bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Structural Adhesion: Experimental and Numerical Analysis of Repair Mortar in Reinforced Concrete
Authors: Bakir Dursun , Savaş Sedat , Tuğrul Tunç Esra
Published: January 2025

The utilization of ecological and cost-effective construction materials has emerged as a critical necessity in contemporary circumstances. It is essential to investigate the use of repair mortar as opposed to epoxy, which offers adhesion to concrete, to guarantee structural integrity under dynamic stresses. In this study, we performed an experimental and computational analysis of the load-bearing capacity of repair mortar to evaluate the adhesion between reinforced concrete structural elements and a geogrid. We performed triaxial bending, compression, splitting, shear bond strength, angle, and adhesion tests on specimens, which were constructed from repair mortar. We constructed 10 × 10 × 50 cm unreinforced beam specimens and 15 × 25 × 200 cm reinforced concrete beams and wrapped the geogrid in the stress zones of the beams by bonding it with repair mortar. We then performed four-point flexural tests on the geogrid specimens wrapped with repair mortar in the tensile zones of these beams. The mechanical properties obtained from these experiments allowed us to create a numerical model. For the first time in the literature, this study investigated the effectiveness of repair mortar compared with epoxy, as well as the innovative use of repair mortar to improve adhesion between the concrete surface and the geogrid. In the literature, reinforcement materials encasing concrete structural elements have utilized epoxy; however, an example of the application of a geogrid wrapped around structural elements with repair mortar has not been previously published. It was concluded that epoxy, effective in adhering to building materials for reinforcement, can bond with structural elements reinforced with a geogrid using repair mortar and may serve as an alternative to epoxy.
Case-Based Axiomatic Design Assistant (CADA): Combining Axiomatic Design and Case-Based Reasoning to Create a Design Knowledge Graph for Pharmaceutical Engineering
Authors: Wölfle Roland , Saur-Amaral Irina , Teixeira Leonor
Published: January 2025

The development of personalized drugs introduces new uncertainties and risks in production machinery design, which can be mitigated through structured workflows. As the commonly used V-Model approach has limitations in dealing with complex multi-domain problems, it is essential to address traceability and relationships between requirements and solutions in a regulated environment to ensure product quality. This study focuses on the conceptual design phase and develops a design methodology called the Case-based Axiomatic Design Assistant (CADA) to address this type of problem. It takes, as a starting point, Axiomatic Design (AD), due to its simplicity and graphical tools for quality evaluation, and Case-Based Reasoning (CBR), due to its capacity to integrate data structures and continuously improve. This combination is put into practice through a visual assistant that utilizes a knowledge graph to represent design elements comprehensively. This article describes the development, implementation, and testing process of CADA, which includes examples of the conceptual design for pharmaceutical manufacturing. The proposed CADA method facilitates systematic requirements analysis, structured reasoning, and solution evaluation, and overcomes the limitations of previous methodologies. It represents a novel approach with an intuitive workflow and advanced graphical capabilities, exemplified in the context of a conceptual design for pharmaceutical manufacturing. The inclusion of intrinsic data labeling capabilities and inference visualization enhances its relevance.
A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
Authors: Qin Zikang , Feng Yumin , Wu Gang , Dong Qing , Han Tianxin
Published: January 2025

Comparing historical map images of the same region from different periods is an effective method for studying urban history and planning. Image registration techniques in the field of computer vision can be applied to this task. However, historical map registration faces unique challenges, including insufficient training data, variations in image sizes, and unavailable texture features. To address these challenges, we constructed a dedicated dataset of over 100 scanned historical maps, including both raw and preprocessed segmented images. We then developed an enhanced SuperGlue-based registration framework, optimized for the specific obstacles posed by historical maps, such as low texture and large image size. Additionally, we proposed a self-supervised fine-tuning feature extraction algorithm and a Transformer-based architecture utilizing graph attention mechanisms to refine feature descriptors and enhance feature matching performance. Experimental results indicate that our solution achieves superior performance compared to existing models, with RMSE reduced by up to 20%, ROCC improved by up to 10%, and processing time shortened by at least 15%.
Super Quasi-Einstein Warped Products Manifolds with Respect to Affine Connections
Authors: Vasiulla Mohd , Ali Mohabbat , Khan Meraj Ali , Aldayel Ibrahim
Published: January 2025

In this paper, we investigate warped products on super quasi-Einstein manifolds under affine connections. We explore their fundamental properties, establish conditions for their existence, and prove that these manifolds can also be nearly quasi-Einstein and pseudo quasi-Einstein. To illustrate, we provide examples in both Riemannian and Lorentzian geometries, confirming their existence. Finally, we construct and analyze an explicit example of a warped product on a super quasi-Einstein manifold with respect to affine connections.
Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes
Authors: Jalili Jalil , Walker Evan , Bowd Christopher , Belghith Akram , Goldbaum Michael H. , Fazio Massimo A. , Girkin Christopher A. , De Moraes Carlos Gustavo , Liebmann Jeffrey M. , Weinreb Robert N. , Zangwill Linda M. , Christopher Mark
Published: January 2025

This study aims to develop deep learning (DL) models to predict the retinal nerve fiber layer (RNFL) thickness changes in glaucoma, facilitating the early diagnosis and monitoring of disease progression. Using the longitudinal data from two glaucoma studies (Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES)), we constructed models using optical coherence tomography (OCT) scans from 251 participants (437 eyes). The models were trained to predict the RNFL thickness at a future visit based on previous scans. We evaluated four models: linear regression (LR), support vector regression (SVR), gradient boosting regression (GBR), and a custom 1D convolutional neural network (CNN). The GBR model achieved the best performance in predicting pointwise RNFL thickness changes (MAE = 5.2 μm, R2 = 0.91), while the custom 1D CNN excelled in predicting changes to average global and sectoral RNFL thickness, providing greater resolution and outperforming the traditional models (MAEs from 2.0–4.2 μm, R2 from 0.94–0.98). Our custom models used a novel approach that incorporated longitudinal OCT imaging to achieve consistent performance across different demographics and disease severities, offering potential clinical decision support for glaucoma diagnosis. Patient-level data splitting enhances the evaluation robustness, while predicting detailed RNFL thickness provides a comprehensive understanding of the structural changes over time.
X-Linked CGD Chorioretinitis in Two Young Girls
Authors: Bailey Johnathan Abraham , Kong Maximilian Daechul , Piamjitchol Chanakarn , Hou Baichun , Exinor Abdhel , Nayak Antara , Heaps Noah , Demirkol Aykut , Tsang Stephen H.
Published: January 2025

Background: Chronic granulomatous disease (CGD) is a rare genetic disorder that causes primary immunodeficiency. In addition to increasing infection susceptibility in various bodily systems, several ocular manifestations have been described in males. This condition is well described in males, due to its X-linked recessive inheritance. However, here we present, to our knowledge, the first cases of X-linked CGD chorioretinitis in female carriers, possibly due to skewed X-inactivation (lyonization). Methods: Comprehensive multimodal imaging, including color fundus photography, short-wavelength autofluorescence, and spectral domain optical coherence tomography (OCT), was conducted. Functional assessment was completed with full-field electroretinogram (ff-ERG). Results: This report details two sisters with X-linked CGD carrier status, both presenting chorioretinal lesions on fundoscopy. Observed features included punched-out chorioretinal lesions, perivascular atrophy, and peripheral pigment changes. Autofluorescence imaging confirmed hypoautofluorescent areas correlating with chorioretinal atrophy, and OCT revealed retinal collapse and ellipsoid zone loss in one sibling. Despite these structural changes, visual function remained stable with minimal progression over time. Subsequent serial ERGs did not show progression. Conclusions: The findings highlight that skewed X-inactivation may contribute to retinal changes in asymptomatic CGD carriers, underscoring the need for awareness of potential ocular manifestations in X-linked genetic disorders in female carriers.
Psychological Determinants of Conflict with the Law and Susceptibility to Rehabilitation in Relation to the Presence of Symptoms of Attention Deficit Hyperactivity Disorder
Authors: Nowogrodzka Agnieszka , Andrusiewicz Mirosław , Mojs Ewa
Published: January 2025

Background/Objectives: Many prison-sentenced individuals exhibit symptoms of mental dysfunctions, including attention deficit hyperactivity disorder (ADHD). The presence of co-occurring mental disorders further complicates their rehabilitation and social reintegration efforts. Given these challenges, understanding the role of specific disorders, such as ADHD, is critical for developing targeted interventions tailored to the needs of incarcerated individuals and improving their outcomes. This research aimed to clarify the relationships among hyperactivity, criminal behavior, and psychological functioning to inform preventative and therapeutic strategies. Methods: This study investigated the complex interplay among attention deficit hyperactivity disorder (ADHD) symptoms, criminal behavior, and various psychological factors in a sample of 391 male inmates from low-security Polish prisons and a control group of non-offending men. Principal component analysis (PCA) and Spearman’s rank correlation were used to analyze the relationships among ADHD severity, type of crime (no crime, property crime, crime involving aggression), family functioning, childhood trauma, early maladaptive schemas, and mental health disorders. Results: The results revealed that while traumatic experiences were present across all groups, stronger family cohesion and support were associated with the absence of ADHD symptoms and criminal behavior. As ADHD severity and criminal behavior escalated, particularly in cases involving aggression, family support diminished, and maladaptive schemas, including “disconnection and rejection” and “excessive vigilance and inhibition”, became more prominent, alongside increased correlations with mental health issues (anxiety and depression). Conclusions: The findings underscore the crucial role of family environment and early intervention in mitigating the risks associated with ADHD and criminal behavior, highlighting the need for comprehensive interventions targeting maladaptive schemas and providing support for both internalizing and externalizing symptoms. Limitations include the retrospective nature of data collection and the exclusive focus on male inmates in low-security facilities.
Binaural Pulse Modulation (BPM) as an Adjunctive Treatment for Anxiety: A Pilot Study
Authors: Leisman Gerry , Wallach Joseph , Machado-Ferrer Yanin , Acosta Mauricio-Chinchilla , Meyer Abraham-Gérard , Lebovits Robert , Donkin Scott
Published: January 2025

Background: Treating psychiatric illnesses or influencing mental states with neurofeedback is challenging, likely due to the limited spatial specificity of EEG and the complications arising from the inadequate signal-to-noise ratio reduction of single-trial EEG. Objective: This pilot study aimed to investigate the feasibility of employing a binaural pulse mode-modulation (BPM) device to reduce anxiety by self-regulation. We desired to determine whether anxiety could be significantly reduced or regulated using BPM-type systems. Methods: Sixty adult participants were examined with self-reported anxiety tests (COVID Stress Scale, Generalized Anxiety Disorder 7, Beck Depression Inventory-II), which were completed before treatment, after four weeks, and after 12 weeks post-treatment. This BPM device produced two frequencies which combined to create a binaural pulse through differential auditory tone presentations. The participant calibrated the suitable target tone for optimal treatment efficacy. Each participant adjusted the binaural pulse to enhance the emotional intensity felt when envisioning an experience with comparable emotional significance or while performing a cognitive task while concurrently listening to music. The “treatment” relied on the individual’s regulation of binaural pulses to obtain the desired state. The training concentrated on particular facets of their psychological challenges while listening to an auditory tone, adjusting a knob until the sound amplified the intended emotional state. Another knob was turned to intensify the emotional state associated with distress reduction. Results: On the self-reported measures, the BPM treatment group was significantly better than the sham treatment (control) groups (p < 0.01). These findings indicate that over the four-week intervention period, BPM was similarly effective. On the GAD-7, the significant difference over time was noted before treatment and at the end of treatment for the experimental group, with the average GAD-7 score at the end of treatment being significantly lower (p < 0.01). Conclusions: BPM seems to induce a short-term alteration in self-reported distress levels during therapy. This study’s limitations are examined, and recommendations for future research are provided.
Impacts of Vertical Greenery on Outdoor Thermal Comfort and Carbon Emission Reduction at the Urban Scale in Turin, Italy
Authors: Dehghan Lotfabad Amir , Hosseini Seyed Morteza , Dabove Paolo , Heiranipour Milad , Sommese Francesco
Published: January 2025

Urban heat islands (UHIs) increase urban warming and reduce outdoor thermal comfort due to changing surface characteristics and climate change. This study investigates the role of green walls (GWs) in mitigating UHI, improving outdoor thermal comfort, and reducing carbon emissions under current and future (2050) scenarios. Focusing on Via della Consolata, Turin, Italy, the study combines remote sensing for UHI detection and numerical simulations for thermal analysis during seasonal extremes. The results show that GWs slightly reduce air temperatures, with a maximum decrease of 1.6 °C in winter (2050), and have cooling effects on mean radiant temperature (up to 2.27 °C) during peak summer solar radiation. GWs also improve outdoor comfort, reducing the Universal Thermal Climate Index by 0.55 °C in the summer of 2050. The energy analysis shows that summer carbon emission intensity is reduced by 31%, despite winter heating demand increasing emissions by 45%. The study highlights the potential of GWs in urban climate adaptation, particularly in dense urban environments with low sky view factors. Seasonal optimization is crucial to balance cooling and heating energy demand. As cities face rising temperatures and heat waves, the integration of GWs offers a sustainable strategy to improve microclimate, reduce carbon emissions, and mitigate the effects of UHI.
Combination of Physico-Chemical and Lead Isotope Analyses for the Provenance Study of the Archaeological Materials: Example of Saadien Ceramics (16th Century, Marrakech Morocco)
Authors: El Halim Mouhssin , Daoudi Lahcen , El Idrissi Hicham El Boudour , El Ouahabi Meriam , Omdi Fatima Ezzahra , Gourfi Abdelali , Ait Hmeid Hanane , Abdellah Hanane Id , Fagel Nathalie
Published: January 2025

This paper aims to study the provenance of archaeological Saadien ceramics (16th century, Marrakech) based on the chemical, mineralogical and lead isotope composition of clays used as raw materials in the manufacture of ceramics in Morocco and collected in the six major potter sites of Marrakech (Ourika I and II, Saada I and II and Mzouda) and Fez (Benjlikh). The clay chemical, mineralogical and isotopic signatures of these raw materials are compared to the compositions of decorated ceramics from El Badi Palace and Saadien Tombs, the most visited archaeological sites in Marrakech, described as World Heritage by UNESCO. The chemical composition was determined using X-ray fluorescence analysis, while the structural changes of the mineral phases during firing were studied using X-ray diffraction over a temperature range between 500–1000 °C. Pb isotopes, on the other hand, were measured using the Nu Plasma MC-ICP-MS technique. Results show that Saadien ceramics were made using calcareous clay from the Fez region. These clays were imported by the artisans from 400 km away to be used in the manufacturing of ceramics in the Saadien buildings of Marrakech. The firing temperature of these materials ranges between 600 and 700 °C for El Badi Palace, and from 800 to 900 °C for the Saadien Tombs ceramics using traditional ovens. This study reveals the mystery behind the source of Saadien ceramics and provides artisans with information about the origin of the raw materials used in Marrakech’s 16th-century buildings, which should be considered for any future restoration of these materials.
Behavioral Inhibition and Fear: The Moderating Role of Emotional Competence and Gender in Preadolescents
Authors: Baroncelli Andrea , Righi Stefania , Facci Carolina , Ciucci Enrica
Published: January 2025

Background: The behavioral inhibition system (BIS) is a key motivational system that shapes human emotions and behaviors; specifically, the BIS regulates avoidance behaviors, and it is linked to negative emotions such as fear and anxiety. Previous studies have demonstrated a link between high BIS scores and attentional bias to threat in children, but literature is inconsistent. This may be due to differences in the individual awareness of emotions or in the accuracy of effectively detecting emotions. Moreover, the past literature has also found gender differences in BIS scores, which may suggest differential processes in boys and girls. Methods: The present study aims to investigate whether BIS scores were associated with an attentional facilitation index of fear in a sample of preadolescents (n = 264; 52.27% girls; M age = 12.98 years; SD = 0.89 years), considering the potential moderating role of (a) the awareness of others’ emotions as assessed by a self-report questionnaire, (b) emotion perception accuracy of fear as assessed by a laboratory task of emotion recognition, and (c) gender. Results: Our results showed that, only in males, higher scores of the BIS were associated with a lower attentional facilitation index of fear in the conditions of low levels of emotional competence (i.e., low levels of self-reported awareness of other emotions or low levels of accuracy recognition of fearful faces). Conclusions: Results were discussed in light of both theories of emotional development and practical clinical implications, with special attention to the emerged gender difference.
Improved Sleep Quality and Sleep Duration After an 8-Week Exergaming Intervention for Exercise Training Among Elementary Schoolchildren in Taiwan
Authors: Kao Hsiao-Fang , Lin Chi-Fang , Lin I-Tung , Hung Yi-Chuan , Chang Ting-Wei , Ho Chien-Chang
Published: January 2025

Objectives: The present study aimed to investigate the effects of an 8-week exergaming intervention for exercise training on sleep quality and sleep duration among elementary schoolchildren in Taiwan. Methods: A randomized controlled trial design was employed, with 68 elementary schoolchildren (aged 10.76 ± 0.49 years) recruited and randomly assigned to either an experimental group (n = 35) or a control group (n = 33). The experimental group participated in an 8-week exergaming intervention for exercise training; this comprised three sessions per week consisting of three rounds per session, with each round lasting 1 min. The control group did not receive any training and maintained their regular daily routines. All participants provided information on their demographic characteristics and anthropometric measurements and completed the Pittsburgh Sleep Quality Index (PSQI) questionnaire at both pre-test (baseline) and post-test (week 8) assessments. Results: After 8 weeks of exergaming intervention, the experimental group showed a significant reduction in the PSQI global score (6.17 ± 2.96 vs. 4.80 ± 1.97, p < 0.0001). The change in the PSQI global score in the experimental group was significantly greater than in the control group (−1.37 ± 1.97 vs. −0.27 ± 1.89, p = 0.022). Additionally, the experimental group demonstrated a significantly longer sleep duration at week 8 compared with the control group (505.15 ± 53.16 vs. 480.51 ± 54.13, p = 0.047). Conclusions: Overall, the findings of this study indicate a beneficial response of elementary schoolchildren to exergaming, with participants reporting improved sleep quality and sleep duration as a result of engaging in the exergaming intervention sessions three times per week for 8 weeks, with three one-minute rounds per session. Therefore, it appears that exergaming may be an effective method for improving certain aspects of sleep quality and sleep duration among children between 10 and 12 years of age.
Evaluation of the Antioxidant and Anti-Cancer Potential of Microwave-Assisted Opuntia humifusa (Korean Cheonnyencho) Aqueous Extract
Authors: Yanamala Poojitha , Youn Jeong-Yun , Thangavel Prakash , Moon Ju-Young , Lee Young-Chul
Published: January 2025

O. humifusa (Korean Cheonnyencho), a prickly pear cactus species, has garnered increased attention owing to its rich phytochemical composition and potential health benefits. In this study, the antioxidant and anti-cancer activities of a microwave-assisted aqueous extract derived from O. humifusa were investigated, and its phytochemical content was characterized. High-performance liquid chromatography (HPLC) was used to identify various bioactive compounds, including polyphenols, flavonoids, and other antioxidants known for their potential health-promoting properties. Furthermore, the individual compounds in the flavonoids were separated using the HPLC fractionation technique. The antioxidant potential of the aqueous extract was evaluated using 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity. The results demonstrated the significant antioxidant activity of the extract, as evidenced by its ability to scavenge free radicals and effectively reduce oxidized molecules. The experiments involved treating colon cancer cells with varying concentrations of the extract (25 to 125 mg/mL) over a 24-h period, resulting in a remarkable dose-dependent inhibition of cell growth. Notably, this inhibitory effect was absent in HDFa cells, highlighting the potential selectivity of O. humifusa in targeting colon cancer cells.
Chimeric Antigen Receptor Cell Therapy: Empowering Treatment Strategies for Solid Tumors
Authors: Jaing Tang-Her , Hsiao Yi-Wen , Wang Yi-Lun
Published: January 2025

Chimeric antigen receptor-T (CAR-T) cell therapy has demonstrated impressive efficacy in the treatment of blood cancers; however, its effectiveness against solid tumors has been significantly limited. The differences arise from a range of difficulties linked to solid tumors, including an unfriendly tumor microenvironment, variability within the tumors, and barriers to CAR-T cell infiltration and longevity at the tumor location. Research shows that the reasons for the decreased effectiveness of CAR-T cells in treating solid tumors are not well understood, highlighting the ongoing need for strategies to address these challenges. Current strategies frequently incorporate combinatorial therapies designed to boost CAR-T cell functionality and enhance their capacity to effectively target solid tumors. However, these strategies remain in the testing phase and necessitate additional validation to assess their potential benefits. CAR-NK (natural killer), CAR-iNKT (invariant natural killer T), and CAR-M (macrophage) cell therapies are emerging as promising strategies for the treatment of solid tumors. Recent studies highlight the construction and optimization of CAR-NK cells, emphasizing their potential to overcome the unique challenges posed by the solid tumor microenvironment, such as hypoxia and metabolic barriers. This review focuses on CAR cell therapy in the treatment of solid tumors.
Molecular Tumor Board-Guided Targeted Treatments for Biliary Tract Cancers in a Publicly Funded Healthcare System
Authors: Beaudry Felix E. G. , Li Zhihao , Borgida Ayelet , Zorigtbaatar Anudari , Wang Xin , Hildebrand Maggie , Hamza Oumaima , Jang Gun Ho , Bucur Roxana , Dodd Anna , Wilson Julie , Auer Rebecca C. , Saibil Samuel , Tsang Erica S. , Vogel Arndt , O’Kane Grainne M. , Gallinger Steven , Knox Jennifer J. , Notta Faiyaz , Sapisochin Gonzalo , Grant Robert C.
Published: January 2025

Comprehensive molecular profiling can identify alterations in biliary tract cancer (BTC) potentially treatable with targeted therapies. However, the impact of whole-genome and transcriptome sequencing (WGTS) on therapeutic decision-making in a public healthcare system is unknown. Here, BTC patients prospectively received WGTS to inform clinical care at a large Canadian academic cancer center. We characterized the proportion of targetable alterations, the treatment recommendations generated by a molecular tumor board, targeted therapies received, patient outcomes, and the financing of these treatments. A total of 55 patients with BTC prospectively underwent WGTS to inform clinical care. Of those 55, 28 (51%, 95% CI 38–64%) harbored targetable alterations. Molecular tumor boards recommended consideration of targeted therapies for 43 (78% CI: 66–87%) of 55 cases. Among the 15 patients who progressed to second-line therapy and harbored targetable alterations, 8 received nine targeted therapies. No targeted therapies were funded through the public system, and most therapies were funded through compassionate access programs from companies. These results highlight the challenges and potential for inequities when implementing precision oncology in a publicly funded healthcare system.
Multinodular and Vacuolating Neuronal Tumors: Imaging Features, Diagnosis, and Management Challenges
Authors: Calandrelli Rosalinda , Mallio Carlo Augusto , Bernetti Caterina , Pilato Fabio
Published: January 2025

Background/Objectives: Multinodular and vacuolating neuronal tumors (MNVTs) are a type of recently identified benign neuroepithelial tumor with debated malformative or neoplastic origins. This review summarizes their neuroanatomical localization, imaging, histopathology, immunohistochemistry, and diagnostic challenges. Methods: A systematic review of PUBMED/MEDLINE was performed in December 2024. Results: Of 118 screened articles, 39 were eligible, covering 299 patients. MNVTs are often asymptomatic “leave me alone” lesions, discovered incidentally, though nonspecific symptoms (59.9%) and seizures (19.7%) are reported. Immunohistochemistry reveals variable profiles, reflecting complex cellular differentiation. The characteristic “bubble-like” MRI pattern along the subcortical ribbon and superficial white matter is a reliable diagnostic feature. Rare cortical involvement and atypical band-like lesions occur. MRI signal intensity varies. Over a mean follow-up of 36 months, lesions were stable or non-recurrent, with only one case of progression. Conclusion: MVNT imaging mimics other glioneuronal lesions, but reliable diagnostic MRI features include a “bubble-clustered” appearance, lack of cortical involvement, absence of enhancement, and temporal lobe predominance. Hemodynamic and metabolic properties support the diagnosis. Most lesions remain stable, requiring no treatment. Surgical resection is reserved for cases with uncontrolled seizures or atypical locations where the diagnosis is unclear.
How Does Banking Concentration Affect Financial Inclusion in the Southern African Region?
Authors: Simatele Munacinga , Bolarinwa Segun Thompson
Published: January 2025

Financial inclusion is an important enabler of economic development and aligns with several United Nations Sustainable Development Goals. In most sub-Saharan African countries, financial inclusion efforts take place in the presence of concentrated and bank-dominated systems. This study investigates the relationship between banking concentration and financial inclusion, focusing on account ownership, savings, and loans from 2010 to 2021. This paper employs dynamic panel threshold modelling to identify concentration thresholds that influence the direction of the relationship. A U-shaped relationship is identified, indicating relatively high levels of bank concentration that can benefit bank account ownership and loans. Thresholds for savings are relatively low. The effect of bank concentration on savings and loans is tempered by mobile phone penetration. Strong property rights and low levels of corruption also moderate this relationship, underscoring the importance of institutional frameworks in fostering trust and reducing informational asymmetries.
Fusing Skeleton-Based Scene Flow for Gesture Recognition on Point Clouds
Authors: Liu Yahui , Jiao Jiajia
Published: January 2025

Dynamic gesture recognition has recently aimed to learn static and motion features by exploiting point clouds from depth images. However, the weak correlation between some pixels and hand gestures makes the extracted dynamic features redundant. When search points and adjacent points in a larger feature space maintain movement consistency, more detailed movements are ignored. To improve the ability to capture fine-grained dynamic features and improve the relevance of point clouds and gestures, we propose a novel method of fusing skeleton-based scene flow for gesture recognition (FSS-GR) for higher recognition accuracy. Firstly, skeletons are automatically converted into pairs of point clouds. Based on the time interval between source and target point clouds and scene flow measurement indicators, four scene flow estimators are obtained. To minimize the additional cost of capturing fine-grained information, scene flow is used as datasets before fusion. Then, the coarse-grained dynamic features from depth images are fused with the obtained scene flow using different strategies, so that the flexible tradeoffs between model complexity and recognition performance are available for various scenarios. The comprehensive experiments and ablation study on SHREC’17 and DHG demonstrate that FSS-GR achieves a higher accuracy than state-of-the-art works.
The Impact of Electricity Generation on CO2 Emissions in Türkiye: An Agent-Based Simulation Approach
Authors: Guven Denizhan , Kayalica Mehmet Ozgur , Sen Omer Lutfi
Published: January 2025

This study investigates the impact of energy, economic, and environmental policies on Türkiye’s energy dynamics and CO2 emissions using climate models and an agent-based simulation (ABM) framework. By integrating climate projections with policy scenarios, it assesses how energy transitions and climate change affect renewable energy sources (RES), cooling demands, and CO2 emissions from electricity generation. Methods include selecting suitable Global Climate Models (GCMs), evaluating climate change impacts on RES performance, and simulating policy effects through ABM across one base and nine policy scenarios from 2023 to 2040. The study highlights the critical role of policy interventions in influencing emissions trends and energy prices. It identifies renewable energy subsidies and low-carbon strategies, such as nuclear power support, as effective tools for reducing emissions and stabilizing energy costs. The methodologies and findings provide actionable insights for policymakers globally, emphasizing the importance of integrating climate data with policy planning.
Boosting Reservoir Prediction Accuracy: A Hybrid Methodology Combining Traditional Reservoir Simulation and Modern Machine Learning Approaches
Authors: Otmane Mohammed , Imtiaz Syed , Jaluta Adel M. , Aborig Amer
Published: January 2025

This study presents a comprehensive investigation into the application of reservoir simulation and machine learning techniques to improve the understanding and prediction of reservoir behavior, focusing on the Sarir C-Main field. The research uses various data sources to develop robust reservoir static and dynamic models, including seismic cubes, well logs, base maps, check shot data, and production history. The methodology involves data quality control, log interpretation, seismic interpretation, horizon and surface interpretation, fault interpretation, gridding, domain conversion, property and petrophysical modeling, well completion, fluid model definition, and rock physics functions. History matching and prediction are performed using simulation cases, and machine learning techniques such as data gathering, cleaning, dynamic time warping (DTW), long short-term memory (LSTM), and transfer learning are applied. The results obtained through the Petrel simulation demonstrate the effectiveness of depletion strategy, history matching, and completion in capturing reservoir behavior. Furthermore, the machine learning techniques, specifically DTW and LSTM, exhibit promising results in predicting oil production. The study concludes that machine learning approaches, such as the LSTM model, offer distinct advantages. They require significantly less time and can yield reliable predictions. By leveraging the power of transfer learning, accurate predictions can be achieved efficiently when limited data are available, offering a more streamlined and practical alternative to traditional reservoir simulation methods.
Characterization of Post-Production Waste from Winemaking of Selected Vitis vinifera L. Varieties Grown in Temperate Climates and Their Energy Valorization
Authors: Kapłan Magdalena , Maj Grzegorz , Klimek Kamila E. , Buczyński Kamil , Borkowska Anna , Sotolář Radek , Danko Richard , Baroň Mojmir
Published: January 2025

The study assessed the yield and quality as well as the energy potential of biomass from stalks and pomace of four grape varieties, Riesling, Chardonnay, Zweigelt, and Merlot Vitis vinifera L., grown in temperate climate conditions. The research is innovative because the evaluation of the energy potential of biomass originating from Vitis vinifera L. has not been carried out so far in the northern wine-growing regions. Field studies were conducted in 2023 in the Experimental Vineyard of the University of Life Sciences in Lublin, located in southeastern Poland. Biometric yield assessment showed that Chardonnay vines were characterized by the lowest mass of clusters and peduncles, number of berries in the cluster, berry diameter, and peduncle size, and at the same time the highest berry mass among the assessed biotypes. Merlot clusters were characterized by the highest mass of clusters and the largest peduncles. Riesling had the most berries in the cluster, the heaviest peduncles, and the highest share of peduncles in the cluster mass (8.99%). For grape pomace, the LHV values range from 15.98 MJ kg−1 for the Chardonnay variety to 16.91 MJ kg−1 for Riesling, while for peduncles, these values range from 15.11 MJ·kg−1 for Merlot and Riesling to 15.26 MJ kg−1 for Chardonnay. The differences in pollutant emissions are more pronounced between grapevine varieties than between types of biomass (pomace vs. peduncles). The greatest variation among varieties was observed for carbon dioxide (CO2) emissions in the pomace category, while the smallest differences were noted for sulfur dioxide (SO2) emissions. Total gas emissions were highest for Zweigelt pomace (7.72 Nm3 kg−1) and lowest for Merlot (6.99 Nm3 kg−1), while for stalks, Chardonnay had the highest values (6.77 Nm3 kg−1) and Merlot the lowest (7.32 Nm3 kg−1). The largest variation among varieties was observed in the pomace category. These results indicate differences in exhaust gas emissions for different plant parts and grape varieties, which are relevant for optimizing production processes and ensuring sustainable development.
Implementation of a Current Harmonics Suppression Strategy for a Six-Phase Permanent Magnet Synchronous Motor
Authors: Lin Yu-Ting , Hwang Jonq-Chin , Tsai Cheng-Ting , Lin Cheng-Tsung
Published: January 2025

This paper proposes a current harmonic suppression strategy that combines harmonic synchronous rotating frame (HSRF) current feedback control and back-electromotive force harmonic (BEMFH) feedforward compensation to suppress the fifth and seventh current harmonics of a six-phase permanent magnet synchronous motor (PMSM). The current harmonics of six-phase PMSMs vary with the current due to manufacturing imperfections and the inverter nonlinearity effect. Using fixed-parameter BEMFH feedforward compensation cannot completely eliminate current harmonics. This paper integrates a closed-loop harmonic current control strategy, using HSRF in the differential mode of the six-phase PMSM rotor rotating frame to effectively mitigate current harmonic variations caused by load changes. The controller adapts a Texas Instrument microcontroller featuring encoder interfaces, complementary pulse width modulation (PWM), and analog–digital converters (ADC) to simplify the board design. The rotor angle feedback is provided by a 12-pole resolver in conjunction with an Analog Device resolver-to-digital converter (RDC). The specifications of the six-phase PMSM are as follows: 12 poles, 1200 rpm, 200 A (rms), and 600 V DC bus. The total harmonic distortion (THD) of the phase current for harmonics below the 21st order was reduced from 31.71% to 4.84% under the test conditions of 1200 rpm rotor speed and 200 A peak phase current. Specifically, the fifth and seventh harmonics were reduced from 29.98% and 9.72% to 2.74% and 1.21%, respectively. These results validate the feasibility of the proposed current harmonic suppression strategy.
Mid-Long-Term Power Load Forecasting of Building Group Based on Modified NGO
Authors: Li Yue-Xu , Zhou Qiang , Zhang Xin-Hui , Chen Jia-Jia , Wang Hao-Dong
Published: January 2025

The mid-long-term forecasting of load in existing building clusters has given relatively little consideration to the prediction of fixed power loads that do not actively participate in renewable energy consumption, which may lead to certain errors in the forecasting results of active renewable energy-consuming loads. Based on power supply dependency, this paper categorizes building electrical loads into fixed loads and those capable of actively consuming renewable energy. Following this categorization, a Modified Northern Goshawk Optimization algorithm (MNGO) is utilized to optimize the XGBoost model, ultimately establishing a mid-long-term load forecasting algorithm tailored for building groups. Initially, a Random Forest (RF) algorithm is deployed to filter the key feature factors influencing the accuracy of load forecasting. Secondly, the Northern Goshawk Optimization (NGO) algorithm is modified to optimize the XGBoost model for the electric load forecasting of building groups. A comparative analysis of the forecasting outcomes reveals that the XGBoost model, refined by the NGO algorithm, significantly diminishes the Mean Absolute Percentage Error (MAPE) and markedly escalates the coefficient of determination (R2), thereby validating the efficacy of the proposed methodology. This approach not only furnishes data support for energy storage planning and ameliorates the capacity for new energy assimilation, but also ensures a stable power supply for buildings reliant on fixed electrical loads.
Fostering Digitalization: How Local Policies Are Transforming Rural Areas in Italy
Authors: Di Stefano Valerio , Paletto Alessandro , Cortignani Raffaele , Di Domenico Giorgia
Published: January 2025

In recent years, several policies and strategies have been developed by the European Union to promote innovation and digitalization in the agricultural and forestry sector, including the Common Agricultural Policy (CAP), which allocates just under EUR 150 billion for the period of 2023–2027. In Italy, digitalization in the agricultural and forestry sector has grown significantly over the past decade, with 3.8% increasing to 15.8% of farms now being computerized. This growth has been fostered by the Italian strategy for digitalization in agriculture, part of the CAP Strategic Plan, implemented at the regional level through the Rural Development Complements (RDCs), adopted in 2023. This study analyzes the RDCs of Italian regions, comparing the strategies adopted in terms of digitalization and innovation from both technical and economic perspectives. This analysis focuses on the interventions of three regional support groups (SRGs)—SRG07, SRG08, SRG09—assessing whether they have been activated in all regions and delves into the political and technical reasons behind any lack of implementation. The study compares the funding allocated for each intervention, highlighting regional differences and underlying causes. The main strengths, weaknesses, opportunities, and threats of digitalization in the agricultural and forestry sector were prioritized through an A’WOT analysis. The major strengths include the provision of job security and sustainability, while the major weaknesses comprise the challenges of the digital divide and a lack of technical training. The opportunities identified include the potential for the development of precision agriculture and eco-sustainable practices, but these are hampered by critical issues such as spatial fragmentation and limited economic resources. This analytical framework offers a comprehensive view of regional dynamics in Italy, providing useful insights for the development of more effective policies that can promote equitable and innovative digitalization in the agricultural and forestry sector.
Advancements in Clothing Thermal Comfort for Cold Intolerance
Authors: Abuhay Amare , Tadesse Melkie Getnet , Berhanu Baye , Malengier Benny , Langenhove Lieva Van
Published: January 2025

Due to constantly shifting environmental and personal circumstances, humans have a wide range of thermal comfort needs. Cold intolerance (CI) is a personalized thermoregulation disorder characterized by a persistently cold-feeling problem, regardless of weather conditions. Improvements in clothing thermal comfort can help maintain proper insulation levels, hence reducing excess heat loss brought on by thermoregulation disorders since the wearer’s thermal comfort is impacted by controllable environmental and personal factors. Despite extensive research on cold-proof clothing, no studies have examined the current status of cold protective clothing systems when taking individual considerations into account, particularly those who use them and have cold sensitivity. There is a significant study gap in research on cold intolerance discomfort and advancements in appropriate cold protection apparel applied to individuals with thermoregulation disorders. Accordingly, this paper reviews the occurrence and severity of cold intolerance and its comfort challenges. It also addresses recent developments in cold protective clothing design, aimed at opening pathways for further investigation into adopting this cutting-edge technology for cold intolerance wear design. This review also aims to clarify the existing opportunities for enhancing the thermal insulation capabilities and other comfort factors of cold protection apparel, which are conducted during the stages of garment design and clothing material/textile manufacture. A thorough assessment of the research on introducing novel surface finishing methods in the pretreatment section and modifying the structural properties of garment materials at the fiber/yarn or weaving stage is conducted. Furthermore, we systematically discuss the potential design solutions regarding fit and size as well as stitching technologies during garment development for thermal insulation enhancement of cold protective clothing design.
Effects of Dietary Protein Levels on Growth Performance, Plasma Parameters, and Digestive Enzyme Activities in Different Intestinal Segments of Megalobrama amblycephala at Two Growth Stages
Authors: Zhang Wuxiao , Xia Silei , Liu Bo , Tian Hongyan , Liu Fei , Yang Wenping , Yu Yebing , Zhao Caiyuan , Dewangan Naresh Kumar , Wang Aimin , Teng Tao
Published: January 2025

An 8-week rearing trial was designed to estimate the dietary protein requirement and evaluate the effects of dietary protein on growth performance, plasma parameters, and digestive enzyme activities of blunt snout bream at two growth stages. Six practical diets were prepared to feed two sizes of fish (larger fish: initial weight of 153.69 ± 0.85 g; smaller fish: initial weight of 40.89 ± 0.28 g) with graded protein levels (26%, 28%, 30%, 32%, 34%, and 36%). Our results show that the final weight, weight gain (WG), and specific growth rate (SGR) of the fish initially rose to peak values and then declined as the dietary protein levels increased. The higher WG and SGR were recorded in the larger fish fed diets containing 30%, 32%, and 34% protein, and in the smaller fish fed a 30% protein diet, all significantly higher than those in the control group (p < 0.05). No significant differences were observed in the feed conversion ratio (FCR), viscerosomatic ratio (VR), hepatosomatic index (HSI), condition factor (CF), or survival rate among the treatments at both growth stages (p > 0.05). The plasma total protein (TP) content was highest at both growth stages in fish fed a 30% protein diet (p < 0.05). As the dietary protein level increased, the plasma urea content of the larger fish increased, peaked in the 34% protein group (p < 0.05), and then remained stable. In contrast, no significant difference in the plasma urea content was seen among the treatment groups of the smaller fish (p > 0.05). Protease activity in the fish foregut at both growth stages peaked in the 32% protein group (p < 0.05). In the midgut of the larger fish, protease activity was higher in the control group, while in the smaller fish, it was higher in the 36% protein group (p < 0.05). In the larger fish, hindgut protease activity was higher in the 34% protein group (p < 0.05), while in the smaller fish, there was no significant difference in the hindgut protease activity among all groups (p > 0.05). The dietary protein levels had no significant effect on lipase activity in the foregut, midgut, or hindgut, or on amylase activity in the foregut or midgut of the fish at the two growth stages (p > 0.05). However, hindgut amylase activity was highest in the control group of the smaller fish (p < 0.05). Based on regression analysis, the optimal dietary protein levels for the larger and smaller fish were 30.45% and 29.95%, respectively. Overall, appropriate dietary protein levels (30%) could improve the growth performance, immune function, and health status of fish at two growth stages and promote the adaptive response of their digestive system, especially the spatial regulation of protease activity in different gastrointestinal regions.