Management Applications Research Articles

Challenges and Optimization Strategies of AI Applications in Supply Chain Management

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.

AI-Driven Energy-Efficient mHealth Applications for Chronic Disease Management: A Review of Optimization Techniques

Mobile health (mHealth) applications provide real-time monitoring, offering valuable support in chronic disease management; however, their operation significantly drains battery life, making long-term use challenging. AI-driven techniques present promising solutions for optimizing energy consumption while maintaining functionality. This study systematically reviews AI-powered approaches for energy-efficient mHealth applications, exploring methods that enhance energy efficiency without compromising monitoring accuracy. A systematic review of AI-driven optimization techniques in mHealth, focusing on energy-saving characteristics of adaptive sampling and task scheduling, was conducted, analyzing 30 studies from 2016 to 2024. The findings reveal that task scheduling achieved energy savings of up to 40%, extending battery life by several hours, while adaptive sampling contributed 25-30% energy savings. Federated learning minimized data transmission, achieving energy savings of up to 25%, while predictive behavior modeling further optimized energy consumption by adjusting resource use based on user interactions. The results highlight that AI-driven techniques significantly reduce energy consumption in mHealth applications, making long-term monitoring more feasible without frequent recharging. Beyond chronic disease management, these techniques hold potential applications in general health monitoring, preventive care, and wellness tracking. Future research should explore advanced machine learning models and energy-harvesting technologies to enhance sustainability in mHealth applications.

Research and Examples on the Application of Financial Derivatives in Risk Avoidance

The fragility of the global financial system, exposed during the 2008 financial crisis, highlighted the challenges posed by the complexity and opacity of the derivatives market. This research examines the application of financial derivatives in risk management, focusing on their role in mitigating risks through the case of JPMorgan Chase during the crisis. The paper focuses on the types and functions of financial derivatives, especially the use of credit default swaps (CDS) and interest rate swaps (IRS) and their risk management applications in financial institutions. Data sources include JPMorgan Chase's public annual report, financial report and related news reports. The results show that JPMorgan Chase has reduced some subprime risk exposure and successfully avoided potential losses of about US$5 billion through the effective use of financial derivatives. However, the use of derivatives also exposes problems such as market liquidity and counterparty risk, reminding financial institutions to carefully manage related risks when using derivatives. In conclusion, while derivatives can be powerful risk management tools, their over-reliance may introduce new systemic risks.

Remote Sensing and Geospatial Analysis in the Big Data Era: A Survey

The present survey examines the role of big data analytics in advancing remote sensing and geospatial analysis. The increasing volume and complexity of geospatial data are driving the adoption of machine learning (ML) and artificial intelligence (AI) techniques, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, to extract meaningful insights from large, diverse datasets. These AI methods enhance the accuracy and efficiency of spatial and temporal data analysis, benefiting applications in environmental monitoring, urban planning, and disaster management. Despite these advancements, challenges related to computational efficiency, data integration, and model transparency remain. This paper also discusses emerging trends and highlights the potential of hybrid approaches, cloud computing, and edge processing in overcoming these challenges. The integration of AI with geospatial data is poised to significantly improve our ability to monitor and manage Earth systems, supporting more informed and sustainable decision-making.

Preparation and thermal conduction mechanism of highly thermally conductive PA6/Al2O3/GNP composites via dynamic reactive blending

AbstractHighly thermally conductive polyamide 6 (PA6)/aluminum oxide (Al2O3)/graphene nanoplatelet (GNP) composites were fabricated through dynamic reactive blending. By utilizing the synergistic effect of low viscosity monomer and dynamic shear, uniform dispersion of Al2O3 and GNP in the PA6 matrix was achieved, forming efficient thermal conductive networks. Structural and morphological analyses confirmed the homogeneous dispersion of fillers and good interfacial bonding characteristics. The results showed that when Al2O3 and GNP contents were 43 and 7 wt%, respectively, the thermal conductivity reached 2.71 W/m·K, representing a 984% enhancement over neat PA6 and a 45.7% improvement compared to melt‐blended composites. The P‐S theoretical model was employed to understand the underlying mechanism, showing good agreement between theoretical calculations and experimental results. LED heat dissipation tests demonstrated excellent thermal response characteristics and temperature stability of the composites in practical thermal management applications, indicating their promising potential in electronic cooling applications.Highlights Dynamic reactive blending was used to prepare thermal conductive composites. Low viscosity of monomer and dynamic shear promoted uniform dispersion of fillers. The composites showed thermal conductivity of 2.71 W/m·K, 984% higher than neat PA6. P‐S model showed enhanced parallel effect reduced interfacial thermal resistance. Composites demonstrated effective heat dissipation in LED thermal management tests.

Sustainable thermal buffering of microencapsulated bio-phase change materials through an engineered biochar dopant

Climate change and unbalanced energy demand and consumption require innovative approaches to the development of sustainable and renewable energy technologies. Phase change materials (PCMs) present exceptional solutions for zero-energy thermal management due to their outstanding energy storage density at an isothermal phase transition. However, the low thermal transport and thermal stability of bulk PCMs, as well as the expensive and complex synthesis of additive materials, hinder their large-scale utilization. In this study, food-waste-derived engineered biochar (FW) is produced via slow pyrolysis to improve the thermal properties of a microencapsulated bio-PCM (B28). The thermal performance of biochar-PCM composites is evaluated based on two biochar preparation systems: varying activation temperatures (carbonized at 400 °C followed by KOH activation at different temperatures (500–800 °C)) and varying mass ratios between KOH and biochar. The introduction of a low (0.63 wt%) engineered biochar dopant significantly improves the thermal diffusivity of B28 by more than 1.3-fold. The biochar-PCM microcapsule composites present fusion and crystalline isothermal phase transition temperatures of 29.4 ± 0.38 °C and 16.7 ± 0.13 °C, respectively. Moreover, the bio-PCM exhibits a highly efficient energy per unit mass of 61.6 kJ kg–1, which is 101.7% of the energy storage capacity of bulk B28. Additionally, the composite demonstrates high thermal stability with decomposition occurring above 195 °C, thus enabling an increase of > 20 °C in the onset decomposition point compared with pristine B28. Further analysis reveals the impact of the KOH/biochar mass ratio on the thermal properties of bio-PCM. Sample FW6PCM, in which the biochar is activated at 600 °C with a KOH/biochar mass ratio of 1, exhibits the highest enthalpy storage capacity. This study suggests a promising strategy for designing high-performance, eco-friendly, and scalable bio-based composite PCMs by overcoming the long-standing bottleneck of microcapsules, which is crucial for advanced thermal management applications such as cooling and green buildings.Graphical

Innovative Technology Solutions for Classroom Management

The increasing integration of technology into various aspects of life has revolutionized education, particularly in classroom management. This study examines innovative technology solutions designed to address challenges in traditional classroom settings, such as disruptions, low engagement, and diverse learning needs. A comprehensive analysis of the limitations of traditional techniques is presented alongside the benefits and features of technological alternatives. The research also includes a discussion on the development and implementation of a proof-of-concept classroom management application, emphasizing its usability, adaptability, and role in enhancing teaching methodologies. Case studies highlight successful implementations, showcasing the potential of these solutions to transform classroom dynamics and improve educational outcomes. Keywords: Classroom Management, Educational Technology, Student Engagement, Teaching Methodologies, Personalized Learning.

DIGITAL TWIN APPLICATION IN CONSTRUCTION COST MANAGEMENT

In line with the current construction revolution, it is time for the construction industry to embrace innovation and technology. This is corresponding with the National Construction Policy 2030 (NCP 2030) that comes out with the aim to digitalize the entire construction industry towards the IR 4.0. The focus is to boost the nation's construction industry's competitiveness and recognition worldwide. The construction industry has undergone a significant transformation in recent years such as BIM, IoT including Digital Twin due to the incorporation of digital technologies. A digital twin is a virtual representation of a physical asset. It is still a relatively new concept in the construction industry, but it offers an innovative method for improving cost management strategies in construction projects. Applications of the digital twin in construction cost management have the potential to revolutionize conventional methods. Therefore, this study seeks to determine the level of understanding of construction industry players on the concept of digital twin applications in construction cost management by providing the concept and to explore the challenges and strategies in implementing the digital twin applications in construction cost management. This research employed a mixed-method approach by means of questionnaire survey and interview for data collection. 35 samples that consist of construction industry players from different organizations participated in this study. The data collected from the survey and interview are analyses through descriptive and content analysis. Overall, the findings find out the understanding of digital twin applications with its challenges and strategies to overcome it. This research contributes to the body of knowledge regarding digital twin applications and construction cost management.

Pathogenicity of Serratia marcescens strains as biological control agent: Implications for sustainable pest management.

The escalating demand for sustainable and eco-friendly pest management strategies has raised interest in harnessing the pathogenic potential of microorganisms. Serratia marcescens, a Gram-negative bacterium, has emerged as a potential biological control agent for sustainable pest management. This review critically examines the history, biology, identification, and pathogenicity of S. marcescens strain with their potential application in pest management. The diverse mechanisms employed by the strain to exert control over pests, including the production of metabolites and the induction of systemic resistance in plants, are examined. The review also summarizes the ecological significance and global distribution of S. marcescens associated with the use of S. marcescens in biological control strategies. Furthermore, the usage efficacy of S. marcescens over other conventional chemicals is discussed. A comprehensive understanding of the pathogenic potential of S. marcescens strains as biological control agents is crucial for developing effective and sustainable pest management strategies. This review consolidates current research advances on S. marcescens, and provides insights into the prospects and challenges of using S. marcescens for integrated pest management.

Comprehensive management application of sulodexide on proteinuria: 2 case reports

Proteinuria is an important sign of kidney function decline and one of the common clinical manifestations in patients with chronic kidney disease and diabetic kidney disease, which are often accompanied by a gradual decline in kidney function. Therefore, effective management of proteinuria is of great significance for delaying the progression of kidney disease and protecting kidney function. Sulodexide is a mixture of fast-moving heparin and dermatan sulfate with anticoagulant, thrombolytic, anticardiovascular, lipid-lowering, and renal-protective effects. It has potential application value in the comprehensive management of proteinuria. This study reported two different chronic disease cases to explore the comprehensive management application of sulodexide in patients with long-term poor proteinuria control. Case 1 is a 45 years-old male with type 2 diabetes mellitus and hypertension accompanied by persistent proteinuria and intermittent edema in both legs. Case 2 is a 52 years-old female with chronic nephritis and rheumatoid arthritis accompanied by persistent proteinuria and edema in both legs. Two patients showed a decrease in urinary protein levels and stable control of the remaining signs of disease after the addition of sulodexide to their comprehensive treatment regimens, indicating that sulodexide plays an important role in the comprehensive management of proteinuria.

A Neural Network-Based Recruitment System for Optimizing Human Resources in Business Process Management Applications

Business processes have been formally studied from various perspectives since the onset of the industrial age. Despite this prolonged scrutiny, processes remain poorly understood, unmanaged, and ineffectively executed. Business school curricula are predominantly function-specific and limited, while IT programs concentrate on narrow technical competencies, neglecting the 'process view' and 'integration' aspects, which are often left to individual students or academics. This paper examines the deficiencies in current business and IS/IT education and reviews the historical context of business processes. It asserts the importance of business process management and underscores its potential to bridge gaps in business education. Additionally, it discusses the strategies adopted by business schools and the challenges faced in BPM education and research.

Pharmacological mechanisms of carvacrol against hepatocellular carcinoma by network pharmacology and molecular docking

Background Preclinical studies have demonstrated that carvacrol possesses various biological and pharmacological properties, including anti-hepatocellular carcinoma (HCC) effects. However, the molecular basis of its therapeutic action on HCC remains unclear. Objective The aim of this study was to investigate and further validate the multi-target therapeutic mechanism of carvacrol against HCC. Methods The chemical structure of carvacrol was obtained from the PubChem database, and its potential targets were identified using SwissTargetPrediction, HERB, and BATMAN-TCM. HCC-specific genes were screened from the TCGA-LIHC cohort. The therapeutic targets of carvacrol against HCC were determined through the intersection of these datasets. Subsequently, a multivariate Cox regression prognostic model was established. Molecular docking was performed to analyze the interactions between carvacrol and its therapeutic targets. Additionally, molecular dynamics simulations were conducted to validate the molecular docking results using Discovery Studio 2019 software. Results A total of 223 carvacrol targets and 882 HCC-specific genes were identified. Fifteen therapeutic targets of carvacrol against HCC were obtained, including CA2, AR, ALB, AURKA, ALPL, EPHX2, BCHE, IL1RN, AGRN, CRP, DMGDH, APOA1, SOX9, HPX, and CHKA. The prognostic model accurately and independently predicted survival outcomes. AGRN and AURKA were significantly associated with HCC overall survival. Molecular docking and molecular dynamics simulations demonstrated that carvacrol exhibited strong potential for stable binding to the therapeutic targets AGRN and AURKA. Conclusion Our findings elucidate the multi-target mechanism of action of carvacrol against HCC, providing a foundation for future research on its application in HCC management.

Biomineralized Nanocomposite-Integrated Microneedle Patch for Combined Brachytherapy and Photothermal Therapy in Postoperative Melanoma Recurrence and Infectious Wound Healing.

In the surgical management of malignant melanoma, incomplete tumor resection and large-area cutaneous defects are major contributors to high locoregional recurrence and uncontrolled wound infections, resulting in poor prognosis and prolonged recovery times for patients. Herein, a versatile nanocomposite microneedle patch (referred to as GM-Ag2S/Ca32P) is designed to simultaneously eliminate residual tumor post-surgery and promote the healing of infectious wounds. This microneedle patch effectively penetrates subcutaneous tissues, delivering therapeutic payloads to infiltrating tumor cells and bacteria. The Ag2S/Ca32P nanocomposites encapsulated within the microneedle patch decompose in the acidic microenvironment of tumors and bacterial biofilms, releasing radioactive 32P and Ag2S nanodots, which enhance tumor eradication and bacteria killing through synergistic brachytherapy and photothermal therapy (PTT). Moreover, the nanocomposite microneedle patch promotes scar-free wound healing by reducing inflammation, and promoting granulation tissue formation, collagen deposition, and angiogenesis, thanks to localized hyperthermia, radiation, and the swelling and biodegradation of the microneedle matrices. This microneedle patch-based postoperative adjuvant therapy offers a comprehensive strategy for addressing melanoma recurrence and infectious wound healing, with promising potential for clinical application in postsurgical management.

3D Printing Elastocaloric TiNiCu Thermoelectric Shape Memory Alloys.

The development of new thermoelectric conversion and cooling materials is an important means of addressing global climate and heat emissions in the future. While heavy and toxic elements like tellurium and lead are traditionally used to make thermoelectric materials with poor mechanical properties, recent decades have seen a gradual push towards greener and more sustainable alternatives. One such potential alternative material for thermoelectric and thermal management applications would be the Nitinol (TiNi) shape memory alloy, due to their superior mechanical properties. In this study, we have investigated the use of 3D melt printing techniques that can be used to achieve thermoelectric performance and efficiency of elastic memory alloys below 500 °C. The electrical and thermal properties of TiNiCu materials and their relation to morphology were investigated. All the alloys show similar effect sizes, their fatigue behavior is however different. By adjusting the composition of Ti and Ni elements and we have obtained memory alloys with high thermoelectric properties, with a 50% increase in power factor and a 100% increase in ZT values. This work demonstrates the feasibility of using novel 3D-printed SMAs for thermoelectric and cooling applications, providing an experimental basis for the future preparation of low-cost, efficient, long service lifetime and stable materials for thermal energy harvesting and management.

Comparison of Evapotranspiration Calculated and Estimated By Different High-Resolution Gridded Analyses For Northeast Brazil

Objective: to present the results of a climatological analysis of evapotranspiration (ET) in the Brazilian Northeast (NEB) between 1961 and 2020, highlighting its seasonality, the identification of homogeneous groups by cluster analysis, and positive trends over the decades. In addition, the text compares the accuracy of two datasets (MODGroETa and ERA5-Land), emphasizing the superiority of ERA5-Land for ET estimates, especially after bias correction. The central purpose is to highlight the relevance of continuously monitoring and evaluating ET data for different regions and time scales. Method: For this study, meteorological data from the National Institute of Meteorology (INMET) and high-resolution gridded analyses (0.1° x 0.1°) adjusted by elevation were used. These data underwent rigorous quality control and altitude correction. It used atmospheric reanalysis data with high spatial resolution (9 km) and hourly temporal frequency, based on observations from satellites, surface stations and snow data. The remote sensing model derived from MOD16 was used to estimate evapotranspiration (ET) based on the Penman-Monteith equation. It is available with a spatial resolution of 0.1° and a decadal time scale. Multivariate statistical technique applied to identify homogeneous areas in Northeast Brazil with similar ET characteristics, based on the monthly distribution of data. Results and Discussion: The climatology of ET (1961-2020) in the NEB showed strong seasonality. Between January and April, the highest values (>4 mm/day) are concentrated in the semi-arid region, decreasing in the west of Piauí and Maranhão (<3.5 mm/day). From May to July, ET decreases in the east, while from September to December, the interior registers maximums (>5.5 mm/day). Cluster analysis identified six homogeneous regions, independent of rainfall patterns, with higher ET from September to November and lower values between April and June, showing significant seasonal variations in the NEB. Research implications: The research contributes to water management, agricultural zoning and environmental conservation in the Brazilian Northeast (NEB). It identifies trends in evapotranspiration (ET) associated with climate change, provides subsidies for improving climate models and public policies, and fosters future research into adaptation and mitigation of impacts on ecosystems and communities. Originality/Value: The study is original in identifying regional patterns of evapotranspiration in the NEB, combining cluster analysis and multiple data sources. Its value lies in its practical application for water management, agricultural zoning, environmental conservation and public policies, strengthening climate adaptation strategies and mitigating impacts on vulnerable ecosystems and communities.

The Present State and Potential Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.

An aberrant increase in cancer incidences has demanded extreme attention globally despite advancements in diagnostic and management strategies. The high mortality rate is concerning, and tumour heterogeneity at the genetic, phenotypic, and pathological levels exacerbates the problem. In this context, lack of early diagnostic techniques and therapeutic resistance to drugs, sole awareness among the public, coupled with the unavailability of these modern technologies in developing and low-income countries, negatively impact cancer management. One of the prime necessities of the world today is the enhancement of early detection of cancers. Several independent studies have shown that screening individuals for cancer can improve patient survival but are bogged down by risk classification and major problems in patient selection. Artificial intelligence (AI) has significantly advanced the field of oncology, addressing various medical challenges, particularly in cancer management. Leveraging extensive medical datasets and innovative computational technologies, AI, especially through deep learning (DL), has found applications across multiple facets of oncology research. These applications range from early cancer detection, diagnosis, classification, and grading, molecular characterization of tumours, prediction of patient outcomes and treatment responses, personalized treatment, and novel anti-cancer drug discovery. Over the past decade, AI/ML has emerged as a valuable tool in cancer prognosis, risk assessment, and treatment selection for cancer patients. Several patents have been and are being filed and granted. Some of those inventions were explored and are being explored in clinical settings as well. In this review, we will discuss the current status, recent advancements, clinical trials, challenges, and opportunities associated with AI/ML applications in cancer detection and management. We are optimistic about the potential of AI/ML in improving outcomes for cancer and the need for further research and development in this field.

Synergistic Effects of Photocatalysis, Ozone Treatment, and Metal Catalysts on the Decomposition of Acetaldehyde

This study explores the synergistic interactions between photocatalysis, ozone treatment, and metal catalysts in the decomposition of acetaldehyde, a representative volatile organic compound (VOC). The study addresses the growing need for efficient air purification technologies by integrating advanced oxidation processes. Metal catalysts, particularly manganese oxide-based materials, were combined with photocatalysis and ozonation to investigate their impact on acetaldehyde removal efficiency. Experimental results revealed that the treatment integrating these methods significantly outperformed conventional single-process treatments. Metal catalysts facilitated the initial oxidation of acetaldehyde, while photocatalysis accelerated subsequent stages, including the mineralisation of intermediates. Ozone contributed additional reactive oxidative species, further enhancing decomposition rates. These findings provide valuable insights into the design of efficient VOC removal systems, demonstrating that integrating metal catalysts with photocatalytic and ozonation processes offers a promising strategy for improving air purification technologies. This approach has potential applications in environmental remediation and indoor air quality management.

Legal Protections for Patients with Medical Conditions in Online Health Consultations

Legal protections in telemedicine protect patients' rights, privacy, and care quality, but research often overlooks patients' perspectives. Therefore, understanding evolving legislation and patient-centered legal concepts improves patient care. This article aims to explore legal protections for patients with medical conditions in online health consultations. This study used a systematic literature review (SLR) approach to examine articles related to legal protections for patients, medical conditions, and online health consultations. We searched the Scopus and PubMed databases for articles in three categories: legal protections for patients, medical conditions, and online health consultations over the last five years (2019-2024). Using the Publish and Perish and Mendeley Reference Manager applications, we selected 389 papers from the Scopus database and 46 from the PubMed database based on specific criteria. Therefore, we conducted a PRISMA analysis on the 59 full-text research articles that we included. The results show that legal protections for patients in online health consultations include confidentiality, informed consent, licensure, data protection, emergency response protocols, professional standards, telemedicine compliance, care quality, and redress mechanisms. Solutions include streamlining licensing processes and enhancing cross-border regulations. The study emphasizes the need for patient advocacy, legal protection, data protection, and ethical compliance in online medical consultations, addressing barriers like dissatisfaction and lack of funds.

A Study on the Role of LPA and ATX in Breast Cancer in Iraq

Breast cancer is the most common malignancy among women and a leading cause of cancer-related mortality. Early detection is crucial for improving treatment outcomes. This study investigates the roles of lysophosphatidic acid (LPA) and autotaxin (ATX) in the early diagnosis of breast cancer among Iraqi women. A case-control study was conducted involving 75 women diagnosed with breast cancer and 75 healthy controls. Blood samples were collected, and biochemical parameters, including LPA and ATX levels, were measured using the ELISA technique. Statistical analyses were performed with SPSS version 24, and receiver operating characteristic (ROC) analysis was used to evaluate diagnostic capabilities. The majority of breast cancer patients were aged 50-59 years (33.3%). Histologically, invasive ductal carcinoma was the most prevalent subtype (82.6%). Biochemical analysis revealed significant differences in alanine aminotransferase, total serum bilirubin, and alkaline phosphatase levels between patients and controls. LPA levels were significantly elevated in the patient group (868.48±142.11 pg/ml) compared to controls (212.01±54.94 pg/ml), while ATX levels were also higher (2252.20±399.46 pg/ml vs. 951.40±209.21 pg/ml). ROC analysis indicated that both LPA and ATX exhibited high diagnostic sensitivity (98%) and specificity (100%). In Iraqi women, elevated serum levels of LPA and ATX may serve as potential diagnostic biomarkers for breast cancer, highlighting their role in disease progression. Further studies are warranted to explore their clinical applications in breast cancer diagnosis and management

Development and Application of Family Management in Nursing Care

The concept of family management has gained increasing acceptance in the United States for over two decades and is today applied in care worldwide. However, this concept is still largely novel to clinical professionals in Taiwan. This article was written to present the history and current application of family management to promote greater recognition of the related opportunities and potentials in the nursing field. Understanding the family management style framework helps nursing professionals better grasp the dynamic situation in families while dealing with the primary health issues of patients. Awareness of adjustability and regulation of the family allows nursing professionals to understand and provide care to the entire family unit rather than to individual family members. Adapting family management measures and identifying the typology of family management may also be an efficient approach to conducting family evaluations and developing appropriate interventions. We reflect on these issues and offer suggestions on family management to inspire nursing professionals to pursue related research and clinical practicums to improve family nursing capabilities in the nursing field.