Management Applications Research Articles

Enhancing transparency and fairness in automated credit decisions: an explainable novel hybrid machine learning approach

This paper uses a generalised stacking method to introduce a novel hybrid model that combines a one-dimensional convolutional neural network 1DCNN with extreme gradient boosting XGBoost. We compared the predictive accuracies of the proposed hybrid architecture with three conventional algorithms-1DCNN, XGBoost and logistic regression (LR) using a dataset of over twenty thousand peer-to-peer (P2P) consumer credit observations. By leveraging the SHAP algorithm, the research provides a detailed analysis of feature importance, contributing to the model’s predictions and offering insights into the overall and individual significance of different features. The findings demonstrate that the hybrid model outperforms the LR, XGBoost and 1DCNN models in terms of classification accuracy. Furthermore, the research addresses concern regarding fairness and bias by showing that removing potentially discriminatory features, such as age and gender, does not significantly impact the hybrid model’s classification capabilities. This suggests that fair and unbiased credit scoring models can achieve high effectiveness levels without compromising accuracy. This paper makes significant contributions to academic research and practical applications in credit risk management by presenting a hybrid model that offers superior classification accuracy and promotes interpretability using the model agnostic SHAP framework.

An experimental and comparative study on passive and active PCM cooling of a battery with/out copper mesh and investigation of PCM mixtures

The carbon emission contribution to global warming accelerated both research on and transition to electric vehicles (EVs). Drivers demand high power, fast acceleration and less charging times. All these demands require high C rate charging/discharging demands from batteries. The rate of heat generation is exponentially proportional to C rates which decreases battery lifetime and may lead to thermal runaway. However, a battery thermal management system decreases thermal runaway risk and decelerates battery degradation via controlling battery temperature. In this paper, we first document the thermal conductivity enhancement via copper foam into phase change material (PCM) domain to uncover their possible use in EV thermal management applications. Maximum 15.93 times increment is achieved with a specific copper foam. Then, physical properties and behaviors of distinct PCM mixtures are documented. Homogeneity of mixtures is associated with the chemistry of PCMs and the mixture melting point is proportional to the volume weighted average of melting temperatures. The results document that the PCM with relatively lower melting point is beneficial when end of discharge temperatures considered, except for high discharge rate of 2C. Temperature uniformity across the battery increases with relatively higher melting point PCM. Experiments also document that the amount of PCM volume lost via insertion of copper foam yields higher end of discharge temperatures. Overall, both PCM and copper foam enhances temperature homogeneity and their benefit becomes more sensible during drive cycles relative to continuous charge/discharge use cases.

Non-Classical Heat Transfer and Recent Progress

Abstract Heat transfer in solids has traditionally been described by Fourier's law, which assumes local equilibrium and a diffusive transport regime. However, advancements in nanotechnology and the development of novel materials have revealed non-classical heat transfer phenomena that extend beyond this traditional framework. These phenomena, which can be broadly categorized into those governed by kinetic theory and those extending beyond it, include ballistic transport, phonon hydrodynamics, coherent phonon transport, Anderson localization, and glass-like heat transfer, among others. Recent theoretical and experimental studies have focused on characterizing these non-classical behaviors using methods such as the Boltzmann transport equation, molecular dynamics, and advanced spectroscopy techniques. In particular, the dual nature of phonons, exhibiting both particle-like and wave-like characteristics, is fundamental to understanding these phenomena. This review summarizes state-of-the-art findings in the field, highlighting the importance of integrating both particle and wave models to fully capture the complexities of heat transfer in modern materials. The emergence of new research areas, such as chiral and topological phonons, further underscores the potential for advancing phonon engineering. These developments open up exciting opportunities for designing materials with tailored thermal properties and new device mechanisms, potentially leading to applications in thermal management, energy technologies, and quantum science.

Novel hydrogen-bonded organic frameworks-based coating for fat oil and grease deposition control in the sewer system

Hydrogen-bonded organic frameworks (HOFs) represent an emerging class of porous materials characterized by crystalline frame structures, self-assembled from organic molecules through hydrogen bonding. This study demonstrates that HOFs fragment into small particles while preserving their original structure when dispersed in a polymeric epoxy solution. The intermolecular hydrogen bond structural and electronic properties of the LH4-HOF complex as well as the mechanism of HOF incorporated epoxy were extensively studied using density functional theory. LH4-HOF has a high Langmuir surface area of 2758.3 m2/g and nonlocal density functional theory pore size distributions of 3 A°. This unique solution processability enables the fabrication of coatings with high stability in aqueous systems. Results from a 30-day leaching test under controlled conditions (pH 7, temperature 20 ± 2 °C) indicate that LH4-HOF incorporated epoxy-coated concrete samples exhibited an over 85 % reduction in calcium release compared to uncoated samples, with epoxy-coated samples alone showing a 60 % reduction in calcium leaching. Additionally, the LH4-HOF-incorporated epoxy coating reduced the formation of fats, oils and grease deposits by more than 73 % on the concrete samples. Thus, this novel coating approach offers a sustainable solution with significant potential applications in sewer management.

Research on a Non-Stationary Groundwater Level Prediction Model Based on VMD-iTransformer and Its Application in Sustainable Water Resource Management of Ecological Reserves

The precise forecasting of groundwater levels significantly influences plant growth and the sustainable management of ecosystems. Nonetheless, the non-stationary characteristics of groundwater level data often hinder the current deep learning algorithms from precisely capturing variations in groundwater levels. We used Variational Mode Decomposition (VMD) and an enhanced Transformer model to address this issue. Our objective was to develop a deep learning model called VMD-iTransformer, which aims to forecast variations in the groundwater level. This research used nine groundwater level monitoring stations located in Hangjinqi Ecological Reserve in Kubuqi Desert, China, as case studies to forecast the groundwater level over four months. To enhance the predictive performance of VMD-iTransformer, we introduced a novel approach to model the fluctuations in groundwater levels in the Kubuqi Desert region. This technique aims to achieve precise predictions of the non-stationary groundwater level conditions. Compared with the classic Transformer model, our deep learning model more effectively captured the non-stationarity of groundwater level variations and enhanced the prediction accuracy by 70% in the test set. The novelty of this deep learning model lies in its initial decomposition of multimodal signals using an adaptive approach, followed by the reconfiguration of the conventional Transformer model’s structure (via self-attention and inversion of a feed-forward neural network (FNN)) to effectively address the challenge of multivariate time prediction. Through the evaluation of the prediction results, we determined that the method had a mean absolute error (MAE) of 0.0251, a root mean square error (RMSE) of 0.0262, a mean absolute percentage error (MAPE) of 1.2811%, and a coefficient of determination (R2) of 0.9287. This study validated VMD and the iTransformer deep learning model, offering a novel modeling approach for precisely predicting fluctuations in groundwater levels in a non-stationary context, thereby aiding sustainable water resource management in ecological reserves. The VMD-iTransformer model enhances projections of the water level, facilitating the reasonable distribution of water resources and the long-term preservation of ecosystems, providing technical assistance for ecosystems’ vitality and sustainable regional development.

Thermo-hydraulic performance characteristics of novel G-Prime and FRD Triply Periodic Minimal Surface (TPMS) geometries

Efficient thermal management is crucial for numerous applications, from electronics cooling and automobile systems to aerospace systems, necessitating the exploration of advanced heat transfer technologies like TPMS structures. Compared to conventional heat exchangers and heat sinks, lattice structures have improved by around 20–30 % performance. However, the variation in performance of the various lattice structures, such as FRD, G-Prime, etc., is yet unknown. Thus, this study investigates the thermo-hydraulic performance of various Triply Periodic Minimal Surface (TPMS) structures, including novel G-Prime and FRD geometries, through numerical simulations and experimental validation. The TPMS geometries were modeled using LattGen software and voxelized with 20 % relative density. Computational Fluid Dynamics (CFD) simulations were performed using Ansys Fluent with the k-epsilon turbulence model, and experiments were conducted on 3D-printed samples for validation. The numerical results reveal that G-Prime-2 and FRD Prime exhibit the highest heat transfer performance while demonstrating higher pressure drops than other TPMS geometries. Experimental validation agrees with numerical predictions, confirming the superior thermo-hydraulic performance of G-Prime-2 and FRD Prime for heat transfer enhancement applications. The results contribute to understanding TPMS thermo-hydraulic performance and enable informed geometry selection for thermal management applications.

Human Resource Management Based On Islamic Values At Madrasah Aliyah Nurul Qarnain In Facing The Challenges Of The Digital Era

In order to meet the difficulties of the digital age, this study intends to examine how Islamic educational institutions apply Human Resource management based on Islamic principles.. This study uses a qualitative method with a case study approach on an educational institution that applies Islamic principles in human resource management, such as trust, justice, and excellence. The research results show that the integration of Islamic values in human resource management significantly improves the performance, loyalty, and satisfaction of educators. However, the challenges in digital literacy are still felt by most educators, even though institutions have provided regular technology training. The implementation of a collaborative culture through deliberation has proven effective in enhancing innovation and the involvement of educators in the decision-making process. The use of digital technology tailored to Islamic values, such as sharia-compliant human resource management applications, also strengthens aspects of transparency and justice in institutional management. In conclusion, Human Resource management based on Islamic values, supported by digital technology, is capable of improving performance quality and creating a harmonious work environment that is relevant in addressing the challenges of the digital era.

Blockchain for video watermarking: An enhanced copyright protection approach for video forensics based on perceptual hash function.

As a direct result of advancements in digital technology and the Internet, the copyright protection and information integrity of multimedia that are being published across the Internet have emerged as a major and urgent issue that needs to be addressed. The technique of digital watermarking may be used to protect intellectual property. In terms of authentication, resilience, storage, and capacity of digital watermarking information, there is still room for development. Blockchain's potential in video copyright protection and management applications has motivated researchers. Copyright owners and consumers may now communicate directly via copyright protection apps built on the blockchain, eliminating the need for distributers and the associated fees. Nonetheless, the current blockchain-based video watermarking solutions require storing a significant number of coordinates depending on the watermark size and are susceptible to video frame attacks on the video frame texture region. This study proposes an enhanced video copyright management approach that incorporates digital watermarking, the blockchain, and a perceptual hash function. Watermark information is stored on a blockchain structure, which also acts as a timestamp for verification purposes. To verify watermark data without the source video, a perceptual hash function is employed to compute a hash value based on the structural information of video frames. The contribution is in learning how to extract a hash function from a small number of video frames that still adequately represent a large amount of video content while also reducing the number of unnecessary video frames and the amount of computation required to summarize and index that content in a blockchain. This expedites the dissemination of copyrighted works and increases their security and readability, hence facilitating their circulation on the Internet. Our experimental results demonstrate that this approach is memory efficient, as it only needs to store one key for each key frame, regardless of the size of the watermark. Additionally, the overall robustness is greatly improved by using the blockchain's random hash function. Therefore, new and important advancements in video watermarking have been realized because of this effort.

A comparative study of drone and manual herbicide application for weed management in wet direct-seeded rice (Oryza sativa L.)

A field experiment was conducted at Tamil Nadu Agricultural University, Coimbatore, during the Navarai season (January-April) of 2024 to assess the efficiency of drone-based herbicide application in terms of weed control, energy use, and cost effectiveness in wet direct-seeded rice (Oryza sativa L.). The study compared the effectiveness of weed control using herbicide combinations applied as pre-emergence followed by early post-emergence, with both drone and knapsack sprayer. The treatments included the application of pretilachlor followed by early post-emergence bispyribac sodium, and pyrazosulfuron followed by penoxsulam + cyhalofop butyl, using both drone and knapsack sprayer. Additionally, weed-free and unweeded control plots were included. Results indicated that the application of pretilachlor followed by bispyribac sodium using a drone sprayer significantly reduced weed density and weed dry weight compared to the unweeded plot. This treatment also resulted in a higher grain yield (5286 kg ha-1). Moreover, drone application of pretilachlor followed by bispyribac sodium provided a higher net return (? 51631/ha), benefit-cost ratio (2.17), energy-use efficiency (9.53), and energy productivity (0.30 kg/MJ). The experiment concluded that drone-based spraying of pretilachlor followed by bispyribac sodium is an effective weed management strategy for wet direct-seeded rice, offering superior yield attributes, energy use efficiency, and profitability.

Application of WeChat-based cognitive behavioural stress management for early-stage cervical cancer patients: a randomised controlled study

Background This randomised controlled study was aimed at investigating the effects of WeChat-based cognitive behavioural stress management (WB-CBSM) on the mental health of patients with early-stage cervical cancer treated with surgical resection. Methods A total of 184 patients with early-stage cervical cancer were randomised to receive either WB-CBSM (n = 92) or normal care (NC) (n = 92) for 8 weeks. Results Compared with the NC group, the WB-CBSM group exhibited reduced Hospital Anxiety and Depression Scale (HADS)–anxiety scores at months (M)1 (t = 2.022, P = 0.045), M3 (t = 2.575, P = 0.011), and M6 (t = 2.709, P = 0.007); anxiety rates at M3 (χ2 = 4.834, P = 0.028) and M6 (χ2 = 5.182, P = 0.023); HADS–depression scores at M3 (t = 2.069, P = 0.040) and M6 (t = 2.449, P = 0.015); and depression rates at M6 (χ2 = 4.268, P = 0.039). Moreover, the WB-CBSM group showed increased Functional Assessment of Chronic Illness Therapy–Spiritual Well-Being scale scores at M1 (t = −2.001, P = 0.047), M3 (t = −2.281, P = 0.024), and M6 (t = −3.501, P = 0.001); the Quality of Life Questionnaire-Core 30 (QLQ-C30) global health score at M3 (t = −2.034, P = 0.043) and M6 (t = −2.426, P = 0.016); and QLQ-C30 function score at M3 (t = −2.009, P = 0.046). However, the groups exhibited decreased EuroQol-5 dimension scores at M3 (t = 2.056, P = 0.041) and M6 ((t = 2.242, P = 0.026) and QLQ-C30 symptom scores at M1 (t = 2.026, P = 0.044) and M3 (t = 2.210, P = 0.028). Conclusion WB-CBSM reduced anxiety and depression and improved the spiritual well-being and quality of life of patients with early-stage cervical cancer treated with surgical resection.

Maize Phenotypic Parameters Based on the Constrained Region Point Cloud Phenotyping Algorithm as a Developed Method

As one of the world’s most crucial food crops, maize plays a pivotal role in ensuring food security and driving economic growth. The diversification of maize variety breeding is significantly enhancing the cumulative benefits in these areas. Precise measurement of phenotypic data is pivotal for the selection and breeding of maize varieties in cultivation and production. However, in outdoor environments, conventional phenotyping methods, including point cloud processing techniques based on region growing algorithms and clustering segmentation, encounter significant challenges due to the low density and frequent loss of point cloud data. These issues substantially compromise measurement accuracy and computational efficiency. Consequently, this paper introduces a Constrained Region Point Cloud Phenotyping (CRPCP) algorithm that proficiently detects the phenotypic traits of multiple maize plants in sparse outdoor point cloud data. The CRPCP algorithm consists primarily of three core components: (1) a constrained region growth algorithm for effective segmentation of maize stem point clouds in complex backgrounds; (2) a radial basis interpolation technique to bridge gaps in point cloud data caused by environmental factors; and (3) a multi-level parallel decomposition strategy leveraging scene blocking and plant instances to enable high-throughput real-time computation. The results demonstrate that the CRPCP algorithm achieves a segmentation accuracy of 96.2%. When assessing maize plant height, the algorithm demonstrated a strong correlation with manual measurements, evidenced by a coefficient of determination R2 of 0.9534, a root mean square error (RMSE) of 0.4835 cm, and a mean absolute error (MAE) of 0.383 cm. In evaluating the diameter at breast height (DBH) of the plants, the algorithm yielded an R2 of 0.9407, an RMSE of 0.0368 cm, and an MAE of 0.031 cm. Compared to the PointNet point cloud segmentation method, the CRPCP algorithm reduced segmentation time by more than 44.7%. The CRPCP algorithm proposed in this paper enables efficient segmentation and precise phenotypic measurement of low-density maize multi-plant point cloud data in outdoor environments. This algorithm offers an automated, high-precision, and highly efficient solution for large-scale field phenotypic analysis, with broad applicability in precision breeding, agronomic management, and yield prediction.

Design and Development of Diabestfriend Application for Digital Self Care Management of Type 2 Diabetes Mellitus Patients

Objective: The availability of the results of the Android-based Diabestfriend Application for digital self-care management of type 2 diabetes mellitus patients. Theoretical Framework: The numerous interventions that come under the more general term "self-care" and are helpful in understanding the self-care suite have been categorized into three domains: self-awareness, self-testing, and self-care. Methods: The ADDIE development model is used in this study in three stages. Ten patients with type 2 diabetes who completed questionnaires, along with validators from one media expert and one material expert, comprise the implementation stage, which is the application assessment stage. The questionnaires consist of three sections: a total of 16 questions for patient application assessment, 21 questions for media expert validation, and 25 questions for material expert validation. Results and Discussion: The validation score of media experts was 83.8 with the very good category, the validation score of the material experts was 81.6 with the very good category and the response score of diabetes mellitus patients was 88.95 with the very good category. Research Implications: Further research on the efficacy of the Diabestfriend Application as a digital self-care management tool in type 2 diabetes mellitus patients will provide insight into how the use of the application affects the patients' level of self-care management. Originality/Value: The Diabestfriend Application is appropriate for use as a digital self-care management tool for patients with type 2 diabetes mellitus, according to validation from media experts, material experts, and 10 diabetes patients.

Seasonal variations of microbial communities and viral diversity in fishery-enhanced marine ranching sediments: insights into metabolic potentials and ecological interactions

BackgroundThe ecosystems of marine ranching have enhanced marine biodiversity and ecological balance and have promoted the natural recovery and enhancement of fishery resources. The microbial communities of these ecosystems, including bacteria, fungi, protists, and viruses, are the drivers of biogeochemical cycles. Although seasonal changes in microbial communities are critical for ecosystem functioning, the current understanding of microbial-driven metabolic properties and their viral communities in marine sediments remains limited. Here, we employed amplicon (16S and 18S) and metagenomic approaches aiming to reveal the seasonal patterns of microbial communities, bacterial-eukaryotic interactions, whole metabolic potential, and their coupling mechanisms with carbon (C), nitrogen (N), and sulfur (S) cycling in marine ranching sediments. Additionally, the characterization and diversity of viral communities in different seasons were explored in marine ranching sediments.ResultsThe current study demonstrated that seasonal variations dramatically affected the diversity of microbial communities in marine ranching sediments and the bacterial-eukaryotic interkingdom co-occurrence networks. Metabolic reconstruction of the 113 medium to high-quality metagenome-assembled genomes (MAGs) was conducted, and a total of 8 MAGs involved in key metabolic genes and pathways (methane oxidation - denitrification - S oxidation), suggesting a possible coupling effect between the C, N, and S cycles. In total, 338 viral operational taxonomic units (vOTUs) were identified, all possessing specific ecological characteristics in different seasons and primarily belonging to Caudoviricetes, revealing their widespread distribution and variety in marine sediment ecosystems. In addition, predicted virus-host linkages showed that high host specificity was observed, with few viruses associated with specific hosts.ConclusionsThis finding deepens our knowledge of element cycling and viral diversity in fisheries enrichment ecosystems, providing insights into microbial-virus interactions in marine sediments and their effects on biogeochemical cycling. These findings have potential applications in marine ranching management and ecological conservation.DRyDF7ykR-XALqAtu34uZtVideo

Integrating MXene Film With Recyclable Polyethylene Glycol-co-Polyphosphazene Copolymer as Solid-Solid Phase Change Material for Versatile Applications.

Phase-change materials (PCMs) stand a pivotal advancement in thermal energy storage and management due to their reversible phase transitions to store and release an abundance of heat energy. However, conventional solid-liquid PCMs suffer from fluidity and leakage in their molten state, limiting their applications at advanced levels. Herein, a novel Zn2+-crosslinked polyethylene glycol-co-polyphosphazene copolymer (PCEPN-Zn) as a solid-solid PCM through dynamic metal-ligand coordination is first designed and synthesized. The as-synthesized PCEPN-Zn is further integrated with an MXene film to construct a double-layered phase-change composite through layer-by layer adhesion. Owing to the introduction of MXene film with low emissivity, good light absorptivity, and nonflammability, the resultant phase-change composite not only presents a high latent-heat capacity, good thermal stability, high thermal reliability, and excellent shape stability, but also exhibits a superior self-healing ability, good recyclability, high adhesivity, and good flame-retardant performance. It can be easily adhered to on most objects for various application scenarios. With a combination of the excellent functions derived from PCEPN-Zn and MXene film, the developed phase-change composite exhibits broad prospects for versatile applications in the thermal management of CPUs and Li-ion batteries, thermal infrared stealth of high-temperature objects, heat therapy in the clinic, and fire-safety for various scenarios.

Developing a Workflow for Transforming BIM Models into Immersive Virtual Reality Experiences

Abstract. The integration of Extended Reality (XR) and Building Information Modelling (BIM) has emerged as a promising approach for enhancing interaction with spatial data in the built environment. While the potential of BIM for advanced visualization has been recognized, there remains a need for standardized workflows that facilitate the transformation of BIM models into immersive virtual reality (VR) experiences. This research contributes to the ongoing development of BIM-VR integration by presenting a workflow that leverages Revit, Twinmotion, and Datasmith to create visually rich and interactive VR environments. The established process focuses on enhancing textures, incorporating dynamic lighting and weather simulations, and populating the virtual space with realistic elements. The workflow also addresses the integration of interactable objects to elevate user engagement and the use of cutting-edge VR headsets for an immersive experience. By providing a structured approach to bridging complex BIM data with VR, this research aims to support architects, engineers, construction professionals, and clients in design collaboration, project review, and decision-making across the building lifecycle. The workflow's potential for facilities management and training applications is also explored. Through a case study, the research demonstrates the workflow's effectiveness in facilitating a deeper understanding of architectural spaces before construction, highlighting its potential for improving efficiency and reducing rework. The paper also discusses the challenges and opportunities associated with integrating BIM with GIS and sensor data to create comprehensive digital twins. This research contributes to the growing body of knowledge on BIM-VR integration and underscores the importance of continued development in this field to enhance the design, communication, and experience of the built environment.

Convolution Bounds on Quantile Aggregation

Advancing Risk Assessment: New Ways To Compute Quantile Aggregation This issue features a pivotal study on quantile aggregation amid dependence uncertainty, an area critical to finance, risk management, and statistics. The authors introduce “convolution bounds,” derived from a recent inf-convolution formula of quantiles and related risk measures. The obtained analytical tools unify existing results and enhance the understanding of quantile methods by providing general, sharp, and computationally efficient solutions. The results offer insights into the extremal dependence structures, with several implications in risk management and economic analysis applications. For more detailed insights, read the full paper, “Convolution Bounds on Quantile Aggregation” (reference: [insert reference]).

Development and Testing of a Mobile App for Management of Gestational Diabetes in Nepal: Protocol for a User-Centered Design Study and Exploratory Randomized Controlled Trial.

The prevalence of gestational diabetes mellitus (GDM) is increasing, particularly in low- and middle-income countries (LMICs) like Nepal. GDM self-management, including intensive dietary and lifestyle modifications and blood glucose monitoring, is critical to maintain glycemic control and prevent adverse outcomes. However, in resource-limited settings, several barriers hinder optimal self-management. Mobile health (mHealth) technology holds promise as a strategy to augment GDM treatment by promoting healthy behaviors and supporting self-management, but this approach has not yet been tested in any LMIC. This report describes the protocol to develop a culturally tailored mHealth app that supports self-management and treatment of GDM (GDM-Dhulikhel Hospital [GDM-DH] app, phase 1) and test its usability and preliminary efficacy (phase 2) among patients with GDM in a periurban hospital setting in Nepal. The study will be conducted at Dhulikhel Hospital in Dhulikhel, Nepal. In the development phase (phase 1), a prototype of the GDM-DH app will be developed based on expert reviews and a user-centered design approach. To understand facilitators and barriers to GDM self-management and to gather feedback on the prototype, focus groups and in-depth interviews will be conducted with patients with GDM (n=12), health care providers (n=5), and family members (n=3), with plans to recruit further if saturation is not achieved. Feedback will be used to build a minimum viable product, which will undergo user testing with 18 patients with GDM using a think-aloud protocol. The final GDM-DH app will be developed based on user feedback and following an iterative product design and user testing process. In the randomized controlled trial phase (phase 2), newly diagnosed patients with GDM (n=120) will be recruited and randomized to either standard care alone or standard care plus the GDM-DH app from 24-30 weeks gestation until delivery. In this proof-of-concept trial, feasibility outcomes will be app usage, self-monitoring adherence, and app usability and acceptability. Exploratory treatment outcomes will be maternal glycemic control at 6 weeks post partum, birth weight, and rates of labor induction and cesarean delivery. Qualitative data obtained from phase 1 will be analyzed using thematic analysis. In phase 2, independent 2-tailed t tests or chi-square analyses will examine differences in outcomes between the 2 treatment conditions. As of July 2024, we have completed phase 1. Phase 2 is underway. The first participant was enrolled in October 2021, with 99 participants enrolled as of July 2024. We anticipate completing recruitment by December 2024 and disseminating findings by December 2025. App-based lifestyle interventions for GDM management are not common in LMICs, where GDM prevalence is rapidly increasing. This proof-of-concept trial will provide valuable insights into the potential of leveraging mHealth app-based platforms for GDM self-management in LMICs. ClinicalTrials.gov NCT04198857; https://clinicaltrials.gov/study/NCT04198857. DERR1-10.2196/59423.

Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant

This study focuses on enhancing heat and mass transfer in an electronic cooling system such as a rectangular cavity that contains equidistant heated chips along the bottom wall. The cavity of the present study is filled with ethylene glycol (30:70) based hybrid nano coolants of different volume fractions (ϕ) of Multi-walled Carbon Nanotube (MWCNT), Aluminum Oxide (Al2O3), and Copper Oxide (CuO). The set of equations controlling the thermo-solutal natural convection within the enclosure is simulated using the Galerkin weighted residual finite element method (FEM). The study has shown a good agreement of numerical results with different experimental reports within the framework of the present study. A solution space is constructed based on governing parameters such as Rayleigh number, buoyancy ratio, and Lewis number. A hybrid nano-coolant containing ϕMWCNT = 1.5 %, ϕCuO = 0.5 %, and ϕAl2O3 = 2 % showed a 3.11% improvement in heat transfer rate compared to the base fluid, highlighting its potential for thermal management applications. This study also investigates various machine learning models for predicting the heat and mass transfer rate, and an error analysis is conducted on the K-Nearest Neighbour Regressor, Random Forest Regressor, Decision Tree Regressor, and ANN model. The ANN model with 6-50-100-50-2 architecture showcases the best fit with the mean squared error of 0.8923 and an R² value of 99.96 % on testing data. The ANN model exhibits its capability to predict heat transfer and mass transfer rates within the error ranges from 1–2 % and 2–3 %, respectively, even at a strong thermal buoyancy force (Ra = 10⁵). This accuracy showcases a novel use of ANN to efficiently predict thermo-fluidic transport behaviors of hybrid nanofluids, offering a faster alternative to resource-intensive simulations. The present study opens new possibilities for real-time, cost-effective cooling solutions, particularly in microelectronics and renewable energy, while maintaining high prediction accuracy by integrating machine learning with the nanotechnology approach.

A Comprehensive Review of Remote Sensing Technologies for Improved Geological Disaster Management

ABSTRACTOver the past two decades, the frequency of natural hazard incidents has steadily risen, leading to substantial human casualties, infrastructure destruction, societal and economic disruption. The occurrence of disasters, both of natural and human origin, has exhibited an upward trend in frequency over the past few decades on a global scale, posing a significant threat to diverse populations. Over time, remote sensing technologies have proven to be effective in analysing and monitoring diverse natural disasters, including but not limited to droughts, earthquakes, tsunamis, landslides and cyclones. The significant extent of its coverage capacity and the ability to repeat observations make its application economically efficient. This paper aims to elucidate the fundamental contributions and role of remote sensing in disaster management applications. In a comprehensive analysis, this study explores recent practical applications in the context of disaster events. The utilisation of diverse methodologies and functions of remote sensing in disaster monitoring and control is further expanded to encompass the domain of disaster risk management, employing cutting‐edge sensors and satellites from emerging technological advancements. This paper also addresses challenges related to disaster monitoring, detection and management. Emergencies, particularly during typical catastrophe scenarios, often witness partial disruptions in communication networks. Consequently, the role of alternative networks becomes paramount in enabling effective disaster detection and management strategies. Emerging issues are highlighted, and potential directions for future research are outlined and also support sustainable development goal 13 (climate action).

Exfoliation of boron nitride nanosheets by liquid phase method based on deep eutectic solvents and their thermal management application

AbstractBoron nitride had emerged as an innovative thermally conductive filler, garnering increasing attention from researchers. However, there was a need to create new methods for efficiently producing functionalized boron nitride nanosheets with enhanced thermal conductivity and dispersion. In this study, a novel approach was introduced to utilize deep eutectic solvents as additives to facilitate the exfoliation of boron nitride nanosheets. This method offered significant advantages in terms of cost‐effectiveness (low additives level) and high yield (about 60%). Furthermore, the resulting epoxy resin composite material exhibited outstanding thermal conductivity and thermal management properties. The thermal conductivity of EP/d‐BNNS composites reached 1.02 W/mK at 20 wt% d‐BNNS loading. Additionally, the composite material demonstrated good thermal stability and low dielectric properties. This work demonstrated that this proposed approach represents an effective means for the scalable production of boron nitride nanosheets.Highlights The boron nitride nanosheets were obtained by deep eutectic solvents (DES) ball milling method. The exfoliated boron nitride exhibits superior dispersion stability. The thermal conductivity of EP composites is significantly improved.