Advanced Capabilities Research Articles

Advancing corporate sustainability via big data analytics, blockchain innovation, and organizational dynamics—A cross‐validated predictive approach

AbstractIn today's business landscape, the paramount focus of firms is to achieve advanced corporate sustainability. This objective has led to the development of big data analytics, reliable big and cloud data analytics capabilities, and blockchain technology as powerful tools worldwide that have helped firms increase sustainable performance. Capitalizing on these technologies, this study provides new insight into the role data‐driven competitive sustainability, data‐driven culture, and organizational management information in steering firms' performance. Using a quantitative research approach, data was collected through a structured survey administered to managers and IT professionals across various industries. The relationships between the variables were analyzed using structural equation modeling (SEM), confirming that all proposed hypotheses were supported. The results demonstrate the significant positive impact of big data analytics capabilities, reliable big and cloud data analytics capabilities, and blockchain technology on corporate sustainable performance and data‐driven competitive sustainability. Furthermore, data‐driven competitive sustainability was found to mediate the relationships between BDA, RBCDA, blockchain technology, and corporate sustainable performance. Additionally, OMIS‐fit and data‐driven culture were identified as critical moderators that enhance the effects of these technological capabilities on competitive sustainability and corporate performance. These findings provide valuable insights for organizations seeking to leverage advanced technological capabilities to achieve a sustainable competitive advantage in emerging economies.

Dental evidence for extended growth in early Homo from Dmanisi.

Human life history is characterized by an extended period of immaturity during which there is a disjunction between cerebral and somatic growth rates1. This mode of ontogeny is thought to be essential for the acquisition of advanced cognitive capabilities in a socially complex environment while the brain is still growing2. Key information about when and how this pattern evolved can be gleaned from the teeth of fossil hominins because dental development informs about the pace of life history3-5. Here we show that the first evolutionary steps towards an extended growth phase occurred in the genus Homo at least 1.77 million years ago, before any substantial increase in brain size. We used synchrotron phase-contrast tomography6 to track the microstructural development of the dentition of a subadult early Homo individual from Dmanisi, Georgia. The individual died at the age of 11.4 ± 0.6 years, shortly before reaching dental maturity. Tooth growth rates were high, similar to rates in living great apes. However, the Dmanisi individual showed a human-like delayed formation of the posterior relative to the anterior dentition, and a late growth spurt of the dentition as a whole. The unique combination of great-ape-like and human-like features of dental ontogeny suggests that early Homo had evolved an extended growth phase before a general slow-down in life history, possibly related to biocultural reproduction7 rather than brain growth.

Feasibility Assessment of Hybrid Renewable Energy Based EV Charging Station in Libya

This study presents an assessment of the feasibility of implementing a hybrid renewable energy-based electric vehicle (EV) charging station at a residential building in Tripoli, Libya. Utilizing the advanced capabilities of HOMER Grid software, the research evaluates multiple scenarios involving combinations of solar and wind energy sources integrated with energy storage and the utility grid. This analysis provides a novel approach to enhancing urban energy systems with renewable technologies in a region traditionally reliant on fossil fuels. Key contributions of this study include the demonstration of an innovative integration strategy that combines solar and wind power with battery storage to ensure a reliable and efficient energy supply for EV charging. Furthermore, the study addresses the practical implications for local energy policy, suggesting that such hybrid systems can significantly enhance energy security and support sustainable urban development. The authors studied five scenarios using HOMER. The results reveals that the annual total costs and payback periods are as follows: for Scenario 1 (wind/utility grid), the expenditure totals US$1,554,416 and payback period of 4.8/5.8 years; for Scenario 2 (solar/wind/Utility grid), the amount is US$1,554,506 and payback period of 4.8/5.8 years; and for Scenario 3(solar/wind/storage/utility grid), it escalates slightly to US$1,554,731, all predicated on the utility grid tariffs and payback period of 4.8/5.8 years. Furthermore, in Scenario 4 (solar/utility grid), the annual total cost is significantly reduced to US$30,589 and a payback period of 8.1/14.3 years, while Scenario 5 (solar/storage/utility grid) incurs an even lower expenditure of US$28,572, again based on the utility grid tariffs and a payback period of 14.0 years. The findings contribute valuable insights into the scalability and adaptability of renewable energy solutions, providing a robust framework for policymakers and planners considering similar implementations in other regions. Overall, the research underscores the potential of integrated renewable energy systems to transform urban energy infrastructures, promoting a sustainable and resilient energy future. The HOMER Grid analysis shows that configurations with energy storage are more cost-effective in the long run, even though they require higher initial costs. It also offers important insights into the economic viability and optimization of hybrid renewable energy systems for an EV charging station in Tripoli, Libya. These results highlight the significance of making calculated investments in renewable energy infrastructure and supporting policies for the development of sustainable energy.

Investigating the mediating effect of logistics capabilities on the relationship between logistics information sharing and logistics performance

PurposeThe purpose of this study is to investigate the impact of logistics capabilities on the relationship between information sharing (INS) and logistics performance of Sudanese industrial companies.Design/methodology/approachThis descriptive study investigates the relationship between INS and logistics performance in Sudanese industrial companies. A five-point scale questionnaire surveyed a non-probability sample of 262 logistics, supply chain and operations managers. Structural equation modeling has been used to test the relationship between the variables.FindingsThe results revealed that there is no direct positive relationship between logistics INS and logistics performance, while there is an indirect relationship through logistics capabilities. The findings confirmed that there is a positive relationship between logistics INS and logistics capabilities.Research limitations/implicationsThere are several limitations in this study. This study is limited to certain businesses and has a small sample size, which may impact the capacity to apply the findings to a broader context. The sample was based on the current manufacturing companies in Sudan, which are distinguished by certain characteristics in terms of number, business stability and other factors. Because of these factors, the findings may not accurately reflect the actual situation. Industrywise, this study focused on manufacturers, whereas logistics is based on a chain of partners and involved parties. Including them in any future investigation could lead to meaningful findings and discoveries. Furthermore, the data collection’s cross-sectional form may not comprehensively reflect the temporal dynamics of logistical activities. Future research may address these constraints by investigating the efficacy of diverse logistical capabilities in other businesses and circumstances. The construct of the variables is a single-dimension construct, which does not reflect all the practices associated with INS, logistics capabilities and logistics performance. Furthermore, there is an opportunity to explore further the impact of INS on important logistical performance metrics, such as order lead time, on-time delivery and inventory management. One potential area for future investigation is the study of logistics information-sharing technologies, specifically utilizing data analytics and machine learning. In such a context, deep insight and understanding of logistics capabilities and performance require more qualitative analysis; therefore, future research could fill this gap and provide deeper insight.Practical implicationsFor practitioners in the small and medium-sized enterprise sector in Sudan, these findings suggest enhancing their operations, particularly by investing in information-sharing technologies that improve stakeholder coordination. Actively engage supply chain partners in the logistics information system to ensure timely and accurate information flows to all. However, it’s important to be aware of potential challenges, such as the high costs and complex design of these systems and potential resistance to change within businesses. Policymakers play a crucial role in this process, as they can use these findings to establish industry-wide standards or incentives that promote the use of advanced logistical capabilities; besides that, policymakers need to invest in building genuine information channels and systems that inform the industry and clear the ambiguity.Originality/valueThis study establishes a relationship between INS and logistics performance, which will be interpreted by logistics capabilities to enhance the logistics performance of Sudanese logistic businesses in an underdeveloped context characterized by a weak logistics industry and logistics capabilities. It suggests that companies should prioritize logistics INS and investment in INS technology to enhance their logistics capabilities.

Learning Languages in the 21st Century: the Digital and Ai Transformation

The article deals with the problem of the learning languages in the 21st century: the digital and AI transformation. The aim of this article is to explore and clarify the transformative impact of digital technologies on foreign language teaching. This article explores the evolution of language learning from classroom-based methods to the incorporation of digital instruments, Artificial Intelligence (AI), and Information and Communication Technologies (ICT). Digital tools such as language learning applications, online dictionaries, multimedia resources, and language exchange platforms have made language acquisition more accessible and engaging. AI technologies introduce intelligent tutoring systems, personalized learning experiences, and advanced language processing capabilities, while ICT tools like Learning Management Systems (LMS) and virtual classrooms facilitate effective communication and information processing. The benefits of these advancements are multifaceted, including increased accessibility and flexibility, enhanced engagement and motivation, personalized learning paths, and improved collaboration and communication. However, challenges such as the digital divide, variability in resource quality, data privacy concerns, and the need for continuous teacher training must be addressed. Looking ahead, future trends such as augmented and virtual reality, blockchain technology, AI-driven content creation, IoT integration, and multimodal learning experiences promise to further transform the landscape of language education. These innovations will pave the way for more immersive and interconnected language learning experiences, contributing to a more multilingual global society.

AI assistance in enterprise UX design workflows: enhancing design brief creation for designers

The study explores the impact of AI tools on the daily tasks of designers in corporate environments, with a focus on the creation and evaluation processes of design briefs. Given ChatGPT’s advanced natural language processing capabilities and its potential to meet the complex communication and analysis needs of design work, this tool was selected to investigate its application in designers’ workflows. Through expert interviews, experimental testing, and third-party expert evaluations, we collected and analyzed data to understand the impact of AI on work processes. The findings indicate that AI tools significantly enhance both operational experience and subjective perceptions across most tasks. Additionally, the study provides a visual comparison of the testing process through a user experience map, highlighting AI’s positive influence on work efficiency, information retrieval, verification, analysis, communication, and decision-making. However, challenges remain in ensuring information authenticity, protecting content copyright, and maintaining professional identity. The primary objective is to gain a comprehensive understanding of the current state of AI application in business contexts and its impact on designers’ roles. By analyzing real-world feedback, the research aims to identify the strengths and weaknesses of AI solutions in enterprises and offer practical recommendations. The study underscores the importance of integrating AI thinking into workflows and adopting a human-centric approach for the future development of corporate work environments.

DbPTM 2025 update: comprehensive integration of PTMs and proteomic data for advanced insights into cancer research.

Post-translational modifications (PTMs) are essential for modulating protein function and influencing stability, activity and signaling processes. The dbPTM 2025 update significantly expands the database to include over 2.79 million PTM sites, of which 2.243 million are experimentally validated from 48 databases and over 80 000 research articles. This version integrates proteomic data from 13 cancer types, with a particular focus on phosphoproteomic data and kinase activity profiles, allowing the exploration of personalized phosphorylation patterns in tumor samples. Integrating kinase-substrate phosphorylations with E3 ligase-substrate interactions, dbPTM 2025 provides a detailed map of PTM regulatory networks, offering insights into cancer-specific post-translational regulations. This update also includes advanced search capabilities, enabling users to efficiently query PTM data across species, PTM typesand modified residues. The platform's new features-interactive visualization tools and streamlined data downloads-allow researchers to access and analyze PTM data easily. dbPTM 2025 also enhances functional annotations, regulatory networks and disease associations, broadening its application for cancer research and the study of disease-associated PTMs. Through these enhancements, dbPTM 2025 is a comprehensive, user-friendly resource, facilitating the study of PTMs and their roles in cancer research. The database is now freely accessible at https://biomics.lab.nycu.edu.tw/dbPTM/.

Web-GIS Application for Hydrogeological Risk Prevention: The Case Study of Cervo Valley

Natural disasters have increasingly threatened human life, infrastructure, and ecosystems, exacerbated by climate change, urbanization, and deforestation. Effective disaster risk management is crucial to mitigate these impacts. Traditionally, Geographic Information Systems (GISs) have provided spatial data analysis capabilities, but the advent of Web-GIS applications has revolutionized this field. Web-GIS platforms enable real-time data access and facilitate enhanced stakeholder collaboration. This paper details the development of a Web-GIS application tailored for hydrogeological risk management in Cervo Valley, part of the NODES—Nord Ovest Digitale e Sostenibile project under Italy’s National Recovery and Resilience Plan (NRRP). The application integrates both static and dynamic geospatial data to create an interactive interface for evaluating and planning responses to hydrogeological hazards, specifically floods, landslides, and debris flow cones. By utilizing advanced Web-GIS capabilities, the project aims to refine the risk management practices and decision-making processes, thereby bolstering territorial resilience and addressing contemporary spatial challenges with enhanced precision and efficiency.

NIMG-63. LEVERAGING LLMS FOR ACCURATE DIFFERENTIATION OF RADIATION NECROSIS AND TUMOR PROGRESSION IN BRAIN MRI REPORTS: A STUDY ON AUTOMATED SCORING AND CLINICAL IMPLICATIONS

Abstract BACKGROUND Accurate differentiation between radiation necrosis and tumor progression or recurrence in patients treated with stereotactic radiosurgery for brain metastases is critical for guiding clinical management. This study leverages the advanced natural language processing capabilities of Meta Llama3, an artificial intelligence (AI) large language model (LLM), combined with prompt engineering, to rapidly categorize brain magnetic resonance imaging (MRI) radiology reports. Our objective was to develop an automated scoring system to classify concern for radiation necrosis, tumor progression, equivocal findings, or stable exams. METHODS Using a comprehensive dataset of reports annotated by expert radiologists, we ran inference on a 70-billion parameter Llama3 model (temperature 0.2, top_p 0.9) with specific prompts designed to capture the nuanced language and diagnostic criteria related to radiation necrosis or tumor progression. RESULTS The first pass was performed on a training dataset of 107 reports and did not predefine the clinical conditions. This demonstrated 43.4% accuracy in scoring when compared to a human user’s classification of each report. Agreement between the human reader and Llama3 was assessed using the Gwet agreement coefficient, AC1=0.411 (99%CI 0.396-0.426). Multiple iterations of targeted prompt engineering were then employed to narrow the definition of radiation necrosis and tumor progression, with specific examples and nuanced language used to achieve a higher degree of accuracy at 72.0%, AC1=0.719 (99%CI 0.717-0.722). DISCUSSION/FUTURE DIRECTIONS This surpasses recent demonstration of lower human-LLM agreement in radiographic score assignment, with further room for calibration on a dataset of several thousand reports. Next, we will correlate automated interpretations with actual clinical management decisions for radiation necrosis (e.g., initiation of steroids, bevacizumab, Laser Interstitial Thermal Therapy, and/or repeat imaging). This automated scoring system holds significant potential for LLMs in clinical applications. Future work will focus on integrating this model into clinical workflows and expanding its capabilities to include longitudinal monitoring of patient outcomes.

SentinelGuard Pro: Deploying Cutting‐Edge FusionNet for Unerring Detection and Enforcement of Wrong Parking Incidents

ABSTRACTWrong parking incidents pose a pervasive challenge in urban environments, disrupting the smooth flow of traffic, compromising safety and contributing to various logistical issues. Unauthorized parking occurs when vehicles are parked in locations not designated for such purposes, leading to a myriad of problems for both authorities and the general public. This research introduces a pioneering approach to confront the persistent challenge of unauthorized parking incidents in urban environments. The study focuses on harnessing the advanced capabilities of the FusionNet model to enhance the accuracy of license plate detection. This paper introduces the YOLO v8 Model, a deep learning architecture designed to enhance urban parking management by accurately detecting vehicles parked in unauthorized slots. The objective is to enhance parking management efficiency by accurately detecting vehicles and their occupancy status in designated parking areas. The methodology begins with data collection and preprocessing of images of parking spaces, followed by the training of YOLO v8 to identify vehicles and parking spaces in real time. Leveraging a diverse dataset encompassing various parking scenarios, including instances of unauthorized parking, the model achieves an accuracy of 98.50% in identifying vehicles outside designated areas. This model segments characters from detected license plates, enabling the accurate extraction of alphanumeric information associated with each vehicle. The integrated system provides timely identification of parking violations and facilitates effective enforcement actions through captured license plate data. Results demonstrate the model's effectiveness in real‐world scenarios, showcasing its potential for improving urban safety and efficiency. The implementation of FusionNet in the Python programming language, the proposed solution aims to streamline parking management, improve compliance with parking regulations and enhance overall urban mobility., with robust precision 96.17%, specificity 97.42% and sensitivity 96.19%, surpassing other MobileNet, CNN, ANN, DNN and EfficientNet models.

Leveraging Microsoft sentinel and logic apps for automated cyber threat response

An integrated approach to automated cyber threat response is explored in this paper, with Microsoft Sentinel's Security Information and Event Management (SIEM) capabilities being leveraged alongside Logic Apps' workflow automation within the Azure ecosystem. Efficient identification and automated mitigation of security incidents are enabled by a combination of AI-driven analytics and advanced threat-hunting capabilities, resulting in a substantial reduction of manual intervention and response times. The approach is demonstrated to contribute scientifically across three core areas: (1) a novel integration of Sentinel's SIEM with Logic Apps is proposed to streamline response workflows using automated playbooks; (2) the effectiveness of the system is assessed through multiple cyber threat scenarios, including automated account blocking and virtual machine isolation in response to identified threats; and (3) Sentinel's performance is evaluated relative to other SIEM solutions, such as Splunk and IBM QRadar, particularly in Azure-centric and hybrid environments. The potential of Microsoft Sentinel and Logic Apps to advance proactive cybersecurity defenses is underscored, while key limitations in scalability and cross-platform adaptability are also identified.

The Self-Improving Customer Service System: How AI Learns from Every Interaction

This article explores the transformative impact of self-improving AI systems in customer service, examining the parallels between AI and human learning processes, the mechanics of AI self-improvement, and the advantages of AI's learning at scale. It delves into real-world applications and impacts across various industries, highlighting improvements in personalization, efficiency, accuracy, customer satisfaction, and cost reduction. The article also addresses ethical considerations such as data privacy, transparency, and bias mitigation, while offering a future outlook on advanced AI capabilities in emotional intelligence, predictive service, and seamless human-AI collaboration. Through a comprehensive analysis of current technologies and future possibilities, this study provides insights into how self-improving AI is revolutionizing customer service, promising more efficient, personalized, and satisfying experiences for customers.

Asymmetric interval numbers: A new approach to modeling uncertainty

This paper introduces Asymmetric Interval Numbers (AINs), a novel type of interval numbers that combines the straightforward identification characteristic of classical interval numbers with the advanced capability for modeling uncertainty found in fuzzy sets, all while maintaining simplicity. AINs incorporate the expected value within the interval, providing a more accurate representation of the uncertainty of the data compared to the classical interval numbers. We define basic arithmetic operations for AINs, discuss their properties, and provide proofs. Additionally, we present theorems on symmetry and asymmetry for fundamental binary and unary operations, enhancing the mathematical framework for AINs. Several illustrative examples demonstrate the practical application of AINs. Although challenges remain, such as exploring performance with different distributions and reducing overestimation, AINs present a promising advancement in interval arithmetic. This study underscores the practical and theoretical implications of AINs, paving the way for further research and application in diverse scientific and industrial contexts.

Understanding Digital Forensic Tools: Their Features, Applicability and Key Short Comings. A Compendium

The Paper provides a comprehensive overview of various digital forensic investigation tools. The paper outlines the significance of digital forensics in collecting and preserving evidence for computer crimes and cybersecurity incidents. It discusses several prominent tools, including Encase Professional, Magnet Axiom, Passware Kit Forensic, Falcon Neo, Tableau USB Write Blocker, FTK Imager, and Autopsy, detailing their features, applications, and shortcomings. Key features highlighted include data acquisition methods, advanced file analysis capabilities, and reporting functionalities. The paper also addresses the potential limitations of these tools, such as issues with user authentication, performance under load, and chain of custody concerns. By examining these tools' strengths and weaknesses, the document aims to provide insights into their applicability in various investigative contexts and emphasize the importance of selecting appropriate tools for effective digital evidence management.

Genomes OnLine Database (GOLD) v.10: new features and updates.

The Genomes OnLine Database (GOLD; https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute is a comprehensive online metadata repository designed to catalog and manage information related to (meta)genomic sequence projects. GOLD provides a centralized platform where researchers can access a wide array of metadata from its four organization levels namely Study, Organism/Biosample, Sequencing Project and Analysis Project. GOLD continues to serve as a valuable resource and has seen significant growth and expansion since its inception in 1997. With its expanded role as a collaborative platform, it not only actively imports data from other primary repositories like National Center for Biotechnology Information but also supports contributions from researchers worldwide. This collaborative approach has enriched the database with diverse datasets, creating a more integrated resource to enhance scientific insights. As genomic research becomes increasingly integral to various scientific disciplines, more researchers and institutions are turning to GOLD for their metadata needs. To meet this growing demand, GOLD has expanded by adding diverse metadata fields, intuitive features, advanced search capabilities and enhanced data visualization tools, making it easier for users to find and interpret relevant information. This manuscript provides an update and highlights the new features introduced over the last 2 years.

Optimized Machine Learning Algorithms for Predicting the Punching Shear Resistance of Flat Slabs with Transverse Reinforcement

In the calculation of reinforced concrete (RC) flat slabs with transverse reinforcement, punching shear resistance is one of the most critical factors. It is true that design provisions may be implemented, but they often result in significant biases and deviations from expectations. This study aims to present an optimized machine learning (ML) algorithm for estimating the punching shear resistance. Four machine learning (ML) algorithms (SVR, DT, RF, and XGBoost) with Bayesian optimization (BO) are presented in this paper to provide accurate predictions for flat slabs. The adoptability and optimization of the models are achieved through the analysis of a database of 337 test specimens with nine design parameters. Machine learning (ML) techniques are used to estimate punching shear resistance, which is compared with design provisions and equations relating to critical shear crack theory (CSCT). According to this study, Bayesian optimization is still capable of improving the performance of conventional machine learning algorithms, while the XGBoost-based model offers advanced capabilities. Predictions based on BO-XGBoost are in good agreement with actual values (MAE, RMSE, and R2 are 0.09 MN, 0.14 MN, and 0.92, respectively) in test set. Following a detailed explanation using Shapley additive explanation (SHAP), a high-performance ML approach is used to investigate the predictive results. With the proposed optimized algorithms, it is possible to determine the punching shear resistance of flat slabs with transverse reinforcement during the preliminary stages of the construction.

Adaptation of Object Detection Algorithms for Road Indicator Lights in Complex Scenes

In the realm of autonomous driving, practical driving scenarios are fraught with numerous complexities, including inclement weather conditions, nighttime blurriness, and ambient light sources that significantly hinder drivers’ ability to discern road indicators. Furthermore, the dynamic nature of road indicators, which are constantly evolving, poses additional challenges for computer vision-based detection systems. To address these issues, this paper introduces a road indicator light detection model, leveraging the advanced capabilities of YOLOv8. We have ingeniously integrated the robust backbone of YOLOv8 with four distinct attention mechanism modules—Convolutional Block Attention Module (CBAM), Efficient Channel Attention (ECA), Shuffle Attention (SA), and Global Attention Mechanism (GAM)—to significantly enhance the model performance in capturing nuanced features of road indicators and boosting the accuracy of detecting minute objects. Additionally, we employ the Asymptotic Feature Pyramid Network (AFPN) strategy, which optimizes the fusion of features across different scales, ensuring not only an enhanced performance but also maintaining real-time capability. These innovative attention modules empower the model by recalibrating the significance of both channel and spatial dimensions within the feature maps, enabling it to hone in on the most pertinent object characteristics. To tackle the challenges posed by samples rich in small, occluded, background-similar objects, and those that are inherently difficult to recognize, we have incorporated the Focaler-IOU loss function. This loss function deftly reduces the contribution of easily detectable samples to the overall loss, thereby intensifying the focus on challenging samples. This strategic balancing of hard-to-detect versus easy-to-detect samples effectively elevates the model’s detection performance. Experimental evaluations conducted on both a public traffic signal dataset and a proprietary headlight dataset have yielded impressive results, with both mAP50 and mAP50:95 metrics experiencing significant improvements exceeding two percentage points. Notably, the enhancements observed in the headlight dataset are particularly profound, signifying a significant step forward toward realizing safer and more reliable assisted driving technologies.

GPT-based data-driven urban building energy modeling (GPT-UBEM): Concept, methodology, and case studies

Achieving carbon neutrality is a critical global goal, with urban building energy modeling (UBEM) playing a pivotal role by providing data-driven insights to optimize energy consumption and reduce emissions. This paper introduces GPT-based urban building energy modeling (GPT-UBEM), a novel approach utilizing GPT’s advanced capabilities to address key UBEM challenges using GPT-4o. The study aimed to demonstrate the effectiveness of GPT-UBEM in performing UBEM tasks and to explore its potential in overcoming traditional limitations. Specifically, (1) basic analytics of urban data, (2) data analysis and energy prediction, (3) building feature engineering and optimization, and (4) energy signature analysis were conducted in four case studies. These analyses were applied to 2,000 buildings in Seoul and 31 buildings in Gangwon-do, South Korea. Through case study, the findings highlighted the ability of GPT-UBEM to integrate diverse data sources, optimize building features for high accuracy in prediction models, and provide valuable insights for urban planners and policymakers through the use of expert domain knowledge and intervention. Additionally, based on the results derived from GPT-UBEM in this study, the current limitations of GPT-UBEM (L1 to L3) and future research directions (F1 to F4) have been outlined.

Dynamic tensile behaviors and crack propagation in fiber-reinforced cementitious composites: Experimental investigation and Peridynamic simulation

Fiber-Reinforced Cementitious Composites (FRCC) have gained significant attention in engineering applications due to their superior mechanical properties and toughness, particularly under high strain rate conditions. This study performed dynamic tensile tests on FRCC at high strain rates (35-110 s⁻¹) using a Split Hopkinson Tensile Bar (SHTB) apparatus. Additionally, a novel Peridynamic (PD) model was developed for the SHTB and FRCC system, leveraging the advanced capabilities of the emerging PD theory. The study compared and analyzed dynamic tensile strength, ultimate tensile strain, strain rate effects, failure modes, and crack development in FRCC with different fiber ratios at various high strain rates, using both experimental data and PD simulations. The results show that the PD-SHTB-FRCC dynamic model developed in this study exhibits high consistency between numerical simulations and experimental findings, effectively capturing the processes of crack initiation, propagation, and complete failure in FRCC specimens. The dynamic tensile properties of FRCC improve significantly with increased strain rates, with polyethylene (PE) fibers providing superior reinforcement compared to steel fibers. Notably, the dynamic tensile strength, peak tensile stress, and ultimate tensile strain of FRCC increase significantly with rising strain rates, with specimens containing higher PE fiber content showing a more pronounced enhancement effect. For strain rates between 42.6 s⁻¹ and 76.1 s⁻¹, considering dynamic tensile strength, ultimate tensile strain, and peak tensile stress, the optimal combination for resisting dynamic tensile loads was 1.5% PE fibers and 0.5% steel fibers. At a strain rate of 99.8 s⁻¹, a 2% PE fiber ratio alone provided the best performance.

Review Paper on Agricultural Terrain Traversal Rocker Bogie Mechanism

Abstract: This paper details the design and implementation of a terrain traversal system leveraging the rocker-bogie mechanism, specifically tailored for agricultural applications where uneven terrain presents significant challenges. The rocker-bogie suspension system, renowned for its application in planetary rovers, is innovatively adapted to navigate the complexities of agricultural fields. The primary objective of this research is to create a prototype that minimizes soil disturbance while effectively maneuvering over obstacles such as rocks, furrows, and varying soil conditions. Through this approach, the prototype aims to enhance operational efficiency in critical agricultural tasks, including planting, monitoring, and maintaining crops. The system's design incorporates advanced navigation capabilities, potentially integrating sensors for obstacle detection and GPS for precise positioning. This research addresses the pressing need for low-cost, scalable solutions in the realm of agricultural mechanization, ultimately contributing to improved productivity and reduced labor costs in farming operations. The findings from this study are anticipated to provide significant insights into the future of agricultural technology, paving the way for further innovations in automated systems that can adapt to the dynamic challenges of modern agriculture. The successful deployment of this prototype not only underscores the feasibility of the rocker-bogie mechanism in agricultural settings but also highlights its potential to revolutionize practices in the industry.