bluuebunny/crossref_metadata_2025 huggingface dataset:
What Drives Banks to Provide Green Loans? Corporate Governance and Ownership Structure Perspectives of Vietnamese Listed Banks
Authors: Hoque Ariful , Le Duong Thuy , Le Thi
Published: September 2024

This study delves into the influence of banks’ governance and ownership structures on green lending. To examine this, we utilized the two-step system GMM and PCSE methods on the panel data of Vietnamese commercial banks spanning from 2010 to 2023. The findings suggest that board characteristics, precisely board size, board independence, and gender diversity, play a significant role in encouraging banks to provide green credit. The study highlights the importance of ownership structure in green lending. Banks with a high percentage of government ownership tend to fund more green projects, while foreign counterparts are reluctant to fund green finance. A mechanism test is also conducted to point out that banks’ disclosure of their green loan commitments is an influential channel whereby corporate governance and ownership structure impact green loans. Additionally, this research finds that the issuance of the Green Loan Principles in 2018 can facilitate banks’ governance of sustainable lending.
Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile
Authors: Rodríguez-López Lien , Bravo Alvarez Lisandra , Duran-Llacer Iongel , Ruíz-Guirola David E. , Montejo-Sánchez Samuel , Martínez-Retureta Rebeca , López-Morales Ernesto , Bourrel Luc , Frappart Frédéric , Urrutia Roberto
Published: September 2024

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). In all cases, each machine learning model shows robust performance, with promising results in predicting chlorophyll-a concentrations. Among these models, LSTM stands out as the most effective, with the best metrics in the estimation, the best performance was Case 1, with R2 = 0.89, an RSME of 0.32 µg/L, an MAE 1.25 µg/L and an MSE 0.25 (µg/L)2, consistently outperforming the others according to the static metrics used for validation. This finding underscores the effectiveness of LSTM in capturing the complex temporal relationships inherent in the dataset. However, increasing the dataset in Case 3 shows a better performance of TCNs (R2 = 0.96; MSE = 0.33 (µg/L)2; RMSE = 0.13 µg/L; and MAE = 0.06 µg/L). The successful application of machine learning algorithms emphasizes their potential to elucidate the dynamics of algal biomass in Lake Ranco, located in the southern region of Chile. These results not only contribute to a deeper understanding of the lake ecosystem but also highlight the utility of advanced computational techniques in environmental research and management.
The Operational and Climate Land Surface Temperature Products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B
Authors: Ghent Darren , Anand Jasdeep Singh , Veal Karen , Remedios John
Published: September 2024

Land Surface Temperature (LST) is integral to our understanding of the radiative energy budget of the Earth’s surface since it provides the best approximation to the thermodynamic temperature that drives the outgoing longwave flux from surface to atmosphere. Since 5 July 2017, an operational LST product has been available from the Sentinel-3A mission, with the corresponding product being available from Sentinel-3B since 17 November 2018. Here, we present the first paper describing formal products, including algorithms, for the Sea and Land Surface Temperature Radiometer (SLSTR) instruments onboard Sentinel-3A and 3B (SLSTR-A and SLSTR-B, respectively). We evaluate the quality of both the Land Surface Temperature Climate Change Initiative (LST_cci) product and the Copernicus operational LST product (SL_2_LST) for the years 2018 to 2021. The evaluation takes the form of a validation against ground-based observations of LST across eleven well-established in situ stations. For the validation, the mean absolute daytime and night-time difference against the in situ measurements for the LST_cci product is 0.77 K and 0.50 K, respectively, for SLSTR-A, and 0.91 K and 0.54 K, respectively, for SLSTR-B. These are an improvement on the corresponding statistics for the SL_2_LST product, which are 1.45 K (daytime) and 0.76 (night-time) for SLSTR-A, and 1.29 K (daytime) and 0.77 (night-time) for SLSTR-B. The key influencing factors in this improvement include an upgraded database of reference states for the generation of retrieval coefficients, higher stratification of the auxiliary data for the biome and fractional vegetation, and enhanced cloud masking.
Assessing Evapotranspiration Changes in Response to Cropland Expansion in Tropical Climates
Authors: Laipelt Leonardo , Rossi Julia Brusso , de Andrade Bruno Comini , Scherer-Warren Morris , Ruhoff Anderson
Published: September 2024

The expansion of cropland in tropical regions has significantly accelerated in recent decades, triggering an escalation in water demand and changing the total water loss to the atmosphere (evapotranspiration). Additionally, the increase in areas dedicated to agriculture in tropical climates coincides with an increased frequency of drought events, leading to a series of conflicts among water users. However, detailed studies on the impacts of changes in water use due to agriculture expansion, including irrigation, are still lacking. Furthermore, the higher presence of clouds in tropical environments poses challenges for the availability of high-resolution data for vegetation monitoring via satellite images. This study aims to analyze 37 years of agricultural expansion using the Landsat collection and a satellite-based model (geeSEBAL) to assess changes in evapotranspiration resulting from cropland expansion in tropical climates, focusing on the São Marcos River Basin in Brazil. It also used a methodology for estimating daily evapotranspiration on days without satellite images. The results showed a 34% increase in evapotranspiration from rainfed areas, mainly driven by soybean cultivation. In addition, irrigated areas increased their water use, despite not significantly changing water use at the basin scale. Conversely, natural vegetation areas decreased their evapotranspiration rates by 22%, suggesting possible further implications with advancing changes in land use and land cover. Thus, this study underscores the importance of using satellite-based evapotranspiration estimates to enhance our understanding of water use across different land use types and scales, thereby improving water management strategies on a large scale.
Mapping the Influence of Olympic Games’ Urban Planning on the Land Surface Temperatures: An Estimation Using Landsat Series and Google Earth Engine
Authors: Padró Joan-Cristian , Della Sala Valerio , Castelló-Bueno Marc , Vicente-Salar Rafael
Published: September 2024

The Olympic Games are a sporting event and a catalyst for urban development in their host city. In this study, we utilized remote sensing and GIS techniques to examine the impact of the Olympic infrastructure on the surface temperature of urban areas. Using Landsat Series Collection 2 Tier 1 Level 2 data and cloud computing provided by Google Earth Engine (GEE), this study examines the effects of various forms of Olympic Games facility urban planning in different historical moments and location typologies, as follows: monocentric, polycentric, peripheric and clustered Olympic ring. The GEE code applies to the Olympic Games that occurred from Paris 2024 to Montreal 1976. However, this paper focuses specifically on the representative cases of Paris 2024, Tokyo 2020, Rio 2016, Beijing 2008, Sydney 2000, Barcelona 1992, Seoul 1988, and Montreal 1976. The study is not only concerned with obtaining absolute land surface temperatures (LST), but rather the relative influence of mega-event infrastructures on mitigating or increasing the urban heat. As such, the locally normalized land surface temperature (NLST) was utilized for this purpose. In some cities (Paris, Tokyo, Beijing, and Barcelona), it has been determined that Olympic planning has resulted in the development of green spaces, creating “green spots” that contribute to lower-than-average temperatures. However, it should be noted that there is a significant variation in temperature within intensely built-up areas, such as Olympic villages and the surrounding areas of the Olympic stadium, which can become “hotspots.” Therefore, it is important to acknowledge that different planning typologies of Olympic infrastructure can have varying impacts on city heat islands, with the polycentric and clustered Olympic ring typologies displaying a mitigating effect. This research contributes to a cloud computing method that can be updated for future Olympic Games or adapted for other mega-events and utilizes a widely available remote sensing data source to study a specific urban planning context.
Identification of Internal Tides in ECCO Estimates of Sea Surface Salinity in the Andaman Sea
Authors: Subrahmanyam Bulusu , Murty V. S. N. , Hall Sarah B. , Trott Corinne B.
Published: September 2024

We used NASA’s high-resolution (1/48° or 2.3 km, hourly) Estimating the Circulation and Climate of the Ocean (ECCO) estimates of salinity at a 1 m depth from November 2011 to October 2012 to detect semi-diurnal and diurnal internal tides (ITs) in the Andaman Sea and determine their characteristics in three 2° × 2° boxes off the Myanmar coast (box A), central Andaman Sea (box B), and off the Thailand coast (box C). We also used observed salinity and temperature data for the above period at the BD12-moored buoy in the central Andaman Sea. ECCO salinity data were bandpass-filtered with 11–14 h and 22–26 h periods. Large variations in filtered ECCO salinity (~0.1 psu) in the boxes corresponded with near-surface imprints of propagating ITs. Observed data from the box B domain reveals strong salinity stratification (halocline) in the upper 40 m. Our analyses reveal that the shallow halocline affects the signatures of propagating semi-diurnal ITs reaching the surface, but diurnal ITs propagating in the halocline reach up to the surface and bring variability in ECCO salinity. In box A, the semi-diurnal IT characteristics are higher speeds (0.96 m/s) with larger wavelengths (45 km), that are closer to theoretical mode 2 estimates, but the diurnal ITs propagating in the box A domain, with a possible source over the shelf of Gulf of Martaban, attain lower values (0.45 m/s, 38 km). In box B, the propagation speed is lower (higher) for semi-diurnal (diurnal) ITs. Estimates for box C are closer to those for box A.
Projecting Response of Ecological Vulnerability to Future Climate Change and Human Policies in the Yellow River Basin, China
Authors: Zhang Xiaoyuan , Wang Shudong , Liu Kai , Huang Xiankai , Shi Jinlian , Li Xueke
Published: September 2024

Exploring the dynamic response of land use and ecological vulnerability (EV) to future climate change and human ecological restoration policies is crucial for optimizing regional ecosystem services and formulating sustainable socioeconomic development strategies. This study comprehensively assesses future land use changes and EV in the Yellow River Basin (YRB), a climate-sensitive and ecologically fragile area, by integrating climate change, land management, and ecological protection policies under various scenarios. To achieve this, we developed an EV assessment framework combining a scenario weight matrix, Markov chain, Patch-generating Land Use Simulation model, and exposure–sensitivity–adaptation. We further explored the spatiotemporal variations of EV and their potential socioeconomic impacts at the watershed scale. Our results show significant geospatial variations in future EV under the three scenarios, with the northern region of the upstream area being the most severely affected. Under the ecological conservation management scenario and historical trend scenario, the ecological environment of the basin improves, with a decrease in very high vulnerability areas by 4.45% and 3.08%, respectively, due to the protection and restoration of ecological land. Conversely, under the urban development and construction scenario, intensified climate change and increased land use artificialization exacerbate EV, with medium and high vulnerability areas increasing by 1.86% and 7.78%, respectively. The population in high and very high vulnerability areas is projected to constitute 32.75–33.68% and 34.59–39.21% of the YRB’s total population in 2040 and 2060, respectively, and may continue to grow. Overall, our scenario analysis effectively demonstrates the positive impact of ecological protection on reducing EV and the negative impact of urban expansion and economic development on increasing EV. Our work offers new insights into land resource allocation and the development of ecological restoration policies.
AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification
Authors: Yang Xiaofei , Luo Yuxiong , Zhang Zhen , Tang Dong , Zhou Zheng , Tang Haojin
Published: September 2024

Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectively. However, CNNs struggle in modeling long-term dependencies, and transformers may miss subtle spatial-spectral features. To address these challenges, this paper proposes an innovative hybrid HSI classification method aggregating hierarchical spatial-spectral features from a CNN and long pixel dependencies from a transformer. The proposed aggregation multi-hierarchical feature network (AMHFN) is designed to capture various hierarchical features and long dependencies from HSI, improving classification accuracy and efficiency. The proposed AMHFN consists of three key modules: (a) a Local-Pixel Embedding module (LPEM) for capturing prominent spatial-spectral features; (b) a Multi-Scale Convolutional Extraction (MSCE) module to capture multi-scale local spatial-spectral features and aggregate hierarchical local features; (c) a Multi-Scale Global Extraction (MSGE) module to explore multi-scale global dependencies and integrate multi-scale hierarchical global dependencies. Rigorous experiments on three public hyperspectral image (HSI) datasets demonstrated the superior performance of the proposed AMHFN method.
Eddy Current-Based Delamination Imaging in CFRP Using Erosion and Thresholding Approaches
Authors: Pasadas Dario J. , Barzegar Mohsen , Ribeiro Artur L. , Ramos Helena G.
Published: September 2024

Carbon fiber reinforced plastic (CFRP) is a composite material known for its high strength-to-weight ratio, stiffness, and corrosion and fatigue resistance, making it suitable for its use in structural components. However, CFRP can be subject to various types of damage, such as delamination, matrix cracking, or fiber breakage, requiring nondestructive evaluation to ensure structural integrity. In this context, damage imaging algorithms are important for assessing the condition of this material. This paper presents signal and image processing methods for delamination characterization of thin CFRP plates using eddy current testing (ECT). The measurement system included an inductive ECT probe with three coil elements, which has the characteristic of allowing eddy currents to be induced in the specimen with two different configurations. In this study, the peak amplitude of the induced voltage in the receiver element and the phase shift between the excitation and receiver signals were considered as damage-sensitive features. Using the ECT probe, C-scans were performed in the vicinity of delamination defects of different sizes. The dimensions and shape of the ECT probe were considered by applying the erosion method in the damage imaging process. Different thresholding approaches were also investigated to extract the size of the defective areas. To evaluate the impact of this application, a comparison is made between the results obtained before and after thresholding using histogram analysis. The evaluation of damage imaging for three different delamination sizes is presented for quantitative analysis.
Sensitive Coatings Based on Molecular-Imprinted Polymers for Triazine Pesticides’ Detection
Authors: Latif Usman , Yaqub Sadaf , Dickert Franz L.
Published: September 2024

Triazine pesticide (atrazine and its derivatives) detection sensors have been developed to thoroughly check for the presence of these chemicals and ultimately prevent their exposure to humans. Sensitive coatings were designed by utilizing molecular imprinting technology, which aims to create artificial receptors for the detection of chlorotriazine pesticides with gravimetric transducers. Initially, imprinted polymers were developed, using acrylate and methacrylate monomers containing hydrophilic and hydrophobic side chains, specifically for atrazine, which shares a basic heterocyclic triazine structure with its structural analogs. By adjusting the ratio of the acid to the cross-linker and introducing acrylate ester as a copolymer, optimal non-covalent interactions were achieved with the hydrophobic core of triazine molecules and their amino groups. A maximum sensor response of 546 Hz (frequency shift/layer height equal to 87.36) was observed for a sensitive coating composed of 46% methacrylic acid and 54% ethylene glycol dimethacrylate, with a demonstrated layer height of 250 nm (6.25 kHz). The molecularly imprinted copolymer demonstrated fully reversible sensor responses, not only for atrazine but also for its metabolites, like des-ethyl atrazine, and structural analogs, such as propazine and terbuthylazine. The efficiency of modified molecularly imprinted polymers for targeted analytes was tested by combining them with a universally applicable quartz crystal microbalance transducer. The stable selectivity pattern of the developed sensor provides an excellent basis for a pattern recognition procedure.
Structural Damage Detection under Ambient Excitation Using Symbolic Three-Order Square Matrix Formed by Specific-Interval-Sampled Time-Domain Signals
Authors: Meng Shuang , Li Dongsheng
Published: September 2024

In the structural health monitoring of vibration systems, varying excitation always affects the accuracy of damage identification. The proposed symbolic three-order square matrix damage detection method with the matrix norm as a damage indicator can solve the difficult problem of damage identification under ambient excitation. The new sampling pattern extracts data from signals in the time domain at specific intervals based on the structural properties with the help of the autocorrelation coefficient. Then, the data extracted are converted into symbols and arranged into a three-order square matrix, and the Frobenius norm of the matrix is used for structural damage identification as a reliable damage indicator. In this process, the transmissibility function is employed to eliminate the effects of varying excitation. First, the method was verified by a cracked simply supported beam—a simulated Abaqus model. Then, a wooden truss bridge in the laboratory and an actual engineering scenario under ambient excitation together demonstrated the effectiveness and accuracy of the damage identification method and proved the proposed method to be robust to different types of damage under ambient excitation. Compared with other related methods, this method is more intuitive and efficient.
Development of Surface EMG for Gait Analysis and Rehabilitation of Hemiparetic Patients
Authors: Pradon Didier , Tong Li , Chalitsios Christos , Roche Nicolas
Published: September 2024

Background: The quantification of electromyographic activity using surface electrodes is invaluable for understanding gait disorders in patients with central nervous system lesions. We propose to evaluate a commercially available low-cost system compared to a reference system in participants with stroke-related movement disorders in functional situations. Methods: Three hemiparetic participants performed three functional tasks: two treadmill walks at different speeds and a sit-to-stand test. The vastus lateralis and gastrocnemius medialis muscles were equipped with two EMG sensors. The comparison between the two EMG systems was based on 883 identified cycles. Spearman’s correlation coefficients (SCs), linear correlation coefficients (LCCs), and cross-correlation coefficients (CCCs) were calculated. Results: The main results indicate good to very good similarity of the EMG signals collected from the two tested sEMG systems. In the comfortable-walking condition, an SC of 0.894 ± 0.091 and an LCC of 0.909 ± 0.094 were noted. In the fast-walking condition, an SC of 0.918 ± 0.064 and an LCC of 0.935 ± 0.056 were observed. For the 1 min sit-to-stand test, an SC of 0.880 ± 0.058 and an LCC of 0.881 ± 0.065 were noted. Conclusions: This study demonstrates good to very good similarity between the two sEMG systems, enabling the analysis of muscle activity during functional tasks.
OS-PSO: A Modified Ratio of Exponentially Weighted Averages-Based Optical and SAR Image Registration
Authors: Zhang Hui , Song Yu , Hu Jingfang , Li Yansheng , Li Yang , Gao Guowei
Published: September 2024

Optical and synthetic aperture radar (SAR) images exhibit non-negligible intensity differences due to their unique imaging mechanisms, which makes it difficult for classical SIFT-based algorithms to obtain sufficiently correct correspondences when processing the registration of these two types of images. To tackle this problem, an accurate optical and SAR image registration algorithm based on the SIFT algorithm (OS-PSO) is proposed. First, a modified ratio of exponentially weighted averages (MROEWA) operator is introduced to resolve the sudden dark patches in SAR images, thus generating more consistent gradients between optical and SAR images. Next, we innovatively construct the Harris scale space to replace the traditional difference in the Gaussian (DoG) scale space, identify repeatable key-points by searching for local maxima, and perform localization refinement on the identified key-points to improve their accuracy. Immediately after that, the gradient location orientation histogram (GLOH) method is adopted to construct the feature descriptors. Finally, we propose an enhanced matching method. The transformed relation is obtained in the initial matching stage using the nearest neighbor distance ratio (NNDR) and fast sample consensus (FSC) methods. And the re-matching takes into account the location, scale, and main direction of key-points to increase the number of correctly corresponding points. The proposed OS-PSO algorithm has been implemented on the Gaofen and Sentinel series with excellent results. The superior performance of the designed registration system can also be applied in complex scenarios, including urban, suburban, river, farmland, and lake areas, with more efficiency and accuracy than the state-of-the-art methods based on the WHU-OPT-SAR dataset and the BISTU-OPT-SAR dataset.
Inertial Motion Capture-Driven Digital Human for Ergonomic Validation: A Case Study of Core Drilling
Authors: Zhao Quan , Lu Tao , Tao Menglun , Cheng Siyi , Wen Guojun
Published: September 2024

In the evolving realm of ergonomics, there is a growing demand for enhanced comfortability, visibility, and accessibility in the operation of engineering machinery. This study introduces an innovative approach to assess the ergonomics of a driller’s cabin by utilizing a digital human. Through the utilization of inertial motion capture sensors, the method enables the operation of a virtual driller animated by real human movements, thereby producing more precise and realistic human–machine interaction data. Additionally, this study develops a simplified model for the human upper limbs, facilitating the calculation of joint forces and torques. An ergonomic analysis platform, encompassing a virtual driller’s cabin and a digital human model, is constructed using Unity 3D. This platform enables the quantitative evaluation of comfortability, visibility, and accessibility. Its versatility extends beyond the current scope, offering substantial support for product development and enhancement.
Simultaneously Tuning Charge Separation and Surface Reaction Kinetics on ZnIn2S4 Photoanode by P-Doping for Highly Efficient Photoelectrochemical Water Splitting and Urea Oxidation
Authors: Sun Jiamin , Tang Ling , Li Chenglong , Quan Jingjing , Xu Li , Ning Xingming , Chen Pei , Weng Qiang , An Zhongwei , Chen Xinbing
Published: September 2024

ZnIn2S4 nanosheets are a promising photoanode for driving photoelectrochemical (PEC) hydrogen fuel production; nevertheless, poor charge separation and sluggish surface reaction kinetics hinder its PEC performance to an extreme degree. Herein, a facile element doping strategy (i.e., P element) was developed to obtain the desired photoanode. As a result, the ZnIn2S4-P (ZIS-P5) photoanode exhibits a remarkable photocurrent density of 1.66 mA cm−2 at 1.23 V versus a reversible hydrogen electrode (VRHE) and a much lower onset potential of 0.12 V vs. RHE for water oxidation. Careful electrochemical analysis confirms that the P doping and sulfur vacancies (Sv) not only facilitate the hole transfer, but also boost surface reaction kinetics. Finally, the “killing two birds with one stone” goal can be achieved. Moreover, the optimized photoanode also presents high PEC performance for urea oxidation, obtaining a photocurrent density of 4.13 mA cm−2 at 1.23 V vs. RHE. This work provides an eco-friendly, simple and effective method to realize highly efficient solar-to-hydrogen conversion.
Breaking Siloed Policies: Applying a Gender-Based Analysis Plus (GBA+) to Homelessness during Pregnancy in Canada
Authors: Chyzzy Barbara , Guruge Sepali , Schwan Kaitlin , Lee Joon , Stewart Stacia
Published: September 2024

Amongst women and gender diverse (WGD) populations experiencing homelessness in Canada, one of the most vulnerable and understudied subgroups are those who are pregnant. A key barrier to accessing housing for this population are policies that lead to siloed sector work and complicated and inaccessible services. Frequent relocation and fragmented access to essential prenatal and postnatal support are the result. Experiences of homelessness for WGD people are distinct from that of cisgender men; the former tend to experience ‘hidden homelessness’ and are more likely to rely on relational, precarious, and sometimes dangerous housing options. The homelessness sector, its policies, and services tend to be cis-male-centric because of the greater visibility of homelessness in cis-men and fail to meet pregnant WGD people’s needs. This paper describes the findings from a one-day symposium that was held in Toronto, Canada, in June 2023 that aimed to address the siloed approach to housing provision for pregnant WGD people experiencing homelessness. A key focus was to understand how to incorporate a gendered and intersectional discourse into practice and policy. Adopting a gender-based analysis plus (GBA+) approach within policymaking can help illuminate and address why certain groups of WGD people are disproportionately affected by homelessness, including Indigenous Peoples, recent immigrants, racialized people, and those experiencing intimate partner violence, poverty, and substance use.
Association of VDR Polymorphisms with Muscle Mass Development in Elite Young Soccer Players: A Pilot Study
Authors: Flore Laura , Robledo Renato , Dettori Laura , Scorcu Marco , Francalacci Paolo , Tocco Filippo , Massidda Myosotis , Calò Carla Maria
Published: September 2024

The vitamin D receptor (VDR) is an important candidate gene in musculoskeletal phenotypes. Polymorphisms in the VDR have been previously associated with several pathologies and muscular strength in athletes and elderly people; however, the literature reported contradictory results. The object of this research was to verify the association between the most studied VDR variants (rs2228570, rs7975232, and rs1544410) and the increase in muscle mass in elite young soccer players. A sample of 55 soccer players (15–18 years old) from a professional team were selected for this study. DNA was extracted by the salting-out method, and polymorphisms were genotyped by PCR-RFLP, followed by 2% agarose gel electrophoresis. To test the effect of the three SNPs (single nucleotide polymorphisms), a logistic regression analysis was applied. The body composition determination was carried out through the skinfold thickness method, and the muscular area of the arm and lower limb were calculated using the Frisancho formula. All three polymorphisms met the Hardy–Weinberg equilibrium (p > 0.05) and their frequencies fell within the worldwide variability. A significant correlation between rs1544410 and the increase in calf muscle mass was observed. Individuals carrying the A allele showed higher calf muscular mass than those carrying the G allele (p = 0.034). Moreover, a haplotype analysis applied to the two SNPs in linkage disequilibrium (rs7975232 and rs1544410) showed that the AG haplotype appeared negatively correlated to the calf muscle area. In conclusion, we confirm an association between VDR polymorphisms and muscular mass that could encourage the genetic screening of the VDR gene to identify a potential risk of injury and for individual nutritional interventions.
Sustainable Operations Management in the Energy Sector: A Comprehensive Review of the Literature from 2000 to 2024
Authors: Losada-Agudelo Mariana , Souyris Sebastian
Published: September 2024

This study centers on sustainable operations management within the energy sector, identifying and synthesizing effective strategies for integrating sustainability into business practices. We perform a systematic literature review covering contributions from January 2000 to June 2024 extracted from Web of Science and Scopus databases. The methodology includes an explicit search and selection protocol to ensure relevant and unbiased insights into the evolution of sustainable practices in the energy sector. The results indicate an increase in publications over the years, particularly in areas such as low-carbon economies, environmental management, and innovation, all of which are crucial for reducing carbon footprints and enhancing operational sustainability. This study categorizes existing research into five main streams: Closed Loop Supply Chains (CLSC), Low Carbon Economy (LCE), Environmental Management and Performance (EMP), Innovation (INN), and Social Responsibility (SR). The review underscores the significant gap between current practices and the potential for incorporating renewable energy sources into existing systems. In addition, it highlights the need for robust governmental policies and international cooperation in order to foster a more rapid transition towards sustainable operations on the energy sector. Furthermore, our findings suggest that despite technological advances, significant implementation gaps remain that require focused research and policy adjustments in order to achieve sustainability targets in the energy sector.
Do Runoff Water Harvesting Ponds Affect Farmers Cropping Choices? Insights from Smallholders in the West African Sahel
Authors: Sanogo Mamadou , Yonaba Roland , Lawane Abdou , Zorom Malicki , Tassembédo Fonzia , Ali Sahad Hamed , Bazié Isidore
Published: September 2024

Supplemental irrigation based on runoff harvesting is a sustainable solution in the current context of water scarcity that is prevalent in Sahelian countries. Runoff water harvesting ponds (RWHPs) are increasingly being utilized for vegetable cultivation by producers. This study aims to analyze the cropping choices of producers benefiting from RWHPs in the Kadiogo and Bazèga provinces of Burkina Faso in the West African Sahel. A sample of 27 surveyed producers revealed a dominant preference for vegetable crops (93.46% of the total production) over cereals. The cropping choices are influenced by factors such as the crop resistance to dry spells, the water demand, the economic return, and the market demand. For the effective utilization of the basins, crop choices should consider the water retention capacity of the basin. Additionally, to enhance the retention capacity, it is advisable to line them using appropriate waterproofing techniques. Similarly, the selection of basin installation sites should consider the soil characteristics and site-specific considerations. The findings of this research highlight the potential of runoff water harvesting basins to significantly improve agricultural productivity and resilience in the West African Sahel, thereby contributing to enhanced food security and improved livelihoods for local farmers.
Deformation, Seepage, and Energy Characteristics of Gas-Containing Coal Rocks under Complex Stress Paths
Authors: Zhang Dongming , Mao Xingfeng , Guo Zhenglin , Geng Jiabo
Published: September 2024

The exploitation and utilization of coal resources are closely related to sustainable social and economic development. To uncover the deformation and seepage patterns of coal on the mining process, this study devised a new stress program with simultaneous changes in axial and confining pressures, then performed coal seepage experiments at various gas pressures. The results show that the residual deformation exhibited a stepwise change, the relative residual deformation at the same level decreased gradually, and the increase in gas pressure led to a reduction in residual deformation. In each stress grade, the absolute permeability damage rate increased gradually, while the relative permeability damage rate decreased with the number of cycles, and the growth of gas pressure could decrease the permeability damage rate. The higher gas pressure led to a lower average energy dissipation ratio at each stress level and increased the rate of growth of elastic energy before destruction of the specimens. A higher gas pressure led to a quicker rate of change in damage variables at high stress levels. The findings have implications for the effective mining and sustainable development of coal resources.
Unlocking Green Patterns: The Local and Spatial Impacts of Green Finance on Urban Green Total Factor Productivity
Authors: Xiang Jiyou , Tan Linfang , Gao Da
Published: September 2024

The urgency of global climate change and environmental degradation has become increasingly apparent, and green finance, as a pioneering financial tool, is providing critical support to unlock regional green patterns. Based on the data of China’s prefecture level from 2010 to 2021, this study examines the causal relationship and mechanism of green finance (GF) and urban green total factor productivity (GTFP) using the spatial Durbin model. The results show the following: (1) Green finance can not only improve local GTFP, but also has a spatial spillover effect, and it is still valid after a robustness test, which means that the development of GF can significantly promote urban green transformation. (2) The local effect and spatial spillover effect of green finance are more obvious in coastal and developed areas. (3) After deconstructing the mechanism of green transformation, this paper finds that improving urban energy utilization efficiency, mitigating the capital mismatch degree, and enhancing new quality productivity are important impact channels for green finance to enhance urban GTFP. These conclusions not only provide a theoretical reference for GF to help with the construction of a high-quality “Double Cycle” new development pattern, but also promote low-carbon transformation. This study has obvious application value and provides experience for other developing countries to seek green transformation from the perspective of green finance practice.
Co-Creation with AI in B2B Markets: A Systematic Literature Review
Authors: Fehrenbach David , Herrando Carolina , Martín-De Hoyos María José
Published: September 2024

Artificial intelligence (AI) has significantly disrupted B2B markets, impacting companies at the product, service, and organizational levels. A key focus is on how to leverage the power of AI to augment and automate activities to create value for customers. One specific form of value creation investigated in marketing is co-creation between parties. Introducing AI into the co-creation process is exciting due to its technological characteristics and the anticipated business value it can bring. This study explores the state of the art in co-creation with AI in B2B markets. It examines how buyers, suppliers, and technology providers interact, along with their motives and characteristics. Furthermore, it investigates the processes enabling these interactions, from the form of AI used and AI tool integration to the necessary capabilities of other actors involved. Finally, this study examines the content of co-creation described in the existing literature and the value created jointly. This review contributes to delineating the interaction between human and non-human actors in a B2B co-creation ecosystem. The implications of this research provide B2B companies with a discussion about the actors, motives, characteristics, processes, and content of co-creation with AI in B2B drivers and barriers of AI for co-creation, mapping the way for success.
Ensemble Learning Algorithms for Solar Radiation Prediction in Santo Domingo: Measurements and Evaluation
Authors: Ramírez-Rivera Francisco A. , Guerrero-Rodríguez Néstor F.
Published: September 2024

Solar radiation is a fundamental parameter for solar photovoltaic (PV) technology. Reliable solar radiation prediction has become valuable for designing solar PV systems, guaranteeing their performance, operational efficiency, safety in operations, grid dispatchment, and financial planning. However, high quality ground-based solar radiation measurements are scarce, especially for very short-term time horizons. Most existing studies trained machine learning (ML) models using datasets with time horizons of 1 h or 1 day, whereas very few studies reported using a dataset with a 1 min time horizon. In this study, a comprehensive evaluation of nine ensemble learning algorithms (ELAs) was performed to estimate solar radiation in Santo Domingo with a 1 min time horizon dataset, collected from a local weather station. The ensemble learning models evaluated included seven homogeneous ensembles: Random Forest (RF), Extra Tree (ET), adaptive gradient boosting (AGB), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting (LGBM), histogram-based gradient boosting (HGB); and two heterogeneous ensembles: voting and stacking. RF, ET, GB, and HGB were combined to develop voting and stacking ensembles, with linear regression (LR) being adopted in the second layer of the stacking ensemble. Six technical metrics, including mean squared error (MSE), root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2), were used as criteria to determine the prediction quality of the developed ensemble algorithms. A comparison of the results indicates that the HGB algorithm offers superior prediction performance among the homogeneous ensemble learning models, while overall, the stacking ensemble provides the best accuracy, with metric values of MSE = 3218.27, RMSE = 56.73, rRMSE = 12.700, MAE = 29.87, MAPE = 10.60, and R2 = 0.964.
Exploring Stakeholders in Elderly Community Retrofit Projects: A Tripartite Evolutionary Game Analysis
Authors: Guo Li , Dzeng Ren-Jye , Hao Shuya , Zhang Chaojie , Zhang Shuang , Tang Liyaning
Published: September 2024

Renovating aging housing is a critical project at the grassroots of social governance and a significant aspect of public welfare. However, renovation processes often encounter difficulties due to conflicts among muti-level stakeholders, influenced by multiple factors. This paper investigates the stakeholders involved in Elderly Community Retrofit Projects (ECRPs), categorizing them into three primary groups: government organizations, renovation enterprises, and elderly families. Through the study of evolutionary game models, it was found that bounded rational actors continually adjust their optimal strategies in response to environmental changes. The government occupies a central role among stakeholders involved in ECRP. During renovation processes, governments and enterprises should provide elderly households with material or other welfare subsidies as much as possible to promote their active cooperation and participation. The integrity of enterprises is closely tied to the strength of governmental enforcement measures; hence, governments should establish a unified standard system, clarify regulatory content, and foster the orderly development of ECRPs.
Land Cover and Spatial Distribution of Surface Water Loss Hotspots in Italy
Authors: Palazzoli Irene , Lelli Gianluca , Ceola Serena
Published: September 2024

Increasing water withdrawals and changes in land cover/use are critically altering surface water bodies, often causing a noticeable reduction in their area. Such anthropogenic modification of surface waters needs to be thoroughly examined to recognize the dynamics through which humans affect the loss of surface water. By leveraging remotely-sensed data and employing a distance–decay model, we investigate the loss of surface water resources that occurred in Italy between 1984 and 2021 and explore its association with land cover change and potential human pressure. In particular, we first estimate the land cover conversion across locations experiencing surface water loss. Next, we identify and analytically model the influence of irrigated and built-up areas, which heavily rely on surface waters, on the spatial distribution of surface water losses across river basin districts and river basins in Italy. Our results reveal that surface water losses are mainly located in northern Italy, where they have been primarily replaced by cropland and vegetation. As expected, we find that surface water losses tend to be more concentrated in the proximity of both irrigated and built-up areas yet showing differences in their spatial occurrence and extent. These observed spatial patterns are well captured by our analytical model, which outlines the predominant role of irrigated areas, mainly across northern Italy and Sicily, and more dominant effects of built-up areas across the Apennines and in Sardinia. By highlighting land cover patterns following the loss of surface water and evaluating the relative distribution of surface water losses with respect to areas of human pressure, our analysis provides key information that could support water management and prevent future conditions of water scarcity due to unsustainable water exploitation.
Research on the Fire Resilience Assessment of Ancient Architectural Complexes Based on the AHP-CRITIC Method
Authors: Yu Songtao , Liu Houdong , Kang Qian , Cheng Juan , Gong Yingli , Ke Yuxian
Published: September 2024

Ancient architectural complexes are an important part of human cultural heritage, carrying a wealth of historical and cultural information. However, fire safety issues in these complexes are becoming increasingly prominent, and it is urgent to assess and enhance their fire resilience to support sustainable development. This paper takes ancient architectural complexes as the research object and establishes a fire resilience evaluation indicator system for ancient architectural complexes, which includes 25 third-level indicators categorized under architectural characteristics, facility factors, management factors, and social factors. Then, this paper combines the AHP method and the CRITIC method to determine the weight of each indicator. The results show that architectural features and facility factors are key level 2 indicators affecting the fire resilience of ancient architectural complexes. The fire resistance rate, building materials, automatic alarm system, etc., are key level 3 indicators influencing the fire resilience of ancient architectural complexes. It is suggested that efforts should be made to enhance the fire resilience of ancient architectural complexes by improving the fire resistance rate, strengthening smart early warning systems, and intensifying the ensuring of firefighting responses. This paper provides valuable insights and recommendations for effectively preventing fire disasters in ancient architectural complexes, thereby supporting their sustainable management and long-term conservation.
Nexus among Digital Leadership, Digital Transformation, and Digital Innovation for Sustainable Financial Performance: Revealing the Influence of Environmental Dynamism
Authors: Mollah Md. Alamgir , Amin Mohammad Bin , Debnath Gouranga Chandra , Hosain Md. Sajjad , Rahaman Md. Atikur , Abdullah Masuk
Published: September 2024

In the era of digitalization, the role of IT firms and the capabilities of digital leadership for driving digital transformation and managing environmental dynamism for the proliferation of digital innovation and organizational performance for sustainability are still scant, producing a knowledge gap. To fill this gap, this study aims to investigate the role of digital leadership in enhancing organizational performance, with the mediating impact of digital transformation and digital innovation, and the moderating role of environmental dynamism in the relationship between digital transformation and innovation. To investigate the conceptual model, we used survey data of 416 responses from small, medium, and large IT organizations in Bangladesh and deployed the AMOS 24 package software for the analysis of structural equation modeling (SEM). The results showed that digital leadership has a significant impact on digital transformation, digital innovation, and organizational performance. Digital innovation partially mediates the relationship between digital leadership and organizational performance. Although environmental dynamism has no significant effect in relation to digital leadership and innovation, it has an indirect influence on the structural model. The results from before and after moderation proved that environmental dynamism might not have a significant moderating effect on relationships, but it has significant power to change other interrelationships and hamper organizational sustainability.