bluuebunny/crossref_metadata_2025 huggingface dataset:
Selecting Optimal Long Short-Term Memory (LSTM) Architectures for Online Estimation of Mean Arterial Pressure (MAP) in Patients Undergoing General Anesthesia
Authors: Ben Othman Ghada , Copot Dana , Yumuk Erhan , Neckebroek Martine , Ionescu Clara M.
Published: June 2024

In the realm of anesthetic management during surgical procedures, the reliable estimation of mean arterial pressure (MAP) is critical for ensuring patient safety and optimizing drug administration. This paper investigates the determination of the optimal Long Short-Term Memory (LSTM) architectures aimed at enhancing the estimation of MAP. Using data from a trial involving 70 patients undergoing Total Intravenous Anesthesia (TIVA) provides the effect-site concentrations of Propofol and Remifentanil as key input variables for LSTM models. Our solution categorizes the selection strategies into three distinct methodologies: (i) a population-based method applying a single model across all patients, (ii) a patient-specific method tailoring models to individual physiological responses, and (iii) a novel category-specific method that groups patients based on the correlation between input variables, the effect-site concentrations of Propofol and Remifentanil, and MAP output. The novelty of this paper lies in the proposed method to identify the optimal architecture, evaluating 288 models to fine-tune the best model for each patient and category. Our findings suggest that the patient-specific model outperforms others, highlighting the benefits of personalized model architectures in medical artificial intelligence (AI) applications. The category-specific models provide a pragmatic solution, with reasonable accuracy and enhanced computational efficiency. By contrast, the population-based models, while efficient, have a lower estimation accuracy. This study confirms the significance of sophisticated LSTM architectures in medical AI, providing insights into their potential for advancing patient-specific anesthetic care by accurately online estimating MAP.
Fast Assessment of Quality of Water Containing Inorganic Pollutants Using Laser Biospeckles in Microbioassay
Authors: Devi Arti , Kadono Hirofumi , Rajagopalan Uma Maheshwari
Published: June 2024

Recently, bioassay techniques have been gaining prominence in assessing water toxicity, offering comprehensive evaluations without identifying the individual chemical component. However, microscopic observation is a crucial component in microbioassays to know the critical features of the targeted microorganisms. However, as the microorganism’s size becomes smaller, observation becomes more difficult due to the narrower focal depth of the imaging system. To address this challenge, we propose a novel laser biospeckle non-imaging technique utilizing biospeckle patterns generated by microorganisms, enabling non-imaging assessments of their swimming ability. Paramecium and Euglena were used as microorganisms. Paramecium and Euglena were subjected to varying concentrations of heavy metal pollutants (Zn(NO3)2·6H2O and FeSO4·7H2O), and their swimming activity was quantified using a dynamic biospeckle analysis. The results show a concentration-dependent effect of Zn on both species, leading to decreased swimming ability at increased concentration. Conversely, Fe exhibited varying effects on Paramecia and Euglena, with the latter displaying tolerance at lower concentrations but a notable response at higher concentrations. The advantage of the method is that owing to the non-imaging system, an enormous number of microorganisms can be processed. Moreover, the method allows for an immediate and statistically significant estimation of their swimming ability in response to environmental pollution.
A Multi-Stage Approach to UAV Detection, Identification, and Tracking Using Region-of-Interest Management and Rate-Adaptive Video Coding
Authors: Lee Dongkyu ‘Roy’ , Kim Sanghong , Yoon Namkyung , Seo Wonki , Kim Hwangnam
Published: June 2024

The drone industry has opened its market to ordinary people, making drones prevalent in daily life. However, safety and security issues have been raised as the number of accidents rises (e.g., losing control and colliding with people or invading secured properties). For safety and security purposes, observers and surveillance systems must be aware of UAVs invading aerial spaces. This paper introduces a UAV tracking system with ROI-based video coding capabilities that can efficiently encode videos with a dynamic coding rate. The proposed system initially uses deep learning-based UAV detection to locate the UAV and determine the ROI surrounding the detected UAVs. Afterward, the ROI is tracked using optical flow, which is relatively light in computational load. Furthermore, our devised module for effective compression, XROI-DCT, is applied to non-ROI regions, so a different coding rate is applied depending on the region during encoding. The proposed UAV tracking system is implemented and evaluated by utilizing videos from YouTube, Kaggle, and a video of 3DR Solo2 taken by the authors. The evaluation verifies that the proposed system can detect and track UAVs significantly faster than YOLOv7 and efficiently encode a video, compressing 70% of the video based on the ROI. Additionally, it can successfully identify the UAV model with a high accuracy of 0.9869 ROC–AUC score.
Investigating Spatiotemporal Effects of Back-Support Exoskeletons Using Unloaded Cyclic Trunk Flexion–Extension Task Paradigm
Authors: Kuber Pranav Madhav , Rashedi Ehsan
Published: June 2024

Back-Support Industrial Exoskeletons (BSIEs) are designed to reduce muscle effort during repetitive tasks that involve trunk bending. We recruited twelve participants to perform 30 cycles of 45° trunk bending with/without the assistance of BSIEs and with/without postural asymmetry, first without any back fatigue, and then at the medium–high level of perceived back fatigue. To study the benefits of BSIEs, the effects of being in a fatigued state were assessed by comparing the muscle demands, kinematics, and stability measures during bending, retraction, and their transition portions per cycle across the study conditions. Overall, the BSIEs caused a minimal decrease in the lower-back activity (0–1.8%), caused by the increased demands during the retraction portion. A substantial decrease in leg activity was observed (10–18%). Asymmetry increased the right-lower-back and leg demands. Medium–high fatigue caused an increase in the lower-back activity (8–12%) during bending and retraction. The BSIEs caused slower movements and improved the stability by lowering the maximum distance of the Center of Pressure (COP) during the transition portion, as well as by lowering the mean velocity of the COP during the bending/retraction portions. This controlled study demonstrated the use of a cyclic trunk flexion–extension paradigm to study the effects of BSIEs, and the outcomes can help with understanding the temporal effects of using BSIEs on physiological measures, ultimately benefiting their proper implementation.
Mechanical Response and Deterioration Mechanisms in Freeze–Thaw Environments for Crushed Stone Stabilized with Industrial Solid Waste
Authors: Li Yongxiang , Ji Kuiliang , Lv Li , Li Xiao
Published: June 2024

The conflict between industrial solid waste treatment and environmental protection in Inner Mongolia is becoming increasingly prominent. Using industrial solid waste such as mineral powder, fly ash and wet calcium carbide slag as raw materials, using the alkali excitation method to prepare geopolymer, and replacing part of the cement for pavement base can effectively absorb industrial solid waste and realize the dual goals of waste utilization and environmental protection. Through mechanical properties tests before and after a freeze–thaw cycle and micro tests such as scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP), the strength variation rule and mechanism of geopolymer-cement stabilized aggregate under freeze–thaw cycles were deeply investigated. The relationship between different porosity indexes and mechanical properties in mercury intrusion porosimetry (MIP) was established by grey relation analysis. The results prove that a mixture with impaired properties after freeze–thaw cycles and the anti-freezing performance of the mixture with 20% geopolymer content are better than that of the mixture with no geopolymer content and 40% geopolymer content. The loss rates of unconfined compressive strength (UCS) after 5, 10 and 20 freeze–thaw cycles were 9.5%, 27.6% and 36.4%, respectively. The appropriate addition of geopolymer can enhance the anti-freezing performance of a stable aggregate. Following freezing and thawing cycles, the unconfined compressive strength (UCS) damage of the mixture is mainly influenced by a rise in total porosity, and the grey correlation degree is 0.75. The increase in more harmful pores and total porosity mainly results in an indirect tensile strength (ITS) loss. The grey correlation degree is 0.91. The compressive rebound modulus (CRM) is not affected by the change in pores but decreases with a rise in the geopolymer dosage.
Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
Authors: Wang Yuxue , Dong Hao , Bai Songyu , Yu Yang , Duan Qingwei
Published: June 2024

In order to rapidly detect pest types in farmland and mitigate their adverse effects on agricultural production, we proposed an improved Yolox-tiny-based target detection method for farmland pests. This method enhances the detection accuracy of farmland pests by limiting downsampling and incorporating the Convolution Block Attention Module (CBAM). In the experiments, images of pests common to seven types of farmland and particularly harmful to crops were processed through the original Yolox-tiny model after preprocessing and partial target expansion for comparative training and testing. The results indicate that the improved Yolox-tiny model increased the average precision by 7.18%, from 63.55% to 70.73%, demonstrating enhanced precision in detecting farmland pest targets compared to the original model.
Enhanced and Combined Representations in Extended Reality through Creative Industries
Authors: Anastasovitis Eleftherios , Roumeliotis Manos
Published: June 2024

The urgent need for research and study with nondestructive and noninvasive methods and the preservation of cultural heritage led to the development and application of methodologies for the multi-level digitization of cultural elements. Photogrammetry and three-dimensional scanning offer photorealistic and accurate digital representations, while X-rays and computed tomography reveal properties and characteristics of the internal and invisible structure of objects. However, the investigation of and access to these datasets are, in several cases, limited due to the increased computing resources and the special knowledge required for their processing and analysis. The evolution of immersive technologies and the creative industry of video games offers unique user experiences. Game engines are the ideal platform to host the development of easy-to-use applications that combine heterogeneous data while simultaneously integrating immersive and emerging technologies. This article seeks to shed light on how heterogeneous digital representations of 3D imaging and tomography can be harmoniously combined in a virtual space and, through simple interactions, provide holistic knowledge and enhanced experience to end users. This research builds on previous experience concerning the virtual museum for the Antikythera Mechanism and describes a conceptual framework for the design and development of an affordable and easy-to-use display tool for combined representations of heterogeneous datasets in the virtual space. Our solution was validated by 62 users who participated in tests and evaluations. The results show that the proposed methodology met its objectives. Apart from cultural heritage, the specific methodology could be easily extended and adapted for training purposes in a wide field of application, such as in education, health, engineering, industry, and more.
From Nature to Technology: Exploring the Potential of Plant-Based Materials and Modified Plants in Biomimetics, Bionics, and Green Innovations
Authors: Barbinta-Patrascu Marcela-Elisabeta , Bita Bogdan , Negut Irina
Published: June 2024

This review explores the extensive applications of plants in areas of biomimetics and bioinspiration, highlighting their role in developing sustainable solutions across various fields such as medicine, materials science, and environmental technology. Plants not only serve essential ecological functions but also provide a rich source of inspiration for innovations in green nanotechnology, biomedicine, and architecture. In the past decade, the focus has shifted towards utilizing plant-based and vegetal waste materials in creating eco-friendly and cost-effective materials with remarkable properties. These materials are employed in making advancements in drug delivery, environmental remediation, and the production of renewable energy. Specifically, the review discusses the use of (nano)bionic plants capable of detecting explosives and environmental contaminants, underscoring their potential in improving quality of life and even in lifesaving applications. The work also refers to the architectural inspirations drawn from the plant world to develop novel design concepts that are both functional and aesthetic. It elaborates on how engineered plants and vegetal waste have been transformed into value-added materials through innovative applications, especially highlighting their roles in wastewater treatment and as electronic components. Moreover, the integration of plants in the synthesis of biocompatible materials for medical applications such as tissue engineering scaffolds and artificial muscles demonstrates their versatility and capacity to replace more traditional synthetic materials, aligning with global sustainability goals. This paper provides a comprehensive overview of the current and potential uses of living plants in technological advancements, advocating for a deeper exploration of vegetal materials to address pressing environmental and technological challenges.
Partial Information Decomposition: Redundancy as Information Bottleneck
Authors: Kolchinsky Artemy
Published: June 2024

The partial information decomposition (PID) aims to quantify the amount of redundant information that a set of sources provides about a target. Here, we show that this goal can be formulated as a type of information bottleneck (IB) problem, termed the “redundancy bottleneck” (RB). The RB formalizes a tradeoff between prediction and compression: it extracts information from the sources that best predict the target, without revealing which source provided the information. It can be understood as a generalization of “Blackwell redundancy”, which we previously proposed as a principled measure of PID redundancy. The “RB curve” quantifies the prediction–compression tradeoff at multiple scales. This curve can also be quantified for individual sources, allowing subsets of redundant sources to be identified without combinatorial optimization. We provide an efficient iterative algorithm for computing the RB curve.
Debt Puzzle: A Comparative Analysis of Public Debt’s Impact on Production Efficiency across OECD Countries
Authors: Al-qalawi Usama R. , Al-Rabbaie Arqam
Published: June 2024

Debt is a fundamental component of modern economic systems. It serves as a source of financing for government, business, and individual projects. Many earlier studies concentrated on the direct relationship between debt and economic performance using different econometric methodologies. This work investigates the effect of debt on production efficiency, extracted from the estimated production function. Unlike previous econometric approaches, we employ a production stochastic frontier analysis (SFA) on data for 18 OECD countries spanning from Quarter 1, 2015, to Quarter 3, 2021, to capture the short-run effect of debt on the production efficiency and, thus, output growth. The results show that, in the short run, as debt increases by $1 billion, efficiency increases by 0.04%. Additionally, we found that the most indebted countries are the most efficient countries. In our sample, those were the UK and France. Furthermore, the average efficiency for the 18 OECD countries was 70.07.
The Design and the Control Principle of a Direct Low-Speed PMSM Servo-Drive Operating under a Sign-Changing Load on the Shaft
Authors: Makarchuk Oleksandr , Całus Dariusz
Published: June 2024

The paper relates to the development of an algorithm applicable for maintaining the rotational speed of low-speed drives using PMSM motors and operating under a sign-changing load. The moment of inertia of rotating parts does not play the role of a mechanical stabilizer for the speeds discussed in the article. Simulation studies are presented with the aim of developing a rotational speed control algorithm that utilizes only positional feedback and the previously assumed sign-changing load on the shaft. For the purposes of this research, a mathematical model was developed to calculate transient processes in a PMSM machine operating in the conditions of a sign-changing load on the shaft. This model assumes a deterministic control principle adapted to the known nature of the load change. In this model, the mutual influence occurring between the phase fluxes, the electromagnetic torque, the electric currents and the rotor position angle are established on the basis of FEM analysis of a two-dimensional magnetic field using a quasi-stationary approximation. Principles applicable for controlling a direct low-speed servo drive based on a PMSM machine operating with a known variable shaft load using only positional feedback and a predetermined shaft load change law are defined. The proposed regulation method is verified in an experimental manner. For this purpose, an experimental setup was built, which includes a PMSM with a load imitator on a variable sign shaft, an inverter providing sine-shaped power supply to the machine and a digital dual-processor control system. The discussed rotational speed stabilization algorithm was implemented in the form of a program for a microcontroller, which forms a part of the control system. The results of experimental tests confirm the adequacy of mathematical modeling and the effectiveness of the proposed rotational speed stabilization algorithm.
Eco-Efficiency of Pellet Production from Dedicated Poplar Plantations
Authors: Sperandio Giulio , Suardi Alessandro , Acampora Andrea , Civitarese Vincenzo
Published: June 2024

Biomass, due to its neutrality in terms of greenhouse gas emissions into the atmosphere during its life cycle, is considered an interesting renewable source for energy production as an alternative to the use of more polluting fossil fuels. Among the different wood fuels, pellets are convenient for use in dedicated stoves, and pellet heating systems have a high energy efficiency. The aim of this work was to estimate the economic and global warming potential (GWP100a) generated along the thermal energy supply chain of wood pellets, starting from the production of raw biomass from dedicated poplar cultivations and ending with the use of pellets in stoves by the end-user to produce thermal energy and ash. The Eco-Efficiency Indicator (EEI) was used to link the economic and environmental performance for eight proposed scenarios, obtained by combining different levels of mechanisation for poplar harvesting and wood biomass management before arrival at the pellet plant. For the thermal energy produced by the poplar wood pellet, the GWP100a ranged from 1.5 × 10−2 to 2.1 × 10−2 kg CO2−eq MJ−1 for three-year-old plantations and from 1.9 × 10−2 to 2.4 × 10−2 kg CO2−eq MJ−1, for six-year-old plantations. In terms of eco-efficiency of the baseline scenario (EEIb), the most favourable scenarios remain those linked to the use of biomass from three-year-old poplar plantations, with EEIb values ranging from 0.31 to 0.60 € kgCO2−eq−1, compared to from 0.29 to 0.36 € kgCO2−eq−1 for pellets obtained from biomass produced from six-year-old poplar plantations. In terms of the Global Eco-Efficiency Indicator (EEIg), which also takes into account the positive effect on the reduction of greenhouse gases due to the storage of carbon in the soil by the plantations and the reduction of emissions from avoided fossil fuels, the most favourable scenarios remain those linked to the use of biomass from three-year-old poplar plantations, with EEIg values that vary in the range of 0.60 ÷ 1.04 € kgCO2−eq−1, compared to 0.55 ÷ 0.62 € kg CO2−eq−1 for thermal energy obtained using biomass from six-year-old poplar plantations.
Blockchain-Based Joint Auction Model for Distributed Energy in Industrial Park Microgrids
Authors: Wang Li , Zhang Zihao , Fan Jinheng , Zeng Shunqi , Pan Shixian , Chen Haoyong
Published: June 2024

To address the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, this paper proposes a blockchain-based joint auction architecture for distributed energy in microgrids inside and outside industrial parks. By combining blockchain technology and auction theory, the architecture integrates the physical energy transactions within industrial parks with the distributed transactions in external microgrids to meet the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, optimizing resource allocation and improving system resilience. In the microgrid auction mechanism for industrial parks, considering distributed energy providers (sellers) and distributed energy buyers, an auction mechanism with power transmission distance, average electricity price, and enterprise nature as its main attributes was constructed to maximize social welfare, realizing efficient energy flow in a multi-microgrid environment and enabling coordinated mutual benefits for producers and consumers within the region. Finally, a case study was conducted on the joint auction mechanism for microgrids inside and outside industrial parks, including the impacts of market dynamics and user preferences on electricity prices using different trading methods, the computational results using different trading matching methods (comparing single-attribute and multi-attribute methods), and multi-dimensional verification of user satisfaction with peer-to-peer transactions in a blockchain environment. The effectiveness of the joint trading between physical energy transactions within industrial parks and external microgrids was demonstrated, which could efficiently coordinate energy allocation inside and outside the parks and reduce the cost of energy configuration.
Long Short-Term Renewable Energy Sources Prediction for Grid-Management Systems Based on Stacking Ensemble Model
Authors: Fekih Hassen Wiem , Challouf Maher
Published: June 2024

The transition towards sustainable energy systems necessitates effective management of renewable energy sources alongside conventional grid infrastructure. This paper presents a comprehensive approach to optimizing grid management by integrating Photovoltaic (PV), wind, and grid energies to minimize costs and enhance sustainability. A key focus lies in developing an accurate scheduling algorithm utilizing Mixed Integer Programming (MIP), enabling dynamic allocation of energy resources to meet demand while minimizing reliance on cost-intensive grid energy. An ensemble learning technique, specifically a stacking algorithm, is employed to construct a robust forecasting pipeline for PV and wind energy generation. The forecasting model achieves remarkable accuracy with a Root Mean Squared Error (RMSE) of less than 0.1 for short-term (15 min and one day ahead) and long-term (one week and one month ahead) predictions. By combining optimization and forecasting methodologies, this research contributes to advancing grid management systems capable of harnessing renewable energy sources efficiently, thus facilitating cost savings and fostering sustainability in the energy sector.
Optimal EMS Design for a 4-MW-Class Hydrogen Tugboat: A Comparative Analysis Using DP-Based Performance Evaluation
Authors: Hwang Seonghyeon , Lee Changhyeong , Ryu Juyeol , Lim Jongwoong , Chung Sohmyung , Park Sungho
Published: June 2024

In the current trend of hydrogen fuel cell-powered ships, batteries are used together with fuel cells to overcome the limitations of fuel cell technology. However, performance differences arise depending on fuel cell and battery configurations, load profiles, and energy management system (EMS) algorithms. We designed four hybrid controllers to optimize EMS algorithms for achieving maximum performance based on target profiles and hardware. The selected EMS is based on a State Machine, an Equivalent Consumption Minimization Strategy (ECMS), Economic Model Predictive Control (EMPC), and Dynamic Programming (DP). We used DP to evaluate the optimal design state and fuel efficiency of each controller. To evaluate controller performance, we obtained a 4-MW-class tug load profile as a reference and performed simulations based on Nedstack’s fuel cells and a lithium-ion battery model. The constraints were set according to the description of each equipment manual, and the optimal controller was derived based on the amount of hydrogen consumed by each EMS under the condition of completely tracking the load profile. As a result of simulating the hybrid fuel cell–battery system by applying the load profile of the tugboat, we found that the 4-MW EMPC, which requires more state variables and control inputs, is the most fuel-efficient controller.
An Agile Approach for Adopting Sustainable Energy Solutions with Advanced Computational Techniques
Authors: Konneh David Abdul , Howlader Harun Or Rashid , Elkholy M. H. , Senjyu Tomonobu
Published: June 2024

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policymakers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision-Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale, but the findings also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.
Thermal and Moisture Management in the Microclimate of Socks for Diabetic Foot Care: The Role of Mohair-Wool Content
Authors: Gericke Adine , Venkataraman Mohanapriya
Published: June 2024

In diabetic patients, optimised plantar health necessitates meticulously designed hosiery. These specialised socks facilitate a healthy microclimate at the skin–textile interface. This requires that stable conditions of temperature and humidity are maintained during wear. This study investigated the thermal resistance and moisture management properties of socks for diabetics. Fabrics and socks were evaluated on the Alambeta and thermal foot manikin instruments and in wear trials. A novel in vitro method, mimicking in-use conditions, was employed to validate findings and assess sock performance during wear. Fabric structure, especially thickness, had a greater impact on thermal resistance than fibre composition, suggesting that socks with different levels of thermal resistance can be customised according to individual preferences. In terms of moisture management, mohair–wool socks outperformed polyester socks, maintaining significantly lower humidity between the skin and the sock, and meeting the requirement to prevent the drying out of the microclimate significantly better. The enhanced moisture vapour sorption exhibited by the mohair–wool fabric contributes to this effect. Overall, the findings suggest that mohair–wool is an excellent fibre choice for diabetic socks, due to its unique moisture management properties and the possibility to tailor thermal properties through fabric structural design.
Fire-RPG: An Urban Fire Detection Network Providing Warnings in Advance
Authors: Li Xiangsheng , Liang Yongquan
Published: June 2024

Urban fires are characterized by concealed ignition points and rapid escalation, making the traditional methods of detecting early stage fire accidents inefficient. Thus, we focused on the features of early stage fire accidents, such as faint flames and thin smoke, and established a dataset. We found that these features are mostly medium-sized and small-sized objects. We proposed a model based on YOLOv8s, Fire-RPG. Firstly, we introduced an extra very small object detection layer to enhance the detection performance for early fire features. Next, we optimized the model structure with the bottleneck in GhostV2Net, which reduced the computational time and the parameters. The Wise-IoUv3 loss function was utilized to decrease the harmful effects of low-quality data in the dataset. Finally, we integrated the low-cost yet high-performance RepVGG block and the CBAM attention mechanism to enhance learning capabilities. The RepVGG block enhances the extraction ability of the backbone and neck structures, while CBAM focuses the attention of the model on specific size objects. Our experiments showed that Fire-RPG achieved an mAP of 81.3%, an improvement of 2.2%. In addition, Fire-RPG maintained high detection performance across various fire scenarios. Therefore, our model can provide timely warnings and accurate detection services.
Nanofillers in Novel Food Packaging Systems and Their Toxicity Issues
Authors: Zhou Xiangyu , Zhou Xiaoyu , Zhou Longli , Jia Ming , Xiong Ying
Published: June 2024

Background: Environmental concerns about petroleum-based plastic packaging materials and the growing demand for food have inspired researchers and the food industry to develop food packaging with better food preservation and biodegradability. Nanocomposites consisting of nanofillers, and synthetic/biopolymers can be applied to improve the physiochemical and antimicrobial properties and sustainability of food packaging. Scope and approach: This review summarized the recent advances in nanofiller and their applications in improved food packaging systems (e.g., nanoclay, carbon nanotubes), active food packaging (e.g., silver nanoparticles (Ag NPs), zinc oxide nanoparticles (ZnO NPs)), intelligent food packaging, and degradable packaging (e.g., titanium dioxide nanoparticles (e.g., TiO2 NPs)). Additionally, the migration processes and related assessment methods for nanofillers were considered, as well as the use of nanofillers to reduce migration. The potential cytotoxicity and ecotoxicity of nanofillers were also reviewed. Key findings: The incorporation of nanofillers may increase Young’s modulus (YM) while decreasing the elongation at break (EAB) (y = −1.55x + 1.38, R2 = 0.128, r = −0.358, p = 0.018) and decreasing the water vapor (WVP) and oxygen permeability (OP) (y = 0.30x − 0.57, R2 = 0.039, r = 0.197, p = 0.065). Meanwhile, the addition of metal-based NPs could also extend the shelf-life of food products by lowering lipid oxidation by an average of approx. 350.74% and weight loss by approx. 28.39% during the longest storage period, and significantly increasing antibacterial efficacy against S. aureus compared to the neat polymer films (p = 0.034). Moreover, the migration process of nanofillers may be negligible but still requires further research. Additionally, the ecotoxicity of nanofillers is unclear, as the final distribution of nanocomposites in the environment is unknown. Conclusions: Nanotechnology helps to overcome the challenges associated with traditional packaging materials. Strong regulatory frameworks and safety standards are needed to ensure the appropriate use of nanocomposites. There is also a need to explore how to realize the economic and technical requirements for large-scale implementation of nanocomposite technologies.
Structural and Release Properties of Combined Curcumin Controlled-Release Tablets Formulated with Chitosan/Sodium Alginate/HPMC
Authors: Lin Jing-Ting , Chiang Yi-Chan , Li Po-Hsien , Chiang Po-Yuan
Published: June 2024

Controlled-release tablets offer several benefits, such as controlled release, odor masking, ease of use, stability, extended shelf life, and reduced production costs. This study developed combined curcumin controlled-release tablets (CCCTs) to increase the bioavailability of curcumin with hydroxypropyl methylcellulose (HPMC), chitosan, and sodium alginate. The hardness of the CCCTs was 5.63–1.98 kgf, friability was 0.00–1.22%, and disintegration time was 0.00–401.25 min. Differential scanning calorimetry and Fourier-transform infrared spectroscopy indicated a high compatibility between the excipients and curcumin. CCCTs with chitosan formed a gel structure, impeded disintegration, and reduced the release rate to 72.5% in simulated gastric fluid. In simulated intestinal fluid, CCCT with the HPMC–sodium alginate group formed a polyelectrolyte membrane hydrogel to prolong release from 6 to 12 h. This study developed various CCCT formulations that can be delivered through the gastric or intestinal tracts, using chitosan and HPMC–sodium alginate as excipients, respectively. CCCT can be used as a reference strategy for controlled-release curcumin delivery in the functional and healthcare supplement development.
Positive Solutions and Their Existence of a Nonlinear Hadamard Fractional-Order Differential Equation with a Singular Source Item Using Spectral Analysis
Authors: Li Cheng , Guo Limin
Published: June 2024

Based on the spectral analysis method, Gelfand’s formula, and the cones fixed point theorem, some positive solutions with their existence of a nonlinear infinite-point Hadamard fractional-order differential equation is achieved on the interval [a, b] under some conditions, and particularly the nonlinear term allows singularities for time and spatial parameters in the present study. Finally, an analysis case is carried out to reveal the principal results.
Adsorption Capacity of Activated Carbon-Encapsulated Hollow-Type Spherical Bacterial Cellulose Gels for Uremic Toxins in a Simulated Human Gastrointestinal Environment
Authors: Hirai Aya , Suzuki Masashige , Sato Kaito , Hoshi Toru , Aoyagi Takao
Published: June 2024

To reduce the risk of the adsorption of granular activated carbon in the gastrointestinal tract, we successfully produced a hollow-type spherical bacterial cellulose (HSBC) gel containing activated carbon with a particle size of 6 μm. In this study, the aim of which was to develop an effective formulation, we evaluated the stability of activated-carbon-encapsulating HSBC gels under various pH conditions. Activated-carbon-encapsulating HSBC gels (ACEGs) retained the activated carbon without leaking when subjected to agitation in acidic or basic environments. The saturated adsorption amount, calculated using the Langmuir adsorption isotherm, was affected by the target adsorbate and pH conditions. These results indicate that ACEGs can adsorb uremic toxins and their precursors similarly to conventional uremic toxin adsorbents while preventing direct contact between the encapsulated activated carbon and the gastrointestinal tract. Compared to powdered activated carbon, the ACEG is less likely to be adsorbed in the gastrointestinal tract. Therefore, the proposed ACEG is a promising new formulation that will contribute to the treatment of renal failure and improve patients’ compliance with medication.
The Effectiveness of Mobile Applications in Improving Nursing Students’ Knowledge Related to Pressure Injury Prevention
Authors: Alkhazali Mo`ath Nayef , Totur Dikmen Burcu , Bayraktar Nurhan
Published: June 2024

The study’s main goal was to compare the effects of a mobile application versus traditional methods of teaching on nursing students’ acquisition of knowledge about pressure injury prevention. In addition, a secondary aim was to discover nursing students’ viewpoints related to the advantages and disadvantages of using mobile applications as an educational method. A randomized controlled study design was implemented during November and December of 2023 in a nursing faculty with 60 undergraduate students in their second nursing year. A total of 30 students were assigned to the mobile application group, while the other 30 students were assigned to the traditional lecture group. The study was executed in three stages: pre-test, educational intervention, and post-test. The results of the study during the pre-test showed that there were no statistically significant differences in the mean scores of pressure injury themes between the two groups. However, the post-test scores for all PI themes were higher in the mobile application group compared to the traditional lecture group. Furthermore, five advantages of the mobile application were highlighted by students: “improvement of students’ knowledge and skills”, “self-confidence”, “stress reduction”, “enhancement of competence”, and “stimulation of learning motivation”. This study demonstrated the effectiveness of the mobile application method in enhancing nursing students’ knowledge and prophylaxis of pressure injury. Therefore, the mobile application method is recommended as an innovative approach to teaching.
Unveiling the Evolution of Virtual Reality in Medicine: A Bibliometric Analysis of Research Hotspots and Trends over the Past 12 Years
Authors: Zuo Guangxi , Wang Ruoyu , Wan Cheng , Zhang Zhe , Zhang Shaochong , Yang Weihua
Published: June 2024

Background: Virtual reality (VR), widely used in the medical field, may affect future medical training and treatment. Therefore, this study examined VR’s potential uses and research directions in medicine. Methods: Citation data were downloaded from the Web of Science Core Collection database (WoSCC) to evaluate VR in medicine in articles published between 1 January 2012 and 31 December 2023. These data were analyzed using CiteSpace 6.2. R2 software. Present limitations and future opportunities were summarized based on the data. Results: A total of 2143 related publications from 86 countries and regions were analyzed. The country with the highest number of publications is the USA, with 461 articles. The University of London has the most publications among institutions, with 43 articles. The burst keywords represent the research frontier from 2020 to 2023, such as “task analysis”, “deep learning”, and “machine learning”. Conclusion: The number of publications on VR applications in the medical field has been steadily increasing year by year. The USA is the leading country in this area, while the University of London stands out as the most published, and most influential institution. Currently, there is a strong focus on integrating VR and AI to address complex issues such as medical education and training, rehabilitation, and surgical navigation. Looking ahead, the future trend involves integrating VR, augmented reality (AR), and mixed reality (MR) with the Internet of Things (IoT), wireless sensor networks (WSNs), big data analysis (BDA), and cloud computing (CC) technologies to develop intelligent healthcare systems within hospitals or medical centers.
Exploring Telemedicine Usage Intention Using Technology Acceptance Model and Social Capital Theory
Authors: Kung Liang-Hsi , Yan Yu-Hua , Kung Chih-Ming
Published: June 2024

Telemedicine technology has emerged as a pivotal solution to enhance the accessibility and efficiency of healthcare services. This study investigates the factors influencing the acceptance of telemedicine technology among healthcare professionals in Taiwan. Employing a quantitative research approach, we utilized a survey instrument adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Data were collected from 325 healthcare professionals across diverse medical fields. The results indicate that perceived usefulness, ease of use, social influence, and convenience significantly influence intention to use telemedicine. Moreover, age moderates the relationship between perceived usefulness and usage intention. These findings underscore the importance of addressing both technological and social factors in promoting the adoption of telemedicine among healthcare professionals. Policy implications and recommendations for enhancing telemedicine implementation are discussed based on the study findings. Specifically, our findings highlight that perceived usefulness, ease of use, social influence, and convenience significantly impact the intention to use telemedicine technology. Age significantly moderates the relationship between perceived usefulness and usage intention. These results not only theoretically support the UTAUT model but also provide practical strategies to advance the application of telemedicine technology.
Seasonal Trends in Suicide Attempts-Keywords Related Searches: A Google Trends Analysis
Authors: Klimiuk Krzysztof Bartosz , Krefta Dawid , Krawczyk Michał , Balwicki Łukasz
Published: June 2024

Suicide is a significant public health concern globally, with its varying rates influenced by numerous factors, including seasonal changes. Online search behaviors, particularly searches related to suicide and mental health, have been proposed as real-time indicators of suicidal ideation in populations. In this study, a cross-sectional time series analysis was conducted, utilizing data on suicide attempts from the Polish Police Headquarters and online search behavior from Google Trends over a decade. Suicide attempt data were analyzed alongside the frequency of Google searches for suicide-related keywords derived from the Polish Corpus of Suicide Notes. A total of 66 keywords were selected for analysis to identify seasonal trends and patterns in search behavior. The study employed linear regression, Seasonal Mann-Kendall tests, and TBATS models to analyze the data. Suicide rates show seasonal patterns, peaking in warmer months. However, keyword searches did not strongly correlate with peak suicide months. This study enhances our understanding of suicide-related search trends and their potential connection to suicide rates. It suggests avenues for more effective prevention efforts and the potential for future algorithms to predict suicide rates and identify at-risk groups.
Molecular Characterization and Expression Analysis of a Gene Encoding 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGR) from Bipolaris eleusines, an Ophiobolin A-Producing Fungus
Authors: Zhang Jianping , Yang Ke , Tang Wei , Yang Yongjie , Yu Xiaoyue , Lu Yongliang , Yu Liuqing
Published: June 2024

Ophibolin A, a fungal sesterterpene, exerts a pivotal influence in a diverse array of biological processes, encompassing herbicidal, bactericidal, fungicidal, and cytotoxic activities. Sixty genes associated with sesterterpene compound biosynthesis were obtained from Bipolaris eleusines via transcriptome sequencing, and those closely linked to ophiobolin A biosynthesis were subsequently filtered. A gene encoding 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) that catalyzes the first committed step of ophiobolin biosynthesis in the mevalonic acid (MVA) pathway was isolated and characterized using RACE (Rapid Amplification of cDNA Ends) technology from ophiobolin A-producing fungus, B. eleusines. The full-length cDNA of the B. eleusines HMGR gene (BeHMGR) was 3906 bp and contained a 3474 bp open reading frame (ORF) encoding 1157 amino acids. Sequence analysis revealed that deduced BeHMGR had high homology to the known HMGRs from Pyrenophora tritici-repentis and Leptosphaeria maculans. It had a calculated molecular mass of about 124.65 kDa and an isoelectric point (pI) of 6.90. It contained two putative HMG-CoA-binding motifs and two NADP(H)-binding motifs. Induced expression analysis of the BeHMGR gene by methyl jasmonate treatment using quantitative fluorescence PCR showed that it significantly elevated after 3 h of methyl jasmonate treatment, peaked at 6 h, and then gradually decreased. This demonstrates that BeHMGR gene expression is induced by methyl jasmonate.
Experimental Study on the Strength Deterioration and Mechanism of Stabilized River Silt Reinforced with Cement and Alginate Fibers
Authors: Wang Ying , Wang Chaojie , Hu Zhenhua , Sun Rong
Published: June 2024

River silt deposited by water in coastal areas is unsuitable for engineering construction. Thus, the in situ stabilization treatment of river silt as the bearing layer has been an important research area in geotechnical engineering. The strength degradation behavior and mechanism of stabilized river silt reinforced with cement and alginate fibers (AFCS) in different engineering environments are crucial for engineering applications. Therefore, freeze–thaw (F–T) cycle tests, wetting-drying (W–D) cycle tests, water immersion tests and seawater erosion tests were conducted to explore the strength attenuation of stabilized river silt reinforced with the same cement content (9% by wet weight) and different fiber contents (0%, 0.3%, 0.6% and 0.9% by weight of wet soil) and fiber lengths (3 mm, 6 mm and 9 mm). The reinforcement and damage mechanism of AFCS was analyzed by scanning electron microscopy (SEM) imaging. The results indicate that the strength of AFCS was improved from 84% to 180% at 15 F–T cycle tests, and the strength of AFCS was improved by 26% and 40% at 30 W–D cycles, which showed better stability and excellent characteristics owing to the hygroscopic characteristics of alginate fiber arousing the release of calcium and magnesium ions within the alginate. Also, the strength attenuation of AFCS was reduced with the increase in the length and content of alginate fibers. Further, the strength of specimens in the freshwater environment was higher than that in the seawater environment at the same fiber content, and the softening coefficient of AFCS in the freshwater environment was above 0.85, indicating that the AFCS had good water stability. The optimal fiber content was found to be 0.6% based on the unconfined compressive strength (UCS) reduction in specimens cured in seawater and a freshwater environment. And the strength of AFCS was improved by about 10% compared with that of cement-stabilized soil (CS) in a seawater environment. A stable spatial network structure inside the soil was formed, in which the reinforcing effect of fibers was affected by mechanical connection, friction and interfacial bonding. However, noticeable cracks developed in the immersed and F–T specimens. These microscopic characteristics contributed to decreased mechanical properties for AFCS. The results of this research provide a reference for the engineering application of AFCS.