bluuebunny/crossref_metadata_2025 huggingface dataset:
Conceptual Approach to Permanent Magnet Synchronous Motor Turn-to-Turn Short Circuit and Uniform Demagnetization Fault Diagnosis
Authors: Yu Yinquan , Yuan Chun , Zeng Dequan , Carbone Giuseppe , Hu Yiming , Yang Jinwen
Published: December 2024

Permanent magnet synchronous motors (PMSMs) play a crucial role in industrial production, and in response to the problem of PMSM turn-to-turn short-circuit and demagnetization faults affecting production safety, this paper proposes a PMSM turn-to-turn short-circuit and demagnetization fault diagnostic method based on a convolutional neural network and bidirectional long and short-term memory neural network (CNN-BiLSTM). Firstly, analyzing the PMSM turn-to-turn short-circuit and demagnetization faults, one takes the PMSM stator current as the fault signal and optimizes the variational modal decomposition (VMD) by using the Gray Wolf Optimization (GWO) algorithm in order to achieve efficient noise reduction processing of the stator current signal and improve the fault feature content in the stator current signal. Finally, the fault diagnostics are classified by using the CNN-BiLSTM, which collects advanced optimization algorithms and deep learning networks. The effectiveness of the method is verified by simulation experiment results. This scheme has high practical value and broad application prospects in the field of PMSM turn-to-turn short circuit and demagnetization fault diagnosis.
Neural Network-Based Descent Control for Landers with Sloshing and Mass Variation: A Cascade and Adaptive PID Strategy
Authors: Ortega Angel Guillermo , Shirin Afroza
Published: December 2024

Autonomous control of lunar landers is essential for successful space missions, where precision and efficiency are crucial. This study presents a novel control strategy that leverages proportional, integral, and derivative (PID) controllers to manage the altitude, attitude, and position of a lunar lander, considering time-varying mass and sloshing behavior. Additionally, neural network models are developed, to approximate the lander’s mass properties as they change during descent. The challenge lies in the significant mass variations due to fuel, oxidizer, and pressurant consumption, which affect the lander’s inertia and sloshing behavior and complicate control efforts. We have developed a control-oriented model incorporating these mass dynamics, employing multiple PID controllers to linearize the system and enhance control precision. Altitude is maintained by one PID controller, while two others adjust the thrust vector control (TVC) gimbal angles to manage pitch and roll, with a fourth controller governing yaw via a reaction control system (RCS). A cascade PD controller further manages position by feeding commands to the attitude controllers, ensuring the lander reaches its target location. The lander’s TVC mechanism, equipped with a spherical gimbal, provides thrust in the desired direction, with control angles α and β regulated by the PID controllers. To improve the model’s accuracy, we have introduced time delays caused by fluid dynamics and actuator response, modeled via computational fluid dynamics (CFD). Fluid sloshing effects are also simulated as external forces acting on the lander. The neural networks are trained using data derived from computer-aided design (CAD) simulations of the lander vehicle, specifically the inertia tensor and the center of mass (COM) based on the varying mass levels in the tanks. The trained neural networks (NNs) can then use lander tank levels and orientation to inform and accurately predict the lander’s COM and inertia tensor in real time during the mission. The implications of this research are significant for future lunar missions, offering enhanced safety and efficiency in vehicle descent and landing operations. Our approach allows for real-time estimation of the lander’s state and for precise execution of maneuvers, verified through complex numerical simulations of the descent, hover, and landing phases.
Influence of Ice Growth Mode on the Ice Thickness and Shape Prediction of Two-Dimensional Airfoil
Authors: Shen Xiaobin , Zhao Jingyu , Ye Zekun , Wang Huanfa , Lin Guiping
Published: December 2024

Computational results of aircraft icing and predictions of ice shape are not only determined by the solutions of air-supercooled droplet two-phase flow and icing thermodynamic models of surface water film, but are also influenced by the growth mode of the ice layer. Two ice growth modes were established in a two-dimensional (2D) icing process simulation framework to calculate the ice thickness and ice shape, depending on whether surface deformation of the icing process was considered. Ice accretion simulations were performed with the two ice growth modes for an NACA0012 airfoil under rime ice and mixed ice conditions, and the results of ice amount, ice thickness, and ice shape were compared and analyzed. Under the same amount of ice formation, the ice thickness and ice shape obtained using different ice growth modes vary. The ice thickness and the ice shape size are relatively large without considering surface deformation, whereas the results with growth correction show a certain degree of reduction, which is more noticeable around the leading edge and the ice horns. However, the degrees of difference in ice thickness and ice shape are not the same, and the deviation in ice thickness is more obvious. Furthermore, the ice thickness and ice shape obtained using the ice growth correction mode are more consistent with experimental data and commercial software results, verifying the accuracy of the ice simulation method and the necessity of considering ice surface deformation. This paper is an essential guide for understanding the icing mechanism and accurately predicting two-dimensional ice shape.
Genotype-Driven Phenotype Prediction in Onion Breeding: Machine Learning Models for Enhanced Bulb Weight Selection
Authors: Choi Junhwa , Cho Sunghyun , Choi Subin , Jung Myunghee , Lim Yu-jin , Lee Eunchae , Lim Jaewon , Park Han Yong , Shin Younhee
Published: December 2024

Onions (Allium cepa L.) are a globally significant horticultural crop, ranking second only to tomatoes in terms of cultivation and consumption. However, due to the crop’s complex genome structure, lengthy growth cycle, self-incompatibility, and susceptibility to disease, onion breeding is challenging. To address these issues, we implemented digital breeding techniques utilizing genomic data from 98 elite onion lines. We identified 51,499 high-quality variants and employed these data to construct a genomic estimated breeding value (GEBV) model and apply machine learning methods for bulb weight prediction. Validation with 260 new individuals revealed that the machine learning model achieved an accuracy of 83.2% and required only thirty-nine SNPs. Subsequent in silico crossbreeding simulations indicated that offspring from the top 5% of elite lines exhibited the highest bulb weights, aligning with traditional phenotypic selection methods. This approach demonstrates that early-stage selection based on genotypic information followed by crossbreeding can achieve economically viable breeding results. This methodology is not restricted to bulb weight and can be applied to various horticultural traits, significantly improving the efficiency of onion breeding through advanced digital technologies. The integration of genomic data, machine learning, and computer simulations provides a powerful framework for data-driven breeding strategies, accelerating the development of superior onion varieties to meet global demand.
Far-Infrared Radiation Heating-Assisted Pulsed Vacuum Drying (FIR-PVD) Enhanced the Drying Efficiency and Quality Attributes of Raspberries
Authors: Liu Zi-Liang , Wang Shan-Yu
Published: December 2024

An emerging drying method, far-infrared radiation heating-assisted pulsed vacuum drying (FIR-PVD), was employed to dry raspberries. In this study, the impacts of FIR-PVD, freeze drying (FD), and hot air drying (HAD) on drying characteristics and quality attributes, including color, rehydration ratio, content of total phenolics (TP), content of total monomeric anthocyanins (TMA), antioxidant activity, and microstructural attributes of dried raspberries, were examined. Results indicated that FIR-PVD notably shortened the drying time by 47.78% compared to HAD and by 73.89% compared to FD. The FIR-PVD samples exhibited the highest TP content, DPPH radical scavenging activity, and FRAP value, which were 17.73%, 6.09%, and 38.16% higher than those of the FD samples, respectively, and 2.78%, 2.77%, and 18.74% higher than those of the HAD samples. Significant correlations (p < 0.05) were observed between antioxidant capacity, as measured by DPPH and FRAP assays, and TP content. However, FD at a low temperature led to a higher TMA content than FIR-PVD and HAD. FIR-PVD resulted in the highest ΔE values of dried products due to the lightness enhancement. In addition, the dried products obtained by FIR-PVD had better rehydration capacity. These findings indicate that FIR-PVD presents a promising alternative method for drying raspberries, as it enhances drying efficiency and improves the quality attributes of the dried products.
First Results of Management of Powdery Mildew in Grapevine Using Sulphur, Silicate and Equisetum arvense Formulations
Authors: Calzarano Francesco , Pagnani Giancarlo , Seghetti Leonardo , D’Agostino Vincenzo , Di Marco Stefano
Published: December 2024

The management of grapevine from diseases is now particularly focused on the development of environmentally friendly strategies. Although sulphur is not in itself a toxic substance, its extensive use in powdery mildew, in organic vineyards especially, may cause environmental problems and plant phytotoxicity and affect the health and safety of viticultural workers. The activity of sulphur applied at doses reduced up to 50% in tank mixtures with silicates or Equisetum arvense-based products was assessed on powdery mildew in grapevine. Two-year trials were carried out applying the products every 7–10 days in the period of greatest risk for disease infections, in two organic vineyards in the Abruzzo region, Italy. In both vineyards throughout all trial years, at harvest, disease incidence and severity on leaves and bunches in the silicon + sulphur treatments were generally significantly lower compared to both the sulphur at full dose and to the control. In all trials, in both vineyards, a strong activity of silicon + sulphur mixtures towards disease on leaves was observed. This effect could be decisive in lowering the potential inoculum in the following season. In the Ari vineyard, vines treated with the mixtures always increased yield quantity and quality, given the higher contents of soluble solids and lower levels of total acidity. In the Cellino vineyard, probably due to the young age of the vines, increases in yield quantity were observed, with lesser effects on quality. The promising activity of the mixtures needs further studies to confirm these positive results. Furthermore, in both vineyards, the source of primary inoculum of the pathogen was observed as chasmothecia on the leaves.
The Effects of Animal, Collection Time, and Interval on the Microbiota Structure, Metabolism, and Degradative Potential of Rumen Fluid Inoculum Collected by Esophageal Probe from Hay-Fed Cows
Authors: Simoni Marica , Mavrommatis Alexandros , Cresceri Andrea , Severgnini Marco , Penasa Mauro , Santinello Matteo , Castiglioni Bianca , Cremonesi Paola , Tsiplakou Eleni , Righi Federico
Published: December 2024

Rumen fluid (RF) is the greatest source of uncontrolled inter-assay variations in in vitro digestibility tests. This study aimed to investigate the effect of donor cow (DC) individual variability, time relative to feeding, and weekly sampling intervals on RF microbiota structure and metabolism, and on its degradative capacity expressed as enzymatic activity (EA). RF was collected using an esophageal probe from 4 Holstein hay-fed DCs before feeding, and 4 and 8 h post-feeding, over 3 days with one-week intervals, for a total of 3 weeks. Rumen bacteriome ß-diversity was affected by DCs and the one-week interval but not by time relative to feeding. However, volatile fatty acids and NDF and DM digestibility were not altered based on DCs. Propionic, lactic, and butyric acids were consistent between weeks, with some variations in lactic and butyric acids 8 h after feeding. Amylase and cellulase activities fluctuated between the weekly intervals, while xylanase activity increased in RF collected at 4 and 8 h after feeding (p = 0.030) and in DC3 compared to the other DCs (p < 0.001). Our results reinforce the importance of pooling RF from multiple cows and increasing the number of replicates to enhance the accuracy of the analysis.
Natural Language Processing-Based Deep Learning to Predict the Loss of Consciousness Event Using Emergency Department Text Records
Authors: Park Hang A. , Jeon Inyeop , Shin Seung-Ho , Seo Soo Young , Lee Jae Jun , Kim Chulho , Park Ju Ok
Published: December 2024

The increasing adoption of electronic medical records (EMRs) presents a unique opportunity to enhance trauma care through data-driven insights. However, extracting meaningful and actionable information from unstructured clinical text remains a significant challenge. Addressing this gap, this study focuses on the application of natural language processing (NLP) techniques to extract injury-related variables and classify trauma patients based on the presence of loss of consciousness (LOC). A dataset of 23,308 trauma patient EMRs, including pre-diagnosis and post-diagnosis free-text notes, was analyzed using a bilingual (English and Korean) pre-trained RoBERTa model. The patients were categorized into four groups based on the presence of LOC and head trauma. To address class imbalance in LOC labeling, deep learning models were trained with weighted loss functions, achieving a high area under the curve (AUC) of 0.91. Local Interpretable Model-agnostic Explanations analysis further demonstrated the model’s ability to identify critical terms related to head injuries and consciousness. NLP can effectively identify LOC in trauma patients’ EMRs, with weighted loss functions addressing data imbalances. These findings can inform the development of AI tools to improve trauma care and decision-making.
Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
Authors: Žeger Ivana , Šetka Ivan , Marić Domagoj , Grgic Sonja
Published: December 2024

Decolorization is an image processing technique that converts a color input image into a grayscale image. This paper discusses the decolorization process and provides an overview of the methods based on the different principles used: basic conversion from RGB to YUV format using ITU Recommendations 601, 709, and 2020; basic conversion from RGB to LAB color space; the method using cumulative distribution function of color channels; one global decolorization method; and one based on deep learning. The grayscale images produced by these methods were evaluated using four objective metrics, allowing for a thorough analysis and comparison of the decolorization results. Additionally, the execution speed of the algorithms was assessed, providing insight into their performance efficiency. The results demonstrate that different metrics evaluate the decolorization methods differently, highlighting the importance of selecting an appropriate metric that aligns with the subsequent image processing tasks following decolorization. Furthermore, it was shown that the decolorization methods depend on the content of the images, performing better on natural images than on artificially generated ones. The decolorization methods were also examined in the context of object segmentation and edge detection. The results from segmentation and edge detection were aligned with the decolorization results, revealing that certain objective metrics for evaluating decolorization more effectively assessed the properties of the decolorized images, which are crucial for successful object segmentation and edge detection.
Three-Dimensional Broad Learning Gravity Data Inversion Using Single-Anomaly Training Samples
Authors: Xu Guang-Jing , Zu Qiang , Yang Xiao-Hui , Tao Tao , Han Peng , Luo Qiang , Han Sixu , He Zhan-Xiang
Published: December 2024

Gravity data inversion is of critical importance in geophysics, encompassing a range of applications, such as the exploration of geological resources, the identification of geological structures, and the detection of groundwater resources. This study proposes a three-dimensional (3D) machine learning approach to enhance the efficiency of the aforementioned exploration tasks by leveraging gravity data. The mapping relationship between gravity data and subsurface density structures is modeled by the broad learning network, distinguished by its high training efficiency and robust modeling capability. Notably, the proposed inversion method obviates the constraints on the number of anomalies prior to the inversion process. This is achieved by setting one anomaly with varied locations for different training samples. Numerical and field data applications demonstrate the efficiency of the proposed 3D machine learning gravity data inversion method, especially in automatically determining the number of anomalies. In particular, the proposed method produced accurate density inversion results in the field application, aiding in the identification of potential oil and gas reservoirs in the target region and offering the potential for broader application in other resource exploration. The proposed inversion method can promote the construction of density structures of subsurfaces based on gravity data.
Adaptive Learning in AI Agents for the Metaverse: The ALMAA Framework
Authors: Xia Yina , Shin Seong-Yoon , Lee Hyun-Ae
Published: December 2024

This study investigates the adaptability of Artificial Intelligence (AI) agents in the Metaverse, focusing on their ability to enhance responsiveness, decision-making, and engagement through the proposed Adaptive Learning Model for AI Agents (ALMAA) framework. The research does not introduce new interventions to existing platforms like Epic Games or AltspaceVR but instead analyzes how their operations align with adaptive learning principles. By examining these platforms, the study demonstrates the alignment between real-world practices and theoretical constructs, offering insights into how adaptive AI systems operate in dynamic virtual environments. Case observations highlight key metrics such as user interaction efficiency, contextual decision accuracy, and predictive engagement strategies. The data, derived from detailed user interaction logs and feedback reports, underscore the practical application of adaptive learning in optimizing user satisfaction and system performance. Statistical analyses reveal notable gains in response speed, predictive precision, and user engagement, validating the theoretical framework’s relevance. This paper positions the ALMAA framework as a critical lens for understanding and analyzing adaptive AI in virtual settings. It emphasizes theoretical exploration rather than experimental application, providing a foundation for future research into scalable, user-centered AI systems tailored for the Metaverse’s evolving demands.
Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
Authors: Jovanovic Dolecek Gordana , Fernandez-Vazquez Alfonso
Published: December 2024

This work presents the design of optimal and multiplierless compensators for Chebyshev sharpened comb decimation filters. The narrowband and wideband compensators are proposed. For the narrowband, the compensator with a magnitude response is proposed in a sinusoidal form, while for the wideband, two compensators with magnitude responses of two sinusoidal functions are introduced. The optimum design is performed using particle swarm optimization (PSO), while the multiplierless design is realized by presenting optimum parameters in a signed-power-of-two (SPT) form. Unlike the methods in the literature, this approach presents flexibility in design, allowing for an exchange between the quality of optimization and the complexity. Comparisons with the compensators from the literature demonstrated that the proposed method provides much better compensation while requiring fewer or slightly increased number of adders. Possible practical applications and potential future research work are also included.
Improving Transferability of Physical Adversarial Attacks on Object Detectors Through Multi-Model Optimization
Authors: Dimitriu Adonisz , Michaletzky Tamás Vilmos , Remeli Viktor
Published: December 2024

Physical adversarial attacks face significant challenges in achieving transferability across different object detection models, especially in real-world conditions. This is primarily due to variations in model architectures, training data, and detection strategies, which can make adversarial examples highly model-specific. This study introduces a multi-model adversarial training approach to improve the transferability of adversarial textures across diverse detection models, including one-stage, two-stage, and transformer-based architectures. Using the Truck Adversarial Camouflage Optimization (TACO) framework and a novel combination of YOLOv8n, YOLOv5m, and YOLOv3 models for optimization, our approach achieves an AP@0.5 detection score of 0.0972—over 50% lower than textures trained on single models alone. This result highlights the importance of multi-model training in enhancing attack effectiveness across object detectors, contributing to improved adversarial effectiveness.
Median U-Turn Intersection Critical Parameter Research and Operational Performance Evaluation
Authors: Zhao Changxiang , Liu Xuewen , Wu Tianhao , Zhang Weiwei
Published: December 2024

As one of the unconventional intersection designs, the median U-turn (MUT) intersection design has been confirmed to have the potential to improve intersection efficiency and produce lower vehicle delays, and it has been widely applied to many urban networks. Most existing studies have successfully evaluated the operational performance of MUT intersections by analyzing the overall traffic flow, particularly for intersection delay and capacity. However, the impact of the critical parameters and behavior of individual vehicles in MUT intersection operations has not been adequately studied. These limitations cannot provide a complete and objective evaluation of MUT intersections. Therefore, a comprehensive cellular automata model is proposed to simulate vehicle dynamic variations, considering road channelization information and vehicle random movements. This model will be compared to the VISSIM model to validate rationality by using field data, and it shows a good agreement between the simulation results of both models. In addition, sensitive experiments reveal that properly adjusting the elements, including the separation distance, the proportion of left-turn vehicles, and green phase percentage, can improve the operation of MUT intersections to varying degrees. By appropriately improving intersection features and conducting reasonable evaluations, the overall performance and sustainability of the MUT intersections in Xi’an city can be enhanced. Finally, this paper offers a valuable guideline for the evaluation and modeling strategies of MUT intersections.
Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
Authors: Bakhtiar Sadia , Khan Farid Ullah , Fu Hailing , Hajjaj Amal Z. , Theodossiades Stephanos
Published: December 2024

Energy harvesting technology plays an important role in converting ambient energy into useful electrical energy to power wireless sensing and system monitoring, especially for systems operating in isolated, abandoned or embedded locations where battery replacement or recharging is not a feasible solution. This paper provides an integrative study of the methodologies and technologies of energy harvesting from fluid flow-induced vibration (FIV). The recent research endeavors contributing to flow-based energy harvesting have been reviewed to present the state-of-the-art issues and challenges. Several mechanisms on FIVs including vortex-induced vibrations (VIVs), flutter, galloping and wake galloping are thoroughly discussed in terms of device architecture, operating principles, energy transduction, voltage production and power generation. Additionally, advantages and disadvantages of each FIV energy harvesting mechanism are also talked about. Power enhancement methods, such as induced nonlinearities, optimized harvester’s configuration, hybridization and coupling of aerodynamic instabilities, for boosting the harvester’s output are also elucidated and categorized. Moreover, rotary wind energy harvesters are reviewed and discussed. Finally, the challenges and potential directions related to the flow-based energy harvesters (FBEHs) are also mentioned to provide an insight to researchers on the development of sustainable energy solutions for remote wireless sensing and monitoring systems.
Fatigue Crack Propagation Analysis of Rail Surface Under Mixed Initial Crack Patterns
Authors: Liu Jianhua , Yang Weiqi , Wang Zhongmei
Published: December 2024

Prolonged rolling contact fatigue between wheels and rails results in the formation of surface cracks on the rail and accurately analyzing the crack expansion behavior is essential to ensuring the safe operation of the train. Drawing upon the principles of fracture mechanics and finite element theory, this study establishes a finite element model of wheel–rail rolling contact that incorporates the presence of cracks. The method utilizes an interaction integral to calculate the stress intensity factors at the leading edge of the crack; then, the Paris formula is used to solve the crack spreading rate. It systematically examines the effects of the initial crack angle, the coefficient of friction of wheels to rails, and crack size on the behavior of fatigue crack propagation. The results indicate that the cracks primarily extend in the depth direction of the rail, transforming the semi-circular surface cracks into elliptical cracks with the major axis oriented along the rail’s width. Crack propagation is primarily driven by model II and III composite crack propagation, with their expansion rates influenced by operating conditions. In contrast, mode-I expansion is less sensitive to these conditions. Under single-variable loading conditions, a smaller initial crack angle results in a faster crack growth rate. Increasing crack length accelerates crack growth, while a higher friction coefficient inhibits it.
Fluid Interaction Analysis for Rotor-Stator Contact in Response to Fluid Motion and Viscosity Effect
Authors: Sozinando Desejo Filipeson , Tchomeni Bernard Xavier , Alugongo Alfayo Anyika
Published: December 2024

Fluid–structure interaction introduces critical failure modes due to varying stiffness and changing contact states in rotor-stator systems. This is further aggravated by stress fluctuations due to shaft impact with a fixed stator when the shaft rotates. In this paper, the investigation of imbalance and rotor-stator contact on a rotating shaft was carried out in viscous fluid. The shaft was modelled as a vertical elastic rotor system based on a vertically oriented elastic rotor operating in an incompressible medium. Implicit representation of the rotating system including the rotor-stator contact and the hydrodynamic resistance was formulated for the coupled system using the energy principle and the Navier–Stokes equations. Additionally, the monolithic approach included an implicit strategy of the rotor-stator fluid interaction interface conditions in the solution methodology. Advanced time-frequency methods, such as Hilbert transform, continuous wavelet transform, and estimated instantaneous frequency maps, were applied to extract the vibration features of the dynamic response of the faulted rotor. Time-varying stiffness due to friction is thought to be the main reason for the frequency fluctuation, as indicated by historical records of the vibration displacement, whirling orbit patterns of the centre shaft, and the amplitude–frequency curve. It has also been demonstrated that the augmented mass associated with the rotor and stator decreases the natural frequencies, while the amplitude signal remains relatively constant. This behaviour indicates a quasi-steady-state oscillatory condition, which minimises the energy fluctuations caused by viscous effects.
ML-Based Pain Recognition Model Using Mixup Data Augmentation
Authors: Shantharam Raghu M. , Schwenker Friedhelm
Published: December 2024

Machine learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where patient communication may be limited. However, ML models often face challenges such as memorization and sensitivity to adversarial examples. Regularization techniques like mixup, which trains models on convex combinations of data pairs, address these issues by enhancing model generalization. While mixup has proven effective in image, speech, and text datasets, its application to time-series signals like electrodermal activity (EDA) is less explored. This research uses ML for pain recognition with EDA signals from the BioVid Heat Pain Database to distinguish pain by applying mixup regularization to manually extracted EDA features and using a support vector machine (SVM) for classification. The results show that this approach achieves an average accuracy of 75.87% using leave-one-subject-out cross-validation (LOSOCV) compared to 74.61% without mixup. This demonstrates mixup’s efficacy in improving ML model accuracy for pain recognition from EDA signals. This study highlights the potential of mixup in ML as a promising approach to enhance pain assessment in healthcare.
Children’s Exposure to Volatile Organic Compounds: A Comparative Analysis of Assessments in Households, Schools, and Indoor Swimming Pools
Authors: Gabriel Marta Fonseca , Felgueiras Fátima , Feliciano Manuel
Published: December 2024

Chemical pollution is an increasing worldwide concern, with children being especially vulnerable to the harmful effects of air pollution. This study aimed to characterize the mixture of volatile organic compounds (VOCs) present in indoor air across residential, educational, and recreational settings. It analyzed data on VOC concentrations from previous sampling campaigns conducted in households with children, primary schools, and indoor swimming pools (70 buildings, 151 indoor spaces) in northern Portugal. The findings reveal the co-occurrence of 16 VOCs (1,2,4-trimethylbenzene, benzene, ethylbenzene, m/o/p-xylenes, styrene, toluene, tetrachloroethylene, 2-ethylhexanol, butanol, acetophenone, ethyl acetate, benzaldehyde, decanal, nonanal, 1-methoxy-2-propanol and limonene) across all three settings, primarily associated to emissions from building materials and detergents. However, distinct patterns were also observed in the VOCs detected across the three indoor environments: in homes, the predominant VOCs were primarily released from cleaning and fragranced products; in schools, from ammonia-based cleaners and occupant activities; and in swimming pools, the predominant airborne chemicals were disinfection by-products resulting from the chemical dynamics associated with water disinfection. Overall, the findings highlight the need for additional research to deepen our understanding of the risks posed by combined exposure to multiple indoor air chemicals for children. These results also underscore the importance of developing and enforcing regulations to monitor VOC levels in environments frequented by children and implementing preventive measures to minimize their exposure to harmful chemicals.
On Qi’s Normalized Remainder of Maclaurin Power Series Expansion of Logarithm of Secant Function
Authors: Zhang Hong-Chao , Guo Bai-Ni , Du Wei-Shih
Published: December 2024

In the study, the authors introduce Qi’s normalized remainder of the Maclaurin power series expansion of the function lnsecx=−lncosx; in view of a monotonicity rule for the ratio of two Maclaurin power series and by virtue of the logarithmic convexity of the function (2x−1)ζ(x) on (1,∞), they prove the logarithmic convexity of Qi’s normalized remainder; with the aid of a monotonicity rule for the ratio of two Maclaurin power series, the authors present the monotonic property of the ratio between two Qi’s normalized remainders.
Stochastic Models for Ontogenetic Growth
Authors: Hoang Chau , Phan Tuan Anh , Tian Jianjun Paul
Published: December 2024

Based on allometric theory and scaling laws, numerous mathematical models have been proposed to study ontogenetic growth patterns of animals. Although deterministic models have provided valuable insight into growth dynamics, animal growth often deviates from strict deterministic patterns due to stochastic factors such as genetic variation and environmental fluctuations. In this study, we extend a general model for ontogenetic growth proposed by West et al. to stochastic models for ontogenetic growth by incorporating stochasticity using white noise. According to data variance fitting for stochasticity, we propose two stochastic models for ontogenetic growth, one is for determinate growth and one is for indeterminate growth. To develop a universal stochastic process for ontogenetic growth across diverse species, we approximate stochastic trajectories of two stochastic models, apply random time change, and obtain a geometric Brownian motion with a multiplier of an exponential time factor. We conduct detailed mathematical analysis and numerical analysis for our stochastic models. Our stochastic models not only predict average growth well but also variations in growth within species. This stochastic framework may be extended to studies of other growth phenomena.
Defining a Critical Partition Zone for Sagittal Alignment in Lumbar Spine Fusion Surgery: A Systematic Review
Authors: Mi Le Jie-Ren , Wu Wen-Tien , Chen Chih-Wei , Jaw Fu-Shan , Yang Shu-Hua , Yeh Kuang-Ting
Published: December 2024

Background: Sagittal alignment in the lumbar spine is essential for spinal stability and functionality, with significant implications in surgical planning for spinal deformity correction. However, standardized lumbar partitioning, particularly identifying a critical sagittal alignment zone, remains underdefined. This study aims to establish a reliable lumbar partition to guide surgical decisions and optimize clinical outcomes. Methods: A systematic review of four major biomedical databases yielded 32 studies, of which 4 met the inclusion criteria. Studies on asymptomatic adults with segmental lordosis data stratified by pelvic incidence were analyzed. Lumbar lordosis values were converted to percentages, allowing for cross-study comparison. Sensitivity analysis and bias assessment were performed to ensure methodological rigor. Results: The findings identified the L3–L5 interval, especially around the L4 vertebra, as a critical biomechanical zone across various populations and pelvic incidence groups. Individuals with higher pelvic incidence had concentrated lordosis in lower segments, while those with lower pelvic incidence had greater lordosis in upper segments, underscoring the L3–L5 region’s stability as a surgical reference. Conclusions: The L3–L5 interval serves as a key partition zone for sagittal alignment, providing a stable reference for lumbar spine fusion. These findings offer a foundational clinical reference, potentially improving alignment outcomes and reducing postoperative complications.
The Effect of Conditioned Medium from Angiopoietin-1 Gene-Modified Mesenchymal Stem Cells on Wound Healing in a Diabetic Mouse Model
Authors: Deng Qiong , Pan Shenzhen , Du Fangzhou , Sang Hongfei , Cai Zhixin , Xu Xiaoyu , Wei Qian , Yu Shuang , Zhang Jingzhong , Li Chenglong
Published: December 2024

Introduction: Mesenchymal stem cells (MSCs) have been introduced as a promising treatment for diabetic wounds. The effects of stem cell therapy are thought to be caused by bioactive molecules secreted by stem cells. Stem cell-based gene therapies can target bioactive molecules. Therefore, treatment using conditioned medium (CM) derived from genetically engineered stem cells has been proposed as an alternative option for diabetic ulcer care. Methods: MSCs derived from human umbilical cords were obtained and engineered to overexpress the angiogenin-1 gene (MSCsAng1) through plasmid transfection. This study extracted conditioned medium from MSCs (MSC-CM) or MSCsAng1(MSCAng1-CM) for wound treatment applications. Via in vitro experiments, the proangiogenic effects of MSCAng1-CM were assessed via the migration and tube formation of human umbilical vein endothelial cells (HUVECs). Furthermore, the efficacy of MSCAng1-CM in promoting wound healing, re-epithelialization, hair follicle, and angiogenesis was evaluated via a diabetic mouse skin defect model. Results: In vitro assays demonstrated that MSCAng1-CM significantly enhanced HUVECs’ functions, including migration and tube formation. In vivo assays revealed that MSCAng1-CM exhibited notable advancements in healing speed, re-epithelialization, hair follicle, and angiogenesis. Conclusion: These results indicate that MSCAng1-CM can promote wound healing in diabetic mice and make the vascular structure in regenerated tissues more stable without inducing tissue fibrosis, providing a new therapeutic strategy for treating diabetic skin wounds. This provides a valuable theoretical basis for further research on regenerative medicine and cell therapy.
Digitalized 3D Spinal Decompression and Correction Device Improved Initial Brace Corrections and Patients’ Comfort Among Adolescents with Idiopathic Scoliosis: A Single-Centre, Single-Blinded Randomized Controlled Trial
Authors: Jie Yi , Li Mengyao , Dong Anqin , Luo Yu-Yan , Luo Chang-Liang , Li Jing , Zheng Pengyuan , Zhang Xinmin , Wong Man Sang , Ma Christina Zong-Hao , Zhang Ming
Published: December 2024

This study aimed to evaluate the efficacy of a novel three-dimensional (3D) spinal decompression and correction device in improving the in-brace correction and patient comfort level for adolescents with idiopathic scoliosis (AIS), and to assess the impact of the number of vertebrae involved in the scoliotic curve on the correction’s effectiveness. A single-centre, single-blinded randomized controlled trial (RCT) was conducted in 110 AIS patients aged 10–18 years who were randomly allocated into four groups receiving 0–3 days of device intervention. Each session lasted for 30 min and was conducted twice daily. Significant improvements were observed in both the in-brace correction ratio and patient comfort level, particularly in the 2- and 3-day intervention groups (p < 0.001). The number of involved vertebrae for a scoliotic curve was positively correlated with the in-brace correction ratio in the no intervention (or 0-day) and 1-day intervention groups, while this correlation varied in the 2- and 3-day intervention groups. These findings suggested that the prolonged use of the 3D device could improve the correction ratios and patient comfort, while the role of vertebrae involvement in predicting the initial correction may require further exploration to optimize personalized treatment strategies in future clinical practice.
The Effect of Atorvastatin on Oncogenic miRNAs in Hematological Malignancies: A Central Study
Authors: Hashem Jood , Alsukhni Farah , Abushukair Hassan , Ayesh Mahmoud
Published: December 2024

The efficacy of statins as anti-cancer drugs has been demonstrated in several malignancies but has been poorly investigated in hematological malignancies (HM). By studying its effect on oncogenic miRNAs, we investigated the effect of statin therapy on HM patients. The data were used to identify enriched pathways that were altered due to statin treatment. The main aim of this study was to identify significantly differentially expressed miRNAs and involved regulatory pathways post-atorvastatin treatment in HM patients. A panel of 95 plasma circulating miRNAs involved in tumorigenesis, apoptosis, and differentiation were relatively quantified using qPCR for blood samples obtained from 12 HM patients, 4 with Chronic Myeloid Leukemia (CML), 4 with Non-Hodgkin Lymphoma (NHL), and 4 with Essential Thrombocythemia. Pre- and post-administration of a 6-week atorvastatin course miRNA expression levels were measured. Significantly differentially expressed miRNAs were those with a fold change >2 or <0.5 using the Livak method with a two-sided p-value < 0.05. To further understand the underlying mechanism of statin regulated miRNA, GO and KEGG pathway enrichment analyses were conducted for identified target genes using the DAVID 6.8 bioinformatics tool. Out of 95 miRNAs, 14 exhibited significant fold changes post-treatment with statins including miR-198, miR-29a+b+c, miR-204, miR-222, miR-224, miR-155, miR-128b, miR-296, miR-199a+b, miR-194, miR-125a, miR-200a, and the let-7-family that were upregulated and miR-150 that was downregulated post-statin treatment. Higher mir-222, mir-194, mir-128b, and mir-199b expressions were significantly associated with better overall survival using the Cancer Genomic Atlas leukemia and lymphoma patient cohorts. Enrichment analysis identified the PI3k-Akt pathway as well as other pathways involved in the epithelial–mesenchymal transition. Atorvastatin upregulated several tumor suppressor genes involved in mediating better prognosis. The data can be used to enhance personalized treatments for patients with hematological malignancies by helping to predict the different pathways that may be targeted and, therefore, result in better survival outcomes in these patients.
Biotechnology Revolution Shaping the Future of Diabetes Management
Authors: Kundnani Nilima Rajpal , Lolescu Bogdan , Dinu Anca-Raluca , Berceanu-Vaduva Delia Mira , Dumitrescu Patrick , Tamaș Tudor-Paul , Sharma Abhinav , Popa Mihaela-Diana
Published: December 2024

Introduction: Diabetes mellitus (DM) has a millennia-long history, with early references dating back to ancient Egypt and India. However, it was not until the 20th century that the connection between diabetes and insulin was fully understood. The sequencing of insulin in the 1950s initiated the convergence of biotechnology and diabetes management, leading to the development of recombinant human insulin in 1982. This marked the start of peptide-based therapies in DM. Recombinant peptides for DM treatment: Numerous recombinant peptides have been developed since, starting with modified insulin molecules, with the aim of bettering DM management through fine-tuning the glycemic response to insulin. Peptide-based therapies in DM have expanded substantially beyond insulin to include agonists of Glucagon-like peptide-1 receptor and Glucose-dependent insulinotropic polypeptide receptor, glucagon receptor antagonists, and even peptides exerting multiple receptor agonist effects, for better metabolic control. Insulin pumps, continuous glucose monitoring, and automated insulin delivery systems: The development of modern delivery systems combined with real-time glucose monitoring has significantly advanced diabetes care. Insulin pumps evolved from early large devices to modern sensor-augmented pumps with automated shutoff features and hybrid closed-loop systems, requiring minimal user input. The second-generation systems have demonstrated superior outcomes, proving highly effective in diabetes management. Islet cell transplantation, organoids, and biological pancreas augmentation represent innovative approaches to diabetes management. Islet cell transplantation aims to restore insulin production by transplanting donor beta cells, though challenges persist regarding graft survival and the need for immunosuppression. Organoids are a promising platform for generating insulin-producing cells, although far from clinical use. Biological pancreas augmentation relies on therapies that promote beta-cell (re)generation, reduce stress, and induce immune tolerance. Further biotechnology-driven perspectives in DM will include metabolic control via biotechnology-enabled tools such as custom-designed insulin hybrid molecules, machine-learning algorithms to control peptide release, and engineering cells for optimal peptide production and secretion.
Control of Corner Separation in Compressor Cascade Using Biomimetic Fish Scales Structure
Authors: Shen Jin-Long , Yeh Szu-I
Published: December 2024

In this study, a fish scale structure with low viscous drag was proposed and applied to the suction surface of a compressor cascade to reduce total pressure loss and suppress corner separation, a key source of compressor inefficiency. By using CFD simulations, the biomimetic structure was identified and integrated into the cascade design. To evaluate its effects, we analyzed secondary flow structures using 2D projected streamlines, axial velocity density (AVD), and vortex visualization techniques. The results show that the fish scale structure effectively reduces the volume of low-energy fluid by 18.36% and decreases total pressure loss at the outlet by 3.5%. Additionally, the AVD iso-surface proved instrumental in identifying low-energy fluid regions, which correlate closely with total pressure loss distribution. These findings highlight the potential of biomimetic-inspired designs to improve compressor performance by mitigating corner separation and reducing flow losses.
Estimation of Spacecraft Angular Velocity Based on the Optical Flow of Star Images Using an Optimized Kalman Filter
Authors: Si Jiaqian , Niu Yanxiong , Niu Haisha , Liu Zixuan , Liu Danni
Published: December 2024

Biomimetic vision is a promising method for efficient navigation and perception, showing great potential in modern navigation systems. Optical flow information, which comes from changes in an image on an organism’s retina as it moves relative to objects, is crucial in this process. Similarly, the star sensor is a critical component to obtain the optical flow for attitude measurement using sequences of star images. Accurate information on angular velocity obtained from star sensors could guarantee the proper functioning of spacecraft in complex environments. In this study, an optimized Kalman filtering method based on the optical flow of star images for spacecraft angular velocity estimation is proposed. The optimized Kalman filtering method introduces an adaptive factor to enhance the adaptability under dynamic conditions and improve the accuracy of angular velocity estimation. This method only requires optical flow from two consecutive star images. In simulation experiments, the proposed method has been compared with the classic Kalman filtering method. The results demonstrate the high precision and robust performance of the proposed method.
Bioenergy Production from Sorghum Distillers Grains via Dark Fermentation
Authors: Lu Ching-Chun , Lin Chiu-Yue
Published: December 2024

Sorghum distillers grains (SDGs) produced from a sorghum liquor company were used for generating biohydrogen via dark fermentation at pH 4.5–6.5 and 55 °C with a batch test, and the biohydrogen electricity generation potential was evaluated. The experimental results show that pH markedly affects hydrogen concentration, hydrogen production rate (HPR) and hydrogen yield (HY), in that high acidic pH values result in high values. The HPR and HY ranged from 0.76 to 3.2 L/L-d and 21.4 to 62.3 mL/g chemical oxygen demand, respectively. These hydrogen production values were used to evaluate bioelectricity generation using a newly developed gas/liquid-fuel engine. The results show a new and prospective biomass source for biohydrogen production, bioelectricity generation and simultaneously solving the problem of treating SDGs when producing kaoliang liquor. Applications of the experimental results are also discussed.