bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Brand Awareness and Positioning Appropriateness on Consumers' Purchase Intention in the Coffee Industry - A Case Study of Starbucks and Luckin Coffee
Authors: Yin Chenxuan
Published: February 2025

With the improvement of people's living standards and the popularization of coffee culture, coffee has gradually become one of the important choices for Chinese consumers' daily drinks. However, with the fierce competition in the coffee market, nowadays various coffee brands are emerging, and how consumers can make a choice among the dazzling coffee brands has become a matter of concern. How brands can stand out among competitors and formulate suitable marketing strategies should not be neglected. This research uses literature analysis and case study method to analyze the impact of brand awareness and positioning appropriateness on consumers' purchase intention in the coffee industry, mainly based on the examples of Starbucks and Luckin Coffee, so as to help the enterprises to better formulate their market strategies and achieve sustainable development in the highly competitive coffee market. The study finds that high brand awareness and positioning appropriateness have a positive effect on consumers' purchase intention. The positive synergy between brand awareness and positioning appropriateness has a positive effect on consumers' purchase intention.
Navigating the High Costs of Energy Storage: Challenges and Solutions for Sustainable Integration
Authors: Xu Jingwen
Published: February 2025

As global energy consumption continues to rise, it is imperative that energy supply systems are aligned to meet this growing demand. However, the current state of the energy industry is characterized by prohibitively high costs associated with energy storage systems, alongside significant inefficiencies and energy wastage. This paper first introduce two main energy storage system: Lithium-ion battery, Compressed Air Energy Storage(CAES), explores the underlying factors contributing to these elevated costs and examines the current energy landscapes of key regions, including Asian countries, particularly China, the United States, and Europe. Through a comparative analysis, the paper identifies the differences and underlying causes that drive these disparities. Furthermore, it proposes strategic solutions to address these cost challenges, such as reforming tax systems to incentivize innovation, enhancing supply chain efficiency through better integration and localization, and investing in research and development to drive technological advancements. By tackling these issues, the global energy sector can advance towards more cost-effective and sustainable energy storage solutions, thereby supporting the transition to a low-carbon future and ensuring energy security for generations to come.
Geopolitical Impacts of China's Belt and Road Initiative: Opportunities and Risks Across Asia, Africa, and Europe
Authors: Zhao Momo
Published: February 2025

Taking the Belt and Road Initiative as the object of study, this paper explores how the Belt and Road strategy enhances China's geopolitical discourse and its specific impacts in Asia, Africa and Europe, and at the same time explores the opportunities and potential risks of the Initiative, revealing the role of the Belt and Road in international relations and providing a reference for national policymaking. Launched in 2013, the Belt and Road Initiative (BRI) has become a crucial strategy for China to enhance globalization and foster international collaboration. By investing in infrastructure and promoting trade across Asia, Africa, and Europe, the BRI has significantly expanded China's influence and established it as a key player among emerging economies. However, this initiative has also raised concerns about geopolitical implications and shifts in global power dynamics, particularly regarding dependency on Chinese investments. As China strengthens its ties with countries along the BRI route, it faces challenges from Western nations wary of its expanding influence. Overall, the BRI represents both opportunities for economic growth and risks associated with increased competition and geopolitical tensions on the global stage.
The Impact of Digital Platforms on Online Impulse Buying of Luxury Goods: The Role of Emotional Triggers and Social Influences
Authors: Zhang Zichen
Published: February 2025

The emergence of social media has significantly changed peoples behavior, especially when it comes to luxury goods. This paper explores the psychological and social drivers of online impulse buying of luxury goods, focusing on the role of shopping apps and social media platforms. Emotional factors such as happiness, satisfaction and fear of missing out (FOMO) caused by impulse buying are combined with social influences such as peer following trends and influencer promotion, which further encourage impulse buying. The online world plays an important role, many shopping apps use advanced computing, and social media perfectly combines business and entertainment. Luxury consumption and digital platforms have been widely discussed in recent studies. This behavior is mainly seen in young people and high-income groups, who are particularly sensitive to trends in luxury goods. The study finally emphasizes that as the availability of luxury goods online continues to increase, it will increase the cost of purchase. This study provides insights for marketers to optimize their strategies and can also provide solutions for consumers to make rational purchases.
Investigating the Link Between Executive Compensation Incentives and a Firms ESG Performance
Authors: Tang Congyuan
Published: February 2025

Utilising data on American firms from 2013 to 2023, this study explores how various executive compensation incentives, ranging from monetary to equity-basedincentives, impact thefirms ESG (environmental, social and governance) performance. The study also explores whether financial and equity-based motivations advancesustainable development. By conducting an empiricalanalysis of publicly listed corporations, this study fills the gap in the literature. Moreover, it offers insights into the way executive compensation is structured tomore closely align with ESG performance, which helps in promoting sustainabledevelopment.
Research on Cross-border E-commerce Supply Chain Risk Management and Countermeasures -- Taking TikTok as an Example
Authors: Sun Qizhou , Xiao Yiwei , Zhang Yiyan
Published: February 2025

With the rapid development of globalization and the Internet, cross-border e-commerce has become an important form of international trade, possessing significant growth potential. However, its supply chain is relatively complex and faces numerous risks, including delivery issues, policy changes, and warehouse management challenges. Although existing research has focused on the identification and management of supply chain risks, in-depth analyses of specific enterprises, such as TikTok Shop, remain limited. This study takes TikTok Shop as a case to explore risk management and response strategies within its cross-border e-commerce supply chain. Through literature review, SWOT analysis, and other methodologies, the main risks and their underlying causes encountered in TikTok Shops supply chain operations are systematically analyzed. The research finds that TikTok Shop primarily faces issues of unstable logistics, changes in international policies, and inadequate warehouse management. In response to these risks, this paper proposes strategies such as establishing corresponding risk response mechanisms and optimizing the warehouse system, aiming to enhance the stability and competitiveness of the supply chain. These findings provide strategic insights and references for cross-border e-commerce enterprises.
Enhancing Agile Responsiveness in Flexible Supply Chains: A Case Study of Automobile Companies
Authors: Dong Aohua , Pu Xingyu
Published: February 2025

As the leading industry in China's current development, the automobile industry is seeking international development under the background of globalization. In order to gain a competitive advantage, the management model of the flexible supply chain is introduced to quickly adapt to market changes and improve the agile responsiveness of the supply chain. This paper first gives an overview of the research, introduces the development status faced by automobile companies, and explains the basic concepts of flexible supply chains. Then the research content is expanded, and a multi-angle analysis is adopted. From the front end to the back end of the supply chain, the four links of the procurement system and supplier management, production system and design development, inventory system and logistics efficiency, and the role of the audit system in the supply chain are analyzed and studied to improve the agile responsiveness of the flexible supply chain of automobile companies. Finally, for the four links, the multi-point supply strategy, "three-ization" strategy, digital strategy and strategy to improve audit transparency are summarized. In the future, automobile companies will also use a variety of strategies to improve the agile responsiveness of the flexible supply chain to adapt to the ever-changing market and new challenges.
Application and Strategy Research of Data Analysis and Statistical Methods in Supply Chain Process and Supply Demand Optimization
Authors: Dai Haoran
Published: February 2025

This study digs into the importance and usefulness of data analysis and statistical methods in making supply chain processes better and balancing supply and demand. It looks at its usefulness in many real-life situations, like figuring out the best way to use resources, making the supply chain more open, managing suppliers better, making logistics more efficient, and lowering business risks. By stressing how important it is to combine data analysis and statistical methods with supply chain process optimization, real-life business examples make the usefulness and practicality of data analysis and statistical methods stand out. At the same time, the study deeply explores the challenges and opportunities encountered in the practical application of data analysis and statistical methods. Finally, we provide an in-depth summary of the research findings and offer suggestions for future research. The goal is to provide forward-looking insights and a solid theoretical foundation for the ongoing improvement and innovation of supply chain management, as well as help businesses better adapt to the tough international market competition environment.
Warehouse Optimization Strategies for Improved Supply Chain Efficiency
Authors: Sun Ran
Published: February 2025

This study focuses on the improvement of supply chain efficiency by warehouse system optimization. Combined with the development trend of information technology, the application prospect of new technologies such as intelligent warehousing, automation equipment and warehouse management system (WMS) in warehouse optimization is discussed. Specific optimization strategies, including warehouse location optimization, partition storage, moving line planning and job process reengineering, are proposed to improve storage efficiency from multiple dimensions. In order to fully evaluate the effect of these optimization measures, the evaluation index system of this study includes key aspects such as reduced operating costs, improved inventory turnover, reduced order response time and improved customer satisfaction. Through these indicators, the study evaluated the positive role of warehouse optimization in improving overall operational efficiency. In addition, this study also focuses on the impact of warehousing optimization on all aspects of the supply chain, including the optimization effects of supplier collaboration, logistics distribution and information sharing. By improving the collaborative efficiency of these links, the optimized storage system can significantly improve the overall operational efficiency of the supply chain. The purpose of this study is to provide theoretical guidance with practical value for enterprises, help enterprises to enhance the competitiveness of supply chain in the fierce market competition, and maintain sustainable development advantages.
E-commerce Enterprise's Supply Chain Green Transformation Strategy
Authors: Zhang Xiaoran
Published: February 2025

With the increasing awareness of social environmental protection and the widespread concept of sustainable development, enterprises gradually realize the importance of environmental responsibility in the process of operation. For e-commerce enterprises, their green transformation in supply chain management has become an inevitable trend. Especially in the modern consumption model, e-commerce enterprises not only need to improve operational efficiency and customer satisfaction but also must actively respond to increasingly stringent environmental regulations and consumer demand for green products. Therefore, promoting the green transformation of the supply chain not only helps to reduce the environmental impact but also enhances the social image of the enterprise and enhances the competitiveness of the brand. Taking e-commerce enterprises as the research object, this paper systematically combs the core issues of green transformation of the supply chain, including specific challenges such as cost problems, shortage of technology and talents, and changes in policies and regulations. In view of these problems, optimization strategies are put forward from the aspects of reducing cost, introducing technology and talents, and coping with policy changes. This paper aims to provide scientific reference suggestions for the green supply chain transformation of e-commerce enterprises to help them achieve sustainable development goals.
Challenges and Optimization Strategies of AI Applications in Supply Chain Management
Authors: Zhu Chenyuxi
Published: February 2025

As globalization accelerates and market dynamics grow increasingly complex, supply chain management has emerged as a pivotal factor in determining corporate competitiveness. Concurrently, the rapid advancement of artificial intelligence (AI) technology has positioned its application as a critical solution for addressing supply chain optimization challenges. This study reviews the challenges faced by AI applications in supply chain management, proposes feasible optimization solutions and suggestions, and aims to help enterprises correctly apply AI technology to achieve cost reduction and efficiency improvement. Research has found that AI applications currently face challenges such as high initial investment, data security, and talent shortages. To address these issues, the study focuses on three critical industriesautomotive, medical, and information technologyanalyzing their unique challenges and exploring targeted solutions. This article suggests that enterprises reduce investment risks, improve data access mechanisms, and strengthen talent cultivation and introduction, to promote the broader integration of artificial intelligence within supply chain operations.
Approximation Design for Low Power Consumption in Digital Signal Processing Architecture: A Literature Review
Authors: Zhong Tianhong , Shu Songqi , Jing Haoyuan
Published: February 2025

Given the power demands of DSP operations in portable or embedded devices, especially in error-resilient applications such as the multimedia and image processing, approximation strategy is proposed as a viable solution. By deliberately reducing computational precision, various DSP components such as adders, multipliers, and compressors can be designed with lower power requirements. This review explores the optimization of these components at different design levels, including architectural and circuit levels. Specifically, this paper goes deeper into the design of approximate adders, compressors, and multipliers, essentially highlighting their impact on power efficiency and computational accuracy for image/video compression applications. Through analyzing and discussing the different designs, this paper elaborates the trade-offs between power savings and error rates, ultimately demonstrating the potential of approximation techniques in reducing power consumption without significantly compromising performance.
ModuleAnalyst: A Small But Useful Tool
Authors: Liu Haochen
Published: February 2025

Many coding beginners of JavaScript, which has a bunch of third-party modules, do not know well about the structure of that module and have no idea how to use it. This paper presents a useful tool called Module-analyst, which can extract from a module and then represent its interface. Module-analyst based on existing functions such as "object.getOwnPropertyNames" in JavaScript, etc. And this tool managed to make the output of those functions more understandable and readable
Vitamin and Mineral Combined in Diabetes: Modulating HbA1c Across Different Demographics
Authors: Niu Ruijie , Xia Jinyang
Published: February 2025

The increasing incidence of diabetes and the serious complications it causes has become a major
global health problem. This issue demonstrates the need for society to develop effective nutrition strategies
in the area of public health. The study used data from the 2017-2018 National Health and Nutrition
Examination Survey (NHANES) to examine how vitamin and mineral supplements could be combined
to help prevent and manage diabetes. By looking at the effects of these supplement combinations on
glycosylated hemoglobin (HbA1c) levels, the study found that calcium can enhance the role of vitamin
D in lowering glycosylated hemoglobin levels, and in addition, vitamin B12 and vitamin D together have
a strong effect in reducing these levels. The study highlights that certain combinations of vitamins and
minerals, particularly calcium, caffeine, alcohol, vitamin B12, vitamin C, and vitamin D, are most effective
at restoring HBA1c levels to normal.
Q-MultiTempGAN: Post-training Quantization of Multitemporal Multispectral Image Compression Models
Authors: Gharagoz Naser Sadeghi , Karaca Ali Can
Published: February 2025

Multispectral (MS) satellites generate a large volume of data and compression of this data is a critical task for remote sensing applications. Multitemporal images, captured at different dates over the same scene, contain temporal correlations that can be used for compression. This study is built on MultiTempGAN, a lightweight generative adversarial network (GAN) designed for multitemporal MS image compression, and investigates the impact of post-training model quantization on compression performance. By reducing the precision of model parameters to various bit-widths, quantization significantly reduces the model size while preserving image quality. Experiments conducted on Sentinel-2 satellites MS image pairs show that the quantized models achieve similar signal-to-noise ratio (SNR) and bit-per-pixel (bpp) metrics as the original model, with minimal impact on image reconstruction and compression efficiency. In addition, the reduction in model size facilitates more resource-efficient deployment, supporting large-scale remote sensing applications. These findings highlight the potential of model quantization to optimize deep learning-based compression techniques, enabling scalable and efficient handling of MS data without sacrificing accuracy and performance.
Enhancing Pose Estimation with Depth Integration: A Comparative Study of ArUco Markers and Depth Data
Authors: Song Yifei
Published: February 2025

Pose estimation is a fundamental technique in computer vision, allowing systems to recognize and analyze the positions and movements of objects or individuals in three-dimensional space. Its applications extend to healthcare for physical therapy, sports for performance analysis, and surveillance for behavior monitoring. The development of advanced algorithms and hardware has significantly improved its accuracy and real-time processing capabilities, driving innovation across industries. This study examines the integration of depth data with ArUco marker detection to improve pose estimation precision. By comparing translation vector (tvec) calculations using RGB-only methods and depth-enhanced approaches, this paper demonstrates that depth data provides superior consistency, particularly at extended distances. The results suggest that depth integration could address the limitations of traditional marker systems, improving accuracy and robustness. This work highlights the potential of depth-based enhancements and outlines future directions for comprehensive evaluation and system refinement.
Design and Function of Lower-Limb Prosthetics Based on Biomechanics and Bionic Simulation Using Deep Learning
Authors: Bao Junzhe
Published: February 2025

By mimicking the biomechanics of a missing biological leg, robotic prosthetic legs hold the potential to significantly enhance mobility and quality of life for millions of lower-limb amputees. Unfortunately, compared to traditional passive prosthetics, existing powered prosthetics are heavier, bulkier, and have shorter battery life, which severely limits their clinical feasibility and hinders their practical use in the daily lives of amputees. This paper reviews and explores optimization strategies for the design of bionic robotic prosthetic legs, focusing on challenges related to the stability of biomechanical bionic prosthetics in complex terrain. It discusses how advancements in materials, structural design, and sensor technology can improve stability and adaptability. In the future, biomechanical bionic prosthetics will become more intelligent and personalized. They will possess features like automatic adjustment, 3D - printed customization, and the capacity to modify control parameters and movement patterns in accordance with individual requirements. The research also highlights the potential of neural interface technology to bring new opportunities for biomechanical bionic prosthetics, enabling natural control, efficient signal transmission, and the integration of virtual technologies to assist in rehabilitation.
Text Information Analysis and Sentiment AnalysisA Case Study of Barrage Comments on Disney Animated Films
Authors: Lan Xi
Published: February 2025

With the rise of digital media, video barragereal-time user comments overlaid on videoshas become a prominent feature of online video platforms, particularly in China. This study examines Bilibili, a leading Chinese barrage video-sharing platform, which in 2023 acquired streaming rights to several Disney animated classics, including Frozen, Zootopia, and Coco. By analysing barrage data generated during the viewing of these films, the research aims to uncover valuable insights into users feedback, emotional engagement, and interactive behaviours. The findings highlight the overall positive emotional resonance among viewers, indicating a favourable reception of the Disney animated films on the platform. The study explores the role of barrage data in enhancing the viewing experience. It provides fresh perspectives for the digital entertainment industry, guiding it towards becoming more user-friendly and innovative. Through in-depth analysis, this research contributes to understanding audience feedback, emotional experiences, and interactive behaviours, transforming individual viewing experiences into communal social events.
AIoT: The Integrating of Artificial Intelligence and the Internet of Things
Authors: Liu Weijie
Published: February 2025

At present, artificial intelligence has emerged as a significant field all over the world, offering diverse enhancements across many industries when combined with it. This study primarily examines the combination of the Internet of Things and artificial intelligence, highlighting practical applications and areas requiring enhancement. Mainly from a literature review utilising the three keywords: artificial intelligence, the Internet of Things, and application for search and screening, it was discovered that there are several issues in the application of this field. There are issues with delayed data transmission, compatibility, and anomaly detection in the field of autonomous driving. In the field of wearable devices, there are data security issues. In the field of industrial Internet of Things, there are problems such as low transmission efficiency, high latency, and inability to effectively resist external network attacks. In addition, there are also trust issues in the field of artificial intelligence where decisions and predictions cannot be understood by people. In besides this, this study provides several of recommendations for improvements in further research.
Analysis of Key Factors Shaping the Performance and Reliability of AI Applications
Authors: Wang Weiming
Published: February 2025

Artificial Intelligence (AI) technologies have revolutionized a wide range of industries by providing cutting-edge solutions that streamline operations, improve decision-making accuracy, and deliver highly personalized user experiences. From automating routine tasks in manufacturing and logistics to enabling advanced data analysis in healthcare and finance, AI has become a critical tool for enhancing productivity and optimizing outcomes.This paper examines three critical AI applicationsautonomous driving, natural language processing (NLP), and facial recognitionto analyze the factors influencing their performance and reliability. The study identifies data quality, algorithm optimization, and deployment environment as pivotal elements that determine the effectiveness and fairness of these systems. Through a comparative analysis, this paper highlights how challenges such as data diversity, algorithmic bias, and environmental constraints impact system outcomes. It also explores strategies for improving accuracy, adaptability, and fairness in real-world settings. Given the rapid evolution of AI, the study emphasizes the importance of continuous innovation and incorporating user feedback into system design. Future research directions include analyzing the adaptive capabilities of AI systems and developing methods for better integrating user insights, ensuring AI's sustained advancement in addressing complex societal needs.
An Enhanced LSTM-based Sales Forecasting Model for Functional Beverages in Cross-Cultural Markets
Authors: Ma Xiaowen , Bi Wenyu , Li Maoxi , Liang Penghao , Wu Jiang
Published: February 2025

This paper proposes an enhanced Long Short-Term Memory (LSTM) based forecasting model explicitly designed for functional beverage sales prediction in cross-cultural markets. Traditional forecasting methods often struggle with capturing the complex interplay between cultural factors and sales patterns, leading to suboptimal prediction accuracy in diverse market environments. The proposed model incorporates innovative architectural modifications to the standard LSTM structure, integrating cultural-aware gates and specialized feature engineering techniques to capture market-specific characteristics. The research utilizes comprehensive sales data from six major markets across North America, Asia, and Europe from 2019 to 2023. The enhanced model demonstrates superior performance with a 28.4% improvement in prediction accuracy compared to traditional methods, achieving an average RMSE of 0.134 across all tested markets. The model's effectiveness is particularly evident in markets with high cultural diversity, where it achieved a 31.5% reduction in prediction error compared to conventional approaches. The research findings establish that cultural dimensions account for approximately 37.5% of sales variation across markets, highlighting the critical importance of cultural feature integration in sales forecasting. The practical implementation of the model resulted in a 23.7% reduction in inventory holding costs and improved resource allocation efficiency. This research contributes to the theoretical understanding of cross-cultural market dynamics and practical applications in global business operations.
In Vitro Evaluation of the Fracture Strength among Three Different Denture Base Materials
Authors: Narjes Mohsen  , Mohamed Elmaroush  , Sarra Ben Hamida  , Fatima Abusua
Published: February 2025

Failure in the form of denture fracture is commonly encountered by both removable prosthodontics wearers and dentists, it is mainly attributed to material properties, technical features, stresses while in function or due to impact as a consequence of dropping the dentures. Traditionally, Heat-Polymerized Polymethylmethacrylate (PMMA) has been the gold standard for denture bases due to its ease of processing and acceptable mechanical properties. However, newer materials, injectable flexible nylon-based resin and CAD/CAM Polyether ether Ketone (PEEK) denture bases are being introduced with claims of superior strength and durability. The aim of this study was to investigate the fracture strength of three commonly used denture base materials in Libya, which may aid in selecting the best material for a given application and predicting potential material behavior under load when holding the durability of the material in mind. A total of 36 samples were tested. These samples were divided into three groups, with 12 samples each: Group I heat polymerized acrylic resin PMMA, group II Injectable flexible nylon-based resin and group III CAD/CAM (PEEK). The fracture strength test was carried out on a universal testing machine at a crosshead speed of 5 mm/min. The results were analyzed using ANOVA, and multiple comparisons were undertaken using Tukey’s HSD test. Statistical significance was set at P ≤ 0.001. The result of this study shows there were significant differences in the fracture strength among the three groups (P< 0.001). Moreover, CAD/CAM (PEEK) denture base material exhibited significantly the highest fracture strength (234.17N ± 13.5) whereas the least fracture strength value was measured with Injectable flexible nylon-based resin denture base material (75.25N±10.5). The study concluded that, within the limitations of this study, the results demonstrate that CAD/CAM (PEEK) outperforms Injectable flexible nylon-based resin and heat polymerized acrylic denture base materials in terms of the fracture strength, supporting its potential as an advanced material for denture construction. However, further clinical studies are recommended.
Rekayasa Teknologi Konstruksi dan Manufaktur: Optimalisasi Material dan Strategi Teknikal dalam Infrastruktur Sumatera Utara
Authors: Napitupulu Abidan , Saputra Andi , Siburian Mananda Ture
Published: February 2025

Structural performance in a variety of loading scenarios is an important aspect of civil engineering. This study compares the capacity of the structure based on existing conditions and simulation results using the ATC-40 and FEMA 356 methods. The main parameters analyzed include drift ratio, base shear, and displacement. The data sources used in this study consist of three main aspects, namely field and laboratory measurements. The data analysis in this study uses a combination of quantitative methods and numerical simulations. The results of the analysis showed that the drift ratio decreased from 1.5% in the existing conditions to 1.2% and 1.3% in the ATC-40 and FEMA 356 simulations. The base shear is also reduced from 500 kN to 480 kN and 470 kN, while the displacement is reduced from 35 mm to 32 mm and 30 mm. In addition, analysis of the stress distribution of various materials such as SS304L, NiCr, and 20% HTM showed significant differences in tensile strength, modulus of elasticity, and elongation. This study provides important insights for designing safer and more efficient structures to withstand dynamic loads
Quarry operations and soil health: Tracing metal pollution in Awi and Njagachan communities, Nigeria
Authors: Udiba Udiba Ugumanim , Modey Dorothy Kuka , Ama John , Amah Joseph Etim , Sam-Uket Nwuyi Okori , Emuru Edward Odey , Chukwubuike Confidence Ikenna
Published: February 2025

This study investigated the influence of quarry operations on soil health by evaluating the metal pollution status of soils around major quarry communities (Awi and Njagachan) in Akamkpa, Nigeria. Soil samples were subjected to wet digestion and quantified using an Atomic Absorption Spectrophotometer (Model AA-6800, Japan). The concentrations of lead (63.34–96.34 mg/kg), cadmium (4.29–7.40 mg/kg), mercury (2.34–3.76 mg/kg), arsenic (2.48–5.21 mg/kg), and selenium (1.22–2.75 mg/kg) were all below the U.S. Environmental Protection Agency (US-EPA), Dutch remediation levels, and other soil guidelines. However, significant spatial variation in metal levels across quarry sites points to anthropogenic influences, with quarries likely contributing to the elevated metal concentrations. Contamination factors indicated moderate contamination by lead, considerable contamination by cadmium, and very high contamination by mercury. The degree of contamination was high for all quarries except Ding Zing quarry, which showed a very high degree during the wet season. Ecological risk assessment revealed low potential risk from lead and arsenic, moderate to high risk from cadmium, and very high risk from mercury. Geo-accumulation indices suggested that soils were largely unpolluted by lead and arsenic but ranged from unpolluted to moderately polluted by cadmium and mercury. The study concludes that quarry activities contribute to elevated metal concentrations, posing varying levels of ecological risk. Continuous monitoring is strongly recommended to prevent potential long-term human and environmental health risks, with a focus on addressing mercury contamination. Regulatory measures should be enforced to mitigate further pollution. 
Using Natural Enemies to Control Greenhouse Pests
Authors: AL-Naabi Samia
Published: February 2025

Biological pest control, often known as biological pest control or biocontrol, is a sustainable and ecologically friendly method of pest management in agriculture. It entails using living organisms, including predators, parasitoids, and diseases, to control pest populations and reduce agricultural loss. The study used the experimental method. The experiment was conducted in Al Batinah South Governorate from 2019-2021 to address crop loss and pesticide overuse in tomato and cucumber crops. Then, it was extended to Al Sharqiyah North, Ad Dakhiliyah, and Al Batinah North Governorates, using biological control methods like Green lacewing and Ladybird. The study used frequencies, percentages and standard deviations as statistical methods. The study employed the experimental method. In the current study, the researcher used certain natural enemies to test their effects on protecting tomato and cucumber crops in greenhouses from harmful pests in the Sultanate of Oman. The results revealed that Green lacewing and Ladybiboth both significantly eliminated whiteflies in tomato and cucumber crops by effectively controlling their populations. The study found that both the ladybird beetle and the green lacewing insect effectively reduced pest populations in tomato and cucumber plants over 10 days. The green lacewing insect led to an average reduction of 80% in pests, while the ladybird beetle was more effective, resulting in a 70% average reduction. Thus, the novelty of this research lies in its exploration of the use of natural enemies like the green lacewing and ladybird as a sustainable, eco-friendly alternative to chemical pesticides for controlling greenhouse pests, emphasizing the importance of sustainable pest management practices and integrating natural enemies with cultural practices and monitoring techniques.
Management Skills and Organizational Issues of a Public Secondary School
Authors: Freolo Dexter M.
Published: February 2025

This study investigates the effectiveness of management skills in addressing organizational issues at Malabog National High School in Albay, Philippines. Using a quantitative approach, data were gathered from 120 participants, including teachers, department heads, and master teachers, using the Organizational Culture Assessment Instrument (OCAI) and the Management Skills Assessment Instrument (MSAI). Findings reveal that communication and strategic planning are predominant among school leaders, correlating strongly with positive dimensions of organizational culture such as leadership effectiveness and goal alignment. The study identifies significant differences in perception among regular teachers, department heads, and master teachers regarding the effectiveness of these skills. Recommendations include implementing targeted professional development programs to enhance communication and strategic planning, fostering a collaborative culture across all staff levels, and integrating regular assessments of organizational culture to drive continuous improvement. By aligning management strategies with the school’s mission and goals, Malabog National High School can strengthen its organizational effectiveness and improved educational quality and student outcomes. This research contributes to the field by providing actionable insights for school administrators and policymakers aiming to enhance management practices in public secondary schools.
Comparative Study on the Functional, Pasting and Physicochemical Properties of Native and Pregelatinized Cocoyam Starch (Xanthosoma sagittifolium)
Authors: Amanyunose A. A. , Olanipekun B. F. , Adelakun O. E.
Published: February 2025

This study sought to extract and modify cocoyam starch for industrial use, as it is an underutilized tuber in Nigeria. Pregelatinization was employed to change cocoyam starch, and the functional, physicochemical, and pasting properties and proximate composition were evaluated using standard methods. The results show that pregelatinization improved the starch’s water absorption capacity (164.333-249.333%), oil absorption capacity (97-106.333%), bulk density (0732-0769g/ml), and solubility index (8.667-14.667%), but swelling power dropped (9.553-7.147g.g-1). Native cocoyam starch had the lowest gelation capacity (8%), while pregelatinized cocoyam starch had a gelation capacity of 6%. Native cocoyam starch outperformed pregelatinized cocoyam starch in terms of peak viscosity (3724.5-944.5 RVU), trough viscosity (3041-822 RVU), breakdown viscosity (683.5-162.5 RVU), and final viscosity (5516.5-1923 RVU). After pregelatinization, cocoyam starch had a higher pasting temperature (83.05-84.4oC) and peak time (5.03-7.0 min). The proximate composition indicates a small increase in carbohydrate and protein concentrations while moisture levels fall.
Understanding COVID-19 Pandemic, Impaired Smelling (anosmia) Incidence and Outcome among Patients in Dubai: Retrospective Study
Authors: Alsalem Fateh Almohammad
Published: February 2025

The onset of anosmia, or temporary loss of smell, has emerged as a prominent neurological manifestation and an early indicator of COVID-19 infection. This retrospective study, conducted by the Dubai Health Authority (DHA) staff at the DHA COVID-19 Command & Control Center in Dubai – UAE, from May to July 2020, aimed to investigate the prevalence and characteristics of anosmia in COVID-19 patients. A total of 1075 patients were enrolled in the study, comprising 878 males (81.4%) and 201 females (18.6%). The median age ranged from 31 to 40 years. Patients with anosmia were compared to those without, with a particular focus on gender disparities and associated comorbidities, including diabetes, hypertension, heart disease, lung disease, and thyroid disease. The study found that 22% of COVID-19 patients presented with anosmia, the initial symptom in 47% of cases. Notably, 95% of patients experiencing anosmia recovered within two months. While there was no significant correlation between anosmia and comorbidities, such as diabetes, hypertension, heart disease, lung disease, and thyroid disease, there was also no association observed between anosmia and other common COVID-19 symptoms, such as fever, cough, shortness of breath, and diarrhoea. Anosmia, occurring in approximately 22% of COVID-19 cases, is a noteworthy early indicator of the infection. Despite its prevalence, anosmia has no significant correlation with comorbidities or other symptomatic manifestations of COVID-19, which underscores the importance of recognising anosmia as a distinct clinical feature in diagnosing and managing COVID-19.
Artificial Intelligence in Farm Management: Integrating Smart Systems for Optimal Agricultural Practices
Authors: Padhiary Mrutyunjay , Kumar Kundan , Hussain Nabiul , Roy Dipak , Barbhuiya Javed Akhtar , Roy Pankaj
Published: February 2025

The introduction of artificial intelligence (AI) in agriculture has made significant improvements in farm management possible by offering innovative ways to optimize farming operations. This review article brings together over 100 articles published in the last ten years through a systematic search of databases on specific keywords related to AI and agriculture. The research paper covers the use of AI in machinery automation, pest detection, irrigation control, and monitoring agriculture. The results obtained show a 25% increase in crop yields in precision farming techniques by AI and machine learning and a decrease in water usage by up to 30% as opposed to traditional farming practices. In addition, AI-based pest identification has reduced pesticide application by 20% and encouraged sustainable agriculture. Crop yield estimation has now improved in terms of decision-making capability since it has significantly yielded 92% accuracy levels by using AI-driven predictive models. Further, studies indicate that the maintenance cost is decreased by 18% and fuel consumption is decreased by 15% in optimized operations with AI-based farm machinery management systems. The agriculture industry can increase productivity and sustainability to a greater extent by implementing AI in the management of farms to overcome the problems arising out of the world’s ever-increasing population.
Evaluating Tuberculosis Awareness and Screening Outcomes among Household Contacts of Index Cases: A Cross-Sectional Study in Ghana
Authors: Larbi-Debrah Patrick , Gmanyami Jonathan Mawutor , Andoono Amos , Nsiah Richmond Bediako , Nyarko Dominic , Atsu-Ofori Obed , Ayambire Geoffrey Akungoe , Kyei Shadrack , Morrison Isaac , Ayirebi Isaac , Obeng Lynna Naa Adede , Yusif Khadijatu Adiss , Nyarko Enoch , Mensah Isaac Owusu , Zakaria Saida Abdul-Rahman , Badiekang Samuel Malogae , Yeboah Akua Kumi , Prempeh Frank
Published: February 2025

This study assessed TB prevalence and factors affecting screening among household contacts in Ga West Municipality, Ghana. A cross-sectional descriptive study was conducted with 107 household contacts of TB index cases identified from health facility records. A structured questionnaire collected data on demographics, TB knowledge, screening, and treatment status. Utilizing STATA 14, the statistical analysis included descriptive statistics and Pearson chi-square tests, with a significance level set at p < 0.05. The mean age of participants was 35.7 years (SD ± 11.8), predominantly male (58.9%) and urban dwellers (78.5%). Of the participants, 57.9% were screened for TB, with a positive rate of 20% (n=9). Among those diagnosed, 88.9% (n=8) initiated treatment. Awareness of TB was high (90.7%), yet only 6.7% could identify five key symptoms of TB. Significant associations were found between educational level and TB awareness (χ²(4) = 17.237, p = 0.002), between residence and screening likelihood (χ²(1) = 4.26, p = 0.039), and individuals residing within 5 km of a health facility had a higher likelihood of being screened compared to those living farther away (χ²(1) = 10.41, p = 0.001). This study found that despite high awareness of TB transmission, screening rates among household contacts were low; enhanced outreach and education efforts are needed to improve knowledge and screening, particularly for urban residents. The predominance of male pulmonary cases was noted, highlighting the necessity for targeted efforts to mitigate TB transmission and further research on barriers to screening.
Developing predictive models for identifying dormant users and optimizing re-engagement strategies in digital communities
Authors: Kaukuntla Preetham Reddy
Published: February 2025

User engagement is critical to online community success and sustainability in an ever-changing digital landscape. However, the active user base tends to shrink, as more users remain inactive or dormant after their initial log-in. This paper thus focuses on developing predictive models of detecting sleeper users in digital communities and identifies optimized re-engagement strategies that mitigate user churn. Leverage machine-learning techniques such as random forests, support vector machines (SVM), and logistic regression, among others, to predict dormancy by users at different points of interaction patterns, behavior, and engagement metrics. The paper will also elaborate on the personalization of re-engagement strategies, including targeted notifications, incentive programs, and personalized content for enhancing user retention. This work in this domain should continue by integrating data across various channels, conducting real-time predictive modelling about external factors influencing these communities, and refining such models and strategies.
Architecting high availability solutions with google cloud load balancing
Authors: Rapolu Upesh Kumar
Published: February 2025

The following research paper has provided a vivid description of the term High Availability. It has proved to be effective for cloud-based applications for determining fluent service delivery. At the same time, it has explored the integration of Google Cloud Load Balancing that plays a vital character in transforming high availability architecture. Furthermore, this has considered the essential components that are needed for the overall enhancement of high availability such as improved performance, simplified management, enhanced security and scalability. Analysing the approaches has been rendered to control the risks leading towards a progressive road while using Google Cloud Load Balancing.
Robo advisors a study of retirement planning revolution
Authors: Ethirajulu Balaji
Published: February 2025

Robo advisors, financial planning services powered by algorithms, are changing the game of retirement. By offering independent investment recommendations, these automated systems offer a low-cost, open-ended solution to managing retirement savings. This paper reviews the role of robo-advisors in retirement planning: pros, cons, and trends. By focusing on the success stories of top platforms, we discuss the effectiveness and satisfaction of robo-advisors. Machine learning and artificial intelligence are likely to improve these systems even further, becoming an integral part of retiring citizens’ financial security.