bluuebunny/crossref_metadata_2025 huggingface dataset:
Promoting Peace through Christian Education in the Family
Authors: Osewska Elżbieta , Stala Józef
Published: January 2024

Peace has always been a topic of great importance and the origins of thinking about peace and education for peace can be traced back to ancient philosophy. In the 21st century, the presence of peace has been desired by all nations, societies and people of goodwill, especially due to so many international conflicts and wars (the dramatic situation in the Middle East, Sub-Saharan Africa and Ukraine). Historical and socio-cultural developments concerning peace and violence urge pedagogues, psychologists, sociologists and theologians to seek the foundations of education that promote peace. Taking into consideration the given context and importance of peace, this article will reflect on the issue of Christian education for peace, especially in the family environment. Educators dealing with the issue of peace education refer to various inspirations and sources of knowledge. As this article is written from the perspective of the Christian understanding of peace, the authors will first refer to the teaching of Pope John Paul II as a strong promotor of peace. Papal documents and speeches consequently present that peace has its roots in the upbringing of human beings; therefore, in the next part of this article, Christianity serving the good of the family, the assumptions of Christian education towards peace in the family and practical indications will be shown.
Two Sets of High-Conductivity Systems with Different Scales Reveal the Seismogenic Mechanism of Earthquakes in the Songyuan Area, Northeastern China
Authors: Jia Xiaodong , Li Zhuoyang , Han Jiangtao , Hou Hesheng , Xin Zhonghua , Liu Lijia , Liu Wenyu
Published: January 2024

To reveal the deep seismogenic environment and mechanism of earthquakes in Songyuan City, Northeastern China, 59 broadband magnetotelluric sites in the Songyuan area were arranged in this study at a spacing of 5 km. In addition, two intersecting magnetotelluric profiles, with a total of 23 measuring sites and a spacing of 2 km, were established near the Ningjiang earthquake swarm. Using a nonlinear conjugate gradient (NLCG) algorithm, resistivity structures in the lithosphere were obtained at different scales using three-dimensional (3D) inversion. The research results show that: a deep high-conductivity system (<10 Ω·m) was identified at 25–85 km depth in the lithosphere under Songyuan, corresponding closely to a region of high heat flow. It is inferred to be the molten material of mantle upwelling. In addition, a shallow high-conductivity system (<10 Ω·m) was identified beneath the Ningjiang earthquake swarm, which is interpreted to correspond to the Fuyu North fault. It is the main seismo-controlling structure of the Ningjiang earthquake swarm. The deep seismogenic environment and seismogenic mechanism of the Ningjiang earthquake swarm can be described as a deep upwelling of molten mantle material, which provides the power source. The deep magma intruded into the lower crust and accumulated, then intruded along faults and fissures, resulting in the activation of the North Fuyu fault and triggering the Ningjiang earthquake. It is attributed to the activation of shallow faults caused by the upwelling of molten mantle material.
Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence
Authors: Raj Nawin , Pasfield-Neofitou Sarah
Published: January 2024

Pacific Island countries are vulnerable to the impacts of climate change, which include the risks of increased ocean temperatures, sea level rise and coastal wetland loss. The destruction of wetlands leads not only to a loss of carbon sequestration but also triggers the release of already sequestered carbon, in turn exacerbating global warming. These climate change effects are interrelated, and small island nations continuously need to develop adaptive and mitigative strategies to deal with them. However, accurate and reliable research is needed to know the extent of the climate change effects with future predictions. Hence, this study develops a new hybrid Convolutional Neural Network (CNN) Multi-Layer Bidirectional Long Short-Term Memory (BiLSTM) deep learning model with Multivariate Variational Mode Decomposition (MVMD) to predict the sea level for study sites in the Solomon Islands and Federated States of Micronesia (FSM). Three other artificial intelligence (AI) models (Random Forest (FR), multilinear regression (MLR) and multi-layer perceptron (MLP) are used to benchmark the CNN-BiLSTM model. In addition to this, remotely sensed satellite Landsat imagery data are also used to assess and predict coastal wetland changes using a Random Forest (RF) classification model in the two small Pacific Island states. The CNN-BiLSTM model was found to provide the most accurate predictions (with a correlation coefficient of >0.99), and similarly a high level of accuracy (>0.98) was achieved using a Random Forest (RF) model to detect wetlands in both study sites. The mean sea levels were found to have risen 6.0 ± 2.1 mm/year in the Solomon Islands and 7.2 ± 2.2 mm/year in the FSM over the past two decades. Coastal wetlands in general were found to have decreased in total area for both study sites. The Solomon Islands recorded a greater decline in coastal wetland between 2009 and 2022.
Remote Sensing for Maritime Traffic Understanding
Authors: Reggiannini Marco , Salerno Emanuele , Bacciu Clara , D’Errico Andrea , Lo Duca Angelica , Marchetti Andrea , Martinelli Massimo , Mercurio Costanzo , Mistretta Antonino , Righi Marco , Tampucci Marco , Paola Claudio Di
Published: January 2024

The capability of prompt response in the case of critical circumstances occurring within a maritime scenario depends on the awareness level of the competent authorities. From this perspective, a quick and integrated surveillance service represents a tool of utmost importance. This is even more true when the main purpose is to tackle illegal activities such as smuggling, waste flooding, or malicious vessel trafficking. This work presents an improved version of the OSIRIS system, a previously developed Information and Communication Technology framework devoted to understanding the maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. A number of dedicated processing units are cascaded with the objective of (i) detecting the presence of vessel targets in the input imagery, (ii) estimating the vessel types on the basis of their geometric and scatterometric features, (iii) estimating the vessel kinematics, (iv) classifying the navigation behavior of the vessel and predicting its route, and, eventually, (v) integrating the several outcomes within a webGIS interface to easily assess the traffic status inside the considered area. The entire processing pipeline has been tested on satellite imagery captured within the Mediterranean Sea or extracted from public annotated datasets.
Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures
Authors: Kim Youngho , Han Incheol , Jung Jeyong , Yang Sumin , Lee Seunghee , Koo Bummo , Ahn Soonjae , Nam Yejin , Song Sung-Hyuk
Published: January 2024

The quantification of comfort in binding parts, essential human–machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of binding parts by measuring electrodermal activity (EDA) and tissue oxygen saturation (StO2). In Experiment 1, EDA was measured from 13 healthy subjects under three different pressure conditions (10, 20, and 30 kPa) for 1 min using a pneumatic cuff on the right thigh. In Experiment 2, EDA and StO2 were measured from 10 healthy subjects for 5 min. To analyze the correlation between EDA parameters and the decrease in StO2, a survey using the visual analog scale (VAS) was conducted to assess the level of discomfort at each pressure. The EDA signal was decomposed into phasic and tonic components, and the EDA parameters were extracted from these two components. RM ANOVA and a post hoc paired t-test were used to determine significant differences in parameters as the pressure increased. The results showed that EDA parameters and the decrease in StO2 significantly increased with the pressure increase. Among the extracted parameters, the decrease in StO2 and the mean SCL proved to be effective indicators. Such analysis outcomes would be highly beneficial for studies focusing on the comfort assessment of the binding parts of rehabilitation robots.
How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery
Authors: Kocejko Tomasz , Matuszkiewicz Nikodem , Durawa Piotr , Madajczak Aleksander , Kwiatkowski Jakub
Published: January 2024

This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10–20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.
Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
Authors: Jeong Seung-Min , Noh Tae-Kyeong , Kim Do-Soon
Published: January 2024

A spectral image analysis has the potential to replace traditional approaches for assessing plant responses to different types of stresses, including herbicides, through non-destructive and high-throughput screening (HTS). Therefore, this study was conducted to develop a rapid bioassay method using a multi-well plate and spectral image analysis for the diagnosis of herbicide activity and modes of action. Crabgrass (Digitaria ciliaris), as a model weed, was cultivated in multi-well plates and subsequently treated with six herbicides (paraquat, tiafenacil, penoxsulam, isoxaflutole, glufosinate, and glyphosate) with different modes of action when the crabgrass reached the 1-leaf stage, using only a quarter of the recommended dose. To detect the plant’s response to herbicides, plant spectral images were acquired after herbicide treatment using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors and analyzed for diagnosing herbicide efficacy and modes of action. A principal component analysis (PCA), using all spectral data, successfully distinguished herbicides and clustered depending on their modes of action. The performed experiments showed that the multi-well plate assay combined with a spectral image analysis can be successfully applied for herbicide bioassays. In addition, the use of spectral image sensors, especially CF images, would facilitate HTS by enabling the rapid observation of herbicide responses at as early as 3 h after herbicide treatment.
Effects of Training and Calibration Data on Surface Electromyogram-Based Recognition for Upper Limb Amputees
Authors: Yao Pan , Wang Kaifeng , Xia Weiwei , Guo Yusen , Liu Tiezhu , Han Mengdi , Gou Guangyang , Liu Chunxiu , Xue Ning
Published: January 2024

Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. By using cross-session calibration and increasing the amount of training data, it is possible to reduce these variations. The impact of varying the amount of calibration and training data on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of the amount of training data on benchmark performance. Two amputees who had undergone amputations years prior were recruited, and seven sessions of data were collected for analysis from each of them. Ninapro DB6, a publicly available database containing data from ten healthy subjects across ten sessions, was also included in this study. The experimental results show that the calibration data improved the average accuracy by 3.03%, 6.16%, and 9.73% for the two subjects and Ninapro DB6, respectively, compared to the baseline results. Moreover, it was discovered that increasing the number of training sessions was more effective in improving accuracy than increasing the number of trials. Three potential strategies are proposed in light of these findings to enhance cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, while also offering effective strategies to maximize the utilization of the entire dataset.
A Vibration-Based Methodology to Monitor Road Surface: A Process to Overcome the Speed Effect
Authors: Meocci Monica
Published: January 2024

Road pavement monitoring represents the starting point for the pavement maintenance process. To quickly fix a damaged road, relevant authorities need a high-efficiency methodology that allows them to obtain data describing the current conditions of a road network. In urban areas, large-scale monitoring campaigns may be more expensive and not fast enough to describe how pavement degradation has evolved over time. Furthermore, at low speeds, many technologies are inadequate for monitoring the streets. In such a context, employing black-box-equipped vehicles to perform a routine inspection could be an excellent starting point. However, the vibration-based methodologies used to detect road anomalies are strongly affected by the speed of the monitoring vehicles. This study uses a statistical method to analyze the effects of speed on road pavement conditions at different severity levels, through data recorded by taxi vehicles. Likewise, the study introduces a process to overcome the speed effect in the measurements. The process relies on a machine learning approach to define the decision boundaries to predict the severity level of the road surface condition based on two recorded parameters only: speed and pavement deterioration index. The methodology has succeeded in predicting the correct damage severity level in more than 80% of the dataset, through a user-friendly real-time method.
Compound Fault Characteristic Analysis for Fault Diagnosis of a Planetary Gear Train
Authors: Ren Yulin , Li Guoyan , Li Xiong , Zhang Jingbin , Liu Runjun , Shi Sifan
Published: January 2024

The carrier eccentricity error and gear compound faults are most likely to occur simultaneously in an actual planetary gear train (PGT). Various faults and errors are coupled with each other to generate a complex dynamic response, which makes the diagnosis of PGT faults difficult in practice. In order to analyze the joint effect of the error and the compound faults in a PGT, a carrier eccentricity error model is proposed and incorporated into the TVMS model by considering the time-varying center distance, line of action (LOA), meshing angle, and contact ratio. Then, the TVMS of the cracked gear is derived based on the potential energy method. On this basis, the dynamic model of a PGT with both the carrier eccentricity error and compound gear cracks as internal excitations are established. Furthermore, the meshing characteristics and dynamic responses of the PGT are simulated to investigate the compound fault features. A series of experiments are conducted to further analyze the influence of the compound fault on the vibration response. The relevant conclusions can provide a reference for the compound fault diagnosis of a PGT in practice.
Simple Conditional Spatial Query Mask Deformable Detection Transformer: A Detection Approach for Multi-Style Strokes of Chinese Characters
Authors: Zhou Tian , Xie Wu , Zhang Huimin , Fan Yong
Published: January 2024

In the Chinese character writing task performed by robotic arms, the stroke category and position information should be extracted through object detection. Detection algorithms based on predefined anchor frames have difficulty resolving the differences among the many different styles of Chinese character strokes. Deformable detection transformer (deformable DETR) algorithms without predefined anchor frames result in some invalid sampling points with no contribution to the feature update of the current reference point due to the random sampling of sampling points in the deformable attention module. These processes cause a reduction in the speed of the vector learning stroke features in the detection head. In view of this problem, a new detection method for multi-style strokes of Chinese characters, called the simple conditional spatial query mask deformable DETR (SCSQ-MDD), is proposed in this paper. Firstly, a mask prediction layer is jointly determined using the shallow feature map of the Chinese character image and the query vector of the transformer encoder, which is used to filter the points with actual contributions and resample the points without contributions to address the randomness of the correlation calculation among the reference points. Secondly, by separating the content query and spatial query of the transformer decoder, the dependence of the prediction task on the content embedding is relaxed. Finally, the detection model without predefined anchor frames based on the SCSQ-MDD is constructed. Experiments are conducted using a multi-style Chinese character stroke dataset to evaluate the performance of the SCSQ-MDD. The mean average precision (mAP) value is improved by 3.8% and the mean average recall (mAR) value is improved by 1.1% compared with the deformable DETR in the testing stage, illustrating the effectiveness of the proposed method.
A Novel Field-Free Line Generator for Mechanically Scanned Magnetic Particle Imaging
Authors: Kim Tae Yi , Jeong Jae Chan , Seo Beom Su , Krause Hans Joachim , Hong Hyo Bong
Published: January 2024

In this study, we propose an efficient field-free line (FFL) generator for mechanically driven FFL magnetic particle imaging (MPI) applications. The novel FFL generator comprises pairs of Halbach arrays and bar magnets. The proposed design generates high-gradient FFLs with low-mass permanent magnets, realizing fine spatial resolutions in MPI. We investigate the magnetic field generated using simulations and experiments. Our results show that the FFL generator yields a high gradient of 4.76 T/m at a cylindrical field of view of 30 mm diameter and a 70 mm open bore. A spatial resolution of less than 3.5 mm was obtained in the mechanically driven FFL-MPI.
Blockchain of Things: Benefits, Challenges and Future Directions
Authors: Biswas Kamanashis , Chowdhury Mohammad Jabed Morshed , Usman Muhammad
Published: January 2024

As Internet of Things (IoT) technologies become increasingly integrated into our daily lives through a multitude of Internet-enabled devices, the efficient, secure, and cost-effective management of the vast amount of data generated by these devices poses a significant challenge [...]
HapticWhirl, a Flywheel-Gimbal Handheld Haptic Controller for Exploring Multimodal Haptic Feedback
Authors: Berna Moya Jose Luis , van Oosterhout Anke , Marshall Mark T. , Martinez Plasencia Diego
Published: January 2024

Most haptic actuators available on the market today can generate only a single modality of stimuli. This ultimately limits the capacity of a kinaesthetic haptic controller to deliver more expressive feedback, requiring a haptic controller to integrate multiple actuators to generate complex haptic stimuli, with a corresponding complexity of construction and control. To address this, we designed a haptic controller to deliver several modalities of kinaesthetic haptic feedback using a single actuator: a flywheel, the orientation of which is controlled by two gimbals capable of rotating over 360 degrees, in combination with a flywheel brake. This enables the controller to generate multiple haptic feedback modalities, such as torque feedback, impact simulation, low-frequency high-amplitude vibrations, inertial effects (the sensation of momentum), and complex haptic output effects such as the experience of vortex-like forces (whirl effects). By combining these diverse haptic effects, the controller enriches the haptic dimension of VR environments. This paper presents the device’s design, implementation, and characterization, and proposes potential applications for future work.
Application of Machine Vision Techniques in Low-Cost Devices to Improve Efficiency in Precision Farming
Authors: Jaramillo-Hernández Juan Felipe , Julian Vicente , Marco-Detchart Cedric , Rincón Jaime Andrés
Published: January 2024

In the context of recent technological advancements driven by distributed work and open-source resources, computer vision stands out as an innovative force, transforming how machines interact with and comprehend the visual world around us. This work conceives, designs, implements, and operates a computer vision and artificial intelligence method for object detection with integrated depth estimation. With applications ranging from autonomous fruit-harvesting systems to phenotyping tasks, the proposed Depth Object Detector (DOD) is trained and evaluated using the Microsoft Common Objects in Context dataset and the MinneApple dataset for object and fruit detection, respectively. The DOD is benchmarked against current state-of-the-art models. The results demonstrate the proposed method’s efficiency for operation on embedded systems, with a favorable balance between accuracy and speed, making it well suited for real-time applications on edge devices in the context of the Internet of things.
Enhancing Performance and Promoting Sustainability in Female Handball: The Impact of Olympic Movement Training on Jumping, Throwing, Sprinting, and Change of Direction
Authors: Orduña-Borraz Estela , Mainer-Pardos Elena , Marco-Contreras Luis Alberto , Lozano Demetrio
Published: January 2024

Improving women’s handball through increased performance and sustainability is crucial. Strength training, especially with Olympic movements, develops strength, power, and speed, key factors for success in team sports. The aim of this study is to provide a comprehensive analysis of the impact of Olympic movements on performance variables such as jumps, throws, sprints, and changes of direction, and additionally, to promote a more sustainable and holistic approach to overall health and wellbeing. Twenty-one women handball players were divided into two groups (the experimental group (EG) [n = 11; age: 15.91 ± 0.70 years; BMI: 21.37] and the control group (CG) [n = 10; age: 15.60 ± 0.52; BMI: 22.31]). All participants performed four assessment tests to determine jump height (Abalakov test), throwing speed (throw test), running speed (20 m sprint) and change of direction ability (V-cut test). Measurements were carried out before and after the intervention. For six weeks, the control group performed the strength work established by the club twice a week while the intervention group additionally performed training with Olympic movements. Significant differences (p < 0.05) were found between the pre and post measurement of the control group and the intervention group in jump height, throwing speed, and running speed, being higher in the intervention group. For change of direction, no significant differences were found. Between groups, significant differences were observed at the end of the intervention for jump height and running speed. The conclusion of this study is that the experimental group achieved greater improvements in jumping performance, throwing speed, and running speed in women handball players.
Emerging Sustainability in Carbon Capture and Use Strategies for V4 Countries via Biochemical Pathways: A Review
Authors: Krátký Lukáš , Ledakowicz Stanislaw , Slezak Radoslaw , Bělohlav Vojtěch , Peciar Peter , Petrik Máté , Jirout Tomáš , Peciar Marián , Siménfalvi Zoltán , Šulc Radek , Szamosi Zoltán
Published: January 2024

The world is moving towards decarbonization policies in the energy and industrial sectors to bring down carbon dioxide release and reach net zero emissions. Technologies to capture CO2 and use it as a feedstock to produce CO2-based chemicals and biofuels via chemical or biochemical conversion pathways can potentially reduce the amount of CO2 released. The paper serves the innovative scientific knowledge for CO2 transformation via a biochemical pathway to microalgal biomass with its subsequent treatment to biofuels and bioproducts assuming milder climatic conditions (Central or Eastern Europe, Visegrad countries or climatically related world regions). The recent trends were critically reviewed for microalgal biorefinery to reach the sustainability of microalgal-based chemicals with added value, digestion, hydrothermal liquefaction, pyrolysis, and gasification of microalgal residues. Knowledge-based chemical process engineering analysis, systematic data synthesis, and critical technical evaluation of available life cycle assessment studies evaluated the sustainability of microalgal biorefinery pathways. The research showed that biological CO2 fixation using water, seawater or wastewater to produce third-generation biomass is a promising alternative for bioethanol production via pretreatment, enzymatic hydrolysis, digestion, and distillation, and can be realized on a large scale in an economically viable and environmentally sound manner. Its best economically promising and sustainable pathway is perceived in producing microalgal-based nutraceuticals, bioactive medical products, and food products such as proteins, pigments, and vitamins. Machine learning methods for data mining, process control, process optimization, and geometrical configuration of reactors and bioreactors are the crucial research needs and challenges to implementing microalgal biorefinery in an operational environment.
Alternatives for the Optimization and Reduction in the Carbon Footprint in Island Electricity Systems (IESs)
Authors: Lozano Medina Juan Carlos , Perez-Baez Sebastian , Leon-Zerpa Federico , Mendieta-Pino Carlos A.
Published: January 2024

The penetration of renewable energies in island electricity systems (IESs) poses a series of challenges, which include, among others, grid stability, the response to demand, and the security of the supply. Based on the current characteristics of electricity demand on the islands of the Canary Archipelago (Spain) and their electricity production systems, this study presents a series of alternative scenarios to reduce greenhouse gas (GHG) emissions and increase the penetration of renewable energies. The goal is to optimize combustion-based (nonrenewable) energy production and combine it with renewable-based production that meets the requirements of dynamic response, safety, scaling, and integration with nonrenewable systems in terms of efficiency and power. As verified in the research background, the combination of power producing equipment that is generally employed on the islands is not the best combination to reduce pollution. The aim of this work is to find other possible combinations with better results. A methodology is developed and followed to obtain the lowest GHG production and to determine the measures to be applied based on: (a) changing the fuel type by switching to natural gas in the equipment that allows it; (b) using optimal combinations of the least polluting energy production equipment; (c) integrating, to the extent that it is possible, the Chira-Soria pumped hydroelectric energy storage plant into the Gran Canaria electricity system. A series of alternative scenarios are generated with different operating conditions which show the possibility of increasing the renewable installed capacity in the Canary Islands by up to 36.78% (70% in Gran Canaria), with a 65.13% reduction in GHG emissions and a 71.45% reduction in fuel consumption. The results of this study contribute, through the different measures determined through our research, to the mitigation of GHG emissions.
Ensemble Deep Learning for Automated Damage Detection of Trailers at Intermodal Terminals
Authors: Cimili Pavel , Voegl Jana , Hirsch Patrick , Gronalt Manfred
Published: January 2024

Efficient damage detection of trailers is essential for improving processes at inland intermodal terminals. This paper presents an automated damage detection (ADD) algorithm for trailers utilizing ensemble learning based on YOLOv8 and RetinaNet networks. The algorithm achieves 88.33% accuracy and an 81.08% F1-score on the real-life trailer damage dataset by leveraging the strengths of each object detection model. YOLOv8 is trained explicitly for detecting belt damage, while RetinaNet handles detecting other damage types and is used for cropping trailers from images. These one-stage detectors outperformed the two-stage Faster R-CNN in all tested tasks within this research. Furthermore, the algorithm incorporates slice-aided hyper inference, which significantly contributes to the efficient processing of high-resolution trailer images. Integrating the proposed ADD solution into terminal operating systems allows a substantial workload reduction at the ingate of intermodal terminals and supports, therefore, more sustainable transportation solutions.
ST-HO: Symmetry-Enhanced Energy-Efficient DAG Task Offloading Algorithm in Intelligent Transport System
Authors: Gao Zhibin , Luo Gaoyu , Zhan Shanhao , Liu Bang , Huang Lianfen , Chao Han-Chieh
Published: January 2024

In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered.
Respiratory and Gut Microbiome Modification during Respiratory Syncytial Virus Infection: A Systematic Review
Authors: Yagi Kazuma , Lukacs Nicholas W. , Huffnagle Gary B. , Kato Hideo , Asai Nobuhiro
Published: January 2024

Background: Respiratory syncytial virus (RSV) infection is a major cause of lower respiratory tract infection, especially in infants, and increases the risk of recurrent wheezing and asthma. Recently, researchers have proposed a possible association between respiratory diseases and microbiome alterations. However, this connection has not been fully established. Herein, we conducted a systematic literature review to evaluate the reported evidence of microbiome alterations in patients with RSV infection. Methods: The systematic literature review on the association between RSV and microbiome in humans was conducted by searching PubMed, EMBASE, Scopus, and CINAHL from 2012 until February 2022. The results were analyzed qualitatively, focusing on the relationship between microbiome and RSV infection with available key microbiome-related parameters. Results: In the 405 articles identified by searching databases, 12 (Respiratory tract: 9, Gut: 2, Both: 1) articles in line with the research aims were eligible for this qualitative review. The types of samples for the respiratory tract microbiome and the sequencing methods utilized varied from study to study. This review revealed that the overall microbial composition in both the respiratory tract and gut in RSV-infected patients was different from that in healthy controls. Our generated results demonstrated an increase in the abundance of Haemophilus and Streptococcus, which could contribute to the distinctive separation based on the beta diversity in the respiratory tract. Conclusions: The respiratory tract and gut microbiome changed in patients with RSV infection. Further research with a well-organized longitudinal design is warranted to clarify the impact of microbiome alterations on disease pathogenesis.
The Rapidly Changing Patterns in Bacterial Co-Infections Reveal Peaks in Limited Gram Negatives during COVID-19 and Their Sharp Drop Post-Vaccination, Implying Potential Evolution of Co-Protection during Vaccine–Virus–Bacterial Interplay
Authors: Said Kamaleldin B. , Alsolami Ahmed , Alshammari Khalid F. , Moussa Safia , Alshammeri Fawaz , Alghozwi Mohammed H. , Alshammari Sulaiman F. , Alharbi Nawaf F. , Khalifa Amany M. , Mahmoud Madiha R. , Alshammari Kawthar , Ghoniem Mohamed E.
Published: January 2024

SARS-CoV-2 has caused the most devastating pandemic of all time in recent human history. However, there is a serious paucity of high-quality data on aggravating factors and mechanisms of co-infection. This study aimed to identify the trending patterns of bacterial co-infections and types and associated outcomes in three phases of the pandemic. Using quality hospital data, we have investigated the SARS-CoV-2 fatality rates, profiles, and types of bacterial co-infections before, during, and after COVID-19 vaccination. Out of 389 isolates used in different aspects, 298 were examined before and during the pandemic (n = 149 before, n = 149 during). In this group, death rates were 32% during compared to only 7.4% before the pandemic with significant association (p-value = 0.000000075). However, the death rate was 34% in co-infected (n = 170) compared to non-co-infected patients (n = 128), indicating a highly significant value (p-value = 0.00000000000088). However, analysis of patients without other serious respiratory problems (n = 28) indicated that among the remaining 270 patients, death occurred in 30% of co-infected patients (n = 150) and only 0.8% of non-co-infected (n = 120) with a high significant p-value = 0.00000000076. The trending patterns of co-infections before, during, and after vaccination showed a significant decline in Staphylococcus aureus with concomitant peaks in Gram negatives n = 149 before/n = 149 during, including Klebsiella pneumonian = 11/49 before/during, E. coli n = 10/24, A. baumannii n = 8/25, Ps. aeruginosa n = 5/16, and S. aureus 13/1. Nevertheless, in the post-vaccination phase (n = 91), gender-specific co-infections were examined for potential differences in susceptibility. Methicillin-resistant S. aureus dominated both genders followed by E. coli in males and females, with the latter gender showing higher rates of isolations in both species. Klebsiella pneumoniae declined to third place in male patients. The drastic decline in K. pneumoniae and Gram negatives post-vaccination strongly implied a potential co-protection in vaccines. Future analysis would gain more insights into molecular mimicry.
Global Meta-Analysis of Nitrate Leaching Vulnerability in Synthetic and Organic Fertilizers over the Past Four Decades
Authors: Hina Naila Sumreen
Published: January 2024

The significance of nitrogen in agricultural ecosystems cannot be overstated; however, it can pose a threat to the environment when it leaches into groundwater. This comprehensive meta-analysis sheds light on the complex relationship between organic and inorganic fertilizers and nitrogen leaching, encompassing 39 years of data. The results indicate that the impact of fertilizers is dependent on crop type, soil properties, and fertilization practices. Vegetables treated with synthetic fertilizers were found to have the highest levels of nitrogen leaching, while grasses exhibited the lowest. Soil texture was also determined to be a significant factor, with coarser soils leading to higher levels of leaching than medium or fine soils. The meta-analysis showed that organic sources resulted in an average of 16% higher losses of nitrate-N, but there was no significant difference between organic and synthetic N fertilizers in terms of leaching overall. These findings provide valuable insights for the responsible management of nitrogen and to further our understanding of the impact of fertilizers on nitrate pollution.
Characteristics of Groundwater Microbial Community Composition and Environmental Response in the Yimuquan Aquifer, North China Plain
Authors: Zhang Huaisheng , Lv Yonggao , Zhang Tao , Zhang Lei , Ma Xuemei , Liu Xuesong , Lian Sheng
Published: January 2024

To explore the intricate interrelationship between groundwater microbial communities and hydrochemical characteristics in the Mancheng District, Baoding City, samples from karst groundwater and porous groundwater were collected. Hydrochemical analysis and the 16s RNA gene V4-V5 region sequencing method were used to detail the water environmental characteristics and microbial community distribution pattern in the study area. The hydrochemical results reveal notable differences in major ion contents and other hydrochemical indices between karst water and porous water. Microbial community analysis demonstrates that Proteobacteria, Actinobacteria, and Firmicutes are the dominant taxa in both types of water bodies, albeit with significant variations in their proportions across distinct water bodies. The observed disparities in microbial communities between karst water and porous water underscore the influence of environmental factors such as ion concentration and pH on microbial distribution. Redundancy Analysis (RDA) indicates notable associations between microbial community composition and environmental factors in both types of groundwater environments. Studying the distribution pattern of microbial communities and the connection with environmental factors in two types of groundwater lays a crucial foundation for interpreting the functional roles of microbes in groundwater ecosystems.
Land Subsidence Due to Groundwater Exploitation in Unconfined Aquifers: Experimental and Numerical Assessment with Computational Fluid Dynamics
Authors: Chalá Dayana Carolina , Quiñones-Bolaños Edgar , Mehrvar Mehrab
Published: January 2024

Land subsidence is a global challenge that enhances the vulnerability of aquifers where climate change and driving forces are occurring simultaneously. To comprehensively analyze this issue, integrated modeling tools are essential. This study advances the simulation of subsidence using Computational Fluid Dynamics (CFD); it assessed the effects of exploitation and recharge of groundwater on the vertical displacement of coarse and fine sands in a laboratory-scale aquifer. A model was developed by integrating the Navier–Stokes equations to study the groundwater flow and Terzaghi’s law for the vertical displacement of sands. The boundary conditions used were Dirichlet based on the changes in the hydraulic head over time. The specific storage coefficient was used to calibrate the model. The findings confirmed that subsidence occurs at slower rates in soil with fine sands with average particle diameters of 0.39 mm than in coarse sands with average particle diameters of 0.67 mm. The maximum discrepancy between the experimental and the numerical reaffirms that CFD platforms can be used to simulate subsidence dynamics and potentially allow the simultaneous simulation of other dynamics. Concluding remarks and recommendations are highlighted considering the up-to-date advances and future work to improve the research on subsidence in unconfined aquifers.
Assessment of Multi-Regional Comprehensive Benefits of the South-to-North Water Diversion Project in China
Authors: Yang Mingming , Qin Changhai , Zhu Yongnan , Zhao Yong , He Guohua , Wang Lizhen
Published: January 2024

Inter-basin water transfer (IBWT) projects are an effective means of addressing regional water resource imbalances. However, owing to the long construction cycle, large investment amount, and wide impact range, water diversion projects exhibit delayed and complex benefits, often lacking clear comprehension. In this study, we established a multi-regional comprehensive benefit assessment framework for the IBWT, considering spatiotemporal and multi-dimensional value effects. Using the South-to-North Water Diversion Project (SNWDP) in China as an example, we assessed its comprehensive benefits from 2003 to 2020. The results showed that the comprehensive benefits of the project were USD 207 billion, encompassing economic and ecological benefits, accounting for 71.6% and 28.4%, respectively. In 2020, the benefits of the SNWDP amounted to USD 39.3 billion, with a per-unit area benefit range of USD −1.03 to 4.27. The operation of the SNWDP effectively alleviated groundwater overexploitation in water-receiving areas. However, without the SNWDP, the total loss caused by industrial development limitations due to water scarcity would have been USD 154.3 billion. These results indicate the importance of a practical framework for assessing IWBT project benefits, aiding managers in assessment tasks, and facilitating the prediction and adjustment of project benefits.
Zeta Diversity Reveals the Small Wetland Complex Promotes Biodiversity
Authors: Chen Jun , Zheng Fuchao , Li Dianpeng , Zhou Rixiu , Jv Wenming , Leng Xin , Xia Lu , An Shuqing
Published: January 2024

Small wetlands provide crucial ecosystem services but are facing widespread loss and degradation. At present, small wetland complexes provide an alternative avenue for conservation; we used a dataset to illustrate how zeta diversity can be assessed in a small wetland complex. The small wetland complex studied consisted of nine hydrologically connected ponds, and all the ponds were connected by narrow ditches. Data collected included the composition of both zooplankton and phytoplankton communities, water physicochemical parameters, and hydrological connectivity. Two ways in which connectivity affects biodiversity, dispersal filtration (path distance-controlled biological diffusion) and environmental filtration (environment-selected species), were quantified. As a result, (1) 46 zooplankton and 119 phytoplankton species were identified, with species richness distribution significantly affected by water properties (explained variances of 68% and 39%, respectively), reflecting significant environmental filtration, and (2) both zooplankton and phytoplankton showed zeta diversity distance decay (p < 0.001), reflecting significant dispersal filtration, and (3) relatively rare species dominated community turnover, in which environmental filtration was far stronger than dispersal filtration. Overall, the small wetland complex in this study was characterized by hydrological connectivity, which not only allowed moderate biological connectivity but also promoted the coexistence of diverse habitats and communities. We emphasize the applicability of zeta diversity for assessing small wetland complexes, further demonstrating the value of small wetland complexes in promoting biodiversity such as species richness and species turnover.
Effective therapy for menopausal syndrome without hormones and side effects. Illusion? Reality!
Authors:   FSBEI CPE RMACPE of the Ministry of Health of the Russian Federation, Penza, Vinogradova O. P. , Sergeeva-Kondrachenko M. Yu. ,   FSBEI CPE RMACPE of the Ministry of Health of the Russian Federation, Penza, Biryuchkova O. A. ,   FSBEI CPE RMACPE of the Ministry of Health of the Russian Federation, Penza, Panina E. S. ,   FSBEI HPE Penza State University, Penza, Petrova E. V. ,   FSBEI HPE Penza State University, Penza, Gorina O. S. ,   State Budgetary Healthcare Institution «Penza Regional Clinical Hospital named after N. N. Burdenko», Penza, Elistratov D. G. ,   Medical Center «Secrets of Longevity», Penza
Published: January 2024

The article describes the problems of the menopausal transition period that cause a woman significant discomfort and reduce her quality of life, characterizes the most common methods of treating menopausal syndrome, and suggests possible reasons for their lack of effectiveness. Femoklim, a fundamentally new anti-menopausal drug for the treatment of complications of age-related hormonal changes, is analyzed. It takes due regard to all possible mechanisms of the occurrence of menopausal syndrome, has a complex versatile effect on the female body, and does not provoke the development of cancer.
Improvement of the methodology for calculating depreciation deductions of fixed assets of agricultural organizations
Authors:   Dagestan state agricultural university named after M. M. Dzhambulatova, Makhachkala, Russia, Yusufov A. M. , Orujova Z. A. ,   Dagestan state agricultural university named after M. M. Dzhambulatova, Makhachkala, Russia, Yusufova A. N. ,   Dagestan state agricultural university named after M. M. Dzhambulatova, Makhachkala, Russia
Published: January 2024

The article deals with the problems of depreciation deductions for fixed assets in modern economic conditions. Special attention is paid to the calculation methodology and comparison of the results of the current methods of its accrual. An improved method of calculating depreciation in accordance with the performance and physical wear of fixed assets is proposed. Graphs and tables have been compiled justifying the recommended methodology from the practice of agricultural organizations. The assessment of the proposed new methods of depreciation is given on the basis of criteria selected for this purpose and reflected in the work. The results of the study made it possible to recognize the accrual of depreciation in accordance with physical wear and performance as a more acceptable method that does not have the disadvantages characteristic of the currently practiced methods of calculating depreciation charges. The approach chosen in the study is simple and easily assimilated by accountants, requiring compliance with the conditions of accounting support. The recommended method of depreciation calculation is proposed to be included in the federal accounting standard 6/2020 “Fixed assets” for practical use.