bluuebunny/crossref_metadata_2025 huggingface dataset:
Fixed-Point Iteration Method for Uncertain Parameters in Dynamic Response of Systems with Viscoelastic Elements
Authors: Łasecka-Plura Magdalena
Published: May 2024

The paper presents a method for determining the dynamic response of systems containing viscoelastic damping elements with uncertain design parameters. A viscoelastic material is characterized using classical and fractional rheological models. The assumption is made that the lower and upper bounds of the uncertain parameters are known and represented as interval values, which are then subjected to interval arithmetic operations. The equations of motion are transformed into the frequency domain using Laplace transformation. To evaluate the uncertain dynamic response, the frequency response function is determined by transforming the equations of motion into a system of linear interval equations. Nevertheless, direct interval arithmetic often leads to significant overestimation. To address this issue, this paper employs the element-by-element technique along with a specific transformation to minimize redundancy. The system of interval equations obtained is solved iteratively using the fixed-point iteration method. As demonstrated in the examples, this method, which combines the iterative solving of interval equations with the proposed technique of equation formulation, enables a solution to be found rapidly and significantly reduces overestimation. Notably, this approach has been applied to systems containing viscoelastic elements for the first time. Additionally, the proposed notation accommodates both parallel and series configurations of damping elements and springs within rheological models.
Multidisciplinary Design Optimization of Cooling Turbine Blade: An Integrated Approach with R/ICSM
Authors: Wang Wenjun , Xiang Lan , Kang Enzi , Xia Jiahao , Shi Shanguang , Wang Cunfu , Yan Cheng
Published: May 2024

This paper presents an efficient integrated multidisciplinary design optimization method for shaping a high-pressure cooling turbine blade in aero engines. This approach utilizes a novel regression/interpolation combination surrogate model (R/ICSM), facilitating comprehensive design optimization through collaborative coupling feature parameterization modeling and numerical simulation analysis across various disciplines. The optimized blade adjusts the load distribution on its surface, effectively eliminating flow separation at the tip and trailing edge. Notably, the optimized blade achieves a 0.69% increase in isentropic efficiency while satisfying aerodynamic, strength, and structural constraints. This highlights the effectiveness and progressiveness of the multidisciplinary design optimization method for a cooling turbine blade based on the R/ICSM in enhancing overall performance. It offers a novel and feasible approach for turbine blade design optimization and provides valuable insights for future research and applications.
The Impact of Pulsed Electric Field Treatment and Shelf Temperature on Quality of Freeze-Dried Pumpkin
Authors: Rastorhuiev Oleksii , Matys Aleksandra , Wiktor Artur , Rybak Katarzyna , Lammerskitten Alica , Toepfl Stefan , Schnäckel Wolfram , Gondek Ewa , Parniakov Oleksii
Published: May 2024

Pulsed electric field (PEF) treatment is known as a method that can intensify heat- and mass-transfer-based processes such as osmotic dehydration, drying, or freeze-drying. However, the literature about its impact on quality of freeze-dried products is limited to a few raw materials. The aim of this study was to analyze the effect of PEF on the cell disintegration index, selected bioactive compounds, and physical quality parameters of freeze-dried pumpkin. The final quality of the freeze-dried product was evaluated by residual moisture content, color analysis, total phenolic content, total carotenoid content, sugars content, and hygroscopic properties. The application of PEF treatment induced the disintegration of pumpkin cells even at low energy input (0.11 kJ/kg), and the saturation level of electroporation was reached after 4 kJ/kg. PEF treatment at 2 kJ/kg allowed 40% more total carotenoids to be retained in comparison to the untreated sample. Furthermore, all PEF-treated freeze-dried pumpkin samples exhibited lower sucrose content but had higher glucose and fructose contents in comparison to the reference samples. However, this effect was more pronounced when the shelf temperature was equal to 40 °C.
Impact of Augmented Reality on Assistance and Training in Industry 4.0: Qualitative Evaluation and Meta-Analysis
Authors: Morales Méndez Ginés , del Cerro Velázquez Francisco
Published: May 2024

In the context of Industry 4.0, industrial environments are at a crossroads, facing the challenge of greater flexibility and significant technical skills gaps. In this situs, Augmented Reality (AR) emerges as a transformative tool, enhancing the synergy between technical staff and emerging technologies. This article focuses on exploring the integration of AR in Industry 4.0, with a particular emphasis on its role in improving technical assistance and training. The research addresses the ways in which AR not only facilitates more efficient processes but also acts as an essential bridge for training and skills development in constantly changing technological environments. It investigates the significant impact of AR on both optimising work processes and training workers to meet the emerging challenges of Industry 4.0. Through a qualitative analysis, the studies are categorised according to their application domains, grouping them into specific thematic areas. Subsequently, a meta-analysis is conducted to determine the actual impact of AR in the sector. The findings reveal a positive and significant correlation between the implementation of AR and its effectiveness in assistance and training in the framework of Industry 4.0. Finally, the article delves into an analysis of current limitations and challenges, providing insights into possible developments and trends in the use of AR for assistance and training in Industry 4.0.
Small-Cell Combs Offer as Favorable Conditions of Rearing Worker Bees as Standard-Cell Combs in the Temperate Climate in Spring
Authors: Dziechciarz Piotr , Strachecka Aneta , Borsuk Grzegorz , Olszewski Krzysztof
Published: May 2024

During the spring development of bee colonies, small-cell combs were found to create equally favorable conditions for worker bee rearing as standard-cell combs, since the workers reared in the small-cell combs did not differ significantly in the majority of morphometric traits, including the length of wings and the sum of the widths of the third and fourth tergites, from those reared in standard-cell combs. Moreover, they had a significantly longer and wider thorax. It can be assumed that the workers reared in small-cell combs collect nectar as effectively as those reared in standard-cell combs, as both groups did not differ in the proboscis length. It was confirmed that the body size of workers is relatively constant and is less influenced by the width of comb cells than was assumed previously, as the values of their morphometric parameters did not increase proportionally with the increasing cell width. The colony kept on small-cell combs provided worse rearing conditions for workers reared in standard-cell combs than the colony kept on standard-cell combs, which may have been related to the less abundant feeding of larvae by workers reared in small-cell combs.
Implementation of a Generative AI Algorithm for Virtually Increasing the Sample Size of Clinical Studies
Authors: Nikolopoulos Anastasios , Karalis Vangelis D.
Published: May 2024

Determining the appropriate sample size is crucial in clinical studies due to the potential limitations of small sample sizes in detecting true effects. This work introduces the use of Wasserstein Generative Adversarial Networks (WGANs) to create virtual subjects and reduce the need for recruiting actual human volunteers. The proposed idea suggests that only a small subset (“sample”) of the true population can be used along with WGANs to create a virtual population (“generated” dataset). To demonstrate the suitability of the WGAN-based approach, a new methodological procedure was also required to be established and applied. Monte Carlo simulations of clinical studies were performed to compare the performance of the WGAN-synthesized virtual subjects (i.e., the “generated” dataset) against both the entire population (the so-called “original” dataset) and a subset of it, the “sample”. After training and tuning the WGAN, various scenarios were explored, and the comparative performance of the three datasets was evaluated, as well as the similarity in the results against the population data. Across all scenarios tested, integrating WGANs and their corresponding generated populations consistently exhibited superior performance compared with those from samples alone. The generated datasets also exhibited quite similar performance compared with the “original” (i.e., population) data. By introducing virtual patients, WGANs effectively augment sample size, reducing the risk of type II errors. The proposed WGAN approach has the potential to decrease costs, time, and ethical concerns associated with human participation in clinical trials.
Automated Assessment Tool for 3D Computer-Aided Design Models
Authors: Eltaief Ameni , Ben Amor Sabrine , Louhichi Borhen , Alrasheedi Nashmi H. , Seibi Abdennour
Published: May 2024

Computer-aided design (CAD) has become an integral part of engineering education, particularly for those studying mechanical engineering. By providing practical skills that are highly valued in the engineering industry, proficiency in CAD systems enhances students’ employability. Generally, CAD systems provide students with the tools and knowledge necessary to excel in their engineering education and future careers. In order to help teachers to give the best training to their students and to make the right evaluations, an automatized tool is needed to support the evaluation of CAD models during training sessions. After an extensive bibliographical search, this paper proposes a CAD Model Automatized Assessment (MAA) Tool for mechanical courses called the CAD MAA Tool. This tool is mainly based on a developed model that takes into account different aspects of modeling, such as geometric, feature-based, and parametric modeling. To correctly evaluate a given part compared to a reference one, the proposed model uses different coefficients fixed by the teacher according to their teaching strategies or course objectives.
High-Precision BEV-Based Road Recognition Method for Warehouse AMR Based on IndoorPathNet and Transfer Learning
Authors: Zhang Tianwei , He Ci , Li Shiwen , Lai Rong , Wang Zili , Qiu Lemiao , Zhang Shuyou
Published: May 2024

The rapid development and application of AMRs is important for Industry 4.0 and smart logistics. For large-scale dynamic flat warehouses, vision-based road recognition amidst complex obstacles is paramount for improving navigation efficiency and flexibility, while avoiding frequent manual settings. However, current mainstream road recognition methods face significant challenges of unsatisfactory accuracy and efficiency, as well as the lack of a large-scale high-quality dataset. To address this, this paper introduces IndoorPathNet, a transfer-learning-based Bird’s Eye View (BEV) indoor path segmentation network that furnishes directional guidance to AMRs through real-time segmented indoor pathway maps. IndoorPathNet employs a lightweight U-shaped architecture integrated with spatial self-attention mechanisms to augment the speed and accuracy of indoor pathway segmentation. Moreover, it surmounts the challenge of training posed by the scarcity of publicly available semantic datasets for warehouses through the strategic employment of transfer learning. Comparative experiments conducted between IndoorPathNet and four other lightweight models on the Urban Aerial Vehicle Image Dataset (UAVID) yielded a maximum Intersection Over Union (IOU) of 82.2%. On the Warehouse Indoor Path Dataset, the maximum IOU attained was 98.4% while achieving a processing speed of 9.81 frames per second (FPS) with a 1024 × 1024 input on a single 3060 GPU.
Thermochromic Yarns’ Application in Fever Detection for Young Children
Authors: Xia Sibei , Liu Chuanlan , Zhang Yanbo , Cameron Bruce
Published: May 2024

The existing literature emphasizes the significance of and needs for developing body temperature monitoring devices that can consistently and discreetly assess the temperatures of young children. Such products could offer a method to check children’s body temperature and alleviate parents’ concerns over fever. However, research gaps and challenges exist in preserving material flexibility, conducting tests in a controlled setting that mimics body temperature, and investigating consumer perceptions of this type of functional textile product. Therefore, this research aimed to investigate functional textiles using thermochromic yarns for young children’s body temperature detection, particularly focusing on testing prototypes in a controlled environment and empirically investigating target consumers’ perceptions of such products. Experimental prototype testing and a consumer survey were conducted in this study. The findings validate the practicality and market potential of such products. We also proposed alternative mediums for implementing the functional yarn and recommendations for developing these products based on target consumers’ concerns and suggestions. This research helped identify potential economic development possibilities for functional textiles.
Flow Velocity Computation in Solid–Liquid Two-Phase Flow by a Hybrid Network CNN–RKSVM
Authors: Li Kun , Yue Shihong , Liu Liping
Published: May 2024

As an advanced detection technique, electrical resistive tomography (ERT) has been applied to detect the solid–liquid two-phase flow velocity based on available ERT measurements. The flow velocity computation by ERT must depend on the relative algorithms, including both the cross-correlation (CC) principle and convolutional neural networks (CNNs). However, these two types of algorithms have poor accuracy and generalization under complex measuring conditions and various flow patterns. To address this issue, in this paper, a hybrid network is proposed that combines a CNN with a reproducing kernel-based support vector machine (RKSVM) technique. The features hidden in ERT measurements are extracted using the CNN, and then the flow velocity is computed by the RKSVM in a high-dimensional feature space. According to the ERT measurements in an actual experimental platform, the results show that the hybrid network has higher accuracy and generalization ability for flow velocity computation compared with the existing CC, RKSVM, and CNN methods.
Research on Application of Improved Quantum Optimization Algorithm in Path Planning
Authors: Du Zuoqiang , Li Hui
Published: May 2024

For the building emergency evacuation path planning problem, existing algorithms suffer from low convergence efficiency and the problem of getting trapped in local optima. The Bloch Spherical Quantum Genetic Algorithm (BQGA) based on the least-squares principle for single-robot path planning and Bloch Spherical Quantum Bee Colony Algorithm (QABC) for multi-robots path planning are studied. Firstly, the characteristics of three-dimensional path planning are analyzed, and a linear decreasing inertia weighting approach is used to balance the global search ability of chromosomes and accelerate the search performance of the algorithm. Then, the application algorithm can generate a clear motion trajectory in the raster map. Thirdly, the least squares approach is used to fit the results, thus obtaining a progressive path. Finally, multi-robots path planning approaches based on QABC are discussed, respectively. The experimental results show that BQGA and QABC do not need to have a priori knowledge of the map, and they have strong reliability and practicality and can effectively avoid local optimum. In terms of convergence speed, BQGA improved by 3.39% and 2.41%, respectively, while QABC improved by 13.31% and 17.87%, respectively. They are more effective in sparse paths.
Numerical Simulation and Development of a Continuous Microwave-Assisted Pilot Plant for Shelled Almond Processing
Authors: Mescia Luciano , Leone Alessandro , Lamacchia Claudio Maria , Ferraris Angela , Caggiano Domenico , Berardi Antonio , Tamborrino Antonia
Published: May 2024

This paper outlines the numerical modeling procedure aimed at defining the guidelines for the development of a continuous microwave-assisted pilot plant for shelled almond disinfestation, as an alternative to the use of chemicals. To this end, a 3D Multiphysics numerical tool involving both electromagnetic and thermal models was developed to predict the temperature and electric field profiles inside the microwave treatment chamber. Three different microwave sources arrangements were simulated and the accuracy of the model was verified under different residence times of almonds in the treatment chamber using the developed prototype. The modeling results demonstrated that the arrangement having five microwave sources, each delivering a maximum power of 1.5 kW and frequency of 2.45 GHz, ensures good heating uniformity. The obtained results proved that the model enables the accurate prediction of the temperature trend (root-mean-square error/RMSE = 0.82). A strong linear regression was detected for the standard deviation between the simulated and experimental data (linear regression, R2 = 0.91). The very low COV value for the experimental temperature data demonstrated the heating uniformity as the treatment time changed. The developed model and the simulation strategy used may provide useful design guidance for microwave-assisted continuous plants for disinfestation, with a significant impact on the almond industry.
Long-Term and Seasonal Changes in Emission Sources of Atmospheric Particulate-Bound Pyrene and 1-Nitropyrene in Four Selected Cities in the Western Pacific
Authors: Hayakawa Kazuichi
Published: May 2024

Estimating the source contribution to polycyclic aromatic hydrocarbons (PAHs) and nitropolycyclic aromatic hydrocarbons (NPAHs) in the atmosphere is necessary for developing effective disease control and pollution control measures. The NPAH-PAH combination method (NP method) was used to elucidate the contributions of vehicles and coal/biomass combustion to seasonal and long-term urban atmospheric particulate matter (PM)-bound Pyr and 1-NP concentrations in Kanazawa, Kitakyushu, Shenyang and Shanghai in the Western Pacific region from 1997 to 2021. Among the four cities, Kanazawa demonstrated the lowest Pyr concentration. The contribution of vehicles to Pyr before and after 2010 was 35% and 5%, respectively. The 1-NP concentration was reduced by a factor of more than 1/10. These changes can be attributed to the emission control from vehicles. Kitakyushu revealed the second-lowest Pyr and the lowest 1-NP concentrations. Coal combustion was found to be the main contributor to Pyr, while its contribution to 1-NP increased from 9% to 19%. The large contribution of coal combustion is attributed to iron manufacturers. Shenyang demonstrated the highest atmospheric Pyr concentration with its largest seasonal change. Vehicles are the largest contributors to 1-NP. However, coal combustion, including winter coal heating, contributed 97% or more to Pyr and more than 14% to 1-NP. Shanghai revealed the second-highest Pyr and 1-NP concentrations, but the former was substantially lower than that in Shenyang. Coal combustion was the major contributor, but the contribution of vehicles to Pyr was larger before 2010, which was similar to Kanazawa.
Machine Learning-Based Forecasting of Metocean Data for Offshore Engineering Applications
Authors: Barooni Mohammad , Ghaderpour Taleghani Shiva , Bahrami Masoumeh , Sedigh Parviz , Velioglu Sogut Deniz
Published: May 2024

The advancement towards utilizing renewable energy sources is crucial for mitigating environmental issues such as air pollution and climate change. Offshore wind turbines, particularly floating offshore wind turbines (FOWTs), are developed to harness the stronger, steadier winds available over deep waters. Accurate metocean data forecasts, encompassing wind speed and wave height, are crucial for offshore wind farms’ optimal placement, operation, and maintenance and contribute significantly to FOWT’s efficiency, safety, and lifespan. This study examines the application of three machine learning (ML) models, including Facebook Prophet, Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX), and long short-term memory (LSTM), to forecast wind speeds and significant wave heights, using data from a buoy situated in the Pacific Ocean. The models are evaluated based on their ability to predict 1-, 3-, and 30-day future wind speed and wave height values, with performances assessed through Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. Among the models, LSTM displayed superior performance, effectively capturing the complex temporal dependencies in the data. Incorporating exogenous variables, such as atmospheric conditions and gust speed, further refined the predictions.The study’s findings highlight the potential of machine learning (ML) models to enhance the integration and reliability of renewable energy sources through accurate metocean forecasting.
One-Dimensional BSDEs with Jumps and Logarithmic Growth
Authors: Bouhadjar El Mountasar Billah , Khelfallah Nabil , Eddahbi Mhamed
Published: May 2024

In this study, we explore backward stochastic differential equations driven by a Poisson process and an independent Brownian motion, denoted for short as BSDEJs. The generator exhibits logarithmic growth in both the state variable and the Brownian component while maintaining Lipschitz continuity with respect to the jump component. Our study rigorously establishes the existence and uniqueness of solutions within suitable functional spaces. Additionally, we relax the Lipschitz condition on the Poisson component, permitting the generator to exhibit logarithmic growth with respect to all variables. Taking a step further, we employ an exponential transformation to establish an equivalence between a solution of a BSDEJ exhibiting quadratic growth in the z-variable and a BSDEJ showing a logarithmic growth with respect to y and z.
An Intuitionistic Fuzzy Multi-Criteria Approach for Prioritizing Failures That Cause Overproduction: A Case Study in Process Manufacturing
Authors: Sudžum Ranka , Nestić Snežana , Komatina Nikola , Kraišnik Milija
Published: May 2024

Overproduction is one of the most significant wastes of Lean that can occur in any manufacturing company. Identifying and prioritizing failures that lead to overproduction are crucial tasks for operational managers and engineers. Therefore, this paper presents a new approach for determining the priority of failures that cause overproduction, based on an intuitionistic fuzzy Multi-Criteria Optimization model and the Failure Mode and Effects Analysis framework. The existing vagueness in the relative importance of risk factors and their values is described using natural language words, which are modeled with trapezoidal intuitionistic fuzzy numbers. Determining the relative importance of risk factors is defined as a fuzzy group decision-making problem, and the weight vector is obtained by applying the proposed Analytical Hierarchy Process with trapezoidal intuitionistic fuzzy numbers. The compromise solution, as well as the stability check of the obtained compromise solution, is achieved using the proposed Multi-Criteria Optimization and Compromise Solution with trapezoidal intuitionistic fuzzy numbers. The proposed model was applied to data collected from a process manufacturing company.
Analyzing the Attractiveness of Food Images Using an Ensemble of Deep Learning Models Trained via Social Media Images
Authors: Morinaga Tanyaboon , Patanukhom Karn , Somchit Yuthapong
Published: May 2024

With the growth of digital media and social networks, sharing visual content has become common in people’s daily lives. In the food industry, visually appealing food images can attract attention, drive engagement, and influence consumer behavior. Therefore, it is crucial for businesses to understand what constitutes attractive food images. Assessing the attractiveness of food images poses significant challenges due to the lack of large labeled datasets that align with diverse public preferences. Additionally, it is challenging for computer assessments to approach human judgment in evaluating aesthetic quality. This paper presents a novel framework that circumvents the need for explicit human annotation by leveraging user engagement data that are readily available on social media platforms. We propose procedures to collect, filter, and automatically label the attractiveness classes of food images based on their user engagement levels. The data gathered from social media are used to create predictive models for category-specific attractiveness assessments. Our experiments across five food categories demonstrate the efficiency of our approach. The experimental results show that our proposed user-engagement-based attractiveness class labeling achieves a high consistency of 97.2% compared to human judgments obtained through A/B testing. Separate attractiveness assessment models were created for each food category using convolutional neural networks (CNNs). When analyzing unseen food images, our models achieve a consistency of 76.0% compared to human judgments. The experimental results suggest that the food image dataset collected from social networks, using the proposed framework, can be successfully utilized for learning food attractiveness assessment models.
How Do Cartilage Lubrication Mechanisms Fail in Osteoarthritis? A Comprehensive Review
Authors: Rajankunte Mahadeshwara Manoj , Al-Jawad Maisoon , Hall Richard M. , Pandit Hemant , El-Gendy Reem , Bryant Michael
Published: May 2024

Cartilage degeneration is a characteristic of osteoarthritis (OA), which is often observed in aging populations. This degeneration is due to the breakdown of articular cartilage (AC) mechanical and tribological properties primarily attributed to lubrication failure. Understanding the reasons behind these failures and identifying potential solutions could have significant economic and societal implications, ultimately enhancing quality of life. This review provides an overview of developments in the field of AC, focusing on its mechanical and tribological properties. The emphasis is on the role of lubrication in degraded AC, offering insights into its structure and function relationship. Further, it explores the fundamental connection between AC mechano-tribological properties and the advancement of its degradation and puts forth recommendations for strategies to boost its lubrication efficiency.
Influence of Bilberry Extract on Neuronal Cell Toxicity
Authors: König Svenja , Bakuradze Tamara , Jesser Sandy , Sreeja Harshitha Ashoka , Carlsson Max J. , Fahrer Jörg , Kins Stefan , Richling Elke
Published: May 2024

Increased intake of dietary antioxidants such as anthocyanins, which are enriched in colourful fruits, is a promising alternative to reduce the risk of degenerative diseases such as Alzheimer’s Disease (AD). Since Amyloid β (Aβ) is one of the key components contributing to AD pathology, probably by reactive oxygen species (ROS) induction, this study investigated the preventive effect of anthocyanin-rich bilberry extract (BE) and its anthocyanin fraction (ACN) on ROS generation and cell toxicity. The results showed a significant and concentration-dependent decrease in neuroblastoma cell (SH-SY5Y) viability by BE or ACN, whereas no cell toxicity was observed in HeLa cells. Incubation with BE and ACN for 24 h diminished the generation of induced ROS levels in SH-SY5Y and HeLa cells. In addition, low concentrations of BE (1–5 µg/mL) showed protective effects against Aβ-induced cytotoxicity in SH-SY5Y cells. In conclusion, our results suggest antioxidant and protective effects of BE and ACN, which could potentially be used to delay the course of neurodegenerative diseases such as AD. Further studies are needed to clarify the high potential of anthocyanins and their in vivo metabolites on neuronal function.
Fire-Induced Changes in Soil Properties and Bacterial Communities in Rotational Shifting Cultivation Fields in Northern Thailand
Authors: Arunrat Noppol , Sansupa Chakriya , Sereenonchai Sukanya , Hatano Ryusuke , Lal Rattan
Published: May 2024

Fire is a common practice in rotational shifting cultivation (RSC), but little is known about the dynamics of bacterial populations and the impact of fire disturbance in northern Thailand. To fill the research gap, this study aims to investigate the dynamics of soil bacterial communities and examine how the soil’s physicochemical properties influence the bacterial communities in RSC fields over a period of one year following a fire. Surface soil samples (0–2 cm depth) were collected from sites with 6 (RSC-6Y) and 12 (RSC-12Y) years of fallow in Chiang Mai Province, northern Thailand at six different time points: before burning, 5 min after burning (summer), 3 months after burning (rainy season), 6 months after burning (rainy season), 9 months after burning (winter), and 12 months after burning (summer). The results revealed a reduction in the soil bacterial communities’ diversity and an increase in soil nutrient levels immediately after the fire. The fire significantly influenced the abundance of Firmicutes, Proteobacteria, Acidobacteria, and Planctomycetes, but not that of Actinobacteria. At the genus level, Bacillus, Conexibacter, and Chthoniobacter showed increased abundance following the fire. During the rainy season, a recovery in the abundance of the soil bacterial communities was observed, although soil nutrient availability declined. Soil physicochemical properties such as pH, organic matter, organic carbon, electrical conductivity, cation exchange capacity, nitrate-nitrogen, available phosphorus, exchangeable potassium, total nitrogen, bulk density, sand, and silt contents significantly influenced the composition of bacterial communities. Alpha diversity indices indicated a decrease in diversity immediately after burning, followed by an increase from the early rainy season until the summer season, indicating that seasonal variation affected the composition of the soil bacterial communities. After one year of burning, an increase in bacterial richness was observed, while the diversity of the bacterial communities reverted to pre-burning levels.
Padina Minor Extract Confers Resistance against Candida Albicans Infection: Evaluation in a Zebrafish Model
Authors: Wu Chang-Cheng , Ding De-Sing , Lo Yi-Hao , Pan Chieh-Yu , Wen Zhi-Hong
Published: May 2024

Padina minor is a seaweed rich in polysaccharides often used in food, feed, fertilizers, and antibacterial drugs. This study is the first to evaluate the effect of feeding zebrafish with Padina minor extract on preventing and treating C. albicans infections. This study evaluated the growth, survival, and disease resistance effects of P. minor extract on zebrafish. The fish were divided into four groups: three groups treated with 1%, 5%, or 10% P. minor extract and one untreated group (c, control). Subsequently, we analyzed how the extract affected the immune function of zebrafish infected with C. albicans. Based on the lethal concentration (LC50) calculated in the first stage, 1% was used as the effective therapeutic concentration. The results showed that the growth rate of the 1% feed group was the best, and no significant difference in survival rates between the four groups was observed. Feeding with 1% P. minor extract downregulated the expression of key inflammatory genes like tumor necrosis factor-alpha (TNF-α), interleukin-1β (IL-1β), and IL-10, effectively preventing and treating C. albicans infections in zebrafish. This study is a preliminary evaluation of the therapeutic efficacy of P. minor extracts against C. albicans.
Enhancing Selenium Accumulation in Rhodotorula mucilaginosa Strain 6S Using a Proteomic Approach for Aquafeed Development
Authors: Díaz-Navarrete Paola , Sáez-Arteaga Alberto , Marileo Luis , Alors David , Correa-Galeote David , Dantagnan Patricio
Published: May 2024

It is known that selenium (Se) is an essential trace element, important for the growth and other biological functions of fish. One of its most important functions is to contribute to the preservation of certain biological components, such as DNA, proteins, and lipids, providing protection against free radicals resulting from normal metabolism. The objective of this study was to evaluate and optimize selenium accumulation in the native yeast Rhodotorula mucilaginosa 6S. Sodium selenite was evaluated at different concentrations (5–10–15–20–30–40 mg/L). Similarly, the effects of different concentrations of nitrogen sources and pH on cell growth and selenium accumulation in the yeast were analyzed. Subsequently, the best cultivation conditions were scaled up to a 2 L reactor with constant aeration, and the proteome of the yeast cultured with and without sodium selenite was evaluated. The optimal conditions for biomass generation and selenium accumulation were found with ammonium chloride and pH 5.5. Incorporating sodium selenite (30 mg/L) during the exponential phase in the bioreactor after 72 h of cultivation resulted in 10 g/L of biomass, with 0.25 mg total Se/g biomass, composed of 25% proteins, 15% lipids, and 0.850 mg total carotenoids/g biomass. The analysis of the proteomes associated with yeast cultivation with and without selenium revealed a total of 1871 proteins. The results obtained showed that the dynamic changes in the proteome, in response to selenium in the experimental medium, are directly related to catalytic activity and oxidoreductase activity in the yeast. R. mucilaginosa 6S could be an alternative for the generation of selenium-rich biomass with a composition of other nutritional compounds also of interest in aquaculture, such as proteins, lipids, and pigments.
Network Pharmacology Analysis of the Potential Pharmacological Mechanism of a Sleep Cocktail
Authors: Liang Yuyun , Lv Yanrong , Qin Jing , Deng Wenbin
Published: May 2024

Insomnia, also known as sleeplessness, is a sleep disorder due to which people have trouble sleeping, followed by daytime sleepiness, low energy, irritability, and a depressed mood. It may result in an increased risk of accidents of all kinds as well as problems focusing and learning. Dietary supplements have become popular products for alleviating insomnia, while the lenient requirements for pre-market research result in unintelligible mechanisms of different combinations of dietary supplements. In this study, we aim to systematically identify the molecular mechanisms of a sleep cocktail’s pharmacological effects based on findings from network pharmacology and molecular docking. A total of 249 targets of the sleep cocktail for the treatment of insomnia were identified and enrichment analysis revealed multiple pathways involved in the nervous system and inflammation. Protein–protein interaction (PPI) network analysis and molecular complex detection (MCODE) analysis yielded 10 hub genes, including AKT1, ADORA1, BCL2, CREB1, IL6, JUN, RELA, STAT3, TNF, and TP53. Results from weighted correlation network analysis (WGCNA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of insomnia-related transcriptome data from peripheral blood mononuclear cells (PBMCs) showed that a sleep cocktail may also ease insomnia via regulating the inflammatory response. Molecular docking results reveal good affinity of Sleep Cocktail to 9 selected key targets. It is noteworthy that the crucial target HSP90AA1 binds to melatonin most stably, which was further validated by MD simulation.
Impact of Inherited Thrombophilia in Women with Obstetric Antiphospholipid Syndrome: A Single-Center Study and Literature Review
Authors: Camacho Sáez Blanca , Martínez-Taboada Víctor M. , Merino Ana , Comins-Boo Alejandra , González-Mesones Belén , Del Barrio-Longarela Sara , Riancho-Zarrabeitia Leyre , López-Hoyos Marcos , Hernández José L.
Published: May 2024

Inherited thrombophilia (IT) has been implicated as a potential causal factor of adverse pregnancy outcomes (APOs), including recurrent miscarriage with and without the presence of antiphospholipid syndrome (APS). The aim of this study was to assess the prevalence and impact of IT on fetal–maternal outcomes and thrombotic risk in women within the spectrum of obstetric APS. Three hundred and twenty-eight women with APS-related obstetric morbidity ever pregnant were included. Of these, 74 met the APS classification criteria, 169 were non-criteria (NC)-APS, and 85 were seronegative (SN)-APS. Patients with other autoimmune diseases were excluded. APOs included early pregnancy loss, fetal death, preeclampsia, abruptio placentae, and preterm birth. Successful pregnancy was defined as the achievement of a live newborn. A literature search was also performed. The mean age of the overall group was 33.9 ± 5.3 years, and the patients were followed up for 35 (11–79) months. During the study period, there were 1332 pregnancies. Nearly 14% of the patients had an associated IT. IT patients more frequently received the standard-of-care (SoC) therapy. The presence of IT was not associated with worse maternal–fetal outcomes in patients treated with SoC treatment. Overall, IT patients had a lower frequency of newborns without treatment, especially those without definite APS. In addition, IT did not increase the risk of thrombosis during pregnancy or the postpartum period. A detailed analysis of the literature review identified only four publications related to our study and did not show conclusive evidence of the impact of IT on patients with obstetric APS. The group of women with APS-related obstetric morbidity and IT who did not receive treatment, especially those without definite APS, had a worse prognosis in terms of a live birth. However, with SoC therapy, the prognosis is similar in those patients without IT. The association of IT with APS does not seem to predispose to the development of thrombosis during pregnancy and/or the postpartum period.
Acinonyx jubatus-Inspired Quadruped Robotics: Integrating Neural Oscillators for Enhanced Locomotion Control
Authors: Hernández-Flores Eric Alberto , Hernández-Rodríguez Yazmín Mariela , Munguía-Fuentes Rosario , Bayareh-Mancilla Rafael , Cigarroa-Mayorga Oscar Eduardo
Published: May 2024

This study presents the design, simulation, and prototype creation of a quadruped robot inspired by the Acinonyx jubatus (cheetah), specifically designed to replicate its distinctive walking, trotting, and galloping locomotion patterns. Following a detailed examination of the cheetah’s skeletal muscle anatomy and biomechanics, a simplified model of the robot with 12 degrees of freedom was conducted. The mathematical transformation hierarchy model was established, and direct kinematics were simulated. A bio-inspired control approach was introduced, employing a Central Pattern Generator model based on Wilson–Cowan neural oscillators for each limb, interconnected by synaptic weights. This approach assisted in the simulation of oscillatory signals for relative phases corresponding to four distinct gaits in a system-level simulation platform. The design phase was conducted using CAD software (SolidWorks 2018), resulting in a 1:3-scale robot mirroring the cheetah’s actual proportions. Movement simulations were performed in a virtual mechanics software environment, leading to the construction of a prototype measuring 35.5 cm in length, 21 cm in width, 27 cm in height (when standing), and weighing approximately 2.1 kg. The experimental validation of the prototype’s limb angular positions and trajectories was achieved through the image processing of video-recorded movements, demonstrating a high correlation (0.9025 to 0.9560) in joint angular positions, except for the knee joint, where a correlation of 0.7071 was noted. This comprehensive approach from theoretical analysis to practical implementation showcases the potential of bio-inspired robotics in emulating complex biological locomotion.
Morphology Control of Zr-Based Luminescent Metal-Organic Frameworks for Aflatoxin B1 Detection
Authors: Zhu Fang , Chai Qiuxue , Xiong Dinghui , Zhu Nuanfei , Zhou Jialong , Wu Ruoxi , Zhang Zhen
Published: May 2024

Metal–organic frameworks (MOFs) have gained significant prominence as sensing materials owing to their unique properties. However, understanding the correlation between the morphology, properties, and sensing performance in these MOF-based sensors remains a challenge, limiting their applications and potential for improvement. In this study, Zr-MOF was chosen as an ideal model to explore the impact of the MOF morphology on the sensing performance, given its remarkable stability and structural variability. Three luminescent MOFs (namely rod-like Zr-LMOF, prismoid-like Zr-LMOF, and ellipsoid-like Zr-LMOF) were synthesized by adjusting the quantities of the benzoic acid and the reaction time. More importantly, the sensing performance of these Zr-LMOFs in response to aflatoxin B1 (AFB1) was thoroughly examined. Notably, the ellipsoid-like Zr-LMOF exhibited significantly higher sensitivity compared to other Zr-LMOFs, attributed to its large specific surface area and pore volume. Additionally, an in-depth investigation into the detection mechanism of AFB1 by Zr-LMOFs was conducted. Building upon these insights, a ratiometric fluorescence sensor was developed by coordinating Eu3+ with ellipsoid-like Zr-LMOF, achieving a remarkably lower detection limit of 2.82 nM for AFB1. This study contributes to an improved comprehension of the relationship between the MOF morphology and the sensing characteristics while presenting an effective approach for AFB1 detection.
Fluorogenic Aptamer-Based Hybridization Chain Reaction for Signal-Amplified Imaging of Apurinic/Apyrimidinic Endonuclease 1 in Living Cells
Authors: Liu Meixi , Tan Yunjie , Zhou Chen , Fu Zhaoming , Huang Ru , Li Jin , Li Le
Published: May 2024

A fluorogenic aptamer (FA)-based hybridization chain reaction (HCR) could provide a sensitive and label-free signal amplification method for imaging molecules in living cells. However, existing FA-HCR methods usually face some problems, such as a complicated design and significant background leakage, which greatly limit their application. Herein, we developed an FA-centered HCR (FAC-HCR) method based on a remote toehold-mediated strand displacement reaction. Compared to traditional HCRs mediated by four hairpin probes (HPs) and two HPs, the FAC-HCR displayed significantly decreased background leakage and improved sensitivity. Furthermore, the FAC-HCR was used to test a non-nucleic acid target, apurinic/apyrimidinic endonuclease 1 (APE1), an important BER-involved endonuclease. The fluorescence analysis results confirmed that FAC-HCR can reach a detection limit of 0.1174 U/mL. By using the two HPs for FAC-HCR with polyetherimide-based nanoparticles, the activity of APE1 in living cells can be imaged. In summary, this study could provide a new idea to design an FA-based HCR and improve the performance of HCRs in live cell imaging.
Are There Biological Correlates of Response to Yoga-Based Interventions in Depression? A Critical Scoping Review
Authors: Rajkumar Ravi Philip
Published: May 2024

Depression is the most common mental disorder worldwide. Both antidepressants and psychotherapy are effective in treating depression, but the response to these treatments is often incomplete. Yoga-based interventions (YBIs) have been advocated by some researchers as a promising form of alternative treatment for depression. Recent research has attempted to identify the biological mechanisms associated with the antidepressant actions of YBIs. In this scoping review, conducted according to the PRISMA-ScR guidelines, the PubMed and Scopus databases were searched to retrieve research on biomarkers of response to YBIs in patients with depression. These studies were also critically reviewed to evaluate their methodological quality and any sources of bias. Nineteen studies were included in the review. Based on these studies, there is preliminary evidence that YBIs may be associated with increased serum brain-derived neurotrophic factor (BDNF) and reduced serum cortisol and interleukin-6 (IL-6) in patients with depression. However, many of these changes were also observed in the control arms, and the overall quality of the research was low. At present, it cannot be concluded that there are reliable biomarkers of response to YBIs in depression, though there are some potential biological correlates. Further advances in this field will depend critically on improvements in study design, particularly the minimization of sources of bias and the selection of more specific and sensitive biomarkers based on existing evidence from other treatment modalities.
Social Undermining and Promotive Voice: The Moderating Effects of Procedural Justice
Authors: Fayzullaev Abdulkhamid Komil ugli , Shin Soo Young
Published: May 2024

Organizations are increasingly depending on their employees to contribute suggestions aimed at enhancing organizational processes, boosting overall efficiency, and fostering innovation. However, some factors might hinder employees from expressing their thoughts. While there is evidence suggesting an effect of supervisor social undermining behavior on employee voice, the impact on innovative types of voice, specifically promotive voice, remains uncertain. This study aims to explore the association between supervisor social undermining and employee promotive voice. Moreover, this research investigates how employee perceptions of procedural justice moderate this relationship, utilizing the Conservation of Resources theory. Data were collected from 115 highly skilled employees, and hierarchical regression analysis was employed to assess the proposed hypotheses. The findings suggest that when individuals encounter social undermining behavior from their supervisor, they are less inclined to engage in promotive voice behavior. Interestingly, the results indicate that this relationship becomes stronger when individuals possess higher perceptions of procedural justice. To the best of our knowledge, this study is the first to investigate the impact of supervisor social undermining on promotive voice while considering procedural justice as a moderator in this relationship. The findings of this study suggest several theoretical and practical implications and provide directions for future research.
A New Approach to Interior Design: Generating Creative Interior Design Videos of Various Design Styles from Indoor Texture-Free 3D Models
Authors: Shao Zichun , Chen Junming , Zeng Hui , Hu Wenjie , Xu Qiuyi , Zhang Yu
Published: May 2024

Interior design requires designer creativity and significant workforce investments. Meanwhile, Artificial Intelligence (AI) is crucial for enhancing the creativity and efficiency of interior design. Therefore, this study proposes an innovative method to generate multistyle interior design and videos with AI. First, this study created a new indoor dataset to train an AI that can generate a specified design style. Subsequently, video generation and super-resolution modules are integrated to establish an end-to-end workflow that generates interior design videos from texture-free 3D models. The proposed method utilizes AI to produce diverse interior design videos directly, thus replacing the tedious tasks of texture selection, lighting arrangement, and video rendering in traditional design processes. The research results indicate that the proposed method can effectively provide diverse interior design videos, thereby enriching design presentation and improving design efficiency. Additionally, the proposed workflow is versatile and scalable, thus holding significant reference value for transforming traditional design toward intelligence.