bluuebunny/crossref_metadata_2025 huggingface dataset:
Digital Transformation of the Shaolin Cultural Industry: Pathways and Challenges for Achieving Sustainable Development
Authors: Qilin Zhang , Zhang Linlin , Li Han , Estrada Marco Valerio
Published: January 2025

In the context of the rapid advancement of global digitalization, traditional cultural industries are facing substantial transformations and challenges. This paper explores the digital transformation of the Shaolin cultural industry, analyzing its opportunities and challenges in achieving sustainable development. As a significant component of traditional Chinese culture, Shaolin culture embodies profound historical and cultural values; however, the path to transmission and innovation is not smooth in the modernization process. Employing grounded theory methodology combined with NVivo qualitative analysis and SWOT analysis, the study thoroughly examines the key issues faced by the Shaolin cultural industry during its digitalization process and potential solutions. The findings indicate that digitalization not only serves as an essential avenue for the transformation of the Shaolin cultural industry in terms of cultural transmission and innovation but also acts as a powerful driver for local economic and social development, providing theoretical support for the sustainable development of Shaolin culture.
Probing the Gravitational Effects of Rotating Masses in a Vacuum via the Proposed Equations
Authors: Ahmad Muhammad
Published: January 2025

This study investigates the gravitational influence of a rotating object on a nearby smaller object in a vacuum environment. Our results, which are based on the proposed equations, demonstrate the substantial gravitational effects induced by the rotating object. These findings have implications for unconventional gravity-generation methods for orbiting satellites and terrestrial applications. Additionally, this study enhances our understanding of the interplay between angular velocity and gravitational forces.
What Does It Mean to Spurn Educational Constructivism? How Traditionalistic Proclivities Generate Unnecessary Error
Authors: Panarello Christian
Published: January 2025

Despite the influence that Dewey has affected to scholars and social critics, the model of education that he criticised, particularly in his works How We Think and Democracy and Education, has been unchanged: significantly, through two world wars, eras of progressivism attached to social and labor purposes, and the modern technocracy. This relatively brief article dictates the significance of – quite simply – not engaging with the Constructivist education that is more productive, egalitarian, and educational than otherwise.
A Game-Based Scheme for Prime Minister Approval in Korea-Like Presidential Systems
Authors: Cheng Yiping
Published: January 2025

This paper addresses the issue of gridlocks in the approval of prime ministers in semi-presidential countries and presidential countries with prime ministers, focusing on the case of South Korea. A game-based scheme for prime minister approval specifically designed for presidential systems like Korea's is then proposed, along with two supporting provisions. The scheme has an adjustable parameter tentatively set at 3/5 to ensure a balanced system, and it also introduces a new form of cohabitation — hard cohabitation — where the prime minister is not nominated by the president. The supporting provisions aim to cope with two challenges likely to arise from hard cohabitation: potential cabinet instability and difficulties in cooperation between the president and the prime minister. The reform package proposed here is thus believed to be smooth, fair, and effective, and is strongly hoped to be adopted by South Korea or other similar countries in the future.
Quaternary Evaporative Facies Interpretation by Seismic Sedimentology in Biogas-Bearing Sanhu Depression, Qaidam Basin, NW China
Authors: Liu Guoyong , Xu Zhaohui , Li Jiangtao , Song Yong , Zeng Hongliu , Zhu Xiaomin , Li Jian , Wen Zhonglin , Tian Jixian , Lin Chunming , Jiang Lei
Published: January 2025

S-wave seismic data are unaffected by natural gas trapped in strata, making them a valuable tool for seismic sedimentology. In this study, S-wave seismic data were utilized to construct an isochronous framework and analyze evaporative facies in the Quaternary biogenic gas-bearing Taidong area, Sanhu Depression, Qaidam Basin, NW China, with calibration from wireline logs and modern analogs. Techniques of phase rotation, frequency decomposition, R (Red), G (Green), B (Blue) fusion, and stratal slices were integrated to reconstruct seismic geomorphological features within the isochronous framework. Linear and sub-circular morphologies, resembling those observed in modern saline pans such as Lake Chad, were identified. Observations from Upper Pleistocene outcrops of anhydrite and halite at Yanshan, east of the Taidong area, salinity measurements from Well TX1 to the north, and lithological and paleo-environmental records from boreholes SG-5, SG-1, and SG-1b to the northwest support the seismic findings. The RGB-fused slices generated from the S-wave seismic data in Taidong area indicate a progressive increase in the occurrence of evaporative features from the K2 standard zone upwards. The vertical occurrence of evaporative facies in the Taidong area mirrors the contemporary regional and global paleo-environmental changes. The interpretation of Quaternary stratal slices from seismic sedimentology—spanning K2, K1, and K0 standard zones—reveals a transition from a freshwater lake to brackish, saline, and finally, a dry saline pan, overlaid by silt. This analysis provides valuable insights into locating evaporites as cap rocks for biogenic gas accumulation and also into mining the evaporite resources in shallow layers of the Taidong area.
Standardized Assessment Framework for Evaluations of Large Language Models in Medicine (SAFE-LLM)
Authors: Mohammadi Ida , Firouzabadi Shahryar Rajai , Kohandel Gargari Omid , Habibi Gholamreza
Published: January 2025

Large language models (LLMs) are AI-powered systems that have demonstrated significant potential in various fields, including medicine. Despite their promise, the methods for evaluating their performance in medical contexts remain inconsistent. This paper introduces the Standardized Assessment Framework for Evaluations of Large Language Models (SAFE-LLM) to streamline and standardize the evaluation of LLMs in healthcare. SAFE-LLM assesses five domains: accuracy, comprehensiveness, supplementation, consistency, and fluency. Accuracy refers to the correctness of the model's response, comprehensiveness to the detail and reasoning provided, supplementation to additional relevant information, consistency to uniformity in repeated answers, and fluency to the coherence of responses. Each prompt is given three times, with responses evaluated by two independent experts. Discrepancies between evaluations trigger a third assessment to ensure reliability. Grading is performed on a scale specific to each domain, with a maximum possible score of seven points. The SAFE-LLM score can be applied to individual answers or averaged across responses for a holistic assessment. This framework aims to unify evaluation standards, facilitating the comparison and improvement of LLMs in medical applications. Developing standardized evaluation tools like SAFE-LLM is critical for integrating AI into healthcare effectively. This framework is a preliminary step towards more rigorous and comparable assessments of LLMs, enhancing their applicability and trustworthiness in medical settings.
Photodynamic Therapy for Thyroid Cancer
Authors: Inglot Julia , Strzelczyk Joanna Katarzyna , Bartusik-Aebisher Dorota , Aebisher David
Published: January 2025

Background/Objectives: Thyroid cancer is the most common cancer of the en-docrine system worldwide. Despite many available therapeutic options, the mortality rate of some subtypes, including anaplastic thyroid cancer, is still sig-nificant. Photodynamic therapy brings hope, which, through local activation of cell death pathways, as well as its effect on the immune system and vessels feeding the tumor, brings effects incomparable to classical methods. Photosensi-tizers particularly used in the case of thyroid cancer are hypericin, porphyrin, photophrin, radachlorin or 5-aminolevulinic acid. Even better effects are achieved by administering sulforaphane, carboplatin or genistein before therapy
Methods: For this research we review articles in regards to provide a critical summary of the existing literature on thyroid cancer to explain the current state of scientific evidence on this topic.
Conclusions: Photodynamic therapy is undoubtedly a technique of the future, the main advantages of which are low invasiveness, the possibility of combining with other treatment methods, or the possibility of outpatient use.
Geometrical Optics Stability Analysis of Rotating Visco-Diffusive Flows
Authors: Kirillov Oleg
Published: January 2025

Geometrical optics stability analysis has proven effective in addressing stability problems in ideal hydrodynamics and magnetohydrodynamics for 3D flows of both compressible and incompressible fluids. The method models perturbations as high frequency wavelets, evolving along fluid trajectories. Detecting local instabilities reduces to solving ODEs for the wave vector and amplitude of the wavelet envelope along streamlines, with coefficients derived from the background flow. While viscosity and diffusivity were historically considered stabilizing, recent studies incorporating both in the geometrical optics framework revealed their destabilizing potential in visco-diffusive and multi-diffusive flows. This extension has enabled analysis across a broad range of Prandtl, Schmidt, and magnetic Prandtl numbers, uncovering new instability citeria. This paper reviews these advancements and explores future applications and directions for the method.
Revolutionizing Infrastructure Resilience: AI-Driven Predictive Maintenance and Structural Health Monitoring
Authors: Shaik Mohammad Abrartaha , Sneha P.
Published: January 2025

Rapid urbanization calls for robust infrastructure systems that prevent failures and ensure long-term reliability. This study delves into the transformative potential of AI-driven predictive maintenance and structural health monitoring (SHM), utilizing advanced sensor networks for real-time anomaly detection. These intelligent frameworks empower decision-making and reduce lifecycle costs by integrating machine learning algorithms, data privacy protocols, and robust cybersecurity measures. Through case studies spanning transportation, energy, and smart cities, the paper demonstrates how these technologies enhance operational efficiency and sustainability. By enabling early fault detection and timely interventions, they contribute to building resilient infrastructure systems capable of withstanding increasing urban demands. The research also addresses key challenges such as scalability, interoperability, and adherence to international standards, which are critical for widespread adoption. It further explores future directions, emphasizing the need for interdisciplinary collaboration to optimize these solutions. Ultimately, the study underscores the vital role of innovative technologies in fostering sustainable, adaptive infrastructure management in an ever-evolving urban landscape.
Functions of MYB Transcription Factors Response and Tolerance to Abiotic Stresses in Plants
Authors: Ma Ziming , Zhong Yu
Published: January 2025

For the abiotic stress in adversity stress is an unfavourable environmental factor that severely affects the plant growth and development, plants have adapted to these stresses through long term evolution to improve their ability to survive. As one of the largest families of transcription factors (TFs) in plants, the MYB transcription factors can play important roles in plants. For example, they can participate in the regulation of plant growth and development, hormone signal transduction, and response to adversity stress. The N-terminus of the MYB family of transcription factors contains a typical MYB structural domain, and they can be classified into different subgroups according to the number of R repeats in the MYB structural domain. Upon activation by external abiotic stress signals, MYB transcription factors can bind to cis-acting elements in the promoter regions of downstream target genes either alone or in interaction with other proteins, and participate in the regulation of downstream expression of stress-response-related genes, thereby regulating plant tolerance to abiotic stress. MYB transcription factors also respond to abiotic stresses by participating in plant hormone signalling pathways. For example, abscisic acid, jasmonic acid, brassinolide, salicylic acid and so on. In this review, the structure and classification of the plant MYB transcription factors family and its mode of action are reviewed, role of MYB transcription factors hormone metabolism in plants and the mechanisms of MYB transcription factors involved in responding to abiotic stresses such as drought, salt, low and high temperatures, nutrients and heavy metals in recent years, and an outlook is given on the key research directions in the future, which will provide excellent genetic resources and theoretical support for the future genetic improvement of crop stress tolerance and plant breeding.
Evaluation of Food-Grade Additives on the Viability of Ten Shigella flexneri Phages in Food to Improve Safety in Agricultural Products
Authors: Tomat David , Casabonne Cecilia , Aquili Virginia , Quiberoni Andrea
Published: January 2025

Bacteriophages can be used as biocontrol agents in Agriculture to improve food safety, provided they can remain viable in food environments. The viability of ten Shigella phages (AShi, Shi3, Shi22, Shi30, Shi33, Shi34, Shi40, Shi88, Shi93 and Shi113) was evaluated against different additives and biocides used daily in food applications. In addition, the influence of additives on phage viability in a food matrix was investigated. Treatments with lactic and citric acid were the most effective to inactivate phages. In addition, the acetic acid was the most phage-friendly treatment evaluated. Preservatives such as acetate, lactate, benzoate, sorbate and propionate proved to be highly compatible with all the phages tested. Regarding the influence of the food matrix on phage viability, an equal or higher viability was found for most phages tested when was compared with the corresponding organic acid. Finally, when phages were exposed to sodium hypochlorite, ethanol, QAC and H2O2, most of them were sensitive to long incubations and high concentrations. However, when biocide concentrations employed are low, 10E3-10E4 PFU mL-1 phage particles remains viable. Thus, the phages evaluated could be used in combination with additives and biocides as a biocontrol tool against the foodborne pathogen S. flexneri in agricultural products.
Intelligent Analysis of Flow Field in Cleaning Chamber for Combine Harvester Based on YOLOv8 and Reasoning Mechanism
Authors: Li Qinglin , Wan Ruihai , Wu Zhaoyue , Yan Yuting , Zhang Xihan
Published: January 2025

As the main working part of the combined harvester, the cleaning device affects the cleaning performance of the machine. The simulation of flow field in cleaning chamber has become an important mean of the design. Now post-processing analysis of the flow field simulation still relies on the researchers’ experience, so it is difficult to obtain information from the post-processing automatically. The experience of researchers is difficult to express and spread. This paper studied an intelligent method to analyse the simulation result data, which was based on the object detection algorithm and the reasoning mechanism. YOLOv8, one of the deep learning object detection algorithm, was selected to identify key point data from flow field in the cleaning chamber. First the training data set was constructed by scatter plot drawing, data enhancement, random screening and other technologies. Then the flow field in the cleaning chamber was divided into 6 key areas by identifying the key points of the flow field. And the analysis of the reasonable wind velocity in the areas and the cleaning results of grain were completed by reasoning mechanism based on rules and examples. Finally a system based on the above method was established by Python software. With the help of the method and the system in this paper, the flow field characteristic in the cleaning chamber and the effects of wind upon cleaning effect could be obtained automatically if the physical property of the crop, geometric parameters of the cleaning chamber, working parameters of the machine were given.
Dependence of GaN Exciton Energy on Temperature
Authors: Liu Xiancheng , Chen Peng , Xie Zili , Xiu Xiangqian , Chen Dunjun , Zhao Hong , Shi Yi , Zhang Rong , Zheng Youdou
Published: January 2025

In this paper, the relationship between GaN exciton energy and temperature was studied with high-quality and strain-free GaN epilayers. The traditional Varshni’s model and Bose-Einstein model are empirical models that lack consideration of physical mechanisms, which makes their models have poor applicability in wide band gap materials. Consider the interaction of electrons and phonons, we use singular functions, linear functions and power functions to express the phonon density of GaN, 2BE, single-linear, power-law-delta and power-law-v models are proposed. All of them are fitting well with the experimental data. Among them, the singular-linear model summarizes the contributions of acoustic phonon and optical phonon. The error of sigular-liner model is smaller than that of 1BE and varshni models in almost the whole temperature range.Therefore, the singular-linear model is better choices.
Cloud Security: Counteracting Evolving Threats in a Digital Age
Authors: Jern Chan Jian , Yan Chan Wei , Khuan Ho Hoong , Enze Huang , Kwong Wee Jason Tang , Han Lee Zu , Xuan Leong Yu , Lai Soon Lim , Shih Ong Wei , On Phoon Chun , Jun Wong , Sindiramutty Siva Raja
Published: January 2025

This report is to investigate the development of cloud computing and its security paradigms, highlighting key components such as data, applications, operating systems, virtualization, servers, storage, and networking, which form the foundation of cloud security. It explores the threats inherent in cloud environments, including account hijacking, malware, data leakage, DoS/DDoS attacks, and social engineering, detailing their impact and countermeasures. Notable security technologies like encryption, Identity and Access Management (IAM), cloud firewalls, and disaster recovery planning are discussed as critical safeguards. Limitations of cloud computing security, including bandwidth issues, redundancy challenges, and control constraints, are analysed alongside emerging trends like AI-driven threat detection, the adoption of zero-trust security models, and unified multi-cloud security approaches. The report proposes advanced countermeasures, including enhanced zero-trust models with behavioural biometrics and adaptive honeypots, as well as AI-augmented threat detection systems utilizing neural networks and reinforcement learning. The findings underscore the importance of continuous innovation and adaptive strategies in cloud computing security to mitigate evolving threats and ensure secure, resilient cloud infrastructures. Future advancements in regulatory compliance, AI integration, and predictive analytics will further shape the trajectory of cloud security in the face of growing technological complexities.
Dynamic Anonymous Access Control Based on Dual-Mode Single-Packet Authentication
Authors: Wang Yunfan , He Chuan , Xi Zesheng , Zhang Bo , Zhang Tao
Published: January 2025

Anonymous access control is crucial in network security to protect user privacy and support anonymous communication while balancing the need for confidentiality and traceability in specific scenarios. This control mechanism is important for maintaining data security, identity protection and security auditing. Traditional anonymous access control methods currently have two challenges: High performance overhead and Vulnerability to cyber-attacks. To cope with the above challenges, this paper proposes a dynamic anonymous access control method based on dual-mode single-packet authentication. The method utilizes dual-mode single-packet authentication to complete user authentication in the anonymous access phase, dynamically evaluates the user’s trust level through the trust evaluation module, and finally decides whether to allow the user to access the network resources. Experimentally verified, the dual-mode single-packet authentication scheme is more advantageous than other schemes regarding communication overhead and computation overhead, and the access efficiency is further improved.
Modeling the Knowledge Production Function Based on Bibliometric Information
Authors: Dolgonosov Boris M.
Published: January 2025

The amount of knowledge is an integral indicator of the development of society. The article examines the knowledge production on a global scale. The measure of knowledge is the number of accumulated publications in the form of patents, articles and books. The rate of knowledge production depends on population size and average human productivity. Productivity generally depends on accumulated knowledge and plays a decisive role in the dynamics of knowledge and related demographic dynamics. Constructing this dependence in the general case, applying it to each type of publication, checking its adequacy and finding parameters is the goal of this study. First, existing econometric models are analyzed, and then a generalized model is developed, of which some literature models are special cases. An important question is under what conditions human productivity can be considered approximately constant, and under what conditions it grows significantly as knowledge accumulates. Finding these conditions provides a criterion that distinguishes a less developed pre-information society from an advanced information society, and allows us to clear up when this transition occurred. The results obtained allow us to forecast the further development of various forms of knowledge in the world.
Quantum Geometric Theory of Temporal Fields: From Philosophical Foundations to Mathematical Framework
Authors: Karmiris Panagiotis
Published: January 2025

This work presents a mathematical framework formalizing philosophical insights about the cyclic nature of time and universal evolution. Drawing from ancient philosophical traditions and modern physical theories, we develop a quantum geometric approach that treats time as an active field shaping cosmic evolution. By employing a minisuperspace approximation incorporating temporal degrees of freedom, we demonstrate how this framework naturally explains dark energy through graviton propagation in temporal dimensions. Our approach maintains consistency with current observational constraints from DES Y3 and gravitational wave observations [6, 14], while offering new insights into the quantum nature of spacetime. The theory makes specific predictions for next-generation experiments including Euclid, the Einstein Telescope, and pulsar timing arrays, providing multiple avenues for empirical verification of these fundamental ideas about the nature of time and reality.
Trends in Labor Analgesia: Analysis of Patients’ Web Searches Across Europe Using a Machine Learning Model
Authors: Leoni Matteo Luigi Giuseppe , Rekatsina Martina , Varrassi Giustino , Cascella Marco , Pasqualucci Alberto , Sansone Pasquale , Todde Cristina , Caruso Annalisa , Mercieri Marco
Published: January 2025

BackgroundEpidural analgesia is widely regarded as the gold standard for pain relief during labor. Despite its effectiveness, significant disparities in adoption persist due to cultural, medical, and informational factors. This study aimed to analyze online search behaviors related to epidural analgesia in the six most populous European countries, evaluate temporal trends, and assess the predictive power of machine learning models for search volumes.MethodsWeekly search data from 2020 to 2024 were obtained from Google Trends for France, Germany, Italy, Spain, Turkey, and the United Kingdom (UK). Data were analyzed using linear regression, time-series decomposition, and Mann-Kendall tests to identify monotonic trends. An Auto Regressive Integrated Moving Average (ARIMA) model was developed to forecast search volumes for 2025. Machine learning models such as Random Forest (RF) and Gradient Boosting Machine (GBM), were employed to evaluate the influence of variables such as country and temporal factors on search patterns. Model performance was assessed using specific metric (R², RMSE, MAE, and MBE) and statistical comparisons were made between the models.ResultsFrance and Turkey exhibited significant downward trends in search interest, while Germany showed a slight upward trend, and Italy, Spain, and the UK demonstrated stable patterns. ARIMA forecast indicated stable search volumes for most countries, with the UK reaching the highest activity. RF outperformed GBM, achieving R² values of 0.92 (testing) and 0.93 (training), with "Country" identified as the most influential predictor. Associated queries highlighted common public concerns, including epidural timing, risks, and side effects.ConclusionsThese findings reveal the value of understanding public interest in epidural analgesia to address concerns effectively. Healthcare providers should guide patients toward reliable online information. Future initiatives should include educational tools, national health programs, and interdisciplinary collaboration to enhance informed decision-making and optimize maternal care outcomes.
Does the Prevalence of Temporomandibular Disorders Increase as a Result of Tooth Loss or Prosthetic Rehabilitation?
Authors: Takács Emőke , Süveges Ibolya , Schmidt Péter , Ambrus Szilvia , Jász Bálint , Hermann Péter , Körmendi Szandra , Jász Máté
Published: January 2025

Objectives: The relationship between occlusion and temporomandibular joint disorder has been the subject of extensive research, yet the findings have been contradictory. In this study we re-examined data from a national screening examination from a novel perspective. The aim was to examine the correlation between partial edentulism, preexisting prosthodontic appliances, and TMD symptoms. Methods: A retrospective analysis was conducted on data from a representative national oral epidemiological pathfinder survey. In our analysis, we investigated the registered TMD symptoms in three groups: patients with extensive tooth loss; patients with replaced teeth and patients with stable occlusion were considered as control group. Results: The present study utilised data from a total of 3,342 patients. A significantly higher prevalence of TMD symptoms was observed in the group with extensive tooth loss (28.2%) compared to the control group (21.56%, p=0.0141). Similarly, a significantly higher prevalence of TMD symptoms was observed in the group with restored dentition (25.3%, p=0.0480) compared to the control group. No significant difference was observed in the frequency of symptoms between the group with tooth loss and the group with replaced teeth. Conclusions: Significant correlation was found between the three examined groups. A comparison of the control group with both the extensive tooth loss and the restored dentition groups revealed a significantly higher prevalence of TMD symptoms in the latter two groups. This indicates that edentulism may be a contributory factor in the development of TMD symptoms, although replacing the missing teeth alone does not appear to eliminate this effect.
A Novel Preclinical Model to Assess Intestinal Barrier Integrity Using Canine Enteroids and Colonoids
Authors: Corbett Megan P , Gabriel Vojtech , Livania Vanessa , Díaz-Regañón David , Ralston Abigail , Zdyrski Christopher , Liu Dongjie , Minkler Sarah , Mao Sichao , Wickham Hannah , Lincoln Addison , Paukner Karel , Atherly Todd , Merodio Maria M. , Sahoo Dipak Kumar , Meyerholz David K , Allenspach Karin , Mochel Jonathan P
Published: January 2025

While two-dimensional (2D) cell cultures, such as Caco-2 and Madin-Darby canine kidney (MDCK) cells are widely used in a variety of biological models, these two-dimensional in vitro systems present inherent limitations in replicating the complexities of in vivo biology. Recent progress in three-dimensional organoid technology has the potential to address these limitations. In this study, the characteristics of conventional 2D cell culture systems were compared to those of canine intestinal organoids (enteroids, ENT and colonoids, COL). Light microscopy and transmission electron microscopy were employed to evaluate the microanatomy of ENT, COL, Caco-2, and MDCK cell monolayers, while transepithelial electrical resistance (TEER) values were measured to assess monolayer integrity. The TEER values of canine ENT monolayers more closely approximated reported TEER values for human small intestines compared to Caco-2 and MDCK monolayers. Additionally, canine ENT demonstrated greater monolayer stability than Caco-2 and MDCK cells. Notably, while all systems displayed tight junctions, canine ENT and COL exclusively produced mucus. These findings highlight the potential of the canine organoid system as a more biologically relevant model for in vitro studies, addressing the limitations of conventional 2D cell culture systems.
Retention of Copper and Zinc from Traffic Area Runoff by Topsoil of Vegetated Infiltration Swales Amended with Recycled Demolition Waste
Authors: Knoll Sebastian , Moritz Janna , Stinshoff Philipp , Helmreich Brigitte
Published: January 2025

Infiltration swales are a prospective key component of water-sensitive urban planning. The utilization of appropriate soil amendments is intended to facilitate their retention of pollutants from stormwater runoff of traffic areas. Little is known about the possibility of utilizing processed construction and demolition waste (CDW) as an amendment to improve pollutant retention. We conducted batch and field tests to investigate i) the leaching of metals from soil substrates containing CDW and ii) their retention potential for copper (Cu) and zinc (Zn) when charged with real traffic area runoff. To gain a comprehensive understanding of the chemical interactions, we iii) employed sequential extractions using an optimized protocol from treated and untreated soil substrates. In batch tests, the potential of vanadium leaching from technosols amended with brick-dominated CDW was apparent. When charged with traffic area runoff, the retentions of Cu and Zn in the technosols were comparable to those of the control soil without CDW. However, the simulation of high rainfall intensities reduced Cu and Zn retention in the technosols and the control. The results from the subsequent sequential extraction of Cu and Zn imply shifts in the chemical binding in the technosols compared to the control.
Diversity and Genetic Structure of Maize Landraces Cultivated in the Zoque Region from Chiapas Mexico
Authors: Hernández Eduardo de la Cruz , Andueza-Noh Rubén H. , Latournerie-Moreno Luis , Sánchez Esaú Ruiz , Gordillo Ruiz Mercedes C. , Pérez Gilberto Rodríguez
Published: January 2025

In Mesoamerica, maize is one of the most important food crops. Its center of origin, domestication and diversity is located in México. In southern Mexico, the state of Chiapas is considered one of the areas with the highest maize landrace diversity. However, information on their genetic diversity, conservation status, and potential use of maize landraces throughout the entire Chiapas region is lacking. One region from Chiapas, Mexico, where local farmers use and preserve a wide diversity of maize landraces is the Zoque region. Until now, however, the genetic diversity of these maize landraces has not been studied. The aim of this study was to analyze the diversity and genetic structure of maize landraces cultivated in the Zoque Region from Chiapas, Mexico, by using 17 maize landraces and 48 ISSR loci. The analysis revealed two genetic groups based on geographical origins. The genetic diversity level was moderate (Hbay=0.29 and I=0.36) and distributed mainly within landraces (70%). The maize landraces 22 and 28 have greater diversity values (Hbay=0.36, I=0.45 and Hbay=0.35, I=0.45, respectively). The results indicated that the maize landraces cultivated in the Zoque region from Chiapas Mexico constitute a valuable genetic resource that can be used for genetic improvement and in conservation programs.
Enhancing Anti-Money Laundering Detection with Self-Attention Graph Neural Networks
Authors: Xu Qian , Wang Sizhe , Tao Yixin
Published: January 2025

Money laundering remains a significant global issue, undermining financial stability and security. This study introduces a Self-Attention-GNN Model enhanced with a self-attention mechanism to improve the detection of money laundering activities in a large, imbalanced dataset of financial transactions. The dataset, covering 97 days and including approximately 180 million transactions, contains 223,000 labeled laundering cases. By representing financial transactions as a graph—where entities such as accounts and banks are nodes, and transactions are edges—the model captures intricate relational and structural dependencies within the transaction network. The addition of the self-attention mechanism enables the model to dynamically adjust feature aggregation, focusing on the most relevant nodes and edges, which significantly improves the model’s ability to identify laundering activities. Despite the challenges posed by class imbalance, the model achieves robust performance in detecting illicit transactions while reducing false positives. The paper also discusses potential strategies for further optimizing precision and recall, such as advanced graph architectures, oversampling methods, and enhanced node embedding techniques. Overall, this research highlights the power of graph-based deep learning approaches for anti-money laundering (AML) applications, demonstrating how structural and relational dependencies within financial networks can be leveraged to enhance detection accuracy.
Novel Data Mining Methodologies for Environmental, Social and Governance Analytics: A Comprehensive Framework for Sustainable Investment
Authors: Singh Saran S.
Published: January 2025

This paper presents a systematic analysis of novel data mining applications in Environmental, Social, and Governance (ESG) assessment, addressing the growing complexity of sustainable investment decisions. Through empirical examination of machine learning methodologies, including deep learning architectures and natural language processing, we demonstrate enhanced capabilities in processing unstructured ESG data and identifying latent patterns in corporate sustainability metrics. Our research establishes a comprehensive framework for integrating diverse analytical techniques, achieving 85% accuracy in governance anomaly detection and significant improvements in environmental risk assessment through hierarchical clustering. The study reveals substantial correlations between ESG performance and financial outcomes, whilst identifying critical challenges in data standardisation and algorithmic bias mitigation. The findings contribute to both theoretical understanding and practical implementation of data-driven ESG analysis, offering valuable insights for investment professionals and corporate stakeholders. This research advances the field of sustainable finance analytics through innovative methodological approaches to ESG assessment.
Building Trust Through Reliability: A Qualitative Approach to Understanding Customer Loyalty
Authors: Anderson Matthew
Published: January 2025

This study explores the critical relationship between reliability, trust, and customer loyalty, focusing on how consistent service delivery and emotional engagement influence long-term customer relationships. Through a qualitative approach, it examines how customers perceive reliability and its impact on their trust in brands, emphasizing the foundational role of reliability in fostering loyalty. The findings reveal that consistent delivery of promises, such as timely service and quality products, is a non-negotiable factor for trust. Emotional engagement, personalization, and tailored customer experiences were also found to deepen the bond between customers and brands, strengthening loyalty over time. Furthermore, the study highlights the importance of transparency and proactive communication in navigating service disruptions and failures, demonstrating that trust can be maintained even in challenging situations when brands adopt a transparent and customer-focused approach. Service recovery processes were identified as pivotal moments that determine whether trust is retained or eroded. Customers highly value swift and meaningful resolutions to service failures, which signal a brand’s commitment to their satisfaction. The research also underscores the role of brand reputation and social proof in shaping initial perceptions of reliability, with long-standing brands benefiting from their established credibility. By integrating reliability, emotional engagement, and transparent communication, brands can cultivate enduring trust and loyalty. These findings provide valuable insights for businesses aiming to build resilient customer relationships, emphasizing that loyalty is the result of a dynamic interplay of factors that consistently meet customers' practical and emotional expectations.
Liver Sinusoidal Endothelial Cells in the Regulation of Immune Responses and Fibrosis in MASH Liver Disease
Authors: Puri Munish
Published: January 2025

Liver Sinusoidal Endothelial Cells (LSECs) play a crucial role in maintaining liver homeostasis, regulating immune responses, and regulating fibrosis in liver diseases. This review explores the unique functions of LSECs in liver pathology, particularly their roles in immune tolerance, antigen presentation, and the modulation of hepatic stellate cells (HSCs) during fibrosis. LSECs act as key regulators of immune balance in the liver by preventing excessive immune activation while also filtering antigens and interacting with immune cells, including Kupffer cells and T cells.
Metabolic dysfunction-associated steatohepatitis (MASH) is a severe form of liver disease characterized by steatosis (fatty liver), inflammation, and fibrosis in the liver. It progresses from metabolic dysfunction-associated steatotic liver disease (MASLD), commonly known as non-alcoholic fatty liver disease (NAFLD). MASH is significant because it can lead to advanced liver dysfunction, such as cirrhosis and liver cancer. The prevalence of MASH is increasing globally, particularly in the United States, and is closely linked to rising rates of obesity and type 2 diabetes. Early diagnosis and intervention are vital to prevent severe outcomes, highlighting the importance of studying LSECs in MASH liver disease. However, during chronic liver diseases such as NAFLD, alcoholic liver disease (ALD), and viral hepatitis, LSECs undergo dysfunction, leading to their capillarization, loss of fenestrations, and promotion of pro-fibrotic signaling pathways such as TGF-β.
The review also discusses the dynamic interaction between LSECs, HSCs, and other hepatic cells during the progression of liver diseases, emphasizing how changes in LSEC phenotype contribute to liver scarring and fibrosis. Furthermore, it highlights the potential of LSECs as therapeutic targets for modulating immune responses and preventing fibrosis in liver diseases. By restoring LSEC function and targeting pathways associated with their dysfunction, novel therapies could be developed to halt or reverse liver disease progression. The findings of this review reinforce the importance of LSECs in liver pathology and suggest that they hold significant promises as targets for future treatment strategies aimed at addressing chronic liver diseases.
Adversarial Text Generation with Dynamic Contextual Perturbation
Authors: Waghela Hetvi , Sen Jaydip , Rakshit Sneha , Dasgupta Subhasis
Published: January 2025

Adversarial attacks on Natural Language Processing (NLP) models expose vulnerabilities by introducing subtle perturbations to input text, often leading to misclassification while maintaining human readability. Existing methods typically focus on word-level or local text segment alterations, overlooking the broader context, which results in detectable or semantically inconsistent perturbations. We propose a novel adversarial text attack scheme named Dynamic Contextual Perturbation (DCP). DCP dynamically generates context-aware perturbations across sentences, paragraphs, and documents, ensuring semantic fidelity and fluency. Leveraging the capabilities of pre-trained language models, DCP iteratively refines perturbations through an adversarial objective function that balances the dual objectives of inducing model misclassification and preserving the naturalness of the text. This comprehensive approach allows DCP to produce more sophisticated and effective adversarial examples that better mimic natural language patterns. Our experimental results, conducted on various NLP models and datasets, demonstrate the efficacy of DCP in challenging the robustness of state-of-the-art NLP systems. By integrating dynamic contextual analysis, DCP significantly enhances the subtlety and impact of adversarial attacks. This study highlights the critical role of context in adversarial attacks and lays the groundwork for creating more robust NLP systems capable of withstanding sophisticated adversarial strategies.
Source Characteristics Influence AI-Enabled Orthopaedic Text Simplification
Authors: Andalib Saman , Solomon Sean S. , Picton Bryce G. , Spina Aidin C. , Scolaro John A. , Nelson Ariana M.
Published: January 2025

Background:

This study assesses the effectiveness of large language models (LLMs) in simplifying complex language within orthopaedic patient education materials (PEMs) and identifies predictive factors for successful text transformation.

Methods:

We transformed 48 orthopaedic PEMs using GPT-4, GPT-3.5, Claude 2, and Llama 2. The readability, quantified by the Flesch-Kincaid Reading Ease (FKRE) and Flesch-Kincaid Grade Level (FKGL) scores, was measured before and after transformation. Analysis included text characteristics such as syllable count, word length, and sentence length. Statistical and machine learning methods evaluated the correlations and predictive capacity of these features for transformation success.

Results:

All LLMs improved FKRE and FKGL scores (p < 0.01). GPT-4 showed superior performance, transforming PEMs to a seventh-grade reading level (mean FKGL, 6.72 ± 0.99), with higher FKRE and lower FKGL than other models. GPT-3.5, Claude 2, and Llama 2 significantly shortened sentences and overall text length (p < 0.01). Importantly, correlation analysis revealed that transformation success varied substantially with the model used, depending on original text factors such as word length and sentence complexity.

Conclusions:

LLMs successfully simplify orthopaedic PEMs, with GPT-4 leading in readability improvement. This study highlights the importance of initial text characteristics in determining the effectiveness of LLM transformations, offering insights for optimizing orthopaedic health literacy initiatives using artificial intelligence (AI).

Clinical Relevance:

This study provides critical insights into the ability of LLMs to simplify complex orthopaedic PEMs, enhancing their readability without compromising informational integrity. By identifying predictive factors for successful text transformation, this research supports the application of AI in improving health literacy, potentially leading to better patient comprehension and outcomes in orthopaedic care.
Challenges of Estimating Future Liabilities of Asset Retirement Obligations in Upstream Oil and Gas: A Literature Review
Authors: Aldhaferi Khaled
Published: January 2025

Estimating asset retirement obligations (AROs) in the upstream oil and gas sector is a complex and critical issue, driven by the need to account for long-term liabilities associated with decommissioning infrastructures, such as offshore platforms and wells. This literature review examines the multifaceted challenges involved in predicting future ARO liabilities, focusing on the technical, financial, regulatory, and environmental uncertainties that complicate accurate estimation. The review explores key accounting frameworks, including International Financial Reporting Standards (IFRS) and United States Generally Accepted Accounting Principles (US GAAP), which guide the recognition and measurement of AROs, and highlights the significant impact of regulatory changes, fluctuating commodity prices and evolving environmental standards on cost predictions. Environmental factors, such as climate change and ecosystem sensitivity, further exacerbate the difficulty of estimating decommissioning costs, while technological advancements offer both opportunities and uncertainties. In addition, the review assesses financial considerations, including discount rate assumptions and currency fluctuations, which introduce variability in financial reporting and risk assessment.

This review identifies key strategies to mitigate risks associated with ARO underestimation, such as scenario analysis, collaborative decommissioning efforts, and strengthening internal controls. It also highlights the importance of incorporating environmental, social, and governance (ESG) criteria and investing in technological research and development. Finally, the review emphasizes the need for future research on advanced decommissioning technologies, cross-industry comparisons, and enhanced financial instruments to address the growing complexity of ARO estimation in the upstream oil and gas industry.
Effects of Levetiracetam an Antiepileptic Drug on the Multi-biological Parameters in African catfish Clarias gariepinus (Burchell, 1822)
Authors: Nzei Joy Ihuoma , Agwu Ekenma Julia , Uba Perpetua Ebube , Chukwuemeka Inwelegbu Valentine
Published: January 2025

This study investigated the impact of levetiracetam on the multi-biological parameters in juvenile Clarias gariepinus. Fish were exposed to 0.2, 0.6, and 0.8mg/L of levetiracetam and filtered dechlorinated water (control) for 10 days, followed by a recovery period of 5 days. Blood samples were collected periodically for haematological and serum biochemical profiling, and the fish were sacrificed for the liver and brain tissues' analysis. Skin colouration, mucus secretion, and air-gulping increased with concentration and duration of exposure, while swimming rate and opercular movement decreased. A significant (p < 0.05) dose- and duration-dependent decrease was recorded in the red blood cells, packed cell volume, haemoglobin, and white blood cell counts, while the opposite was recorded in the mean corpuscular haemoglobin, volume, and haemoglobin concentrations. Compared to the control, there were no significant differences in the values of the differential leukocyte counts, except for lymphocytes, which were significantly (p < 0.05) lower in the highest concentration at the end of the exposure period. Aspartate and alanine aminotransferase, and alkaline phosphatase activities increased significantly (p < 0.05), while the values for protein and glucose were reduced compared to the control. The oxidative stress biomarkers, liver catalase, superoxide dismutase, and glutathione peroxidase activities decreased significantly (p < 0.05), while malondialdehyde and acetylcholinesterase activities increased significantly (p < 0.05) compared to the control. The activities were duration- and concentration-dependent. No significant difference in the correction factor, while the hepatosomatic index decreased significantly (p < 0.05) compared to the control. The parameters returned to normal after post 5-day withdrawal of levetiracetam administration. The study indicated that levetiracetam is toxic to fish.