Search Results (12,027)

Mid-deep geothermal heat pump systems (MD-GHPs) use mid-deep borehole heat exchangers (MDBHEs) to extract heat from the geothermal energy at a depth of 2–3 km, and have been used for space heating in China over the last decade. This paper proposes a comprehensive and multilevel evaluation-index system to analyze and evaluate the energy performance of MD-GHPs. The multilevel evaluation index system consists of a target layer, a criterion layer, and an index layer, where the criterion layer is subdivided into six aspects and the index layer includes 26 specific indices, reflecting the geothermal resources, heat transfer performance of the MDBHEs, energy efficiency of the heat pump systems, building space heating demand, grid dynamic response capability, and energy-saving and economic benefits. Then, based on both expert survey results and case study data, the entropy weight method and the analytic hierarchy process are integrated to determine indicator weight coefficients among the multilevel evaluation indices, comprehensively considering both subjective and objective analyses. Furthermore, a fuzzy comprehensive evaluation model is conducted to integrate these weighted indices into a multi-criteria evaluation of MD-GHP performance. Finally, the proposed method was applied to evaluate the practical performance of four projects, returning scores of 61.56, 58.33, 72.73, and 78.41. These evaluations enable an overall assessment of the energy performance of MD-GHPs, reflecting the technical weaknesses and offering optimization guidance for system design and operation. Full article

This study aims to present a methodological approach based on the objectives of the Nature Restoration Law and the concept of Forest Landscape Restoration to identify areas that are best suited for the implementation of Nature-based Solutions for the improvement of landscape and habitat status in the city of Campobasso (1028.64 km²). Using open data (ISPRA ecosystem services and regional land use capability), an expert based approach (questionnaire), and a multicriteria analysis (Analytical Hierarchy Process), the Total Ecosystem Services Value index was determined as a weighted additive sum of the criteria considered. The index was then classified into eight clusters, and the land use “Cropland” was extracted. Cluster 1 croplands (740.09 Ha) were identified as the areas to be allocated to Nature-based Solutions since they were those characterized by fewer ecosystem services provisioning, while Cluster 8 croplands (482.88 Ha) were identified as valuable areas to be preserved. It was then possible to compare the “Forest” areas currently present in the study area with those of a possible future scenario, represented by the areas occupied today by forest with the addition of Cluster 1 croplands. A landscape analysis was conducted; it showed greater dispersion and fragmentation of forest patches in the future scenario, but also greater connectivity and thus greater ecological functionality of the patches. Full article

This systematic review investigated how Higher Education Institutions (HEIs) optimise data analytics in postgraduate programmes to enhance student achievement. Existing research explores the theoretical benefits of data analytics but lacks practical guidance on strategies to effectively implement and utilise data analytics for student success. As such, this review aimed to identify data analytics approaches used by HEIs and explore challenges and best practices in their application. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Five databases were searched. Studies that examined data analytics in HEIs postgraduate programmes and their impact on student learning were included. Studies that were solely theoretical or in non-postgraduate settings were excluded. Twenty-six studies were included. Quality assessment using the Critical Appraisal Skills Programme (CASP) Checklist was employed. The review identified various data analytics approaches including descriptive, predictive, and prescriptive analytics, among others. These approaches can improve foundational skills, create supportive learning environments, and optimise teaching strategies. However, limitations (standardised tests, data integration) and privacy concerns were acknowledged. Recommendations include developing a comprehensive evaluation system, equipping educators with the skills to utilise diverse analytics to enhance student achievement, fostering open communication about data use, and cultivating a data-literate student body. While diverse approaches were explored, the review’s lack of specific contextual details may limit the generalisability of findings. To mitigate this, the review categorised techniques and provided references for further exploration. Full article

Autism spectrum disorder (commonly known as autism) is a complex and prevalent neurodevelopmental condition characterized by challenges in social behavior, restricted interests, and repetitive behaviors. It is projected that the annual cost of autism spectrum disorder in the US will reach USD 461 billion by 2025. However, despite being a major public health problem, effective treatment for the underlying symptoms remains elusive. As numerous literature data indicate the role of gut microbiota in autism prognosis, particularly in terms of alleviating gastrointestinal (GI) symptoms, high hopes have been placed on probiotics for autism treatment. Approximately twenty clinical studies have been conducted using single or mixed probiotic cultures. However, unequivocal results on the effect of probiotics on people with autism have not been obtained. The small sample sizes, differences in age of participants, choice of probiotics, dose and duration of treatment, outcome measures, and analytical methods used are largely inconsistent, making it challenging to draw distinctive conclusions. Here, we discuss the experimental evidence for specific gut bacteria and their metabolites and how they affect autism in light of the phenotypic and etiological complexity and heterogeneity. We propose a personalized medicine approach for using probiotics to increase the quality of life of individuals with autism by selecting specific probiotics to improve particular features of the condition. Full article

This work aimed to evaluate the accuracy of analytical models for predicting the behavior of concrete-filled steel tubular (CFST) columns via finite element analysis coupled with physical nonlinearity. The methodology involved an extensive review of experimental tests from the literature, numerical modeling of columns with different configurations, and a comparison of the results obtained with available experimental data. Several characteristics were evaluated, such as the load capacity, confinement factor, and relative slenderness. The numerical model agreed well with the experimental results, with a less than 10% relative error. The results indicated that analytical models of the Chinese (GB 50936) and European (EC4) codes overestimated some load capacity values (up to 14.9% and 8.7%, respectively). In comparison, the American (AISC 360) and Brazilian (NBR 8800) standards underestimated the ultimate loads (23.3% and 31.6%, respectively). An approach coefficient β is proposed, contributing to safer and more efficient design practices in structural engineering. Full article

The article formulates the state of the problem of improving the theoretical calculation of the nanofiltration kinetic characteristics in the time cycle of separation of industrial solutions containing copper(II), iron(III), trisodium phosphate and OP-10 (a wetting agent used in electroplating, a product of treating a mixture of mono- and dialkylphenols with ethylene oxide) using the equations of convective diffusion, hydrodynamics and mass transfer. To calculate the kinetic characteristics of the nanofiltration process, the mathematical model was improved by numerically solving the equations of convective diffusion, the Navier–Stokes equation and the flow continuity equation in a polar coordinate system with initial and boundary conditions. The theoretical results obtained in the process of an analytical solution of the system of equations allow calculating changes in concentrations in the permeate and retentate tracts and the permeate volume during nanofiltration separation. The acceptability of the developed nanofiltration method for separating solutions is assessed by comparing the calculated data according to the mathematical model with the experimental data obtained on the nanofiltration unit during separation of solutions containing copper(II), iron(III), trisodium phosphate and OP-10. Full article

Understanding the intricate relationship between drought stress and corn yield is crucial for ensuring food security and sustainable agriculture in the face of climate change. This study investigates the subtle effects of drought stress on corn physiological, morphological, and spectral characteristics at different growth stages, in order to construct a new drought index to characterize drought characteristics, so as to provide valuable insights for maize recovery mechanism and yield prediction. Specific conclusions are as follows. Firstly, the impact of drought stress on corn growth and development shows a gradient effect, with the most significant effects observed during the elongation stage and tasseling stage. Notably, Soil and Plant Analyzer Development (SPAD) and Leaf Area Index (LAI) are significantly affected during the silking stage, while plant height and stem width remain relatively unaffected. Secondly, spectral feature analysis reveals that, from the elongation stage to the silking stage, canopy reflectance exhibits peak–valley variations. Drought severity correlates positively with reflectance in the visible and shortwave infrared bands and negatively with reflectance in the near-infrared band. Canopy spectra during the silking stage are more affected by moderate and severe drought stress. Thirdly, LAI shows a significant positive correlation with yield, indicating its reliability in explaining yield variations. Finally, the yield-related drought index (YI) constructed based on Convolutional Neural Network (CNN), Random Forest (RF) and Multiple Linear Regression (MLR) methods has a good effect on revealing drought characteristics (R = 0.9332, p < 0.001). This study underscores the importance of understanding corn responses to drought stress at various growth stages for effective yield prediction and agricultural management strategies. Full article

Seafood preservation techniques have evolved from ancient methods to modern innovations like canning, freezing, and surimi production. Canning in the 19th century introduced airtight containers, while commercial freezing technologies like flash freezing extended shelf life. Surimi pastes in the 20th century led to affordable imitation seafood products. Emerging technologies continue to enhance seafood preservation methods. Moreover, the integration of digital technology, automation, and data sharing, known as Industry 4.0, is transforming various industries. This integration encompasses blockchain technology, automation, robotics, and big data analytics, aiming to enhance production, sustainability, traceability, and efficiency in fish processing. With a focus on the seafood market dynamics affecting these advances, this research was conducted with the aim to understand how technical breakthroughs in the seafood business are dispersed and implemented across different nations. We aim to determine the correspondence between the technological sophistication of machinery in seafood processing companies and map it across different countries across the globe to obtain an understanding of the generation of technology used in prominence. Variations in adoption rates and technological trends reflect regional market dynamics. The Seafood Expo ASIA 2023 study looked at the use of Industry 4.0 technologies, operational procedures, and technology adoption in the global seafood processing industry. Notably, countries like Norway, the Republic of Korea, Spain, Turkey, and the Netherlands have rapidly embraced Industry 4.0 technologies. The market factors driving these technological advancements across different countries include rising consumer demand for sustainable seafood, economic incentives, and global competition. A correspondence analysis was employed to analyze the correspondence between countries and the level of technological sophistication in the machinery used. We successfully mapped the level of technology utilized in machinery across global seafood processing companies, providing insights into the technological advancements shaping the industry. Full article

Project cost forecasting is a complex and critical process, and it is of paramount importance for the successful implementation of engineering projects. Accurately forecasting project costs can help project managers and relevant decision-makers make informed decisions, thereby avoiding unnecessary cost overruns and time delays. Furthermore, accurately forecasting project costs can make important contributions to better controlling engineering costs, optimizing resource allocation, and reducing project risks. To establish a high-precision cost forecasting model for construction projects in Guangdong Province, based on case data of construction projects in Guangdong Province, this paper first uses the Analytic Hierarchy Process (AHP) to obtain the characteristic parameters that affect project costs. Then, a neural network training and testing dataset is constructed, and a genetic algorithm (GA) is used to optimize the initial weights and biases of the neural network. The GA-BP neural network is used to establish a cost forecasting model for construction projects in Guangdong Province. Finally, by using parameter sensitivity analysis theory, the importance of the characteristic values that affect the project cost is ranked, and the optimal direction for controlling the project cost is obtained. The results showed: (1) The determination coefficient between the forecasting and actual values of the project cost forecasting model based on the BP neural network testing set is 0.87. After GA optimization, the determination coefficient between the forecasting and actual values of the GA-BP neural network testing set is 0.94. The accuracy of the intelligent forecast model for construction project cost in Guangdong Province has been significantly improved after optimization through GA. (2) Based on sensitivity analysis of neural network parameters, the most significant factor affecting the cost of construction projects in Guangdong Province is the number of above-ground floors, followed by the main structure type, foundation structure, above-ground building area, total building area, underground building area, fortification intensity, and building height. The results of parameter sensitivity analysis indicate the direction for cost control in construction projects. The research results of this paper provide theoretical guidance for cost control in construction projects. Full article

Nandrolone, or 19-nortestosterone, is an anabolic steroid derived from testosterone, known for its androgenic and anabolic effects. Often used illicitly by athletes to boost performance, its use is banned by the World Anti-Doping Agency (WADA) in and out of competition. Nandrolone’s main metabolites, 19-norandrosterone (19-NA) and 19-noretiocholanolone (19-NE), are typically detected in urine. This systematic review, registered with PROSPERO and following PRISMA guidelines, examines nandrolone’s metabolism, factors affecting its natural production, and the analytical methods used in doping tests. Searches on PubMed, Scopus, and Web of Science yielded 517 studies, of which 57 were selected for analysis after excluding duplicates and unrelated articles. Descriptive statistics were applied to assess data on metabolic pathways, endogenous production influences, and detection techniques. Based on this review, it clearly emerges that the only technique that can distinguish endogenous production from an exogenous intake is gas chromatography/combustion/isotope ratio mass spectrometry (GC-C-IRMS). In addition, factors influencing endogenous production are considered and explored. Overall, this review provides useful information regarding nandrolone and its main metabolites. Full article

The sources of uncertainty in wall-to-wall AGB maps propagate from the tree to pixel, but uncertainty due to forest cover mapping is rarely incorporated into the error propagation process. This study aimed to (1) elaborate an analytical procedure to incorporate forest-mapping-related uncertainty into the error propagation from plot and pixel predictions; (2) develop a stratified estimator with a model-assisted estimator for small and large areas; and (3) estimate the effect of ignoring the mapping uncertainty on the confidence intervals (CIs) for totals. Data consist of a subset of the Brazilian national forest inventory (NFI) database, comprising 75 counties that, once aggregated, served as strata for the stratified estimator. On-ground data were gathered from 152 clusters (plots) and remotely sensed data from Landsat-8 scenes. Four major contributions are highlighted. First, we describe how to incorporate forest-mapping-related uncertainty into the CIs of any forest attribute and spatial resolution. Second, stratified estimators perform better than non-stratified estimators for forest area estimation when the response variable is forest/non-forest. Comparing our stratified estimators, this study indicated greater precision for the stratified estimator than for the regression estimator. Third, using the ratio estimator, we found evidence that the simple field plot information provided by the NFI clusters is sufficient to estimate the proportion forest for large regions as accurately as remote-sensing-based methods, albeit with less precision. Fourth, ignoring forest-mapping-related uncertainty erroneously narrows the CI width as the estimate of proportion forest area decreases. At the small-area level, forest-mapping-related uncertainty led to CIs for total AGB as much as 63% wider in extreme cases. At the large-area level, the CI was 5–7% wider. Full article

Background: In northern countries, spring requires the removal of large volumes of abrasive materials used in winter road maintenance. This sweeping process, crucial for safety and environmental protection, has traditionally relied on conventional mechanical brooms. Recent technological innovations, however, have introduced more efficient and environmentally friendly sweeping solutions; Methods: This study provides a comprehensive comparative analysis of the environmental and operational performance of these innovative sweeping systems versus conventional methods. Using simulation models based on real-world data and integrating fuel consumption models, the analysis replicates sweeping behaviors to assess both operational and environmental performance. A sensitivity analysis was conducted using these models, focusing on key parameters such as the collection rate, the number of trucks, the payload capacity, and the truck unloading duration; Results: The results show that the innovative sweeping system achieves an average 45% reduction in GHG emissions per kilometer compared to the conventional system, consistently demonstrating superior environmental efficiency across all resources configurations; Conclusions: These insights offer valuable guidance for service providers by identifying effective resource configurations that align with both environmental and operational objectives. The approach adopted in this study demonstrates the potential to develop decision-making support tools that balance operational and environmental pillars of sustainability, encouraging policy decision-makers to adopt greener procurement policies. Future research should explore the integration of advanced technologies such as IoT, AI-driven analytics, and digital twin systems, along with life cycle assessments, to further support sustainable logistics in road maintenance. Full article

Compared with those of berries, the stems and leaves of the genus Vaccinium are important and underestimated sources of polyphenols with high antioxidant activity. In the course of this work, aqueous methanol extracts of the aerial parts of common bilberry (Vaccinium myrtillus L.) and bog bilberry (Vaccinium uliginosum L.) were studied to analyze the component compositions of their biologically active polyphenolic compounds. The aqueous methanol fractions of the stems and leaves of the studied samples contained 8.7 and 4.6% extractives, respectively, and were comparable in total polyphenol content, but presented significant differences in antioxidant activity. The identification of polyphenolic compounds was carried out via the following two-stage analytical procedure: (1) non-targeted screening of dominant structures via the 2D NMR method and (2) analysis of HPLC-HRMS data via the scanning of precursor ions for a specific ion. A total of 56 phenolic compounds were identified, including the glycosides quercetin, proanthocyanidins, and catechins, as well as various conjugates of caffeic and p-coumaric acids, including iridoids. Some of the latter, such as caffeoyl and p-coumaroyl hydroxydihydromonotropein, as well as a number of lignan glycosides, were described for the first time in V. uliginósum and V. myrtillus. Full article

Green innovation provides powerful incentives to achieve sustained social progress. However, the available research examines the financial drivers of green innovation, overlooking the impact of digital government development and the institutional environment. The integration of digital government construction with the institutional environment, and the coupling of the two with green innovation, will paint a picture of the future that promotes sustainable social progress and the modernization of governance. This research utilizes data from 31 provinces in China from 2018 to 2022 to study the impact of digital government construction and the institutional environment on the provincial green innovation efficiency. An empirical analysis is conducted on the basis of analyzing the spatiotemporal evolution and pattern of digital government construction, the institutional environment and the provincial green innovation efficiency. Firstly, digital government construction emphasizes data openness and sharing, and data become a key link between those inside and outside the government. The digital platform becomes an important carrier connecting the government and multiple subjects in collaborative innovation to continuously shape a new digital governance ecology. The netting of digital ecology is conducive to the institutional environment, serving to break the path dependence and create a more open, inclusive and synergistic institutional environment. Based on this, we consider that digital government construction positively affects the institutional environment, and this is verified. Secondly, a good government–market relationship, mature market development, a large market service scale, a complete property rights system and a fair legal system brought about by the improved institutional environment provide macro-external environmental support for enhanced innovation dynamics. Based on this, it is proposed that the institutional environment positively affects the provincial green innovation efficiency. Meanwhile, building on embeddedness theory, the industrial embeddedness of the institutional environment for green innovation highlights the scattered distribution of innovation components. Geographical embeddedness stresses indigenous resource distribution grounded in space vicinity and clustering. The better the institutional environment, the greater the forces of disempowerment at the industrial tier and the easier it is for resources to flow out. This may potentially have a detrimental role in improving the local green innovation efficiency. In view of this, it is proposed that the institutional environment negatively affects the provincial green innovation efficiency, and this is verified. Thirdly, digital government construction, as an important aspect of constructing a digital governance system and implementing the strategy of a strong network state, can effectively release the multiplier effect of digital technology in ecological environment governance and green innovation, continuously enhancing the provincial green innovation efficiency. In view of this, it is proposed that digital government construction positively affects the provincial green innovation efficiency, and this is verified. When the institutional environment is used as a mediating variable, digital government construction will have a certain non-linear impact in terms of provincial green innovation efficiency improvement. Building on the evidence-based analysis results, it is found that the institutional environment plays a competitive mediating role. This study integrates digital government construction, the institutional environment and the provincial green innovation efficiency under a unified analytical structure, offering theoretical inspiration and operational directions to enhance the provincial green innovation efficiency. Full article

The prediction and analysis of students’ academic performance are essential tools for educators and learners to improve teaching and learning methods. Effective predictive methods assist learners in targeted studying based on forecast results, while effective analytical methods help educators design appropriate educational content. However, in actual educational environments, factors influencing student performance are multidimensional across both temporal and spatial dimensions. Therefore, a student performance prediction and analysis method incorporating multidimensional spatiotemporal features has been proposed in this study. Due to the complexity and nonlinearity of learning behaviors in the educational process, predicting students’ academic performance effectively is challenging. Nevertheless, machine learning algorithms possess significant advantages in handling data complexity and nonlinearity. Initially, a multidimensional spatiotemporal feature dataset was constructed by combining three categories of features: students’ basic information, performance at various stages of the semester, and educational indicators from their places of origin (considering both temporal aspects, i.e., performance at various stages of the semester, and spatial aspects, i.e., educational indicators from their places of origin). Subsequently, six machine learning models were trained using this dataset to predict student performance, and experimental results confirmed their accuracy. Furthermore, SHAP analysis was utilized to extract factors significantly impacting the experimental outcomes. Subsequently, this study conducted data ablation experiments, the results of which proved the rationality of the feature selection in this study. Finally, this study proposed a feasible solution for guiding teaching strategies by integrating spatiotemporal multi-dimensional features in the analysis of student performance prediction in actual teaching processes. Full article