ALBERT

Description: This paper proposes an equivalent circuit model to obtain the transient electrical stress quantitatively in medium voltage medium frequency transformers in modern power electronics. To verify this model, transient simulation is performed on a 1.5 kV/1 kHz transformer, revealing voltage overshoot quantitatively between turns and layers of the transformer’s HV winding. Effects of rise time of the input pulse voltage, stray capacitance of the winding insulation, and their interactions on the voltage overshot magnitude are presented. With these results, we propose limiting the voltage overshoot and, thereafter, enhancing medium voltage medium frequency transformer’s insulation capability, which throws light on the transformer’s insulation design. Additionally, guidance on the future studies on aging and endurance lifetime of the medium voltage medium frequency transformer’s insulation could be given.

Description: COVID-19 might have long-term mental health impacts. We aim to investigate the longitudinal changes in mental problems from initial COVID-19 peak to its aftermath among general public in China. Depression, anxiety and insomnia were assessed among a large-sample nationwide cohort of 10,492 adults during the initial COVID-19 peak (28 February 2020 to 11 March 2020) and its aftermath (8 July 2020 to 8 August 2020) using the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Insomnia Severity Index. We used generalized estimating equations and linear mixed models to explore factors associated with long-term mental health symptoms during COVID-19. During the five months, mental health symptoms remained consistently elevated (baseline 46.4%; follow-up 45.1%). Long-term depression, anxiety and insomnia were associated with several personal and work-related factors including quarantine (adjusted OR for any mental health symptoms 1.31, 95%CI 1.22–1.41, p 〈 0.001), increases in work burden after resuming work (1.77, 1.65–1.90, p 〈 0.001), occupational exposure risk to COVID-19 (1.26, 1.14–1.40, p 〈 0.001) and living in places severely affected by initial COVID-19 peak (1.21, 1.04–1.41, p = 0.01) or by a COVID-19 resurgence (1.38, 1.26–1.50, p 〈 0.001). Compliance with self-protection measures, such as wearing face masks (0.74, 0.61–0.90, p = 0.003), was associated with lower long-term risk of mental problems. The findings reveal a pronounced and prolonged mental health burden from the initial COVID-19 peak through to its aftermath in China. We should regularly monitor the mental health status of vulnerable populations throughout COVID-19.

Description: Developments in the design of wind turbines with augmentation are advancing around the globe with the goal of generating electricity close to the user in built-up areas. This is certain to help lessen the power generation load as well as distribution and transmission network costs by reducing the distance between the user and the power source. The main objectives driving the development and advancement of vertical-axis wind turbines are increasing the power coefficient and the torque coefficient by optimizing the upstream wind striking on the rotor blades. Unlike horizontal-axis wind turbines, vertical axis turbines generate not only positive torque but also negative torque during operation. The negative torque generated by the returning blade is a key issue for vertical-axis wind turbines (VAWTs) that is counterproductive. Installation of wind deflectors for flow augmentation helps to reduce the negative torque generated by the returning blades as well as enhance the positive torque by creating a diversion in the upstream wind towards the forwarding blade during operation. This paper reviews various designs, experiments, and CFD simulations of wind deflectors reported to date. Optimization techniques for VAWTs incorporating wind deflectors are discussed in detail. The main focus of the review was on the installation position and orientation of the deflectors and their potential contribution to increasing the power coefficient. Topics for future study are suggested in the conclusion section of the paper.

Description: Malaysia implemented its first Movement Control Order (MCO) during the early phase of the COVID-19 pandemic to slow the transmission of the virus. This study aimed to explore the public perception of the MCO implementation and people’s experiences during this period. The study employed qualitative explorative in-depth interviews conducted with 23 Malaysian adults from various demographic backgrounds. Thematic analysis was performed using NVivo 12. Three main themes were identified: a period of information surge, heterogeneous emotional response, and attempts to adapt. During the MCO, the participants obtained information from multiple platforms. They suggested the need for clear and repeated instructions to avoid confusion and misinformation. They also acknowledged the importance of the MCO in breaking the chain of transmission and safeguarding high-risk groups; however, they also expressed that stricter enforcement from the authorities was warranted. The changes in the participants’ work–life routines, lack of physical interaction, and uncertainty about their health and the economy due to the MCO negatively impacted their psychological states. Despite these challenges, the participants attempted to adapt to life under the MCO in different ways. The findings imply that during a crisis, the public tends to seek clear and reliable information, experience emotional turmoil, and adapt to changes. The MCO implementation can be improved through an effective communication strategy and efforts to battle misinformation.

Description: This empirical study assesses the effect of CO2 emissions, urbanization, energy consumption, and agriculture on Thailand’s economic growth using a dataset between 1970 and 2018. The ARDL and the frequency domain causality (FDC) approaches were applied to assess these interconnections. The outcome of the bounds test suggested a long-term association among the variables of investigation. The ARDL outcomes reveal that urbanization, agriculture, energy consumption, and CO2 emissions positively trigger Thailand’s economic growth. Additionally, the frequency domain causality test was used to detect a causal connection between the series. The main benefit of this technique is that it can detect a causal connection between series at different frequencies. To the understanding of the authors, this is the first study in the case of Thailand that will apply the FDC approach to capture the causal linkage between GDP and the regressors. The outcomes of the causality test suggested that CO2 emissions, urbanization, energy consumption, and agriculture can predict Thailand’s economic growth in the long term. These outcomes have far-reaching implications for economic performance and Thailand’s macroeconomic indicators.

Description: The number of medically compromised dental patients is increasing every year with the increase in the super-aged population. Many of these patients have underlying psychiatric problems and diseases, which need to be recognized by dental professionals for better treatment outcomes. The aim of this narrative review article is to summarize the psychological and psychiatric backgrounds of medically compromised patients who are frequently visited and taken care of by dentists using findings from recent systematic reviews and meta-analyses. Anxiety and symptoms of depression, post-traumatic stress disorders, panic disorders, poor cognitive functions, and poor quality of life were some of the common psychological backgrounds in medically compromised patients. Additionally, the consequences of these psychological problems and the considerations that need to be taken by the dentist while treating these patients have been discussed. Dental professionals should be aware of and recognize the different psychological backgrounds of medically compromised dental patients in order to provide appropriate dental treatment and to prevent oral conditions from worsening.

Description: Water uptake is a seminal process in seed germination. Salt and polyethylene glycol (PEG) are known to retard seed germination rates and percentages, which is often attributed to osmotic effects. Here, we quantified water uptake in wheat seeds killed with a hot needle, finding evidence of three distinct water uptake pools. The fast pool was unaffected by salt, and likely represents cell walls and other apoplastic material. Water uptake into the medium and slow pools was slowed by salt addition, with the medium pool thought to be cellular, while the slow pool is presumably related to endosperm hydration. Salt caused a minor decrease in the water uptake rates and maximum seed water content, while PEG strongly suppressed both parameters. Seeds transferred between water and salt solutions followed the water uptake trajectories of the solution into which they were transferred. Seeds transferred from PEG to water achieved final seed water contents similar to water control seeds, while seeds transferred from water to PEG achieved significantly higher final water contents than PEG controls. This work confirms that salt and PEG have distinct effects on water uptake by wheat seeds.

Description: The south of Portugal is one of the regions that will be most affected by the impacts of climate change (CC), with an expected increase in water scarcity. Irrigated super-intensive olive orchards occupy a large area of the used agricultural surface in the Alentejo region, south of Portugal, making it necessary to adapt this crop to the effects of CC. This study assessed the impacts of CC and defined adaptation measures concerning irrigation management of the super-intensive olive orchard. To compute the crop irrigation requirement (CIR), the soil water balance model ISAREG was combined with climate data relative to the reference period 1971–2000 and to the representative concentration pathways RCP4.5 and RCP8.5 for the periods 2011–2040, 2041–2070, and 2071–2100. The growing degree-days (GDD) approach was used to estimate olive phenology for these CC scenarios. Unchanged irrigation management with an average CIR increase up to 16% in RCP4.5 and 31% in RCP8.5 is expected. By adopting higher levels of water deficit, water savings of up to 22% can be realized. The results showed that the anticipated CIR increase for the CC scenarios can be mitigated through regulated deficit irrigation strategies.

Description: This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods that have been independently validated previously in clinical feasibility studies and deploys them together in a practical prostate cancer biopsy system. NaviPBx is based entirely on free open-source software and will be shared as a free open-source program with no restriction on its use. NaviPBx is set to be deployed and sustained nationwide through the Senegalese Military Health Service. This paper reports on the results of the design process of NaviPBx. Our approach concentrates on “frugal technology”, intended to be affordable for low–middle income (LMIC) countries. Our project promises the wide-scale application of prostate biopsy and will foster time-efficient development and programmatic implementation of ultrasound-guided diagnostic and therapeutic interventions in Senegal and beyond.

Description: The number of fatalities in the construction industry is highest among all industries; thus, various losses in the form of fatalities in construction represent a significant factor for safety management. If a fatality occurs, it is important to estimate the overall loss, as a significant financial loss occurs for each project participant. However, in studies of the cost of accidents involving a fatality conducted abroad, the framework cannot be applied as it is because insurance types, systems, and legal cost systems differ by country. Therefore, we developed a framework for calculating the fatality loss considering various aspects of labor, industry, and regulations in Korea. This was done in four steps: (i) data collection and criteria selection; (ii) proposed framework development; (iii) conduct of questionnaire surveys for the case study; and (iv) analysis and evaluation of the case study. As a result of the data analysis after the case study of general construction companies, the total fatality loss was estimated to be 2,198,260 USD/person. The contributions of this study are the development of a framework composed of newly discovered items that can acquire reliable data in consideration of the properties of the construction industry.

Description: Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. However, in the practical realm of software development, the software project that needs to be predicted is generally a brand new software project, and there is not enough labeled data to build a defect prediction model; therefore, traditional methods are no longer applicable. Cross-project defect prediction uses the labeled data of the same type of project similar to the target project to build the defect prediction model, so as to solve the problem of data loss in traditional methods. However, the difference in data distribution between the same type of project and the target project reduces the performance of defect prediction. To solve this problem, this paper proposes a cross-project defect prediction method based on manifold feature transformation. This method transforms the original feature space of the project into a manifold space, then reduces the difference in data distribution of the transformed source project and the transformed target project in the manifold space, and finally uses the transformed source project to train a naive Bayes prediction model with better performance. A comparative experiment was carried out using the Relink dataset and the AEEEM dataset. The experimental results show that compared with the benchmark method and several cross-project defect prediction methods, the proposed method effectively reduces the difference in data distribution between the source project and the target project, and obtains a higher F1 value, which is an indicator commonly used to measure the performance of the two-class model.

Description: Objective. The main objective of this study was to investigate the relationship between poverty, religion, and suicidal thoughts among U.S. youth. The disparities regarding gender, race, and ethnicity with regard to suicidal thoughts were also assessed. Methods. A cross-sectional correlational research design was used for this study and a national representative sample of 1945 young adults aged 18 to 25 was selected from the 2014 National Survey on Drug Use and Health. Logistic regression analysis with interaction effects was utilized to determine if poverty and religion were associated with suicidal thoughts. Results. About 43 percent of the sample reported having suicidal thoughts when things got worse and this prevalence rate varied by gender and race/ethnicity with white males self-disclosing the highest rate of suicidal thoughts. After adjusting for demographic and socioeconomic characteristics, black males who lived up to two times the poverty line had a higher likelihood of suicidal thoughts (p = 0.011), and religion protected against suicidal thoughts (p = 0.012). Youth with lower education and poor health were more inclined to have suicidal thoughts than their peers. Conclusions. Suicide is the second leading cause of death for American young adults aged 18 to 25. Understanding these differences between social determinants of suicide can help public health researchers strategize how to make evidence-based recommendations for suicide prevention efforts.

Description: A field experiment was conducted to investigate the effectiveness of different herbicides for controlling wild onion (Asphodelus tenuifolius) in cumin (Cuminum cyminum L.) during the rabi seasons (2018–2019 and 2019–2020) at Agricultural Research Station, Agriculture University, Jodhpur, Rajasthan. The experiment comprised eight herbicidal weed management treatments for wild onion applied to cumin in a three-replication randomized block design. Among the herbicidal weed management treatments, early post-emergence (8 DAS) application of oxyfluorfen 200 g/ha resulted in the lowest weed density and dry matter of Asphodilus tenuifolius, with maximum weed (Asphodilus tenuifolius) control efficiency at 40 days after sowing (DAS) during both experimental years. Likewise, the highest total efficiency of weed control was recorded with the application of oxyfluorfen 200 g/ha at 8 DAS. Oxyflourfen 200 g/ha used early post emergence (8 DAS) reduced the weed index more effectively than the other herbicides. It also recorded the highest number of branches/plant, plant height, umbels/plant, umbellates/umbel, seeds/umbellates, and seed yield. However, application of oxyflourfen @ 200 g/ha 8 DAS–early POE and pendimethalin 38.7 CS 500 g/ha + oxyfluorfen @ 150 g/ha 8 DAS–early POE were statistically similar in terms of plant growth, yield, and yield attributes. The net returns (366.49 USD/ha in 2018–2019 and 175.72 USD/ha in 2019–2020) and B:C ratio (1.70 and 1.33 in 2018–2019 and 2019–2020, respectively) were also superior, with oxyfluorfen 200 g/ha applied early post emergence.

Description: The aims of this study were (1) to design a user-friendly instrument to assess and optimize patient information material (PIM), (2) to develop an English version, and (3) to test its psychometric properties. The instrument was needed to optimize the top-down developed PIM of the psycho-oncological care programme isPO. First, a literature-based PIM checklist was developed by a team of patient representatives, cancer care experts and professional researchers. Next, the checklist’s reliability and validity were analysed by having cancer survivors assess the initial and optimized version of the isPO-leaflet. The User-friendly Patient Information Material Checklist (UPIM-Check), developed participatorily, was found to be effective for evaluating PIM. It uses a traffic light scale, and suggestions for improvement can be given for each criterion. Its reliability appeared to be excellent (α = 0.927). The optimized leaflet was rated significantly better than the initial one. The UPIM-Check is a reliable and valid instrument, which enables end-users (e.g., patients) to assess and optimize the quality of PIM according to scientific criteria and the needs of end-users. A bottom-up approach was essential for developing and validating the UPIM-Check. End-users constantly contributed with their specific knowledge. Thus, their position as co-researchers was significantly strengthened.

Description: This paper combines the interval analysis tools with the nonlinear model predictive control (NMPC). The NMPC strategy is formulated based on an uncertain dynamic model expressed as nonlinear ordinary differential equations (ODEs). All the dynamic parameters are identified in a guaranteed way considering the various uncertainties on the embedded sensors and the system’s design. The NMPC problem is solved at each time step using validated simulation and interval analysis methods to compute the optimal and safe control inputs over a finite prediction horizon. This approach considers several constraints which are crucial for the system’s safety and stability, namely the state and the control limits. The proposed controller consists of two steps: filtering and branching procedures enabling to find the input intervals that fulfill the state constraints and ensure the convergence to the reference set. Then, the optimization procedure allows for computing the optimal and punctual control input that must be sent to the system’s actuators for the pendulum stabilization. The validated NMPC capabilities are illustrated through several simulations under the DynIbex library and experiments using an inverted pendulum.

Description: Flow-induced vibration is a canonical issue in various engineering fields, leading to fatigue or immediate damage to structures. This paper numerically investigates flow-induced vibrations of a cylinder interacting with the wake of another cylinder at a Reynolds number Re = 150. It sheds light on the effects of mass ratio m*, damping ratio, and mass-damping ratio m*ζ on vibration amplitude ratio A/D at different reduced velocities Ur and cylinder spacing ratios L/D = 1.5 and 3.0. A couple of interesting observations are made. The m* has a greater influence on A/D than ζ although both m* and ζ cause reductions in A/D. The m* effect on A/D is strong for m* = 2–16 but weak for m* 〉 16. As opposed to a single isolated cylinder case, the mass-damping m*ζ is not found to be a unique parameter for a cylinder oscillating in a wake. The vortices in the wake decay rapidly at small ζ. Alternate reattachment of the gap shear layers on the wake cylinder fuels the vibration of the wake cylinder for L/D = 1.5 while the impingement and switch of the gap vortices do the same for L/D = 3.0.

Description: Visual cognitive strategies in construction hazard recognition (CHR) signifies prominent value for the development of CHR computer vision techniques and safety training. Nonetheless, most studies are based on either sparse fixations or cross-sectional (accumulative) statistics, which lack consideration of temporality and yielding limited visual pattern information. This research aims to investigate the temporal visual search patterns for CHR and the cognitive strategies they imply. An experimental study was designed to simulate CHR and document participants’ visual behavior. Temporal qualitative comparative analysis (TQCA) was applied to analyze the CHR visual sequences. The results were triangulated based on post-event interviews and show that: (1) In the potential electrical contact hazards, the intersection of the energy-releasing source and wire that reflected their interaction is the cognitively driven visual area that participants tend to prioritize; (2) in the PPE-related hazards, two different visual strategies, i.e., “scene-related” and “norm-guided”, can usually be generalized according to the participants’ visual cognitive logic, corresponding to the bottom-up (experience oriented) and top-down (safety knowledge oriented) cognitive models. This paper extended recognition-by-components (RBC) model and gestalt model as well as providing feasible practical guide for safety trainings and theoretical foundations of computer vision techniques for CHR.

Description: This study empirically explored the activity profiles of Korean older adults by considering a wide range of activities simultaneously and further investigated the socioeconomic factors associated with activity profiles. Gender differences in activity profiles were examined in-depth. Latent class analysis (LCA) was used to identify activity profiles based on a nationally representative sample of older adults from the most recent two waves of the Korean Time Use Survey (n = 3034 for 2014 and n = 3960 for 2019). Multinomial logistic regression analysis was employed to further examine the factors associated with the activity profiles. The findings revealed four distinct activity groups, although there were differences in activity profiles between the two waves. Several sociodemographic factors, such as gender, age, assets and income, were significantly associated with the activity profiles. Findings from this study can inform policy makers seeking interventions that enhance the overall well-being of older adults through activity engagement.

Description: This comparative study inspects the heat transfer characteristics of magnetohydrodynamic (MHD) nanofluid flow. The model employed is a two-phase fluid flow model. Water is utilized as the base fluid, and zinc and titanium oxide (Zn and TiO2) are used as two different types of nanoparticles. The rotation of nanofluid is considered along the z-axis, with velocity ω*. A similarity transformation is used to transform the leading structure of partial differential equations to ordinary differential equations. By using a powerful mathematical BVP-4C technique, numerical results are obtained. This study aims to describe the possessions of different constraints on temperature and velocity for rotating nanofluid with a magnetic effect. The outcomes for the rotating nanofluid flow and heat transference properties for both types of nanoparticles are highlighted with the help of graphs and tables. The impact of physical concentrations such as heat transference rates and coefficients of skin friction are examined. It is noted that rotation increases the heat flux and decreases skin friction. In this comparative study, Zn-water nanofluid was demonstrated to be a worthy heat transporter as compared to TiO2-water nanofluid.

Description: This paper reveals the relationship between the Miller plateau voltage and the displacement currents through the gate–drain capacitance (CGD) and the drain–source capacitance (CDS) in the switching process of a power transistor. The corrected turn-on Miller plateau voltage and turn-off Miller plateau voltage are different even with a constant current load. Using the proposed new Miller plateau, the turn-on and turn-off sequences can be more accurately analyzed, and the switching power loss can be more accurately predicted accordingly. Switching loss models based on the new Miller plateau have also been proposed. The experimental test result of the power MOSFET (NCE2030K) verified the relationship between the Miller plateau voltage and the displacement currents through CGD and CDS. A carefully designed verification test bench featuring a power MOSFET written in Verilog-A proved the prediction accuracy of the switching waveform and switching loss with the new proposed Miller plateau. The average relative error of the loss model using the new plateau is reduced to 1/2∼1/4 of the average relative error of the loss model using the old plateau; the proposed loss model using the new plateau, which also takes the gate current’s variation into account, further reduces the error to around 5%.

Description: The Society of Automotive Engineers (SAE) defines six levels of driving automation, ranging from Level 0 to Level 5. Automated driving systems perform entire dynamic driving tasks for Levels 3–5 automated vehicles. Delegating dynamic driving tasks from driver to automated driving systems can eliminate crashes attributed to driver errors. Sharing status, sharing intent, seeking agreement, or sharing prescriptive information between road users and vehicles dedicated to automated driving systems can further enhance dynamic driving task performance, safety, and traffic operations. Extensive simulation is required to reduce operating costs and achieve an acceptable risk level before testing cooperative automated driving systems in laboratory environments, test tracks, or public roads. Cooperative automated driving systems can be simulated using a vehicle dynamics simulation tool (e.g., CarMaker and CarSim) or a traffic microsimulation tool (e.g., Vissim and Aimsun). Vehicle dynamics simulation tools are mainly used for verification and validation purposes on a small scale, while traffic microsimulation tools are mainly used for verification purposes on a large scale. Vehicle dynamics simulation tools can simulate longitudinal, lateral, and vertical dynamics for only a few vehicles in each scenario (e.g., up to ten vehicles in CarMaker and up to twenty vehicles in CarSim). Conventional traffic microsimulation tools can simulate vehicle-following, lane-changing, and gap-acceptance behaviors for many vehicles in each scenario without simulating vehicle powertrain. Vehicle dynamics simulation tools are more compute-intensive but more accurate than traffic microsimulation tools. Due to software architecture or computing power limitations, simplifying assumptions underlying convectional traffic microsimulation tools may have been a necessary compromise long ago. There is, therefore, a need for a simulation tool to optimize computational complexity and accuracy to simulate many vehicles in each scenario with reasonable accuracy. This research proposes a traffic microsimulation tool that employs a simplified vehicle powertrain model and a model-based fault detection method to simulate many vehicles with reasonable accuracy at each simulation time step under noise and unknown inputs. Our traffic microsimulation tool considers driver characteristics, vehicle model, grade, pavement conditions, operating mode, vehicle-to-vehicle communication vulnerabilities, and traffic conditions to estimate longitudinal control variables with reasonable accuracy at each simulation time step for many conventional vehicles, vehicles dedicated to automated driving systems, and vehicles equipped with cooperative automated driving systems. Proposed vehicle-following model and longitudinal control functions are verified for fourteen vehicle models, operating in manual, automated, and cooperative automated modes over two driving schedules under three malicious fault magnitudes on transmitted accelerations.

Description: Psychological distress and psychosocial factors are studied in the sports context in players of various specialties, but are only little studied with coaches who carry out their work with these athletes; that is where we put the emphasis, trying to determine the perception of coaches on psychological distress and psychosocial risk factors that may influence their sports work in times of a pandemic. It is an ex post facto study with a single-group retrospective design, with a representative sample of 94 coaches out of a possible 109. The Kessler Psychological Distress Scale and the short version of the ISTAS21 Psychosocial Risk Assessment at Work Questionnaire were adapted to the sports context. The results show that the youngest, those with the least experience and level 1 and level 2 coaches show the highest levels of stress. According to the psychosocial risk assessment, level 1 and 2 coaches, with experience between 6 to 10 years, are in the risk zone. Therefore, it is important to work with a group of coaches who are in the psychosocial risk zone and with high levels of psychological discomfort in order to avoid mental, emotional and physical stress, for the good performance of their work in the best possible conditions.

Description: This article discusses the problems of exhaust gas emissions in the context of the possibility of their reduction through the use of fuels with hydrogen as an additive or hydrotreatment. These fuels, thanks to their properties, may be a suitable response to more and more demanding restrictions on exhaust emissions. The use of such fuels in reactivity controlled dual fuel engines (RCCI) is currently the most effective way of using them in internal combustion (IC) engines. Low-temperature combustion in this type of engine allows the use of all modern fuels intended for combustion engines with high thermal efficiency. Thermal efficiency higher than in classic engines allows for additional reduction of CO2 emissions. In this work, the research on this subject was compiled, and conclusions were drawn as to further possibilities of popularizing the use of these fuels in a wide spectrum of applications and the prospect of using them on a mass scale.

Description: The 2020–2021 global pandemic has brought significant changes to social and economic life. Companies must begin to rethink their business models and values to meet these new challenges. Given the process of intermediation, which has an indirect and catalytic impact, banks have a responsibility and opportunity to transform the economy by, for example, lending to projects that encourage decarbonization and/or green energy. The purpose of this paper is to examine the MagNet Hungarian Community Bank’s approach as a values-based bank in order to compare how the operation of the bank differs from that of traditional ones—even if these apply the Corporate Social Responsibility approach. The findings of an exploratory study of MagNet’s responsible and sustainable way of doing business can help other sectors and companies cope with the global crisis and be a part of the transition to an inclusive, fair, and decarbonised economy. The data for this study were collected using semi-structured interviews with eleven bankers and three customers of MagNet Bank in 2019, as well as bank documents. Using this information, we created a business model for the bank, using the Business Model Canvas method. Within the business model we highlighted how MagNet Bank integrates principles of the Global Alliance for Banking on Values, with the aim of truly integrating sustainability into the core of their corporate genetic makeup, instead of as a partial or insincere measure. With the projection of the models onto each other, we emphasize the role of values in the business model. The combination of the models indicates potential learning points for the further development and long-term success of the bank and serves as an example of good practices for others. This is especially relevant when considering the greater need for improved crisis and risk management due to the pandemic and for the integration of sustainability considerations into business operations which have increased the impetus in the financial sector towards sustainability.

Description: In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods—Mutual Information, ReliefF, Chi Square, and Xvariance—and then each feature from the union set was assessed by three classification algorithms—support vector machine, naïve Bayes, and k-nearest neighbors—and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost.

Description: Generational renewal and the incorporation of women into the rural world are fundamental for the sustainability and modernisation of the agricultural sector. Hence the special government protection granted to the sector, which appears in both European legislation on rural issues and in the global strategy of the 2030 Agenda and the Sustainable Rural Development Goals involving a commitment to measures aimed at helping young farmers to set up agricultural holdings and especially at encouraging women to engage in farm management. In the case of Spain, this is nothing new, given that Law 35/2011 on the Shared Ownership of Agricultural Holdings became a veritable revolution in terms of gender in the rural world when it came into force. The results section discusses the practical application of funding for young farmers to start up an agricultural holding through a technical and economic study, and a case study of a first-time farm set up by a young female farmer. The main contribution of this work lies in answering two questions. The first one is to evaluate the efficiency of the subsidies for young people to set up farms in terms of offering a decent, stable livelihood for young men and women in Spain and the Valencian Region. The second one attempts to verify the effectiveness of both European and Spanish active public policies to encourae young men and women to join the agricultural sector.

Description: With the rapid development of deep learning, computer vision has assisted in solving a variety of problems in engineering construction. However, very few computer vision-based approaches have been proposed on work productivity’s evaluation. Therefore, taking a super high-rise project as a research case, using the detected object information obtained by a deep learning algorithm, a computer vision-based method for evaluating the productivity of assembling reinforcement is proposed. Firstly, a detector that can accurately distinguish various entities related to assembling reinforcement based on CenterNet is established. DLA34 is selected as the backbone. The mAP reaches 0.9682, and the speed of detecting a single image can be as low as 0.076 s. Secondly, the trained detector is used to detect the video frames, and images with detected boxes and documents with coordinates can be obtained. The position relationship between the detected work objects and detected workers is used to determine how many workers (N) have participated in the task. The time (T) to perform the process can be obtained from the change of coordinates of the work object. Finally, the productivity is evaluated according to N and T. The authors use four actual construction videos for validation, and the results show that the productivity evaluation is generally consistent with the actual conditions. The contribution of this research to construction management is twofold: On the one hand, without affecting the normal behavior of workers, a connection between construction individuals and work object is established, and the work productivity evaluation is realized. On the other hand, the proposed method has a positive effect on improving the efficiency of construction management.

Description: Creating a widely excepted model on the measure of intelligence became inevitable due to the existence of an abundance of different intelligent systems. Measuring intelligence would provide feedback for the developers and ultimately lead us to create better artificial systems. In the present paper, we show a solution where learning as a process is examined, aiming to detect pre-written solutions and separate them from the knowledge acquired by the system. In our approach, we examine image recognition software by executing different transformations on objects and detect if the software was resilient to it. A system with the required intelligence is supposed to become resilient to the transformation after experiencing it several times. The method is successfully tested on a simple neural network, which is not able to learn most of the transformations examined. The method can be applied to any image recognition software to test its abstraction capabilities.

Description: In recent years, Germany has experienced an increasing number of extreme wet and dry years. In the North German lowlands, wet grassland sites with shallow water table conditions are widespread landscape elements. They are characterized by a special water and nutrient balance that reacts very sensitively to changes in the hydrological system. Studies on evapotranspiration (ETa) and the development of groundwater levels were carried out at two typical wet grassland sites with shallow water table conditions. A weighable groundwater lysimeter system in the Spreewald wetland (SPW) and an eddy covariance station in Havelländisches Luch (HL) were used to measure ETa. The results show that even these shallow water table sites cannot sufficiently meet the vegetation’s water demands in extreme dry conditions. The groundwater levels drop to values deeper than 1 m below the surface. As a result, water supply to the vegetation is temporarily limited. The mean crop coefficients (Kc) of these wet grassland sites reach values of 1.1 in the vegetation period with a sufficient water supply, but drop to around 0.8 in dry years when the water supply is limited. Areas with small catchment areas, such as HL, are more seriously affected by the dry meteorological conditions than areas with sufficient inflows from larger catchment areas, such as SPW.

Description: Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment.

Description: Microcredit financing is extensively considered as an effective development method for poverty mitigation and women empowerment. Nevertheless, relevant studies reflected opposing outcomes on microfinance effects consisting of positive, zero, and negative impacts. Thus, this research investigated Al-Amal Bank’s microcredit impacts on women empowerment in Yemen, one of the poorest Middle Eastern nations. A panel dataset and primary and secondary data were gathered through household surveys and propensity score matching to restrict intangible variables’ possible effects. The empirical results revealed that microcredit had a significant positive effect on monthly household incomes and accumulated asset values. Although microcredit facilitated female entrepreneurship and income generation for improved household incomes and expenditure, no influence was found on female household decisions and mobility following the patriarchal system practised in many Arabian nations, including Yemen. Hence, the study finding has theoretically and practically contributed to the body of knowledge in three ways. First, a novel proof of how microcredit interactions affected several Yemeni women empowerment elements was identified. This study also provides new insight into the empowerment theory by explaining how access to microcredit influences numerous features of women’s economic and social empowerment. Lastly, social and family traditions significantly influenced female attributes and lifestyles by reflecting how communal and family rituals affected microcredit impacts on women empowerment and vice versa. Conversely, this study guides Yemeni policymakers and those from other nations on extending financial services for self-development to reduce poverty and drive women empowerment rather than relying on government and international agencies.

Description: Global warming and climate change are threatening life on earth. These changes are due to human activities resulting in the emission of greenhouse gases. This is caused by intensive industrial activities and excessive fuel energy consumption. Recently, the scheduling of production systems has been judged to be an effective way to reduce energy consumption. This is the field of green scheduling, which aims to allocate jobs to machines in order to minimize total costs, with a focus on the sustainable use of energy. Several studies have investigated parallel-machine shops, with a special focus on reducing and minimizing the total consumed energy. Few studies explicitly include the idle energy of parallel machines, which is the energy consumed when the machines are idle. In addition, very few studies have considered the elimination of idle machine times as an efficient way to reduce the total consumed energy. This is the no-idle machine constraint, which is the green aspect of the research. In this context, this paper addresses the green parallel-machine scheduling problem, including release dates, delivery times, and no-idle machines, with the objective of minimizing the maximum completion time. This problem is of practical interest since it is encountered in several industry processes, such as the steel and automobile industries. A mixed-integer linear programming (MILP) model is proposed for use in obtaining exact solutions for small-sized instances. Due to the NP-hardness of the studied problem, and encouraged by the successful adaptation of metaheuristics for green scheduling problems, three genetic algorithms (GAs) using three different crossover operators and a simulated annealing algorithm (SA) were developed for large-sized problems. A new family of lower bounds is proposed. This was intended for the evaluation of the performance of the proposed algorithms over the average percent of relative deviation (ARPD). In addition, the green aspect was evaluated over the percentage of saved energy, while eliminating the idle-machine times. An extensive experimental study was carried out on a benchmark of test problems with up to 200 jobs and eight machines. This experimental study showed that one GA variant dominated the other proposed procedures. Furthermore, the obtained numerical results provide strong evidence that the proposed GA variant outperformed the existing procedures from the literature. The experimental study also showed that the adoption of the no-idle machine time constraints made it possible to reduce the total consumed energy by 29.57%, while the makespan (cost) increased by only 0.12%.

Description: According to star graph and winding distribution, winding MMFs of three kinds of 12-slot/10-pole multi-phase and multi-layer winding layout are analyzed by the improved winding function method. Analysis results show that multi-phase and multi-layer winding can suppress even order and (12n ± 1) order harmonics, thereby reducing the eddy-current loss in PMs. Based on the unfolded LPMSM model, rotor MMF, air gap flux density, and no-load back-EMF are analyzed by the analytical permeance function method, which is validated by Teslameter and no-load experiment. Winding MMF is validated by FEM. An axial-flux integrated starting/generator (ISG) with double-three-phase four-layer (DTP-FL) winding, segmented armature and PM, and centrifugal fan is designed and manufactured. The no-load and load test with two groups of resistance established has validated the reasonability of the mentioned method. Experiments show that the axial-flux ISG prototype can run at a relative temperature rise.

Description: Suicide represents a very important issue in public health. For approaching attitudes toward suicide, we have developed an instrument that, following previous recommendations, assesses specific thoughts related to the perception of suicide utility in the press. First of all, we will test the psychometric properties of the scale we created ad hoc for assessing suicide utility perception. After that, we expect to find that the suicide utility perception in the press will have a statistically significant impact on positive attitudes toward terminally ill patients’ suicide (Hypothesis 1). In addition, this relationship will be mediated by suicide legitimation (Hypothesis 2). This mediation will be moderated by depressive symptomatology (Hypothesis 3). The sample was composed of 66 Spanish participants. Suicide legitimation was significantly related to the positive evaluation of terminally ill people’s euthanasia. Finally, when the levels of depression’s psychological concomitants increased, the support for terminally ill people’s euthanasia increased as well. Implications and limitations have been discussed.

Description: Patient-controlled epidural analgesia is widely used to control postoperative pain following major intra-abdominal surgeries. However, determining the optimal infusion dose that can produce effective analgesia while reducing side effects remains a task to be solved. Postoperative pain and adverse effects between variable-rate feedback infusion (VFIM group, n = 36) and conventional fixed-rate basal infusion (CFIM group, n = 36) of fentanyl/ropivacaine-based patient-controlled epidural analgesia were evaluated. In the CFIM group, the basal infusion rate was fixed (5 mL/h), whereas, in the VFIM group, the basal infusion rate was increased by 0.5 mL/h each time a bolus dose was administered and decreased by 0.3 mL/h when a bolus dose was not administered for 2 h. Patients in the VFIM group experienced significantly less pain at one to six hours after surgery than those in the CFIM group. Further, the number of patients who suffered from postoperative nausea was significantly lower in the VFIM group than in the CFIM group until six hours after surgery. The variable-rate feedback infusion mode of patient-controlled epidural analgesia may provide better analgesia accompanied with significantly less nausea in the early postoperative period than the conventional fixed-rate basal infusion mode following open gastrectomy.

Description: The free-piston Stirling engine is a closed-cycle regenerative heat engine that converts heat energy into mechanical work, and requires a spring element for vibratory operations of the displacer and power pistons. In this study, the geometry of the flexural spring design was optimized through structural finite element analyses and fatigue test evaluations. First, we constructed a target design space considering the required natural frequency of the displacer spring assembly under the geometric constraints of total mass and module height. The design of experiments was employed to construct simulation cases for design factors such as the outer diameter, thickness, and number of spirals in the spring sheet. As a result, the optimized design values were obtained to satisfy the design requirements. We also fabricated a test spring specimen and conducted fatigue tests using a linear actuator system developed to have the same motion as the engine. The test results indicated that the optimized spiral spring had no fracture under operating conditions with the design piston amplitude, revealing the effectiveness of the design method.

Description: Lead-acid batteries utilised in electrical substations release hydrogen and oxygen when these are charged. These gases could be dangerous and cause a risk of fire if they are not properly ventilated. Therefore, this research seeks to design and implement a network control panel for heating, ventilation, and air conditioning system (HVACS). This is achieved by using a specific range of controllers, which have more than thirty loops of proportional, integral, and derivative (PID) control to achieve a cost-effective design. It performs the required function of extracting hydrogen and oxygen, maintaining the desired temperature of the battery storage room within recommended limits (i.e., 25 ± 1 °C tolerance) without compromising quality, as set out in the user requirement specification. The system control panel allows the user to access control parameters such as changing temperature set-points, fan-speed, sensor database, etc. It does this automatically and allows no human interface after all necessary settings and installation are completed. The hardware is configured to detect extreme hydrogen and oxygen gas content in the battery room and to ensure that the HVACS extracts the gas content to the outside environment. The system’s results show that the network control panel operates effectively as per the recommended system requirements. Therefore, the effective operation of the HVACS ensures sufficient gas ventilation, thus mitigating the risk of fire in a typical battery storage room. Furthermore, this also enhances battery lifespan because of regulated operating temperature, which is conducive to minimise the effect of sulfation in lead–acid batteries (LAB). The extraction of toxic gases, regulation of temperature, ensuring suitable humidity in UPS battery room is important as it provides longer operational service of equipment, thus reducing frequent maintenance in these rooms. This benefits the electricity supply industry and helps in saving for unplanned maintenance costs.

Description: Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation.

Description: Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics using only a single image without data-driven training such that it can be used for various image restoration problems. However, super-resolution tasks are difficult to perform with DIP when the target image is noisy. The super-resolved image becomes noisy because the reconstruction loss of DIP does not consider the noise in the target image. Furthermore, when the target image contains noise, the optimization process of DIP becomes unstable and sensitive to noise. In this paper, we propose a noise-robust and stable framework based on DIP. To this end, we propose a noise-estimation method using the generative adversarial network (GAN) and self-supervision loss (SSL). We show that a generator of DIP can learn the distribution of noise in the target image with the proposed framework. Moreover, we argue that the optimization process of DIP is stabilized when the proposed self-supervision loss is incorporated. The experiments show that the proposed method quantitatively and qualitatively outperforms existing single image super-resolution methods for noisy images.

Description: Nuclear and renewables energies are the two variants for low-carbon energy and the evolving grid suggests possible synergies between them. Nuclear energy introduces supple operations based on power demand, while renewables such as PV and wind hybrid systems depend on the presence and strength of sunlight or wind. For grid stability, there is need to improve their performance in order to overcome the impact of this disadvantage. The paper is a step in this direction as it addresses a detailed comprehensive dynamic modeling and an efficient control of grid-connected energy sources such as PV or wind system to increase system reliability and to ensure the power quality and safe operation of critical demands. The behavior of the suggested hybrid system is tested at different climate circumstances such as variation of sun radiation and wind speed. The PV is equipped with a boost converter and a three-phase pulse width modulation (PWM) inverter. The wind energy comprises a doubly fed generator (DFIG) based on a variable-speed wind turbine. The two controllers’ rotor-side and grid-side converters of DFIG have the ability to generate and observe reactive power, to keep constant speed of the rotor and control the DC-link voltage. The proposed scheme was investigated using MATLAB software. The maximum power point tracking (MPPT) was used for two systems, PV and wind, in varying weather conditions. The simulation results prove that the voltage at the point of common coupling was constant. Furthermore, the injected current of the grid side was in sinusoidal form and was synchronized with grid side voltage. In addition, the injected power-to-utility grid was around power delivered by the hybrid PV and wind system.

Description: This paper analyzed the degradation pathways of petroleum hydrocarbon degradation bacteria, screened the main degradation pathways, and found the petroleum hydrocarbon degradation enzymes corresponding to each step of the degradation pathway. Through the Copeland method, the best inoculation program of petroleum hydrocarbon degradation bacteria in a polluted site was selected as follows: single oxygenation path was dominated by Streptomyces avermitilis, hydroxylation path was dominated by Methylosinus trichosporium OB3b, secondary oxygenation path was dominated by Pseudomonas aeruginosa, secondary hydroxylation path was dominated by Methylococcus capsulatus, double oxygenation path was dominated by Acinetobacter baylyi ADP1, hydrolysis path was dominated by Rhodococcus erythropolis, and CoA path was dominated by Geobacter metallireducens GS-15 to repair petroleum hydrocarbon contaminated sites. The Copeland method score for this solution is 22, which is the highest among the 375 solutions designed in this paper, indicating that it has the best degradation effect. Meanwhile, we verified its effect by the Cdocker method, and the Cdocker energy of this solution is −285.811 kcal/mol, which has the highest absolute value. Among the inoculation programs of the top 13 petroleum hydrocarbon degradation bacteria, the effect of the best inoculation program of petroleum hydrocarbon degradation bacteria was 18% higher than that of the 13th group, verifying that this solution has the best overall degradation effect. The inoculation program of petroleum hydrocarbon degradation bacteria designed in this paper considered the main pathways of petroleum hydrocarbon pollutant degradation, especially highlighting the degradability of petroleum hydrocarbon intermediate degradation products, and enriching the theoretical program of microbial remediation of petroleum hydrocarbon contaminated sites.

Description: The characteristics of bridge pile-group foundation have a significant influence on the dynamic performance of the superstructure. Most of the existing analysis methods for the pile-group foundation impedance take the trait of strong specialty, which cannot be generalized in practical projects. Therefore, a project-oriented numerical solution algorithm is proposed to compute the dynamic impedance of bridge pile-group foundation. Based on the theory of viscous-spring artificial boundary, the derivation and solution of the impedance function are transferred to numerical modeling and harmonic analysis, which can be carried out through the finite element method. By taking a typical pile-group foundation as a case study, the results based on the algorithm are compared with those from existing literature. Moreover, an impact experiment of a real pile-group foundation was implemented, the results of which are also compared with those resulting from the proposed numerical algorithm. Both comparisons show that the proposed numerical algorithm satisfies engineering precision, thus showing good effectiveness in application.

Description: Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-growing datasets. In Phase I of this research, we scanned the MICCAI (Medical Image Computing and Computer-Assisted Intervention) conference proceedings from 2011 to 2018. We identified 907 papers involving human MRI, CT or fMRI datasets and extracted their sizes. The median dataset size had grown by 3–10 times from 2011 to 2018, depending on imaging modality. Statistical analysis revealed exponential growth of the geometric mean dataset size with an annual growth of 21% for MRI, 24% for CT and 31% for fMRI. Thereupon, we had issued a forecast for dataset sizes in MICCAI 2019 well before the conference. In Phase II of this research, we examined the MICCAI 2019 proceedings and analyzed 308 relevant papers. The MICCAI 2019 statistics compare well with the forecast. The revised annual growth rates of the geometric mean dataset size are 27% for MRI, 30% for CT and 32% for fMRI. We predict the respective dataset sizes in the MICCAI 2020 conference (that we have not yet analyzed) and the future MICCAI 2021 conference.

Description: Climate change has inherent multidisciplinary characteristics, and predicting the future of a single field of work has a limit. Therefore, this study proposes a water-centric nexus approach for the agriculture and forest sectors for improving the response to climate change in the Korean Peninsula. Two spatial models, i.e., Environmental Policy Integrated Climate and Integrated Valuation of Ecosystem Services and Tradeoffs, were used to assess the extent of changes in agricultural water demand, forest water supply, and their balance at the watershed level in the current and future climatic conditions. Climate changed has increased the agricultural water demand and forest water supply significantly in all future scenarios and periods. Comparing the results with RCP8.5 2070s and the baseline, the agricultural water demand and forest water supply increased by 35% and 28%, respectively. Water balance assessment at the main watershed level in the Korean Peninsula revealed that although most scenarios of the future water supply increases offset the demand growth, a risk to water balance exists in case of a low forest ratio or smaller watershed. For instance, the western plains, which are the granary regions of South and North Korea, indicate a higher risk than other areas. These results show that the land-use balance can be an essential factor in a water-centric adaptation to climate change. Ultimately, the water-centric nexus approach can make synergies by overcoming increasing water demands attributable to climate change.

Description: Autosomal dominant hypophosphatemic rickets (ADHR) is an extremely rare form of genetic rickets caused by mutations in the fibroblast growth factor 23 gene. ADHR is characterized by hypophosphatemia secondary to isolated renal phosphate wasting. Only a few cases of ADHR have been reported in the literature to date. We describe the case of a 17-month-old girl who presented with severe failure to thrive (length: −4.08 standard deviation (SD), weight: −2.2 SD) and hypotonia. Hypophosphatemia, decreased tubular phosphate reabsorption (69%), and rachitic lesions were found. Genetic analysis showed the heterozygous variant c.536G〉A (NM_020638.3:c.536G〉A) in exon 3 of the FGF23 gene, leading to the diagnosis of ADHR. She was treated with phosphate salts and oral alfacalcidol. After 4 years of treatment, at 5 years of age, the patient’s ADHR resolved spontaneously. Considering the lack of knowledge regarding ADHR, we reviewed the literature to describe the features of this rare and poorly understood disease. Eleven ADHR pediatric cases have been described thus far, with cases tending to be more common in females than males. Similar to the general population, two groups of patients with ADHR can be described depending on the mutations present: patients with an R179 and R176 mutation have early-onset of disease and higher frequency of rickets, and a milder and late-onset of disease, respectively. Symptoms and disease severity may fluctuate. Spontaneous remission may occur during the pediatric age.

Description: The optimal expansion of AC medium-voltage distribution grids for rural applications is addressed in this study from a heuristic perspective. The optimal routes of a distribution feeder are selected by applying the concept of a minimum spanning tree by limiting the number of branches that are connected to a substation (mixed-integer linear programming formulation). In order to choose the caliber of the conductors for the selected feeder routes, the maximum expected current that is absorbed by the loads is calculated, thereby defining the minimum thermal bound of the conductor caliber. With the topology and the initial selection of the conductors, a tabu search algorithm (TSA) is implemented to refine the solution with the help of a three-phase power flow simulation in MATLAB for three different load conditions, i.e., maximum, medium, and minimum consumption with values of 100%, 60%, and 30%, respectively. This helps in calculating the annual costs of the energy losses that will be summed with the investment cost in conductors for determining the final costs of the planning project. Numerical simulations in two test feeders comprising 9 and 25 nodes with one substation show the effectiveness of the proposed methodology regarding the final grid planning cost; in addition, the heuristic selection of the calibers using the minimum expected current absorbed by the loads provides at least 70% of the calibers that are contained in the final solution of the problem. This demonstrates the importance of using adequate starting points to potentiate metaheuristic optimizers such as the TSA.

Description: Perceptions of the harmfulness of tobacco products may be a determinant of smoking behaviors. This study aimed to: (1) assess the perception of harmfulness of various tobacco products and e-cigarettes in Poland as well as (2) to assess the awareness of the health effects of using tobacco and e-cigarettes. A cross-sectional survey was conducted in 2019 with a nationally representative sample of 1011 individuals aged 15 and over. In the studied group, 22.3% were smokers. Smokeless tobacco was most likely to be perceived as less harmful than cigarettes (25%), followed by water pipe (24.5%), heated tobacco products (22%), e-cigarettes (21.6%), slim cigarettes (17.1%), flavored cigarettes (except menthol ones) (16.1%), menthol cigarettes (15.6%) and cigarillos (12.6%). In this study, 10% of respondents denied that smoking causes serious diseases. Most of the respondents (88.9%) were aware that smoking causes lung cancer (88.9%), but only 70.4% were aware that smoking causes stroke. Smokers compared to non-smokers were less likely to declare that smoking causes a stroke (OR: 0.43, 95%CI: 0.31–0.59; p 〈 0.001) or myocardial infarction (OR: 0.41, 95%CI: 0.29–0.60; p 〈 0.001). There were no significant differences (p 〉 0.05) in the perception of harmfulness of various tobacco products and e-cigarettes by gender, age, or occupational status.

Description: Drowning is a leading cause of fatality among children in the United States, and pool/spa aquatic structures represent common locations of submersion incidents. This study employed narrative case review to understand characteristics related to permission, supervision, and precipitating events in childhood submersion incidents. Retroactive analysis of 1537 fatal and non-fatal submersion incidents among children age 13 years old and younger was conducted using the U.S. Consumer Products Safety Commission In-Depth Investigations dataset from 2000–2017. Narrative descriptions were coded according to the themes of permission, supervision, and precipitating events. In most (86%) incidents, the child did not have permitted water access, and 80% of narratives indicated the child was alone at time of incident. These attributes were significantly associated with a fatal outcome (No permission: OR 11.98, 95% CI 7.97–18.06; Alone: OR 34.93, 95% CI 19.69–61.96). The average length of inactive supervision time was 15.6 min; this duration significantly differed by non-fatal (3.2 min) and fatal (16.1 min) outcomes (p 〈 0.001). More than half of cases occurred under the supervision type of a parent (56%), followed by grandparents (14%) and childcare provider (10%). Submersion incidents with a non-parent supervisor were two times more likely to result in a fatal outcome (OR 1.87, 95% CI 1.07–3.64). The most frequently occurring precipitating events included outdoor play (46%), a social gathering (36%), and previous water play (15%). Narrative excerpts further illustrate how tragic submersion events can unfold quickly and unpredictably. Education campaigns should target all adults that supervise children and reiterate key findings in that many submersion incidents occur (1) without permitted pool use, (2) without active supervision, and (3) when a caregiver is distracted. Multiple strategies should be utilized to add layers of projection against submersion injury.

Description: This study classified the changes in alcohol use behavior among Korean adults and explored the related factors. The study used data from the 4th (2009) to 14th (2019) waves of the Korean Welfare Panel Study. The subjects were 8267 adults aged 19–60 years. Latent class growth analysis was used to classify the latent classes of alcohol use behavior among Korean adults, and logistic regression analysis was performed to identify the specific factors that form the classes. Additionally, the 11-year trajectories of major variables associated with alcohol use behavior for the derived classes were analyzed using growth mixture modeling. Four classes were identified according to the trajectories of alcohol use behavior. There were statistically significant differences in the trajectories of depression, self-esteem, satisfaction in family relationships, and satisfaction in leisure activities according to the class of alcohol use behavior. In particular, self-esteem and satisfaction in family relationships indicated distinctly decreasing trajectories in the low- to moderate-risk class, which suggested the need for longitudinal analysis of the factors that influence alcohol use behavior. Moreover, it is recommended that interventions for the prevention of high-risk drinking target not only individuals but also family units.

Description: In a nuclear power plant environment, low-voltage cables experience different stresses during their service life which challenge their integrity. A non-destructive and reliable condition monitoring technique is desired to determine the state of these low-voltage cables during service and for the life extension of nuclear power plants. Hence, in this research work, an EPR/CSPE-based low-voltage cable was exposed to γ-rays for five different absorbed doses. The overall behavior of the cable under stress was characterized by frequency and time domain electrical measurements (capacitance, tan δ, and Extended Voltage Response) and a mechanical measurement (elongation at break). Significant variations in the electrical parameters were observed, as was a decline in the elongation at break values. A strong correlation between the measurement methods was observed, showing the ability of the electrical methods to be adopted as a non-destructive condition monitoring technique.

Description: The rehabilitation needs of COVID-19 survivors are increasingly recognized, with a focus on combating respiratory and neuromuscular dysfunctions. The aim here was to explore the perception of Austrian physiotherapists and physiotherapy students on post-COVID-19 rehabilitation care and to identify barriers for the application of sufficient rehabilitation. We analysed current knowledge and practical skills in respiratory physiotherapy, performing a cross-sectional national survey among physiotherapists working in outpatient settings and physiotherapy students in their last academic year of bachelor-level education in Austria. Out of 255 survey participants, one-third already had inquiries to treat post-COVID-19 patients, and the majority of respondents expected a further increased inflow of patients with rehabilitation needs (64.2%). Only 11.2% of respondents reported feeling sufficiently informed about post-COVID-19 rehabilitation. A total of 68.2% of students and up to 48.1% of physiotherapists favoured a COVID-19-specific adaptation already in the basic academic education, and 74.1% of survey participants indicated interest in attending specific training. Concerning respiratory physiotherapy, our data showed discrepancies between the estimation of the importance of specific examination and treatment techniques and the level of current experience. There is a clear lack of experience in implementing effective device-based respiratory therapy. Our data indicate an urgent need to develop new education and training programs with a focus on the interdisciplinary rehabilitation of patients with post-COVID-19 syndrome.

Description: This study’s objective was to describe the relationship between the main risk factors for oral cancer, including tobacco (in the form of cigarettes, smokeless tobacco (SLT), secondhand smoking (SS)), alcohol, and betel quid (BQ), and the occurrence of oral potentially malignant disorders (OPMDs). A community-based case-control study was conducted with a population of 1448 adults aged 40 years or above in northeastern Thailand. Patients aged 60 years or above (OR 1.79, p 〈 0.001) and female patients (OR 2.17, p 〈 0.001) had a significant chance of having OPMDs. Our multivariate analysis showed that the most potent risk factor for OPMDs occurrence was betel quid (BQ) (adjusted OR 4.65, p 〈 0.001), followed by alcohol (OR 3.40, p 〈 0.001). Even former users were at risk of developing OPMDs. The synergistic effect between these main risk factors was significantly shown in the group exposed to SLT, SS, BQ, and alcohol. The most potent synergistic effect was found in the group exposed to SLT, BQ and alcohol with the OR = 20.96.

Description: One strategy to promote physical activity (PA) is for health care providers to give exercise prescriptions (ExRx) that refer to community-based facilities. However, facilitators and barriers specific to urban programs in the US for under-resourced women are unknown. Thus the purpose of this formative research was to explore ExRx barriers and facilitators specific to US under-resourced women to inform future intervention targets and strategies. This mixed-methods community-engaged research was conducted in partnership with an urban women’s only wellness center that exchanged ExRx for free access (1–3 months). Qualitative semi-structured interviews and validated quantitative questionnaires (SF-12, International Physical Activity Questionnaire, Physical Activity Self-Efficacy, Physical Activity Stage of Change, and Barriers to Physical Activity, Social Support for Exercise, and Confusion, Hubbub, and Order Scale) were administered by phone and guided by the socio-ecological model. ExRx utilization was defined as number visits/week divided by membership duration. Means and percentages were compared between ≥1 visit/week vs.

Description: In this paper, the effect of heat injection on productivity of Fuyu oil shale during in-situ pyrolysis was studied by using heat flow coupling analysis method. It is found that fluid conducts heat transmission to the oil shale stratum mainly along the fissure formed by hydraulic fracturing. With the increase of heating time, the oil shale on both sides of fissures were effectively pyrolyzed, and the porosity of the formation increases and the diffusion range of the nitrogen to the oil shale stratum is also improved. After 200 days, the oil shale around the fractures first reaches the pyrolysis temperature, and 700 days later, the average temperature of the oil shale stratum reaches 500 °C; therefore, the whole oil shale can be effectively pyrolyzed. Productivity analysis shows that the best exploitation temperature is 500 °C. When the gas injection rate is in the range of 1.0~11.0 m3/min, different degrees of heat loss will occur, and the output is also different. The pyrolysis time reaches 100~150 days, showing the peak value of daily production, which is between 0.5~3.2 m3/day. The pressure of displacement fluid affects oil shale product recovery in in-situ pyrolysis. High pressure helps to improve the displacement efficiency of oil and gas products and increase the productivity of oil shale in-situ pyrolysis. The best acting pressure is 9.5 MPa.

Description: Allogeneic hematopoietic stem cell transplantation (alloHSCT) is one of the most commonly performed transplantation procedures nowadays. Despite the significant progress made in the treatment, alloHSCT is still associated with numerous complications also affecting the oral cavity. One of them is dentin hypersensitivity (DH)—a sharp, short-term pain that occurs when stimuli act on exposed dentin. Various authors point out that DH may result in a significantly lower quality of life, among other things by impeding the consumption of food as well as causing difficulties in daily oral hygiene. The aim of the study was a preliminary analysis of the incidence rate and severity of DH pain in adult patients during late period after alloHSCT. The impact of chronic graft-versus-host disease (cGvHD) and time after alloHCT were also considered. A total of 80 patients were examined. cGvHD was identified in 52 participants. The incidence rate and severity of DH pain was assessed on the basis of a questionnaire and a clinical examination. DH pain appeared a serious problem in late period after alloHSCT regardless of post-transplant time. DH primarily affected cGvHD patients. The prevention-treatment protocol for DH should be developed for this group.

Description: Low pressure fluid transport (1) applications often require low and precise volumetric flow rates (2) including low leakage to reduce additional costly and complex sensors. A peristaltic pump design (3) was realized, with the fluid’s flexible transport channel formed by a solid cavity and a wobbling plate comprising a rigid and a soft layer (4). In operation, the wobbling plate is driven externally by an electric motor, hence, the soft layer is contracted and unloaded (5) during pump-cycles transporting fluid from low to high pressure sides. A thorough characterization of the pump system is required to design and dimension the components of the peristaltic pump. To capture all these parameters and their dependencies on various operation-states, often complex and long-lasting dynamic 3D FE-simulations are required. We present, here, a holistic design methodology (6) including analytical as well as numerical calculations, and experimental validations for a peristaltic pump with certain specifications of flow-rate range, maximum pressures, and temperatures. An experimental material selection process is established and material data of candidate materials (7) (liquid silicone rubber, acrylonitrile rubber, thermoplastic-elastomer) are directly applied to predict the required drive torque. For the prediction, a semi-physical, analytical model was derived and validated by characterizing the pump prototype.

Description: Many smart city and society applications such as smart health (elderly care, medical applications), smart surveillance, sports, and robotics require the recognition of user activities, an important class of problems known as human activity recognition (HAR). Several issues have hindered progress in HAR research, particularly due to the emergence of fog and edge computing, which brings many new opportunities (a low latency, dynamic and real-time decision making, etc.) but comes with its challenges. This paper focuses on addressing two important research gaps in HAR research: (i) improving the HAR prediction accuracy and (ii) managing the frequent changes in the environment and data related to user activities. To address this, we propose an HAR method based on Soft-Voting and Self-Learning (SVSL). SVSL uses two strategies. First, to enhance accuracy, it combines the capabilities of Deep Learning (DL), Generalized Linear Model (GLM), Random Forest (RF), and AdaBoost classifiers using soft-voting. Second, to classify the most challenging data instances, the SVSL method is equipped with a self-training mechanism that generates training data and retrains itself. We investigate the performance of our proposed SVSL method using two publicly available datasets on six human activities related to lying, sitting, and walking positions. The first dataset consists of 562 features and the second dataset consists of five features. The data are collected using the accelerometer and gyroscope smartphone sensors. The results show that the proposed method provides 6.26%, 1.75%, 1.51%, and 4.40% better prediction accuracy (average over the two datasets) compared to GLM, DL, RF, and AdaBoost, respectively. We also analyze and compare the class-wise performance of the SVSL methods with that of DL, GLM, RF, and AdaBoost.

Description: Social distancing was found to prevent COVID-19 contagion. Therefore, understanding the factors associated with the public’s adherence is important. Acknowledging the importance of emotional wellbeing regarding older people’s health, and understanding their emotional state during the pandemic, are crucial. This study assessed factors associated with older people’s adherence to social distancing and their emotional status. A cross-sectional online survey was conducted among 1822 respondents above the age of 60. Distancing adherence, negative emotion, trust, social support, threat perception, attitudes, and subjective norms were assessed, and a path analysis was performed. Adherence was positively associated with attitudes (β = 0.10; p 〈 0.001), and with subjective norms (β = 0.19; p 〈 0.001). Negative emotions were positively associated with threat perception (β = 0.33; p 〈 0.001), and negatively associated with social support (β = −0.13; p 〈 0.001) and subjective norms (β = −0.10; p 〈 0.001). Attitudes mediated the relationship of threat perception (95% CI = 0.009, 0.034), trust (95% CI = 0.008, 0.029), and social support (95% CI = 0.006, 0.023) with distancing adherence. Subjective norms mediated the relationship between threat perception (95% CI = 0.014, 0.034), trust (95% CI = 0.026, 0.055), and social support (95% CI = 0.002, 0.048) with distancing adherence. Subjective norms mediated the relationship between threat perception (95% CI = −0.022, −0.006), trust (95% CI = −0.034, −0.010), and social support (95% CI = −0.029, −0.009) with negative emotions. When promoting social distancing adherence, subjective norms and attitudes must be considered, as they play a role in promoting adherence and negative-emotion regulation.

Description: Single cell protein (SCP) is the first product of the fermentation process and has proven to be a good protein alternative. Food competition is becoming more intense as the world’s population continues to grow. Soon, SCP may be able to compensate for a protein deficit. Various global businesses are focusing on SCP production, and the scope of its application is expanding as time and knowledge increases. High quantities of SCP can be produced by microorganisms, such as algae, yeast, fungi and bacteria, due to their fast development rate and the significant level of protein in their chemical structure. Beside proteins, SCP contains carbohydrates, nucleic acids, lipids, minerals, vitamins and several important amino acids. SCP has been an effective substitute for more expensive protein sources such as fish and soybean products. In conclusion, SCP can easily replace traditional protein sources in human and animal feed without detrimental effects. Potential substrate candidates and optimization strategies for SCP production have been extensively studied. This review article focuses on the various aspects of SCP, from its production, using different substrates, player microorganisms and nutritional benefits, to its economic aspects.

Description: This paper analyzes the impact of a Weather-Based Crop Insurance Scheme (WBCIS) on the Technical Efficiency (TE) of smallholder groundnut farmers in the context of climate change in India. We use Data Envelopment Analysis (DEA) to study the TE of smallholder farmers, which range between 0.58 and 1, with a mean of 0.79. Using the Propensity Score Matching (PSM) technique, we find that the TE of smallholder farmers improves when they participate in a WBCIS using three matching methods. Increasing the coverage of farmers under a WBCIS can help in reducing smallholder farmers vulnerability to climate change.

Description: Building Information Modeling (BIM) is recognized as one of the most significant technological breakthroughs in the Architecture, Engineering, and Construction (AEC) industry. The pace of implementation of BIM in AEC has increased during the past decade with an enhanced focus on sustainable construction. However, BIM implementation lags its potential because of several factors such as readiness issues, lack of previous experience in BIM, and lack of market demand for BIM. To evaluate and solve these issues, understanding the current BIM implementation in construction organizations is required. Motivated by this need, the main objective of this study is to propose a tool for the measurement of BIM implementation levels within an organization. Various sets of indexes are developed based on their pertinent Critical Success Factors (CSFs). A detailed literature review followed by a questionnaire survey involving 99 respondents is conducted, and results are analyzed to formulate a BIMp-Chart to calculate and visualize the BIM implementation level of an organization. Subsequently, the applicability of the BIMp-Chart is assessed by comparing and analyzing datasets of four organizations from different regions, including Qatar, Portugal, and Egypt, and a multinational organization to develop a global measurement tool. Through measuring and comparing BIM implementation levels, the BIMp-Chart can help the practitioners identify the implementation areas in an organization for proper BIM implementation. This study helps understand the fundamental elements of BIM implementation and provides a decision support system for construction organizations to devise proper strategies for the effectual management of the BIM implementation process.

Description: The electricity sector aims to achieve a balanced progress in all three dimensions of the energy trilemma: affordability, decarbonisation and security of supply. Separate strategies for decarbonisation and security of supply have been pursued; each with close attention to minimising costs, thus consistent with the affordability aspect of the trilemma. However, while it is evident that the pathway for decarbonisation increases pressure on security of supply, the pressures that cost-minimising security of supply measures are putting on decarbonisation goes unaddressed. The United Kingdom (UK) is a global leader in the transition towards a decarbonised economy and aims to achieve net-zero emissions by 2050. As a major part of the UK, Great Britain (GB) has achieved greater than 50% of low-carbon electricity generation and the grid’s carbon intensity has dropped by 36% over the period 2015–2019. However, balancing services that provide security of supply uses only 8% of low-carbon generation. Their carbon intensity is double the grid’s average and this gap is widening. This is an effect of a systemic reliance on carbon-intensive fuels. Financial support for capital investment for flexible low-carbon technologies is much needed. The GB context suggests that an integrated strategy covering all three dimensions of the trilemma might achieve an improved balance between them and unlock an affordable, net-zero emissions and secure power system.

Description: Deep learning has recently emerged as a successful approach to produce accurate subgrid-scale (SGS) models for Large Eddy Simulations (LES) in combustion. However, the ability of these models to generalize to configurations far from their training distribution is still mainly unexplored, thus impeding their application to practical configurations. In this work, a convolutional neural network (CNN) model for the progress-variable SGS variance field is trained on a canonical premixed turbulent flame and evaluated a priori on a significantly more complex slot burner jet flame. Despite the extensive differences between the two configurations, the CNN generalizes well and outperforms existing algebraic models. Conditions for this successful generalization are discussed, including the effect of the filter size and flame–turbulence interaction parameters. The CNN is then integrated into an analytical reaction rate closure relying on a single-step chemical source term formulation and a presumed beta PDF (probability density function) approach. The proposed closure is able to accurately recover filtered reaction rate values on both training and generalization flames.

Description: The SARS-CoV-2 virus has spread worldwide, and the World Health Organization has declared COVID-19 pandemic, proclaiming that the entire world must overcome it together. The chest X-ray and computed tomography datasets of individuals with COVID-19 remain limited, which can cause lower performance of deep learning model. In this study, we developed a model for the diagnosis of COVID-19 by solving the classification problem using a self-supervised learning technique with a convolution attention module. Self-supervised learning using a U-shaped convolutional neural network model combined with a convolution block attention module (CBAM) using over 100,000 chest X-Ray images with structure similarity (SSIM) index captures image representations extremely well. The system we proposed consists of fine-tuning the weights of the encoder after a self-supervised learning pretext task, interpreting the chest X-ray representation in the encoder using convolutional layers, and diagnosing the chest X-ray image as the classification model. Additionally, considering the CBAM further improves the averaged accuracy of 98.6%, thereby outperforming the baseline model (97.8%) by 0.8%. The proposed model classifies the three classes of normal, pneumonia, and COVID-19 extremely accurately, along with other metrics such as specificity and sensitivity that are similar to accuracy. The average area under the curve (AUC) is 0.994 in the COVID-19 class, indicating that our proposed model exhibits outstanding classification performance.

Description: Defective hard candies are usually produced due to inadequate feeding or insufficient cooling during the candy production process. The human-based inspection strategy needs to be brought up to date with the rapid developments in the confectionery industry. In this paper, a detection and classification method for defective hard candies based on convolutional neural networks (CNNs) is proposed. First, the threshold_li method is used to distinguish between hard candy and background. Second, a segmentation algorithm based on concave point detection and ellipse fitting is used to split the adhesive hard candies. Finally, a classification model based on CNNs is constructed for defective hard candies. According to the types of defective hard candies, 2552 hard candies samples were collected; 70% were used for model training, 15% were used for validation, and 15% were used for testing. Defective hard candy classification models based on CNNs (Alexnet, Googlenet, VGG16, Resnet-18, Resnet34, Resnet50, MobileNetV2, and MnasNet0_5) were constructed and tested. The results show that the classification performances of these deep learning models are similar except MnasNet0_5 with the classification accuracy of 84.28%, and the Resnet50-based classification model is the best (98.71%). This research has certain theoretical reference significance for the intelligent classification of granular products.

Description: Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

Description: Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This includes not only forecasting the expected level, but also putting error bounds on the forecast. The National Electricity Market (NEM) in Australia operates on a five minute basis. We used statistical forecasting tools to generate forecasts with prediction intervals, trialing them on one wind and one solar farm. In classical time series forecasting, construction of prediction intervals is rudimentary if the error variance is constant—termed homoscedastic. However, if the variance changes—either conditionally as with wind farms, or systematically because of diurnal effects as with solar farms—the task is much more complicated. The tools were trained on segments of historical data and then tested on data not used in the training. Results from the testing set showed good performance using metrics, including Coverage and Interval Score. The methods used can be adapted to various time scales for short term forecasting.

Description: Bisphosphonates are widely used in the treatment of women at risk of osteoporotic hip fracture; however, the overall effectiveness of bisphosphonates in the prevention of osteoporotic fractures has not been studied in real life. To investigate whether the use of bisphosphonates in women aged 50 years and over is associated with a decrease in hospitalization for osteoporotic hip fractures, a historical prospective cohort study was conducted between 2009 and 2016 from a permanent representative sample consisting of 1/97 of the French health insurance beneficiaries. Bisphosphonate use was defined according to medication persistence and adherence regarding bisphosphonate dispensations. The primary outcome was the hospitalization rate for osteoporotic hip fracture. Among the 81,268 women included, 2005 were exposed to bisphosphonates. The median time of bisphosphonate exposure was 12 (IQR, 3–29) and 17 (IQR, 5–42) months for the persistence and adherence definitions, respectively. Exposure to bisphosphonates was not associated with a decrease in hospitalization for hip fracture: weighted HRadherence = 0.66 (95% CI, 0.33 to 1.33); HRpersistance = 0.77 (95% CI, 0.38 to 1.57). In real life, bisphosphonate use does not appear to reduce hospitalization for hip fractures, as to date, it is probably prescribed as primary prevention and for a duration too short to be effective.

Description: Measuring innovation is a challenging but essential task to improve business performance. To tackle this task, key performance indicators (KPIs) can be used to measure and monitor innovation. The objective of this study is to explore how KPIs, designed for measuring innovation, are used in practice. To achieve this objective, the author draws upon literature on business performance in accounting and innovation, yet moves away from the functional view. Instead, the author focuses explicitly on how organizational members, through their use of KPIs in innovation, make sense of conflicting interpretations and integrate them into their practices. A qualitative in-depth case study was conducted at the innovation department of an organization in the process industry that operates production sites and sales organizations worldwide. In total, 28 interviews and complementary observations were undertaken at several organizational levels (multi-level). The empirical evidence suggests that strategic change, attributed to commoditization, affects the predetermined KPIs in use. Notably, these KPIs in innovation are used, despite their poor fit to innovation subject to commoditization. From a relational perspective, this study indicates that in innovation, KPIs are usually complemented by or supplemented with other information, as stand-alone KPIs exhibit a significant degree of incompleteness. In contrast to conventional studies in innovation and management accounting, this study explores the use of key performance indicators (KPIs) in innovation from an interpretative perspective. This perspective advances our understanding of the actual use of KPIs and uncovers the complexity of accounting and innovation, which involve numerous angles and organizational levels. Practically, the findings of this study will inform managers in innovation about the use of KPIs in innovation and the challenges individual organizational members face when using them. In innovation, KPIs appear to be subjective and used in unintended ways. Thus, understanding how KPIs are used in innovation is a game of reading between the lines, and these KPIs can be regarded as misfits.

Description: Previous studies have shown that the physical functioning of older adults directly affects their depressive symptoms, and suicide is also closely associated with depression. This study determined the effects of physical functioning on depressive symptoms and suicidal ideation among older Korean adults. This study used data from the 2017 National Survey of Older Persons. Among the 10,299 participants in the entire data set, 10,083 participants were analyzed, excluding 216 participants who did not respond to the dependent variables. Data analyses included frequency, chi-squared tests, and binary logistic regression. The results indicated that physical functioning among older adults was associated with reduced depressive symptoms and suicidal ideation. Compared to the group that had non-limited activities of daily living (ADL) function, the group with limitations was 1.66 times more likely to show depressive symptoms (OR: 1.66, 95% CI: 1.36–2.02). Similar trends were observed in instrumental activities of daily living (IADL) (OR: 1.85, 95% CI: 1.58–2.16). When suicidal ideation was set as a dependent variable, IADL had a statistically significant impact (OR: 1.41, 95% CI: 1.14–1.74); however, ADL did not seem to have an impact. Moreover, both ADL (OR: 1.62, 95% CI: 1.35–1.94) and IADL (OR: 1.72, 95% CI: 1.49–1.97) had statistically significant effects when combined with depressive symptoms and suicidal ideation. Better physical functioning was associated with a reduction in depressive symptoms and suicidal ideation. This study emphasizes the importance of physical functioning when examining older adults’ mental health.

Description: Handover usually deals with the mobility of the end users in a mobile network to assure about the ongoing session of a user. It is observed that frequent handover results in call dropping due to latency. In order to overcome this issue, a fuzzy based handover decision scheme for mobile devices using a predictive model is proposed. First, an MFNN (Multi-layer Feed Forward Network) is used to determine the next cell of the user along with best hand off time. To obtain the best access network, multiple-attribute Access Network Selection Function (ANSF) is used. The fuzzy rule is applied by considering the parameter data rate, reliability, signal strength, battery power and mobility as input and the output obtained is the optimal network. The proposed scheme selects the best access network and enhances the quality of services.

Description: The accurate and timely assessment of pasture quantity and quality (i.e., nutritive characteristics) is vital for effective pasture management. Remotely sensed data can be used to predict pasture quantity and quality. This study investigated the ability of Sentinel-2 multispectral bands, convolved from proximal hyperspectral data, in predicting various pasture quality and quantity parameters. Field data (quantitative and spectral) were gathered for experimental plots representing four pasture types—perennial ryegrass monoculture and three mixtures of swards representing increasing species diversity. Spectral reflectance data at the canopy level were used to generate Sentinel-2 bands and calculate normalised difference indices with each possible band pair. The suitability of these indices for prediction of pasture parameters was assessed. Pasture quantity parameters (biomass and Leaf Area Index) had a stronger influence on overall reflectance than the quality parameters. Indices involving the 1610 nm band were optimal for acid detergent fibre, crude protein, organic matter and water-soluble carbohydrate concentration, while being less affected by biomass or LAI. The study emphasises the importance of accounting for the quantity parameters in the spectral data-based models for pasture quality predictions. These explorative findings inform the development of future pasture quantity and quality models, particularly focusing on diverse swards.

Description: One of the key challenges of developing countries is to tackle the mitigation of the impacts of rapid and uncontrolled urbanization. Assessing this phenomenon is crucial to lessen the consequences for the environment and society. ‘Literature has been concentrated in planning strategies for the cities’ adaptation and engagements to the principles of green development ensuring a long-term quality of life for their citizens. Hereby, smart technologies and applications consist of two of the most encouraging concepts for solutions for achieving the 2030 and 2050 horizon targets towards clean energy transition and carbon neutrality. In academia, scholars have already raised the importance of ‘smartness’ to define the adaptative patterns for the global pressures of climate change and uncontrolled urban growth. The mitigation of these phenomena is crucial to ensure the cities’ future and lessen their impacts. This study seeks a strategic and smart-driven vision to leverage smartness on the phenomenon of rapid urbanization that occurred in the case of Beijing, China. Defining and evaluating the environmental impacts in line with the RIAM approach as one of its main targets. Future works can be focused on addressing solutions in similar cases in further developing countries to not only overcome environmental, but also economic, social, and digital complications.

Description: The objective of this paper is to evaluate the contribution of energy storage systems to resource adequacy of power systems experiencing increased levels of renewables penetration. To this end, a coherent methodology for the assessment of system capacity adequacy and the calculation of energy storage capacity value is presented, utilizing the Monte Carlo technique. The main focus is on short-duration storage, mainly battery energy storage systems (BESS), whose capacity values are determined for different power and energy configurations. Alternative operating policies (OPs) are implemented, prioritizing system cost or reliability, to demonstrate the significant effect storage management may have on its contribution to system adequacy. A medium-sized island system is used as a study case, applying a mixed integer linear programming (MILP) generation scheduling model to simulate BESS and system operation under each OP, in order to determine capacity contribution and overall performance in terms of renewable energy sources (RES) penetration, system operating cost and BESS lifetime expectancy. This study reveals that BESS contribution to system adequacy can be significant (capacity credit values up to ~85%), with energy capacity proving to be the most significant parameter. Energy storage may at the same time enhance system reliability, reduce generation cost and support RES integration, provided that it is appropriately managed; a combined reliability-oriented and cost-driven management approach is shown to yield optimal results.

Description: The physical-geographical features of the mountainous area of Montenegro cause difficulties in farmers’ life and work. The organization of the agricultural production faces a number of problems that limit the overall development of rural areas. Some agricultural crops, such as raspberry, have found optimal growth conditions and produce appropriate yields associated with good fruit quality in such mountainous area. The Willamette variety dominates the production and has broadly expanded, as some new varieties, most notably, Tulameen, Fertȍdi Zamatos, and Glen Ample. The aim of this paper was to examine the biological and production characteristics of two raspberry varieties—one floricane (Tulameen) and one primocane (Polka)—grown in two localities. We confirmed the general rule that at lower altitudes, these varieties (570 m a.s.l, Bijelo Polje) are characterized by earlier vegetation, flowering, and fruit ripening in comparison to plants at higher altitude (1040 m a.s.l, Mojkovac). The Tulameen variety started flowering on 15 May in Bijelo Polje (the flowering phenophase lasted for 29 days, until 12 June) and on 25 May in Mojkovac (the flowering phenophase lasted for 27 days, until 20 June). That is a delay of 11 days in relation to different locations. The Polka variety started flowering on 25 June in Bijelo Polje (57 days, until 20 August) and on 1 July in Mojkovac (flowering for 67 days, until 5 September). That is a delay of 7 days. The Tulameen variety started maturation on 14 June in Bijelo Polje (maturation lasted for 27 days, until 10 July) and on 22 June in Mojkovac (26 days, until 17 July), with a delay of 9 days. The Polka variety started maturation on 23 July in Bijelo Polje (the maturation phenophase lasted for 55 days, until 15 September) and on 5 August in Mojkovac (52 days, until 25 September), with a delay of 14 days. The results showed that the Polka variety had significantly higher total phenol content than the Tulameen variety (4.43 and 4.03 mg, respectively). In terms of locality, the Mojkovac raspberries had higher total phenol content than the Bijelo Polje raspberries. Polka raspberries also had a higher content of total flavonoids than Tulameen, whereas differences between localities in regard to the content of total flavonoids in the fruit were not significant. The content of condensed tannins and gallotannins in the raspberry fruit was similar in relation to both varieties and localities. Finally, the total antioxidant capacity was significantly higher in the Polka compared to the Tulameen variety, whereas the differences between localities were not statistically significant.

Description: This research proposes a generic smart cloud-based system in order to accommodate multiple scenarios where agriculture farms using Internet of Things (IoTs) need to be monitored remotely. The real-time and stored data are analyzed by specialists and farmers. The cloud acts as a central digital data store where information is collected from diverse sources in huge volumes and variety, such as audio, video, image, text, and digital maps. Artificial Intelligence (AI) based machine learning models such as Support Vector Machine (SVM), which is one of many classification types, are used to accurately classify the data. The classified data are assigned to the virtual machines where these data are processed and finally available to the end-users via underlying datacenters. This processed form of digital information is then used by the farmers to improve their farming skills and to update them as pre-disaster recovery for smart agri-food. Furthermore, it will provide general and specific information about international markets relating to their crops. This proposed system discovers the feasibility of the developed digital agri-farm using IoT-based cloud and provides solutions to problems. Overall, the approach works well and achieved performance efficiency in terms of execution time by 14%, throughput time by 5%, overhead time by 9%, and energy efficiency by 13.2% in the presence of competing smart farming baselines.

Description: The COVID-19 pandemic is a public health emergency concern and a challenge to students’ mental health due to changes in education and social isolation. The aim of this research was to expand knowledge about the relations that shape the level of anxiety amongst men and women who are studying during the pandemic in terms of the relations towards their sense of social support and their nutritional behaviors. A State–Trait Anxiety Inventory was used to measure anxiety level, alongside supplementary questions such as the feeling of support from close ones, concentration of attention on nutrition during the pandemic and externally derived factors (university, specialization). Analysis of the regression was applied to the examination of the dependency between the anxiety level (in both forms of its occurrence—as state-anxiety and as trait-anxiety). We observed that the pandemic situation affected a level of state-anxiety above average (mean value of 46–48 points) even when students felt social support. Nutrition habits and chosen education type are associated with trait-anxiety level, which was also elevated (mean values of 49–50 points). Chosen factors had a partial influence on the anxiety level of students, therefore their mental health should concern shaping positive nutrition habits and social support.

Description: In this paper, we construct novel numerical algorithms to solve the heat or diffusion equation. We start with 105 different leapfrog-hopscotch algorithm combinations and narrow this selection down to five during subsequent tests. We demonstrate the performance of these top five methods in the case of large systems with random parameters and discontinuous initial conditions, by comparing them with other methods. We verify the methods by reproducing an analytical solution using a non-equidistant mesh. Then, we construct a new nontrivial analytical solution containing the Kummer functions for the heat equation with time-dependent coefficients, and also reproduce this solution. The new methods are then applied to the nonlinear Fisher equation. Finally, we analytically prove that the order of accuracy of the methods is two, and present evidence that they are unconditionally stable.

Description: IP fragmentation is still prevalent on the Internet. Defragmented traffic is a prerequisite for many network processing algorithms. This work focuses on the size and organization of a flow table, which is an essential ingredient of the hardware IP defragmentation block. Previous research suggests that fragmented IP traffic is highly local, and a relatively small flow table (on the order of a thousand entries) can process most of the traffic. Samples of IP traffic were obtained from public data sources and used for a statistical analysis, revealing the key factors in achieving design goals. The findings were backed by an extensive design space exploration of the software defragmentation model, which resulted in the efficiency estimates. To provide a robust score of the simulation model, a new validation technique is employed that helps to overcome the limitations of the samples.

Description: As gestational diabetes, which is increasing steadily around the world, can cause complications in the mother and fetus, it is essential to change eating habits and eating behavior to prevent this. According to the 2020 American Diabetes Association recommendations, the food plan should be designed for the adequate calorie intake to achieve glycemic goals and consequently promote maternal and fetal health. Thus, the following study has used the qualitative theme analysis method to assess what it means for 28 South Korean women, who were diagnosed with gestational diabetes for the first time, to change their eating habits and behaviors. As a result, themes were derived related to reflection on daily life, formation of new relationships in the same group, efforts that must be made, rediscovery of couples, and lifestyles reborn as new roles. Based on the results of the study, it is shown that the study participants recovered the peace in their mental state after the crisis of gestational diabetes to pursue relaxation and ultimately higher quality of life by following the plan to fulfill healthy achievements, such as changing their eating habits and behaviors. Therefore, future research and support measures to help the healthy behaviors should be sought by comprehensively exploring the effects of women’s experiences in changing their eating habits and behaviors.

Description: Among the various factors that could influence health-related quality of life (HRQoL) in adolescence, body mass index (BMI) seems to play a key role as a main anthropometric parameter. The aim of this cross-sectional study was to determine, in a sample of Italian adolescents, whether HRQoL is associated with the different weight status categories (underweight, normal weight, overweight, obese), according to BMI cut-off points for children, even considering sex differences. Data were collected from 1707 adolescents (n = 828 males) in seven schools. HRQoL was analyzed using the Italian version of KIDSCREEN-52. Males were more overweight and obese than females (13% vs. 10% and 4% vs. 2%, p 〈 0.05, respectively). In females, BMI categories are associated with physical well-being (p 〈 0.05), emotion/mood (p 〈 0.05), self-perception (p 〈 0.001), financial resources (p 〈 0.05), and bullying behavior (p 〈 0.05). In males, weight status is linked to physical well-being dimension (p 〈 0.001) and perception of self (p 〈 0.05). Our results may suggest that there is an association between weight status categories and HRQoL, more pronounced in females than in males. Interestingly, the weight status correlated more with the psychological dimension mainly in females, whereas in males, a stronger association between weight and physical status was observed, suggesting that given the complex, multifaceted, and dynamic nature of relationship between health-related quality of life and weight status in adolescents, multiple factors must be considered.

Description: The treatment of chronic pelvic pain (CPP) with botulinum neurotoxin type A (BoNT/A) has increased lately, but more studies assessing its effect are needed. This study aimed to evaluate the evolution of patients after BoNT/A infiltration and identify potential responders to treatment. Twenty-four women with CPP associated with dyspareunia were treated with 90 units of BoNT/A injected into their pelvic floor muscle (PFM). Clinical status and PFM activity were monitored in a previous visit (PV) and 12 and 24 weeks after the infiltration (W12, W24) by validated clinical questionnaires and surface electromyography (sEMG). The influence of patients’ characteristics on the reduction in pain at W12 and W24 was also assessed. After treatment, pain scores and the impact of symptoms on quality of life dropped significantly, sexual function improved and sEMG signal amplitude decreased on both sides of the PFM with no adverse events. Headaches and bilateral pelvic pain were risk factors for a smaller pain improvement at W24, while lower back pain was a protective factor. Apart from reporting a significant clinical improvement of patients with CPP associated with dyspareunia after BoNT/A infiltration, this study shows that clinical characteristics should be analyzed in detail to identify potential responders to treatment.

Description: The use of gradient descent training to optimize the performance of a rule-fact network expert system via updating the network’s rule weightings was previously demonstrated. Along with this, four training techniques were proposed: two used a single path for optimization and two use multiple paths. The performance of the single path techniques was previously evaluated under a variety of experimental conditions. The multiple path techniques, when compared, outperformed the single path ones; however, these techniques were not evaluated with different network types, training velocities or training levels. This paper considers the multi-path techniques under a similar variety of experimental conditions to the prior assessment of the single-path techniques and demonstrates their effectiveness under multiple operating conditions.

Description: The article presents the results of preliminary research aimed at determining the possibility of using microencapsulated phase change material (mPCM) slurries as a working fluid in installations with a flat liquid solar collector. In the tests, the following were used as the working fluid: water (reference liquid) and 10% wt. and 20% wt. of an aqueous solution of the product under the trade name MICRONAL® 5428 X. As the product contained 43% mPCM, the mass fraction of mPCM in the working liquid was 4.3% and 8.6%, respectively. The research was carried out in laboratory conditions in the range of irradiance I = 250–950 W/m2. Each of the three working fluids flowed through the collector in the amount of 20 kg/h, 40 kg/h, and 80 kg/h. The working fluid was supplied to the collector with a constant temperature Tin = 20 ± 0.5 °C. It was found that the temperature of the working fluid at the collector outlet increases with the increase in the radiation intensity, but the temperature achieved depended on the type of working fluid. The greater the share of mPCM in the working liquid, the lower the temperature of the liquid leaving the solar collector. It was found that the type of working fluid does not influence the achieved thermal power of the collector. The negative influence of mPCM on the operation of the solar collector was not noticed; the positive aspect of using mPCM in the solar installation should be emphasized—the reduced temperature of the medium allows the reduction in heat losses to the environment from the installation, especially in a low-temperature environment.

Description: Temperature change is an important meteorological indicator reflecting weather stability. This study aimed to examine the effects of ambient temperature change on non-accidental mortality using diurnal temperature change (DTR) and temperature change between neighboring days (TCN) from two perspectives, intra-day and inter-day temperature change, and further, to explore seasonal variations of mortality, identify the susceptible population and investigate the interaction between temperature change and apparent temperature (AT). We collected daily data on cause-specific mortality, air pollutants and meteorological indicators in Shenzhen, China, from 1 January 2013 to 29 December 2017. A Quasi-Poisson generalized linear regression combined with distributed lag non-linear models (DLNMs) were conducted to estimate the effects of season on temperature change-related mortality. In addition, a non-parametric bivariate response surface model was used to explore the interaction between temperature change and AT. The cumulative effect of DTR was a U-shaped curve for non-accidental mortality, whereas the curve for TCN was nearly monotonic. The overall relative risks (RRs) of non-accidental, cardiovascular and respiratory mortality were 1.407 (95% CI: 1.233–1.606), 1.470 (95% CI: 1.220–1.771) and 1.741 (95% CI: 1.157–2.620) from exposure to extreme large DTR (99th) in cold seasons. However, no statistically significant effects were observed in warm seasons. As for TCN, the effects were higher in cold seasons than warm seasons, with the largest RR of 1.611 (95% CI: 1.384–1.876). The elderly and females were more sensitive, and low apparent temperature had a higher effect on temperature change-related non-accidental mortality. Temperature change was positively correlated with an increased risk of non-accidental mortality in Shenzhen. Both female and elderly people are more vulnerable to the potential adverse effects, especially in cold seasons. Low AT may enhance the effects of temperature change.

Description: We aimed to develop machine learning classifiers as a risk-prevention mechanism to help medical professionals with little or no knowledge of the patient’s languages in order to predict the likelihood of clinically significant mistakes or incomprehensible MT outputs based on the features of English source information as input to the MT systems. A MNB classifier was developed to provide intuitive probabilistic predictions of erroneous health translation outputs based on the computational modelling of a small number of optimised features of the original English source texts. The best performing multinominal Naïve Bayes classifier (MNB) using a small number of optimised features (8) achieved statistically higher AUC (M = 0.760, SD = 0.03) than the classifier using high-dimension natural features (135) (M = 0.631, SD = 0.006, p 〈 0.0001, SE = 0.004) and the automatically optimised classifier (22) (M = 0.7231, SD = 0.0084, p 〈 0.0001, SE = 0.004). Furthermore, MNB (8) had statistically higher sensitivity (M = 0.885, SD = 0.100) compared with the full-feature classifier (135) (M = 0.577, SD = 0.155, p 〈 0.0001, SE = 0.005) and the automatically optimised classifier (22) (M = 0.731, SD = 0.139, p 〈 0.0001, SE = 0.0023). Finally, MNB (8) reached statistically higher specificity (M = 0.667, SD = 0.138) compared to the full-feature classifier (135) (M = 0.567, SD = 0.139, p = 0.0002, SE = 0.026) and the automatically optimised classifier (22) (M = 0.633, SD = 0.141, p = 0.0133, SE = 0.026).

Description: Wildfires are increasing in frequency, size, and intensity, and increasingly affect highly populated areas. Wildfire smoke impacts cardiorespiratory health; children are at increased risk due to smaller airways, a higher metabolic rate and ongoing development. The objective of this systematic review was to describe the risk of pediatric respiratory symptoms and healthcare visits following exposure to wildfire smoke. Medical and scientific databases and the grey literature were searched from inception until December 2020. Included studies evaluated pediatric respiratory-related healthcare visits or symptoms associated with wildfire smoke exposure. Prescribed burns, non-respiratory symptoms and non-pediatric studies were excluded. Risk of bias was evaluated using the National Toxicology Program’s Office of Health Assessment and Translation Risk of Bias Rating Tool. Data are presented narratively due to study heterogeneity. Of 2138 results, 1167 titles and abstracts were screened after duplicate removal; 65 full text screens identified 5 pre-post and 11 cross-sectional studies of rural, urban and mixed sites from the USA, Australia, Canada and Spain. There is a significant increase in respiratory emergency department visits and asthma hospitalizations within the first 3 days of exposure to wildfire smoke, particularly in children 〈 5 years old.

Description: Rice production in Tanzania, with 67% of its territory considered semi-dry and having average annual rainfall of 300 mm, must be increased to feed an ever-growing population. Water for irrigation and low soil fertility are among the main challenges. One way to decrease water consumption in paddy fields is to change the irrigation regime for rice production, replacing continuous flooding with alternate wetting and drying. In order to assess the impact of different irrigation regimes and nitrogen fertilizer applications on growth, yield, and water productivity of rice, a greenhouse pot experiment with soil from lowland rice ecology was conducted at Sokoine University of Agriculture, Tanzania during the 2019 cropping season. The experiment was split-plot based on randomized complete block design with 12 treatments and 3 replications. Water regimes were the main factors comparing continuous flooding (CF) and alternate wetting and drying (AWD) with nitrogen fertilizer levels as the subfactor, comparing absolute control (no fertilizer) with 0 (P and K fertilizers), 60, 90, 120, and 150 kg Nha−1. Alternate wetting and drying (AWD) significantly improved water productivity by 8.3% over CF (p 〈 0.05). Water productivity (WP) ranged from 0.6 to 1.5 kg of rice per m3 of water. Average water use ranged from 36 to 82 L per season, and water saving was up to 34.3%. Alternate wetting and drying significantly improved yields (p 〈 0.05) by 13.3%, and the yield ranged from 21.8 to 118.2 g pot−1. The combination of AWD water management and 60 kg N ha−1 nitrogen fertilization application was found to be the optimal management, however there was no significant difference between 60 and 90 kg N ha−1, in which case 60 kg N ha−1 is recommended because it lowers costs and raises net income. Nitrogen levels significantly affected water productivity, water use, and number of irrigations. Nitrogen levels had significant effect (p 〈 0.05) on plant height, number of tillers, flag leaf area, chlorophyll content, total tillers, number of productive tillers, panicle weight, panicle length, 1000-grain weight, straw yield, grain yield, and grain harvest index. The results showed that less water can be used to produce more crops under alternative wetting and drying irrigation practices. The results are important for water-scarce areas, providing useful information to policy makers, farmers, agricultural departments, and water management boards in devising future climate-smart adaptation and mitigation strategies.

Description: During the COVID-19 outbreak, college students experienced different periods of isolation on campus, which has had an impact on students’ mental health. Based on ART theory, this study randomly selected students at Northwest A&F University, Shaanxi, China and distributed questionnaires in order to evaluate the psychological recovery effect of campus environment during the epidemic. The results showed that: (1) There were significant differences in the psychological restoration of four types of campus environments. Blue space had the greatest effect, followed by green space and sports grounds, while grey space had the least. (2) Time of stay had a very significant impact on psychological restoration. Longer time of exposure is not necessarily correlated with a better recovery experience. (3) In the different campus environments, extent is easier to be perceived followed by fascination and compatibility, and the weakest is being away. At the time of stay level, no significant difference was found in the perception of compatibility. Time of stay was negatively correlated with fascination and compatibility. These findings can provide theoretical and practical bases for campus environmental planning and construction following the COVID-19 epidemic.

Description: Thermodynamic analysis of a gasification process was conducted assuming that it is composed of two successive stages, namely: pyrolysis reaction followed by a stage of gasification reaction. This approach allows formulation the models of selected gasification processes dominating in industrial applications namely: Shell (coal), SES (coal), and DFB (dual fluid bed, biomass) gasification. It was shown that the enthalpy of fuel formation is essential for the correctness of computed results. The specific computational formula for a wide range of fuels enthalpy of formation was developed. The following categories were evaluated in terms of energy balance: total reaction enthalpy of gasification process, enthalpy of pyrolysis reaction, enthalpy of gasification reaction, heat demand for pyrolysis reaction, and heat demand for gasification reactions. The discussion of heat demand for particular stages of gasification related to the various processes was performed concluding the importance of the pyrolysis stage.