Diverse Applications Research Articles

Development of a ReaxFF reactive force field for ternary phosphate-based bioactive glasses.

Phosphate-based glasses (PBGs) in the CaO-Na2O-P2O5 system have diverse applications as biomaterials due to their unique dissolution properties. A reactive force field (ReaxFF) has been developed to simulate these materials at the atomic level. The ReaxFF parameters of PBGs, including the interaction between phosphorus and calcium atoms, have been developed using a published code based on genetic algorithms. The training data, including the atomic charges, atomic forces, bond and angle parameters, and different differential energies, are chosen and measured from static quantum-mechanical calculations and abinitio molecular dynamics of PBGs. We did different short- and medium-range structural analyses on the bulk simulated PBGs with different compositions to validate the developed potential. Radial and angular distribution functions and coordination numbers of network formers and modifiers, as well as the network connectivity of the glass, are in agreement with experimental and previous simulations using both shell-model classical force fields and abinitio simulated data; for example, the coordination number of phosphorus is 4.0. This successful development of ReaxFF parameters being able to describe the bulk PBGs enables us to work on the dissolution behavior of the glasses, including the interaction of water molecules with PBGs, in future works.

Copper-Nanoparticle-Decorated Hydrothermal Carbonaceous Carbon-Polydimethylsiloxane Nanocomposites: Unveiling Potential in Simultaneous Light-Driven Interfacial Water Evaporation and Power Generation.

This study introduces a hydrothermal synthesis method that uses glucose and Cu2+ ions to create a Cu-nanoparticle (NP)-decorated hydrothermal carbonaceous carbon hybrid material (Cu-HTCC). Glucose serves both as a reducing agent, efficiently transforming Cu2+ ions into elemental Cu nanostructures, and as a precursor for HTCC microstructures. An enhanced plasmon-induced electric field resulting from Cu NPs supported on microstructure matrices, coupled with a distinctive localized π-electronic configuration in the hybrid material, as confirmed by X-ray photoelectron spectroscopic analysis, lead to the heightened optical absorption in the visible-near-infrared range. Consequently, flexible nanocomposites of Cu-HTCC/PDMS and Cu-HTCC@PDMS (PDMS=polydimethylsiloxane) are designed as 2 and 3D structures, respectively, that exhibit broad-spectrum solar absorption. These composites promise efficient photo-assisted thermoelectric power generation and water evaporation, demonstrating commendable mechanical stability and flexibility. Notably, the Cu-HTCC@PDMS composite sponge simultaneously exhibits commendable efficiency in both water evaporation (1.47kgm-2h-1) and power generation (32.1mV) under 1 sunlight illumination. These findings unveil new possibilities for innovative photothermal functional materials in diverse solar-driven applications.

Stress Analysis of Transmission Shafts: A Multi-Faceted Investigation

Transmission shafts constitute pivotal components in mechanical systems, responsible for transferring power from the engine to various mechanisms. The integrity and longevity of these systems hinge on the robustness of transmission shafts, necessitating comprehensive stress analysis. This paper presents an in-depth exploration into stress analysis methodologies applied to transmission shafts. Analytical, numerical, and experimental approaches are scrutinized, with a focus on their efficacy, limitations, and advancements. Material properties, geometric configurations, loading conditions, and manufacturing intricacies are dissected to discern their influence on shaft behavior. Through this comprehensive examination, this paper endeavors to contribute to the advancement of transmission shaft design, ensuring reliability and performance across diverse engineering applications.

Electromagnetic–thermal–mass transport triple hybrid model for energy-efficient hydrogen release from metal particles by induction heating

Hydrogen storage in metals is anticipated to be a highly promising technical approach for managing hydrogen in diverse energy applications, emphasizing the need to establish efficient operating conditions. In this study, we developed a numerical model that can simultaneously calculate hydrogen and heat transport combined with electromagnetic induction heating of metal particles. Using the developed numerical simulator, we investigated the behavior of the hydrogen release process from the hydrogen-absorbing metal and proposed important indicators for the operation. The fractions of the electromagnetic energy absorbed by the metal particle used for the hydrogen release, sensible heat, interfacial heat conduction, and thermal radiation were systematically evaluated. Considering the trade-off between temperature rise and the hydrogen concentration gradient, an optimal diameter of metal particles existed for the maximum hydrogen release rate. Similarly, the trade-off between temperature rise and energy consumption revealed an optimal electromagnetic power density for the heat utilization fraction for hydrogen release. Our electromagnetic–thermal–mass transport triple hybrid model constitutes a useful numerical simulator for the design of energy-efficient hydrogen storage systems.

ADVANCED SHOPLIFTING PREVENTION AND ALERT STSYEM

Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using YOLOv7, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed. We can use camera for Human Motion Detection. The Camera is used to catch the live images of the area in which it is being implemented, if any object is moving. The captured images are stored for further work. If motion is found in this video, the computer will start recording, buzz an alarm and send SMS to people listed in its database. In this way the system will provide the security against any misdeed. Keywords: YOLOv7, SMS, E-mail, CCTV, SMTP, RFID

Asymmetric Carbene Transfer: Enhancing Chemical Diversity for Drug Discovery.

The exploration of chemical space for disease targets is of paramount importance. However, the current repertoire of chemically synthesized compounds, numbering around 108, merely scratches the surface in comparison to the theoretical diversity of 1060. Organic chemists are actively engaged in pioneering methodologies to expand this constrained space. Among these innovative approaches, Asymmetric Carbene Transfer (ACT) emerges as a potent strategy for enhancing molecular diversity. This article critically examines the efficacy of ACT in synthesizing pharmaceutically relevant molecules, encompassing natural products and bioactive compounds. We highlight its pivotal role in the synthesis of 25 compounds. By unraveling the diverse applications of ACT, this review illuminates its potential impact on drug discovery. Furthermore, we propose future methodology directions, contributing to the ongoing discourse within the medicinal chemistry community. In summary, this review article navigates the landscape of ACT, showcasing its prowess in expanding chemical space for drug discovery. Through a nuanced exploration of its applications and a forward-looking approach, we contribute to the collective understanding of ACT's potential and chart a course for future advancements in ACT for drug discovery.

Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing

Abstract Background Natural language processing (NLP), a subset of artificial intelligence (AI), aims to decipher unstructured human language. This study showcases NLP's application in surgical health care, focusing on vestibular schwannoma (VS). By employing an NLP platform, we identify prevalent text concepts in VS patients' electronic health care records (EHRs), creating concept panels covering symptomatology, comorbidities, and management. Through a case study, we illustrate NLP's potential in predicting postoperative cerebrospinal fluid (CSF) leaks. Methods An NLP model analyzed EHRs of surgically managed VS patients from 2008 to 2018 in a single center. The model underwent unsupervised (trained on one million documents from EHR) and supervised (300 documents annotated in duplicate) learning phases, extracting text concepts and generating concept panels related to symptoms, comorbidities, and management. Statistical analysis correlated concept occurrences with postoperative complications, notably CSF leaks. Results Analysis included 292 patients' records, yielding 6,901 unique concepts and 360,929 occurrences. Concept panels highlighted key associations with postoperative CSF leaks, including “antibiotics,” “sepsis,” and “intensive care unit admission.” The NLP model demonstrated high accuracy (precision 0.92, recall 0.96, macro F1 0.93). Conclusion Our NLP model effectively extracted concepts from VS patients' EHRs, facilitating personalized concept panels with diverse applications. NLP shows promise in surgical settings, aiding in early diagnosis, complication prediction, and patient care. Further validation of NLP's predictive capabilities is warranted.

Extracting Latent Variables From Forecast Ensembles and Advancements in Similarity Metric Utilizing Optimal Transport

AbstractThis study presents a novel methodology for extracting latent variables from high‐dimensional sparse data, particularly emphasizing spatial distributions such as precipitation distribution. This approach utilizes multidimensional scaling with a distance matrix derived from a new similarity metric, the Unbalanced Optimal Transport Score (UOTS). UOTS effectively captures discrepancies in spatial distributions while preserving physical units. This is similar to mean absolute error, however it considers location errors, providing a more robust measure crucial for understanding differences between observations, forecasts, and ensembles. Probability distribution estimation of these latent variables enhances the analytical utility, quantifying ensemble characteristics. The adaptability of the method to spatiotemporal data and its ability to handle errors suggest its potential as a promising tool for diverse research applications.

Boosting Upconversion Efficiency in Optically Inert Shelled Structures with Electroactive Membrane through Electron Donation.

This study innovatively addresses challenges in enhancing upconversion efficiency in lanthanide-based nanoparticles (UCNPs) by exploiting Shewanella oneidensis MR-1, a microorganism capable of extracellular electron transfer. Electroactive membranes, rich in c-type cytochromes, are extracted from bacteria and integrated into membrane-integrated liposomes (MILs), encapsulating core-shelled UCNPs with an optically inactive shell, forming UCNP@MIL constructs. The electroactive membrane, tailored to donate electrons through the inert shell, independently boosts upconversion emission under near-infrared excitation (980nm or 1550nm), bypassing ligand-sensitized UCNPs. The optically inactive shell restricts energy migration, emphasizing electroactive membrane electron donation. Density functional theory calculations elucidate efficient electron transfer due to the electroactive membrane hemes' highest occupied molecular orbital being higher than the valence band maximum of the optically inactive shell, crucial for enhancing energy transfer to emitter ions. The introduction of a SiO2 insulator coating diminishes light enhancement, underscoring the importance of unimpeded electron transfer. Luminescence enhancement remains resilient to variations in emitter or sensitizing ions, highlighting the robustness of the electron transfer-induced phenomenon. However, altering the inert shell material diminishes enhancement, emphasizing the role of electron transfer. This methodology holds significant promise for diverse biological applications. UCNP@MIL offers an advantage in cellular uptake, which proves beneficial for cell imaging. This article is protected by copyright. All rights reserved.

Characterization of Lipid Extracts from Different Colors of Peach Palm Fruits—Red, Yellow, Green, and White—Obtained through Ultrasound-Assisted Green Extraction

This study represents a pioneering investigation and comparative analysis of lipid extracts from four different colors of peach palm (Bactris gasipaes Kunt) fruits—red, yellow, green, and white—by employing a green method based on ethanolic ultrasound-assisted extraction. This study examined the extraction yield, physico-chemical-quality attributes, chromatographic profiles (GC), color measurements, total carotenoid content, differential thermogravimetry (TG/DTA), and infrared spectroscopy (FTIR). The obtained lipid extracts displayed a high quality, considering the physico-chemical parameters of the Codex Alimentarius, and a fatty acids profile characterized by unsaturated fatty acids, notably omegas (ω-3, ω-6, and ω-9). The indices of atherogenicity (A.I.), thrombogenicity (I.T.), and hypocholesterolemic and hypercholesterolemic ratios revealed superior outcomes for the red peach palm lipid extract (approximately 0.35, 0.52, and 2.75, respectively), along with higher levels of β-carotene (748.36 µg of β-carotene per 100 g−1 of lipid extract) compared to the yellow, green, and white counterparts. Consequently, this research successfully demonstrates the efficacy of using a green extraction method in preserving the lipid’s quality, which can display cardiovascular functionality and thermal stability. These findings underscore the considerable potential of peach palm lipid extract as a valuable raw material for diverse industrial applications across various sectors. The results support its utilization in the production of functional food products and nutraceuticals due to its favorable fatty acid composition, potent antioxidant properties exhibited by its high β-carotene content, and notable cardiovascular functionality indices.

Molecular-caged metal-organic frameworks for energy management.

Metal-organic frameworks (MOFs) hold great promise for diverse applications when combined with polymers. However, a persistent challenge lies in the susceptibility of exposed MOF pores to molecule and polymer penetration, compromising the porosity and overall performance. Here, we design a molecular-caged MOF (MC-MOF) to achieve contracted window without sacrificing the MOF porosity by torsional conjugated ligands. These molecular cages effectively shield against the undesired molecule penetration during polymerization, thereby preserving the pristine porosity of MC-MOF and providing outstanding light and thermal management to the composites. The polymer containing 0.5 wt % MC-MOF achieves an 83% transmittance and an exceptional haze of 93% at 550 nanometers, coupled with remarkable thermal insulation. These MC-MOF/polymer composites offer the potential for more uniform daylighting and reduced energy consumption in sustainable buildings when compared to traditional glass materials. This work delivers a general method to uphold MOF porosity in polymers through molecular cage design, advancing MOF-polymer applications in energy and sustainability.

Mobile User Traffic Generation via Multi-Scale Hierarchical GAN

Mobile user traffic facilitates diverse applications, including network planning and optimization, whereas large-scale mobile user traffic is hardly available due to privacy concerns. One alternative solution is to generate mobile user traffic data for downstream applications. However, existing generation models cannot simulate the multi-scale temporal dynamics in mobile user traffic on individual and aggregate levels. In this work, we propose a multi-scale hierarchical generative adversarial network (MSH-GAN) containing multiple generators and a multi-class discriminator. Specifically, the mobile traffic usage behavior exhibits a mixture of multiple behavior patterns, which are called micro-scale behavior patterns and are modeled by different pattern generators in our model. Moreover, the traffic usage behavior of different users exhibits strong clustering characteristics, with the co-existence of users with similar and different traffic usage behaviors. Thus, we model each cluster of users as a class in the discriminator’s output, referred to as macro-scale user clusters. Then, the gap between micro-scale behavior patterns and macro-scale user clusters is bridged by introducing the switch mode generators, which describe the traffic usage behavior in switching between different patterns. All users share the pattern generators. In contrast, the switch mode generators are only shared by a specific cluster of users, which models the multi-scale hierarchical structure of the traffic usage behavior of massive users. Finally, we urge MSH-GAN to learn the multi-scale temporal dynamics via a combined loss function, including adversarial loss, clustering loss, aggregated loss, and regularity terms. Extensive experiment results demonstrate that MSH-GAN outperforms state-of-art baselines by at least 118.17% in critical data fidelity and usability metrics. Moreover, observations show that MSH-GAN can simulate traffic patterns and pattern switch behaviors.

Design Strategies for Anti‐Freeze Electrolytes in Aqueous Energy Storage Devices at Low Temperatures

AbstractWith the continuous development of electrochemical energy storage technology, especially in the current pursuit of environmental sustainability and safety, aqueous energy storage devices, due to their high safety, environmental friendliness, and cost‐effectiveness, are becoming an important direction of development in the field of energy storage. Diverse application scenarios require that energy storage systems be capable of continuous power supply under low temperature conditions. However, conventional aqueous electrolytes freeze at extremely low temperatures, causing limited ion transport and slow reaction kinetics, degrading the performance of the energy storage system. The design of low‐temperature anti‐freeze aqueous electrolytes has become an effective way to address this issue. In this review, the deep connection between hydrogen bonds (HBs) interactions in aqueous electrolytes and the liquid‐to‐solid conversion process, and the fundamental principles of the anti‐freeze mechanism is first explored. Subsequently, a systematic categorization and discussion of the design strategies for low‐temperature anti‐freeze aqueous electrolytes are conducted. Finally, potential directions are proposed. This review aims to provide comprehensive scientific guidance and technical reference for the development of anti‐freeze aqueous electrolytes with excellent low‐temperature performance, thereby promoting the application and innovation of aqueous energy storage devices in low‐temperature environments.

Recent Applications of Chitosan and Its Derivatives in Antibacterial, Anticancer, Wound Healing, and Tissue Engineering Fields

Chitosan, a versatile biopolymer derived from chitin, has garnered significant attention in various biomedical applications due to its unique properties, such as biocompatibility, biodegradability, and mucoadhesiveness. This review provides an overview of the diverse applications of chitosan and its derivatives in the antibacterial, anticancer, wound healing, and tissue engineering fields. In antibacterial applications, chitosan exhibits potent antimicrobial properties by disrupting microbial membranes and DNA, making it a promising natural preservative and agent against bacterial infections. Its role in cancer therapy involves the development of chitosan-based nanocarriers for targeted drug delivery, enhancing therapeutic efficacy while minimising side effects. Chitosan also plays a crucial role in wound healing by promoting cell proliferation, angiogenesis, and regulating inflammatory responses. Additionally, chitosan serves as a multifunctional scaffold in tissue engineering, facilitating the regeneration of diverse tissues such as cartilage, bone, and neural tissue by promoting cell adhesion and proliferation. The extensive range of applications for chitosan in pharmaceutical and biomedical sciences is not only highlighted by the comprehensive scope of this review, but it also establishes it as a fundamental component for forthcoming research in biomedicine.

Method and application of surface modification of Cerium dioxide

Airborne pollutants, such as nitrogen oxides (NOx), and waterborne contaminants, including heavy metals, dyes, and other pollutants, pose substantial environmental threats and jeopardize human health. Additionally, certain limitations have hindered the widespread application of some batteries, primarily attributed to elevated operating temperatures and the decreasing use of fossil fuels. In response to these challenges, cerium dioxide (CeO2) nanoparticles have garnered considerable attention due to their unique and advantageous physical and chemical properties. Furthermore, the incorporation of CeO2 into various coating materials has demonstrated the potential to significantly enhance their performance. In the realm of biotechnology, research into CeO2 nanoparticles continues to expand, encompassing a range of applications such as the scavenging of reactive oxygen species (ROS) in biological systems, the development of anti‐inflammatory agents, and cancer research. Nevertheless, in these diverse applications, CeO2 still exhibits certain limitations that hinder the achievement of desired outcomes. The modification of CeO2 nanoparticles presents a promising avenue to address these limitations, thereby improving their overall efficacy. As such, this review aims to comprehensively compare and summarize various methods for the modification of CeO2 nanoparticles, elucidate their practical applications, and provide insights into the potential challenges and future prospects in this field.This article is protected by copyright. All rights reserved.

Synergistic Activity of Noble Trimetallic Nanofluids: Unveiling Unprecedented Antimicrobial Potential and Computational Insights.

Nanofluids hold significant promise in diverse applications, particularly in biomedicine, where noble trimetallic nanofluids outperformed their monometallic counterparts. The composition, morphology, and size of these nanofluids play pivotal roles in their functionality. Controlled synthesis methods have garnered attention, focusing on precise morphology, content, biocompatibility, and versatile chemistry. Understanding how reaction parameters such as time, reducing agents, stabilizers, precursor concentration, temperature, and pH affect size and shape during synthesis is crucial. Trimetallic nanofluids, with their ideal composition, size, surface structure, and synergistic properties, are gaining traction in antimicrobial applications. These nanofluids were tested against seven microorganisms, demonstrating a heightened antimicrobial efficacy. Computational analyses, including molecular docking, dynamics, density functional theory (DFT), molecular electrostatic potential (MESP) analysis, and absorption, distribution, metabolism, elimination, and toxicology studies (ADMET) provided insights into binding interactions, energy, reactivity, and safety profiles, affirming the antimicrobial potential of trimetallic nanofluids. These findings emphasize the importance of controlled synthesis and computational validation in harnessing the unique properties of trimetallic nanofluids for biomedical applications.

Fibrous Structures: An Overview of Their Responsiveness to External Stimuli towards Intended Application

In recent decades, the interest in responsive fibrous structures has surged, propelling them into diverse applications: from wearable textiles that adapt to their surroundings, to filtration membranes dynamically altering selectivity, these structures showcase remarkable versatility. Various stimuli, including temperature, light, pH, electricity, and chemical compounds, can serve as triggers to unleash physical or chemical changes in response. Processing methodologies such as weaving or knitting using responsive yarns, electrospinning, as well as coating procedures, enable the integration of responsive materials into fibrous structures. They can respond to these stimuli, and comprise shape memory materials, temperature-responsive polymers, chromic materials, phase change materials, photothermal materials, among others. The resulting effects can manifest in a variety of ways, from pore adjustments and altered permeability to shape changing, color changing, and thermal regulation. This review aims to explore the realm of fibrous structures, delving into their responsiveness to external stimuli, with a focus on temperature, light, and pH.

Electromechanical efficiency index of skeletal muscle and its applicability: a systematic review

Introduction: The electromechanical efficiency of skeletal muscle represents the dissociation between electrical and mechanical events within a muscle. It has been widely studied, with varying methods for its measurement and calculation. For this reason, the purpose of this literature review was to integrate the available research to date and provide more insights about this measure.Methods: A systematic search of the literature was performed across three online databases: PubMed, ScienceDirect, and SPORTDiscus. This yielded 1284 reports, of which 10 met the inclusion criteria. Included studies have used different methods to measure the electromechanical efficiency (EME) index, including electromyography (EMG), mechanomyography and tensiomyography (TMG).Results: The EME index was used to assess muscle conditions such as muscle atrophy, pain syndromes, or to monitor rehabilitation in patients with knee problems, fatigue and the effects of exercise and rehabilitation. TMG has been shown to be one of the most reliable methods to obtain the EME index, but its use precludes obtaining the index during voluntary muscle contractions.Conclusion: Standardizing the EME index is crucial for its diverse applications in clinical, sport, and rehabilitation contexts. Future research should prioritize standardization of measurement protocols for establishing the most repeatable, and reliable approach that can be used for inter-individual comparisons or for assessing an individual for multiple times over a longer period.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023440333 Identifier: CRD42023440333.

Fairness Testing of Machine Translation Systems

Machine translation is integral to international communication and extensively employed in diverse human-related applications. Despite remarkable progress, fairness issues persist within current machine translation systems. In this paper, we propose FairMT, an automated fairness testing approach tailored for machine translation systems. FairMT operates on the assumption that translations of semantically similar sentences, containing protected attributes from distinct demographic groups, should maintain comparable meanings. It comprises three key steps: (1) test input generation, producing inputs covering various demographic groups; (2) test oracle generation, identifying potential unfair translations based on semantic similarity measurements; and (3) regression, discerning genuine fairness issues from those caused by low-quality translation. Leveraging FairMT, we conduct an empirical study on three leading machine translation systems—Google Translate, T5, and Transformer. Our investigation uncovers up to 832, 1,984, and 2,627 unfair translations across the three systems, respectively. Intriguingly, we observe that fair translations tend to exhibit superior translation performance, challenging the conventional wisdom of a fairness-performance trade-off prevalent in the fairness literature.

Tension and Shear Behaviour of Basalt Fiber Bio-Composites with Digital Image Correlation and Acoustic Emission Monitoring

This research investigates the mechanical behavior and damage evolution in cross-ply basalt fiber composites subjected to different loading modes. A modified Arcan rig for simultaneous acoustic emission (AE) monitoring was designed and manufactured to apply quasi-isotropic shear, combined tensile and shear loading, and pure tensile loading on specimens with a central notch. Digital image correlation (DIC) was applied for high-resolution strain measurements. The measured failure strengths of the bio-composite specimens under different loading angles are presented. The different competing failure mechanisms that contribute to the local reduction in stress concentration are described. Different damage mechanisms trigger elastic waves in the composite, with distinct AE signatures that closely follow the sequence of fracture mechanisms. AE monitoring is employed to capture signals associated with structural damage initiation and progression. The characteristic parameters of AE signals are correlated with crack modes and damage mechanisms. The evolution of AE parameters during the peak load transition is presented, which enables the timely AE detection of the maximum load transition. The combination of DIC and AE monitoring improves understanding of the mechanical response and failure mechanisms in cross-ply basalt fiber composites, offering valuable insights for possible performance monitoring and structural reliability in diverse engineering applications.