Search Results (1,995)

This article presents the development and implementation of an IoT-enabled, off-grid solar power supply prototype designed to power a range of electrical devices. The developed system comprises a Photovoltaic panel, a Maximum Power Point Tracking (MPPT) charger, a 2.5 kWh/24 V high-performance LiFePO4 battery bank with a Battery Management System, an embedded controller with IoT connectivity, and DC/DC and DC/AC converters. The PV panel serves as the primary energy source, with the MPPT controller optimizing battery charging, while the DC/DC and DC/AC converters supply power to the connected electrical devices. The article includes a case study of a developed platform for powering an information and advertising system. The system features a predictive energy management algorithm, which optimizes the appliance operation based on daily solar irradiance forecasts and real-time battery State-of-Charge monitoring. The IoT-enabled controller obtains solar irradiance forecasts from an online meteorological service via API calls and uses these data to estimate energy availability for the next day. Using this prediction, the system schedules and prioritizes the operations of connected electrical devices dynamically to optimize the performance and prevent critical battery discharge. The IoT-based controller is equipped with both Wi-Fi and an LTE modem, enabling communication with online services via wireless or cellular networks. Full article

The efficacy of monetary policy depends on an accurate model of bank behavior, yet the existing literature has a significant blind spot: the central role of deposit interest rate setting. This paper argues that the deposit rate is the primary arena where banks’ strategic and asymmetric responses to policy signals are revealed. Motivated by the unique dual-benchmark system in Indonesia, where a prudential deposit insurance rate actively competes with the central bank’s policy rate, this study addresses a conceptual problem with global relevance, namely, how monetary policy transmission functions when confronted with conflicting policy signals. To investigate this gap, this paper employs a Systematic Literature Review (SLR), combined with bibliometric analysis. By synthesizing findings from 63 articles selected via the PRISMA protocol, this review first maps the intellectual structure of the field, confirming that while themes of monetary policy and bank behavior are mature, the crucial dimension of deposit rate setting, particularly within a dual-benchmark context, remains a ‘missing link’. The primary contribution of this study is, therefore, building a conceptual framework that recenters the deposit interest rate as the fundamental indicator for assessing asymmetric bank behavior and identifying policy distortions. The findings provide a structured foundation for future empirical research and offer critical insights for regulators on the implications for monetary policy transmission and financial system stability. Full article

With the development of large language model (LLM) technology, AI-assisted education systems are gradually being widely used. Learning Path Recommendation (LPR) is an important task in personalized instructional scenarios. AI-assisted LPR is gaining traction for its ability to generate learning content based on a student’s personalized needs. However, the native-LLM has the problem of hallucination, which may lead to the inability to generate learning content; in addition, the evaluation results of the LLM on students’ knowledge status are usually conservative and have a large margin of error. To address these issues, this work proposes a novel approach for LPR enhanced by knowledge tracing (KT) and LLM. Our method operates in a “generate-and-retrieve” manner: the LLM acts as a pedagogical planner that generates contextual reference exercises based on the student’s needs. Subsequently, a retrieval mechanism constructs the concrete learning path by retrieving the top-N most semantically similar exercises from an established exercise bank, ensuring the recommendations are both pedagogically sound and practically available. The KT plays the role of an evaluator in the iterative process. Rather than generating semantic instructions directly, it provides a quantitative and structured performance metric. Specifically, given a candidate learning path generated by the LLM, the KT model simulates the student’s knowledge state after completing the path and computes a knowledge promotion score. This score quantitatively measures the effectiveness of the proposed path for the current student, thereby guiding the refinement of subsequent recommendations. This iterative interaction between the KT and the LLM continuously refines the candidate learning items until an optimal learning path is generated. Experimental validations on public datasets demonstrate that our model surpasses baseline methods. Full article

This study explores the effect of the energy transition on financial stability in the context of 13 OECD countries during the period from 2009 to 2019. In this sense, the soundness of the financial system is expressed through two dimensions: the Zscore and the volume of non-performing loans (NPLs). Using a dynamic panel estimation with the Generalized Method of Moments (GMM), the results highlight the complex effects of the energy transition on financial stability. Switching from fossil to clean energy improves the Zscore and reduces NPLs. In addition, the study reveals heterogeneous impacts depending on the renewable energy source involved. In fact, wind energy makes a positive contribution to both dimensions of financial stability. By linking the dynamics of the energy transition with the resilience of the banking sector, this study provides new insights into how sustainable energy policies can foster long-term financial sustainability. The effects of solar power and hydroelectricity, while positive overall, are not without nuances. Specifically, the former reduces the NPLs but also the Zscore, while the latter has the opposite effect on both aspects of financial stability. At this point, it is crucial to take into account the varying effects of different renewable energy sources when assessing the financial repercussions of the energy transition. Full article

The storage of different forms of energy is becoming increasingly important in the energy system sector, due to the significant fluctuations that renewable energy sources influence on urban energy systems. Nowadays, these sources have been promoted for the transition towards modern energy systems at different scales, due to their reduced emissions of greenhouse gases. Yet, many doubts remain about their efficacy in urban settlements worldwide. For this reason, to promote the fast implementation of renewable energy technologies around the world, it is of great importance to design and develop free-access and user-friendly tools to help stakeholders in the planning and management of urban energy districts. The present study has proposed an evaluation tool to model decentralised energy storage systems using Urban Building Energy Models, including an optimisation method to size the best capacity in each building of a district. The developed models simulate two storage technologies: battery power banks and heated water tanks. To present the outcomes of the tool, these models have been tested in two scenarios of Portugal, located in a densely populated area and the most isolated region of the country. Among the most important findings of the results are their ability to evaluate the performance of individual buildings by group archetype and total district metrics, using different temporal periods in a single model to identify the buildings taking most advantage of storage technologies. In addition, the optimisation algorithm efficiently estimated the ideal size of each storage technology, reducing the need of unnecessary capacity. Full article

Crystallography plays a crucial role in understanding the functions of macromolecules by determining their three-dimensional structures at the atomic level. This review outlines the history of crystallization, explains the principles of crystallization, and provides a comprehensive retrospective on the role of crystallography in enzymology, with a particular focus on the seven Enzyme Commission (EC) classes. For each class, we highlight representative enzymes and the specific mechanistic insights enabled by crystal structures, oxidoreductases (the “yellow enzyme” lineage), transferases (phosphotransferase systems), hydrolases (RNase III and chymotrypsin), lyases (fumarase), isomerases (pseudouridine synthases), ligases (E3 ubiquitin ligases), and translocases (ATP synthase), emphasizing cofactor usage, conformational change, regulation, and implications for disease and drug discovery. We also compile EC-wide statistics from the Protein Data Bank (PDB) to quantify structural coverage. The limitations and challenges of current crystallization techniques are addressed, along with alternative experimental methods for structural elucidation. In addition, emerging computational tools and biomolecular design are also discussed. By reviewing the trajectory of enzymology and crystallography, we demonstrated their profound impact on biochemistry and therapeutic discovery. Full article

Monetary policy and central banks faced significant challenges in recent decades, like the Great Recession and the 2008–2009 financial crisis, and the Global Inflation Surge of 2021–2022. The introduction of blockchain technology triggered major financial innovations. Nevertheless, the adoption of digital currencies and stablecoins in particular has been limited and does not have wide and everyday use, like national currencies. To understand non-national currency usage better, we examine free banking in Scotland and the U.S., and specifically note issuance. Lessons from these periods suggest the importance of reserves and coordination mechanisms. Based on these free banking cases, we propose that banks and corporations should have the freedom to issue their own stablecoins. More specifically, we examine the freedom for regulated banks to issue their own stablecoins in a competitive environment, learning from historical precedents how to manage such a system. Free banking stablecoins could provide significant benefits, especially in countries with unstable monetary systems, like emerging economies. Such benefits can range from better monetary policy, inflation targeting, and stability, to a broader range of innovative financial markets and services that can contribute towards entrepreneurship, investments, and economic development. Citizens, entrepreneurs, and domestic and foreign investors can gain from these benefits. At the same time, the banking sector and financial institutions can maintain an important role and further expand and develop by offering innovative financial services in an evolving and challenging environment due to financial technology and disintermediation. Finally, governments and central banks could also benefit from increased financial inclusion, higher economic growth and development, but also from more competition and financial stability, and from financial innovation and technology services. Full article

This study addresses the critical challenge of balancing coastal wetland conservation with urban sustainable development, a pivotal issue for ecological civilization in rapidly developing regions. Through an in-depth analysis of Qingdao and Weihai—exemplary cases in Shandong Province—this research systematically investigates mechanisms for achieving synergistic win–win outcomes. Employing a mixed-methods approach, including systems analysis to deconstruct governance frameworks, comparative case study to identify transferable strategies, and policy deduction to formulate actionable pathways, the study reveals how integrated approaches yield tangible results. Qingdao’s “Five Ocean Usages” concept and Weihai’s segmented coastal zoning have significantly improved key ecological metrics. By contrast, Qinhuangdao faces pronounced challenges, including degraded wetlands, spatial conflict between ports and core habitats, and underdeveloped synergistic governance. To address these, the study proposes a targeted strategy for Qinhuangdao, emphasizing a data-informed “wetland+” multi-format integration plan, the establishment of wetland mitigation banking and green finance instruments, digitally enabled public participation, and deeper policy alignment with national strategies such as Maritime Power. This research provides both a replicable analytical framework and practical guidance for coastal cities seeking to realize “development within protection and protection within development”. Full article

Financial fraud represents a critical global challenge with substantial economic and social consequences. This comprehensive review synthesizes the current knowledge on machine learning approaches for financial fraud detection, examining their effectiveness across diverse fraud scenarios. We analyze various fraud types, including credit card fraud, financial statement fraud, insurance fraud, and money laundering, along with their specific detection challenges. The review outlines supervised, unsupervised, and hybrid learning approaches, discussing their applications and performance in different fraud detection contexts. We examine commonly used datasets in fraud detection research and evaluate performance metrics for assessing these systems. The review is further grounded by two case studies applying supervised models to real-world banking data, illustrating the practical challenges of implementing fraud detection systems in operational environments. Through our analysis of the recent literature, we identify persistent challenges, including data imbalance, concept drift, and privacy concerns, while highlighting the emerging trends in deep learning and ensemble methods. This review provides valuable insights for researchers, financial institutions, and practitioners working to develop more effective, adaptive, and interpretable fraud detection systems capable of operating within real-world financial environments. Full article

Vegetation colonizing fluvial sandbars provides many noteworthy functions in river and floodplain systems, but it also influences hydrodynamic processes, mainly during flooding events. Numerical modelling is generally used to evaluate the impact of floods, but its reliability is very much connected with the accuracy of the bed and bank roughness, which is eventually altered by the presence of vegetation and its height. However, for the sake of simplicity, most models tend to ignore how the sandbar roughness varies over space and time, as a function of the local vegetation dynamics (spatial distribution and height). To determine the long-term dynamic vegetation condition using remote sensing multispectral indexes, this study leverages a deep-learning method to establish a relationship between vegetation height (h), a critical parameter for vegetation roughness estimation, and vegetation indexes (VIs) collected by an uncrewed aerial vehicle (UAV). A field campaign was performed in October 2024 covering the Baishazhou sandbar, located along a straight section of the Wuhan reach of the Changjiang River Basin, China. The results show that the R² and RMSE between the real and predicted vegetation height by the trained Fully Connected Neural Network (FCNN) are 0.85, 1.10 m, and the relative error reaches a maximum of 17.2%, meaning that the trained FCNN model performs rather well. Despite being tested on a single case study, the workflow presented here demonstrates the opportunity to use UAVs for depicting vegetation characteristics such as height over large areas, eventually using them to inform numerical models that consider sandbar roughness. Full article

SThe green finance structure refers to the configuration of financial instruments within the green finance system, the optimization of which is crucial for efficient resource allocation and corporate green transformation. Using panel data from Chinese A-share listed companies from 2014 to 2021, this study empirically examines the relationship between green finance structure and corporate green transformation. The results reveal a significant inverted U-shaped relationship, indicating that a coordinated balance between market-based and bank-based instruments most effectively promotes green transformation. This relationship is influenced by technological and institutional environments: in high-tech industries and regions with weaker environmental regulations, a more market-oriented green finance structure is associated with stronger transformation performance. Further analysis identifies a significant synergistic effect between green credit and green bonds, showing that their complementarity can further enhance corporate green transformation and varies across different technological and institutional contexts. Heterogeneity analysis indicates that the inverted U-shaped pattern is more pronounced in western regions and among firms with stronger internal control systems, while eastern and central regions exhibit a more linear positive relationship. Overall, this study introduces a structural perspective to explain the role of green finance in supporting corporate sustainability transitions and provides new empirical evidence for optimizing the green financial system. Full article

This study examines the key factors influencing online banking adoption in India in the post-COVID-19 period. Building on the Technology Acceptance Model (TAM), the research integrates traditional factors—perceived ease of use (PEOU), accessibility (ABS), and reliability of the banking system (RBS)—with a novel construct, perceived environmental, social, and governance performance of banks (PESGB). Data were collected through a structured questionnaire administered to Indian banking customers, and the proposed model was tested using covariance-based structural equation modeling (CB-SEM). The results demonstrate that PEOU, ABS, and PESGB significantly and positively influence customers’ intention to adopt online banking, whereas RBS does not show a significant effect. These findings suggest that in the post-pandemic era, customers prioritize usability, accessibility, and sustainability over traditional concerns of reliability. The study contributes to the extension of TAM by incorporating ESG considerations and offers practical implications for banks to enhance digital adoption by promoting user-friendly services and aligning digital transformation strategies with sustainability commitments. Full article

Bean common mosaic virus (BCMV; Potyvirus phaseovulgaris) is one of the primary viruses that severely impacts the yield and quality of common beans (Phaseolus vulgaris L.) and has a worldwide distribution. Utilizing small RNA sequencing and RT-PCR validation, this study identified widespread co-infection by multiple viruses in field-collected common bean samples, with BCMV being the dominant viral species. A novel isolate, designated DY9, was obtained from these field samples. Pathotype characterization confirmed DY9 as pathotype PG-III, while previous studies reported all other PG-III members as Bean common mosaic necrosis virus (BCMNV). Whole-genome sequencing and phylogenetic analysis revealed that DY9 was genetically closer to BCMV and diverged significantly from known PG-III isolates. Based on these findings, we constructed an infectious clone of DY9. To address the genetic instability of Potyvirus in the Escherichia coli (E. coli) expression system, we discovered that inserting Intron 2 (derived from the NiR gene of P. vulgaris, GenBank: U10419.1) at position 2431 of the HC-Pro gene and targeting Intron 1 (derived from the ST LS1 gene of Solanum tuberosum, GenBank: X04753.1) at position 4240 of the CI gene significantly improved the stability of the cloning vector. The clone was verified to systemically infect common bean plants and induce typical mosaic symptoms. Infectivity was validated through RT-PCR, RT-qPCR, Western blotting, and transmission electron microscopy. This study represents the first successful construction of an infectious clone for pathotype PG-III BCMV, providing a critical reverse genetics tool for dissecting viral pathogenesis and identifying resistance genes. These findings not only expand the genetic diversity of BCMV but also offer a methodological reference for constructing infectious clones of Potyvirus species. Full article

In-Lieu Fee programs are an important mechanism for compensatory mitigation in the United States and received wide-spread standardization after the regulatory mitigation rule change of 2008. On public lands, they are especially important for pooling funds from numerous small-scale impacts that might otherwise go unmitigated. This study examines the use cases of fee program credits on public lands since 2008. Using data from the Regulatory In-Lieu Fee and Bank Information Tracking System, I analyzed eleven active In-Lieu Fee programs approved post-2008 across 78 service areas, encompassing 1043 credit transactions. Transactions were categorized by credit amount, proportion, target ecosystems, and impact designations. The analysis highlights the influence of residential and commercial development, alongside resource extraction, as major contributors to fee program transactions, underscoring the program’s role in mitigating various development pressures. Residential, commercial, and government projects frequently co-occur within service areas, which can support policy planning to anticipate potential cumulative impacts and expected future impacts and credit demands. Furthermore, my analysis shows that impacts from resource extraction require proportionally larger offsets than those from residential or recreational activities. The findings suggest that programs on public lands can fill a niche distinct from mitigation banks, as they address a multitude of impacts while further allowing for the pooling of resources and funds from small-scale impacts, while the use of advance credits remains contentious for achieving no net loss. Full article

In an era of global warming, sustainable agriculture, which emphasises the conservation of biodiversity and the rational use of natural resources, is growing in importance. One of the key elements is to increase the genetic diversity of crops through the use of crop wild relatives (CWRs) and local varieties, which provide a source of genes for resistance to biotic and abiotic stresses. Modern agricultural systems are characterised by low biodiversity, which increases the susceptibility of plants to diseases and pests. Growing mixtures of varieties, both intra- and interspecific, is a practical strategy to increase plant resistance, stabilise yields and reduce pathogen pressure. This manuscript has a review character and synthesises the current literature on the use of CWRs, local varieties, and variety mixtures in sustainable agriculture. The main research question of the study is to what extent plant genetic resources, including CWRs and local varieties, as well as the cultivation of variety mixtures, can promote plant resistance, stabilise yields and contribute to sustainable agriculture under climate change. The objectives of the study are to assess the role of genetic resources and variety mixtures in maintaining biodiversity and yield stability, and to analyse the potential of CWRs and local varieties in enhancing plant resistance. Additionally, the study investigates the impact of variety mixtures in reducing disease and pest development, and identifies barriers to the use of genetic resources in breeding along with strategies to overcome them. The study takes an interdisciplinary approach including literature and gene bank data analysis (in situ and ex situ), field trials of cultivar mixtures under different environmental conditions, genetic and molecular analysis of CWRs, the use of modern genome editing techniques (CRISPR/Cas9) and assessment of ecological mechanisms of mixed crops such as barrier effect, and induced resistance and complementarity. In addition, the study considers collaboration with participatory and evolutionary breeding programmes (EPBs/PPBs) to adapt local varieties to specific environmental conditions. The results of the study indicate that the integration of plant genetic resources with the practice of cultivating variety mixtures creates a synergistic model that enhances plant resilience and stabilises yields. This approach also promotes agroecosystem conservation, contributing to sustainable agriculture under climate change. Full article