Scientific Applications Research Articles

Research Trends and Hot Topics of Wearable Sensors in Wound Care Over Past 18 Years: A Bibliometric Analysis

ObjectiveThis study determined the development trends, analyzed collaboration networks, and identified research hotspots in the field of wearable sensors for wound care from 2007–2024 using a rigorous bibliometric analysis approach. MethodsBibliometric and scientometric analyses were performed utilizing data sourced from the Web of Science Core Collection database. This study examined publication trends, contributions from various countries and institutions, author productivity, keyword prevalence, and citation patterns to discern research hotspots and potential future avenues in the application of wearable sensors for wound care. ResultsThis study included 1177 articles, which demonstrated a marked increase in publications since 2016 and underscores the burgeoning interest in wearable sensors for wound care. China and the United States have emerged as prominent contributors to the research field, exhibiting numerous international collaborations. An analysis of keywords and citation bursts highlighted wound healing, hydrogels, and sensors as the key research foci with recent trends shifting towards the integration of wearable technology with advanced materials and artificial intelligence for advanced wound management. The research landscape is characterized by a diverse network of international collaborations and an emphasis on interdisciplinary approaches that integrate materials science, sensor technology, and clinical applications. ConclusionThe utilization of wearable sensors in wound care constitutes a rapidly progressing area of research, garnering significant interest and promising avenues for future advances. The integration of wearable sensors with advanced materials and AI technologies presents a frontier of opportunity for innovating wound care methodologies, enhancing patient outcomes, and optimizing the allocation of healthcare resources.

Research Trends on MoTrPAC and Exercise Science Applications

PURPOSE: Exercise plays a crucial role in maintaining human health and is widely recognized for its effectiveness in preventing and managing chronic diseases. However, the molecular mechanisms by which exercise exerts its beneficial effects on the body remain unclear. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) is a collaborative research initiative that brings together multiple institutions and organizations to investigate the molecular mechanisms of exercise.METHODS: This article provides a comprehensive overview of the MoTrPAC, detailing its structure, research strategies, and methodologies. We reviewed the consortium’s primary research objectives, methodologies, key findings, and potential applications in exercise science. This analysis was conducted through a systematic review of online databases and search engines, including PubMed, Web of Science, and Google Scholar.RESULTS: The MoTrPAC uses animal models and human studies to explore the physiological effects of exercise on various tissues. Research by the consortium has demonstrated that different types and intensities of exercise induce specific gene expression changes, activate diverse metabolic pathways, such as fatty acid oxidation, glucose metabolism, and protein synthesis, and restructure protein interaction networks in muscle tissue. These studies have identified novel biomarkers that can be used to assess the molecular effects of exercise.CONCLUSIONS: MoTrPAC significantly advances our understanding of the molecular mechanisms underlying the effects of exercise. The consortium’s findings have the potential to inform the development of personalized exercise prescriptions and health management strategies. Future research should include more diverse populations and investigate the long-term effects of exercise to enhance our understanding of the profound impact of physical activity on human health.

The role of data science in transforming business operations: Case studies from enterprises

Data science has emerged as a pivotal force in transforming business operations across various industries, driving innovation, operational efficiency, and strategic decision-making. This review paper explores the multifaceted role of data science in business, examining key concepts, historical integration, and strategic advantages. It discusses the application of data science in diverse business domains, highlighting techniques such as predictive analytics, sentiment analysis, and optimization that have revolutionized marketing, supply chain management, finance, and customer service. The paper further analyzes data science's tangible and intangible benefits, including cost reduction, improved customer experience, and enhanced productivity, which collectively contribute to a competitive edge in the market. The paper reflects on emerging trends like artificial intelligence, machine learning, ethical data practices, and the integration of blockchain and IoT, which are set to shape the future of data-driven business operations. It offers recommendations for businesses to prepare for this evolving landscape, emphasizing the importance of a data-centric culture, robust infrastructure, and collaboration. The paper concludes by underscoring the critical role of data science in fostering sustained business success in an increasingly data-driven world. Keywords: Data Science, Business Transformation, Predictive Analytics, Operational Efficiency, Artificial Intelligence, Strategic Decision-Making.

Achieving precise multiparameter measurements with distributed optical fiber sensor using wavelength diversity and deep neural networks

The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications.

Software stewardship and advancement of a high-performance computing scientific application: QMCPACK

We provide an overview of the software engineering efforts and their impact in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code targeting high-performance computing (HPC) systems. Aspects included are: (i) strategic expansion of continuous integration (CI) targeting CPUs, using GitHub Actions own runners, and NVIDIA and AMD GPUs used in pre-exascale systems, (ii) incremental reduction of memory leaks using sanitizers, (iii) incorporation of Docker containers for CI and reproducibility, and (iv) refactoring efforts to improve maintainability, testing coverage, and memory lifetime management. We quantify the value of these improvements by providing metrics to illustrate the shift towards a predictive, rather than reactive, maintenance approach. Our goal, in documenting the impact of these efforts on QMCPACK, is to contribute to the body of knowledge on the importance of research software engineering (RSE) for the stewardship and advancement of community HPC codes to enable scientific discovery at scale.

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning.

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

Implementation of Social Science Theory in Civic Education for School Students

Civic education plays an important role in shaping Indonesian students into democratic, responsible and active citizens in social, political and economic life. Through an understanding of the political system, rights and responsibilities, community participation, social and cultural awareness, critical thinking, ethics, global citizenship and communication skills, citizenship education provides a foundation for students' active engagement and understanding of global roles. The purpose of this study is to identify and analyze the theories in social science that underlie citizenship education, as well as analyze how the application of social science can assist students in understanding and exercising their rights and responsibilities as citizens. This research uses a qualitative method with a literature study approach to examine the relationship between civic education and social science theories. The results show that the integration of social sciences such as sociology, psychology, education, politics and law in the civic education curriculum strengthens students' understanding of their rights and responsibilities. In addition, social sciences help advance civic engagement and responsibility, equipping students to face challenges in the 21st century. In conclusion, the application of Social Sciences in citizenship education prepares students to become informed and responsible citizens, capable of contributing positively in local and global communities.

Recent Advances in the Synthesis of N-fluoroalkyl Amides/Sulfonamides and Their Carbonyl/Sulfonyl Derivatives.

Organofluorine compounds have consistently demonstrated practical applications in the life sciences due to the fascinating properties imparted by the fluorine substituents. In recent years, significant advancements have been made in the synthesis of N-fluoroalkyl carbonyl and sulfonyl compounds. This review offers a current overview of the various synthetic routes for N-fluoroalkyl amides/sulfonamides and their transformation to new unexplored N-fluoroalkyl carbonyl/sulfonyl derivatives, categorized into three parts based on the different fluoroalkyl groups.

Statistical applications in the biomedical sciences: A review

This study covers applications areas of statistics in biological science. The integration of statistical methods into the biological sciences has revolutionized data analysis, enhancing the accuracy of data analysis and interpretation. This review aims to provide a comprehensive overview of the key statistical techniques used in biomedical research, highlighting their applications, advantages, and limitations. We conducted a systematic literature search across major databases, we focused on studies that have employed descriptive statistics, inferential statistics, regression analysis, multivariate analysis, survival analysis, Bayesian statistics, and machine learning methods in biomedical sciences. Our findings reveal that statistical methods are indispensable in various areas, including genomics, proteomics, epidemiology, ecology, clinical trials, and pharmacology. Each section of this review details the specific applications of these methods, supported by relevant case studies that illustrate their practical implementation and impact. This review underscores the indispensable role of statistics in biological research, providing actionable insights and recommendations for researchers aiming to leverage statistical tools to drive scientific discovery.

Novel one-pot synthesis of nitrogen-containing derivatives via C-N bond formation and C-O bond cleavage

Ortho-aminophenols are significant organic compounds known for their versatility and wide-ranging applications in materials science, synthesis, pharmaceuticals, agriculture, and industry. In this work, we present novel synthetic methods that utilize readily available and cost-effective materials, namely catechol, Cd(NO3)2 as a catalyst, and H2O/EtOH solvent. These proposed synthesis methods involve mild, one-pot reactions that facilitate the formation of both C-N and cleavage of C-O bond. This approach leads to the production of various nitrogen-containing derivatives with remarkably high yields and efficiency.

Snapshot coherent diffraction imaging across ultra-broadband spectra

Ultrafast imaging simultaneously pursuing high temporal and spatial resolution is a key technique to study the dynamics in the microscopic world. However, the broadband spectra of ultra-short pulses bring a major challenge to traditional coherent diffraction imaging (CDI), as they result in an indistinct diffraction pattern, thereby complicating image reconstruction. To address this, we introduce, to our knowledge, a new ultra-broadband coherent imaging method, and empirically demonstrate its efficacy in facilitating high-resolution and rapid image reconstruction of achromatic objects. The existing full bandwidth limitation for snapshot CDI is enhanced to ∼60% experimentally, restricted solely by our laser bandwidth. Simulations indicate the applicability of our method for CDI operations with a bandwidth as high as ∼140%, potentially supporting ultrafast imaging with temporal resolution into ∼50-attosecond scale. Even deployed with a comb-like harmonic spectrum encompassing multiple octaves, our method remains effective. Furthermore, we establish the capability of our approach in reconstructing a super-broadband spectrum for CDI applications with high fidelity. Given these advancements, we anticipate that our method will contribute significantly to attosecond imaging, thereby advancing cutting-edge applications in material science, quantum physics, and biological research.

De Novo Design and Characterization of Amphiphilic Peptides with Basic Side Chains for Tailored Interfacial Chemistries.

Lysine-leucine (LK) peptides have been used as model systems and platforms for 2D material design for decades. LK peptides are amphiphilic sequences designed to bind and fold at hydrophobic surfaces through hydrophobic leucine side chains and hydrophilic lysine side chains extending into the aqueous subphase. The hydrophobic periodicity of the sequence dictates the secondary structure at the interface. This robust design makes them ideal candidates for controlling interfacial chemistry. This study presents the de novo design and characterization of two novel peptides: LRα14 and LHα14, which substitute lysine with arginine and histidine, respectively, in the helical LKα14 sequence. This modification is intended to expand the LK peptide platform to a new basic interfacial chemistry. We explore the stability of the new LRα14 and LHα14 designs with respect to changes in pH and salt concentration in bulk solution and at the interface using circular dichroism (UV-CD) and vibrational sum-frequency generation spectroscopy, respectively. Notably, the structural stability of the peptides remains unaffected across a wide range of pH and ionic strength values. At the same time, the variation of side-chain chemistry leads to a wide spectrum of interfacial water structures. By extension of the LK platform to include arginine and histidine, this study broadens the toolbox for designing tailored interfacial chemistries with applications in material and biomedical sciences.

Electric-field-enhanced second-harmonic domain contrast and nonreciprocity in a van der Waals antiferromagnet

Imaging antiferromagnetic 180° domains with actively controlled visibility is vital for both fundamental science and sophisticated applications. While optical second-harmonic generation (SHG) is a well-known technique for distinguishing such domains in non-centrosymmetric antiferromagnets, a general material-based strategy to control domain contrast remains elusive. Using van der Waals antiferromagnet MnPS3 as a proof of concept, we demonstrate the tuning of nonreciprocity-induced domain contrast in SHG through applying an in-plane electric field that transforms the magnetic point group to its unitary subgroup. The interference among intrinsic electric-dipole, magnetic-dipole, and field-induced electric-dipole transitions, each carrying distinct characters under space-inversion (P\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mathcal{P}}}$$\\end{document}) and time-reversal (T\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mathcal{T}}}$$\\end{document}) operations, enables large tuning of domain contrast and nonreciprocity in a broad spectral range. This strategy, generically applicable to systems characterized by PT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mathcal{P}}}{{\\mathcal{T}}}$$\\end{document}-symmetric magnetic groups with a polar unitary subgroup, offers a path to fast electrical modulation of nonlinear nonreciprocal photonic behaviors using antiferromagnets.

The Utilization of Google Sites Media in Writing Skills in the 5.0 Era

High school teachers in Tegal City have difficulty realizing professionalism in managing meaningful learning. One of the reasons is that teachers are not used to using google sites learning media which includes innovative, efficient, and effective learning materials and media and are student-oriented in the 5.0 era. The solution to overcome this problem is to empower the Indonesian Subject Teacher Conference (MGMP) through the development of google sites media in writing skills in the field of Indonesian studies to improve meaningful learning in the 5.0 era for high school teachers in Tegal City. The goal is to maximize the MGMP program and foster teacher professionalism and optimize the ability of educators to carry out learning. The methods used are training methods and guidance methods. The training method is used to teach google sites media content and the guidance method is used in the practice of designing google sites media in writing skills using Canva, the preparation of teaching modules, learning materials, and learning models. With these two methods, it is hoped that the target audience for the application of science and technology can increase professionalism for teachers to improve the quality of learning.

Porous Magnetic Soft Grippers for Fast and Gentle Grasping of Delicate Living Objects.

Magnetic soft grippers have attracted intensive interest due to their untethered controllability, rapid response, and biological safety. However, manipulating living objects requires a simultaneous increase in shape adaptability and gripping force, which are typically mutually exclusive. Increasing the magnetic particle content enhances the magnetic strength but also increases the elastic modulus, leading to low adaptability and high impact force. Here, a porous magnetic soft gripper (PMSG) is developed by integrating a porous structure into a magnetic silicone elastomer. The design of porous hard magnetic composite is characterized by high magnetization, low modulus, and rough surface. It offers the PMSG good compliance, high gripping force, and low impact force at fast gripping. The PMSG is capable of performing a variety of tasks, including the fast and gentle grasping of delicate living objects. The study provides insight into the design of novel magnetic grippers and may offer a promising outlook for biomedical or scientific applications in the manipulation of delicate organisms.

Minimizing distance between distribution functions: discrete counterparts to continuous random variables with applications in non-life insurance and stochastic reliability

In this work, we propose a novel family of procedures for deriving a discrete counterpart to a continuous probability distribution. They are based on a class of distances between cumulative distribution functions, including the Cramér, the Cramér-von Mises, and the Anderson-Darling distances as particular cases. The discrete counterpart is defined and derived as the random variable which minimizes its distance to the assigned continuous probability distribution among all the discrete random variables supported on the set of integers (or positive integers). Applications are provided with reference to the exponential and the normal distributions, among others; the discrete counterparts are derived, and their main properties are discussed, also in comparison with the one obtained through an existing discretization technique based on the preservation of the cumulative distribution function at integer values. Parameter estimation for these discrete analogs is discussed, along with an analysis of two real datasets, where they are compared in terms of goodness-of-fit with some popular discrete distributions. Furthermore, in order to highlight the effectiveness and the benefits derived from the proposed discretization procedures, we illustrate two practical applications in actuarial science and in reliability engineering. In the former case, the problem of determining the distribution of the total claims amount for a non-life insurance portfolio is considered, where the claim sizes can be modelled as iid random variables, and the number of claims is random as well. Actuaries use a recursive calculation method based on Panjer's formula, which requires an appropriate discretization of the individual claim distribution, and therefore the proposed procedures can be used. Since we consider two simple cases where the distribution of the total claims amount is analytically acquirable, the efficacy of the discretization procedures in the final approximation can be easily assessed and turns out to be satisfactory, especially when compared to the existing discretization. The latter case considers the determination of the reliability parameter for a complex stress-strength model. Here, the approximation by discretization is compared to Monte Carlo simulation and shown to be relevant: with a comparable if not smaller computational effort, discretization leads to similar results as simulation. Such discretizations can also naturally be applied to more complex problems such as scenario generation in stochastic programming. R code for this article is provided as supplementary material.

Mechanochemistry: Fundamental Principles and Applications.

Mechanochemistry is an emerging research field at the interface of physics, mechanics, materials science, and chemistry. Complementary to traditional activation methods in chemistry, such as heat, electricity, and light, mechanochemistry focuses on the activation of chemical reactions by directly or indirectly applying mechanical forces. It has evolved as a powerful tool for controlling chemical reactions in solid state systems, sensing and responding to stresses in polymer materials, regulating interfacial adhesions, and stimulating biological processes. By combining theoretical approaches, simulations and experimental techniques, researchers have gained intricate insights into the mechanisms underlying mechanochemistry. In this review, the physical chemistry principles underpinning mechanochemistry are elucidated and a comprehensive overview of recent significant achievements in the discovery of mechanically responsive chemical processes is provided, with a particular emphasis on their applications in materials science. Additionally, The perspectives and insights into potential future directions for this exciting research field are offered.

Potentials of Additive Manufacturing for Cutting Tools: A Review of Scientific and Industrial Applications

Potentials of Additive Manufacturing for Cutting Tools: A Review of Scientific and Industrial Applications

Economic Analysis and Design of Sustainable Solar Electric Vehicle Carport at Applied Science Private University in Jordan

Electrical vehicles are finding wide acceptance in the Jordanian transportation market; this has caused an accelerating shift in the emissions of greenhouse gases from the direct burning of fossil fuels consumed by the transportation sector towards the power generation sector. On the other hand, as electric vehicles gain more popularity, an extra load is added to the electrical power generation systems, raising essential concerns such as the capability of the power network to support this massive extra load and the increased emission of greenhouse gases caused by power plants. Studies show that Jordan’s weather is known for being bright, sunny, and very suitable for the generation of electric power from solar energy sources. Therefore, with an infrastructure that can support convenient off-grid charging, a huge burden will be taken off the national grid and the environment. Therefore, this work proposes a basic design for an off-grid PV-covered carport and presents a study of the economics and effectiveness of using such a system to charge electric vehicles owned by the students and employees of the Applied Science Private University. The study is based on actual solar irradiance data collected on-site during university working hours (8 a.m.–5 p.m.) to allow students and employees to charge their electric vehicles from an off-grid carport system while on campus. Space limitations for carport design, initial design cost, return on investment, and annual electricity consumption are discussed to demonstrate the benefits of such a system for both the consumer (convenience and low charging cost) and the power company provider (less load to maintain).

Pompeiu-Hausdorff Fuzzy <i>b</i>-metric Spaces Are Associated with a Common Fixed Point and Multivalued Mappings

The notion of fuzzy logic was introduced by Zadeh. Unlike traditional logic theory, where an element either belongs to the set or does not, in fuzzy logic, the affiliation of the element to the set is expressed as a number from the interval [0, 1]. The study of the theory of fuzzy sets was prompted by the presence of uncertainty as an essential part of real-world problems, leading Zadeh to address the problem of indeterminacy. The theory of a fixed point in fuzzy metric spaces can be viewed in different ways, one of which involves the use of fuzzy logic. Fuzzy metric spaces, which are specific types of topological spaces with pleasing ”geometric” characteristics, possess a number of appealing properties and are commonly used in both pure and applied sciences. Metric spaces and their various generalizations frequently occur in computer science applications. For this reason, a new space called a Pompeiu-Hausdorff fuzzy <i>b</i>-metric space is constructed in this paper. In this space, some new fixed point results are also formulated and proven. Additionally, a general common fixed point theorem for a pair of multi-valued mappings in Pompeiu-Hausdorff fuzzy <i>b</i>-metric spaces is investigated. The findings obtained in fuzzy metric spaces, such as those discussed in Remark 3.1, are generalized by the results in this paper, and additional specific findings are produced and supported by examples. The study of denotational semantics and their applications in control theory using fuzzy <i>b</i>-metric spaces and Pompeiu-Hausdorff fuzzy <i>b</i>-metric spaces will be an important next step.