bluuebunny/crossref_metadata_2025 huggingface dataset:
Advancing Algorithmic Adaptability in Hyperspectral Anomaly Detection with Stacking-Based Ensemble Learning
Authors: Wheeler Bradley J. , Karimi Hassan A.
Published: October 2024

Anomaly detection in hyperspectral imaging is crucial for remote sensing, driving the development of numerous algorithms. However, systematic studies reveal a dichotomy where algorithms generally excel at either detecting anomalies in specific datasets or generalizing across heterogeneous datasets (i.e., lack adaptability). A key source of this dichotomy may center on the singular and like biases frequently employed by existing algorithms. Current research lacks experimentation into how integrating insights from diverse biases might counteract problems in singularly biased approaches. Addressing this gap, we propose stacking-based ensemble learning for hyperspectral anomaly detection (SELHAD). SELHAD introduces the integration of hyperspectral anomaly detection algorithms with diverse biases (e.g., Gaussian, density, partition) into a singular ensemble learning model and learns the factor to which each bias should contribute so anomaly detection performance is optimized. Additionally, it introduces bootstrapping strategies into hyperspectral anomaly detection algorithms to further increase robustness. We focused on five representative algorithms embodying common biases in hyperspectral anomaly detection and demonstrated how they result in the previously highlighted dichotomy. Subsequently, we demonstrated how SELHAD learns the interplay between these biases, enabling their collaborative utilization. In doing so, SELHAD transcends the limitations inherent in individual biases, thereby alleviating the dichotomy and advancing toward more adaptable solutions.
Capsule Attention Network for Hyperspectral Image Classification
Authors: Wang Nian , Yang Aitao , Cui Zhigao , Ding Yao , Xue Yuanliang , Su Yanzhao
Published: October 2024

While many neural networks have been proposed for hyperspectral image classification, current backbones cannot achieve accurate results due to the insufficient representation by scalar features and always cause a cumbersome calculation burden. To solve the problem, we propose the capsule attention network (CAN), which combines an activity vector with an attention mechanism to improve HSI classification. In particular, we consider two attention mechanisms to improve the effectiveness of the activity vectors. First, an attention-based feature extraction (AFE) module is proposed to preprocess the spectral-spatial features of HSI data, which effectively mines useful information before the generation of the activity vectors. Second, we propose a self-weighted mechanism (SWM) to distinguish the importance of different capsule convolutions, which enhances the representation of the primary activity vectors. Experiments on four well-known HSI datasets have shown our CAN surpasses state-of-the-art (SOTA) methods on three widely used metrics with a much lower computational burden.
Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization
Authors: Han Yi , Li Xueting , Zhang Tianxian , Yang Xiaobo
Published: October 2024

This paper is mainly devoted to studying the deployment problem of a multi-static radar system (MSRS) within a non-connected deployment region using multi-objective particle swarm optimization (MOPSO). By modeling and reformulating the problem, it can be represented as a multi-objective mixed integer programming (MOMIP), which eliminates the need for additional constraints. To enhance the algorithm performance, integer variables and continuous ones are treated separately employing multiple velocity formulas. The velocity formulas for integer variables are modified using the sigmoid function and genetic operation, leading to the proposal of two MSRS deployment algorithms, namely MOPSO-Sigmoid and MOPSO-Gene. To evaluate the performance of the proposed algorithms, they are compared with two existing MOPSO-based algorithms. The first algorithm is the MSRS deployment algorithm for the non-connected deployment region that addresses the additional constraint problem model. The second algorithm is based on an existing conventional MOPSO algorithm and addresses the equivalent MOMIP problem model. A numerical study demonstrates that MOPSO-Sigmoid and MOPSO-Gene exhibit promising efficiency and effectiveness.
Affordable Real-Time PPP—Combining Low-Cost GNSS Receivers with Galileo HAS Corrections in Static, Pseudo-Kinematic, and UAV Experiments
Authors: Marut Grzegorz , Hadas Tomasz , Kazmierski Kamil , Bosy Jaroslaw
Published: October 2024

The Galileo High Accuracy Service (HAS) is a free of charge Global Navigation Satellite System (GNSS) augmentation service provided by the European Union. It is designed to enable real-time Precise Point Positioning (PPP) with a target accuracy (at the 95% confidence level) of 20 cm and 40 cm in the horizontal and vertical components, respectively, to be achieved within 300 s. The performance of the service has been confirmed with geodetic-grade receivers. However, mass market applications require low-cost GNSS receivers connected to low-cost antennae. This paper focuses on the performance of the real-time static and kinematic positioning achieved with Galileo HAS and low-cost GNSS receivers. The study is limited to GPS + Galileo dual-frequency positioning, thus exploiting the full potential of Galileo HAS SL1. We demonstrate that the target accuracy of Galileo HAS SL1 is reached with both geodetic-grade and low-cost receivers in dual-frequency static and kinematic applications in open-sky conditions. Precision of a few centimeters is reached for static positioning, while kinematic positioning results in subdecimeter precision. Vertical accuracy is limited by missing phase center offset models for low-cost antennas. In general, the performance of low-cost hardware using Galileo HAS for real-time PPP is comparable to that of geodetic-grade hardware. Therefore, combining low-cost GNSS receivers with Galileo HAS is feasible and justified.
The Effects of a Single Vagus Nerve’s Neurodynamics on Heart Rate Variability in Chronic Stress: A Randomized Controlled Trial
Authors: Pérez-Alcalde Ana Isabel , Galán-del-Río Fernando , Fernández-Rodríguez Francisco J. , de la Plaza San Frutos Marta , García-Arrabé María , Giménez María-José , Ruiz-Ruiz Beatriz
Published: October 2024

Background: The modulation of the autonomic nervous system’s activity, particularly increasing its parasympathetic tone, is of significant interest in clinical physiotherapy due to its potential benefits for stress-related conditions and recovery processes. This study evaluated the effectiveness of the addition of neurodynamics in enhancing parasympathetic activation in subjects with chronic stress. Methods: A clinical trial randomly assigned participants to a group with neurodynamics (6 bpm breathing protocol + manual therapy + neurodynamic technique) or a group without neurodynamics (6 bpm breathing protocol + manual therapy only). Metrics of heart rate variability (HRV), including the Mean Heart Rate (Mean HR), standard deviation of intervals between consecutive heartbeats (SDNN), Heart Rate Difference (Diff. HR), Root Mean Square of Successive Differences (RMSSD), number of intervals differing by more than 50 ms (NN50), percentage of consecutive NN intervals that differed by more than 50 ms (pNN50), and the high-frequency component measured in standardized units (HF), were assessed before, during, and after the intervention. Results: During the intervention, the group with neurodynamics showed significant changes in all variables except in the pNN50 and HF while the group without neurodynamics only showed improvements in the Mean HR, SDNN, and RMSSD. In the post-intervention phase, the group with neurodynamics maintained an increase in HRV while the group without neurodynamics experienced a decrease, suggesting an increase in sympathetic activity. Conclusions: Vagal nerve neurodynamics appear to represent an effective method for enhancing parasympathetic activation in patients with chronic stress. The results highlight the importance of a more comprehensive analysis of HRV variables in order to obtain a correct picture of the impact of interventions on the complex and multifaceted functioning of the autonomic nervous system.
Toward a Generic Framework for Mission Planning and Execution with a Heterogeneous Multi-Robot System
Authors: Denguir Mohsen , Touir Ameur , Gazdar Achraf , Qasem Safwan
Published: October 2024

This paper presents a comprehensive framework for mission planning and execution with a heterogeneous multi-robot system, specifically designed to coordinate unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) in dynamic and unstructured environments. The proposed architecture evaluates the mission requirements, allocates tasks, and optimizes resource usage based on the capabilities of the available robots. It then executes the mission utilizing a decentralized control strategy that enables the robots to adapt to environmental changes and maintain formation stability in both 2D and 3D spaces. The framework’s architecture supports loose coupling between its components, enhancing system scalability and maintainability. Key features include a robust task allocation algorithm, and a dynamic formation control mechanism, using a ROS 2 communication protocol that ensures reliable information exchange among robots. The effectiveness of this framework is demonstrated through a case study involving coordinated exploration and data collection tasks, showcasing its ability to manage missions while optimizing robot collaboration. This work advances the field of heterogeneous robotic systems by providing a scalable and adaptable solution for multi-robot coordination in challenging environments.
Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN
Authors: Ali Syed Waqad , Rashid Muhammad Munaf , Yousuf Muhammad Uzair , Shams Sarmad , Asif Muhammad , Rehan Muhammad , Ujjan Ikram Din
Published: October 2024

Respiratory disorders are commonly regarded as complex disorders to diagnose due to their multi-factorial nature, encompassing the interplay between hereditary variables, comorbidities, environmental exposures, and therapies, among other contributing factors. This study presents a Clinical Decision Support System (CDSS) for the early detection of respiratory disorders using a one-dimensional convolutional neural network (1D-CNN) model. The ICBHI 2017 Breathing Sound Database, which contains samples of different breathing sounds, was used in this research. During pre-processing, audio clips were resampled to a uniform rate, and breathing cycles were segmented into individual instances of the lung sound. A One-Dimensional Convolutional Neural Network (1D-CNN) consisting of convolutional layers, max pooling layers, dropout layers, and fully connected layers, was designed to classify the processed clips into four categories: normal, crackles, wheezes, and combined crackles and wheezes. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data. Hyperparameters were optimized using grid search with k−fold cross-validation. The model achieved an overall accuracy of 0.95, outperforming state-of-the-art methods. Particularly, the normal and crackles categories attained the highest F1-scores of 0.97 and 0.95, respectively. The model’s robustness was further validated through 5−fold and 10−fold cross-validation experiments. This research highlighted an essential aspect of diagnosing lung sounds through artificial intelligence and utilized the 1D-CNN to classify lung sounds accurately. The proposed advancement of technology shall enable medical care practitioners to diagnose lung disorders in an improved manner, leading to better patient care.
A Novel Non-Contact Multi-User Online Indoor Positioning Strategy Based on Channel State Information
Authors: Zhuang Yixin , Tian Yue , Li Wenda
Published: October 2024

The IEEE 802.11bf-based wireless fidelity (WiFi) indoor positioning system has gained significant attention recently. It is important to recognize that multi-user online positioning occurs in real wireless environments. This paper proposes an indoor positioning sensing strategy that includes an optimized preprocessing process and a new machine learning (ML) method called NKCK. The NKCK method can be broken down into three components: neighborhood component analysis (NCA) for dimensionality reduction, K-means clustering, and K-nearest neighbor (KNN) classification with cross-validation (CV). The KNN algorithm is particularly suitable for our dataset since it effectively classifies data based on proximity, relying on the spatial relationships between points. Experimental results indicate that the NKCK method outperforms traditional methods, achieving reductions in error rates of 82.4% compared to naive Bayes (NB), 85.0% compared to random forest (RF), 72.1% compared to support vector machine (SVM), 64.7% compared to multilayer perceptron (MLP), 50.0% compared to density-based spatial clustering of applications with noise (DBSCAN)-based methods, 42.0% compared to linear discriminant analysis (LDA)-based channel state information (CSI) amplitude fingerprinting, and 33.0% compared to principal component analysis (PCA)-based approaches. Due to the sensitivity of CSI, our multi-user online positioning system faces challenges in detecting dynamic human activities, such as human tracking, which requires further investigation in the future.
Two-Step Iterative Medical Microwave Tomography
Authors: Zhang Zekun , Liu Heng , Gao Xiang , Zhang Zeyu , He Zhongxia Simon , Liu Luoyuan , Zong Rui , Qin Zhizhen
Published: October 2024

In the field of medical imaging, microwave tomography (MWT) is based on the scattering and absorption characteristics of different tissues to microwaves and can reconstruct the electromagnetic property distribution of biological tissues non-invasively and without ionizing radiation. However, due to the inherently nonlinear and ill-posed characteristics of MWT calculations, actual imaging is prone to overfitting or artifacts. To address this, this paper proposes a two-step iterative imaging approach for rapid medical microwave tomography. This method establishes corresponding objective functions for microwave imaging across multiple frequencies and conducts iterative calculations on images at varying resolutions. This effectively enhances image clarity and accuracy while alleviating the issue of prolonged computational time associated with imaging complex structures at high resolution due to insufficient prior information during iterative processes. In the electromagnetic simulation section, we simulated a three-layer brain model and conducted imaging experiments. The results demonstrate that the algorithm significantly enhances imaging resolution, accurately pinpointing cerebral hemorrhages at different locations using an eight-antenna array and successfully reconstructs tomography images with a hemorrhage area radius of 1 cm. Lastly, experiments were conducted using a medical microwave tomography platform and four simplified human brain models, achieving millimeter-level accuracy in MWT.
Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model
Authors: Li Feifan , Dai Zhuoheng , Jiang Lei , Song Chanfei , Zhong Caiming , Chen Yingna
Published: October 2024

Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. Estimating the RUL enables the assessment of health bearing, maintenance planning, and significant cost reduction, thereby fostering industrial development. Existing models rely on traditional feature engineering with feature changes because operating conditions pose a major challenge to the generalization of RUL prediction models. This study focuses on neural network-based feature engineering and the downstream prediction of the RUL, eliminating the need for specific prior knowledge and simplifying the development and maintenance of models. Initially, a convolutional neural network (CNN) model is employed for feature engineering. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) model is used to capture the time-series degradation characteristics of the engineered features and predict the RUL through regression. Finally, the study examines the influence of operating conditions in the model and integrates domain adaptation to minimize differences in feature distribution, thereby enhancing the model’s generalizability for the RUL prediction.
DDNet: Depth Dominant Network for Semantic Segmentation of RGB-D Images
Authors: Rong Peizhi
Published: October 2024

Convolutional neural networks (CNNs) have been widely applied to parse indoor scenes and segment objects represented by color images. Nonetheless, the lack of geometric and context information is a problem for most RGB-based methods, with which depth features are only used as an auxiliary module in RGB-D semantic segmentation. In this study, a novel depth dominant network (DDNet) is proposed to fully utilize the rich context information in the depth map. The critical insight is that obvious geometric information from the depth image is more conducive to segmentation than RGB data. Compared with other methods, DDNet is a depth-based network with two branches of CNNs to extract color and depth features. As the core of the encoder network, the depth branch is given a larger fusion weight to extract geometric information, while semantic information and complementary geometric information are provided by the color branch for the depth feature maps. The effectiveness of our proposed depth-based architecture has been demonstrated by comprehensive experimental evaluations and ablation studies on challenging RGB-D semantic segmentation benchmarks, including NYUv2 and a subset of ScanNetv2.
Revealing Ecotourism Potential of Streams and Waterfalls: Identification of Influential Factors in a Case Study from Thua Thien Hue Province, Vietnam
Authors: Hien Tran Thi Thu , Chu Hone-Jay
Published: October 2024

Ecotourism is recognized as a fundamental aspect of sustainable tourism development. This study aimed to identify the factors influencing ecotourism in Thua Thien Hue province, Vietnam, specifically focusing on streams and waterfalls, using an ecotourism potential model. This model utilizes quantitative approaches, incorporating questionnaires and spatial information, including remote sensing data. The model is employed to determine the factors affecting ecotourism potential in the 31 locations of streams and waterfalls in Thua Thien Hue province. The model considers ecotourism potential as the dependent variable, while the independent variables are greenness using the normalized difference vegetation index (NDVI), connectivity, accessibility, and infrastructure, e.g., restaurants and hotels. The results revealed that greenness has the most significant positive impact on ecotourism potential around streams and waterfalls in Thua Thien Hue province, followed by accessibility, restaurants, and hotels, while connectivity has a negative impact on ecotourism potential in these areas. This analysis underscores the importance of prioritizing the conservation of natural environments and ecosystems surrounding streams and waterfalls. Furthermore, this study utilizes geospatial methodologies to identify and enhance our understanding of the key factors influencing ecotourism.
The Peer Effects of Residents’ Carbon Emission Behavior: An Empirical Analysis in China
Authors: He Congxian , Shi Ruiqing , Wen Huwei
Published: October 2024

The responsible low-carbon behavior of household residents is a crucial factor for the purpose of achieving carbon neutrality in the economy and society. Based on the peer effects theory, this study constructs a fixed-effects model to empirically analyze the existence, heterogeneity, and action mechanism of peer effects in household carbon emission behavior, which uses panel data from the China Household Finance Survey (CHFS). The results indicate that peer effects have a significantly positive impact on residents’ carbon emission behavior, and the results are verified by the robustness test in various ways. Further mechanism tests show that peer effects influence carbon emission behavior through methods including the learning imitation mechanism and competitive imitation mechanism. In addition, we find that peer effects have different impacts on residents’ carbon emission behavior in varying regions, income levels, education levels, and ages groups. This study aims to embed residents’ carbon emission behavior into the strong relationship between surrounding groups, raise consumers low-carbon awareness through publicity, guidance, and group interaction, form a low-carbon atmosphere for the whole society, and contribute to the realization of Sustainable Development Goals.
Sustainable Activated Carbon from Agricultural Waste: A Study on Adsorption Efficiency for Humic Acid and Methyl Orange Dyes
Authors: Tigrine Zahia , Benhabiles Ouassila , Merabti Leila , Chekir Nadia , Mellal Mounir , Aoudj Salaheddine , Abdeslam Nora Amele , Tassalit Djilali , Lebouachera Seif El Islam , Drouiche Nadjib
Published: October 2024

In this study, porous activated carbon was produced from coffee waste and used as an effective adsorbent for the removal of humic acid (HA) from seawater and methyl orange (MO) dye from aqueous solutions. Phosphoric acid H3PO4 was used as an activating agent for the chemical activation of these agricultural wastes. The characterization of the activated carbon obtained using a scanning electron microscope (SEM), Fourier-transform infrared (FTIR) spectroscopy analysis, X-ray diffraction (XRD) and the Brunauer–Emmett–Teller (BET) method revealed that the activated carbon products exhibited high porosity and the formation of various functional groups. The effects of different parameters were examined using batch adsorption experiments, such as the adsorbent masses, pH, initial pollutant concentration and contact time. The results show that the performance increased with an increased adsorbent mass (up to 0.25 g/L) and decreased initial concentration of the adsorbent tested. On the other hand, this study clearly showed that the adsorption efficiency of the MO on the raw spent coffee grounds (SCGs) waste was around 43%, while no removal was observed for the humic acid. The experiments demonstrated that the activated carbon synthesized from the used coffee grounds (the efficiency was compared with commercial activated carbon (CAC) with a difference of 13%) was a promising alternative to commercially available adsorbents for the removal of humic acid from seawater. To understand and elucidate the adsorption mechanism, various isothermal and kinetic models were studied. The adsorption capacity was analyzed by fitting experimental data to these models. The experimental data for methyl orange dyes were analyzed using Langmuir and Freundlich isothermal models. The Freundlich isotherm model provided a superior fit to the equilibrium data, as indicated by a higher correlation coefficient (R2) than that of the Langmuir model. The maximum adsorption was observed at pH 3. The Freundlich adsorption capacity was found to be 333 mg/g adsorbent. The PAC showed a high adsorption capacity for the MO and HA. The PAC showed the highest adsorption capacities for the HA and MO compared with the other adsorbents used (SCGs and CAC) and would be a good material to increase the adsorption efficiency for humic acid removal in the seawater pretreatment process. In addition, the prepared AC BET surface area was 520.40 m2/g, suggesting a high adsorption capacity. This makes the material potentially suitable for various applications that require a high surface area. These results indicate that high-quality sustainable activated carbon can be efficiently produced from coffee waste, making it suitable for a wide range of adsorbent applications targeting various pollutants.
The Re-Crushing Spatiotemporal Evolution Law of Broken Coal in the Goaf for Sustainable Utilization of Abandoned Mines
Authors: Chen Yanhong , Zhang Cun , Cheng Juhao , Zhao Xiangyu
Published: October 2024

The broken coal samples’ (BCS) re-crushing characteristics in the goaf during roof compaction directly affect the mechanics and seepage characteristics of the caving zone. This will further affect the safety of coal mining and the sustainable utilization of abandoned mines. Thus, the experiment of BCS compaction is carried out with the help of an acoustic emission (AE) monitoring system. The Hurst exponent changes of the AE counts at different stages were obtained using the R/S analysis method. The results indicate that the compaction and re-crushing of the BCS at the laboratory scale have long-term memory. When providing sufficient stress, the AE activity of BCS will continue to develop according to the current trend. Based on the AE breakage location technology, the spatial distribution re-crushing characteristics of the BCS are obtained. Re-crushing of the BCS demonstrates uniform breakage in the horizontal direction and layered breakage in the vertical direction. In the horizontal direction, the boundary area first began to break, and the damage gradually spread evenly to the central area. In the vertical direction, the upper layer was the first to be broken, and then the damage began to shift to the middle and lower layers.
Network Integration as a Tool for Sustainable Business Development
Authors: Shmeleva Nadezhda , Tolstykh Tatyana , Krasnobaeva Viktoria , Boboshko Diana , Lazarenko Denis
Published: October 2024

Business enterprises are required to adopt new economic models and business strategies in line with global trends such as the green agenda, the shortening of innovation cycles, digitalization (networking platforms) and socio-economic security. The models of network integrations (clusters, industrial symbioses, innovation ecosystems, etc.) are developing faster than other business models. Networking allows businesses to synergetically, based on the principles of collaboration, trust and self-organization, increase intellectual, innovative, social, and environmental values, which leads to economic growth and sustainable development. Through networking integration, the business performance of all participants in multi-company projects can be improved, regardless of their size and activities. The purpose of this study is to develop a methodological framework for assessing the synergistic effect and the level of collaborative maturity of participants in network industrial integration. This paper provides a comprehensive analysis and evaluation of network business integration efficiency. The authors’ methodology is based on the AHP hierarchy method, expert evaluation and Harrington’s level of desirability. The methodology is validated on Russian industrial integrations: industrial cluster of Nizhny Novgorod region; pipeline valve manufacturing cluster; South Ural industrial cluster producing spare parts and assembly units for roadbuilding, construction and agricultural vehicles. The results show that the level of collaborative maturity of business partners and the synergies from network integration effect the sustainability of business development. Recommendations for businesses are provided to improve their network integration and collaborative maturity. Furthermore, the findings allow the authors to define directions for further research, considering the interests of the business community, regional authorities, and financial institutions, and provide a framework for researchers to investigate the link between industrial network integration and economic growth.
Energy Citizenship: Revealing the Intrinsic Motivational Factors Suggested by Self-Determination Theory
Authors: Brizga Janis , Vijaikis Aivaras
Published: October 2024

This study investigated the motivational factors driving energy citizenship through the lens of self-determination theory. Utilizing data from a survey of 749 respondents, we examined the role of intrinsic and extrinsic motivations in predicting energy citizenship. Our findings reveal that intrinsic motivations, such as personal responsibility for climate change, community involvement, and the desire to reduce one’s carbon footprint, significantly predict engagement in energy citizenship. Conversely, extrinsic motivations, including financial incentives and external pressures, were not significant predictors. The study underscores the importance of intrinsic motivations in fostering sustained pro-environmental behaviours, particularly as the complexity of these behaviours increases. These insights suggest that policymakers should focus on enhancing intrinsic motivations through education, community engagement, and autonomy-supportive initiatives to promote active participation in sustainable energy practices.
Reheating Constraints and the H0 Tension in Quintessential Inflation
Authors: de Haro Jaume , Pan Supriya
Published: October 2024

In this work, we focus on two important aspects of modern cosmology: reheating and Hubble constant tension within the framework of a unified cosmic theory, namely the quintessential inflation connecting the early inflationary era and late-time cosmic acceleration. In the context of reheating, we use instant preheating and gravitational reheating, two viable reheating mechanisms when the evolution of the universe is not affected by an oscillating regime. After obtaining the reheating temperature, we analyze the number of e-folds and establish its relationship with the reheating temperature. This allows us to connect, for different quintessential inflation models (in particular for models coming from super-symmetric theories such as α-attractors), the reheating temperature with the spectral index of scalar perturbations, thereby enabling us to constrain its values. In the second part of this article, we explore various alternatives to address the H0 tension. From our perspective, this tension suggests that the simple Λ-Cold Dark Matter model, used as the baseline by the Planck team, needs to be refined in order to reconcile its results with the late-time measurements of the Hubble constant. Initially, we establish that quintessential inflation alone cannot mitigate the Hubble tension by solely deviating from the concordance model at low redshifts. The introduction of a phantom fluid, capable of increasing the Hubble rate at the present time, becomes a crucial element in alleviating the Hubble tension, resulting in a deviation from the Λ-Cold Dark Matter model only at low redshifts. On a different note, by utilizing quintessential inflation as a source of early dark energy, thereby diminishing the physical size of the sound horizon close to the baryon–photon decoupling redshift, we observe a reduction in the Hubble tension. This alternative avenue, which has the same effect of a cosmological constant changing its scale close to the recombination, sheds light on the nuanced interplay between the quintessential inflation and the Hubble tension, offering a distinct perspective on addressing this cosmological challenge.
Animal Venoms and Their Components: Molecular Mechanisms of Action V 2.0
Authors: Utkin Yuri
Published: October 2024

The adaptation of living beings to natural environmental conditions occurs as a result of various adaptive mechanisms [...]
Future of Uremic Toxin Management
Authors: Vanholder Raymond , Snauwaert Evelien , Verbeke Francis , Glorieux Griet
Published: October 2024

During the progression of chronic kidney disease (CKD), the retention of uremic toxins plays a key role in the development of uremic syndrome. Knowledge about the nature and biological impact of uremic toxins has grown exponentially over the past decades. However, the science on reducing the concentration and effects of uremic toxins has not advanced in parallel. Additionally, the focus has remained for too long on dialysis strategies, which only benefit the small fraction of people with CKD who suffer from advanced kidney disease, whereas uremic toxicity effects are only partially prevented. This article reviews recent research on alternative methods to counteract uremic toxicity, emphasizing options that are also beneficial in the earlier stages of CKD, with a focus on both established methods and approaches which are still under investigation or at the experimental stage. We will consequently discuss the preservation of kidney function, the prevention of cardiovascular damage, gastro-intestinal interventions, including diet and biotics, and pharmacologic interventions. In the final part, we also review alternative options for extracorporeal uremic toxin removal. The future will reveal which of these options are valid for further development and evidence-based assessment, hopefully leading to a more sustainable treatment model for CKD than the current one.
INCREASING THE RELIABILITY OF DEFECT DETECTION THREAD, FIBRE AND YARN
Authors: Sevostyanov Petr Sa. , Samoilova Tatiana A. , Gorodentseva Lubov M. , Burdin Ilya M.
Published: October 2024

A method and algorithm for increasing the reliability of decision-making in the problem of detecting defects in extended products – threads, wires, cables, fibres, yarn, etc., based on the following principles are proposed: using several statistical criteria at the same time rather than one, developing a rule for coor-dinated decision-making based on the results of testing using several statistical criteria, implementing the testing procedure in the online mode as data are received and checked. The algorithm is a sequential test of the hypothesis about the violation of stationarity by the average level and variability of the data flow from the scanning device using simultaneously used Student, Wilcoxon, Fisher and Ansari – Bradley tests, taking into account the information already accumulated about the flow at the previous stages of testing.
Transfer Learning for Turkish Cuisine Classification
Authors: Alp Sait
Published: October 2024

Thanks to developments in data-oriented domains like deep learning and big data, the integration of artificial intelligence with food category recognition has been a topic of interest for decades. The capacity of image classification to produce more precise outcomes in less time has made it a popular topic in computer vision. For the purpose of food categorization, three well-known CNN-based models—EfficientNetV2M, ResNet101, and VGG16—were fine-tuned in this research. Moreover, the pre-trained Vision Transformer (ViT) was used for feature extraction, followed by classification using a Random Forest (RF) algorithm. All the models were assessed on the TurkishFoods-15 dataset. It was found that the ViT and RF models were most effective in accurately capturing food images, with precision, recall, and F1-score values of 0.91, 0.86, and 0.88 respectively.
Does Social Media Content Quality and Engagement Influence Consumer Satisfaction in the Apparel Industry?
Authors: Princy Hebshibha K , Chitra K
Published: October 2024

This study examines the influence of social media marketing on consumer satisfaction in the apparel industry, with a focus on key factors such as engagement and content quality. The rapid growth of social media has reshaped the way consumers interact with brands, making it essential to understand how these platforms influence consumer behavior. The research problem centers on determining the impact of social media engagement and the quality of social media content on consumer satisfaction. The study aims to explore these relationships and provide insights into effective social media marketing strategies for apparel brands. Using a descriptive research design, the study was conducted in Coimbatore district with a sample size of 139 respondents identified through convenience sampling. Data was collected using structured questionnaires, and relevant statistical tools, including ANOVA, Correlation Analysis, and Multiple Linear Regression, were used to analyze the data. The findings reveal that frequent interaction with brands and engaging visual content are key drivers of consumer satisfaction. Findings of the study reveals that social media marketing plays a crucial role in shaping consumer behavior in the apparel industry, and brands must refine their strategies to meet consumer needs and enhance satisfaction through consistent, high-quality content.
Innovative Solutions for Sustainable Logistics in Food Manufacturing Industries overcome Barriers and Challenges
Authors: Anisha A , Chandrasekar K
Published: October 2024

This paper explores innovative solutions for sustainable logistics in the food manufacturing industry, focusing on how these strategies address key challenges. It examines how advancements in technology, logistics strategies, and eco-friendly practices contribute to sustainability while maintaining efficiency and profitability. The study analyzes data from companies that have adopted modern logistics solutions, such as AI-driven optimization, renewable energy, and circular supply chain models, supplemented by expert interviews to gain practical insights. The findings reveal that these solutions help reduce carbon emissions, minimize food waste, and optimize resource use. Companies that have implemented these strategies have successfully overcome common barriers such as high costs, infrastructure complexity, and regulatory challenges. While the research focuses on large-scale manufacturers, smaller companies remain under explored, suggesting the need for future studies on scalability and policy frameworks to support wider adoption. This paper offers a unique contribution by combining current innovations in sustainable logistics with practical industry challenges, providing actionable insights for food manufacturers and laying the foundation for further research into overcoming financial and technological barriers.
Availability Problem of Drinking Water in an Urban Area - Effects of Family Size and Monthly Income: An Empirical Study
Authors: Suprabha L.
Published: October 2024

The availability of water is a major problem faced by the urban masses and policy makers. Bridging the gap between the demand for and supply of water is a major policy decision confronted by the urban authorities. The ever-increasing urban population and shift in occupational structure results in the increased demand which confronts with lagging supply of water which needs immediate policy attention. The present study is based on the survey of households in urban areas who have piped water connections in their homes. 600 households were selected at random from the total connections available. The data is analysed using the x2 test. The present study aims at examining the relation between size of the family and income of the respondents with that of severity of availability problem of water and comes to the conclusion that severity problem is not connected with the size of the family size but it is connected with the family income of the consumers.
Analysis of Structural, Optical, Morphological and Magnetic Behaviour of Lead Nanoferrite by Two Different Methods
Authors: Subashini A. , Pavithra C.
Published: October 2024

Sol gel and the hydrothermal approach are two distinct techniques that have been successfully used to synthesise lead nanoferrite (PbFe2O4). To determine which of the two approaches is superior, the features of the sample prepared by each have been examined. Vibration Sample Magnetometer (VSM), Fourier Transform Infrared Spectroscopy, UV-VIS Spectral investigations, Scanning Electron Microscopy (SEM), X-Ray Diffraction, and Size, Band Gap Energies, Morphology, and Magnetic Properties of Nanostructures were used to determine and compare its various aspects. The mean diameters of the synthesised lead ferrite nanocrystalline were found to be 33 nm for the sol gel method and 20 nm for the hydrothermal method, respectively, based on X-Ray Diffraction analysis using Debye-Scherer’s formula with the peak. FTIR spectra are used to analyse the vibration characteristics of nanomaterials. The optical characteristics of the synthesised nanomaterials, such as their Energy Band Gap, were determined using UV-VIS Spectral investigations. SEM is used for surface morphological investigations, allowing the structure of the synthesised nanomaterials to be studied. Using VSM, the magnetisation of the produced nanomaterials was examined, and the hysteresis loops were used to calculate the saturation magnetisation (Ms), coercivity (Hc), and retainivity (Mr).
Korvais in Karnatak Music - Improvisation through Training
Authors: Balaji J.
Published: October 2024

Korvai is a Tamil word meaning joining or beading. When rhythmic syllables are joined or beaded together in a logical development and culminate in a structural format of definable Purvanga and Uttaranga then they can be termed as Korvai. Korvais form an essential part of every Karnatak concert. They are being extensively used by Vocalists, Instrumentalists and various types of percussion artistes also. Since, Korvai is an integral and important aspect of a concert, there is a necessity to analyze the process of making them and also quantify them for easier approaches to its various types. There are two parts in a Korvai, that are generally being used to identify them distinctively. They are Purvanga (the first part or the introductory part) and Uttaranga (the ending part or the subsequent part). The purvanga and uttaranga are constant factors irrespective of the rhythmic structure or Tala structure. Same Purvanga can be used with different Uttarangas for same tala or extended to other talas as well. Such being the case, the purvangas and uttarangas are clearly quantifiable in terms of numbers and rhythmic phrases or combination of phrases. When a number is being used, there can be logical build up and hence this article explores such logical build-up using numbers in a structured way converted as cognizable rhythmic varieties. Talas are cyclical in nature having specified fixed number of beats per cycle. For a tala which is having 9 beats per cycle, the total number for 3 or 4 cycles can be calculated by simple multiplication. For example, for 4 cycles the total would be 36 beats. Whenever calculations are made mostly they are done on either as number of beats or on pulses. Both methods can be employed for quick results. In the above case, there were 36 beats which can give us 144 pulses (36 X 4).The purvanga can be anything like 108 and the uttaranga can be 36. The general ratio of purvanga and uttaranga could be 60:40 or 70:30 for. Longer Purvangas of 90:10 or shorter purvangas of 20:80 are generally not preferred. This article also covers various examples where different combinations are improvised. Different types of korvais can also be made using such improvisations.
Porutham - Creating varied Rhythmic Cadences with Chart Templates
Authors: Balaji J , Ramakrishnan Uma
Published: October 2024

Porutham is a Tamil word meaning ‘matching’. The word has varied meanings according to the context in which it is used in Tamil language. Porutham in Astrology means ‘match making between two astrological charts’ at the time of marriage. Porutham also means perfectly matched partners or friends. ‘Nalla Porutham’ is the phrase which means ‘Good Match’. It also means that it will be ideally suited for the particular object.Musically ‘Poruthams’ can be used as part of various cadential forms like Korvai, short ending items like Muktayi, theermanam, arudi etc. This can also be used in ‘Jati Pallavis’, in the Pallavi portion of Ragam, Tanam, Pallavi and in Kritis where neravals are sung. Porutham is generally done as a rhythmic exercise, where Jatis are employed when a percussion artiste uses Poruthams and Swaras are used when a Vocalist/instrumentalist uses them.This article also covers various examples covering porutham ideas for various kritis where neravals are popularly sung. Sample kritis have been taken from different Talas like Adi Tala, Rupaka Tala and Misra Chaapu Tala. Ragam-Tanam-Pallavis and Jati Pallavis have not been covered in this article, as we propose to cover them extensively in a separate article.
Overview of Mobile Cloud Computing: Learning Prospects, an Essentiality to Realism of the Digital World
Authors: Arockia Venice J , . Albin
Published: October 2024

This paper has been worked on the relational significance of ‘Cloud Computing’ as a newer innovation of the digital world is a supportive system that enhancing the storage capacity of resources and data as needed by individuals who believes in its usage. Most effectively, the new concept ‘Cloud Computing’ has been designed to strengthen the network of communication transfer much faster and safer with more storage possibilities. The framework of this identity enables integrated systems to create a wider scenario of computer’s working space to equip effective processes of mobile cloud computing. The trends of this new technology is most favored among all groups of people and in different organizations as it provides security and privacy for the information stored. The paper explicates on the efficiency and efficacy of cloud computing with its potential usage. The necessity of such a service serves the background of necessity in the globalized digitized scenario. It also will investigate the current state of mobile cloud computing security and privacy, with a focus on the risks and vulnerabilities associated with this technology. It also discusses potential solutions and best practices for protecting mobile cloud users and their data that will give the asset to findings identified. This paper proposes several measures including secure communication protocols, strong authentication mechanisms, data encryption and user awareness with linked deliberation to education. In conclusion, it is clear that Cloud Computing is an innovative and evolving field and it requires a comprehensive approach to protect the users and their data for futurity which is the relevance to contribution from this paper.