Research Efforts Research Articles

Antioxidant and Protective Effects of Oleaster Oil Against Silica Nanoparticle-Induced Oxidative Stress and Organ Toxicity in Rats

Nanoparticles have found widespread application in a variety of fields, despite growing worry about their possible hazardous effects on both the environment and human health. In recent years, research efforts have focused on plants and vegetable oils, which have been identified as abundant sources of many bioactive compounds. Many of these substances are known to participate in antioxidant processes. As a result, the current study was designed to investigate the antioxidant and protective properties of oleaster oil against cytotoxicity and oxidative stress induced by silica nanoparticles (SiNPs) in albino Wistar rats. Forty male rats were randomly assigned to four equally sized cohorts: a control group, SiNP-treated group (at a dose of 50 mg/kg), SiNP-treated group supplemented with oleaster oil (at a dose of 2 mL/kg), and those receiving only 2 mL/kg of oleaster oil. The findings demonstrated that SiNPs initiated an oxidative stress environment, as evidenced by higher lipid peroxidation levels and changes in antioxidant defense mechanisms. Antioxidant enzymes were significantly reduced, including glutathione levels between the control and SiNP-exposure treatments (36.01%, 36.59%, 60%), glutathione-S-transferase (29.74%, 29.90%, 13.49%), catalase (24.14%, 28.19%, 30.85%), and tissue superoxide dismutase (11.90%, 37.78%, 37.79%) in the liver, kidney, and heart, respectively. Furthermore, histological investigations revealed significant liver, kidney, and heart damage, as indicated by pathological alterations such as vascular dilatation and congestion, inflammatory cellular infiltration, and hepatocellular dysfunction. Encouragingly, the administration of oleaster oil significantly ameliorated a majority of these detrimental effects. These data suggest a potential protective effect of oleaster oil against the adverse histological effects induced by SiNP injection.

Review of Uncertainty, Carbon Emissions, Greenness Index, and Quality Issues in Green Supply Chains

The ability of closed-loop supply chains (CLSC) and reverse logistics (RL) to improve the triple bottom line (economic, social and environmental values) has increased the development of design and management models for CLSCs and RL. Consequently, there exists an extensive body of literature dedicated to exploring these supply and logistics issues. This paper reviews recent and relevant literature on CLSC and RL with an emphasis on uncertainty, carbon emissions, greenness index, return product quality and reliability considerations. The selected references are organized, reviewed, and analyzed to establish valuable mapping to highlight major findings. Finally, the outcomes are synthesized, and the primary research gaps are emphasized, pointing toward potential avenues for future investigation. These findings reveal that research efforts must be directed towards the development of multi-criteria greenness indices and multi-objective robust optimization models for uncertain quality and reliability of returns.

The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education.

In the field of medicine, uncertainty is inherent. Physicians are asked to make decisions on a daily basis without complete certainty, whether it is in understanding the patient's problem, performing the physical examination, interpreting the findings of diagnostic tests, or proposing a management plan. The reasons for this uncertainty are widespread, including the lack of knowledge about the patient, individual physician limitations, and the limited predictive power of objective diagnostic tools. This uncertainty poses significant problems in providing competent patient care. Research efforts and teaching are attempts to reduce uncertainty that have now become inherent to medicine. Despite this, uncertainty is rampant. Artificial intelligence (AI) tools, which are being rapidly developed and integrated into practice, may change the way we navigate uncertainty. In their strongest forms, AI tools may have the ability to improve data collection on diseases, patient beliefs, values, and preferences, thereby allowing more time for physician-patient communication. By using methods not previously considered, these tools hold the potential to reduce the uncertainty in medicine, such as those arising due to the lack of clinical information and provider skill and bias. Despite this possibility, there has been considerable resistance to the implementation of AI tools in medical practice. In this viewpoint article, we discuss the impact of AI on medical uncertainty and discuss practical approaches to teaching the use of AI tools in medical schools and residency training programs, including AI ethics, real-world skills, and technological aptitude.

Implementing regional blue economy research and innovation strategies: a case study for the Black Sea

The Black Sea is a vital resource with vast potential to boost the societal value of the Blue Economy for its surrounding countries. Improved knowledge and enhanced infrastructure together with better coordination and alignment of research and innovation efforts are critical for the better management of the deteriorated Black Sea ecosystem to help restore and maintain its resilience and enable sustainable use of natural resources. In 2019, the Black Sea Strategic Research and Innovation Agenda (Black Sea SRIA), was developed by the Black Sea experts, in cooperation with European marine institutes and organizations, with the support of the European Commission. Black Sea SRIA presents the priorities and ecosystem-based management options needed for a healthy, sustainable, and resilient Black Sea. Since 2019, efforts focused on developing concrete actions under the Black Sea SRIA Implementation Plan (IP) to contribute to the national blue economy strategies, better structuring of the relevant funding mechanisms for solution-oriented implementation and the international and regional strategies, supporting the co-funding mechanisms. In this study, we present a transnational process that has led to the development of the IP which utilized a bottom-up, co-design and co-creation-based approach. The SRIA and its IP support better governance of the blue economy principles towards sustainable development and conservation of unique Black Sea ecosystems. The Black Sea SRIA and IP address the fundamental research challenges of the region, promote the blue economy, and also build vital support systems and innovative research infrastructure and capacity together with academia, funding organizations, industry, policy, civil society and local communities. The IP is a long-lasting guide to catalyze new ideas and innovations towards and with the Black Sea community with strong implications for other sea basins.

Progress and Hotspot Analysis of Bibliometric-Based Research on Agricultural Irrigation Patterns on Non-Point Pollution

With the constant advancement of irrigation technology and the continuous expansion of irrigation areas, non-point source pollution (NPS) caused by agricultural activities has posed a persistent threat to ecosystems and biological safety. Against this backdrop, it is imperative to lay scientific foundations for green, sustainable, and high-quality agricultural development through a thorough review of the relevant research progress. In this study, bibliometric methods are adopted to comprehensively analyze and visualize the current state and key literature on agricultural irrigation and NPS pollution from 2010 to July 2024. The focus of this study is specifically on summarizing the research hotspots and development trends of different irrigation methods and the mechanisms behind their impacts on NPS pollution. The results indicate that publications from the United States and China account for 63.8% of the total, but the fragmentation of research efforts remains, suggesting a necessity to strengthen international and regional collaboration. There are three institutions with the highest publication output, namely Northwest A&F University, Hohai University, and the Chinese Academy of Sciences. The subjects identified as the key areas of research on irrigation-related NPS pollution (IRR-NPS) include precision irrigation, rapid water pollution response, spatiotemporal management, interdisciplinary integration, wastewater treatment, and crop models. Regarding future research, it is necessary to focus attention on real-time precision irrigation, standardized crop models, data accuracy, spatiotemporal pollution coordination, pollution purification technology development, interdisciplinary integrated governance, and the innovative applications of soil improvement technologies. In addition to offering theoretical support and practical guidance for the management of agricultural NPS pollution, this study also provides management and technical support for policymakers, which is beneficial for advancing agricultural irrigation technology and environmental preservation.

Developments in Localized Surface Plasmon Resonance

AbstractLocalized surface plasmon resonance (LSPR) is a nanoscale phenomenon associated with noble metal nanostructures that has long been studied and has gained considerable interest in recent years. These resonances produce sharp spectral absorption and scattering peaks, along with strong electromagnetic near-field enhancements. Over the past decade, advancements in the fabrication of noble metal nanostructures have propelled significant developments in various scientific and technological aspects of LSPR. One notable application is the detection of molecular interactions near the nanoparticle surface, observable through shifts in the LSPR spectral peak. This document provides an overview of this sensing strategy. Given the broad and expanding scope of this topic, it is impossible to cover every aspect comprehensively in this review. However, we aim to outline major research efforts within the field and review a diverse array of relevant literature. We will provide a detailed summary of the physical principles underlying LSPR sensing and address some existing inconsistencies in the nomenclature used. Our discussion will primarily focus on LSPR sensors that employ metal nanoparticles, rather than on those utilizing extended, fabricated structures. We will concentrate on sensors where LSPR acts as the primary mode of signal transduction, excluding hybrid strategies like those combining LSPR with fluorescence. Additionally, our examination of biological LSPR sensors will largely pertain to label-free detection methods, rather than those that use metal nanoparticles as labels or as means to enhance the efficacy of a label. In the subsequent section of this review, we delve into the analytical theory underpinning LSPR, exploring its physical origins and its dependency on the material properties of noble metals and the surrounding refractive index. We will discuss the behavior of both spherical and spheroidal particles and elaborate on how the LSPR response varies with particle aspect ratio. Further, we detail the fundamentals of nanoparticle-based LSPR sensing. This includes an exploration of single-particle and ensemble measurements and a comparative analysis of scattering, absorption, and extinction phenomena. The discussion will extend to how these principles are applied in practical sensing scenarios, highlighting the key experimental approaches and measurement techniques.

Portfolio selection revisited

AbstractIn 1952, Harry Markowitz formulated portfolio selection as a trade-off between expected, or mean, return and variance. This launched a massive research effort devoted to finding suitable inputs to mean-variance optimization. The estimation problem is high dimensional and a factor model is at the core of many attempts. A factor model can reduce the number of parameters that need to be estimated to a manageable size, but these parameters may incorporate substantial, hidden estimation error. Recent analysis elucidates the nature of this error, identifies a mechanism by which it can corrupt optimization and provides a method for its mitigation. We explore this analysis here by illustrating how to improve the volatility ratio of large optimized portfolios, leading to superior portfolio selection.$$^{*}$$ ∗

Smart Health Monitoring Approach to Diagnose Attention-Deficit Hyperactivity Disorderbased on Real-Time Activity and Heart Rate Variability using Boosting Models

Introduction: Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent chronic mental health condition that significantly impacts the psychological and physical wellbeing of millions of adolescents. Early detection and accurate diagnosis are crucial for effective treatment and mitigating the disorder's adverse effects. Despite extensive research efforts, current methods often fall short in simultaneously accounting for daily motor activity and heart rate variability in ADHD detection. Method: Addressing these gaps, this paper introduces a histogram-based gradient-boosting classifier for analyzing real-time activity and heart-rate variability data to automate ADHD diagnosis. By extracting twelve key features from the data and selecting the most significant ones with the extra tree model, we evaluate these features using various classifiers, including histogram-based gradient boosting, light gradient boosting machine, extreme gradient boosting, gradient boosting, and adaptive boosting. Results: The histogram-based gradient-boosting model, validated through ten-fold crossvalidation, outperforms other classifiers with an accuracy of 99.12%, an F1 measure of 99.12%, and a sensitivity of 99.13%. Additionally, it achieves a specificity of 99.1%, an AUC of 0.9995, and a minimal FDR of 0.88%. These results demonstrate that the proposed approach offers a highly effective and precise solution for automated ADHD diagnosis. Conclusion: The implications of these findings suggest that integrating real-time activity and heart-rate variability data into diagnostic processes can significantly enhance the accuracy and efficiency of ADHD assessment, potentially leading to earlier and more reliable diagnoses, improved patient outcomes, and more tailored treatment strategies.

Unveiling the Pockels coefficient of ferroelectric nitride ScAlN

Nitride ferroelectrics have recently emerged as promising alternatives to oxide ferroelectrics due to their compatibility with mainstream semiconductor processing. ScAlN, in particular, has exhibited remarkable piezoelectric coupling strength (K2) comparable to that of lithium niobate, making it a valuable choice for RF filters in wireless communications. Recently, ScAlN has sparked interest in its use for nanophotonic devices, chiefly due to its large bandgap facilitating operation in blue wavelengths coupled with promises of enhanced nonlinear optical properties such as a large second-order susceptibility (χ(2)). It is still an open question whether ScAlN can outperform oxide ferroelectrics concerning the Pockels effect—an electro-optic coupling extensively utilized in optical communications devices. In this paper, we present a comprehensive theoretical analysis and experimental demonstration of ScAlN’s Pockels effect. Our findings reveal that the electro-optic coupling of ScAlN, despite being weak at low Sc concentration, may be significantly enhanced and exceed LiNbO3 at high levels of Sc doping, which points the direction of continued research efforts to unlock the full potential of ScAlN.

Protocol for scoping review: Mapping the landscape of acute pain management in sports-related musculoskeletal injuries.

Acute pain management is critical in sports-related musculoskeletal injuries to facilitate recovery and minimize long-term impact. While current practices vary, incorporating both pharmacological and non-pharmacological approaches, the quality and breadth of existing evidence have not been thoroughly assessed. This scoping review aims to explore the clinical role of different pain management strategies and provide a comprehensive overview of the field. The review will follow the Joanna Briggs Institute (JBI) methodology for scoping reviews and adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR) guidelines. Searches will be conducted in major peer-reviewed databases and relevant gray literature. Studies involving athletes of any level undergoing treatment for acute musculoskeletal injuries will be considered. Data extraction will include study and participant characteristics, intervention details, reported outcomes, efficacy comparisons, and economic analyses. This review will provide a descriptive synthesis of the data, utilizing statistical analysis, figures, and tables where relevant to introduce the different treatment modalities. In line with PRISMA-P and PRISMA-ScR guidelines, this scoping review incorporates studies of diverse designs. The data synthesis involves descriptive statistics and narrative presentations, aimed at exploring the relationship between study results and research objectives. This scoping review will evaluate various pain management interventions for acute musculoskeletal injuries in sports, mapping the current evidence and identifying gaps in research. The findings will help inform clinical practices and guide future research efforts to optimize pain management strategies in sports medicine.

A novel maximum likelihood based probabilistic behavioral data fusion algorithm for modeling residential energy consumption.

The current research effort is focused on improving the effective use of the multiple disparate sources of data available by proposing a novel maximum likelihood based probabilistic data fusion approach for modeling residential energy consumption. To demonstrate our data fusion algorithm, we consider energy usage by fuel type variables (for electricity and natural gas) in residential dwellings as our dependent variable of interest, drawn from residential energy consumption survey (RECS) data. The national household travel survey (NHTS) dataset was considered to incorporate additional variables that are not available in the RECS data. With a focus on improving the model for the residential energy use by fuel type, our proposed research provides a probabilistic mechanism for appropriately fusing records from the NHTS data with the RECS data. Specifically, instead of strictly matching records with only common attributes, we propose a flexible differential weighting method (probabilistic) based on attribute similarity (or dissimilarity) across the common attributes for the two datasets. The fused dataset is employed to develop an updated model of residential energy use with additional independent variables contributed from the NHTS dataset. The newly estimated energy use model is compared with models estimated RECS data exclusively to see if there is any improvement offered by the newly fused variables. In our analysis, the model fit measures provide strong evidence for model improvement via fusion as well as weighted contribution estimation, thus highlighting the applicability of our proposed fusion algorithm. The analysis is further augmented through a validation exercise that provides evidence that the proposed algorithm offers enhanced explanatory power and predictive capability for the modeling energy use. Our proposed data fusion approach can be widely applied in various sectors including the use of location-based smartphone data to analyze mobility and ridehailing patterns that are likely to influence energy consumption with increasing electric vehicle (EV) adoption.

Characterization of virus species associated with sweet potato virus disease in Costa Rica and riboprobe development for its rapid detection

AbstractSweet potato (Ipomoea batatas) is a crucial crop for food security and economic stability in many regions, including Costa Rica. This study aimed to assess the prevalence and genetic diversity of two major viruses associated with sweet potato virus disease (SPVD) in Costa Rica: sweet potato feathery mottle virus (SPFMV) and sweet potato chlorotic stunt virus (SPCSV). A total of 50 leaf samples displaying typical viral disease symptoms were collected from major sweet potato‐growing regions. Using multiplex reverse transcription (RT)‐PCR, SPFMV was detected in 82% of the samples and SPCSV in 52%, with a coinfection rate of 48%. Genetic analysis revealed that SPFMV isolates belonged predominantly to phylogenetic group B, while SPCSV isolates clustered with the West African (WA) strain. The low genetic diversity within these viral populations suggests a limited number of initial infection events followed by local spread. Riboprobe technology was evaluated as an alternative to RT‐PCR for virus detection. While the riboprobes for SPFMV demonstrated sensitivity comparable to RT‐PCR, the SPCSV riboprobes showed slightly lower sensitivity, particularly when using crude leaf extracts. Despite this, riboprobes offer significant advantages in terms of cost and ease of use for large‐scale screening. Our findings underscore the need for robust diagnostic and management strategies to control SPVD and highlight the importance of continuous monitoring and research to mitigate the impact of viral diseases on sweet potato production in Costa Rica. These insights contribute to the global understanding of SPVD and support collaborative efforts in plant pathology research.

Identification of stable housekeeping genes in mouse liver for studying carbon tetrachloride-induced injury and cellular senescence

Acute liver injury (ALI) presents a challenging problem worldwide, prompting extensive research efforts. Cellular senescence has been found to be induced following ALI, and targeting cellular senescence has shown therapeutic potential. Therefore, understanding the expression of senescence-related genes in ALI can help to explore pathogenesis and treatment of this common disease. Carbon tetrachloride (CCl4) is commonly used to study ALI. Although polymerase chain reaction (PCR) is a convenient and economical molecular biology technique widely used in basic medicine, research on selecting suitable reference genes to obtain objective and reproducible PCR data is scarce. Moreover, evidence supporting the choice of reference genes for experimental studies of CCl4-induced ALI and hepatic senescence in mice is limited. In this study, we obtained murine livers at four time points (0, 12, 24, and 48 h) following CCl4 treatment. We used five algorithms (geNorm, BestKeeper, NormFinder, delta Ct, and RefFinder) to rank 12 candidate genes in real-time reverse-transcription quantitative PCR (RT-qPCR) experiments. Focusing on cellular senescence in this model, we adopted four senescence-associated secretory phenotype (SASP) genes (Il6, Il1b, Ccl2, and Ccl5) as target genes. Our results confirmed Gapdh and Tbp as suitable reference genes in murine CCl4-induced ALI models. Furthermore, we provide a table of published studies recommending reference genes for various liver disease models. This study provides a valuable reference for enhancing the reliability and reproducibility of ALI molecular findings.

Beyond a constant proton relative biological effectiveness: A survey of clinical and research perspectives among proton institutions in Europe and the United States.

Although proton relative biological effectiveness (RBE) depends on factors like linear energy transfer (LET), tissue properties, dose, and biological endpoint, a constant RBE of 1.1 is recommended in clinical practice. This study surveys proton institutions to explore activities using functionalities beyond a constant proton RBE. Research versions of RayStation integrate functionalities considering variable proton RBE, LET, proton track-ends, and dirty dose. A survey of 19 institutions in Europe and the United States, with these functionalities available, investigated clinical adoption and research prospects using a 25-question online questionnaire. Of the 16 institutions that responded (84% response rate), 13 were clinically active. These clinical institutions prescribe RBE=1.1 but also employ planning strategies centered around special beam arrangements to address potentially enhanced RBE effects in serially structured organs at risk (OARs). Clinical plan evaluation encompassed beam angles/spot position (69%), dose-averaged LET (LETd) (46%), and variable RBE distributions (38%). High ratings (discrete scale: 1-5) were reported for the research functionalities using linear LETd-RBE models, LETd, track-end frequency and dirty dose (averages: 4.0-4.8), while LQ-based phenomenological RBE models dependent on LETd scored lower for optimization (average: 2.2) but congruent for evaluation (average: 4.1). The institutions preferred LET reported as LETd (94%), computed in unit-density water (56%), for all protons (63%), and lean toward LETd-based phenomenological RBE models for clinical use (>50%). Proton institutions recognize RBE variability but adhere to a constant RBE while actively mitigating potential enhancements, particularly in serially structured OARs. Research efforts focus on planning techniques that utilize functionalities beyond a constant RBE, emphasizing standardized LET and RBE calculations to facilitate their adoption in clinical practice and improve clinical data collection. LETd calculated in unit-density water for all protons as input to adaptable phenomenological RBE models was the most suggested approach, aligning with predominant clinical LET and variable RBE reporting.

APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR ERROR PREVENTION AND QUALITY CONTROL IN MIG/MAG WELDING PROCESSES: A COMPREHENSIVE REVIEW

Welding techniques, particularly MIG/MAG (Metal Inert Gas/Metal Active Gas) processes, are essential for various industries, including automotive, construction, aerospace, and defense. Ensuring the quality and accuracy of welds is crucial for efficient production and product reliability. However, numerous issues can arise during MIG/MAG welding, often resulting in suboptimal quality. This study presents a comprehensive review of research efforts employing AI techniques to address errors and defects in MIG/MAG welding. The review systematically analyzes relevant studies published since 2018, focusing on three key aspects: datasets employed, methodologies and approaches adopted, and performance metrics reported. The findings reveal a significant adoption of both machine learning and deep learning techniques, with the choice of approach dependent on factors such as the nature of input data, welding process dynamics, and computational requirements. Deep learning models, particularly convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have demonstrated superior performance in tasks like image-based defect detection and time-series analysis for quality prediction. However, traditional machine learning algorithms have also been utilized, often in conjunction with dimensionality reduction or feature selection techniques. The review highlights the diverse range of performance metrics employed, including accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE), with the selection contingent upon the specific task (classification or regression) and desired trade-off between different performance aspects. While many studies have reported promising results, with accuracy rates frequently exceeding 90%, challenges remain in real-world industrial settings, where factors such as noise, occlusions, and rapidly changing welding conditions can pose significant hurdles. This review serves as a comprehensive guide for researchers and practitioners working in the field of AI-assisted error prevention and quality control for MIG/MAG welding processes, highlighting current trends, methodologies, and future research directions.

An Efficient ReRAM-based Accelerator for Asynchronous Iterative Graph Processing

Graph processing has become a central concern for many real-world applications and is well-known for its low compute-to-communication ratios and poor data locality. By integrating computing logic into memory, resistive random access memory (ReRAM) tackles the demand for high memory bandwidth in graph processing. Despite the years’ research efforts, existing ReRAM-based graph processing approaches still face the challenges of redundant computation overhead . It is because the vertices of many subgraphs are ineffectively and repeatedly processed over the ReRAM crossbars for lots of iterations so as to update their states according to the vertices of other subgraphs regardless of the dependencies among the subgraphs. In this paper, we propose ASGraph , a dependency-aware ReRAM-based graph processing accelerator that overcomes the aforementioned performance bottlenecks. Specifically, ASGraph dynamically constructs the subgraph based on the dependencies between vertices’ states and then detects constructed subgraph that owns high value (it is likely that it has accumulated many state propagations from its neighbors and is able to affect more other neighbors) to be preferentially processed. In this way, it makes the vertex states propagate along the dependencies between vertices as much as possible to reduce the redundant computation. Besides, ASGraph employs a hybrid processing scheme to accelerate the state propagations of the tightly connected subgraph, thereby minimizing the redundant computations. Experimental results show that ASGraph achieves 25.5 × and 4.8 × speedup and 70.8 × and 2.2 × energy saving on average compared with the state-of-the-art ReRAM-based graph processing accelerators, i.e., GraphR and GaaS-X, respectively.

Eliminating hazardous pollutants: treatment options for dioxins and surfactants from water and wastewater: an updated review.

Surfactants and dioxins are increasingly being released into the environment due to their excessive usage and their improper disposal. These pollutants cause considerable harm to both humans and the natural environment. Therefore, their removal from water and wastewater, which form major pathways for their transmission, is necessary. Considerable research efforts have been devoted to finding a suitable method for the complete removal of these pollutants. The treatment options for both surfactants and dioxins could be similar but differ in terms of removal efficiencies for each. For example, surfactant removal through coagulation resulted in almost 68%, while for dioxins it attained 98% efficiency. Another method tested for the removal of surfactants is nanobubbling which recorded a 99% removal efficiency, while it was found to be inapplicable for the removal of dioxins due to the difference in the structure of the two products. Worth noting is that among the studied removal methods, biochar-based adsorption stands as one of the most promising techniques in terms of removal efficiency, cost, and sustainability covering the two pollutants. This review deals with the sources and impacts of these pollutants and discusses the recent developments in treatment methods, as compared to already-existing methods, for their elimination from water and wastewater, with the objective of highlighting the most sustainable methods for field application.

Electrodynamic Tether and Brake Sails Combination Deorbit Design

Given the growing threat of an impending space debris crisis, nations worldwide have intensified their research efforts in satellite deorbiting technologies. Electrodynamic tether and braking sails stand out as popular methods for spacecraft deorbiting that do away with the necessity for propellant. However, these methods possess their own set of limitations. This paper presents a holistic dynamical model for a fusion of electrodynamic tether and braking sails. The aim is to avoid the complex nonlinear dynamics during the deployment, retrieval, and dwell time of electrodynamic tether, while compensating for the insufficient trust generated by braking sails in high orbital environments. The objective is to enable satellite to deorbit swiftly and stably under a broader range of conditions. Specifically accomplishing the following three aspects: conceptualizing the design of an ideal equipment, implementing simulated deorbiting process, and conducting an efficiency comparative analysis with prevalent current deorbiting methods. Through numerical simulations, the effectiveness and feasibility of this proposed design have been validated.

An assessment of the seasonally dependent biophysical mechanism and dynamic land use change: A Research Effort from Emerging Urban Agglomeration of West Bengal

The behavior of the biophysical components needs to be closely observed while taking seasonal perspectives into account. This study focuses on seasonal analysis of biophysical components and dynamics of land use changes. This study examines the spatiotemporal seasonal variation among land surface temperature (LST), NDVI, and NDWI, with a focus on the two main sub-tropical climatic seasons summer and winter. We use correlation and regression analysis to assess the season-wise relationship between LST and biophysical indices with the help of grouped marginal plots. The comparison analyses of the biophysical components and land use change for a longer period from 1991-2021 with their matrix was undertaken in five urban agglomerations (UA) like Asansol, Siliguri, Kolkata, English Bazar and Kharagpur. The maximum likelihood classification technique was used to categorize multi-temporal Landsat images for each of the five growing UA in West Bengal. The most intriguing finding is that in all UAs, from 1991 to 2021, built-up area increased to become the predominant land. Thus, the vegetation and agricultural land are in danger in every location of West Bengal, whether it is plain or hilly, because of the land's gradual conversion to concrete or built-up areas over time. Additionally, the result shows that in summer and winter seasons, LST was observed growth due to the rapid urban built-up area expansion. It is also found that LST and NDVI have a close negative relationship, but LST and NDWI do not have any perfect relationship. Therefore, to lessen the warming effect of urban microclimates, policymakers should be concerned about future urban expansion that is both horizontal and vertical. An appropriate strategy for environmentally friendly construction, green buildings, and cool roof technologies should be developed.

Approaching Dynamic Behaviors of Life through Systems Chemistry.

The intricate interplay of metabolic reactions and molecular assembly in living systems enables spatiotemporally organization and gives rise to diverse dynamic behaviors that characterize life. Over the last decades, research efforts have increasingly focused on replicating the remarkable properties and characteristics of living systems, driving the rapid growth of systems chemistry. This young discipline which generally studies interacting molecular networks and emergent system-level properties, behaviors, and functions, offers new concepts and tools to tackle the complexity of life. In this review paper, we have explored seminal research and recent advancements in recreating dynamic behaviors of life with systems chemistry. We believe that the recreation of the dynamic behaviors of life through systems chemistry would set the initial steps to obtain synthetic life de novo.