Agricultural producer service (APS) plays a crucial role in the sustainable development of modern agriculture. Enhancing the stability of contracts between farmers and APS is the key to promoting the high-quality development of the latter. This research aimed to explore the decision-making and moderator effects of transaction costs, service satisfaction, and the stability of APS contracts by constructing a theoretical framework. Based on survey data from 893 farmers in Northeast China’s black soil area, we employed the Mvprobit model to examine the relationship between transaction costs, service satisfaction, and contract stability. The key findings are as follows: Firstly, transaction costs have a dual impact on contract stability in agricultural productive services, acting as both inhibitors and promoters. Higher information and execution costs reduce farmers’ willingness to maintain current cooperative relationships, while higher negotiation costs make farmers more inclined to stick with the status quo. Secondly, farmers’ satisfaction with services positively moderates the influence of transaction costs on contract stability, with highly satisfied farmers being more affected than those with lower satisfaction levels. Lastly, farmers’ personal, family, and social characteristics all shape their preferences during the formation process. To mitigate cooperation risks and bolster cooperation contract stability, this study suggests that the government implement supervision and incentives to reduce transaction costs for farmers when procuring services and enhance the efficiency of farmer–service provider connections. Full article

The purpose of this study was to investigate the impact of voltage drop of a three-phase voltage source inverter on driving brushless motors. This study also proposes an enhanced field-oriented control scheme that accounts for inverter voltage drop, enabling efficient driving at very low motor speeds. Experimental results are provided to verify the theoretical study and the proposed control scheme. Full article

The blood oxygen saturation, which indicates the ratio of oxygenated hemoglobin to total hemoglobin in the blood, is closely related to one’s health status. Oxygen saturation is typically measured using a pulse oximeter. However, this method can cause skin irritation, and in situations where there is a risk of infectious diseases, the use of such contact-based oxygen saturation measurement devices can increase the risk of infection. Therefore, recently, methods for estimating oxygen saturation using facial or hand images have been proposed. In this paper, we propose a method for estimating oxygen saturation from facial images based on a convolutional neural network (CNN). Particularly, instead of arbitrarily calculating the AC and DC components, which are essential for measuring oxygen saturation, we directly utilized signals obtained from facial images to train the model and predict oxygen saturation. Moreover, to account for the time-consuming nature of accurately measuring oxygen saturation, we diversified the model inputs. As a result, for inputs of 10 s, the Pearson correlation coefficient was calculated as 0.570, the mean absolute error was 1.755%, the root mean square error was 2.284%, and the intraclass correlation coefficient was 0.574. For inputs of 20 s, these metrics were calculated as 0.630, 1.720%, 2.219%, and 0.681, respectively. For inputs of 30 s, they were calculated as 0.663, 2.142%, 2.612%, and 0.646, respectively. This confirms that it is possible to estimate oxygen saturation without calculating the AC and DC components, which heavily influence the prediction results. Furthermore, we analyzed how the trained model predicted oxygen saturation through ‘SHapley Additive exPlanations’ and found significant variations in the feature contributions among participants. This indicates that, for more accurate predictions of oxygen saturation, it may be necessary to individually select appropriate color channels for each participant. Full article

The block design test (BDT) has been used for over a century in research and clinical contexts as a measure of spatial cognition, both as a singular ability and as part of more comprehensive intelligence assessment. Traditionally, the BDT has been scored using methods that do not reflect the full potential of individual differences that could be measured by the test. Recent advancements in technology, including eye-tracking, embedded sensor systems, and artificial intelligence, have provided new opportunities to measure and analyze data from the BDT. In this methodological review, we outline the information that BDT can assess, review several recent advancements in measurement and analytic methods, discuss potential future uses of these methods, and advocate for further research using these methods. Full article

The interest in combined heat and solar power (CHP) systems has increased due to the growing demand for sustainable energy with low carbon emissions. An effective technical solution to address this requirement is using a parabolic trough solar collector (PTC) in conjunction with a Rankine cycle (RC) heat engine. The solar-powered Rankine cycle (SPRC) system is a renewable energy technology that can be relied upon for its high efficiency and produces clean energy output. This study describes developing a SPRC system specifically for electricity generation in Aden, Yemen. The system comprises parabolic trough collectors, a thermal storage tank, and a Rankine cycle. A 4E analysis of this system was theoretically investigated, and the effects of various design conditions, namely the boiler’s pinch point temperature and steam extraction from the high-pressure turbine, steam extraction from the intermediate-pressure turbine, and condenser temperature, were studied. Numerical simulations showed that the system produces a 50 MW net. The system’s exergetic and energy efficiencies are 30.7% and 32.4%. The planned system costs 2509 USD/h, the exergoeconomic factor is 79.43%, and the system’s energy cost is 50.19 USD/MWh. The system has a 22.47 kg/MWh environmental carbon footprint. It is also observed that the performance of the cycle is greatly influenced by climatic circumstances. Raising the boiler’s pinch point temperature decreases the system’s performance and raises the environmental impact. Full article

Aluminium can benefit from the high-speed forming technique known as electromagnetic forming (EMF). EMF is increasingly used in automotive applications as a result of this capability. This technology depends on Lorentz force (Magnetic force) in the practical forming application which relies on different process parameters like forming a coil. A finite element model for the EMF process is built and studied in this work using the finite element analysis software ANSYS 2022 R1. The affecting process parameters are investigated using the Design of Experiments (DOE) approach. Response Surface Methodology (RSM) of the DOE approach is used by taking process parameters such as coil size, gap, and current density into account. The number of experiments is reduced by using Central Composite Design (CCD), an RSM model. To determine the optimal level of parameters, a magnetic force optimization study is carried out. The parameters of the EMF process (e.g., magnetic force) are investigated through a developed 2D finite element model and validated with available literature. Full article

This study aimed to develop a laboratory-scale direct air capture unit for evaluating and comparing amine-based adsorbents under temperature vacuum swing adsorption conditions. The experimental campaign conducted with the direct air capture unit allowed for the determination of equilibrium loading, CO₂ uptake capacity, and other main performance parameters of the investigated adsorbent Lewatit VP OC 1065^®. The investigations also helped to understand the co-adsorption of CO₂ and H₂O on the tested material, which is crucial for improving temperature vacuum swing adsorption processes. This was achieved by obtaining pure component isotherms for CO₂ and H₂O and using three different co-adsorption isotherm models from the literature. It was found that the weighted average dual-site Toth model emerged as the most accurate and reliable model for simulating this co-adsorption behaviour. Its predictions closely align with the experimental data, particularly in capturing the adsorption equilibrium at various temperatures. It was also observed that this lab-scale unit offers advantages over thermogravimetric analysis when conducting adsorption experiments on the chosen amine. The final aim of this study is to provide a pathway to develop devices for testing and developing efficient and cost-effective adsorbents for direct air capture. Full article

Anthocyanin is an important pigment that affects plant color change. In this study, the color parameters and anthocyanin content of Ginkgo biloba seed exocarp at different periods were measured, and it was determined that the a* value (redness value) of the seed exocarp was closely related to the color change occurring during the development of the seed exocarp, and the anthocyanin content in the seed exocarp showed an increasing trend. The molecular mechanism of anthocyanin biosynthesis in Ginkgo biloba seed exocarp is still unclear. In order to further understand the molecular mechanism of color change in Ginkgo biloba seed exocarp, the regulation mechanism and accumulation mode of anthocyanin in the seed exocarp at three different periods were analyzed using transcriptomic and metabolomic. A total of four key anthocyanins were screened from the metabolome, including three kinds of Cyanidin 3-arabinoside, Malvidin 3-glucoside and Cyanidin 3-sambubioside 5-glucoside with increased content. Among them, Cyanidin 3-arabinosidehad a strong correlation with the a* value (PCC = 0.914), which have a great influence on the color change of the seed exocarp, and Delphinidin 3-O-3″,6″-O-dimalonylglucoside with decreased content might jointly affect the formation of exocarp color. The transcriptome data show that among the structural genes, ANS (Gb_33402) had the highest correlation with Cyanidin 3-arabinoside (PCC = 0.9217) and in GbANS, only Gb_33402 showed an upregulated expression trend in the three stages of seed exocarp development, which suggesting that it plays an important role in anthocyanin accumulation in the seed exocarp and it may be the key structural gene affecting the formation of seed exocarp color. Among the transcription factors, the differential expression of most transcription factors (MYB, bHLH, b-ZIP, NAC, WDR and AP2/ERF) may jointly affect the formation of seed exocarp color by promoting anthocyanin accumulation. This study elucidates the main anthocyanins that cause the color change of the seed exocarp of Ginkgo biloba and reveals the molecular regulation mechanism of anthocyanins at different developmental stages of the seed exocarp. It provides a theoretical basis and insights for understanding the color change of Ginkgo biloba seed exocarp. Full article

People spend up to 45% of their time in their living space, which has a major impact on their physical and mental health, behaviour, happiness and overall life satisfaction. In these times of rapid change and new, modern demands on living, it is necessary to understand the needs of residents and current design trends in order to incorporate both into the design of new living spaces. The aim of this study is to use the views of residents and architects collected through surveys to rank apartments and identify differences in the definition of what a high-quality apartment would be based on their spatial characteristics. The previously developed Housing Quality Assessment Model was used to assess the 126 most represented apartment layouts in the city of Osijek, Croatia, built since 1930. Apartment layouts were further divided according to the time of their construction, location, and size for comparison purposes. The research shows that residents rated apartments with higher ratings than architects in all categories and that residents and architects gave different ratings on certain characteristics of the apartment, e.g., the existence of additional storage space, the existence of a bathroom window, the size and orientation of living rooms, the type of spatial organisation, etc. Full article

Addressing the pressing issue of food waste is vital for environmental sustainability and resource conservation. While computer vision has been widely used in food waste reduction research, existing food image datasets are typically aggregated into broad categories (e.g., fruits, meat, dairy, etc.) rather than the fine-grained singular food items required for this research. The aim of this study is to develop a model capable of identifying individual food items to be integrated into a mobile application that allows users to photograph their food items, identify them, and offer suggestions for recipes. This research bridges the gap in available datasets and contributes to a more fine-grained approach to utilising existing technology for food waste reduction, emphasising both environmental and research significance. This study evaluates various (n = 7) convolutional neural network architectures for multi-class food image classification, emphasising the nuanced impact of parameter tuning to identify the most effective configurations. The experiments were conducted with a custom dataset comprising 41,949 food images categorised into 20 food item classes. Performance evaluation was based on accuracy and loss. DenseNet architecture emerged as the top-performing out of the seven examined, establishing a baseline performance (training accuracy = 0.74, training loss = 1.25, validation accuracy = 0.68, and validation loss = 2.89) on a predetermined set of parameters, including the RMSProp optimiser, ReLU activation function, ‘0.5’ dropout rate, and a $160\times 160$ image size. Subsequent parameter tuning involved a comprehensive exploration, considering six optimisers, four image sizes, two dropout rates, and five activation functions. The results show the superior generalisation capabilities of the optimised DenseNet, showcasing performance improvements over the established baseline across key metrics. Specifically, the optimised model demonstrated a training accuracy of 0.99, a training loss of 0.01, a validation accuracy of 0.79, and a validation loss of 0.92, highlighting its improved performance compared to the baseline configuration. The optimal DenseNet has been integrated into a mobile application called FridgeSnap, designed to recognise food items and suggest possible recipes to users, thus contributing to the broader mission of minimising food waste. Full article

In order to solve the problems of methods that use a single form of sensing, the ease of causing deformation damage to the targets with a low hardness during grasping, and the slow sliding inhibition of a prosthetic hand when the grasping target slides, which are problems that exist in most current intelligent prosthetic hands, this study introduces an adaptive control strategy for prosthetic hands based on multi-sensor sensing. Using a force-sensing resistor (FSR) to collect changes in signals generated after contact with a target, a prosthetic hand can classify the target’s hardness level and adaptively provide the desired grasping force so as to reduce the deformation of and damage to the target in the process of grasping. A fiber-optic sensor collects the light reflected by the object to identify its surface roughness, so that the prosthetic hand adaptively adjusts the sliding inhibition method according to the surface roughness information to improve the grasping efficiency. By integrating information on the hardness and surface roughness of the target, an adaptive control strategy for a prosthetic hand is proposed. The experimental results showed that the adaptive control strategy was able to reduce the damage to the target by enabling the prosthetic hand to achieve stable grasping; after grasping the target with an initial force and generating sliding, the efficiency of slippage inhibition was improved, the target could be stably grasped in a shorter time, and the hardness, roughness and weight ranges of targets that could be grasped by the prosthetic hand were enlarged, thus improving the success rate of stable grasping under extreme conditions. Full article

This qualitative study employs a thematic network analysis of the literature to explore the implications and evaluations of the ‘Great Reset’. Delving into how complexity theory, especially the principles related to complex systems and coordination, can be applied, this research aims to promote resilience and facilitate comprehensive systemic transformation. This study identifies critical ‘Great Reset’ components that align or conflict with stakeholders’ needs, objectives, and capabilities by examining how diverse stakeholders function within intricate and interconnected systems. Full article

Based on Thermodynamics and its well-established First and Second Laws, this work presents and explores their economics counterparts, introducing new concepts, variables, and equations. This includes, among others, the economic counterparts of temperature, reversibility and irreversibility, and entropy and entropy generation resulting from economic irreversibility. The meaning of the new concepts, variables, equations, and their messages are introduced and discussed considering simple yet relevant economic processes. The economic counterparts of the First and Second Law balance equations are set in addition to the base concepts and Laws. These are effective and valuable tools for the analysis of economic processes. Observations from selected economic activities are analyzed using the new concepts, variables, and equations. Full article

In rural planning, ensuring sustainable water management is pivotal, particularly in addressing the challenges posed by the absence of comprehensive rural water security assessments. Despite the abundance of water resources in mountainous regions, their accessibility and utilization remain significant hurdles for local populations, often hindering sustainable development. This study proposed a rural water accessibility (RWA) model, focusing on village settlements (VSs) as fundamental units for water utilization. The model examines two critical aspects of mountainous water security that are essential for sustainability: the supply–demand relation between VSs and their water sources, and the water availability to characterize difficulties in obtaining water sources in complex terrain. Using data from 1156 natural VSs in Dongchuan District, Kunming, water demand was calculated based on population and local average rural water demand per person. Springs and streams were identified as main and standby water sources, respectively. The RWA model evaluates the supply–demand balance and assesses water availability using the least-cost path (LCP) method. The results establish RWA grades, indicating water security conditions for VSs. This approach effectively identifies supply–demand relations and determines water demand gaps, facilitating targeted water management in rural areas, especially during droughts. It enables managers to accurately gauge the grade of water accessibility for each VS, allowing for prompt and tailored emergency water supply interventions. Furthermore, aggregating the RWA of each VS can provide valuable insights for devising sustainable water management strategies in mountainous regions. Full article

Susurluk Basin is among the basins that may be most affected by drought risk due to its agricultural, economic, and natural resources. In this study, regional hydrological drought risk models were developed for water supply systems in the Susurluk Basin, Turkey. Twenty-four flow observation sites with 25 years or more of data showing natural flow characteristics as much as possible were converted into daily flow data with Q₇, Q₁₅, Q₃₀, and Q₆₀ low-flow indexes. Regionalization was carried out by two-stage multivariate cluster and principal component analysis using the basins’ physical and hydrological characteristics and low-flow statistics, and two homogeneous regions were obtained due to the discordancy, heterogeneity, and goodness of fit tests, which are L-moment approaches. Regional models were performed with ordinary and principal component regression techniques using the physical and hydrological characteristics of the watersheds and regional low-flow frequency analysis. The cross-validation procedure results for ungauged basins show that ordinary regression models are more effective in the lowland first region. In contrast, principal component regression models are more suitable for the mountainous second region. This study’s findings, which are a first for the Susurluk Basin, will have important results in terms of agricultural water management in the region and will help the water authority in water allocation. To investigate whether human impact and climate change impact the prediction of hydrological drought, we recommend seasonal non-stationary frequency analysis with the addition of useful empirical hydrological drought indexes. Full article

Early-onset Alzheimer’s disease (EOAD), defined as Alzheimer’s disease onset before 65 years of age, has been significantly less studied than the “classic” late-onset form (LOAD), although EOAD often presents with a more aggressive disease course, caused by variants in the APP, PSEN1, and PSEN2 genes. EOAD has significant differences from LOAD, including encompassing diverse phenotypic manifestations, increased genetic predisposition, and variations in neuropathological burden and distribution. Phenotypically, EOAD can be manifested with non-amnestic variants, sparing the hippocampi with increased tau burden. The aim of this article is to review the different genetic bases, risk factors, pathological mechanisms, and diagnostic approaches between EOAD and LOAD and to suggest steps to further our understanding. The comprehension of the monogenic form of the disease can provide valuable insights that may serve as a roadmap for understanding the common form of the disease. Full article

The ability to model the kinetics of wine fermentation enables the early detection of abnormal, sluggish or stuck fermentations, and the prediction of present and future rates of energy, CO₂ and ethanol vapor release. While several wine fermentation models have been published, there are only a few that have been successfully adopted for commercial practice. In this work, the mathematical descriptions of wine fermentation are reviewed and compared. The common features of these include descriptions for the kinetics of yeast growth; substrate and nutrient consumption; product formation; and total and viable cell mass. Additional features include the inhibition of growth by ethanol; competitive inhibition of glucose and fructose uptake; glycerol formation; and the calculation of density from solution composition and solute properties. Three models were selected to compare their ability to describe the sugar, ethanol, biomass and nitrogen of benchtop fermentations at four temperatures, previously published. The models are assessed for their goodness of fit to the data in synthetic-medium fermentations and their suitability for analyzing and predicting commercial wine fermentations. Full article

To solve the problem of urban freight demand and build an efficient, practical, intelligent, green, and sustainable new logistics system, this paper considers the application of the subway network to urban freight transportation and studies the location problem of subway transit stations in the urban freight network. According to the differences between different subway stations, the nodal degree, medial centrality, proximity centrality, and regional accessibility are proposed as the evaluation indexes, and the improved fuzzy analytic hierarchy method and entropy weight method are used to calculate the index weight. The TOPSIS evaluation method is used to evaluate the importance of each subway station, and the importance evaluation model of subway stations is constructed. Combined with the distribution location and transportation demand of urban express delivery outlets, a two-tier planning model for the location of subway transfer stations was constructed with total cost and customer satisfaction as the objective functions, and the case studies were carried out by taking Jiangning District, Lishui District, and Gaochun District of Nanjing as the research objects. The results show that Hohai University Focheng West Road, Zhengfang Middle Road, Qunli, and Gaochun can be transformed into subway transfer stations and used as transshipment centers of the urban cargo transportation network. Compared with the original ground transportation network, 52.87% of the ground transportation distance in the optimized transportation network is replaced by subway transportation, and the total cost of logistics transportation is reduced by 8.73%, which verifies the feasibility of subway for urban cargo transportation, reduces logistics transportation costs, and relieves the pressure of ground transportation, which is of great significance for the sustainable development of urban logistics. Full article

Financial inclusion (FI) relates to the access and availability of financial services to society, especially in low-income groups. FI is pivotal in achieving 7 of the 17 Sustainable Development Goals (SDGs). This paper explores the level of FI in the rural areas of Maharashtra and measures the impact of FI on the social conditions of Self-Help Groups (SHGs) prevalent in these areas. The study is based on a 424 SHGs survey conducted in the Pune, Thane, and Palghar districts of Maharashtra, India. The impact of FI on SHGs is evaluated using a Structural Equation Model (SEM). The results of the study show that physical banking services, Business Facilitators (BFs), and Business Correspondents (BCs) improve the social conditions of rural SHGs. Additionally, BCs and BFs mediate the relationship between physical banking services and social conditions. The study also reveals an insignificant association between BCs and BFs and insurance services. The present study highlights the importance of increasing the awareness of insurance policies through financial literacy programs and making timely availability and accessibility of BCs and BFs to enhance financial inclusion in rural areas. Full article

Road safety is a serious concern worldwide, and traffic signs play a critical role in confirming road safety, particularly in the context of AVs. Therefore, there is a need for ongoing advancements in traffic sign evaluation methodologies. This paper comprehensively analyzes the relationship between traffic sign retroreflectivity and LiDAR intensity to enhance visibility and communication on road networks. Using Python 3.10 programming and statistical techniques, we thoroughly analyzed handheld retroreflectivity coefficients alongside LiDAR intensity data from two LiDAR configurations: 2LRLiDAR and 1CLiDAR systems. The study focused specifically on RA1 and RA2 traffic sign classes, exploring correlations between retroreflectivity and intensity and identifying factors that may impact their performance. Our findings reveal variations in retroreflectivity compliance rates among different sign categories and color compositions, emphasizing the necessity for targeted interventions in sign design and production processes. Additionally, we observed distinct patterns in LiDAR intensity distributions, indicating the potential of LiDAR technology for assessing sign visibility. However, the limited correlations between retroreflectivity and LiDAR intensity underscore the need for further investigation and standardization efforts. This study provides valuable insights into optimizing traffic sign effectiveness, ultimately contributing to improved road safety conditions. Full article

Cochlear implants are crucial for addressing severe-to-profound hearing loss, with the success of the procedure requiring careful electrode placement. This scoping review synthesizes the findings from 125 studies examining the factors influencing insertion forces (IFs) and intracochlear pressure (IP), which are crucial for optimizing implantation techniques and enhancing patient outcomes. The review highlights the impact of variables, including insertion depth, speed, and the use of robotic assistance on IFs and IP. Results indicate that higher insertion speeds generally increase IFs and IP in artificial models, a pattern not consistently observed in cadaveric studies due to variations in methodology and sample size. The study also explores the observed minimal impact of robotic assistance on reducing IFs compared to manual methods. Importantly, this review underscores the need for a standardized approach in cochlear implant research to address inconsistencies and improve clinical practices aimed at preserving hearing during implantation. Full article

Finite Control Set Model Predictive Control (FCS-MPC) has the ability to achieve multi-objective optimization, but there are still many challenges. The key to realizing multi-objective optimization in FCS-MPC lies in the design of the cost function. However, the different dimensions of penalty terms in the cost function often lead to difficulties in designing weighting coefficients. Incorrect weighting coefficients may result in truncation errors in calculations of DSPs and FPGAs, thereby affecting the algorithm’s control performance. Therefore, this article focuses on a system driving an induction motor with a three-level Neutral Point Clamped (NPC) inverter, and selects stator current and switching frequency as penalty terms in the cost function. An improved method is proposed to unify the dimensions of both penalty terms in the cost function. By unifying the dimensions of the penalty terms, a simple design of weighting coefficients can be achieved. Subsequently, to balance the inverter’s switching frequency and the dynamic response performance of the motor, a composite cost function is further proposed. Finally, the rationality of the proposed method is validated through simulation and experimental platforms. Full article