ALBERT

Description: Since reinforced concrete (RC) buildings have long service life, cutout openings are generally needed in their load-bearing walls as a sustainable solution in order to meet new requirements of their users. However, the cutout openings decrease the load-bearing capacity of the walls, which may result in the failure of the buildings. In this paper, we investigate the possibility of making a door opening in a load-bearing RC wall of an existing building in Gävle in Sweden. The wall studied in the current paper rests on two individual supports at its two ends; thus, it is considered as a deep beam. However, it is called an examined wall (EW) here. The StruSoft FEM-Design software is used in this study to model, analyze, and design the building based on the Eurocodes and Swedish national annex. The potential need for the EW to be strengthened when the cutout opening is made is also evaluated. It is concluded that strengthening the EW with cutout opening is needed. Different strengthening solutions are proposed for the EW. Moreover, the situation of the EW with the solutions is assessed with regard to the utilization ratio, deflection, and weight. Consequently, it is demonstrated that the proposed strengthening solutions function well for the EW.

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: 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: 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: Pinned base plate connections are the most common base connection used in low-rise steel buildings. In this research, an extensive parametric study is performed using the Finite Element (FE) software Abaqus to determine the elastic rotational stiffness, moment resistance, and energy absorption of the pinned base plate connection connected to a reinforced concrete footing and subjected to an eccentric axial load. The developed FE model is validated using experimental results from the literature. Moreover, an intensive parametric study is conducted to understand the behavior of these connections better. The investigated parameters include the base plate thickness, anchor bolt diameter, and arrangement and number of bolts. The most effective parameters that affect the elastic rotational stiffness and moment resistance of pinned base connections are the anchor bolt arrangement and diameter. The maximum increase in the rotational stiffness was 53% for the anchor bolt diameter of 30 mm when the base plate thickness increased from 12 mm to 30 mm. Based on the base plate thickness, the moment resistance is improved by 150–260% when the bolt diameter increases from 12 mm to 30 mm.

Description: This state-of-the-art review was aimed to conduct a comprehensive literature survey to summarize experiences of crumb rubber (CR) application in concrete within the last 30 years. It shows that certain gaps prevent obtaining a coherent overview of both mechanical behaviour and environmental impact of crumb rubber concrete (CRC) to object to the stereotypes which prevent to use of CR in concrete in the construction industry. Currently, four major barriers can be distinguished for a successful CR application in the concrete industry: (1) the cost of CR recycling, (2) mechanical properties reduction, (3) insufficient research about leaching criteria and ecotoxicological risks and (4) recyclability of CRC. The application of CR in concrete has certainly its advantages and in general cannot be ignored by the construction industry. CR can be applied, for example, as an alternative material to replace natural aggregates and CRC can be used as recycled concrete aggregates (RCA) in the future. A certain diversity for the CR application can be introduced in a more efficient way when surface treatment and concrete mix design optimization are properly developed for each type of CR application in concrete for possible field applications. The role of CRC should not be limited to structures that are less dependent on strength.

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: Projective transformation of spheres onto images produce ellipses, whose centers do not coincide with the projected center of the sphere. This results in an eccentricity error, which must be treated in high precision metrology. This article provides closed formulations for modeling this error in images to enable 3-dimensional (3D) reconstruction of the center of spherical objects. The article also provides a new direct robust method for detecting spherical pattern in point clouds. It was shown that the eccentricity error in an image has only one component in the direction of the major axis of the ellipse. It was also revealed that the eccentricity is zero if and only if the center of the projected sphere lies on the camera’s perspective center. The effectiveness of the robust sphere detection and the eccentricity error modeling method was evaluated on simulated point clouds of spheres and real-world images, respectively. It was observed that the proposed robust sphere fitting method outperformed the popular M-estimator sample consensus in terms of radius and center estimation accuracy by a factor of 13, and 14 on average, respectively. Using the proposed eccentricity adjustment, the estimated 3D center of the sphere using modeled eccentricity was superior to the unmodeled case. It was also observed that the accuracy of the estimated 3D center using modeled eccentricity continuously improved as the number of images increased, whereas the unmodeled eccentricity did not show improvements after eight image views. The results of the investigation show that: (i) the proposed method effectively modeled the eccentricity error, and (ii) the effects of eliminating the eccentricity error in the 3D reconstruction become even more pronounced in a larger number of image views.

Description: In watershed mountain basins, affected in the last decades by strong rainfall events, the role of debris-flow and debris flood processes was investigated. Morphometric parameters have proven to be useful first-approximation indicators in discriminating those processes, especially in large areas of investigation. Computation of morphometric parameters in 19 watershed mountain basins of the western side valley of the Vallo di Diano intermontane basin (southern Italy) was carried out. This procedure was integrated by a semi-automatic elaboration of the potential susceptibility to debris flows, using Flow-R modelling. This software, providing an empirical model of the preliminary susceptibility assessment at a regional scale, was applied in many countries of the world. The implementation of Flow-R modelling requires a GIS application and some thematic base maps extracted using DEMs analysis. A 5-meter-resolution DEM has been used in order to produce the susceptibility maps of the whole study area, and the results are compared and discussed with the real debris flow/flood events that occurred in 1993, 2005, 2010, and 2017 in the studied area. The results have provided a good reliability of Flow-R modelling within small catchment mountain basins.

Description: Deformation monitoring has been brought to the fore and extensively studied in recent years. Global Navigation Satellite System Reflectometry (GNSS-R) techniques have so far been developed in deformation estimation applications, which however, are subject to the influence of mobile satellites. Rather than compensating for the path delay variations caused by mobile satellites, adopting Beidou geostationary Earth orbit (GEO) satellites as transmitters directly eliminates the satellite-motion-induced phase error and thus provides access to stable phase information. This paper presents a novel deformation monitoring concept based on GNSS-R utilizing Beidou GEO satellites. The geometrical properties of the GEO-based bistatic GNSS radar system are explored to build a theoretical connection between deformation quantity and the echo carrier phases. A deformation retrieval algorithm is proposed based on the supporting software receiver, thus allowing echo carrier phases to be extracted and utilized in deformation retrieval. Two field validation experiments are conducted by constructing passive bistatic radars with reflecting plates and ground receiver. Utilizing the proposed algorithm, the experimental results suggested that the GEO-based GNSS reflectometry can achieve deformation estimations with an accuracy of around 1 cm when the extracted phases does not exceed one complete cycle, while better than 3 cm when considering the correct integer number of phase cycles. Consequently, based on the passive bistatic radar system, the potential of achieving continuous, low-cost deformation monitoring makes this novel technique noteworthy.

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: 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: 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: 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: The knowledge of genetic variability and breeding techniques is crucial in crop improvement programs. This information is especially important in underutilized crops such as Bambara groundnut, which have limited breeding systems and genetic diversity information. Hence, this study evaluated the genetic variability and established the relationship between the yield and its components in Bambara groundnut based on seed weight using multivariate analysis. A field trial was conducted in a randomized complete block design with three replications on 28 lines. Data were collected on 12 agro-morphological traits, and a statistical analysis was conducted using SAS version 9.4 software, while the variance component, genotypic and phenotypic coefficient variation, heritability, and genetic advance values were estimated. A cluster analysis was performed using NT-SYS software to estimate the genetic relations among the accessions. The results showed significant variability among the accessions based on the yield and yield component characteristics. The evaluated lines were grouped into seven primary clusters based on the assessed traits using the UPGMA dendrogram. Based on the overall results, G5LR1P3, G1LR1P3, G4LR1P1, G2SR1P1 and G3SR1P4 performed the best for the yield and yield components. These improved lines are recommended for large-scale evaluation and utilization in future breeding programs to develop high-yield Bambara groundnut varieties.

Description: Root-knot nematode (RKN) is a serious threat to crops worldwide due to the difficulty in controlling it and the limited eco-friendly alternatives to deal with the biotic stress it causes. In the present work, water-extractable fractions obtained from vermicompost (WSFv), vermicompost enriched with Trichoderma asperellum (WSFta) and T. virens (WSFtv) were tested as biotechnological tools to reduce the impacts of RKN on gas exchange, water use efficiency (WUE) and nutrient concentration in tomato and bell pepper plants. The plants were infected with 5000 eggs and eventual J2 of RKN and then treated with the water-extractable fractions for seven weeks. It was observed that the addition of WSFta, WSFtv and WSFv increased the CO2 assimilation, stomatal conductance and WUE in the tomato plants. In the bell pepper plants, WSFta, WSFtv, WSFv increased the stomatal conductance, while WUE was higher in the treatment with WSFtv. In fact, the parameters associated with the gas exchange were usually higher in the bell pepper than in the tomato plants. Overall, higher contents of N, Mg, B and Mn were detected when the extracts were applied in both bell pepper and tomato plants. The application of the water-extractable fractions, inoculated or not with Trichoderma, attenuates the RKN damage on the gas exchange parameters and successfully enhanced the nutrient concentration in the infected tomato and bell pepper plants, showing that it could be an important and promising tool for reducing the damage caused by this pathogen. We suggest that both the tomato and pepper plants can cope with the dilemma between growth and stress response via stomata regulation that are modulated by the WSF and Trichoderma.

Description: Container-based lightweight buildings offer a high ecologic and economic potential when they are designed as nearly zero-energy container buildings (NZECBs). Thus, they are relevant to energy transition in achieving an almost climate-neutral building stock. This paper describes and applies design strategies for suitable building concepts and energy systems to be used in NZECBs for different climates. Therefore, different applications in representative climatic zones were selected. Initially, the global climate zones were characterized and analyzed with regard to their potential for self-sufficiency and renewable energies in buildings. The design strategies were further developed and demonstrated for three cases: a single-family house in Sweden, a multi-family house in Germany, and a small school building in rural Ethiopia. For each case, design guidelines were derived and building concepts were developed. On the basis of these input data, various energy concepts were developed in which solar and wind energy, as well as biomass, were integrated as renewable energy sources. All the concepts were simulated and analyzed with the Polysun® software. The various approaches were compared and evaluated, particularly with regard to energy self-sufficiency. Self-sufficiency rates up to 80% were achieved. Finally, the influence of different climate zones on the energy efficiency of the single-family house was studied as well as the influence of the size of battery storage and insulation.

Description: Central counterparties’ (CCPs) role is to take over the counterparty risk during trading. To fulfill its role, a CCP needs to operate a multi-level guarantee system that can absorb losses of clearing members’ defaults. Our main question is how the size of the guarantee system changes and how the cross-guarantee undertaking changes between clearing members and markets if the CCP merges the guarantee systems of different markets. This question is essential from a financial stability perspective since the size and the structure of the guarantee system will affect the loss-absorbing capacity of a CCP. We used Monte Carlo simulation to simulate a 30 year time-series for three different products, which gave us the basis for the value-at-risk-based margin calculation and the stress-test-based default fund calculation. Results show that merging the guarantee systems will always decrease the total value of the guarantees because the margin will decrease, which cannot be offset by the increase in the default fund size. We conclude that it is not optimal from the financial stability perspective to merge the guarantee systems. However, if the CCP wants to provide cheaper services, or if the clearing members are willing to cross-guarantee each other, merging is more suitable.

Description: The paper presents a comparative analysis of recent collision avoidance and real-time path planning algorithms for ships. Compared methods utilize radar remote sensing for target ships detection. Different recently introduced approaches are briefly described and compared. An emphasis is put on input data reception using a radar as a remote sensing device applied in order to detect moving obstacles such as encountered ships. The most promising methods are highlighted and their advantages and limitations are discussed. Concluding remarks include proposals of further research directions in the development of collision avoidance methods utilizing radar remote sensing.

Description: The aftermath of the COVID-19 pandemic is not limited to human lives and health sectors. It has also changed social and economic aspects of the world. This study investigated the Islamic stock market’s reaction and changes in volatility before and during this pandemic. The market model of event study methodology was employed to analyze Islamic stock market reactions in nine different markets around the globe. To examine changes in volatility and persistence of risk, the generalized autoregressive conditional heteroscedasticity (GARCH) method was used. Nine Islamic stock indices were selected for this study from the Thomson Reuters data stream. The results suggest that, in the short run, the Islamic Australian stock index and Islamic GCC stock index remained stable for the first 15 days following news of the pandemic. The Islamic stock indexes of Qatar, UAE, ASEAN, MENA, MENASA, and Bahrain were significantly affected by the outbreak in the short-term. On the other hand, the volatility of Islamic stock indices was substantially amplified after the global health crisis was declared by the WHO. Moreover, volatility shocks tended to persist for a longer period after COVID-19.

Description: Fusarium head blight (FHB) is one of the major threats for wheat production worldwide. It reduces yield, quality, and feeding value of wheat grains. In addition, mycotoxins produced by Fusarium pathogens can have a negative effect on livestock and human health. The aim of this study was to assess changes in technological quality traits and end-use quality of winter wheat varieties after artificial inoculation with Fusarium spp. over three years. Differences in dough development duration and extensibility were measured as the means of relative reductions due to different environments and varieties’ characteristics. Differences in dough softening during kneading were determined as the means of relative increases due to FHB inoculation. In addition, dough had reduced strength, was stickier, and therefore was more difficult to handle, due to a decrease of the average energy value and resistance to extension in FHB-inoculated wheat, compared to naturally infected plants. Dough development time, stability, and resistance usually varied in a similar way, with FHB-resistant varieties showing a good response to FHB inoculation and maintaining good quality. Increasing the level of Fusarium spp. contamination in more FHB-susceptible wheat varieties worsened their technological quality, primarily, the sedimentation value and the gluten index, and hence had a negative effect on the rheological properties.

Description: Suffering from hardware phase biases originating from satellites and the receiver, precise point positioning (PPP) requires a long convergence time to reach centimeter coordinate accuracy, which is a major drawback of this technique and limits its application in time-critical applications. Ambiguity resolution (AR) is the key to a fast convergence time and a high-precision solution for PPP technology and PPP AR products are critical to implement PPP AR. Nowadays, various institutions provide PPP AR products in different forms with different strategies, which allow to enable PPP AR for Global Positioning System (GPS) and Galileo or BeiDou Navigation System (BDS). To give a full evaluation of PPP AR performance with various products, this work comprehensively investigates the positioning performance of GPS-only and multi-GNSS (Global Navigation Satellite System) combination PPP AR with the precise products from CNES, SGG, CODE, and PRIDE Lab using our in-house software. The positioning performance in terms of positioning accuracy, convergence time and fixing rate (FR) as well as time to first fix (TTFF), was assessed by static and kinematic PPP AR models. For GPS-only, combined GPS and Galileo PPP AR with different products, the positioning performances were all comparable with each other. Concretely, the static positioning errors can be reduced by 21.0% (to 0.46 cm), 52.5% (to 0.45 cm), 10.0% (to 1.33 cm) and 21.7% (to 0.33 cm), 47.4% (to 0.34 cm), 9.5% (to 1.16 cm) for GPS-only and GE combination in north, east, up component, respectively, while the reductions are 20.8% (to 1.13 cm), 42.9% (to 1.15 cm), 19.9% (to 3.4 cm) and 20.4% (to 0.72 cm), 44.1% (to 0.66 cm), 10.1% (to 2.44 cm) for kinematic PPP AR. Overall, the positioning performance with CODE products was superior to the others. Furthermore, multi-GNSS observations had significant improvements in PPP performance with float solutions and the TTFF as well as the FR of GPS PPP AR could be improved by adding observations from other GNSS. Additionally, we have released the source code for multi-GNSS PPP AR, anyone can freely access the code and example data from GitHub.

Description: This work aims to study the influence of using construction and demolition waste in the replacement of coarse and fine aggregate to produce recycled aggregate concrete (RAC). A moderate compressive strength concrete made with usual fine and coarse aggregate was used as a benchmark material. Compressive and split tensile tests were performed using 120 cylindrical concrete specimens with 150 mm diameter and 300 mm length. Four-point flexural tests in reinforced beams made with conventional concrete and RAC were performed. The results obtained showed that the use of recycled fine aggregates, in both percentages of substitution investigated—50% and 100%— did not generate any deleterious influence on the values of compressive strength and split tensile strength of the RACs produced. Tin fact, the mechanical strengths of RACs produced with recycled fine aggregate were equal or higher than those from the reference concrete. The same behavior was not observed, however, when the recycled coarse aggregate was used. For this case, decreases in concrete mechanical strengths were observed, especially in compressive strength, with values around 35% lower when compared to the reference concrete. Tensile mechanical tests results confirmed the excellent behavior of all RACs made with replacement of usual fine aggregates by recycled. Bending tests performed in reinforced RAC beams had as objective to evaluate the deformation profile of the beams. The obtained results showed that RAC beams with full replacement of usual fine aggregate by the recycled aggregates have presented little changes in the global behavior, an aspect that encourages its use.

Description: Even though, an innovative inorganic family of geopolymer concretes are eye-catching potential building materials, it is quite essential to comprehend the fire and thermal resistance of these structural materials at a very high temperature and also when experiencing fire with a view to make certain not only the safety and security of lives and properties but also to establish them as more sustainable edifice materials for future. The experimental and field observations of degree of cracking, spalling and loss of strength within the geopolymer concretes subsequent to exposure at elevated temperature and incidences of occurrences of disastrous fires extend an indication of their resistance against such severely catastrophic conditions. The impact of heat and fire on mechanical attributes viz., mechanical-compressive strength, flexural behavior, elastic modulus; durability—thermal shrinkage; chemical stability; the impact of thermal creep on compressive strength; and microstructure properties—XRD, FTIR, NMR, SEM as well as physico-chemical modifications of geopolymer composites subsequent to their exposures at elevated temperatures is reviewed in depth. The present scientific state-of-the-art review manuscript aimed to assess the fire and thermal resistance of geopolymer concrete along with its thermo-chemistry at a towering temperature in order to introduce this novel, most modern, user and eco-benign construction materials as potentially promising, sustainable, durable, thermal and fire-resistant building materials promoting their optimal and apposite applications for construction and infrastructure industries.

Description: The purpose of this study was to assess the suitability of the Romanian Albanus hybrid of Sorghum bicolor as a potential functional food ingredient. Ultrasound-assisted extraction in different solvents, together with spectrophotometric and chromatographic methods, was used to monitor the variation in total phenolic and flavonoid content and the antioxidant activity of raw sorghum grains before and during short germination periods (24, 36 and 48 h). The High-Performance Liquid Chromatography with Diode-Array Detection (HPLC-DAD) investigation of the extracts revealed that the ungerminated sorghum grains extracted with methanol had the highest diversity of phenolic compounds, while the total phenolic content (TPC) was the highest after 36 h of germination in both extract types: 1853 mg GAE/100 g for the methanolic extract and 1726 mg GAE/100 g for the ethanolic extract. The findings of this study showed that the TPC of sorghum extracts is strongly correlated with their antioxidant activity and, overall, that the studied extracts presented a good radical scavenger activity, which supports the benefits of alimentary uses of Sorghum bicolor grains.

Description: TouchTerrain is a simple-to-use web application that makes creating 3D printable terrain models from anywhere on the globe accessible to a wide range of users, from people with no GIS expertise to power users. For coders, a Python-based standalone version is available from the open-source project's GitHub repository. Analyzing 18 months of web analytics gave us a preliminary look at who is using the TouchTerrain web application and what their models are used for; and to map out what terrains on the globe they chose to 3D print. From July 2019 to January 2021, more than 20,000 terrain models were downloaded. Models were created for many different use cases, including education, research, outdoor activities and crafting mementos. Most models were realized with 3D printers, but a sizable minority used CNC machines. Our own experiences with using 3D printed terrain in a university setting have been very positive so far. Anecdotal evidence points to the strong potential for 3D printed terrain models to provide significant help with specific map-related tasks. For the introductory geology laboratory, 3D printed models were used as a form of “training wheels” to aid beginning students in learning to read contour maps, which are still an important tool for geology.

Description: The main reason for the development of the yield gap in crop production is the inefficient management of nitrogen (N). The nitrogen gap (NG) cannot be ameliorated without an indication and quantification of soil characteristics that limit N uptake by a crop plant. The insufficient supply of N to a plant during its cardinal stages of yield formation is a result of two major-variabilities. The first is spatial variability in the soil characteristics responsible for water supply to a plant, also serving as a nutrient carrier. The second is a vertical variability in soil factors, decisive for pools of available nutrients, and their in-season accessibility to the grown crop. The long-term strategy for NG cover should focus first on soil characteristics (humus stock, pH, nutrient content) responsible for water storage and its availability to the currently grown plant. Diagnostics of plant nutrient availability should deliver data on their contents both in the topsoil and subsoil. The combined use of both classical diagnostic tools and spectral imagery is a way to divide a single field into units, differing in productivity. Remote-sensing techniques offer a broad number of tools to define the in-season crop canopy requirement for fertilizer N in homogenous field units.

Description: The basic urban tissue of medieval European cities consisted of brick townhouses. In the cities of northern Europe, these tenements were characterised by a block based on an elongated rectangular plan, covered with a gable roof with a ridge oriented perpendicularly to the street. The side walls of the tenement house were common for both neighbours and constituted a basic structural element. The gable façades were not loaded with ceilings, providing freedom in shaping them. The aim of this work is to determine the reasons why this method of shaping tenement houses in historical city centres has survived to the present day, becoming an archetype for contemporary architectural and construction solutions, despite the passage of time, numerous historical events, war damage, changing architectural styles, fashions and building techniques and technologies. The historical centre of Gdańsk has become the research material in this paper, where by means of such methods as historical source material analysis (iconographic), observation (operationalisation of preserved historical objects), comparative analysis of completed contemporary investments, and 3D modelling of structural systems, an attempt has been made to determine the main factors determining contemporary architectural and structural solutions. The reason for the extraordinary durability of this type of construction model can be found in the enormous rationality and efficiency of this solution. It allows for very intensive use of land, easy access of all front elevations to the main communication routes, cheapness of construction resulting from small spans and use of common structural walls for the neighbouring buildings, ease of shaping gable elevations, and fire safety. Aesthetic considerations are probably also important here, although it should be assumed that their significance began to grow only in the second half of the 19th century. However, it seems that the most important factor which made the model of the mediaeval bourgeois tenement house become an archetype for contemporary architectural and construction solutions is the timeless message contained in this model, a specific code allowing it to be unambiguously identified as a form of urban house – a place of safe living and at the same time a visible sign of the rich history of European cities, an element creating their cultural and spatial identity, a component of the living, constantly transforming urban fabric.

Description: The development of generalisation (simplification) methods for the geometry of features in digital cartography in most cases involves the improvement of existing algorithms without their validation with respect to the similarity of feature geometry before and after the process. It also consists of the assessment of results from the algorithms, i.e., characteristics that are indispensable for automatic generalisation. The preparation of a fully automatic generalisation for spatial data requires certain standards, as well as unique and verifiable algorithms for particular groups of features. This enables cartographers to draw features from these databases to be used directly on the maps. As a result, collected data and their generalised unique counterparts at various scales should constitute standardised sets, as well as their updating procedures. This paper proposes a solution which consists in contractive self-mapping (contractor for scale s = 1) that fulfils the assumptions of the Banach fixed-point theorem. The method of generalisation of feature geometry that uses the contractive self-mapping approach is well justified due to the fact that a single update of source data can be applied to all scales simultaneously. Feature data at every scale s 〈 1 are generalised through contractive mapping, which leads to a unique solution. Further generalisation of the feature is carried out on larger scale spatial data (not necessarily source data), which reduces the time and cost of the new elaboration. The main part of this article is the theoretical presentation of objectifying the complex process of the generalisation of the geometry of a feature. The use of the inherent characteristics of metric spaces, narrowing mappings, Lipschitz and Cauchy conditions, Salishchev measures, and Banach theorems ensure the uniqueness of the generalisation process. Their application to generalisation makes this process objective, as it ensures that there is a single solution for portraying the generalised features at each scale. The present study is dedicated to researchers concerned with the theory of cartography.

Description: Earthworm activity is a key factor in creating soil aggregates, but introduced organic matter and abiotic factors are also equally important. The purpose of this study was to investigate the stability of aggregates made by earthworms in soils with organic additives. Additionally, the two aggregate stability measurement methods were compared: (i) the wet-sieve method and (ii) the laser diffraction method. A six-month container experiment containing sixteen treatments and controls were made. Each treatment included one of four types of soil texture: sand, loam, silty loam and clay, and one of four additives: straw, peat, compost and compost with added microorganisms. To each treatment, six earthworms were added, two each of species commonly occurring in Polish soils: Dendrodrilus rubidus, Aporrectodea caliginosa and A. rosea. This study confirmed that earthworm activity was the factor favoring aggregate formation. In terms of the investigated organic additives, the efficiency of aggregate creation was as follows: compost with active bacteria, compost, peat and straw. Nevertheless, earthworms alone, without the addition of an organic additive, did not form permanent aggregates. The wet sieving and laser diffractometry methods of measuring aggregate stability were comparable for silty, clayey and loamy soils.

Description: Cassava mosaic disease (CMD) is a major viral disease adversely affecting cassava production in Africa and Asia. Genomic regions conferring resistance to the disease have been mapped in African cassava germplasm through biparental quantitative trait loci (QTL) mapping and genome-wide association studies. To facilitate the utilization of these markers in breeding pipelines to support selections, proof-of-concept technical and biological validation research was carried out using independent pre-breeding and breeding populations. Kompetitive Allele-Specific Polymerase Chain Reaction (KASP) assays were designed from three single nucleotide polymorphism (SNP) markers linked to a major resistance locus on chromosome 12 (S12_7926132, S12_7926163) and a minor locus on chromosome 14 (S14_4626854). The designed assays were robust and easy to score with 〉99% genotype call rate. The overall predictive accuracy (proportion of true positives and true negatives) of the markers (S12_7926132 and S14_4626854) was 0.80 and 0.78 in the pre-breeding and breeding population, respectively. On average, genotypes that carried at least one copy of the resistant allele at the major CMD2 locus had a significantly higher yield advantage. Nevertheless, variation was observed in prediction accuracies for the major locus (S12_7926132) among sub-families from the two populations, suggesting the need for context-specific utilization, for example, by screening for co-segregation of favorable SNP alleles with resistance in the parents being used for crosses. Availability of these validated SNP markers on the uniplex KASP genotyping platform represents an important step in translational genetics toward marker-assisted selection to accelerate introgression of favorable resistant alleles in breeding populations.

Description: Nitrogen (N) management remains a global challenge for the sustainability of diversified farming systems. Field crops are often over-supplied with nitrogen by farmers aiming to high productivity. Although the increase of nitrogen rates leads in many instances to high yields, degree of effectiveness for nitrogen use remains low. Urease and nitrification inhibitors are technologies which have been present in the fertilizers market at least 50 years. Inhibitors exploitation ensures long-term nitrogen release and improved N-uptake by plants and N-storage in seeds and silage. Avail of inhibitors, such as the decline of nitrogen leaching in form of NO3−, reduction of emissions in NH3 form, and rise of yield, are some of the desirable attributes that are derived from their integration in fertilization schedules. This review reports the evaluation of applied nitrogen, with inhibitors, and field crops based on nitrogen indices. The examined N-indicators include Nitrogen use efficiency (NUE), Nitrogen Utilization Efficiency (NUtE,) Nitrogen Agronomic Efficiency (NAE), Nitrogen Harvest Index (NHI), and N uptake. This review gathered all, to the best of our knowledge, available data regarding the utilization of nitrification and urease inhibitors under an exclusively agronomic perspective. Either dual or single use of nitrification and urease inhibitors has been reported to significantly increase yield components and promote nitrogen uptake. To conclude, the assessment of N-related indices is vital to promoting sustainability in diversified farming systems, while the integration of inhibitors in national N fertilizations schemes may contribute to system profitability through enhancement of N-supply to crops.

Description: Semi-natural grasslands are particularly important in mountainous areas of Romania, being the only source of forage for many farmers. The aim of this study was to investigate the changes in forage quantity and quality as a result of Urea Ammonium nitrate (UAN) liquid fertilization. The experiment was carried out in the eastern part of Apuseni Mountains, Romania on a Festuca rubra L.-Agrostis capillaris L. grassland located at 1240 m altitude. Studies were made over three years of experimental trial (2014–2016) and covered four experimental plots in three replicates, as follows: V1–control plot, unfertilized; V2–plot fertilized with 50 kg UAN ha−1 year−1; V3–plot fertilized with 75 kg UAN ha−1 year−1, and V4–plot fertilized with 100 kg UAN ha−1 year−1. The experimental plots were harvested once per year and the botanical composition, dry matter yield and forage quality were assessed. Our results showed important changes in forage quantity, quality and diversity as a result of UAN fertilization. Starting from the second experimental year the dominance/co-dominance ratio changed favoring the species from Poaceae family. Dry matter increased as a result of UAN fertilization but forage quality was negatively affected by the higher percentage of participation of species from other botanical families which have higher crude fiber content and lower crude protein. Based on our results we recommend moderate fertilization with UAN up to 50 kg UAN ha−1 year−1 for semi-natural grasslands located in soil-climatic conditions similar to those in our experiment.

Description: Countries trying to attract foreign direct investment often use various tools to influence the foreign investor’s allocation decision including public subsidies in the form of investment incentives. However, the effects associated with providing these incentives are often questioned, especially in light of the need to achieve at least a minimum level of attractiveness of the business environment. The primary aim of the present study was to examine the effects of investment incentives on foreign direct investment inflows (direct effect) and on selected macroeconomic variables (indirect effects) under the conditions in Slovakia. Findings showed that the preference of specific forms of investment incentives by the government of the Slovak Republic changed slightly in the observed period of 2002–2019. The results of the regression analysis further suggest that while financial incentives have a positive statistically significant direct effect on foreign direct investment inflows, in the case of fiscal incentives, this effect is the opposite. In terms of indirect effects of investment incentives, only a reduction in the unemployment rate through foreign direct investment was found. The study contributes to the literature by providing evidence on the effects of various forms of investment incentives and by offering some implications for investment promotion policy.

Description: The article aims to show the opportunities for the formation of new FinTech startups in Poland and further development of the sector, as well as to identify the most critical threats. The study offers the descriptive and deductive analysis based on the literature review. The empirical part relies on the data from external databases as well as the dataset collected in a survey run among the FinTechs in Poland in January 2020. The paper reveals that Poland is a fast-growing FinTech market which satisfies various requirements such as the number of secure Internet servers, mobile telephone subscriptions, the available labor force, as well as growing tertiary education enrolment. The crucial obstacles to the development of the sector is the uncertainty about the availability of skilled workers in the future and the lack of proper legal regulations.

Description: In this paper, we use a partition function form game to analyze cartel formation among firms in Cournot competition. We assume that a firm obtains a certain cost advantage that allows it to produce goods at a lower unit cost. We show that if the level of the cost advantage is “moderate”, then the firm with the cost advantage leads the cartel formation among the firms. Moreover, if the cost advantage is relatively high, then the formed cartel can also be stable in the sense of the core of a partition function form game. We also show that if the technology for the low-cost production can be copied, then the cost advantage may prevent a cartel from splitting.

Description: The purpose of this study is to find the most important variables that represent the future projections of the Bank of International Settlements’ (BIS) capital adequacy ratio, which is the index of financial soundness in a bank as a comprehensive and important measure of capital adequacy. This study analyzed the past 12 years of data from all domestic banks in South Korea. The research data include all financial information, such as key operating indicators, major business activities, and general information of the financial supervisory service of South Korea from 2008 to 2019. In this study, machine learning techniques, Random Forest Boruta algorithms, Random Forest Recursive Feature Elimination, and Bayesian Regularization Neural Networks (BRNN) were utilized. Among 1929 variables, this study found 38 most important variables for representing the BIS capital adequacy ratio. An additional comparison was executed to confirm the statistical validity of future prediction performance between BRNN and ordinary least squares (OLS) models. BRNN predicted the BIS capital adequacy ratio more robustly and accurately than the OLS models. We believe our findings would appeal to the readership of your journal such as the policymakers, managers and practitioners in the bank-related fields because this study highlights the key findings from the data-driven approaches using machine learning techniques.

Description: In terms of land cover classification, optical images have been proven to have good classification performance. Synthetic Aperture Radar (SAR) has the characteristics of working all-time and all-weather. It has more significant advantages over optical images for the recognition of some scenes, such as water bodies. One of the current challenges is how to fuse the benefits of both to obtain more powerful classification capabilities. This study proposes a classification model based on random forest with the conditional random fields (CRF) for feature-level fusion classification using features extracted from polarized SAR and optical images. In this paper, feature importance is introduced as a weight in the pairwise potential function of the CRF to improve the correction rate of misclassified points. The results show that the dataset combining the two provides significant improvements in feature identification when compared to the dataset using optical or polarized SAR image features alone. Among the four classification models used, the random forest-importance_ conditional random fields (RF-Im_CRF) model developed in this paper obtained the best overall accuracy (OA) and Kappa coefficient, validating the effectiveness of the method.

Description: Detection of small-sized maritime targets is an important task for a marine surveillance radar. Recently, with the emergence of a marine surveillance radar system that has a narrow azimuth beamwidth and rapidly rotating antennas, the available dwell time for detecting a maritime target is usually very short. This short dwell time considerably degrades the performance of conventional detectors, especially those focusing on small-sized targets. In this paper, we propose an efficient detector for small-sized maritime targets to provide a reliable detection performance, even in short dwell times. The proposed scheme is based on a new joint metric, which results from the product of the magnitude and difference features in the Doppler spectra. We discriminate the target bins from sea clutter bins using a statistical discriminator based on the joint metric, whose probability density function follows the product distribution of standard gamma distributions. Compared to conventional detectors, the proposed scheme can provide a robust performance in terms of the average signal-to-clutter ratio as well as the detection rate, especially in shorter dwell times.

Description: As an approach with great potential, the interpretation of space-borne synthetic aperture radar (SAR) images has been applied for monitoring the dynamic evolution of the glacial lakes in recent years. Considering unfavorable factors, such as inherent topography-induced effects and speckle noise in SAR images, it is challenging to accurately map and track the dynamic evolution of the glacial lakes by using multi-temporal SAR images. This paper presents an improved neighborhood-based ratio method utilizing a time series of SAR images to identify the boundaries of the glacial lakes and detect their spatiotemporal changes. The proposed method was applied to monitor the dynamic evolution of the two glacial lakes with periodic water discharge at the terminus of the Gongba Glacier in the southeastern Tibetan Plateau by utilizing 144 Sentinel-1A SAR images collected between October of 2014 and November of 2020. We first generated the reference intensity image (RII) by averaging all the SAR images collected when the water in the glacial lakes was wholly discharged, then calculated the neighborhood-based ratio between RII and each SAR intensity image, and finally identified the boundaries of the glacial lakes by a ratio threshold determined statistically. The time series of areas of the glacial lakes were estimated in this way, and the dates for water recharging and discharging were accordingly determined. The testing results showed that the water of the two glacial lakes began to be recharged in April and reached their peak in August and then remained stable dynamically until they began to shrink in October and were discharged entirely in February of the following year. We observed the expansion process with annual growth rates of 3.19% and 12.63% for these two glacial lakes, respectively, and monitored a glacial lake outburst flood event in July 2018. The validation by comparing with the results derived from Sentinel-2A/B optical images indicates that the accuracy for identifying the boundaries of the glacial lakes with Sentinel-1A SAR images can reach up to 96.49%. Generally, this contribution demonstrates the reliability and precision of SAR images to provide regular updates for the dynamic monitoring of glacial lakes.

Description: The pixel-based semantic segmentation methods take pixels as recognitions units, and are restricted by the limited range of receptive fields, so they cannot carry richer and higher-level semantics. These reduce the accuracy of remote sensing (RS) semantic segmentation to a certain extent. Comparing with the pixel-based methods, the graph neural networks (GNNs) usually use objects as input nodes, so they not only have relatively small computational complexity, but also can carry richer semantic information. However, the traditional GNNs are more rely on the context information of the individual samples and lack geographic prior knowledge that reflects the overall situation of the research area. Therefore, these methods may be disturbed by the confusion of “different objects with the same spectrum” or “violating the first law of geography” in some areas. To address the above problems, we propose a remote sensing semantic segmentation model called knowledge and spatial pyramid distance-based gated graph attention network (KSPGAT), which is based on prior knowledge, spatial pyramid distance and a graph attention network (GAT) with gating mechanism. The model first uses superpixels (geographical objects) to form the nodes of a graph neural network and then uses a novel spatial pyramid distance recognition algorithm to recognize the spatial relationships. Finally, based on the integration of feature similarity and the spatial relationships of geographic objects, a multi-source attention mechanism and gating mechanism are designed to control the process of node aggregation, as a result, the high-level semantics, spatial relationships and prior knowledge can be introduced into a remote sensing semantic segmentation network. The experimental results show that our model improves the overall accuracy by 4.43% compared with the U-Net Network, and 3.80% compared with the baseline GAT network.

Description: People have become more conscientious about the environment in recent years. Increasing environmental awareness drives customers to be more selective about environmentally friendly products and forces governments to adopt environmentally friendly policies. As a result, competition in the market becomes more challenging. Thus, companies cannot remain indifferent to adopting environmentally friendly strategies to be sustainable. In this regard, this study investigates the effect of green innovation on firm performance. We also examined whether the environmental uncertainty moderates the investigated effect. For this purpose, first, data were collected from the first 1000 exporting firms declared in 2019 by the Turkey Exporters Assembly using a survey method. Secondly, factor analyses and regression analyses were performed with the data set obtained from 136 companies. As a result of the analysis, it was determined that green innovation increases both environmental performance and economic performance. It also was found that green innovation positively affects firm performance, but environmental uncertainty reduces this effect. According to these results, it was offered that firms should increase their green innovation activities to achieve better outputs and seek ways to reduce environmental uncertainty to keep these outputs at the maximum level. Finally, the research includes some considerations on the positive implications and potential of green innovation in an open-innovation context.

Description: High-spatial-resolution land-surface temperature is required for several applications such as hydrological or climate studies. Global estimates of surface temperature are available from sensors observing in the infrared (IR), but without ‘all-weather’ observing capability. Passive microwave (MW) instruments can also be used to provide surface-temperature measurements but suffer from coarser spatial resolutions. To increase their resolution, a downscaling methodology applicable over different land environments and at any time of the day is proposed. The method uses a statistical relationship between clear sky-predicting variables and clear-sky temperatures to estimate temperature patterns that can be used in conjunction with coarse measurements to create high-resolution products. Different predicting variables are tested showing the need to use IR-derived information on vegetation, temperature diurnal evolution, and a temporal information. To build a true ‘all-weather’ methodology, the effect of clouds on surface temperatures is accounted for by correcting the clear-sky diurnal cycle amplitude, using cloud parameters from meteorological reanalysis. Testing the method on a coarse IR synthetic data at ∼25 km resolution yields a Root Mean Square Deviations (RMSD) between the ∼5 km high-resolution and downscaled temperatures smaller than 1 ∘C. When applied to observations by the Special Sensor Microwave Imager Sounder (SSMIS) at ∼25 km resolution, the downscaling to ∼5 km yields a smaller RMSD compared to IR observations. These results demonstrate the relevance of the methodology to downscale MW land-surface temperature and its potential to spatially enhanced the current ‘all-weather’ satellite monitoring of surface temperatures.

Description: In Ethiopia land degradation through soil erosion is of major concern. Land degradation mainly results from heavy rainfall events and droughts and is associated with a loss of vegetation and a reduction in soil fertility. To counteract land degradation in Ethiopia, initiatives such as the Sustainable Land Management Programme (SLMP) have been implemented. As vegetation condition is a key indicator of land degradation, this study used satellite remote sensing spatiotemporal trend analysis to examine patterns of vegetation between 2002 and 2018 in degraded land areas and studied the associated climate-related and human-induced factors, potentially through interventions of the SLMP. Due to the heterogeneity of the landscapes of the highlands of the Ethiopian Plateau and the small spatial scale at which human-induced changes take place, this study explored the value of using 30 m resolution Landsat data as the basis for time series analysis. The analysis combined Landsat derived Normalised Difference Vegetation Index (NDVI) data with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) derived rainfall estimates and used Theil-Sen regression, Mann-Kendall trend test and LandTrendr to detect changes in NDVI, rainfall and rain-use efficiency. Ordinary Least Squares (OLS) regression analysis was used to relate changes in vegetation directly to SLMP infrastructure. The key findings of the study are a general trend shift from browning between 2002 and 2010 to greening between 2011 and 2018 along with an overall greening trend between 2002 and 2018. Significant improvements in vegetation condition due to human interventions were found only at a small scale, mainly on degraded hillside locations, along streams or in areas affected by gully erosion. Visual inspections (based on Google Earth) and OLS regression results provide evidence that these can partly be attributed to SLMP interventions. Even from the use of detailed Landsat time series analysis, this study underlines the challenge and limitations to remotely sensed detection of changes in vegetation condition caused by land management interventions aiming at countering land degradation.

Description: This survey paper discusses the Cointegrated Vector AutoRegressive (CVAR) methodology and how it has evolved over the past 30 years. It describes major steps in the econometric development, discusses problems to be solved when confronting theory with the data, and, as a solution, proposes a so-called theory-consistent CVAR scenario. A number of early CVAR applications are motivated by the urge to find out why the empirical results did not support Milton Friedman’s concept of monetary inflation. The paper also proposes a method for combining partial CVAR analyses into a large-scale macroeconomic model. It argues that an empirically-based approach to macroeconomics preferably should be based on Keynesian disequilibrium economics, where imperfect knowledge expectations replace so called rational expectations and where the financial sector plays a key role for understanding the long persistent movements in the data. Finally, the paper argues that the CVAR is potentially a candidate for Haavelmo’s “design of experiment for passive observations” and provides several illustrations.

Description: Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CVthr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV values.

Description: Intermittently closed and open lakes or Lagoons (ICOLLs) are characterised by entrance barriers that form or break down due to the action of wind, waves and currents until the ocean-lagoon exchange becomes discontinuous. Entrance closure raises a variety of management issues that are regulated by monitoring. In this paper, those issues are investigated, and an automated sensor solution is proposed. Based upon a static Lidar paired with an edge computing device. This solar-powered remote sensing device provides an efficient way to automatically survey the lagoon entrance and estimate the berm profile. Additionally, it estimates the dry notch location and its height, critical factors in the management of the lagoon entrances. Generated data provide valuable insights into landscape evolution and berm behaviour during natural and mechanical breach events.

Description: The age of Mars yardangs is significant in studying their development and the evolution of paleoclimate conditions. For planetary surface or landforms, a common method for dating is based on the frequency and size distribution of all the superposed craters after they are formed. However, there is usually a long duration for the yardangs’ formation, and they will alter the superposed craters, making it impossible to give a reliable dating result with the method. An indirect method by analyzing the ages of the superposed layered ejecta was devised in the research. First, the layered ejecta that are superposed on and not altered by the yardangs are identified and mapped. Then, the ages of the layered ejecta are derived according to the crater frequency and size distribution on them. These ages indicate that the yardangs ceased development by these times, and the ages are valuable for studying the evolution of the yardangs. This indirect dating method was applied to the areas of Martian yardangs in the Medusae Fossae Formation (MFF). The ages of the selected six layered ejecta range from ~0.50 Ga to ~1.5 Ga, indicating that the evolution of the corresponding yardangs had been ceased before these times. Analysis of more layered ejecta craters and superposed yardangs implies that yardangs in the MFF have a long history of development and some yardangs are still in active development.

Description: In the past few decades, target detection from remote sensing images gained from aircraft or satellites has become one of the hottest topics. However, the existing algorithms are still limited by the detection of small remote sensing targets. Benefiting from the great development of computing power, deep learning has also made great breakthroughs. Due to a large number of small targets and complexity of background, the task of remote sensing target detection is still a challenge. In this work, we establish a series of feature enhancement modules for the network based on YOLO (You Only Look Once) -V3 to improve the performance of feature extraction. Therefore, we term our proposed network as FE-YOLO. In addition, to realize fast detection, the original Darknet-53 was simplified. Experimental results on remote sensing datasets show that our proposed FE-YOLO performs better than other state-of-the-art target detection models.

Description: Object detection is a significant and challenging problem in the study of remote sensing. Since remote sensing images are typically captured with a bird’s-eye view, the aspect ratios of objects in the same category may obey a Gaussian distribution. Generally, existing object detection methods ignore exploring the distribution character of aspect ratios for improving performance in remote sensing tasks. In this paper, we propose a novel Self-Adaptive Aspect Ratio Anchor (SARA) to explicitly explore aspect ratio variations of objects in remote sensing images. To be concrete, our SARA can self-adaptively learn an appropriate aspect ratio for each category. In this way, we can only utilize a simple squared anchor (related to the strides of feature maps in Feature Pyramid Networks) to regress objects in various aspect ratios. Finally, we adopt an Oriented Box Decoder (OBD) to align the feature maps and encode the orientation information of oriented objects. Our method achieves a promising mAP value of 79.91% on the DOTA dataset.

Description: Automated damage evaluation is of great importance in the maintenance and preservation of heritage structures. Damage investigation of large cultural buildings is time-consuming and labor-intensive, meaning that many buildings are not repaired in a timely manner. Additionally, some buildings in harsh environments are impossible to reach, increasing the difficulty of damage investigation. Oblique images facilitate damage detection in large buildings, yet quantitative damage information, such as area or volume, is difficult to generate. In this paper, we propose a method for quantitative damage evaluation of large heritage buildings in wild areas with repetitive structures based on drone images. Unlike existing methods that focus on building surfaces, we study the damage of building components and extract hidden linear symmetry information, which is useful for localizing missing parts in architectural restoration. First, we reconstruct a 3D mesh model based on the photogrammetric method using high-resolution oblique images captured by drone. Second, we extract 3D objects by applying advanced deep learning methods to the images and projecting the 2D object segmentation results to 3D mesh models. For accurate 2D object extraction, we propose an edge-enhanced method to improve the segmentation accuracy of object edges. 3D object fragments from multiple views are integrated to build complete individual objects according to the geometric features. Third, the damage condition of objects is estimated in 3D space by calculating the volume reduction. To obtain the damage condition of an entire building, we define the damage degree in three levels: no or slight damage, moderate damage and severe damage, and then collect statistics on the number of damaged objects at each level. Finally, through an analysis of the building structure, we extract the linear symmetry surface from the remaining damaged objects and use the symmetry surface to localize the positions of missing objects. This procedure was tested and validated in a case study (the Jiankou Great Wall in China). The experimental results show that in terms of segmentation accuracy, our method obtains results of 93.23% mAP and 84.21% mIoU on oblique images and 72.45% mIoU on the 3D mesh model. Moreover, the proposed method shows effectiveness in performing damage assessment of objects and missing part localization.

Description: A country can be well-comprehended through its core cities. Similarly, we can learn about a city from its hotspots, as they manifest the concentration of urban infrastructures and human activities. Following this philosophy, this paper studies the intra-urban form and function from a complexity science perspective by exploring the power law distribution of hotspot sizes and related socio-economic attributes. To detect hotspots, we rely on spatial clustering of geospatial big data sets, including street data from OpenStreetMap platform and nighttime light (NTL) data from the visible infrared imaging radiometer suite (VIIRS) imagery. Unlike conventional spatial units, which are imposed by governments or authorities (such as census block), the delineation of hotspots is done in a totally bottom-up manner and, more importantly, can help us examine precisely the scaling pattern of urban morphological and functional aspects. This results in two types of urban hotspots—street-based and NTL-based hotspots—being generated across 20 major cities in China. We find that Zipf’s law of hotspot sizes (both types) holds remarkably well for each city, as do the city-size distributions at the country level, indicating a statistically self-similar structure of geographic space. We further find that the urban scaling law can be effectively detected when using NTL-based hotspots as basic units. Furthermore, the comparison between two types of hotspots enables us to gain in-depth insights of urban planning and urban economic development.

Description: Megaproject social responsibility (MSR) is a critical factor regarding the sustainable delivery of megaprojects and MSR behavior to implement MSR. However, the questions of which factors effectively promote MSR behavior and how these factors work remain underexamined. Thus, this study aims to examine how the external institutional pressure and internal factors composed of relational behavior and pure altruistic values affect MSR behavior. On the basis of a conditional process analysis conducted on a set of survey data from various organizations involved in megaprojects in China, the results revealed that the higher the degree of mimetic pressure, the higher the degree of MSR behavior. Concurrently, relational behavior mediates the relationship between normative pressures and MSR behavior. Relational behavior also drives MSR behavior, and pure altruistic values moderate the relationships between institutional pressures and MSR behavior. These findings also provide practical suggestions for policymakers on the implementation and governance of MSR.

Description: Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by means of the angles and distances among them. Hence, contributing the quality improvement of the 3D model obtained through the fine registration process, which is carried out using the ICP method, was our aim. The performance of the new algorithm was assessed using the root mean square error (RMSE) of the 3D transformation in the rough alignment stage as well as a-prior and a-posterior RMSE values of the ICP algorithm. The new algorithm was also compared with the point feature histogram (PFH) descriptor and matching algorithm, accompanying two commonly used detectors. In result of the comparisons, the superiorities and disadvantages of the suggested algorithm were discussed. The measurements for the datasets employed in the experiments were carried out using scanned data of a 6 cm × 6 cm × 10 cm Aristotle sculpture in the laboratory environment, and a building facade in the outdoor as well as using the publically available Stanford bunny sculpture data. In each case study, the proposed algorithm provided satisfying performance with superior accuracy and less iteration number in the ICP process compared to the other coarse registration methods. From the point clouds where coarse registration has been made with the proposed method, the fine registration accuracies in terms of RMSE values with ICP iterations are calculated as ~0.29 cm for Aristotle and Stanford bunny sculptures, ~2.0 cm for the building facade, respectively.

Description: In this paper, land subsidence susceptibility was assessed for Shahryar County in Iran using the adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. Another aim of the present paper was to assess if ensembles of ANFIS with two meta-heuristic algorithms (imperialist competitive algorithm (ICA) and gray wolf optimization (GWO)) would yield a better prediction performance. A remote sensing synthetic aperture radar (SAR) dataset from 2019 to 2020 and the persistent-scatterer SAR interferometry (PS-InSAR) technique were used to obtain a land subsidence inventory of the study area and use it for training and testing models. Resulting PS points were divided into two parts of 70% and 30% for training and testing the models, respectively. For susceptibility analysis, eleven conditioning factors were taken into account: the altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), distance to stream, distance to road, stream density, groundwater drawdown, and land use/land cover (LULC). A frequency ratio (FR) was applied to assess the correlation of factors to subsidence occurrence. The prediction power of the models and their generated land subsidence susceptibility maps (LSSMs) were validated using the root mean square error (RMSE) value and area under curve of receiver operating characteristic (AUC-ROC) analysis. The ROC results showed that ANFIS-ICA had the best accuracy (0.932) among the models (ANFIS-GWO (0.926), ANFIS (0.908)). The results of this work showed that optimizing ANFIS with meta-heuristics considerably improves LSSM accuracy although ANFIS alone had an acceptable result.

Description: The Sporadic-E (Es) layer is an often-observed phenomenon at high latitudes; however, our understanding of the polar cap Es layer is severely limited due to the scarce number of measurements. Here, the first comprehensive study of the polar cap Es layer associated with Global Positioning System (GPS) Total Electron Content (TEC) variations and scintillations is presented with multiple measurements at Resolute, Canada (Canadian Advanced Digital Ionosonde (CADI), Northward-looking face of Resolute Incoherent-Scatter Radar (RISR-N), and GPS receiver). According to the joint observations, the polar cap Es layer is a thin patch structure with variously high electron density, which gradually develops into the lower E region (~100 km) and horizontally extends 〉200 km. Moreover, the TEC variations produced by the polar cap Es layer are pulse-like enhancements with a general amplitude of ~0.5 TECu and are followed by smaller but rapid TEC perturbations. Furthermore, the possible scintillation effects likely associated with the polar cap Es layer are also discussed. As a consequence, the results widely expand our understanding on the polar cap Es layer, in particular on TEC variations.

Description: In both the Geospatial (Geo) and Building Information Modelling (BIM) domains, it is widely acknowledged that the integration of geo-data and BIM-data is beneficial and a crucial step towards solving the multi-disciplinary challenges of our built environment. The result of this integration—broadly termed GeoBIM—has the potential to be particularly beneficial in the context of the construction of large infrastructure projects, which could make use of data relating to the larger spatial extents typically handled in geographical information systems (GIS) as well as the detailed models generated by BIM. To date, GeoBIM integration has mainly been explored for buildings, in a 3D context and for small projects. This paper demonstrates the results of the next level of integration, exploring the addition of the fourth dimension by linking project schedule information to create 4D GeoBIM, examining interoperability challenges and benefits in the context of a number of use cases relating to the enabling works for a major commercial infrastructure project. The integrating power of location and time—knowing where and when data relate to—allows us to explore data interoperability challenges relating to linking real world construction data, created using commercial software, with other data sources; we are then able to demonstrate the benefits of 4D GeoBIM in the context of three decision making scenarios: examining the potential for prioritisation of noise mitigation interventions by identifying apartments closest to the noisiest construction process; development of a 4D location-enabled risk register allowing, for example, work to continue underground if a risk is specific to the top of a building; ensuring construction safety by using 3D buffering to ensure that the required distances between moving construction equipment and surrounding infrastructure are not breached. Additionally, once integrated, we are able to ‘democratize’ the data—make it accessible beyond the BIM and GIS expert group—by embedding it into a 3D/4D open source Web GIS tool.

Description: Soil loss is one of the most important causes of land degradation. It is an inevitable environmental and socio-economic problem that exists in many physiographic regions of the world, which, besides other impacts, has a direct bearing on agricultural productivity. A reliable estimate of soil loss is critical for designing and implementing any mitigation measures. We applied the widely used Revised Universal Soil Loss Equation (RUSLE) in the Khabur River Basin (KhRB) within the NW part of the Zagros Fold and Thrust Belt (ZFTB). The areas such as the NW Zagros range, characterized by rugged topography, steep slope, high rainfall, and sparse vegetation, are most susceptible to soil erosion. We used the Digital Elevation Model (DEM) of the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), and Landsat imagery to estimate annual soil loss using the RUSLE model. In addition, we estimated sediment yield (SY) at sub-basin scale, in the KhRB where a number of dams are planned, and where basic studies on soil erosion are lacking. Estimation of SY will be useful in mitigation of excessive sedimentation affecting dam performance and watershed management in this region. We determined the average annual soil loss and the SY in the KhRB to be 11.16 t.ha−1.y−1 and 57.79 t.ha−1.y−1, respectively. The rainfall and runoff erosivity (R factor), slope length (L factor), and slope steepness (S factor), are the three main factors controlling soil loss in the region. This is the first study to determine soil loss at the sub-basin scale along with identifying suitable locations for check dams to trap the sediment before it enters downstream reservoirs. The study provides valuable input data for design of the dams to prevent excessive siltation. This study also aims at offering a new approach in relating potential soil erosion to the actual erosion and hypsometric integrals.

Description: For 1990–2019, this study presents two-step GMM estimates of EU members’ demands for defense spending based on alternative spatial-weight matrices. In particular, EU spatial connectivity is tied to EU membership status, members’ contiguity, contiguity and power projection, inverse distance, and arms trade. At a Nash equilibrium, our EU demand equations are derived explicitly from a spatially based game-theoretical model of alliances. Myriad spatial linkages among EU members provide a robust free-riding finding, which differs from the spatial and non-spatial literature on EU defense spending. Even though the EU applies common trade policies and allows for unrestricted labor movement among members, members’ defense responses adhered to those of a defense alliance. Moreover, EU defense spending exhibits positive responses to GDP and transnational terrorist attacks, and a negative response to population. During the sample period, EU members did not view Russia as a military threat.

Description: Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

Description: The COVID-19 pandemic has shown how important it is to prepare one’s own financial budget for the unexpected loss of income. In this dimension, the financial education of the society plays an invaluable role. It allows us to account for events that may adversely affect personal finances in our budget management decisions. Therefore, the aim of the article is to check whether households with a higher level of financial and debt literacy have better management skills from the perspective of a household’s budget, which in the face of a crisis reduces the risk of individuals not paying their liabilities. Thus, at the turn of June and July 2020, we conducted surveys among 1300 Polish citizens. Using the multinomial logistic regression, we show that people with a higher financial and debt literacy are less affected by overindebtedness. During the crisis, people who have a higher debt literacy are better prepared to manage credit liabilities; in this situation, financial literacy is less important. In addition, the type of credit experience turned out to be significant. Respondents who have experience with consumer loans (potentially high-margin products) are more likely to have debt repayment problems than those with mortgage loans experiences.

Description: With continuous improvement of earth observation technology, source, and volume of remote sensing data are gradually enriched. It is critical to realize unified organization and to form data sharing service capabilities for massive remote sensing data effectively. We design a hierarchical multi-dimensional hybrid indexing model (HMDH), to address the problems in underlying organization and management, and improve query efficiency. Firstly, we establish remote sensing data grid as the smallest unit carrying and processing spatio-temporal information. We implement the construction of the HMDH in two steps, data classification based on fuzzy clustering algorithm, and classification optimization based on recursive neighborhood search algorithm. Then, we construct a hierarchical “cube” structure, filled with continuous space filling curves, to complete the coding of the HMDH. The HMDH reduces the amount of data to 6–17% and improves the accuracy to more than eight times than traditional grid model. Moreover, it can reduce the query time to 25% in some query scenarios than algorithms selected as the baseline in this paper. The HMDH model proposed can be used to solve the efficiency problems of fast and joint retrieval of remote sensing data. It extends the pattens of data sharing service and has a high application value.

Description: The chemical composition dataset of Aerosol Reanalysis of NASA’s Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRAero) has not been thoroughly evaluated with observation data in mainland China due to the lack of long-term chemical components data. Using the 5-year data of PM10 mass concentrations and chemical compositions obtained from the routine sampling measurements at the World Meteorological Organization the Global Atmosphere Watch Programme regional background stations, Jing Sha (JS) and Lin’An (LA), in central and eastern China, we comprehensively evaluate the surface PM10 concentrations and chemical compositions such as sulfate (SO42−), organic carbon (OC) and black carbon (BC) derived from MERRAero. Overall, the concentrations of PM10, SO42−, OC and BC from the MERRAero agreed well with the measurements, despite a slight and consistent overestimation of BC concentrations and a moderate and persistent underestimation of PM10 concentrations throughout the study period. The MERRAero reanalysis of aerosol compositions performs better during the summertime than wintertime. By considering the nitrate particles in PM10 reconstruction, MERRAero performance can be significantly improved. The unreasonable seasonal variations of PM10 chemical compositions at station LA by MERRAero could be causative factors for the larger MERRAero discrepancies during 2016–2017 than the period of 2011–2013.

Description: Construction is a hazardous industry. The project-based nature and fragmentation in the industry lead to change and uncertainty requiring special expertise. To handle those, construction firms must develop strategies and action plans along with the experience gained from lessons learned. Among the risks, safety risks are of critical importance leading to accidents. Hence, firms need to strengthen their safety programs, review their strategies for safety management, and develop effective safety training sessions to protect their workers. This study focuses on the success factors promoting safety performance. In this respect, a questionnaire was designed and administered to the Engineering News-Record (ENR) 2020 Top 400 Contractors. The questionnaire data was utilized in conducting a factor analysis to group and name the factors considering the total variance. The analysis of the factors resulted in six-factor groups; namely, project and firm-related factors, demographic factors, practical factors, motivational factors, organizational factors, and human-related factors. Project and firm-related factors were found to be the most essential factor group in terms of promoting the effectiveness of safety training. The results of this study are expected to guide industry practitioners in terms of reviewing and revising their safety training programs.

Description: To seek stochastic analogies in key processes related to the hydrological cycle, an extended collection of several billions of records from hundred thousands of worldwide stations is used in this work. The examined processes are the near-surface hourly temperature, dew point, relative humidity, sea level pressure, and atmospheric wind speed, as well as the hourly/daily streamflow and precipitation. Through the use of robust stochastic metrics such as the K-moments and a second-order climacogram (i.e., variance of the averaged process vs. scale), it is found that several stochastic similarities exist in both the marginal structure, in terms of the first four moments, and in the second-order dependence structure. Stochastic similarities are also detected among the examined processes, forming a specific hierarchy among their marginal and dependence structures, similar to the one in the hydrological cycle. Finally, similarities are also traced to the isotropic and nearly Gaussian turbulence, as analyzed through extensive lab recordings of grid turbulence and of turbulent buoyant jet along the axis, which resembles the turbulent shear and buoyant regime that dominates and drives the hydrological-cycle processes in the boundary layer. The results are found to be consistent with other studies in literature such as solar radiation, ocean waves, and evaporation, and they can be also justified by the principle of maximum entropy. Therefore, they allow for the development of a universal stochastic view of the hydrological-cycle under the Hurst–Kolmogorov dynamics, with marginal structures extending from nearly Gaussian to Pareto-type tail behavior, and with dependence structures exhibiting roughness (fractal) behavior at small scales, long-term persistence at large scales, and a transient behavior at intermediate scales.

Description: Search engine marketing accounts for a high percentage of investment in platforms such as Google. Several studies have confirmed that users have a negative bias towards advertisements, so we apply social psychology theories via the elaboration probability model in this analysis. In this research, we modify the types of ads shown on Google’s results pages using the regulatory focus and fit and message framing theory to study attentional and behavioral responses with eye-tracking technology and cognitive responses from self-report measures. The results confirm a negative bias towards ads and a preference for organic results. Although promotion-framed ads seem to be more persuasive than neutral and prevention-framed ads, it was not possible to prove compliance with regulatory fit in this field through survey-based studies.

Description: Agro-industrial wastes can be valorized as biorefinery raw materials through innovative, environmentally friendly bioprocessing for added value products. In this study, a process for citrus waste valorization within the biorefinery concept is proposed, including the development of an effective biocatalyst, based on immobilized cells, for aromatic beer production, and an alternative yeast extract (AYE) production in the same unit. Specifically, orange pulp from discarded oranges was applied as an immobilization carrier of the alcohol-resistant and cryotolerant yeast strain S. cerevisiae AXAZ-1. The yeast culture was produced by minor nutrient supplementation using diluted molasses as substrate. An effective Citrus Waste Brewing Biocatalyst (CWBB) was produced and applied for beer fermentation. The aroma-related compounds in beer produced with free yeast cells or the CWBB were evaluated by solid-phase micro-extraction (SPME) gas chromatography–mass spectrometry (GC–MS). The analysis showed that the beers produced by the CWBB had a more complex volatile profile compared with beer fermented by the free cells. More specifically, the CWBB enhanced the formation of esters and terpenes by 5- and 27-fold, respectively. In the frame of the proposed multiprocessing biorefinery concept, the spent CWBB, after it has completed its cycle of brewing batches, was used as substrate for AYE production through autolysis. The produced AYE significantly affected the yeast growth when compared to commercial yeast extract (CYE). More specifically, it promoted the biomass productivity and biomass yield factor by 60–150% and 110–170%, respectively. Thus, AYE could be successfully used for industrial cell growth as an efficient and cheaper substitute of CYE. Within a circular economy framework, the present study highlights the potential use of citrus waste to produce aromatic beer combined with AYE production as an alternative way to valorize these wastes.

Description: The quest for the attainment of economic development is sought after by all global economies, which by effect is expected to transcend to improving livelihoods and standard of living. However, several factors hinder the process of achieving sustained economic development, especially in developing countries. In this regard, assessing the extent of economic expansion orchestrated by foreign direct investment (FDI) inflows in vulnerable economies such as Sub-Saharan Africa (SSA), particularly in the face of the significant fall in global FDI inflow, is worthwhile. In essence, this study ascertains the impact of FDI inflows and external debt on economic growth amidst decline in FDI inflows and excessive foreign borrowings. The mixed order of integration from the stationarity test underpins the adoption of autoregressive distributed lag (ARDL) approach for data covering the period 1990 to 2018. The empirical results found FDI inflows play a crucial role in achieving economic expansion in the region. On average, FDI inflows, external debt, and foreign aids are more useful in expanding the economy compared to trade openness and exchange rate. Thus, this study recommends the need for SSA to open its economic borders for external capital, viz. FDI. A peaceful economic and political environment is a pre-condition to attract and maintain potential foreign investors. Stability in exchange rates is critical in achieving growth in FDI and other foreign resources. However, caution is required, especially in administration of external resources. Particularly, contracting external debt must strictly be driven by economic reasons rather than political motivation. Borrowed funds could be injected mainly into productive streams with the highest investment returns to boost economic development.

Description: Changes in the land use/cover alter the Earth system processes and affect the provision of ecosystem services, posing a challenge to achieve sustainable development. In the past few decades, the Yellow River (YR) basin faced enormous social and environmental sustainability challenges associated with environmental degradation, soil erosion, vegetation restoration, and economic development, which makes it important to understand the long-term land use/cover dynamics of this region. Here, using three decades of Landsat imagery (17,080 images) and incorporating physiography data, we developed an effective annual land use/cover mapping framework and provided a set of 90 m resolution continuous annual land use/cover maps of the YR basin from 1986 to 2018 based on the Google Earth Engine and the Classification and Regression Trees algorithm. The independent random sampling validations based on the field surveys (640 points) and Google Earth (3456 points) indicated that the overall accuracy of these maps is 78.3% and 80.0%, respectively. The analysis of the land system of the YR basin showed that this region presents complex temporal and spatial changes, and the main change patterns include no change or little change, cropland loss and urban expansion, grassland restoration, increase in orchard and terrace, and increase in forest during the entire study period. The major land use/cover change has occurred in the transitions from forests, grasslands, and croplands to the class of orchard and terrace (19.8% of all change area), which not only increase the greenness but also raised the income, suggesting that YR progress towards sustainable development goals for livelihood security, economic growth, and ecological protection. Based on these data and analysis, we can further understand the role of the land system in the mutual feedback between society and the environment, and provide support for ecological conservation, high-quality development, and the formulation of sustainable management policies in this basin, highlighting the importance of continuous land use/cover information for understanding the interactions between the human and natural systems.

Description: Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images.

Description: The area of dynamic planning of construction sites is unexplored. Although there is a large amount of scientific interest in the literature in dynamic planning of construction site layouts, with different methodologies developed, studies on construction site relocation do not exist. The purpose of this study is to cover this gap in the literature and contribute to the body of knowledge by presenting for the first time a dynamic planning of a construction site and its importance in linear construction projects and to validate this methodology through real case studies. The decisive variables that determine the appropriate site locations and the costs that arise from these choices are analyzed. The choice that maximizes the production rate of the construction site and thus minimizes the costs is further investigated. An algorithm has also been developed that estimates the cost of transportation of the equipment used in the project and thus enables the investigation of the “ideal” location that minimizes this cost. The “ideal” site location is examined according to the time schedule of the project at time intervals that are determined by the work progress. The optimization algorithm aims to minimize the cost that derives from non-productive activities. The validity of the proposed model is tested in four motorway projects. A sensitivity analysis concerning different sequences in the construction methods reveals remarkable changes in cost fluctuations depending on project size. The outcomes show that for the second, third, and fourth projects, dynamic planning is demanded, and the profit gained ranges from 1 to 1.5% of total budget cost. Financing expenses could be covered by this profit. The case studies presented are derived from linear infrastructure projects that are more sensitive to this approach because of their size and their budget that both affect the results.

Description: As cities are exposed to a portfolio of risks, the concept of resilience has risen to prominence over the past two decades. Consequently, a large volume of research has been published on different aspects of urban resilience. However, urban form resilience is still relatively understudied. As a step toward filling this gap, this study examines resilience of nine selected neighborhoods from Shiraz, an old Iranian city. The selected cases represent three different urban form patterns, namely, traditional, semi-planned, and planned. Different indicators related to the physical configuration of lots, blocks, open and green spaces, and street networks are used to examine resilience of each neighborhood to three major stressors, namely, earthquakes, extreme heat events, and floods. Additionally, a combination of Shannon entropy and the VIKOR (VlseKriterijumska Optimizcija I Kaompromisno Resenje in Serbian) method is used to rank the resilience of each neighborhood to each of the three stressors. Results show that, overall, the physical form of the planned neighborhoods is more conducive to urban resilience. In contrast, the urban form of traditional neighborhoods was found to be less resilient. There were, however, some variations depending on the type of stressor considered. The paper concludes by emphasizing the need to consider social and economic factors in future studies of urban form resilience.

Description: The safety of the gas transmission infrastructure is one of the main concerns for infrastructure operating companies. Common gas pipelines’ tightness control is tedious and time-consuming. The development of new methods is highly desirable. This paper focuses on the applications of air-borne methods for inspections of the natural gas pipelines. The main goal of this study is to test an unmanned aerial vehicle (UAV), equipped with a remote sensing methane detector, for natural gas leak detection from the pipeline network. Many studies of the use of the UAV with laser detectors have been presented in the literature. These studies include experiments mainly on the artificial methane sources simulating gas leaks. This study concerns the experiments on a real leakage of natural gas from a pipeline. The vehicle at first monitored the artificial source of methane to determine conditions for further experiments. Then the experiments on the selected section of the natural gas pipelines were conducted. The measurement data, along with spatial coordinates, were collected and analyzed using machine learning methods. The analysis enabled the identification of groups of spatially correlated regions which have increased methane concentrations. Investigations on the flight altitude influence on the accuracy of measurements were also carried out. A range of between 4 m and 15 m was depicted as optimal for data collection in the natural gas pipeline inspections. However, the results from the field experiments showed that areas with increased methane concentrations are significantly more difficult to identify, though they are still noticeable. The experiments also indicate that the lower altitudes of the UAV flights should be chosen. The results showed that UAV monitoring can be used as a tool for the preliminary selection of potentially untight gas pipeline sections.

Description: This paper studies efficient market hypothesis in prediction markets and the results are illustrated for the in-play football betting market using the quoted odds for the English Premier League. Our analysis is based on the martingale property, where the last quoted probability should be the best predictor of the outcome and all previous quotes should be statistically insignificant. We use regression analysis to test for the significance of the previous quotes in both the time setup and the spatial setup based on stopping times, when the quoted probabilities reach certain bounds. The main contribution of this paper is to show how a potentially different distributional opinion based on the violation of the market efficiency can be monetized by optimal trading, where the agent maximizes logarithmic utility function. In particular, the trader can realize a trading profit that corresponds to the likelihood ratio in the situation of one market maker and one market taker, or the Bayes factor in the situation of two or more market takers.

Description: Under the impact of both increasing credit pressure and low economic returns characterizing developed countries, investment levels have decreased over recent years. Moreover, the recent turbulence caused by the COVID-19 crisis has accelerated the latter process. Within this scenario, we consider the so-called Volatility Target (VolTarget) strategy. In particular, we focus our attention on estimating volatility levels of a risky asset to perform a VolTarget simulation over two different time horizons. We first consider a 20 year period, from January 2000 to January 2020, then we analyse the last 12 months to emphasize the effects related to the COVID-19 virus’s diffusion. We propose a hybrid algorithm based on the composition of a GARCH model with a Neural Network (NN) approach. Let us underline that, as an alternative to standard allocation methods based on realized and backward oriented volatilities, we exploited an innovative forward-looking estimation process exploiting a Machine Learning (ML) solution. Our solution provides a more accurate volatility estimation, allowing us to derive an effective investor risk-return profile during market crisis periods. Moreover, we show that, via a forward-looking VolTarget strategy while using an ML-based prediction as the input, the average outcome for an investment in a drawdown plan is more sustainable while representing an efficient risk-control solution for long time period investments.

Description: Phytophthora nicotianae is a devastating oomycete plant pathogen with a wide host range. On tobacco, it causes black shank, a disease that can result in severe economic losses. Deployment of host resistance is one of the most effective means of controlling tobacco black shank, but adaptation to complete and partial resistance by P. nicotianae can limit the long-term effectiveness of the resistance. The molecular basis of adaptation to partial resistance is largely unknown. RNAseq was performed on two isolates of P. nicotianae (adapted to either the susceptible tobacco genotype Hicks or the partially resistant genotype K 326 Wz/Wz) to identify differentially expressed genes (DEGs) during their pathogenic interactions with K 326 Wz/Wz and Hicks. Approximately 69% of the up-regulated DEGs were associated with pathogenicity in the K 326 Wz/Wz-adapted isolate when sampled following infection of its adapted host K 326 Wz/Wz. Thirty-one percent of the up-regulated DEGs were associated with pathogenicity in the Hicks-adapted isolate on K 326 Wz/Wz. A broad spectrum of over-represented gene ontology (GO) terms were assigned to down-regulated genes in the Hicks-adapted isolate. In the host, a series of GO terms involved in nuclear biosynthesis processes were assigned to the down-regulated genes in K 326 Wz/Wz inoculated with K 326 Wz/Wz-adapted isolate. This study enhances our understanding of the molecular mechanisms of P. nicotianae adaptation to partial resistance in tobacco by elucidating how the pathogen recruits pathogenicity-associated genes that impact host biological activities.

Description: The multi-ovary trait of wheat inbred line DUOII is controlled by a dominant gene whose expression can be suppressed by the heterogeneous cytoplasm of TeZhiI (TZI), another inbred line with the nucleus of common wheat and the cytoplasm of Aegilops. DUOII (♀) × TZI (♂) shows multi-ovary trait, while TZI (♀) × DUOII (♂) shows mono-ovary. To elucidate the molecular mechanism regulating heterogeneous cytoplasmic suppression of the multi-ovary gene, we performed an untargeted metabolomic analysis of 2–6 mm young spikes of reciprocal crosses between DUOII and TZI at the critical stage of additional pistil primordium development. We identified 198 annotated differentially expressed metabolites and analyzed them according to their biological functions. The results showed that these metabolites had obvious functional pathways mainly implicated in amino acid, carbohydrate, nicotinate and nicotinamide, and purine metabolism and isoquinoline alkaloid biosynthesis. We also found that shikimate, phosphoglycolic acid, nicotinamide, guanine, and xanthine might play essential roles in cytoplasmic suppression of multi-ovary trait. Chloroplast metabolism was also implicated in the nuclear-cytoplasmic effect of the multi-ovary gene. The findings provide solid theoretical and empirical foundations for future studies elucidating the mechanisms controlling heterogeneous cytoplasmic suppression of the nuclear multi-ovary gene in wheat.

Description: We manually mapped particles ranging in longest axis from 0.3 cm to 95 m on (101955) Bennu for the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) asteroid sample return mission. This enabled the mission to identify candidate sample collection sites and shed light on the processes that have shaped the surface of this rubble-pile asteroid. Building on a global survey of particles, we used higher-resolution data from regional observations to calculate particle size-frequency distributions (PSFDs) and assess the viability of four candidate sites for sample collection (presence of unobstructed particles ≤ 2 cm). The four candidate sites have common characteristics: each is situated within a crater with a relative abundance of sampleable material. Their PSFDs, however, indicate that each site has experienced different geologic processing. The PSFD power-law slopes range from −3.0 ± 0.2 to −2.3 ± 0.1 across the four sites, based on images with a 0.01-m pixel scale. These values are consistent with, or shallower than, the global survey measurements. At one site, Osprey, the particle packing density appears to reach geometric saturation. We evaluate the uncertainty in these measurements and discuss their implications for other remotely sensed and mapped particles, and their importance to OSIRIS-REx sampling operations.