Comprehensive Comparative Analysis Research Articles

Summarization and Translation Using NLP

Abstract: Text summarization and translation are two critical tasks in natural language processing with significant applications in various domains such as news aggregation, document summarization, machine translation, and information retrieval. In recent years, there has been remarkable progress in the development of techniques and models for both tasks, leveraging advancements in deep learning and neural network architectures. This paper presents a comprehensive review and comparative analysis of state-of-the-art methods in text summarization and translation. First, we provide an overview of the different approaches to text summarization, including extractive, abstractive, and hybrid methods, highlighting their strengths and weaknesses. We discuss various evaluation metrics and datasets commonly used for benchmarking summarization systems, shedding light on the challenges and opportunities in this field.

Comprehensive comparative analysis of the periodontal pathogen Porphyromonas gingivalis: exploring the pan-genome, the reconstruction of the gene regulatory network and genome-scale metabolic network.

Porphyromonas gingivalis is a nonmotile, obligate anaerobic, Gram-negative bacterium known for its association with periodontal disease and its involvement in systemic diseases such as atherosclerosis, cardiovascular disease, colon cancer and Alzheimer's disease. This bacterium produces several virulence factors, including capsules, fimbriae, lipopolysaccharides, proteolytic enzymes and hemagglutinins. A comparative genomic analysis revealed the open pangenome of P. gingivalis and identified complete type IV secretion systems (T4SS) in strain KCOM2805 and almost complete type VI secretion systems (T6SS) in strains KCOM2798 and ATCC49417, which is a new discovery as previous studies did not find the proteins involved in secretion systems IV and VI. Conservation of some virulence factors between different strains was observed, regardless of their genetic diversity and origin. In addition, we performed for the first time a reconstruction analysis of the gene regulatory network (GRN), identifying transcription factors and proteins involved in the regulatory mechanisms of bacterial pathogenesis. In particular, QseB regulates the expression of hemagglutinin and arginine deaminase, while Rex may suppress the release of gingipain through interactions with PorV and the formatum/nitrate transporter. Our study highlights the central role of conserved virulence factors and regulatory pathways, particularly QseB and Rex, in P. gingivalis and provides insights into potential therapeutic targets.

Towards Media Monitoring: Detecting Known and Emerging Topics through Multilingual and Crosslingual Text Classification

This study aims to address challenges in media monitoring by enhancing closed-set topic classification in multilingual contexts (where both training and testing occur in several languages) and crosslingual contexts (where training is in English and testing spans all languages). To achieve this goal, we utilized a dataset from the European Media Monitoring webpage, which includes approximately 15,000 article titles across 18 topics in 58 different languages spanning a period of nine months from May 2022 to March 2023. Our research conducted comprehensive comparative analyses of nine approaches, encompassing a spectrum of embedding techniques (word, sentence, and contextual representations) and classifiers (trainable/fine-tunable, memory-based, and generative). Our findings reveal that the LaBSE+FFNN approach achieved the best performance, reaching macro-averaged F1-scores of 0.944 ± 0.015 and 0.946 ± 0.019 in both multilingual and crosslingual scenarios. LaBSE+FFNN’s similar performance in multilingual and crosslingual scenarios eliminates the need for machine translation into English. We also tackled the open-set topic classification problem by training a binary classifier capable of distinguishing between known and new topics with the average loss of ∼0.0017 ± 0.0002. Various feature types were investigated, reaffirming the robustness of LaBSE vectorization. The experiments demonstrate that, depending on the topic, new topics can be identified with accuracies above ∼0.796 and of ∼0.9 on average. Both closed-set and open-set topic classification modules, along with additional mechanisms for clustering new topics to organize and label them, are integrated into our media monitoring system, which is now used by our real client.

Upgraded durian genome reveals the role of chromosome reshuffling during ancestral karyotype evolution, lignin biosynthesis regulation, and stress tolerance.

Durian (Durio zibethinus) is a tropical fruit that has a unique flavor and aroma. It occupies a significant phylogenetic position within the Malvaceae family. Extant core-eudicot plants are reported to share seven ancestral karyotypes that have undergone reshuffling, resulting in an abundant genomic diversity. However, the ancestral karyotypes of the Malvaceae family, as well as the evolution trajectory leading to the 28 chromosomes in durian, remain poorly understood. Here, we report the high-quality assembly of the durian genome with comprehensive comparative genomic analyses. By analyzing the collinear blocks between cacao and durian, we inferred 11 Malvaceae ancestral karyotypes. These blocks were present in a single-copy form in cacao and mainly in triplicates in durian, possibly resulting from a recent whole genome triplication (WGT) event that led to hexaploidization of the durian genome around 20 (17-24) million years ago. A large proportion of the duplicated genes in durian, such as those involved in the lignin biosynthesis module for phenylpropane biosynthesis, are derived directly from whole genome duplication, which makes it an important force in reshaping its genomic architecture. Transcriptome studies have revealed that genes involved in feruloyl-CoA formations were highly preferentially expressed in fruit peels, indicating that the thorns produced on durian fruit may comprise guaiacyl and syringyl lignins. Among all the analyzed transcription factors (TFs), members of the heat shock factor family (HSF) were the most significantly upregulated under heat stress. All subfamilies of genes encoding heat shock proteins (HSPs) in the durian genome appear to have undergone expansion. The potential interactions between HSF Dzi05.397 and HSPs were examined and experimentally verified. Our study provides a high-quality durian genome and reveals the reshuffling mechanism of ancestral Malvaceae chromosomes to produce the durian genome. We also provide insights into the mechanism underlying lignin biosynthesis and heat stress tolerance.

A comprehensive comparative genomic analysis revealed that plant growth promoting traits are ubiquitous in strains of Stenotrophomonas

Stenotrophomonas strains, which are often described as plant growth promoting (PGP) bacteria, are ubiquitous in many environments. A total of 213 genomes of strains of Stenotrophomonas were analyzed using comparative genomics to better understand the ecological roles of these bacteria in the environment. The pan-genome of the 213 strains of Stenotrophomonas consists of 27,186 gene families, including 710 core gene families, 11,039 unique genes and 15,437 accessory genes. Nearly all strains of Stenotrophomonas harbor the genes for GH3-family cellulose degradation and GH2- and GH31-family hemicellulose hydrolase, as well as intact glycolysis and tricarboxylic acid cycle pathways. These abilities suggest that the strains of this genus can easily obtain carbon and energy from the environment. The Stenotrophomonas strains can respond to oxidative stress by synthesizing catalase, superoxide dismutase, methionine sulfoxide reductase, and disulfide isomerase, as well as managing their osmotic balance by accumulating potassium and synthesizing compatible solutes, such as betaine, trehalose, glutamate, and proline. Each Stenotrophomonas strain also contains many genes for resistance to antibiotics and heavy metals. These genes that mediate stress tolerance increase the ability of Stenotrophomonas strains to survive in extreme environments. In addition, many functional genes related to attachment and plant colonization, growth promotion and biocontrol were identified. In detail, the genes associated with flagellar assembly, motility, chemotaxis and biofilm formation enable the strains of Stenotrophomonas to effectively colonize host plants. The presence of genes for phosphate-solubilization and siderophore production and the polyamine, indole-3-acetic acid, and cytokinin biosynthetic pathways confer the ability to promote plant growth. These strains can produce antimicrobial compounds, chitinases, lipases and proteases. Each Stenotrophomonas genome contained 1–9 prophages and 17–60 genomic islands, and the genes related to antibiotic and heavy metal resistance and the biosynthesis of polyamines, indole-3-acetic acid, and cytokinin may be acquired by horizontal gene transfer. This study demonstrates that strains of Stenotrophomonas are highly adaptable for different environments and have strong potential for use as plant growth-promoting bacteria.

Comparative Study on Solvothermal and Sonochemical Synthesis of Zinc Oxide Nanomaterials and Unraveling the Efficacy in Sunscreen Formulations for Enhanced UV Protection

AbstractAs the demand for highly efficient UV‐protective sunscreens continues to rise, the exploration of advanced materials becomes imperative. This paper introduces a novel approach to synthesizing zinc oxide (ZnO) nanomaterials through one‐step solvothermal (ZnO−A) and sonochemical (ZnO−B) methods, eliminating the need for stabilizers or capping agents. The materials have undergone rigorous characterization, focusing on structural and optical properties, and are subsequently evaluated for their UV shielding efficacy. The synthesized ZnO nanomaterials are seamlessly incorporated into a pure cream matrix at varying concentrations (5, 10 and 15 wt. %), offering a versatile application for skin protection against UVA/UVB irradiation. Remarkably, the cream with 15 wt. % ZnO−A emerges as a standout performer, demonstrating immense UV blocking efficacy with 96.44 % for UVA and 97.22 % for UVB. The in vitro sun protection factor (SPF) value of 30.76 further underscores its exceptional performance. This research not only establishes a comprehensive comparative analysis of ZnO−A and ZnO−B nanomaterials but also emphasizes their potential as active ingredients in next‐generation skin care sunscreen formulations. The findings contribute to the ongoing efforts in advancing UV protection technologies, addressing both efficacy and safety considerations.

Technical and financial feasibility of a chemicals recovery and energy and water production from a dairy wastewater treatment plant

Due to the high volume of wastewater produced from dairy factories, it is necessary to integrate a water recovery process with the treatment plant. Today, bipolar membrane electrodialysis units (BMEUs) are increasingly developed for wastewater treatment and reutilizing. This article aims to develop and evaluate (technical and cost analyses) a combined BMEU/batch reverse osmosis unit (BROU) process for the recovery of chemicals and water from the dairy wastewater plant. The combined BROU/BMEU process is able to simultaneously produce water and strong base-acid, and reduce power consumption due to the injection of concentrated feed flow into the BMEU. A comprehensive comparative analysis on the performances of two combined and stand-alone BMEU configurations are developed. The proposed combined technology for dairy factory wastewater treatment is designed on a new structure and configuration that can address superior cost analysis compared to similar technologies. Further, the optimal values of permeate flux and current density as two vital and influencing parameters on the performance of the studied dairy wastewater treatment process were calculated and discussed. From the outcomes, the total cost of production in the combined configuration has been reduced by approximately 26% compared to the stand-alone configuration. Increasing the feed concentration rate using the batch reverse osmosis process for the dairy wastewater treatment process can be an ideal solution from an economic point of view. Moreover, point (current density, feed concentration rate, total unit cost) = 328.9,7,14.37\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left(328.9, 7, 14.37\\right)$$\\end{document} can be considered as an optimal point for the economic performance of the studied wastewater treatment process.

COMPARATIVE ANALYSIS OF BUSINESS MODELS IN RETAIL: FAST CYCLE VS. LONG-TERM SUSTAINABILITY

This study conducts a comprehensive comparative analysis of fast-cycle and long-term sustainability business models in the retail industry. Through a mixed-methods approach, incorporating both quantitative and qualitative data, this research examines the key characteristics, underlying mechanisms, and impacts of these two contrasting models. The findings reveal stark differences in supply chain structures, design and production processes, marketing strategies, and consumer behavior. While fast-cycle models excel in speed, affordability, and trend responsiveness, they are associated with environmental degradation, ethical concerns, and a culture of disposability. Conversely, sustainable models prioritize ethical sourcing, environmental responsibility, and long-term value creation, but face challenges related to cost, scalability, and the need to balance timelessness with trend relevance. The implications for retailers, policymakers, and consumers are discussed, with a focus on the potential for hybrid models that combine elements of both approaches. This research contributes to the growing body of literature on sustainable business practices and provides valuable insights for retailers seeking to navigate the complex landscape of consumer preferences and sustainability demands.

Cost Analysis of Bladder Catheterization After Pelvic Floor Surgery.

Approximately 15-45% of female patients develop transient postoperative urinary retention (POUR) following pelvic reconstructive surgery. Catheter options for bladder drainage include transurethral indwelling catheter (TIC), intermittent self-catheterization (ISC), and suprapubic tube (SPT). Each strategy has risks and benefits; none have been shown to be clinically superior, and to date, no comprehensive comparative economic analysis has been published. The objective of this study was to evaluate the cost of these different bladder catheterization strategies after transvaginal pelvic surgery. A Canadian universal single-payer (government funded) health system perspective was taken, and a decision tree model was constructed to evaluate the costs associated with each catheterization strategy over a 6-week horizon. Base-cases were set based on recently published clinical data of our institutions, 2 academic tertiary care centers, and based on systematic reviews and meta-analyses. Costs were established in consultation with process stakeholders, in addition to published values. The average cost calculated for management of transient POUR after outpatient pelvic reconstructive surgery was 150.69 CAD (median 154.86; interquartile range [IQR] 131.30-176.33) for TIC, 162.28 CAD (median 164.72; IQR 144.36-189.39) for ISC and 255.67 CAD (median 270.63; IQR 234.32-276.82) for SPT. In costing inpatient surgical data, the average cost calculated was 134.22 CAD (median 123.61; IQR 108.87-151.85) for TIC and 224.61 CAD (median 216.07; IQR 203.86-231.23) for SPT. TIC and ISC were found to be significantly less costly than SPT in managing transient POUR following transvaginal pelvic reconstructive surgery.

Temporal action segmentation for video encryption

Videos contain temporal information, enabling them to capture the dynamic changes of actions and provide richer visual effects. Traditional video encryption methods involve decomposing videos into frames and encrypting them frame by frame, which results in significant resource consumption. This paper proposes a video encryption method based on temporal action segmentation. This methodology involves the identification and extraction of pivotal frames from a video dataset, followed by the encryption of these significant key frames. This approach serves to enhance the efficacy of the video encryption algorithm. The method consists of three modules. The first module uses temporal action segmentation to classify video frames and extract important frames for the second module’s input. The second module encrypts the extracted key frames using a chaos-based encryption algorithm, thereby reducing the time cost of video encryption. The third module outputs the encrypted video. During the encryption process, a large amount of key stream is required. To address this, the paper introduces a new pseudo-random sequence generation method called two-dimensional Gramacy&Lee map (2D-GLM). Comprehensive comparative analysis clearly demonstrates that compared to other systems, 2D-GLM exhibits superior performance and can generate a large number of high-performance pseudo-random sequences. The proposed algorithm is tested on GTEA, and the simulation results demonstrate that it can accomplish video encryption tasks with high security.

Comprehensive characterization of extracellular vesicles produced by environmental (Neff) and clinical (T4) strains of Acanthamoeba castellanii.

We conducted a comprehensive comparative analysis of extracellular vesicles (EVs) from two Acanthamoeba castellanii strains, Neff (environmental) and T4 (clinical). Morphological analysis via transmission electron microscopy revealed slightly larger Neff EVs (average = 194.5 nm) compared to more polydisperse T4 EVs (average = 168.4 nm). Nanoparticle tracking analysis (NTA) and dynamic light scattering validated these differences. Proteomic analysis of the EVs identified 1,352 proteins, with 1,107 common, 161 exclusive in Neff, and 84 exclusively in T4 EVs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) mapping revealed distinct molecular functions and biological processes and notably, the T4 EVs enrichment in serine proteases, aligned with its pathogenicity. Lipidomic analysis revealed a prevalence of unsaturated lipid species in Neff EVs, particularly triacylglycerols, phosphatidylethanolamines (PEs), and phosphatidylserine, while T4 EVs were enriched in diacylglycerols and diacylglyceryl trimethylhomoserine, phosphatidylcholine and less unsaturated PEs, suggesting differences in lipid metabolism and membrane permeability. Metabolomic analysis indicated Neff EVs enrichment in glycerolipid metabolism, glycolysis, and nucleotide synthesis, while T4 EVs, methionine metabolism. Furthermore, RNA-seq of EVs revealed differential transcript between the strains, with Neff EVs enriched in transcripts related to gluconeogenesis and translation, suggesting gene regulation and metabolic shift, while in the T4 EVs transcripts were associated with signal transduction and protein kinase activity, indicating rapid responses to environmental changes. In this novel study, data integration highlighted the differences in enzyme profiles, metabolic processes, and potential origins of EVs in the two strains shedding light on the diversity and complexity of A. castellanii EVs and having implications for understanding host-pathogen interactions and developing targeted interventions for Acanthamoeba-related diseases.IMPORTANCEA comprehensive and fully comparative analysis of extracellular vesicles (EVs) from two Acanthamoeba castellanii strains of distinct virulence, a Neff (environmental) and T4 (clinical), revealed striking differences in their morphology and protein, lipid, metabolites, and transcripts levels. Data integration highlighted the differences in enzyme profiles, metabolic processes, and potential distinct origin of EVs from both strains, shedding light on the diversity and complexity of A. castellanii EVs, with direct implications for understanding host-pathogen interactions, disease mechanisms, and developing new therapies for the clinical intervention of Acanthamoeba-related diseases.

Comparative proteomic analysis of the ovarian fluid and eggs of Siberian sturgeon

BackgroundSturgeon species are living fossils that exhibit unique reproductive characteristics, and elucidation of the molecular processes governing the formation and quality of sturgeon eggs is crucial. However, comprehensive data on the protein composition of sturgeon ovarian fluid (OF) and eggs and their functional significance are lacking. To address this knowledge gap, the aim of the present study was to conduct a comprehensive comparative proteomic analysis of Siberian sturgeon OF and eggs using liquid chromatography–mass spectrometry (LC–MS/MS).ResultsA total of 617 proteins were identified in OF, and 565 proteins were identified in eggs. A total of 772 proteins showed differential abundance. Among the differentially abundant proteins, 365 were more abundant in OFs, while 407 were more abundant in eggs. We identified 339 proteins unique to OFs and 287 proteins specific to eggs, and further investigated the top 10 most abundant proteins in each. The functional annotation of the OF proteins highlighted their predominant association with immune system processes, including the complement and coagulation cascade, neutrophil and leukocyte-mediated immunity, cholesterol metabolism, and regulation of the actin cytoskeleton. Analysis of egg proteins revealed enrichment in metabolic pathways, such as oxidative phosphorylation and fatty acid metabolism, and protein ubiquitination and translation. OF-specific proteins included extracellular matrix and secretory vesicles, and eggs were enriched in proteins localized to mitochondria and ribosome components.ConclusionsThis study presents the first comprehensive characterization of the protein composition of sturgeon OF and eggs and elucidates their distinct functional roles. These findings advance our understanding of sturgeon reproduction, OF-egg signaling and the origin of OF proteins. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium with the dataset identifier PXD044168 to ensure accessibility for further research.

RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition.

This article aims to enhance the performance of larger language models (LLMs) on the few-shot biomedical named entity recognition (NER) task by developing a simple and effective method called Retrieving and Chain-of-Thought (RT) framework and to evaluate the improvement after applying RT framework. Given the remarkable advancements in retrieval-based language model and Chain-of-Thought across various natural language processing tasks, we propose a pioneering RT framework designed to amalgamate both approaches. The RT approach encompasses dedicated modules for information retrieval and Chain-of-Thought processes. In the retrieval module, RT discerns pertinent examples from demonstrations during instructional tuning for each input sentence. Subsequently, the Chain-of-Thought module employs a systematic reasoning process to identify entities. We conducted a comprehensive comparative analysis of our RT framework against 16 other models for few-shot NER tasks on BC5CDR and NCBI corpora. Additionally, we explored the impacts of negative samples, output formats, and missing data on performance. Our proposed RT framework outperforms other LMs for few-shot NER tasks with micro-F1 scores of 93.50 and 91.76 on BC5CDR and NCBI corpora, respectively. We found that using both positive and negative samples, Chain-of-Thought (vs Tree-of-Thought) performed better. Additionally, utilization of a partially annotated dataset has a marginal effect of the model performance. This is the first investigation to combine a retrieval-based LLM and Chain-of-Thought methodology to enhance the performance in biomedical few-shot NER. The retrieval-based LLM aids in retrieving the most relevant examples of the input sentence, offering crucial knowledge to predict the entity in the sentence. We also conducted a meticulous examination of our methodology, incorporating an ablation study. The RT framework with LLM has demonstrated state-of-the-art performance on few-shot NER tasks.

Microservices Security Vulnerability Remediation approach using Veracode and Checkmarx

Abstract — As organizations increasingly adopt microservices architectures for building scalable and resilient applications, ensuring the security of these distributed systems becomes paramount. In this empirical study, we conduct a comprehensive comparative analysis to assess the efficacy of three leading security scanning tools, namely Veracode, Snyk, and Checkmarx, in identifying and remedying security vulnerabilities within microservices applications deployed on the AWS and Azure cloud platforms. The study aims to provide nuanced insights into the performance, usability, and integration capabilities of these tools, offering valuable guidance to organizations striving to fortify their microservices-based infrastructures. By meticulously evaluating scanning capabilities, vulnerability detection accuracy, remediation guidance comprehensiveness, CI/CD pipeline integration proficiency, and overall ease of use, our research sheds light on the relative strengths and weaknesses of each tool in the context of modern cloud-native application security. Through meticulously designed experiments utilizing realistic microservices application scenarios encompassing diverse vulnerability types, including injection attacks, authentication bypasses, and insecure configurations, we present a thorough examination of the tools' capabilities and limitations. The findings from our study contribute to the evolving discourse on microservices security, emphasizing the critical importance of selecting appropriate security scanning solutions tailored to the unique requirements and constraints of cloud-based microservices architectures. By leveraging the insights gleaned from our comparative analysis, organizations can make well-informed decisions regarding tool selection and deployment strategies, thereby bolstering the resilience of their microservices ecosystems against an ever-expanding threat landscape.

Adaptive Random Mac Strategy for IoT Security Through Network Forensics Investigation

In the rapidly evolving landscape of digital systems and networks, ensuring robust cyber security measures is of paramount importance. This study delves into the evaluation of a proposed security method's efficiency through a comprehensive comparative analysis conducted under diverse security breach simulations. The objective is to discern the method's effectiveness in safeguarding sensitive information and maintaining system integrity.The research methodology involves the selection of representative security breach scenarios, each emulating distinct attack vectors and potential vulnerabilities. A benchmark security protocol, widely acknowledged for its reliability, serves as a reference for the proposed method's assessment. A series of controlled breach simulations are executed, simulating real-world cyber threats, ranging from sophisticated malware intrusions to social engineering attacks. Key performance metrics, such as response time, resource utilization, and data recovery rates, are meticulously recorded and analyzed for both the benchmark and proposed methods. Furthermore, the impact on system functionality and user experience is evaluated to comprehend the trade-off between heightened security and seamless user interaction. The experiments are conducted across multiple system architectures to account for variations in hardware and software configurations. The findings reveal nuanced insights into the proposed method's efficiency under distinct breach scenarios. It showcases commendable resilience against certain attack vectors while potentially exposing limitations in others. The comparative analysis provides a comprehensive overview of the method's overall efficacy and enables the identification of scenarios where enhancements might be necessary. The performance of the suggested approach under various security breach simulations is empirically demonstrated in this paper, adding to the body of current knowledge. The insights gained from this research aid in refining the method's design and implementation, bolstering its potential to address the dynamic and evolving landscape of cyber security threats. As digital systems continue to play an increasingly integral role in our lives, the significance of such evaluations cannot be overstated, ensuring that our vital digital infrastructure remains secure and resilient.

Theoretical insights into IT governance and compliance in banking: Perspectives from African and U.S. regulatory environments

This review paper provides a comprehensive comparative analysis of IT governance and compliance within the banking sectors of Africa and the U.S., highlighting the intricacies of navigating diverse regulatory environments. Through examining governance strategies, compliance mechanisms, and best practices, the paper underscores the adaptation of frameworks like COBIT, ISO/IEC 27001, and ITIL to meet regional regulatory demands and operational challenges. Key insights reveal significant differences in regulatory landscapes, technological maturity, and cybersecurity and risk management approaches. The analysis identifies best practices and lessons that can be leveraged globally, suggesting areas for future research including emerging technologies and the impact of global regulations. This work aims to enhance understanding of IT governance and compliance, offering valuable perspectives for banks, regulators, and policymakers. Keywords: IT Governance, Compliance, Banking Sector, Regulatory Environment, Cybersecurity, Emerging Technologies.

Enabling near-real-time safety glove detection through edge computing and transfer learning: comparative analysis of edge and cloud computing-based methods

PurposeComputer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.Design/methodology/approachThe developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.FindingsThree DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.Originality/valueThis study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

On cost-efficient parallel iterative solvers for 3D frequency-domain seismic multisource viscoelastic anisotropic wave modeling

Solving large sparse linear systems in 3D frequency-domain seismic wave modeling, especially in viscoelastic anisotropic media, poses significant challenges due to the increasing number of discrete moduli and nonzero elements in the linear system matrix. The computational load surpasses that of acoustic or viscoacoustic media, making it even more challenging when dealing with multisource problems. Popular scientific tools for solving a linear system, such as multifrontal massively parallel sparse direct solver (MUMPS), structured matrix package (STRUMPACK), and portable extensible toolkit for scientific computation (PETSc), can be used, but their applicability to our specific problem has not been comprehensively evaluated. Our study aims to tackle the challenges in solving large, sparse, complex-valued symmetric linear systems with multiple right-hand side vectors for 3D frequency-domain seismic wave modeling. We have adopted the preconditioned conjugate gradient iterative algorithms as the foundation for our research, introducing two highly cost-effective parallel iterative solvers: the parallel symmetric successive overrelaxation conjugate gradient (P-SSORCG) and the parallel incomplete Cholesky conjugate gradient (P-ICCG). These novel solvers are subjected to a comprehensive comparative analysis against well-established scientific tools, such as MUMPS, STRUMPACK, and PETSc, in the context of 3D frequency-domain seismic wave modeling. We show their promising performances in a practical 3D SEG/EAGE overthrust model and demonstrate that the grouped P-SSORCG offers an efficient alternative to parallel direct solvers, particularly in situations wherein computational resources are limited.

DFT aided comparative screening of mechanical, optoelectronic, and transport properties of double perovskites Cs2ScAuX6 (X = Cl, Br, and I) for green energy applications

A comprehensive comparative analysis has been performed to investigate the Cs2ScAuX6 (X = Cl, Br, and I) employing density functional theory. The WIEN2k code has been applied to optimize the structural arrangement of perovskites and calculate various aspects such as mechanical, optoelectronic, and transport characteristics. The results demonstrated that these perovskite compounds had both structural and chemical stability. The analyzed elastic characteristics of the Cs2ScAuX6 have shown that these compounds are mechanically stable, have anisotropy toughness, and provide durability against plastic deformation. The electronic characteristics are determined precisely and accurately using the Trans-Blaha-modified Becke-Johnson (TB-mBJ) potential. The compounds Cs2ScAuCl6, Cs2ScAuBr6, and Cs2ScAuI6 are semiconductors and have band gaps of 1.6, 1.5, and 1.05 eV, respectively, demonstrating an indirect nature. An investigation examines the optical characteristics within the energy range of 0–6 eV. The chosen materials reveal transparency in the lower energy spectrum of incoming photons and exhibit significant absorption and transmission of light. Therefore, it can be inferred that Cs2ScAuX6 materials possess optical properties that make them suitable for utilization in photovoltaic devices. Further, these materials elaborate substantial electrical conductivity, power factors, and figures of merit (ZT), making them efficient thermoelectric resources. At last, these results would be advantageous for experimental researchers and have also unveiled the substantial capability of these substances to be utilized in solar energy harvesting and thermoelectric devices.

In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics

The quantitative analysis of glucose using spectroscopy is a topic of great significance and interest in science and industry. One conundrum in this area is deploying appropriate preprocessing and regression tools. To contribute to addressing this challenge, in this study, we conducted a comprehensive and novel comparative analysis of various machine learning and preprocessing filtering techniques applied to near-infrared, mid-infrared, and a combination of near-infrared and mid-infrared spectroscopy for glucose assay. Our objective was to evaluate the effectiveness of these techniques in accurately predicting glucose levels and to determine which approach was most optimal. Our investigation involved the acquisition of spectral data from samples of glucose solutions using the three aforementioned spectroscopy techniques. The data was subjected to several preprocessing filtering methods, including convolutional moving average, Savitzky-Golay, multiplicative scatter correction, and normalisation. We then applied representative machine learning algorithms from three categories: linear modelling, traditional nonlinear modelling, and artificial neural networks. The evaluation results revealed that linear models exhibited higher predictive accuracy than nonlinear models, whereas artificial neural network models demonstrated comparable performance. Additionally, the comparative analysis of various filtering methods demonstrated that the convolutional moving average and Savitzky-Golay filters yielded the most precise outcomes overall. In conclusion, our study provides valuable insights into the efficacy of different machine learning techniques for glucose measurement and highlights the importance of applying appropriate filtering methods in enhancing predictive accuracy. These findings have important implications for the development of new and improved glucose quantification technologies.