bluuebunny/crossref_metadata_2025 huggingface dataset:
Sulfated Polysaccharides Isolated from Nacre Extract Suppress Chronic Scopolamine Administration-Induced Amyloid-Beta Deposition
Authors: Wako Mayumi , Ohara Kanae , Hasegawa Yasushi
Published: September 2024

Pearl oyster shells are composed of a double layer of calcium carbonate polymorphs: prismatic and nacreous. The nacreous layer is used in functional foods and cosmetics. In an earlier work, we reported that sulfated polysaccharides in nacre extract ameliorated memory impairment induced by a single dose of scopolamine. Here, we investigated whether sulfated polysaccharides suppress amyloid-beta (Aβ) deposition in an Alzheimer’s disease model induced by prolonged administration of scopolamine. Chronic scopolamine administration induces Aβ deposition; however, sulfated polysaccharides suppressed this effect. Additionally, sulfated polysaccharides ameliorated the accumulation of phosphorylated tau, neuroinflammation, and neuronal cell death in the brain, which are common features of patients with Alzheimer’s disease. To further determine the inhibitory mechanisms of Aβ deposition, we assessed the amount of the Aβ-degrading enzyme insulin-degrading enzyme (IDE). In animal experiments, sulfated polysaccharides increased IDE levels in scopolamine-treated mice. To study the effect of sulfated polysaccharides on insulin signaling, which regulates IDE expression, we evaluated the expression levels of phosphorylated Akt and nuclear factor-kB. Sulfated polysaccharides restored the levels of phosphorylated Akt and nuclear factor-kB, which were decreased and increased, respectively, using scopolamine treatment. Overall, our findings suggest that sulfated polysaccharides suppress Aβ deposition by regulating IDE expression.
A Study of the Influence of Synthesis Parameters on the Preparation of High Performance SSZ-13 Membranes
Authors: Taherizadeh Alireza , Simon Adrian , Richter Hannes , Stelter Michael , Voigt Ingolf
Published: September 2024

This study investigated the effect of different synthesis parameters including pre- and post-hydrothermal treatment on the formation of a high-quality SSZ-13 membrane layer. The membranes were identified initially by the gas tightness test, then were characterized by single gas permeation measurements applying H2, He, CO2, N2, CH4, and SF6 at room temperature. The results showed how each parameter affects the performance of the membrane, including structural defects in the formed selective layer, CO2 permeance, and the ideal CO2/CH4 permselectivity. This work focused on optimizing these parameters. An ideal CO2/CH4 permselectivity of up to 122 with CO2 permeance of ~3.72 × 10−6 [mol/(m2sPa)] and CO2/CH4 selectivity of 111 with CO2 permeance of 8.5 × 10−7 [mol/(m2sPa)] in an equimolar mixture at room temperature and pressure drop of 0.15 MPa was achieved. This is one of the highest performances compared to other publications for SSZ-13 or all-Si membranes.
A Review of Smart Photovoltaic Systems Which Are Using Remote-Control, AI, and Cybersecurity Approaches
Authors: Călin (Comșiț) Andreea-Mihaela , Cotfas Daniel Tudor , Cotfas Petru Adrian
Published: September 2024

In recent years, interest in renewable energy and photovoltaic systems has increased significantly. The design and implementation of photovoltaic systems are various, and they are in continuous development due to the technologies used. Photovoltaic systems are becoming increasingly complex due to the constantly changing needs of people, who are using more and more intelligent functions such as remote control and monitoring, power/energy prediction, and detection of broken devices. Advanced remote supervision and control applications use artificial intelligence approaches and expose photovoltaic systems to cyber threats. This article presents a detailed examination of the applications of various remote-control, artificial intelligence, and cybersecurity techniques across a diverse range of solar energy sources. The discussion covers the latest technological innovations, research outcomes, and case studies in the photovoltaics field, as well as potential challenges and the possible solutions to these challenges.
Isolation and Characterization of Novel Oligomeric Proanthocyanidins in Chokeberries Using High-Resolution Mass Spectrometry and Investigation of Their Antioxidant Potential
Authors: Meiners Amelie , Hübner Florian , Esselen Melanie
Published: September 2024

Chokeberries, which belong to the rose family (Rosaceae), have received increasing research attention due to their high content of secondary metabolites, especially oligomeric proanthocyanidins (OPCs). OPC-rich extracts are attributed to various positive health effects, including antioxidant or anti-inflammatory properties, which is why they are sold as food supplements. However, knowledge about the antioxidant properties of single OPCs is quite limited. Several separation steps with different separation techniques were performed to isolate OPCs from a pre-produced extract. More than 90 analytes were detected in the enriched fractions, which include eight OPCs, four cinchonains and one hexoside, including their respective isomers. For the characterization of the OPCs, high-resolution mass spectrometry coupled with liquid chromatography (LC-HRMS) was used. Based on the fragment spectra of the MS2 experiments, conclusions about the fragmentation pathways and the structure of six new OPCs could be drawn. After isolating trimers, tetramers and pentamers, it was possible to test the antioxidant effect in relation to the individual degrees of polymerization (DP) or structures. The Trolox-equivalent antioxidant capacity (TEAC) test showed that all OPCs investigated exhibit antioxidant effects and a first correlation between the antioxidant effect and the DP could be postulated, which suggests new possibilities for the design of food supplements.
Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism
Authors: Wang Jubo , Wang Ruixin
Published: September 2024

Drone area coverage primarily involves using unmanned aerial vehicles (UAVs) for extensive monitoring, surveying, communication, and other tasks over specific regions. The significance and value of this technology are multifaceted. Firstly, UAVs can rapidly and efficiently reach remote or inaccessible areas to perform tasks such as terrain mapping, disaster monitoring, or search and rescue, significantly enhancing response speed and execution efficiency. Secondly, drone area coverage in agricultural monitoring, forestry conservation, and urban planning offers high-precision data support, aiding scientists and decision-makers in making more accurate judgments and decisions. Additionally, drones can serve as temporary communication base stations in areas with poor communication, ensuring the transfer of crucial information. Drone area coverage technology is vital in improving work efficiency, reducing costs, and strengthening decision support. This paper aims to solve the optimization problem of multi-UAV area coverage flight path planning to enhance system efficiency and task execution capability. For multi-center optimization problems, a region decomposition method based on the Voronoi graph is designed, transforming the multi-UAV area coverage issue into the single-UAV area coverage problem, greatly simplifying the complexity and computational process. For the single-UAV area coverage problem and its corresponding area, this paper contrives a convolutional neural network with the channel and spatial attention mechanism (CSAM) to enhance feature fusion capability, enabling the model to focus on core features for solving single-UAV path selection and ultimately generating the optimal path. Simulation results demonstrate that the proposed method achieves excellent performance.
Possibilities of Increasing the Ampacity of Overhead Lines Using High-Temperature Low-Sag Conductors in the Electric Power System of the Slovak Republic
Authors: Margita František , Beňa Ľubomír , Malska Wiesława , Pijarski Paweł
Published: September 2024

The examination of ampacity in overhead transmission lines offers a comprehensive overview, covering its definition, thermal evaluation methodologies, standards, practical applications, and the potential of high-temperature low-sag (HTLS) conductors. By navigating through static and dynamic approaches to thermal evaluation and detailing methodologies prescribed by international standards like those from CIGRE and IEEE, this study provides a solid understanding of how ampacity is determined and optimized. Furthermore, the exploration of HTLS conductors introduces a forward-looking perspective on enhancing transmission capacity while mitigating the need for extensive infrastructure modifications. Through an analysis of regional considerations and preferences using Slovak standards and CIGRE Technical Brochure 601, insights are provided into how environmental factors influence transmission line performance. The analysis of the transition from traditional ACSR (Aluminium Conductor Steel-Reinforced) to advanced ACCC (Aluminium Conductor Composite Core) conductors demonstrates the tangible benefits of adopting advanced conductor technologies in real-world scenarios.
SemiPolypSeg: Leveraging Cross-Pseudo Supervision and Contrastive Learning for Semi-Supervised Polyp Segmentation
Authors: Guo Ping , Liu Guoping , Liu Huan
Published: September 2024

The colonoscopy is the foremost technique for detecting polyps, where accurate segmentation is crucial for effective diagnosis and surgical preparation. Nevertheless, contemporary deep learning-based methods for polyp segmentation face substantial hurdles due to the large amount of labeled data required. To address this, we introduce an innovative semi-supervised learning framework based on cross-pseudo supervision (CPS) and contrastive learning, termed Semi-supervised Polyp Segmentation (SemiPolypSeg), which requires only limited labeled data. First, a new segmentation architecture, the Hybrid Transformer–CNN Segmentation Network (HTCSNet), is proposed to enhance semantic representation and segmentation performance. HTCSNet features a parallel encoder combining transformers and convolutional neural networks, as well as an All-MLP decoder with skip connections to streamline feature fusion and enhance decoding efficiency. Next, the integration of CPS in SemiPolypSeg enforces output consistency across diverse perturbed datasets and models, guided by the consistency loss principle. Finally, patch-wise contrastive loss discerns feature disparities between positive and negative sample pairs as delineated by the projector. Comprehensive evaluation demonstrated our method’s superiority over existing state-of-the-art semi-supervised segmentation algorithms. Specifically, our method achieved Dice Similarity Coefficients (DSCs) of 89.68% and 90.62% on the Kvasir-SEG dataset with 15% and 30% labeled data, respectively, and 89.72% and 90.06% on the CVC-ClinicDB dataset with equivalent ratios.
Tailoring Zinc Oxide Nanoparticles via Microwave-Assisted Hydrothermal Synthesis for Enhanced Antibacterial Properties
Authors: Doicin Irina Elena , Preda Manuela Daniela , Neacsu Ionela Andreea , Ene Vladimir Lucian , Birca Alexandra Catalina , Vasile Bogdan Stefan , Andronescu Ecaterina
Published: September 2024

In recent years, significant advancements in nanotechnology have facilitated the synthesis of zinc oxide (ZnO) nanoparticles with tailored sizes and shapes, offering versatile applications across various fields, particularly in biomedicine. ZnO’s multifunctional properties, such as semiconductor behavior, luminescence, photocatalytic activity, and antibacterial efficacy, make it highly attractive for biomedical applications. This study focuses on synthesizing ZnO nanoparticles via the microwave-assisted hydrothermal method, varying the precursor concentrations (0.3488 mol/L, 0.1744 mol/L, 0.0872 mol/L, 0.0436 mol/L, and 0.0218 mol/L) and reaction times (15, 30, and 60 min). Characterization techniques, including X-ray diffraction, scanning electron microscopy, transmission electron microscopy, BET surface area analysis, and Fourier transform infrared spectroscopy were employed to assess the structural, morphological, and chemical properties. The predominant morphology is observed to be platelets, which exhibit a polygonal shape with beveled corners and occasionally include short rod-like inserts. The thickness of the platelets varies between 10 nm and 50 nm, increasing with the concentration of Zn2+ in the precursor solution. Preliminary antimicrobial studies indicated that all strains (S. aureus, E. coli, and C. albicans) were sensitive to interaction with ZnO, exhibiting inhibition zone diameters greater than 10 mm, particularly for samples with lower precursor concentrations. Cell viability studies on human osteoblast cells demonstrated good compatibility, affirming the potential biomedical applicability of synthesized ZnO nanoparticles. This research underscores the influence of synthesis parameters on the properties of ZnO nanoparticles, offering insights for optimizing their design for biomedical applications.
Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength
Authors: Zhang Rui , Zhou Jian , Wang Zhenyu
Published: September 2024

Given the critical role of true triaxial strength assessment in underground rock and soil engineering design and construction, this study explores sandstone true triaxial strength using data-driven machine learning approaches. Fourteen distinct sandstone true triaxial test datasets were collected from the existing literature and randomly divided into training (70%) and testing (30%) sets. A Multilayer Perceptron (MLP) model was developed with uniaxial compressive strength (UCS, σc), intermediate principal stress (σ2), and minimum principal stress (σ3) as inputs and maximum principal stress (σ1) at failure as the output. The model was optimized using the Harris hawks optimization (HHO) algorithm to fine-tune hyperparameters. By adjusting the model structure and activation function characteristics, the final model was made continuously differentiable, enhancing its potential for numerical analysis applications. Four HHO-MLP models with different activation functions were trained and validated on the training set. Based on the comparison of prediction accuracy and meridian plane analysis, an HHO-MLP model with high predictive accuracy and meridional behavior consistent with theoretical trends was selected. Compared to five traditional strength criteria (Drucker–Prager, Hoek–Brown, Mogi–Coulomb, modified Lade, and modified Weibols–Cook), the optimized HHO-MLP model demonstrated superior predictive performance on both training and testing datasets. It successfully captured the complete strength variation in principal stress space, showing smooth and continuous failure envelopes on the meridian and deviatoric planes. These results underscore the model’s ability to generalize across different stress conditions, highlighting its potential as a powerful tool for predicting the true triaxial strength of sandstone in geotechnical engineering applications.
Automatizing Automatic Controller Design Process: Designing Robust Automatic Controller under High-Amplitude Disturbances Using Particle Swarm Optimized Neural Network Controller
Authors: Gökçe Celal Onur
Published: September 2024

In this study, a novel approach of designing automatic control systems with the help of AI tools is proposed. Given plant dynamics, expected references, and expected disturbances, the design of an optimal neural network-based controller is performed automatically. Several common reference types are studied including step, square, sine, sawtooth, and trapezoid functions. Expected reference–disturbance pairs are used to train the system for finding optimal neural network controller parameters. A separate test set is used to test the system for unexpected reference–disturbance pairs to show the generalization performance of the proposed system. Parameters of a real DC motor are used to test the proposed approach. The real DC motor’s parameters are estimated using a particle swarm optimization (PSO) algorithm. Initially, a proportional–integral (PI) controller is designed using a PSO algorithm to find the simple controller’s parameters optimally and automatically. Starting with the neural network equivalent of the optimal PI controller, the optimal neural network controller is designed using a PSO algorithm for training again. Simulations are conducted with estimated parameters for a diverse set of training and test patterns. The results are compared with the optimal PI controller’s performance and reported in the corresponding section. Encouraging results are obtained, suggesting further research in the proposed direction. For low-disturbance scenarios, even simple controllers can have acceptable performance, but the real quality of a proposed controller should be shown under high-amplitude and difficult disturbances, which is the case in this study. The proposed controller shows higher performance, especially under high disturbances, with an 8.6% reduction in error rate on average compared with the optimal PI controller, and under high-amplitude disturbances, the performance difference is of more than 2.5 folds.
Effect of Pulsed Electric Field on the Drying Kinetics of Apple Slices during Vacuum-Assisted Microwave Drying: Experimental, Mathematical and Computational Intelligence Approaches
Authors: Rashvand Mahdi , Nadimi Mohammad , Paliwal Jitendra , Zhang Hongwei , Feyissa Aberham Hailu
Published: September 2024

One of the challenges in the drying process is decreasing the drying time while preserving the product quality. This work aimed to assess the impact of pulsed electric field (PEF) treatment with varying specific energy levels (15.2–26.8 kJ/kg) in conjunction with a microwave vacuum dryer (operating at energy levels of 100, 200 and 300 W) on the kinetics of drying apple slices (cv. Gravenstein). The findings demonstrated a notable reduction in the moisture ratio with the application of pulsed electric field treatment. Based on the findings, implementing PEF reduced the drying time from 4.2 to 31.4% compared to the untreated sample. Moreover, two mathematical models (viz. Page and Weibull) and two machine learning techniques (viz. artificial neural network and support vector regression) were used to predict the moisture ratio of the dried samples. Page’s and Weibull’s models predicted the moisture ratios with R2 = 0.958 and 0.970, respectively. The optimal topology of machine learning to predict the moisture ratio was derived based on the influential parameters within the artificial neural network (i.e., training algorithm, transfer function and hidden layer neurons) and support vector regression (kernel function). The performance of the artificial neural network (R2 = 0.998, RMSE = 0.038 and MAE = 0.024) surpassed that of support vector regression (R2 = 0.994, RMSE = 0.012 and MAE = 0.009). Overall, the machine learning approach outperformed the mathematical models in terms of performance. Hence, machine learning can be used effectively for both predicting the moisture ratio and facilitating online monitoring and control of the drying processes. Lastly, the attributes of the dried apple slices, including color, mechanical properties and sensory analysis, were evaluated. Drying apple slices using PEF treatment and 100 W of microwave energy not only reduces drying time but also maintains the chemical properties such as the total phenolic content, total flavonoid content, antioxidant activity), vitamin C, color and sensory qualities of the product.
A Deep-Learning-Based Approach to the Classification of Fire Types
Authors: Refaee Eshrag Ali , Sheneamer Abdullah , Assiri Basem
Published: September 2024

The automatic detection of fires and the determination of their causes play a crucial role in mitigating the catastrophic consequences of such events. The literature reveals substantial research on automatic fire detection using machine learning models. However, once a fire is detected, there is a notable gap in the literature concerning the automatic classification of fire types like solid-material fires, flammable gas fires, and electric-based fires. This classification is essential for firefighters to quickly and effectively determine the most appropriate fire suppression method. This work introduces a benchmark dataset comprising over 1353 manually annotated images, classified into five categories, which is publicly released. It introduces a multiclass dataset based on the types of origins of fires. This work also presents a system incorporating eight deep-learning models evaluated for fire detection and fire-type classification. In fire-type classification, this work focuses on four fire types: solid material, chemical, electrical-based, and oil-based fires. Under the single-level, five-way classification setting, our system achieves its best performance with an accuracy score of 94.48%. Meanwhile, under the two-level classification setting, our system achieves its best performance with accuracy scores of 98.16% for fire detection and 97.55% for fire-type classification, using the DenseNet121 and EffecientNet-b0 models, respectively. The results also indicate that electrical and oil-based fires are the most challenging to detect.
Synergistic Optimization of Coal Power and Renewable Energy Based on Generalized Adequacy
Authors: Yue Hao , Wu Bingqing , Li Shunxin , Zhang Yin , Gao Jin , Zhou Yi , Meng Zijian , Wu Zhaoyuan
Published: September 2024

As China accelerates its transition to a low-carbon society, its power system is facing growing challenges in terms of maintaining adequacy amid a rapidly evolving energy structure. The concept of adequacy, traditionally focused on power capacity and generation, has broadened to include dimensions like flexibility and inertia. Against this backdrop, optimizing the integration of coal power and renewable energy to meet the system’s needs for adequacy, flexibility, and frequency stability has become a critical research area. This paper introduces the concept of “Generalized Adequacy”, expanding the traditional understanding of adequacy, and proposes an optimization model for the coordinated development of coal power and renewable energy based on this concept. This study examines the effects of extreme weather, renewable energy penetration, wind–solar ratios, and generalized adequacy constraints using a case study from a central region of China. The findings reveal that extreme weather conditions drive an increase in photovoltaic installations, while higher renewable energy penetration leads to more wind power installations. Accounting for generalized adequacy constraints can moderate the retirement of coal-fired plants, reducing unnecessary inertia support in normal conditions and ensuring dynamic frequency stability during extreme weather events.
Laser-Induced Breakdown Spectroscopy Detection of Heavy Metal Contamination in Soil Samples from North Birmingham, Alabama
Authors: Adhikari Nirmala , Martyshkin Dmitry , Fedorov Vladimir , Das Deblina , Antony Veena , Mirov Sergey
Published: September 2024

High levels of heavy metal contamination in soil present substantial threats to human health and the environment, leading to severe health problems such as neurotoxicity, cancer, kidney issues, chronic obstructive pulmonary disease, and reduced life expectancy. This research aims to identify and analyze heavy metals in soil samples collected from Superfund sites in North Birmingham, Alabama, specifically in affected areas with zip codes 35207 and 35217 and control area 35214. These affected areas were previously used for mining, coal-fired power plants, coke furnaces, smelting, and other potential sources of heavy metal pollution. Laser-induced breakdown spectroscopy (LIBS) was employed to study 60 soil samples systematically collected from affected and control areas. We found that by using LIBS, we could detect arsenic (As), lead (Pb), and manganese (Mn) in all soil samples from the affected areas. The limit of detection (LoD) was 29.5 mg/kg for Pb, 95.5 mg/kg for As, and 327 mg/kg for Mn using specific parameters of the detection system and/or argon gas purging at atmospheric pressure. The results were compared with ICP-MS measurements to validate the accuracy of the LIBS findings. The data showed good linearity for all calibration data at relatively low concentrations and a good correlation with ICP-MS measurements.
Extraction of Soluble Phenols and Flavonoids from Native Mexican Pigmented Corn Kernel Powder by Ultrasound: Optimization Process Using Response Surface Methodology
Authors: Hernández-Estrada Salvador , Anaya-Esparza Luis Miguel , González-Torres Sughey , Hernández-Villaseñor Luis Alfonso , Gómez-Rodríguez Víctor Manuel , Ramírez-Vega Humberto , Villagrán Zuamí , Ruvalcaba-Gómez José Martín , Rodríguez-Barajas Noé , Montalvo-González Efigenia
Published: September 2024

This study focused on optimizing ultrasound-assisted extraction (UAE) conditions (XPC: pulse cycle of 1:1, 2:1, and 3:1 s on/off; XUP: ultrasound power of 80, 90, and 100%; and XET: extraction time of 2, 4, and 6 min) for maximizing the content of soluble phenols (TSPs) and flavonoids (FLAs) from a native Mexican pigmented corn kernel powder through response surface methodology (RSM). Under the Box–Behnken design conditions, the UAE of TSPs ranged from 27.72 to 34.87 mg/g, while FLA content ranged from 16.59 to 27.28 mg/g. The highest content for TSPs was under 4 min XET, 1:1 s on/off XPC, and 100% XUP, while for flavonoids it was under 6 min XET, 2:1 s on/off XPC, and 80% XUP. According to RSM analysis, the optimal UAE conditions for TSPs were found to be XET 3.15 min, 1.58 s on/off XPC, and 100% XUP, and an XET of 4.18 min, 3 s on/off XPC, and 80% XUP were the best experimental conditions for FLAs with a predictive TSP of 35.07 mg/g and FLA of 27.51 mg/g. These data were adjusted in a second-order polynomial model and experimentally validated (TSP = 34.06 mg/g and 27.04 mg/g). Furthermore, the extracts demonstrated antioxidant activity (ABTS, FRAP, and DPPH methods) for optimal UAE for TSPs and FLAs. The antioxidant extract from the native Mexican pigmented corn kernel powder can be used for diverse industrial applications. Thus, the UAE is an effective and sustainable technology for recovering bioactive compounds from maize-based materials.
Innovative Speech-Based Deep Learning Approaches for Parkinson’s Disease Classification: A Systematic Review
Authors: van Gelderen Lisanne , Tejedor-García Cristian
Published: September 2024

Parkinson’s disease (PD), the second most prevalent neurodegenerative disorder worldwide, frequently presents with early-stage speech impairments. Recent advancements in Artificial Intelligence (AI), particularly deep learning (DL), have significantly enhanced PD diagnosis through the analysis of speech data. Nevertheless, the progress of research is restricted by the limited availability of publicly accessible speech-based PD datasets, primarily due to privacy concerns. The goal of this systematic review is to explore the current landscape of speech-based DL approaches for PD classification, based on 33 scientific works published between January 2020 and March 2024. We discuss their available resources, capabilities, and potential limitations, and issues related to bias, explainability, and privacy. Furthermore, this review provides an overview of publicly accessible speech-based datasets and open-source material for PD. The DL approaches identified are categorized into end-to-end (E2E) learning, transfer learning (TL), and deep acoustic feature extraction (DAFE). Among E2E approaches, Convolutional Neural Networks (CNNs) are prevalent, though Transformers are increasingly popular. E2E approaches face challenges such as limited data and computational resources, especially with Transformers. TL addresses these issues by providing more robust PD diagnosis and better generalizability across languages. DAFE aims to improve the explainability and interpretability of results by examining the specific effects of deep features on both other DL approaches and more traditional machine learning (ML) methods. However, it often underperforms compared to E2E and TL approaches.
Infrared Dim and Small Target Detection Based on Local–Global Feature Fusion
Authors: Ling Xiao , Zhang Chuan , Yan Zhijun , Wang Bo , Sheng Qinghong , Li Jun
Published: September 2024

Infrared detection, known for its robust anti-interference capabilities, performs well in all weather conditions and various environments. Its applications include precision guidance, surveillance, and early warning systems. However, detecting infrared dim and small targets presents challenges, such as weak target features, blurred targets with small area percentages, missed detections, and false alarms. To address the issue of insufficient target feature information, this paper proposes a high-precision method for detecting dim and small infrared targets based on the YOLOv7 network model, which integrates both local and non-local bidirectional features. Additionally, a local feature extraction branch is introduced to enhance target information by applying local magnification at the feature extraction layer allowing for the capture of more detailed features. To address the challenge of target and background blending, we propose a strategy involving multi-scale fusion of the local branch and global feature extraction. Additionally, the use of a 1 × 1 convolution structure and concat operation reduces model computation. Compared to the baseline, our method shows a 2.9% improvement in mAP50 on a real infrared dataset, with the detection rate reaching 93.84%. These experimental results underscore the effectiveness of our method in extracting relevant features while suppressing background interference in infrared dim and small target detection (IDSTD), making it more robust.
A Backpropagation-Based Algorithm to Optimize Trip Assignment Probability for Long-Term High-Speed Railway Demand Forecasting in Korea
Authors: Kwak Ho-Chan
Published: September 2024

In Korea, decisions for high-speed railway (HSR) construction are made based on long-term demand forecasting. A calibration process that simulates current trip patterns is an important step in long-term demand forecasting. However, a trial-and-error approach based on iterative parameter adjustment is used for calibration, resulting in time inefficiency. In addition, the all-or-nothing-based optimal strategy algorithm (OSA) used in HSR trip assignment has limited accuracy because it assigns all trips from a zone with multiple accessible stations to only one station. Therefore, this study aimed to develop a backpropagation-based algorithm to optimize trip assignment probability from a zone to multiple accessible HSR stations. In this algorithm, the difference between the estimated volume calculated from the trip assignment probability and observed volumes was defined as loss, and the trip assignment probability was optimized by repeatedly updating in the direction of the reduced loss. The error rate of the backpropagation-based algorithm was compared with that of the OSA using KTDB data; the backpropagation-based algorithm had lower errors than the OSA for most major HSR stations. It was especially superior when applied to areas with multiple HSR stations, such as the Seoul metropolitan area. This algorithm will improve the accuracy and time efficiency of long-term HSR demand forecasting.
Multi-Objective Optimization Design of Porous Gas Journal Bearing Considering the Fluid–Structure Interaction Effect
Authors: Duran-Castillo Azael , Jauregui-Correa Juan Carlos , Benítez-Rangel Juan Primo , Dominguez-Gonzalez Aurelio , De Santiago Oscar Cesar
Published: September 2024

The performance of the porous gas bearing depends on the geometric characteristics, material, fluid properties, and the properties of the porous media, which is a restrictor that controls the gas flow. Its application in industrial environments must support higher loads, higher supply pressure, and, consequently, higher pressure in the lubricant fluid film. Because porous media has a relatively low elastic modulus, it is necessary to consider its deformation when designing porous gas bearings. The design of porous gas bearings is a multi-objective problem in engineering because the optimization objectives commonly are to maximize the load capacity or static stiffness coefficient and minimize the airflow; these objectives conflict. This work presents a multi-objective optimization algorithm based on the nature-inspired Flower Pollination Algorithm enhanced with Non-Dominated Sorting Genetic Algorithm II. The algorithm is applied to optimize the design of a porous gas bearing, maximizing the resultant force and the static stiffness coefficient and minimizing the airflow. The results indicate a better performance of the Multi-Objective Flower Pollination Algorithm than the Multi-Objective Cuckoo Search. The results show a relatively short running time of 6 min for iterations and a low number of iterations of 50.
Research on Short-Term Forecasting Model of Global Atmospheric Temperature and Wind in the near Space Based on Deep Learning
Authors: Sun Xingxin , Zhou Chen , Feng Jian , Yang Huiyun , Zhang Yuqiang , Chen Zhou , Xu Tong , Deng Zhongxin , Zhao Zhengyu , Liu Yi , Lan Ting
Published: September 2024

Developing short-term forecasting models for global atmospheric temperature and wind in near space is crucial for understanding atmospheric dynamics and supporting human activities in this region. While numerical models have been extensively developed, deep learning techniques have recently shown promise in improving atmospheric forecasting accuracy. In this study, convolutional long short-term memory (ConvLSTM) and convolutional gated recurrent unit (ConvGRU) neural networks were applied to build for short-term global-scale forecasting model of atmospheric temperature and wind in near space based on the MERRA-2 reanalysis dataset from 2010–2022. The model results showed that the ConvGRU model outperforms the ConvLSTM model in the short-term forecast results. The ConvGRU model achieved a root mean square error in the first three hours of approximately 1.8 K for temperature predictions, and errors of 4.2 m/s and 3.8 m/s for eastward and northward wind predictions on all 72 isobaric surfaces. Specifically, at a higher altitude (on the 1.65 Pa isobaric surface, approximately 70 km above sea level), the ConvGRU model achieved a RMSE of about 2.85 K for temperature predictions, and 5.67 m/s and 5.17 m/s for eastward and northward wind. This finding is significantly meaningful for short-term temperature and wind forecasts in near space and for exploring the physical mechanisms related to temperature and wind variations in this region.
The Genus Chaetogaster Baer, 1827 (Annelida, Clitellata) in Switzerland: A First Step toward Cataloguing Its Molecular Diversity and Description of New Species on a DNA Sequence Basis
Authors: Vivien Régis , Lafont Michel , Issartel Colin , Ferrari Benoît J. D. , Martin Patrick
Published: September 2024

The genus Chaetogaster belongs to the subfamily Naidinae (Naididae); it includes mostly species of small size and is diverse and abundant in surface coarse sediments in streams. The aim of the present study is to initiate an inventory of lineages (=species) of Chaetogaster in Switzerland. We used 135 specimens collected at 6 sites in 4 streams of 4 cantons. We sequenced the cytochrome c oxidase (COI) gene from all specimens and ITS2 and rDNA 28S from all or several specimens of each lineage that was delimited using COI data, and preserved, for morphological identifications, the anterior part of almost all sequenced specimens. We were able to delimit, based on the calculation of genetic distances and analyses of single-locus data, one lineage for Chaetogaster diaphanus (Gruithuisen, 1828), three within Chaetogaster diastrophus (Gruithuisen, 1828), one for Chaetogaster langi Brestcher, 1896, one for Chaetogaster setosus Svetlov, 1925, and three unidentified Chaetogaster spp. Two lineages of Chaetogaster spp. could correspond to a new morphological group, but this should be confirmed in more specimens. We proposed a new identification key of the nominal species and described the three C. diastrophus lineages and two Chaetogaster spp. as new species. The prospects of the present work are to complete the data of the molecular diversity of this genus in Switzerland and to describe the newly found Chaetogaster species on a molecular/morphological basis.
Novel Fatty Acid Biomarkers in Psoriasis and the Role of Modifiable Factors: Results from the METHAP Clinical Study
Authors: Sarandi Evangelia , Krueger-Krasagakis Sabine , Tsoukalas Dimitris , Evangelou George , Sifaki Maria , Kyriakakis Michael , Paramera Efstathia , Papakonstantinou Evangelos , Rudofsky Gottfried , Tsatsakis Aristides
Published: September 2024

Psoriasis is a chronic, immune-mediated skin condition with significant metabolic complications. Although lipid metabolism is linked to its pathogenesis, reliable biomarkers and the impact of modifiable factors remain underexplored. The aim of the present study was to identify potential biomarkers, study the affected metabolic networks, and assess the role of dietary and lifestyle factors in psoriasis. Plasma samples from 56 patients with psoriasis and 49 healthy controls were analyzed, as part of the Metabolic Biomarkers in Hashimoto’s Thyroiditis and Psoriasis (METHAP) clinical trial. Using Gas Chromatography-Mass Spectrometry 23 fatty acids and their ratios were quantified, revealing significant changes in psoriasis. Specifically, lower levels of α-linoleic acid (C18:3n3), linoleic acid (C18:2n6), and gamma-linolenic acid (C18:3n6) were observed along with higher levels of eicosatrienoic acid (C20:3n3), eicosapentaenoic acid (C20:5n3), and erucic acid (C22:1n9). Total polyunsaturated fatty acids (PUFA) were significantly decreased, and the ratio of saturated to total fatty acids (SFA/Total) was increased in psoriasis (p-values < 0.0001). Linear regression identified α-linoleic acid, linoleic acid, eicosatrienoic acid, and eicosapentaenoic acid as potential biomarkers for psoriasis, adjusting for demographic, dietary, and lifestyle confounders. Network analysis revealed key contributors in the metabolic reprogramming of psoriasis. These findings highlight the association between psoriasis and fatty acid biomarkers of inflammation, insulin resistance and micronutrients deficiency, suggesting their potency in disease management.
Analysis of Multispectral Indices as a Tool for Segmenting and Quantifying the Seaweed Kappaphycus alvarezii in a Commercial Cultivation System
Authors: Innocentini Marcel M. , Rodrigues Ellen F. , Mathion Juliano K. , Caritá Edilson Carlos , Simão Lisandro , Marins Mozart
Published: September 2024

The red seaweed Kappaphycus alvarezii is an economically important gelling agent κappa carrageenan source. Phytochemical analysis has pointed to the presence of various other inorganic and organic compounds, which are expanding the application of biomass as a biostimulant in the agroindustry and as a source of new bioactive molecules in the food, chemical, and pharmaceutical industries. Native to Southeast Asia, K. alvarezii has been introduced as an exotic species in Brazil for commercial large-scale farming. Nowadays, legal farming areas are located in the South and on the South-East coast, but with initiatives to be authorized in the country’s Northeast. The biomass yield in a large-scale farming system can be affected by cultivation techniques and environmental stressors, such as temperature, salinity, water quality, disease, and predators. The use of high-resolution images obtained with unmanned aerial vehicles (UAV or drones) is becoming a popular technology in agriculture, and it has the potential to be employed in seaweed farming to extract a variety of variables and features to predict biomass yield throughout the cultivation period. The present study was conducted to analyze and select multispectral indices obtained from images collected by drone for the detection and quantification of K. alvarezii in a commercial cultivation environment in Brazil. Frequency analysis of pixel values, statistical analyses, and visual interpretations for 24 pre-selected indices was applied according to scores attributed to the efficiency of image segmentation. This analysis resulted in the selection of four indices (ABDI1, ABDI2, CIG, and GNDVI) as the best ones for the segmentation of images in the K. alvarezii commercial farms analyzed. The data obtained are the first step in improving the analysis process of images generated by drones, which will facilitate decision-making and better management, and help scale-up K. alvarezii farming in Brazil.
New Neutrophil Parameters in Diseases with Various Inflammatory Processes
Authors: Rutkowska Elżbieta , Kwiecień Iwona , Raniszewska Agata , Sokołowski Rafał , Bednarek Joanna , Jahnz-Różyk Karina , Chciałowski Andrzej , Rzepecki Piotr
Published: September 2024

The neutrophils evaluation seems interesting in the initial qualifications of patients with various inflammatory processes. In this study, we presented analysis of neutrophils and new parameters of the complexity (NEUT-GI, NE-WX), maturation (IG), size (NE-FSC, NE-WZ), and neutrophil activities (NEUT-RI, NE-WY) in coronavirus disease 2019 (COVID-19), lung cancer (LC), sarcoidosis (SA), and healthy controls (HCs). Peripheral blood (PB) was collected. The new parameters were examined by the Sysmex XN-1500. The mean absolute value for the IG parameter was the highest in the LC group. The differences in NEUT-RI value between COVID-19 and the HC group were observed. No significant differences were noticed between groups in the NEUT-GI granularity parameter. Neutrophil size assessed by NE-FSC parameter was reduced in all groups compared to HCs. The values of complexity (NE-WX), fluorescence (NE-WY), and size (NE-WZ) were the lowest in the HCs, whereas the highest median proportions of NE-WX, NE-WY, and NE-WZ were in LC patients. Patients from the SA group differed significantly from the HC group only for the NE-WZ parameter. We showed the usefulness of neutrophil parameters and their reactivity, morphology, and exhaustion. A more detailed analysis of blood counts may reveal trends that indicate a disease-specific immune response.
Assessment of Platelet Aggregation and Thrombin Generation in Patients with Familial Chylomicronemia Syndrome Treated with Volanesorsen: A Cross-Sectional Study
Authors: Calcaterra Ilenia Lorenza , Santoro Renata , Vitelli Nicoletta , Cirillo Ferdinando , D’Errico Guido , Guerrino Cornelia , Cardiero Giovanna , Di Taranto Maria Donata , Fortunato Giuliana , Iannuzzo Gabriella , Di Minno Matteo Nicola Dario
Published: September 2024

Background: The antisense oligonucleotide against APOC3 mRNA volanesorsen was recently introduced to treat Familial Chylomicronemia Syndrome (FCS). Cases of decreased platelet count are reported among patients treated with volanesorsen. The aim of the study was to evaluate platelet function and thrombin generation (TG) assessment in FCS patients receiving volanesorsen. We performed a cross-sectional study on FCS patients treated with volanesorsen. Methods: Changes in platelet count PLC were assessed from baseline to Tw12 and Tw36. To assess TG, samples were processed by CAT (with PPP-reagent LOW). The results were expressed by the thrombogram graphic (thrombin variation over time); LagTime; endogenous thrombin potential (ETP); peak; time to reach peak (ttpeak), StartTail and Velocity Index. Platelet aggregation was assessed by testing different agonists using the turbidimetry method. Results: Four FCS patients and four matched healthy controls were included in the present study. Changes in PLC were 30% at Tw12 and 34% at Tw36. Thrombin generation results showed values in the normal range (for patients and controls, respectively, LagTime:10.42 ± 4.40 and 9.25 ± 0.99; ttPeak:14.33 ± 4.01 and 13.10 ± 0.67; StartTail: 32.13 ± 3.54 and 29.46 ± 1.69; Velocity Index: 20.21 ± 3.63 and 33.05 ± 13.21; ETP: 599.80 ± 73.47 and 900.2 ± 210.99; peak value: 76.84 ± 1.07 and 123.30 ± 39.45) and no significant difference between cases and controls. Platelet aggregation test showed values in range, with no significant difference compared to healthy controls. Conclusions: Our study showed for the first time that no significant changes in general hemostasis assessed by TG and in platelet function were observed in FCS patients receiving volanesorsen.
Neuropathological mRNA Expression Changes after Single Mild Traumatic Brain Injury in Pigs
Authors: Grovola Michael R. , Cullen D. Kacy
Published: September 2024

Traumatic brain injury (TBI) is a public health concern, with an estimated 42 million cases globally every year. The majority of TBIs are mild TBIs, also known as concussion, and result from the application of mechanical forces on the head. Most patients make a complete recovery and mortality is rare; therefore, studies investigating cellular changes after mild TBI in a clinical setting are limited. To address this constraint, our group utilized a pig model of closed-head rotational acceleration-induced TBI, which recreated the biomechanical loading parameters associated with concussion on a large gyrencephalic brain similar to humans. While our previous research has focused on immunohistochemical characterization of neuropathology, the current study utilized transcriptomic assays to evaluate an array of TBI-induced neurodegenerative analytes. Pigs subjected to mild TBI were survived for 3 days post-injury (DPI) (n = 3), 30 DPI (n = 3), or 1 year post-injury (YPI) (n = 3) and compared to animals undergoing a sham procedure (n = 8). RNA was isolated from whole coronal sections of fixed tissue and multiplexed on a Nanostring neuropathology panel. Differential expression analysis revealed 11 differentially expressed genes at 3 DPI versus sham, including downregulation of the synaptotagmin calcium sensor gene (SYT1), upregulation of the neurofibromin gene (NF1), and upregulation of the Alzheimer’s disease-associated receptor gene (SORL1). There were no differentially expressed genes at 30 DPI or 1 YPI compared to shams. Additionally, high-magnitude undirected global significance scores (GSS) were detected at 3 DPI for chromatin modification and autophagy gene sets, and at 30 DPI for cytokine gene sets, while many dysregulated gene sets were highlighted by directed GSSs out to 1 YPI. This study adds to a growing body of literature on transcriptomic changes in a clinically relevant large animal model of closed-head TBI, which highlights potential therapeutic targets following mild TBI.
Preservation of Mitochondrial Function by SkQ1 in Skin Fibroblasts Derived from Patients with Leber’s Hereditary Optic Neuropathy Is Associated with the PINK1/PRKN-Mediated Mitophagy
Authors: Xu Jin , Li Yan , Yao Shun , Jin Xiuxiu , Yang Mingzhu , Guo Qingge , Qiu Ruiqi , Lei Bo
Published: September 2024

Increased or altered mitochondrial ROS production in the retinal ganglion cells is regarded as the chief culprit of the disease-causing Leber’s hereditary optic neuropathy (LHON). SkQ1 is a rechargeable mitochondria-targeted antioxidant with high specificity and efficiency. SkQ1 has already been used to treat LHON patients, and a phase 2a randomized clinical trial of SkQ1 has demonstrated improvements in eyesight. However, the underlying mechanism of SkQ1 in LHON remains unclear. This study aimed to assess the effects and molecular mechanism of SkQ1 in the preservation of mitochondrial function using skin fibroblasts derived from LHON patients. Our study found that SkQ1 could reduce ROS production and stabilize the mitochondrial membrane. Mechanistically, through network pharmacology and molecular docking, we identified the key targets of SkQ1 as SOD2 and PINK1, which play crucial roles in redox and mitophagy. SkQ1 interacted with PINK1 and downregulated its expression to balance mitochondrial homeostasis. Collectively, the findings of our study reveal that by regulating PINK1/PRKN-mediated mitophagy, SkQ1 preserves mitochondrial function in LHON fibroblasts. The data indicate that SkQ1 may be a novel therapeutic intervention to prevent the progression of LHON.