bluuebunny/crossref_metadata_2025 huggingface dataset:
Release Behavior of the Pineal Hormone Melatonin from Modified Matrix Tablets Based on Poly(L-Lactic Acid) and Its Derivatives
Authors: Vlachou Marilena , Bolbasis Georgios-Marios , Trikali Anna-Evaggelia , Protopapa Chrystalla , Siamidi Angeliki , Sakellaropoulou Aikaterini , Christodoulou Evi , Bikiaris Nikolaos D.
Published: February 2025

Biodegradable polyesters have been researched intensively over the last two decades because of their biodegradability and superb physical properties. However, the use of linear biodegradable polyesters, for the preparation of drug delivery systems (DDS), is hampered by several limitations. In view of this, scientific attention has been shifted to the employment of branched-chain (co-)polymers. In this context, we present herein the development of new melatonin (MLT) tablet formulations, using novel branched polylactide (PLA)-based copolymers of different architectures. Specifically, three PLA-polyol branched polyesters, namely, a three-arm copolymer based on glycerol (PLA-glycerol), a four-arm copolymer based on pentaerythritol (PLA-pentaerythritol), and a six-arm copolymer based on sorbitol (PLA-sorbitol), were utilized. The presence of these polyesters in the formulations was found to be crucial, as the sought MLT release, regarding its use in confronting sleep onset and/or sleep maintenance dysfunctions, was achieved. The copresence of the other excipients in the matrix tablets (lactose monohydrate, hydroxypropylmethylcellulose, microcrystalline cellulose, and sodium alginate) led to a concentration-dependent synergistic effect on the MLT release. To the best of our knowledge, this is the first investigation with these specific polymeric materials, concerning MLT modified release from matrix tablets.
Towards Better Sentiment Analysis in the Turkish Language: Dataset Improvements and Model Innovations
Authors: Zümberoğlu Kevser Büşra , Dik Sümeyye Zülal , Karadeniz Büşra Sinem , Sahmoud Shaaban
Published: February 2025

Sentiment analysis in the Turkish language has gained increasing attention due to the growing availability of Turkish textual data across various domains. However, existing datasets often suffer from limitations such as insufficient size, lack of diversity, and annotation inconsistencies, which hinder the development of robust and accurate sentiment analysis models. In this study, we present a novel enhanced dataset specifically designed to address these challenges, providing a comprehensive and high-quality resource for Turkish sentiment analysis. We perform a comparative evaluation of previously proposed models using our dataset to assess their performance and limitations. Experimental findings demonstrate the effectiveness of the presented dataset and trained models, offering valuable insights for advancing sentiment analysis research in the Turkish language. These results underscore the critical role of the enhanced dataset in bridging the gap between existing datasets and the importance of training the modern sentiment analysis models on scalable, balanced, and curated datasets. This can offer valuable insights for advancing sentiment analysis research in the Turkish language. Furthermore, the experimental results represent an important step in overcoming the challenges associated with Turkish sentiment analysis and improving the performance of existing models.
Modal Analysis and Optimization of Tractor Exhaust System
Authors: Tekin Ayla , Şamlı Halil
Published: February 2025

Excessive vibrations in exhaust systems can significantly reduce a vehicle’s lifespan and compromise performance. These vibrations, caused by factors such as engine operation and road conditions, lead to wear and tear. To address this issue, a finite element analysis (FEA) was conducted on a 90-horsepower tractor’s exhaust system. Using ANSYS WB®, a 3D model was created and modal analysis was performed to determine the system’s natural frequencies and mode shapes. Based on the results, geometric modifications were made to the exhaust system, increasing its stiffness and shifting vibration frequencies to higher values. Consequently, vibration levels, noise, and the risk of component failure were significantly reduced. The redesigned exhaust system was successfully implemented in production. This study demonstrates the effectiveness of FEA in analyzing exhaust system vibrations and facilitating design improvements. By extending vehicle lifespan and providing a quieter, more comfortable driving experience, this research offers valuable insights for automotive and mechanical engineers.
Assessing Abrasion Resistance in Concrete Pavements: A Review
Authors: Subedi Alka , Kim Hyunhwan , Lee Soon-Jae , Lee Moon-Sup
Published: February 2025

Abrasion resistance is a crucial factor in pavement concrete durability, influenced by compressive strength, surface finishing and curing techniques, aggregate characteristics, and testing conditions. Research shows that improving concrete’s abrasion resistance through proper mix design, additives like fibers and silica fume, and optimal curing practices can extend its service life by reducing wear from traffic and environmental forces, but concrete remains vulnerable to abrasion over time. This is a comprehensive review investigating the abrasion resistance in concrete pavements. It explores the mechanisms of abrasion resistance and standard testing methods. It then delves into the numerous factors affecting abrasion resistance, focusing on material properties (aggregates, cement type, admixtures, and water–cement ratio), pavement design (surface texture and reinforcement), and environmental conditions. Finally, the review examines advancements in enhancing abrasion resistance, including new materials and technologies, and concludes by highlighting current limitations in understanding the phenomenon and suggesting avenues for future research. This paper aims to provide a better understanding of materials, techniques, and predictive models to enhance abrasion resistance, which is crucial for enhancing the durability and longevity of concrete pavements.
Morphological and Mechanical Symmetry of the Quadriceps and Hamstrings in Athletes
Authors: Wenzel Vanessa Ines , Leite-Nunes Tiago Dutra , Soldatelli Isadora Miotto , Bischoff Arthur Brum Gonçalves , Lanferdini Fábio Juner
Published: February 2025

Sports performance can be influenced by several multifactorial conditions. Symmetries between sides can interfere with muscle strength, impacting performance. Balance in morphological aspects and muscle strength is desirable between dominant (DO) and non-dominant (ND) lower limbs to improve performance or to prevent injuries. The study aimed to compare quadriceps and hamstring muscle morphology and knee extensor and flexor torque while analyzing whether cross-sectional area (CSA) and echo intensity (EI) can better explain maximal concentric torque in competitive athletes. Sixteen competitive athletes of team sports were evaluated. Quadriceps and hamstring CSA and EI were assessed using a B-mode ultrasound. The maximal torque production capacity of the quadriceps and hamstrings was evaluated using an isokinetic dynamometer protocol. Our backward multiple linear regression models indicated that the quadriceps cross-sectional area (CSA) and echo intensity (EI) of the dominant (DO) and non-dominant (ND) limbs explain 61–79% of concentric torque production at 60°/s and 300°/s, with CSA showing a positive relationship and EI a negative one. Likewise, hamstrings CSA and EI account for 70–77% of torque production at the same angular velocities, both exhibiting positive relationships with torque output. Our data present bilateral mechanical and morphological symmetries in lower limbs of amateur athletes and potential predictive concentric torque capacity of CSA and EI for quadriceps and hamstrings.
Research on Control Technology of Roof-Cutting and Roadway Protection for Narrow Pillar Beneath Close-Distance Goaf
Authors: Zhu Gaolei , Liu Chenyang , Wu Yiyi , Li Hui , Zou Hang
Published: February 2025

Close-distance coal seams are common in underground mining, and their spacing is short, which produces strong mining disturbance. In instances where the upper seam has been mined and a goaf has formed, a notable issue arises during the lower seam’s mining, characterized by substantial deformation of the roadway along the goaf. Field exploration and three-dimensional geological modeling have revealed that the fourth and sixth working faces and pillar of seam No. 5 are all under seam No. 2’s goaf, with an average distance of 16.70 m. Simultaneously, the double compression effect of the pillar, induced by the linkage rotation of key blocks of the lower and upper seams, is analyzed. The induction mechanism and path of the large deformation are expounded. It is thus proposed that the pillar’s width should be determined by gob-side entry, driving beneath the goaf, with the roof near the pillar being cut off in advance to realize the path of cutting off the compressed pillar. Through the simulation comparison of five kinds of pillar width combined with engineering practice, it has been determined that the best width is 8 m, and the abutment pressure is distributed in a double-peak saddle shape, with the result that the load-bearing ability is notably significant. Through the comparative simulation of roof-cutting, it was found that roof-cutting helps the roof to collapse near the pillar-side and decreases the vertical stress peak to 16.46 MPa, the shear stress peak to 5.93 MPa, and the J2 peak to 7.23 × 1013 Pa, which further alleviates the pressure on the pillar. In the field, the haulage roadway’s roof was cut by two-way shaped-charge blasting, and the sandy mudstone (5.90 m) was successfully cut off. Concurrently, anchor cable reinforcement was implemented on the roof and two ribs of the ventilation roadway in proximity to the pillar, thereby ensuring stabilization and mitigating the mining effect. The engineering research provides a case and scheme reference for the operation of gob-side entry driving beneath close-distance goafs worldwide.
An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
Authors: Li Yuanxi , Wang Haiyan , Zhang Dong
Published: February 2025

Entity-relation extraction (ERE) aims to identify entity types and the relationships between them from unstructured texts and is one of the key technologies for constructing knowledge graphs. However, ERE tasks face challenges such as insufficient semantic representations and the complexity of relationship types, which lead to the difficulty of triplet extraction. To address these issues, we propose an entity-relation extraction model that incorporates dependency parsing and a mixture-of-experts architecture. Specifically, we use BERT as a character encoder, while integrating dependency syntax information as a separate encoding path. We apply additive attention to fuse the two pathways of encoding, assigning different weights to each vector in the encoding layer output through a learned weighting process. This enables the model to flexibly adjust the attention given to different features, allowing for a more accurate identification and utilization of syntactic dependencies within a sentence. In the relation classification layer, we employ a mixture-of-experts architecture, allowing each expert to focus on learning different relationship labels, thereby enhancing the model’s ability to accurately identify and capture specific entity relationships. The proposed model achieves superior results to the baseline models on two public ERE datasets, providing a novel and effective solution for entity-relation extraction tasks.
The Acute Effects of Combined Isometric and Plyometric Conditioning Activities on Sprint Acceleration and Jump Performance in Elite Junior Sprinters
Authors: Pietraszewski Przemysław , Gołaś Artur , Zając Adam , Maćkała Krzysztof , Krzysztofik Michał
Published: February 2025

This study investigates the acute effects of a combined isometric and plyometric unilateral conditioning activity (CA) on post-activation performance enhancement (PAPE) in junior elite sprinters. The rationale for combining isometric and plyometric exercises lies in their complementary effects: isometric exercises enhance neuromuscular activation, while plyometric exercises may exploit the stretch–shortening cycle to maximize power output. Thirteen sprinters (10 males, three females) performed countermovement jumps (CMJ) and 50 m sprints before and after the CA protocol, which involved Bulgarian split squats (15% body mass) and single-leg hops. Performance metrics, including sprint times, CMJ height, and modified reactive strength index (RSImod), were recorded and analyzed. Results showed a significant improvement in CMJ height (2.6 ± 3.1%, p < 0.001) and RSI (4.1 ± 7.5%, p = 0.038), alongside a reduction in 20 m sprint time (−0.8 ± 1.4%, p = 0.012). No significant changes were observed in 40 m and 50 m sprint times or ground contact and flight times. These findings suggest that the applied CA selectively enhances sprint acceleration and vertical jumping performance, with a minimal impact on sprint distances above 20 m. This study underscores the potential of incorporating combined isometric and plyometric CAs into sprint training to optimize short-distance performance, though further research is needed to refine exercise protocols and explore long-term effects.
An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
Authors: Zhang Chao , Zhang Xuhui , Yang Wenjuan , Wan Jicheng , Zhang Guangming , Du Yuyang , Tian Sihao , Wang Zeyao
Published: February 2025

This paper proposes a cutting trajectory planning method for boom-type roadheaders using an improved Nondominated Sorting Genetic Algorithm II (NSGA-II) with an elitist strategy. Existing methods often overlook constraints related to cutterhead dimensions and target sections, affecting section formation quality. We develop a kinematic model for coordinate transformations and design a simplified cutterhead and constraint model to generate feasible cutting points. Bi-objective functions—minimizing cutting trajectory length and turning angle—are formulated as a bi-objective traveling salesman problem (BO-TSP) with adjacency constraints. NSGA-II is adapted with enhancements in adjacency constraint handling, population initialization, and genetic operations. Simulations and experiments demonstrate significant improvements in convergence speed and computation time. Virtual cutting experiments confirm trajectory feasibility under varying postures, achieving high formation quality. A comparison of planned and tracked trajectories shows a maximum deviation of 23.879 mm, supporting autonomous cutting control. This method advances cutting trajectory planning for roadway section formation and autonomous roadheader control.
Idempotent Triangular Matrices over Additively Idempotent Semirings: Decompositions into Products of Semicentral Idempotents
Authors: Vladeva Dimitrinka
Published: February 2025

The explicit forms of idempotent and semicentral idempotent triangular matrices over an additively idempotent semiring are obtained. We define a diamond composition of idempotents and give a representation of an idempotent n×n matrix as an (n−1)th degree of a sum of diamond compositions of semicentral idempotents. We construct a decomposition of a strictly upper matrix, a unitriangular matrix, and a nil-clean matrix by semicentral idempotents.
A Survey on the Applications of Cloud Computing in the Industrial Internet of Things
Authors: Dritsas Elias , Trigka Maria
Published: February 2025

The convergence of cloud computing and the Industrial Internet of Things (IIoT) has significantly transformed industrial operations, enabling intelligent, scalable, and efficient systems. This survey provides a comprehensive analysis of the role cloud computing plays in IIoT ecosystems, focusing on its architectural frameworks, service models, and application domains. By leveraging centralized, edge, and hybrid cloud architectures, IIoT systems achieve enhanced real-time processing capabilities, streamlined data management, and optimized resource allocation. Moreover, this study delves into integrating artificial intelligence (AI) and machine learning (ML) in cloud platforms to facilitate predictive analytics, anomaly detection, and operational intelligence in IIoT environments. Security challenges, including secure device-to-cloud communication and privacy concerns, are addressed with innovative solutions like blockchain and AI-powered intrusion detection systems. Future trends, such as adopting 5G, serverless computing, and AI-driven adaptive services, are also discussed, offering a forward-looking perspective on this rapidly evolving domain. Finally, this survey contributes to a well-rounded understanding of cloud computing’s multifaceted aspects and highlights its pivotal role in driving the next generation of industrial innovation and operational excellence.
Genetic and Epigenetic Intersections in COVID-19-Associated Cardiovascular Disease: Emerging Insights and Future Directions
Authors: Sabit Hussein , Arneth Borros , Altrawy Afaf , Ghazy Aysha , Abdelazeem Rawan M. , Adel Amro , Abdel-Ghany Shaimaa , Alqosaibi Amany I. , Deloukas Panos , Taghiyev Zulfugar T.
Published: February 2025

The intersection of COVID-19 and cardiovascular disease (CVD) has emerged as a significant area of research, particularly in understanding the impact of antiplatelet therapies like ticagrelor and clopidogrel. COVID-19 has been associated with acute cardiovascular complications, including myocardial infarction, thrombosis, and heart failure, exacerbated by the virus’s ability to trigger widespread inflammation and endothelial dysfunction. MicroRNAs (miRNAs) play a critical role in regulating these processes by modulating the gene expressions involved in platelet function, inflammation, and vascular homeostasis. This study explores the potential of miRNAs such as miR-223 and miR-126 as biomarkers for predicting resistance or responsiveness to antiplatelet therapies in COVID-19 patients with cardiovascular disease. Identifying miRNA signatures linked to drug efficacy could optimize treatment strategies for patients at high risk of thrombotic events during COVID-19 infection. Moreover, understanding miRNA-mediated pathways offers new insights into how SARS-CoV-2 exacerbates CVD, particularly through mechanisms like cytokine storms and endothelial damage. The findings of this research could lead to personalized therapeutic approaches, improving patient outcomes and reducing mortality in COVID-19-associated cardiovascular events. With global implications, this study addresses the urgent need for effective management of CVD in the context of COVID-19, focusing on the integration of molecular biomarkers to enhance the precision of antiplatelet therapy.
Laboratory Tests in Inflammatory Bowel Disease: An Evidence-Based Approach to Daily Practice
Authors: Durham Katelin , Atagozli Tyler , Elliott David E. , Ince M. Nedim
Published: February 2025

Inflammatory bowel diseases (IBDs) comprise a group of chronic gastrointestinal disorders characterized by periods of relapse and remission. The mainstay of treatment is medical, involving medications such as steroids, immune modulators, monoclonal antibodies (categorized as biologics), and small molecules. These medications can provide profound therapeutic benefits, but they can also cause severe and irreversible toxicities. Clinicians may utilize laboratory tests in the diagnosis and management of IBD including assessment of disease activity, monitoring medication response or toxicity, surveillance of infectious complications, and detection of nutritional deficiencies. Routine use of laboratory tests may help clinicians avoid reactivation of life-threatening infections such as tuberculosis or hepatitis B virus upon initiation of immune suppressive therapy. They can also be used to detect vitamin deficiencies such as B12 deficiency, which has the potential to cause irreversible neurologic damage. While some laboratory tests constitute established practices, the utility of newer tests such therapeutic drug monitoring (TDM) in the era of biologics is an evolving topic. Although clinical assessment with imaging, endoscopic, and histopathological examination is standard practice, laboratory tests serve as valuable adjuncts. We aim to explore the broad range of laboratory tests available to clinicians and to summarize their application in the current management of IBD in daily clinical practice, with special attention to updates in therapeutic drug monitoring.
Radiological Variability in Pancreatic Neuroendocrine Neoplasms: A 10-Year Single-Center Study on Atypical Presentations and Diagnostic Challenges
Authors: Danek Eleanor , Kavnoudias Helen , McLean Catriona , Gerstenmaier Jan F. , Di Muzio Bruno
Published: February 2025

Background: Pancreatic neuroendocrine neoplasms (PNENs) are rare but clinically significant tumors with variable radiological presentations that complicate diagnosis. While typical PNENs are well characterized, atypical features, such as cystic or hypoenhancing patterns, are less understood and can lead to diagnostic delays or misdiagnosis. This study aimed to evaluate atypical radiological presentations of PNENs, focusing on their impact on diagnostic pathways and differentiation from other pancreatic pathologies. Methods: A retrospective review was conducted of all PNEN cases diagnosed at a single tertiary center between 2010 and 2020. Cases with histopathological confirmation and available cross-sectional imaging were included. Radiological features were categorized as typical (solid and hyperenhancing) or atypical (cystic and hypoenhancing). Demographic, radiological, and pathological data were analyzed. Comparisons between typical and atypical PNENs were performed using descriptive and inferential statistics. Results: Among 77 PNEN cases, 39 met the inclusion criteria. Atypical radiological presentations were identified in 46% of cases, including cystic (18%) and hypoenhancing (28%) lesions. Hypoenhancing PNENs were significantly more likely to present with advanced disease (54% vs. 14% in typical PNENs, p = 0.016). In contrast, none of the cystic PNENs exhibited advanced disease. Atypical PNENs posed greater diagnostic challenges, with alternative diagnoses initially considered in 64% of hypoenhancing and 43% of cystic cases compared to 10% of typical PNENs (p = 0.0042). Conclusions: Atypical PNENs, particularly hypoenhancing lesions, present significant diagnostic challenges and are more likely to be associated with advanced disease. These findings highlight the need for improved recognition of atypical imaging patterns and more precise diagnostic strategies. However, the retrospective design and small cohort size limit the generalizability of our findings. Further multicenter studies are warranted to refine the imaging criteria and optimize the differentiation from other pancreatic neoplasms.
Assessing the Clinical Relevance of Soluble PD-1 and PD-L1: A Multi-Cohort Study Across Diverse Tumor Types and Prognostic Implications
Authors: Kushlinskii Nikolay E. , Kovaleva Olga V. , Gratchev Alexei N. , Alferov Alexander A. , Kuzmin Yurii B. , Sokolov Nikolai Y. , Tsekatunov Dmitry A. , Ryzhavskaya Irina B. , Kuznetsov Igor N. , Kushlinskii Dmitry N. , Mamedli Zaman Z. , Stilidi Ivan S.
Published: February 2025

Background/Objectives: Immune checkpoint inhibitors targeting the PD-1/PD-L1 pathway have revolutionized cancer immunotherapy, however the clinical relevance of their soluble forms (sPD-1 and sPD-L1) remains less studied. Soluble PD-1 and PD-L1 have been implicated in tumor progression, prognosis, and treatment response across various malignancies. This study aims to provide a comprehensive analysis of sPD-1 and sPD-L1 levels in serum across diverse tumor types, including rare malignancies, and to evaluate their associations with clinicopathological characteristics and prognostic significance. Methods: In this study we analyzed sPD-1 and sPD-L1 levels in serum samples from 675 cancer patients representing a range of malignancies, including ovarian cancer, breast cancer, gastric cancer, colorectal cancer, renal cell carcinoma, and bone tumors. sPD-1 and sPD-L1 concentrations were measured using ELISA. Statistical analyses were performed to evaluate associations between soluble marker concentrations and clinicopathological factors, including tumor stage, size, histological subtype, and survival outcomes. Results: Elevated sPD-L1 levels were observed in several tumor types, including ovarian cancer, renal cell carcinoma, and gastric cancer, where they were associated with features of advanced disease, such as tumor size, stage, and metastases. In contrast, sPD-1 levels showed limited associations, with significant findings solely in gastric cancer and bone tumors, where levels correlated with histological subtype and differentiation. Prognostic analyses identified sPD-L1 as a marker of poor survival outcomes in ovarian cancer and bone tumors, while sPD-1 displayed no consistent prognostic significance. Conclusions: This study identifies the potential of sPD-L1 as a biomarker for tumor progression and prognosis across multiple malignancies. In contrast, sPD-1 showed limited clinical relevance, suggesting the importance of further investigation. These findings contribute to our understanding of soluble immune checkpoint proteins and their integration into personalized oncology strategies.
Dupilumab in the Treatment of Severe Uncontrolled Chronic Rhinosinusitis with Nasal Polyps (CRSwNP) and Comorbid Asthma—A Multidisciplinary Monocentric Real-Life Study
Authors: Fadda Gian Luca , Rustichelli Chiara , Soccal Simone , Moglio Simone , Serrone Alessandro , Bertolini Francesca , Carriero Vitina , Pizzimenti Stefano , Levra Stefano , Cavallo Giovanni , Ricciardolo Fabio Luigi Massimo , Guida Giuseppe
Published: February 2025

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) and asthma are mutually correlated with Type-2 inflammation. Dupilumab is effective in uncontrolled and relapsing CRSwNP. However, the precise characterization of Type-2 inflammation and the impact of previous surgery on clinical outcomes need clarification. Methods: We present a prospective observational study on a 38 CRSwNP-patient cohort, whose Type-2 endotype was confirmed after a multidisciplinary approach shared among ENTs, pneumologists and allergologists. Patients were treated with dupilumab and evaluated at 15 days and 1-3-6-12-18-24-30 months, focusing on clinical (VAS, nasal polyp score—NPS), radiological (Lund-Mackay) and quality of life (SNOT-22) parameters, as well olfactory function, asthma control, variation of Type-2 markers and number and extent (ACCESS score) of previous surgeries. Results: We confirmed the efficacy of dupilumab in total and sub-items VAS, NPS, SNOT-22 and sniffing score, as well as Lund–Mackay score improvements, observable and significant after 2 weeks of treatment (p < 0.0001) and long-lasting over 30 months. Good to excellent response criteria to biologic treatment at 6 months was observed in 30/32 patients. Comorbid asthma reached rapid control (p < 0.0001) and exhaled nitric oxide normalization was achieved. One single “not adequate” surgery showed a trend to milder improvement, as well as a higher ACCESS score to better olfactory outcome. Conclusions: The accurate selection of uncontrolled relapsing CRSwNP in terms of Type-2 endotyping by multidisciplinary approach can maximize dupilumab efficacy. The number and extent of previous surgeries may differentiate the response, although this effect is difficult to catch in real life. “Adequate” ESS surgery before dupilumab may drive mostly effective disease control.
Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy
Authors: Gkintoni Evgenia , Antonopoulou Hera , Sortwell Andrew , Halkiopoulos Constantinos
Published: February 2025

Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive learning systems, informed by neurophysiological insights, enhance personalized education for K-12 students and adult learners. This study emphasizes the role of Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), and other neurophysiological tools in assessing cognitive states and guiding AI-powered interventions to refine instructional strategies dynamically. Methods: This study reviews n = 103 papers related to the integration of principles of CLT with AI and ML in educational settings. It evaluates the progress made in neuroadaptive learning technologies, especially the real-time management of cognitive load, personalized feedback systems, and the multimodal applications of AI. Besides that, this research examines key hurdles such as data privacy, ethical concerns, algorithmic bias, and scalability issues while pinpointing best practices for robust and effective implementation. Results: The results show that AI and ML significantly improve Learning Efficacy due to managing cognitive load automatically, providing personalized instruction, and adapting learning pathways dynamically based on real-time neurophysiological data. Deep Learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) improve classification accuracy, making AI-powered adaptive learning systems more efficient and scalable. Multimodal approaches enhance system robustness by mitigating signal variability and noise-related limitations by combining EEG with fMRI, Electrocardiography (ECG), and Galvanic Skin Response (GSR). Despite these advances, practical implementation challenges remain, including ethical considerations, data security risks, and accessibility disparities across learner demographics. Conclusions: AI and ML are epitomes of redefinition potentials that solid ethical frameworks, inclusive design, and scalable methodologies must inform. Future studies will be necessary for refining pre-processing techniques, expanding the variety of datasets, and advancing multimodal neuroadaptive learning for developing high-accuracy, affordable, and ethically responsible AI-driven educational systems. The future of AI-enhanced education should be inclusive, equitable, and effective across various learning populations that would surmount technological limitations and ethical dilemmas.
A Visual Preference Analysis of Building Façades in Street View Images: A Case Study of Nanshan District, Shenzhen
Authors: Lee Chia-Chen , Wang Yuxiao , Tang Chenggao , Li Xiang , Yin Jie
Published: February 2025

Human visual perception and visual preferences affect how people perceive and experience the urban environment. Constrained by their methodologies and data availability, the previous studies have struggled to precisely quantify the subjects and objects in urban perception. This research employs visual saliency detection and fractal dimension measurement to quantify the characteristics of urban building façades and visual perception. Through this study, we determine the differences between human visual perception and computer vision. The study shows that (1) human vision exhibits selective preferences, processing building façades with varying complexity levels to form a critical complexity threshold, which is a feature that is not captured by computer vision processing; (2) taking Nanshan District, Shenzhen, as a case study, the value of this threshold is 1.2383; (3) building façades with complexity greater than this threshold are seen as “complex” building façades and vice versa; (4) when perceiving “simple” buildings, human eyes tend to focus on their more complex local areas, whereas for “complex” buildings, they pay more attention to simpler parts. This study provides a reference for conducting quantitative research on urban perception and visual perception.
Comparative Analysis of Residents’ Willingness to Pay for Diverse Low-Carbon Measures in Hangzhou, China: Implications for Urban Sustainability and Policy
Authors: He Jiahao , He Yong , Wu Shuwen , Yu Huifang , Bao Chun
Published: February 2025

Chinese cities have made significant progress in fostering low-carbon societies and piloting a variety of low-carbon measures. Nonetheless, the effective implementation of these initiatives and the long-term upkeep of related amenities rely heavily on resident support. The existing studies provide limited insight into how local residents perceive and endorse different types of low-carbon measures, which often involve varying trade-offs. Addressing this gap, the present study surveyed the willingness to pay (WTP) of residents in Hangzhou—an early adopter of low-carbon practices in China—across five representative low-carbon measures. Survey data were collected from 13 distinct residential neighborhoods. The results indicate that Hangzhou residents are more inclined to financially support measures offering direct personal benefits compared to those benefiting the collective good, with this tendency being notably pronounced among highly educated individuals. Further findings include the following: (1) respondents aware of ongoing low-carbon measures were more willing to pay for them; (2) male respondents, recent migrants (within the past five years), high-income groups, and residents in aging communities tended to contribute higher amounts; (3) providing detailed information on carbon mitigation effects markedly increased both the likelihood and the magnitude of WTP; (4) the promotion of new energy vehicles (NEVs) remains contentious, particularly between NEV owners and gasoline vehicle owners. These findings highlight the need for targeted policies and educational programs to strengthen public awareness and support for low-carbon interventions, thereby advancing sustainability in fast-growing urban centers like Hangzhou. Overall, these findings provide key insights for the formulation of low-carbon city policies and sustainable urban planning, emphasizing the global importance of local socioeconomic dynamics and offering a valuable reference for cities worldwide seeking to advance sustainability transitions and meet international climate targets.
Climate Change Exposure of Agriculture Within Regulated Groundwater Basins of the Southwestern United States
Authors: Parker Lauren E. , Zhang Ning , Kisekka Isaya , Abatzoglou John T. , Elias Emile H. , Steele Caitriana M. , Ostoja Steven M.
Published: February 2025

Agriculture is an important part of the economy of southwestern United States (Southwest). The production of food and fiber in the Southwest is supported by irrigation, much of which is sourced from groundwater. Climate projections suggest an increasing risk of drought and heat, which can affect water supply and demand, and will challenge the future of agricultural production in the Southwest. Also, as groundwater in the Southwest is highly regulated, producers may not be able to readily rely on groundwater to meet increased demand. Climate exposure of five economically-important crops—alfalfa, cotton, pecans, pistachios, and processing tomatoes—was analyzed over twelve regulated groundwater basins by quantifying changes in a suite of both crop-specific and non-specific agroclimatic indicators between contemporary (1981–2020) and future (2045–2074, SSP2-4.5) climates. Generally, groundwater basins that are currently the most exposed to impactful climate conditions remain so under future climate. The crops with the greatest increase in exposure to their respective crop-specific indicators are cotton, which may be impacted by a ~180% increase in exposure to extreme heat days above 38 °C, and processing tomatoes, which may see a ~158% increase in exposure to high temperatures and reduced diurnal temperature range during flowering. These results improve understanding of the potential change in exposure to agroclimatic indicators, including crop-specific indicators, at the scale of regulated groundwater basins. This understanding provides useful information for the long-term implications of climate change on agriculture and agricultural water, and can inform adaptation efforts for coupled agricultural and water security in groundwater-dependent regions. These results may also be useful for assessing the adaptive potential of water conservation actions—some of which are outlined herein—or the suitability of other adaptation responses to the challenges that climate change will pose to agriculture.
Effect of Enzymatic Glycosylation on Film-Processing Properties and Biological Activities of Black Soybean Protein
Authors: Zhang Yinglei , Gong Xue , Wang Jing , Dou Boxin , Hou Lida , Xiao Wei , Chang Jiang , Li Danting
Published: February 2025

In this study, chitooligosaccharides (COS) were introduced into black soybean protein (BSP) using transglutaminase (TG) as a biocatalyst. The film-processing properties and physiological activities of the enzymatically glycosylated black soybean protein (EGBSP) were studied. The results showed that glycosylation decreased the surface hydrophobicity, absolute value of the zeta potential, its minimum solubility, and film permeability of BSP by 69.86%, 6.04%, 36.68%, and 14.91%, respectively, while increasing the tensile strength and elongation at break of its protein film by 56.57% and 172.68%, respectively. The gel time was shortened, and the acid-induced gel properties of EGBSP were similar to those of BSP. The anticancer effect of EGBSP was evaluated by the tumor inhibition rate, flow cytometry, and morphology observation of an ascites tumor in H22 tumor-bearing mice. The immune organs (spleen, thymus), immune cells (lymphocytes, NK cells), and immune factors (IL-2, IL-12) of H22 tumor-bearing mice were detected to evaluate the immunomodulatory effects of EGBSP. The results showed that medium and high doses of BSP had positive effects on immune enhancement and anti-cancer activity of H22 tumor-bearing mice, while almost all doses of EGBSP showed significant effects. These results indicated that glycosylation significantly improved the anti-cancer effect and immunomodulatory activity of H22 tumor-bearing mice while prolonging their overall survival. In conclusion, the glycosylation method using microbial transglutaminase to catalyze the introduction of chitooligosaccharides into black bean protein can improve the film-processing properties and biological activities of BSP more effectively than the enzyme crosslinking method.
Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis
Authors: Khan Mehshan Ahmed , Asadi Houshyar , Qazani Mohammad Reza Chalak , Arogbonlo Adetokunbo , Pedrammehr Siamak , Anwar Adnan , Zhou Hailing , Wei Lei , Bhatti Asim , Oladazimi Sam , Khan Burhan , Nahavandi Saeid
Published: February 2025

Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in the concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR). Various machine learning classification techniques have been utilized to distinguish cognitive states. However, conventional machine learning methods, although simpler to implement, undergo a complex pre-processing phase before network training and demonstrate reduced accuracy due to inadequate data preprocessing. Additionally, previous research in cognitive load assessment using fNIRS has predominantly focused on differentiating between two levels of mental workload. These studies mainly aim to classify low and high levels of cognitive load or distinguish between easy and difficult tasks. To address these limitations associated with conventional methods, this paper conducts a comprehensive exploration of the impact of Long Short-Term Memory (LSTM) layers on the effectiveness of Convolutional Neural Networks (CNNs) within deep learning models. This is to address the issues related to spatial feature overfitting and the lack of temporal dependencies in CNNs discussed in the previous studies. By integrating LSTM layers, the model can capture temporal dependencies in the fNIRS data, allowing for a more comprehensive understanding of cognitive states. The primary objective is to assess how incorporating LSTM layers enhances the performance of CNNs. The experimental results presented in this paper demonstrate that the integration of LSTM layers with convolutional layers results in an increase in the accuracy of deep learning models from 97.40% to 97.92%.
Modified Ant Colony Optimization to Improve Energy Consumption of Cruiser Boundary Tour with Internet of Underwater Things
Authors: Mohammed Hadeel , Ibrahim Mustafa , Raoof Ahmed , Jaleel Amjad , Al-Dujaili Ayad Q.
Published: February 2025

The Internet of Underwater Things (IoUT) holds significant promise for developing a smart ocean. In recent years, there has been swift progress in data collection methods using autonomous underwater vehicles (AUVs) within underwater acoustic sensor networks (UASNs). One of the key challenges in the IoUT is improving both the energy consumption (EC) of underwater vehicles and the value of information (VoI) necessary for completing missions while gathering sensing data. In this paper, a hybrid optimization technique is proposed based on boundary tour modified ant colony optimization (BTMACO). The proposed optimization algorithm was developed to solve the challenging problem of determining the optimal path of an AUV visiting all sensor nodes with minimum energy consumption. The optimization algorithm specifies the best order in which to visit all the sensor nodes, while it also works to adjust the AUV’s information-gathering locations according to the permissible data transmission range. Compared with the related works in the literature, the proposed method showed better performance, and it can find the best route through which to collect sensor information with minimum power consumption and a 6.9% better VoI.
Brain Health for All? Influence of Glycemic Control and Neuropsychiatric Symptoms in Dementia with Lewy Bodies: A Case Report and Literature Review
Authors: Stancu Patrick , Janela Duarte , Gurary Samuel , Sveikata Lukas , Assal Frédéric
Published: February 2025

Background: Dementia with Lewy bodies (DLBs) often presents with neuropsychiatric symptoms (NPSs), yet the role of hyperglycemia, a common cause of delirium in older adults, as a contributing factor remains under-recognized. This article aims to explore the relationship between hyperglycemia and NPSs. Methods: We report the case of a 71-year-old male with DLBs and type 2 diabetes mellitus (T2DM) who experienced worsening NPSs closely associated with periods of hyperglycemia. Initial pharmacological and nonpharmacological interventions were insufficient, prompting adjustments to insulin therapy and dietary modifications to stabilize blood glucose levels. Results: Improved glycemic control resulted in a clinically significant reduction in NPSs. Conclusions: This case suggests a potential link between hyperglycemia and NPSs in DLB patients, emphasizing the importance of maintaining glycemic control in managing NPSs. Although the exact mechanisms remain incompletely understood, adopting a holistic framework for brain health could offer a comprehensive approach to cognitive care. Further studies are needed to elucidate the biological pathways involved, validate these findings in larger populations, and develop evidence-based clinical guidelines.
Antimicrobial and Anticancer Potential of Polyketides Isolated from the Caribbean Marine Sponge Plakortis halichondrioides
Authors: Jiménez-Romero Carlos , Amador Luis A. , Castro-Falcón Gabriel , Rodríguez Abimael D.
Published: February 2025

Background/Objectives: The marine sponge Plakortis halichondrioides is notable for its capacity to accumulate a large array of secondary metabolites. The present research aims at discovering new secondary metabolites from P. halichondrioides with potential applications in medicine. Methods: Plakortilactone (1) and seco-plakortide F acid (2), two new polyketides, along with known manadodioxan D (3), 13-oxo-plakortide F (4), plakortide F (5), and manadodioxan E (6), were isolated from P. halichondrioides. We achieved the structural elucidation of 1 and 2 using modern spectroscopic methods. The relative stereochemistry of 1 was proposed on the basis of 1D- and 2D-NMR data in combination with molecular modeling studies. Additionally, the absolute configuration of 2 was established through chemical correlation to 5. We screened some of the isolated compounds against the malaria parasite Plasmodium falciparum 3D7 non-resistant (wild-type resistant) strain, the tuberculosis bacterium Mycobacterium tuberculosis, the prostate cancer cell line DU-145, and the melanoma cell line A-2058. Results: minimal activity was detected for 2 against these cancer cells. In contrast, 3 and 4 displayed activity against DU-145 cells with IC50 values of 1.6 µg/mL and 4.5 µg/mL, respectively, and A-2058 cells with IC50s of 2.6 µg/mL and 7.7 µg/mL, respectively. In the antiplasmodial activity assay 3 appeared more active (IC50 = 1.7 µg/mL) than 4 (IC50 = 3.1 µg/mL). Meanwhile, 2 displayed only moderate activity (IC50 = 39.3 µg/mL). In the antimycobacterial activity assay 2 exhibited moderate inhibition (MIC = 75.1 µg/mL). In contrast, a 1:1 mixture of 3 and 4 demonstrated higher activity (MIC = 26.3 µg/mL). Conclusions: the biological activity data together with ADMET predictions indicated favorable pharmacokinetic properties for 3.
Evaluating the Effectiveness of the Third Molar Maturity Index (I3m) and Mandibular Condyle Cortication for Determining the Legal Age (18) of Latvian Individuals
Authors: Bokvalde Zanda , Zamure-Damberga Liene , Zolovs Maksims , Neimane Laura
Published: February 2025

Background: It is important to accurately determine the legal age at which a person is considered and treated as an adult; in many countries, it is 18. With the increasing migration flow to European countries, accurate age estimation methods must be implemented. In this study, the third molar maturity (I3m) index and mandible condyle cortication stage were tested to distinguish adult from non-adult Latvian individuals using dental panoramic radiographs. Methods: This study included 716 selected dental panoramic radiographs of patients between the ages of 14 and 22. The lower-left third molar apical root parts were analyzed, and the I3m index was calculated. In addition, the condyle cortication stage was evaluated. Results: All logistic regression models achieved statistically significant results (p < 0.001). The accuracy was high for all groups (males: 0.90, females: 0.87, both genders: 0.89), and the sensitivity was lower than the specificity, particularly for females (sensitivity: 0.55). The I3m index appears to be a strong predictor across all models, while the mandibular condyle cortication stage plays a more nuanced role, depending on sex and the stage of condyle maturation. A higher I3m index value (greater than the cut-off of 0.095 in males) indicates a higher likelihood of being classified as a non-adult male for this model; however, in females, a cut-off point higher than 0.150 indicated a higher likelihood of being classified as a non-adult female. The new proposed cut-off values need to be tested on a new sample. Conclusions: The I3m index is a reliable age estimation tool, and a modified cut-off value could be determined for each gender in Latvian individuals. The condyle cortication stage is a weak tool for chronological age estimation in dental panoramic radiographs.