bluuebunny/crossref_metadata_2025 huggingface dataset:
AFGN: Adaptive Filtering Graph Neural Network for Few-Shot Learning
Authors: Tan Qi , Lai Jialun , Zhao Chenrui , Wu Zongze , Zhang Xie
Published: October 2024

The combination of few-shot learning and graph neural networks can effectively solve the issue of extracting more useful information from limited data. However, most graph-based few-shot models only consider the global feature information extracted by the backbone during the construction process, while ignoring the dependency information hidden within the features. Additionally, the essence of graph convolution is the filtering of graph signals, and the majority of graph-based few-shot models construct fixed, single-property filters to process these graph signals. Therefore, in this paper, we propose an Adaptive Filtering Graph Convolutional Neural Network (AFGN) for few-shot classification. AFGN explores the hidden dependency information within the features, providing a new approach for constructing graph tasks in few-shot scenarios. Furthermore, we design an adaptive filter for the graph convolution of AFGN, which can adaptively adjust its strategy for acquiring high and low-frequency information from graph signals based on different few-shot episodic tasks. We conducted experiments on three standard few-shot benchmarks, including image recognition and fine-grained categorization. The experimental results demonstrate that our AFGN performs better compared to other state-of-the-art models.
Effect of Pre-Treatment, Treatment, and Extraction Technologies on the Bioactive Substances of Coriander
Authors: Mouhoubi Khokha , Brahmi Fatiha , Boulekbache-Makhlouf Lila , Ayouaz Siham , Abbou Amina , Madani Khodir , Mateos-Aparicio Inmaculada , Garcia-Alonso Alejandra
Published: October 2024

Herbs and spices, with their wealth of bioactive compounds, are widely used in food, medicine, and cosmetics. Among them, coriander (Coriandrum sativum L.) is particularly valued for its medicinal and culinary properties. Growing consumer and industrial interest in natural products has led to the development of modern, environmentally friendly extraction techniques designed to improve the yield and quality of extracts while reducing time, energy, and solvent consumption. These processes make it possible to obtain optimal quantities of active compounds, thereby meeting the growing demand for plant-based products. After showing evidence of coriander’s health benefits, this review summarizes research findings on the impact of some treatments and pretreatments on its phytochemical composition. After that, it summarizes different aspects of the use of conventional and non-conventional extraction techniques for coriander’s bioactive constituents, mainly polyphenols and crude and essential oils (EO). Among these methods, microwave-assisted extraction (MAE/MAHD) emerges as one of the most efficient methods, offering higher yields, better-quality extracts, and a significant reduction in energy costs.
FRAMUX-EV: A Framework for Evaluating User Experience in Agile Software Development
Authors: Rojas Luis Felipe , Quiñones Daniela , Cubillos Claudio
Published: October 2024

Agile software development prioritizes customer satisfaction through the continuous delivery of valuable software. However, integrating user experience (UX) evaluations into agile projects remains a significant challenge. Existing proposals address specific stages that apply UX evaluation methods but do not fully consider UX artifacts or UX events for integrating user experience into agile processes. To address this gap and support teams, we propose FRAMUX-EV, a framework for evaluating UX in agile software development using Scrum. FRAMUX-EV introduces seven UX artifacts: (1) UX evaluation methods, (2) UX design system, (3) UX personas, (4) UX responsibilities and roles, (5) UX evaluation repository, (6) UX backlog, and (7) UX sprint backlog; and four UX events: (1) pre-planning UX meeting, (2) pre-review UX meeting, (3) weekly UX meeting, and (4) weekly user meeting. The first version of the framework was developed using a seven-step methodology with a qualitative approach. A survey of 34 practitioners validated the usefulness and ease of integration of FRAMUX-EV components, yielding positive results. These findings suggest the potential of FRAMUX-EV as an interesting proposal for integrating UX into agile software development.
Decarbonizing European Industry: A Novel Technology to Heat Supply Using Waste and Renewable Energy
Authors: Marcos José Daniel , Golpour Iman , Barbero Rubén , Rovira Antonio
Published: October 2024

This study examines the potential for the smart integration of waste and renewable energy sources to supply industrial heat at temperatures between 150 °C and 250 °C, aiming to decarbonize heat demand in European industry. This work is part of a European project (SUSHEAT) which focuses on developing a novel technology that integrates several innovative components: a Stirling cycle high-temperature heat pump (HTHP), a bio-inspired phase change material (PCM) thermal energy storage (TES) system, and a control and integration twin (CIT) system based on smart decision-making algorithms. The objective is to develop highly efficient industrial heat upgrading systems for industrial applications using renewable energy sources and waste heat recovery. To achieve this, the specific heat requirements of different European industries were analyzed. The findings indicate that industrial sectors such as food and beverages, plastics, desalination, textiles, ceramics, pulp and paper, wood products, canned food, agricultural products, mining, and chemicals, typically require process heat at temperatures below 250 °C under conditions well within the range of the SUSHEAT system. Moreover, two case studies, namely the Pelagia and Mandrekas companies, were conducted to validate the effectiveness of the system. An analysis of the annual European heat demand by sector and temperature demonstrated that the theoretical potential heat demand that could be met by the SUSHEAT system is 134.92 TWh annually. Furthermore, an environmental impact assessment estimated an annual significant reduction of 19.40 million tonnes of CO2 emissions. These findings underscore the significant potential of the SUSHEAT system to contribute to the decarbonization of European industry by efficiently meeting heat demand and substantially reducing carbon emissions.
A Simple Method to Evaluate the Bearing Capacity of Concrete-Filled Steel Tubes with Rectangular and Circular Sections: Beams, Columns, and Beam–Columns
Authors: Katsimpini Panagiota , Papagiannopoulos George , Hatzigeorgiou George
Published: October 2024

This study investigates the behavior and capacity of concrete-filled steel tubes (CFTs) with rectangular and circular sections under various loading conditions: axial loads, pure-bending, and combined axial load-bending moments. A finite element-based numerical model is developed and validated against existing experimental data. Using an extensive databank generated from finite element analysis, this research proposes analytical empirical relations for determining the bearing capacity of rectangular and circular composite beams, columns, and beam–columns. These relations provide a direct, concise, and efficient method for calculating the ultimate strength of CFT members, making them valuable for engineering design. Comparisons between the proposed analytical results and previous experimental studies demonstrate the high accuracy of this method in analyzing rectangular and circular CFT member behavior. The findings contribute to a better understanding of CFT structural performance and offer practical tools for designers working with these composite elements.
The Influence of Helium Addition on the Combustion Process in a Hydrogen-Fueled Turbulent Jet Ignition Engine
Authors: Mielcarzewicz Dawid , Pielecha Ireneusz
Published: October 2024

There are considerably fewer requirements for the quality of hydrogen combusted in an engine than its quality for fuel cells. Therefore, the analysis was carried out on the combustion of hydrogen–helium mixtures in an engine with a two-stage combustion system (TJI—Turbulent Jet Ignition). A single-cylinder research engine with a passive and active prechamber was used. A hydrogen–helium mixture was supplied to the main chamber in proportions of 100:0, 90:10, 80:20, 30:70, and 60:40 volume fractions. The prechamber was fueled only with pure hydrogen. Combustion was carried out in the lean charge range (λ = 1.5–3) and at a constant value of the Center of Combustion (CoC = 8–10 deg aTDC). It was found that the helium concentration in the mixture affected the changes in combustion pressure, heat release rate and the amount of heat release. It was observed that increasing the proportion of helium in the mixture by 10% also reduces the IMEP by approximately 10% and reduces the rate of heat release by approximately 20%. In addition, helium influences knock combustion. Limits of MAPO = 1 bar mean assumed that knock combustion occurs in the main chamber at values of λ < 1.9. Increasing the excess air ratio results in a gradual reduction in the temperature of the exhaust gas, which has a very rapid effect on changes in the concentration of nitrogen oxides. Studies carried out on the helium addition in hydrogen fuel indicate that it is possible to use such blends with a partial deterioration of the thermodynamic properties of the two-stage combustion process.
Comparative Study of Temperature and Pressure Variation Patterns in Hydrogen and Natural Gas Storage in Salt Cavern
Authors: Liu Zhongzhong , Liu Yuxuan , Wang Zonghao
Published: October 2024

Clarifying the distribution of temperature and pressure in the wellbore and cavern during hydrogen injection and extraction is crucial for quantitatively assessing cavern stability and wellbore integrity. This paper establishes an integrated flow and heat transfer model for the cavern and wellbore during hydrogen injection and withdrawal, analyzing the variations in temperature and pressure in both the wellbore and the cavern. The temperature and pressure parameters of hydrogen and natural gas within the chamber and wellbore were compared. The specific conclusions are as follows. (1) Under identical injection and withdrawal conditions, the temperature of hydrogen in the chamber was 10 °C higher than that of natural gas, and 16 °C higher in the wellbore. The pressure of hydrogen in the chamber was 2.9 MPa greater than that of natural gas, and 2.6 MPa higher in the wellbore. (2) A comparative analysis was conducted on the impact of surrounding rock’s horizontal and numerical distance on temperature during hydrogen and natural gas injection processes. As the distance from the cavity increases, from 5 to 15 m, the temperature fluctuation in the surrounding rock diminishes progressively, with the temperature effect in the hydrogen storage chamber extending to at least 10 m. (3) The influence of rock thermal conductivity parameters on temperature during the processes of hydrogen injection and natural gas extraction is also compared. The better the thermal conductivity, the deeper the thermal effects penetrate the rock layers, with the specific heat capacity having the most significant impact.
Blocking of Gas–Liquid Coalescing Filters with Accumulated Oil during the On–Off Operation of a Filtration System
Authors: Krasiński Andrzej , Kamocki Szymon , Stor Michał
Published: October 2024

The study aims to eliminate the effect of coalescing filter blocking due to on–off operation by changing the wetting properties of the non-woven fiberglass filter media through their chemical modification with the use of a polydimethylsiloxane (PDMS) solution in hexane and a few commercial products that give the surface oleophobic properties. The best results—high separation efficiency, no redispersion of droplets at the outlet, and low flow resistance—were obtained for materials coated by immersion in a 0.2% PDMS solution, for which a reduction in oleophilicity was found, but the material was not oleophobic and still moderately wetted with the test liquid. The corresponding static contact angle with the VG-46 rotary compressor oil measured on the flat borosilicate glass wafer made of the same material as the fiberglass media was equal to 54° for the PDMS dip-coated surface. Moreover, the good stability of the applied polymer on the material surface was confirmed by the SEM imaging, the FTIR analysis, and maintaining a high performance in multiple tests run for a single coalescing element.
Exploratory Investigation of Head Stability in Children with Cerebral Palsy and Typically Developing Children during a Targeted Stepping Task
Authors: Bailey Harry G. B. , O’Brien Thomas D. , Barton Gabor J. , Bass Alf , Wright David , Pinzone Ornella , Greaves Henrike , Foster Richard J.
Published: October 2024

Children with cerebral palsy (CP) exhibit head instability during simple overground walking, which may comprise sensory input and reduce stepping accuracy. Investigations of head stability during more challenging tasks, where fall risk may be increased, are limited. This study explored differences in head stability between ambulatory children with hemiplegic CP (N = 9) and diplegia (N = 9) (GMFCS I and II) and typically developing (TD) children (N = 8) during a targeted stepping task. All children completed five trials stepping into two successive rectangular floor-based targets whilst walking along an 8 m walkway. Three-dimensional motion capture enabled calculation of head stability and foot placement within and before each target. A two-way mixed-design ANOVA compared differences between all groups and target approach. Children with diplegic CP showed greater sagittal, frontal, and resultant head-to-laboratory and head-to-trunk head instability compared to children with hemiplegic CP and TD children. Anteroposterior foot placement error was significantly greater in children with hemiplegic CP (8.5 ± 5.0 cm) compared to TD children (3.8 ± 1.5 cm). Group differences in head instability were not consistent with group differences in foot placement error. To better understand how head instability might affect fall risk in children with CP, more challenging environments should be tested in future.
Effect of Apple Puree as a Substitute for Fat and Sugar on the Texture and Physical Properties of Muffins
Authors: Dana Huțu , Sonia Amariei
Published: October 2024

In the context of global public health concerns, reducing the fat and sugar content of baked goods has become a major priority. The excessive consumption of sugar and fat is associated with an increased risk of obesity, type II diabetes and cardiovascular disease. This study realizes the optimization of muffin formula by incorporating apple puree as a substitute for sucrose and fat, with the aim of increasing the nutritional value and reducing the caloric content of the product. A comprehensive analysis was performed to evaluate the impact of this substitution on various textural and physical properties of muffins. The firmness, chewiness, cohesiveness, height, volume and weight loss during baking of the muffins were determined. The result of the study showed that in order to obtain optimal textural and physical properties, the sugar and fat in the muffins can be substituted with applesauce in percentages of 34.04% and 43.78%, respectively. This research highlights the potential of apple puree to reduce the sugar and fat content and to lead to obtaining optimal physical and textural properties.
Antimicrobial Action of Lactobacillus spp. Isolated from Yoghurt against Escherichia coli, Salmonella Enteritidis and Listeria monocytogenes: A Pilot Study
Authors: Wiktorczyk-Kapischke Natalia , Grudlewska-Buda Katarzyna , Budzyńska Anna , Chomacka-Gollus Karolina , Gospodarek-Komkowska Eugenia , Skowron Krzysztof
Published: October 2024

Milk has been a dietary staple around the world for centuries. In recent years, consumer interest in healthy foods and organic products has increased due to their health-promoting properties. Fermented dairy products, including yoghurt, are receiving special attention for their properties and the presence of probiotic bacteria. The quantitative and qualitative (MALDI TOF MS) evaluation of lactic acid bacteria (LAB) in different types of yoghurt (with different shelf lives) was carried out. The effect of the Lactobacillus spp. strains isolated from yoghurts (with potential antimicrobial activity) against foodborne pathogenic bacteria (Escherichia coli, Salmonella Enteritidis and Listeria monocytogenes) was evaluated. The presence of Lactobacillus spp. (Lactobacillus rhamnosus and Lactobacillus paracasei) in the tested yoghurts was demonstrated. In the samples tested, not all the lactic acid bacteria (LAB) declared by the manufacturer were identified. The number of live bacteria present in the product was influenced by the type of yoghurt. The number of bacteria did not fall below the World Health Organization (WHO) recommended level by the last day of validity. It was shown that a mixed culture (L. rhamnosus and L. paracasei, isolated from tested yoghurts) had the most significant effect on changing the number of pathogenic microorganisms. The consumption of dairy products, which are a source of LAB, can reduce the risk of foodborne pathogen infections.
Enhancing Greywater Treatment: High-Efficiency Constructed Wetlands with Seashell and Ceramic Brick Substrates
Authors: Feitosa Adriano P. , Rodrigues Kelly , Martins Waleska E. , Rodrigues Sara M. P. R. , Pereira Luciana , Silva Glória M. M.
Published: October 2024

Constructed wetland (CW) systems have been recognized as a sustainable technology for wastewater treatment that can be easily integrated into the local natural environment, offering both low cost and high efficiency. In this study, synthetic greywater was treated using a vertical subsurface flow CW operated in batch mode with 7-day cycles across two phases, operated in parallel: I, non-vegetated, and II, vegetated, with Echinodorus subalatus. The mixed filter bed was composed of seashells, ceramic brick fragments, and sand. No statistically significant differences (p > 0.05) were observed between the non-vegetated and vegetated phases for most parameters. The removal efficiencies of organic matter, anionic surfactants, and total phosphorus in the non-vegetated versus vegetated phases were (91.0 ± 3.8)% versus (94.0 ± 1.1)%; (71.9 ± 14.1)% versus (60.0 ± 9.5)%; and (35.2 ± 4.6)% versus (40.2 ± 15.5)%, respectively. Phosphorus removal exceeded values reported in the literature for both phases, primarily due to the calcium present in the seashells, which increased the electrical conductivity and hardness of the effluent compared to the influent. The macrophyte exhibited leaf desiccation, possibly due to contact with greywater and its young age (30 days), which may have negatively impacted the system’s performance during the vegetated phase.
Utilizing an Arduino Uno-Based System with Integrated Sensor Data Fusion and Filtration Techniques for Enhanced Air Quality Monitoring in Residential Spaces
Authors: Rudavskyi Ivan , Klym Halyna , Kostiv Yuriy , Karbovnyk Ivan , Zhydenko Illia , Popov Anatoli I. , Konuhova Marina
Published: October 2024

This study presents an air quality monitoring system that employs the Arduino Uno microcontroller. The system is augmented with a moving average filter and data fusion techniques from BME680 and CCS811 sensors, which are designed to process and combine data from these sensors. The system was tested and analyzed empirically across a range of residential environments in order to validate its efficacy. The findings indicated that the typical IAQ level in a bedroom was approximately 20 units. However, this level increased significantly, reaching 140 units, within minutes after the introduction of a 17% perfume spray. In contrast, the use of an aromatic diffuser resulted in a smaller increase in IAQ to 40 units, which returned to normal levels after ventilation. Moreover, the analysis demonstrated that the kitchen and bathroom exhibited inferior air quality in comparison to the bedroom. This was evidenced by elevated VOC and humidity levels, which were observed to be 10–20% higher due to the combined effects of household activities and inadequate ventilation. This study makes a significant contribution to the field of air quality monitoring by proposing a solution that employs sensor technology and data processing methods to enhance the quality of life within residential spaces.
Seismic Wave Amplification Characteristics in Slope Sections of Various Inclined Model Grounds
Authors: Jeong Sugeun , Moon Minseo , Kim Daehyeon
Published: October 2024

The collapse of slopes caused by earthquakes can lead to landslides, resulting in significant damage to both lives and structures. Seismic reinforcement of these slopes can protect social systems during an earthquake. In South Korea, where more than 70% of the land is mountainous, the stability of slopes is of paramount importance compared to other countries. While many seismic designs are based on peak ground acceleration (PGA), there is relatively little consideration given to the extent of PGA’s influence, and few studies have been done. This study aims to assess the seismic amplification of slopes with multilayers using a 1 g shaking table and verify the results through numerical analysis after confirming the impact of PGA at specific points. Typically, slope model experiments are conducted on single-layered ground models. However, actual ground conditions consist of multiple layers rather than a single layer, so a multi-layered model was created with different properties for the upper and lower layers. Two multi-layered ground models consisting of two layers were created, one with a flat ground surface and the other with a sloped surface. The properties of the two layers in each model were configured as a single layer to create the slope models. The peak ground acceleration (PGA) of the four ground models was compared, revealing that seismic wave amplification increases as it moves upward, and the amplification is even greater when transitioning from the lower to the upper ground layers, leading to different dynamic behavior of the slope. Through the contour lines, the influence of PGA was further confirmed, and it was found that approximately 60% of the PGA impact occurs at the topmost part of the slope on average. Analysis of the earthquake waves showed that the top of the slope experienced an average amplification of about 31.75% compared to the input motion, while the lower part experienced an average amplification of about 27.85%. Numerical analysis was performed using the ABAQUS program, and the results were compared with the 1 g shaking table experiments through spectral acceleration (SA), showing good agreement with the experimental results.
Research on Human Exposure to Transport-Induced Vibration in Buildings
Authors: Kowalska-Koczwara Alicja , Pachla Filip , Burdzik Rafał
Published: October 2024

The analysis of human perception of vibrations in buildings is a critical aspect of structural engineering, particularly as urbanization intensifies and the proximity of vibration sources to buildings increases. This paper addresses the frequent errors in the assessment and diagnosis of the impact of vibrations on building occupants. Despite stringent standards and detailed methodologies, misinterpretations and incorrect implementations of these guidelines are common, leading to flawed diagnostic studies. These errors often stem from the misuse of measurement equipment, inappropriate selection of measurement points, and a general lack of comprehensive education on vibration analysis. National guidelines, although largely based on ISO standards, vary significantly, contributing to inconsistent practices across Europe. The dominant sources of urban vibrations include vehicle traffic, particularly heavy trucks and rail vehicles, which significantly impact both building structures and human comfort. This paper reviews the methodologies for measuring and interpreting vibrations, emphasizing the importance of correct sensor placement and data analysis. It highlights the necessity of integrating vibrational comfort into building design, considering both external and internal vibration sources. The study also explores the effectiveness of different evaluation methods, such as the RMS and VDV methods, and the impact of various weighting functions on the analysis results. The findings underscore the need for improved education and standardization in the field to ensure accurate assessments and enhance the vibrational comfort of building occupants.
Rapid-Learning Collaborative Pushing and Grasping via Deep Reinforcement Learning and Image Masking
Authors: Huang Chih-Yung , Su Guan-Wen , Shao Yu-Hsiang , Wang Ying-Chung , Yang Shang-Kuo
Published: October 2024

When multiple objects are positioned close together or stacked, pre-grasp operations such as pushing objects can be used to create space for the grasp, thereby improving the grasping success rate. This study develops a model based on a deep Q-learning network architecture and introduces a fully convolutional network to accurately identify pixels in the workspace image that correspond to target locations for exploration. In addition, this study incorporates image masking to limit the exploration area of the robotic arm, ensuring that the agent consistently explores regions containing objects. This approach effectively addresses the sparse reward problem and improves the convergence rate of the model. Experimental results from both simulated and real-world environments show that the proposed method accelerates the learning of effective grasping strategies. When image masking is applied, the success rate in the grasping task reaches 80% after 600 iterations. The time required to reach 80% success rate is 25% shorter when image masking is used compared to when it is not used. The main finding of this study is the direct integration of image masking technique with a deep reinforcement learning (DRL) algorithm, which offers significant advancement in robotic arm control. Furthermore, this study shows that image masking technique can substantially reduce training time and improve the object grasping success rate. This innovation enables the robotic arm to better adapt to scenarios that conventional DRL methods cannot handle, thereby improving training efficiency and performance in complex and dynamic industrial applications.
Machine Learning Approach for Arabic Handwritten Recognition
Authors: Mutawa A. M. , Allaho Mohammad Y. , Al-Hajeri Monirah
Published: October 2024

Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been conducted on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. In the new model, the architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of a similar type with a character error rate of 13.2% and word error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.
High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
Authors: Varshney Atul , Gençoğlan Duygu Nazan
Published: October 2024

This study introduces a novel antenna based on the binary operation of a modified circular patch in conjunction with the Koch fractal. The antenna is intended for applications in the sub-6 GHz band, partial C-band, and X-band. The low-cost antenna is fabricated on a 1.6-mm-thick FR-4 substrate. A frequency-selective surface (FSS) is used to overcome the decreased values of the gain and bandwidth due to the fractal operations. The introduced split ring resonator (SRR) and the antenna substrate dimension reduction reduce the bandwidth and antenna gain. The air gap between the FSS and the antenna not only enhances the antenna gain but also controls the frequency tuning at the design frequency. The antenna size is miniaturized to 36.67%. A monopole antenna ground loaded with an SRR results in improved closest tuning (3.44 GHz) near the design frequency. The antenna achieves a peak gain of 9.37 dBi in this band. The FSS-based antenna results in a 4.65 dBi improvement in the gain value with the FSS. The measured and simulated plots exhibit an excellent match with each other in all three frequency bands at 2.96–4.72 GHz. These bands cover Wi-MAX (3.5 GHz), sub-6 GHz n77 (3300–3800 MHz), n78 (3300–4200 MHz), and approximately n79 (4400–4990 MHz), in addition to C-band applications.
Damage Analysis of Rolling Stock Automatic Coupler under Cyclic Loads
Authors: Lukoševičius Vaidas , Bazaras Žilvinas , Putnaitė Donata , Bazaraitė Eglė
Published: October 2024

To ensure the operational safety of the automatic couplers of a rolling stock subjected to cyclic loading, reliability and residual service life must be determined. This paper involved the prediction of the durability of the automatic coupler SA3 by analytical summation of cyclic damage. During the investigation, the cyclic characteristics and damage modes of the automatic coupler material that influence the accumulation of damage are determined. The stress and strain state assessment model was developed using a 3D finite element method for the automatic coupler housing as a geometrically complex component. A methodology was used to assess damage per load cycle that is applicable to any sequence of automatic coupler load cycles. For this purpose, the authors used low-cycle stationary load dependencies that account for quasi-static and low-cycle fatigue damage. The investigation showed that a coupler may develop a crack due to accumulated quasi-static and fatigue damage. For damage summation, the dependences of low-cycle stationary stress-controlled load accounting for low-cycle, quasi-static fatigue damage are proposed in view of the variation of the load on the automatic coupler during operation depending on the weight of the rolling stock, velocity, and railway relief. The proposed methodology is applicable to the calculation of other housings under similar loading conditions.
Nitrite: From Application to Detection and Development
Authors: Li Haoneng , Song Yang , Zhou Baoqing , Xu Hengyi
Published: October 2024

Nitrite, a collective term for a group of inorganic compounds containing nitrite ions (NO2−), is widely present in the natural environment and in the human body. It has a wide range of applications in the medical, food and environmental fields, such as food additives, water treatment agents and drugs. However, the excessive intake of nitrite poses indirect carcinogenic, teratogenic and mutagenic risks to humans. With the in-depth study of the functional properties of nitrite, there is an increasing demand for accurate and efficient methods for its detection. This paper presents a review of methods for the detection of nitrite, which will cover different principles and technologies, including traditional methods, optical methods, electrochemical sensors, and biosensors, and their prospects. By comparing and evaluating the different methods, it will provide references and valuable suggestions for choosing the most suitable nitrite detection methods and the scientific selection of alternatives for nitrite.
Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
Authors: Bui Thioanh , Liu Juncheng , Cao Jingyu , Wei Geng , Zeng Qian
Published: October 2024

This work was conducted mainly to provide a healthy and safe monitoring system for the elderly living in the home environment. In this paper, two different target fall detection schemes are proposed based on whether the target is visible or not. When the target is visible, a vision-based fall detection algorithm is proposed, where an image of the target captured by a camera is transmitted to the improved You Only Look Once version 5s (YOLOv5s) model for posture detection. In contrast, when the target is invisible, a WiFi-based fall detection algorithm is proposed, where channel state information (CSI) signals are used to estimate the target’s posture with an improved long short-term memory (LSTM) model. In the improved YOLOv5s model, adaptive picture scaling technology named Letterbox is used to maintain consistency in the aspect ratio of images in the dataset, and the weighted bidirectional feature pyramid (BiFPN) and the attention mechanisms of squeeze-and-excitation (SE) and coordinate attention (CA) modules are added to the Backbone network and Neck network, respectively. In the improved LSTM model, the Hampel filter is used to eliminate the noise from CSI signals and the convolutional neural network (CNN) model is combined with the LSTM to process the image made from CSI signals, and thus the object of the improved LSTM model at a point in time is the analysis of the amplitude of 90 CSI signals. The final monitoring result of the health status of the target is the result of combining the fall detection of the improved YOLOv5s and LSTM models with the physiological information of the target. Experimental results show the following: (1) the detection precision, recall rate, and average precision of the improved YOLOv5s model are increased by 7.2%, 9%, and 7.6%, respectively, compared with the original model, and there is almost no missed detection of the target; (2) the detection accuracy of the improved LSTM model is improved by 15.61%, 29.36%, and 52.39% compared with the original LSTM, CNN, and neural network (NN) models, respectively, while the convergence speed is improved by 90% compared with the original LSTM model; and (3) the proposed algorithm can meet the requirements of accurate, real-time, and stable applications of health monitoring.
Applying Deep Generative Neural Networks to Data Augmentation for Consumer Survey Data with a Small Sample Size
Authors: Watanuki Shinya , Edo Katsue , Miura Toshihiko
Published: October 2024

Questionnaire consumer survey research is primarily used for marketing research. To obtain credible results, collecting responses from numerous participants is necessary. However, two crucial challenges prevent marketers from conducting large-sample size surveys. The first is cost, as organizations with limited marketing budgets struggle to gather sufficient data. The second involves rare population groups, where it is difficult to obtain representative samples. Furthermore, the increasing awareness of privacy and security concerns has made it challenging to ask sensitive and personal questions, further complicating respondent recruitment. To address these challenges, we augmented small-sized datawith synthesized data generated using deep generative neural networks (DGNNs). The synthesized data from three types of DGNNs (CTGAN, TVAE, and CopulaGAN) were based on seed data. For validation, 11 datasets were prepared: real data (original and seed), synthesized data (CTGAN, TVAE, and CopulaGAN), and augmented data (original + CTGAN, original + TVAE, original + CopulaGAN, seed + CTGAN, seed + TVAE, and seed + CopulaGAN). The large-sample-sized data, termed “original data”, served as the benchmark, whereas the small-sample-sized data acted as the foundation for synthesizing additional data. These datasets were evaluated using machine learning algorithms, particularly focusing on classification tasks. Conclusively, augmenting and synthesizing consumer survey data have shown potential in enhancing predictive performance, irrespective of the dataset’s size. Nonetheless, the challenge remains to minimize discrepancies between the original data and other datasets concerning the values and orders of feature importance. Although the efficacy of all three approaches should be improved in future work, CopulaGAN more accurately grasps the dependencies between the variables in table data compared with the other two DGNNs. The results provide cues for augmenting data with dependencies between variables in various fields.
Sustainability in Geotechnics
Authors: Correia António Alberto S. , Venda Oliveira Paulo J.
Published: October 2024

The development of humans and societies has posed huge challenges to geotechnics [...]
The Assessment of the Possibility of Using Yellow Mealworm Powder in Chicken and Pork Pâté Production
Authors: Bogusz Radosław , Latoszewska Małgorzata , Szymańska Iwona , Jaworska Danuta , Szulc Karolina , Lipińska Edyta , Florowska Anna , Nowacka Małgorzata , Pietrzak Dorota
Published: October 2024

Meat delicatessen products, including pâtés, are important for consumers’ daily diets. However, due to the complex recipe composition, pâtés can also contain allergens such as gluten. Simultaneously, powdered edible insects are increasingly used to reformulate food products. Therefore, the paper aimed to investigate the feasibility of replacing wheat flour (total content: 9% w/w) with yellow mealworm powder (3:0, 2:1, 1:2, and 0:3) in chicken and pork pâtés and determine its effect on their quality properties. The rheological properties of pâté batter, as well as texture, color parameters, and microbiological and sensory characteristics of pâtés, were assessed. All prepared pâté batters were identified as weak gels. Furthermore, all the examined pâtés met the microbiological quality requirements. Adding yellow mealworm powder to the formulation weakened the pâté structure, resulting in significantly lower shear force and increased spreadability. In addition, it led to a darker and more gray color of the pâtés. It may be seen as indicating no preservatives or as resembling a typical pork pâté. It has been demonstrated that the complete replacement of wheat flour with insect powder significantly reduced the sensory quality of chicken and pork pâtés.
Remaining Useful Life of the Rolling Bearings Prediction Method Based on Transfer Learning Integrated with CNN-GRU-MHA
Authors: Yu Jianghong , Shao Jingwei , Peng Xionglu , Liu Tao , Yao Qishui
Published: October 2024

To accurately predict the remaining useful life (RUL) of rolling bearings under limited data and fluctuating load conditions, we propose a new method for constructing health indicators (HI) and a transfer learning prediction framework, which integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multi-head attention (MHA). Firstly, we combined Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) to fully extract temporal and spatial features from vibration signals. Then, the Multi-head attention mechanism (MHA) was added for weighted processing to improve the expression ability of the model. Finally, a new method for constructing Health indicators (HIs) was proposed in which the noise reduction and normalized vibration signals were taken as a HI, the L1 regularization method was added to avoid overfitting, and the model-based transfer learning method was used to realize the RUL prediction of bearings under small samples and variable load conditions. Experiments were conducted using the PHM2012 dataset from the FEMTO-ST research institute and XJTU-SY dataset. Three sets of 12 migration experiments were conducted under three different operating conditions on the PHM2012 dataset. The results show that the average RMSE of the proposed method was 0.0443, indicating high prediction accuracy under variable loads and small sample conditions. Three different operating conditions and two sets of four migration experiments were conducted on the XJTU-SY dataset, and the results show that the average RMSE of the proposed method was 0.0693, verifying the good generalization of the model under variable load conditions. In summary, the proposed HI construction method and prediction framework can effectively reduce the differences between features, with high stability and good generalizability.
Experimental and Theoretical Comparison and Analysis of Surface-Enhanced Raman Scattering Substrates with Different Morphologies
Authors: Tipaldi Ciro Federico , Vitols Kaspars , Kokis Tots , Trausa Annamarija , Sarakovskis Anatolijs
Published: October 2024

The following research paper concerns the analysis and characterisation of commercially available surface-enhanced Raman scattering (SERS) substrates. SERS has long been a potentially very powerful method with a great deal of interest around it; however, there are still many obstacles which do not allow SERS to be easily applied to real-world detection and analysis problems. As such, research around the various types of substrates is ongoing, in the hope of streamlining and improving the Raman enhancement mechanism. Scanning electron microscope images were obtained for each of the three substrates, and their features and scales were described. Enhanced Raman spectra for Rhodamine B were obtained for a range of concentrations using each of the three substrates, and, in addition, surface enhancement maps are presented. Enhancement factors were calculated for the 1358 cm−1 peak of Rhodamine B. Complementing the experimental work, theoretical FEM modelling in COMSOL Multiphysics was performed, with the resulting calculations yielding an enhancement prediction adequately accurate to the real substrates.
Diverse Humanoid Robot Pose Estimation from Images Using Only Sparse Datasets
Authors: Heo Seokhyeon , Cho Youngdae , Park Jeongwoo , Cho Seokhyun , Tsoy Ziya , Lim Hwasup , Cha Youngwoon
Published: October 2024

We present a novel dataset for humanoid robot pose estimation from images, addressing the critical need for accurate pose estimation to enhance human–robot interaction in extended reality (XR) applications. Despite the importance of this task, large-scale pose datasets for diverse humanoid robots remain scarce. To overcome this limitation, we collected sparse pose datasets for commercially available humanoid robots and augmented them through various synthetic data generation techniques, including AI-assisted image synthesis, foreground removal, and 3D character simulations. Our dataset is the first to provide full-body pose annotations for a wide range of humanoid robots exhibiting diverse motions, including side and back movements, in real-world scenarios. Furthermore, we introduce a new benchmark method for real-time full-body 2D keypoint estimation from a single image. Extensive experiments demonstrate that our extended dataset-based pose estimation approach achieves over 33.9% improvement in accuracy compared to using only sparse datasets. Additionally, our method demonstrates the real-time capability of 42 frames per second (FPS) and maintains full-body pose estimation consistency in side and back motions across 11 differently shaped humanoid robots, utilizing approximately 350 training images per robot.
Under-Represented Speech Dataset from Open Data: Case Study on the Romanian Language
Authors: Păiș Vasile , Barbu Mititelu Verginica , Irimia Elena , Ion Radu , Tufiș Dan
Published: October 2024

This paper introduces the USPDATRO dataset. This is a speech dataset, in the Romanian language, constructed from open data, focusing on under-represented voice types (children, young and old people, and female voices). The paper covers the methodology behind the dataset construction, specific details regarding the dataset, and evaluation of existing Romanian Automatic Speech Recognition (ASR) systems, with different architectures. Results indicate that more under-represented speech content is needed in the training of ASR systems. Our approach can be extended to other low-resourced languages, as long as open data are available.
Natural Language Processing Risk Assessment Application Developed for Marble Quarries
Authors: Eker Hasan
Published: October 2024

In this study, by using the texts describing the hazards and precautions taken during text mining, the necessary processes were carried out to first estimate the probability value and severity value of the risk and then calculate the risk values by Natural Language Processing analysis. In order to be used within the scope of the study, two data sets were generated from the data in the risk assessment report prepared by applying the L-type matrix risk assessment in marble quarries between 2015 and 2021. Stochastic Gradient Descent (SGD) was used for classification and prediction by analyzing text data. One data set was used to analyze the probability value of the risk and the other was used to analyze the severity value of the risk. In light of the results, when a text containing hazard and precaution information was entered, a system was developed that analyzed this text, estimated the probability and severity values, and calculated the risk assessment score. The application of the SGD algorithm to learning models developed on text data yielded an accuracy rate of 91.2% in the risk probability data set and 97.5% in the risk severity data set. The results indicated that the models were capable of conducting automatic risk assessment on text data and of effectively predicting the requisite probability and severity values. Due to the high accuracy rates obtained during the study, this risk assessment software was recommended for use in marble quarries.
Positive Effect of Camelina Intercropping with Legumes on Soil Microbial Diversity by Applying NGS Analysis and Mobile Fluorescence Spectroscopy
Authors: Marcheva Marina , Petkova Mariana , Slavova Vanya , Popov Vladislav
Published: October 2024

Camelina (Camelina sativa (L.) Crantz) is a valuable source of essential amino acids, especially sulphur-containing ones, which are generally lacking in leguminous crops, thus representing an alternative source of protein for both humans and farm animals. Rhizosphere soil samples from five experimental plots with mono- and mixed cultivations of three camelina cultivars, including two introduced varieties Cs1.Pro (Luna) and Cs2.Pro (Lenka) and one Bulgarian variety Cs3.Pro (local Bulgarian landrace) with variety 666 of vetch (Vicia sativa L.) (Cs3-Vs.Pro) and variety Mir of pea (Pisum sativum L.) (Cs3-Ps.Pro), were collected and analysed. The total DNA was isolated from the rhizosphere soils and the presence of the 16S rRNA gene was confirmed by amplification with the universal primer 16SV34. In the present study, the structure of the soil bacterial community in five different plots (Cs1.S.Pro, Cs2.S.Pro, Cs3.S.Pro, Cs3.Vs.S.Pro, and Cs3.Ps.S.Pro) where camelina was grown alone and by being intercropped with pea and vetch was analysed via a metagenomic approach. The number of observed species was highest in the local genotype of the camelina Cs3 grown alone, followed by soil from the intercropped variants Cs3-Vs and CsS-Ps. The soil bacterial communities differed between the sole cultivation of camelina and that grown with joint cultivation with vetch and peas, indicating that legumes considerably affected the growth and development of beneficial microorganisms by aspects such as nitrogen fixing, levels of nitrifying bacteria, and levels of phosphorus-dissolving bacteria, thus helping to provide better plant nutrition. The α-diversity indicated that bacterial communities in the rhizosphere were higher in soils intercropped with vetch and pea. The optical properties of cereals and legumes were determined by their energy structure, which includes both their occupied and free electronic energy levels and the energy levels of the atomic vibrations of the molecules or the crystal lattice.