bluuebunny/crossref_metadata_2025 huggingface dataset:
Factors Contributing to Successful Spontaneous Dog–Human Cooperation
Authors: Csepregi Melitta , Gácsi Márta
Published: July 2023

Dogs’ ability to cooperate with humans is widely acknowledged, but the factors influencing their spontaneous cooperative tendencies are largely unknown. We investigated whether breed function, training experience, and owner-reported social motivation level contribute to spontaneous dog–owner cooperation. Family dogs (N = 100) of three breed groups (non-working dogs, cooperative/independent working breeds) with various training experiences were tested in an ‘out–of–reach’ task with their owners as their partners, who never directly asked for help during the test. We measured dogs’ behaviour along three main components of successful cooperation: paying attention, understanding the problem, and willingness to cooperate. Breed groups had no significant effect on dogs’ behaviour. No factor was associated with the behavioural variables related to not understanding the task. Dogs with high training levels and high social motivation showed more attention-related behaviours and were more likely to help the owner (training level and social motivation were not correlated with each other). Our results highlight the importance of training experience and social motivation in dogs’ attentiveness and spontaneous cooperativity. This also points to the need for careful sample balancing and experimental procedures that do not rely on specific trained skills.
Cefto Real-Life Study: Real-World Data on the Use of Ceftobiprole in a Multicenter Spanish Cohort
Authors: Hidalgo-Tenorio Carmen , Pitto-Robles Inés , Arnés García Daniel , de Novales F. , Morata Laura , Mendez Raul , de Pablo Olga , López de Medrano Vicente , Lleti Miguel , Vizcarra Pilar , Lora-Tamayo Jaime , Arnáiz García Ana , Núñez Leonor , Masiá Mar , Seco Maria , Sadyrbaeva-Dolgova Svetlana
Published: July 2023

Background: Ceftobiprole is a fifth-generation cephalosporin that has been approved in Europe solely for the treatment of community-acquired and nosocomial pneumonia. The objective was to analyze the use of ceftobiprole medocaril (Cefto-M) in Spanish clinical practice in patients with infections in hospital or outpatient parenteral antimicrobial therapy (OPAT). Methods: This retrospective, observational, multicenter study included patients treated from 1 September 2021 to 31 December 2022. Results: A total of 249 individuals were enrolled, aged 66.6 ± 15.4 years, of whom 59.4% were male with a Charlson index of four (IQR 2–6), 13.7% had COVID-19, and 4.8% were in an intensive care unit (ICU). The most frequent type of infection was respiratory (55.8%), followed by skin and soft tissue infection (21.7%). Cefto-M was administered to 67.9% of the patients as an empirical treatment, in which was administered as monotherapy for 7 days (5–10) in 53.8% of cases. The infection-related mortality was 11.2%. The highest mortality rates were identified for ventilator-associated pneumonia (40%) and infections due to methicillin-resistant Staphylococus aureus (20.8%) and Pseudomonas aeruginosa (16.1%). The mortality-related factors were age (OR: 1.1, 95%CI (1.04–1.16)), ICU admission (OR: 42.02, 95%CI (4.49–393.4)), and sepsis/septic shock (OR: 2.94, 95%CI (1.01–8.54)). Conclusions: In real life, Cefto-M is a safe antibiotic, comprising only half of prescriptions for respiratory infections, that is mainly administered as rescue therapy in pluripathological patients with severe infectious diseases.
Aldehyde Dehydrogenase and Aldo-Keto Reductase Enzymes: Basic Concepts and Emerging Roles in Diabetic Retinopathy
Authors: Karan Burak Mugdat , Little Karis , Augustine Josy , Stitt Alan W. , Curtis Tim M.
Published: July 2023

Diabetic retinopathy (DR) is a complication of diabetes mellitus that can lead to vision loss and blindness. It is driven by various biochemical processes and molecular mechanisms, including lipid peroxidation and disrupted aldehyde metabolism, which contributes to retinal tissue damage and the progression of the disease. The elimination and processing of aldehydes in the retina rely on the crucial role played by aldehyde dehydrogenase (ALDH) and aldo-keto reductase (AKR) enzymes. This review article investigates the impact of oxidative stress, lipid-derived aldehydes, and advanced lipoxidation end products (ALEs) on the advancement of DR. It also provides an overview of the ALDH and AKR enzymes expressed in the retina, emphasizing their growing importance in DR. Understanding the relationship between aldehyde metabolism and DR could guide innovative therapeutic strategies to protect the retina and preserve vision in diabetic patients. This review, therefore, also explores various approaches, such as gene therapy and pharmacological compounds that have the potential to augment the expression and activity of ALDH and AKR enzymes, underscoring their potential as effective treatment options for DR.
Oxidative Stress and Performance after Training in Professional Soccer (European Football) Players
Authors: Abate Michele , Pellegrino Raffaello , Di Iorio Angelo , Salini Vincenzo
Published: July 2023

Vitamins, hormones, free radicals, and antioxidant substances significantly influence athletic performance. The aim of this study was to evaluate whether these biological mediators changed during the season and if this was associated with the rate of improvement in performance after training, assessed by means of a standardized test. Professional male soccer players took part in the study. Two evaluations were performed: the first in the pre-season period and the second at the mid-point of the official season, after about 6 months of intensive training and weekly matches. Blood levels of vitamins D, B12, and folic acid, testosterone and cortisol, free radicals, and antioxidant substances were measured. Two hours after breakfast, a Yo-Yo test was performed. The relationships between the biological mediators and the rate of improvement after training (i.e., the increase in meters run in the Yo-Yo test between the pre-season and mid-season periods) were evaluated by means of a linear mixed models analysis. Results: Eighty-two paired tests were performed. The athletes showed better performance after training, with an increase in the meters run of about 20%. No significant relationships between the vitamin and hormone values and the gain in the performance test were observed. Plasmatic levels of free radicals increased significantly, as did the blood antioxidant potential. An indirect relationship between oxidative stress and the improvement in performance was observed (free radicals β ± SE: = −0.33 ± 0.10; p-value = 0.001), with lower levels of oxidative stress being associated with higher levels of performance in the Yo-Yo test. Monitoring the measures of oxidative stress could be a useful additional tool for coaches in training and/or recovery programs tailored to each player.
Natural Extracts Mitigate the Deleterious Effects of Prolonged Intense Physical Exercise on the Cardiovascular and Muscular Systems
Authors: Yehya Marc , Boulghobra Doria , Grillet Pierre-Edouard , Fleitas-Paniagua Pablo R. , Bideaux Patrice , Gayrard Sandrine , Sicard Pierre , Thireau Jérome , Reboul Cyril , Cazorla Olivier
Published: July 2023

Muscle fatigue is a common symptom induced by exercise. A reversible loss of muscle force is observed with variable rates of recovery depending on the causes or underlying mechanisms. It can not only affect locomotion muscles, but can also affect the heart, in particular after intense prolonged exercise such as marathons and ultra-triathlons. The goal of our study was to explore the effect of four different natural extracts with recognized antioxidant properties on the contractile function of skeletal (locomotion) and cardiac muscles after a prolonged exhausting exercise. Male Wistar rats performed a bout of exhausting exercise on a treadmill for about 2.5 h and were compared to sedentary animals. Some rats received oral treatment of a natural extract (rosemary, buckwheat, Powergrape®, or rapeseed) or the placebo 24 h and 1 h before exercise. Experiments were performed 30 min after the race and after 7 days of recovery. All natural extracts had protective effects both in cardiac and skeletal muscles. The extent of protection was different depending on muscle type and the duration post-exercise (just after and after one-week recovery), including antiarrhythmic effect and anti-diastolic dysfunction for the heart, and faster recovery of contractility for the skeletal muscles. Moreover, the muscular protective effect varied between natural extracts. Our study shows that an acute antioxidant supplementation can protect against acute abnormal endogenous ROS toxicity, induced here by prolonged exhausting exercise.
Effects of Tannic Acid Supplementation on the Intestinal Health, Immunity, and Antioxidant Function of Broilers Challenged with Necrotic Enteritis
Authors: Xu Huiping , Zhang Xiaodan , Li Peng , Luo Yimeng , Fu Jianyang , Gong Lu , Lv Zengpeng , Guo Yuming
Published: July 2023

Clostridium perfringens causes necrotic enteritis (NE) after proliferation in the intestine of poultry, resulting in considerable losses to the poultry industry. This study aimed to investigate the impact of tannic acid on the antioxidant, immunity, and gut health of broilers with NE. In the experiment, 630 one-day-old Cobb500 male chicks were randomly divided into six treatment groups, with seven replicate cages and with fifteen birds in each cage. The treatment groups were as follows: control group (NC), challenged group (PC), and challenged NE chickens treated with 250, 500, 750, and 1000 mg/kg tannic acid (PTA1, PTA2, PTA3, and PTA4, respectively). To induce NE, coccidia vaccine and Clostridium perfringens were administered on day 19 and days 22–28, respectively. Indexes related to antioxidant, immune, and intestinal health were measured on days 28 and 35. During the infection period, we observed significant increases in fecal water content, D-LA, TNF-α, and malondialdehyde concentrations (p < 0.05). Conversely, significant decreases were noted in chyme pH and in T-AOC, IL-4, and IL-10 concentrations (p < 0.05). The addition of tannic acid exhibited a linear decrease in fecal water content and TNF-α concentration (p < 0.05). Furthermore, tannic acid supplementation resulted in a quadratic curve decrease in D-LA concentration and linear increases in T-AOC, IL-4, and IL-10 (p < 0.05). Cecal microbiological analysis revealed that Ruminococcaceae and Butyricimona were dominant in PTA3. In conclusion, the dietary addition of tannic acid may reduce the negative effects of NE by increasing antioxidant and anti-inflammatory capacity, improving the intestinal barrier, and regulating the intestinal flora.
A Comparative Regression Analysis between Principal Component and Partial Least Squares Methods for Flight Load Calculation
Authors: Yan Qi , Yang Chao , Wan Zhiqiang
Published: July 2023

This study investigates and compares various multivariate regression methods, including principal component regression (PCR) and partial least squares regression (PLSR), for flight load analysis and demonstrates their high learning efficiency and strong generalization capabilities, making them highly suitable for this purpose. The flight load data of a civil aircraft use altitude, Mach number and load factors as input parameters, which are used as sample data to establish regression models for predicting wing loads under different flight conditions. The accuracy of all regressions are confirmed through evaluation, with PLSR being the most efficient. In the comparison of computational times, it was found that the computational efficiency of regression methods was significantly superior to traditional panel methods. The flight load calculation shows that PCR and PLSR can significantly improve analysis efficiency and provide new insights into efficient flight load analysis.
Study on Rock Failure Criterion Based on Elastic Strain Energy Density
Authors: Cheng Yang , Zhang Liangliang
Published: July 2023

Uniaxial and five conventional triaxial compression tests were conducted on sandstone to obtain the evolution laws of the input energy density, elastic strain energy density, and dissipative energy density. The input and dissipative energy densities increased with increasing axial strain; the elastic strain energy density increased with increasing axial strain at the pre-peak stage and decreased after the peak. According to the linear change rule between the peak elastic strain energy density and confining pressure, the energy density failure criterion of sandstone was established, and the criterion has high precision and few parameters, and the parameters have clear physical meaning. Moreover, the expression of the energy density failure criterion was similar to the classical Hoek-Brown criterion, but its adaptability was more extensive. The strength calculation results for seven different rocks under different confining pressures calculated using the energy density failure criterion were consistent with the experimental values, and the calculation error was smaller than that of the Mohr–Coulomb criterion and Drucker–Prager criterion, verifying the accuracy and applicability of the criterion.
Maneuver Decision-Making Method for Ship Collision Avoidance in Chengshantou Traffic Separation Scheme Waters
Authors: He Yixiong , Du Zijun , Huang Liwen , Yu Deqing , Liu Xiao
Published: July 2023

A maneuvering decision-making model based on time series rolling and feedback compensation methods is proposed to solve the problem of high traffic risk in Chengshantou traffic separation scheme (TSS) waters. Firstly, a digital traffic environment model suitable for the TSS waters is proposed. Secondly, a navigation risk identification method in these waters is constructed based on the digitized traffic environment and situation identification model in the Chengshantou TSS waters. Thirdly, considering the requirements of the rules and good seamanship, minimum course altering is obtained by combining the collision avoidance mechanism. Lastly, a maneuvering decision-making model in the TSS waters based on time series rolling and feedback compensation methods is developed. The simulation results show that the ship can correctly identify the collision risk and appropriately obtain maneuvering decisions, and can resume the planned route under the premise of ensuring safety. When the target ships alter course or change speed, the ship can also make adaptive maneuvering decisions. In summary, the proposed method meets the requirement of safe navigation in Chengshantou waters and provides a theoretical basis for the realization of intelligent navigation in waters similar to TSS.
Research on Virtual Track Train Path-Tracking Control Based on Improved MPC and Hierarchical Framework: A Reconfigurable Approach
Authors: Wang Zehan , Lu Zhenggang , Wei Juyao , Qiu Xiaojie
Published: July 2023

The virtual track train (VTT) is a new urban public transportation system that adopts all-axle steering and distributed drive. The Super autonomous Rail rapid Transit (SRT), as one of them, adopts a four-module six-axle structure. In response to its crucial problem, its path tracking, this article proposes a reconfigurable dynamic modeling method, which has two parts: a multi-body dynamics model with generalized forces at each module’s center of gravity (CG) as the input, and the CG generalized force model, which expresses the CG generalized forces generated by the wheel control inputs. Then, a path-tracking strategy is proposed based on the improved MPC and hierarchical framework. Firstly, the CG generalized forces of each module required for path tracking were calculated, and then the CG generalized force redistribution was performed and the “virtual axle” method was proposed. Finally, the wheel state of each module was allocated. This strategy reduces the complexity of each layer of the controller and it solves the problem of insufficient actuators in the middle two modules of the SRT. Finally, through a hardware-in-the-loop (HIL) real-time simulation and comparison with different control strategies, the control strategy’s effectiveness, adaptability, and robustness were verified.
Research on a BP Neural Network Slope Safety Coefficient Prediction Model Based on Improved Sparrow Algorithm Optimization
Authors: Wang Yiwen , Liu Dongna , Dong Haiyu , Lin Junwei , Zhang Qi , Zhang Xiaohui
Published: July 2023

Through the stability evaluation of a slope, a landslide geological disaster can be identified, and the safety and risk control of a project can be ensured. This work proposes an improved sparrow search algorithm to optimize the slope safety factor prediction model (ISSA–BP) of a BP neural network, through an improvement in two aspects: introducing dynamic weight factors and reverse learning strategies to realize adaptive searches. The optimal value improves a defect in the traditional model, preventing it from easily falling into the local minimum. First, combined with 352 sets of actual slope data, three machine learning models were used to predict the safety factor of the slope. Then, the accuracy index was used for evaluation. Compared with other models, the MAPE, RMSE, and R2 of the ISSA-BP model were 1.64%, 0.0296, and 0.99, respectively, and the error was reduced by 78% compared with the BP neural network, showing better accuracy. Finally, the three models were applied to the slope stability analysis of Tianbao Port in Wenshan Prefecture. The research shows that the predicted value of the ISSA–BP model was the closest to the actual safety factor, which verified the experimental results. The improved ISSA–BP model can effectively predict the safety factor of slopes under different conditions, and it provides a new technology for slope disaster warning and control.
Experimental Investigation of Mechanical Properties of Clay–Cement Slurry Containing Graphene Oxide
Authors: Sun Jinze , Liu Shujie , Zhang Jiwei , Tian Qinghao , Yu Zhijie , Xie Zuodong
Published: July 2023

As a widely used material in underground engineering, clay–cement slurry grouting is known for its initial poor anti-seepage and filtration capacity, the low strength of the resulting stone body, and its tendency towards brittle failure. To explore efficient and environmentally friendly grouting materials, industrial-grade graphene oxide (GO) was incorporated into a clay–cement slurry to create a new type of slurry called a GO composite. These GO composites were then utilized to reinforce fractured formations. Uniaxial compression tests, shear strength tests, permeability tests, and electron microscopy scans were conducted to investigate the strength, permeability, and microscopic features of the GO composite-reinforced fractured formations. Furthermore, the optimization effect and application prospects of graphene oxide on clay–cement slurry materials were evaluated. The experimental results demonstrated that the modified slurry effectively improved the compressive strength (increased by 7.2% to 32.5%) and shear strength (increased by 28.6% to 105.3%) of consolidated fractured gravel. By conducting orthogonal experiments with range analysis, variance analysis, and multiple regression analysis, it was shown that there was a strong correlation between the consolidated body and three factors influencing the permeability coefficient. Among these factors, the OPC content had the most significant impact on the permeability coefficient, followed by the GO content. Graphene oxide was found to promote cement hydration reactions, guide the growth of hydration products on the surface of graphene oxide nanosheets, optimize the pore structure in grouting materials, and reduce microcracks between the slurry and the fractured gravel interface. Electron microscopy characterization and fractal analysis revealed that the addition of graphene oxide effectively reduced the degree of microdamage during the sample’s failure process. This ensured the integrity of the sample during the unstable failure process, enhanced the material’s toughness, and improved its ability to resist loads.
eDA3-X: Distributed Attentional Actor Architecture for Interpretability of Coordinated Behaviors in Multi-Agent Systems
Authors: Motokawa Yoshinari , Sugawara Toshiharu
Published: July 2023

In this paper, we propose an enhanced version of the distributed attentional actor architecture (eDA3-X) for model-free reinforcement learning. This architecture is designed to facilitate the interpretability of learned coordinated behaviors in multi-agent systems through the use of a saliency vector that captures partial observations of the environment. Our proposed method, in principle, can be integrated with any deep reinforcement learning method, as indicated by X, and can help us identify the information in input data that individual agents attend to during and after training. We then validated eDA3-X through experiments in the object collection game. We also analyzed the relationship between cooperative behaviors and three types of attention heatmaps (standard, positional, and class attentions), which provided insight into the information that the agents consider crucial when making decisions. In addition, we investigated how attention is developed by an agent through training experiences. Our experiments indicate that our approach offers a promising solution for understanding coordinated behaviors in multi-agent reinforcement learning.
Performing Arithmetic Operations with Locally Homogeneous Spiking Neural P Systems
Authors: Zhang Xu , Hu Zongrong , Li Jingyi , Liu Ran
Published: July 2023

The parallelism of rule execution in membrane computing provides support for improving computational efficiency. Membrane computing models have been applied in many fields. In arithmetic operations, designing basic arithmetic operation spiking neural P systems using fewer neurons and rule types has been an important field of membrane computing application research in recent years. We discuss the application of locally homogeneous spiking neural P systems in arithmetic operations. The purpose is to design a spiking neural P system with fewer neurons and rule types to perform arithmetic operations. We designed the addition and subtraction of a locally homogeneous spiking neural P system without weight and delay. They include two input neurons to achieve any two binary number subtraction, one input neuron to achieve any two binary number addition and subtraction, and one input neuron to achieve any n binary number addition and subtraction. This is an attempt to apply the locally homogeneous spiking neural P system in arithmetic operations. Compared with the current excellent spiking neural P system performing arithmetic operations, our designed locally homogeneous spiking neural P system is more concise. The system we designed reduces the number of neurons required for n number addition operations by k − 6 and reduces the number of rule types by 5k − 14.
Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention Mechanism
Authors: Liu Jiaqi , Sun Jian , Qi Xiao
Published: July 2023

With the aim of promoting energy conservation and emission reduction, electric vehicles (EVs) have gained significant attention as a strategic industry in many countries. However, the insufficiency of accessible charging infrastructure remains a challenge, hindering the widespread adoption of EVs. To address this issue, we propose a novel approach to optimize the placement of charging stations within a road network, known as the charging station location problem (CSLP). Our method considers multiple factors, including fairness in charging station distribution, benefits associated with their placement, and drivers’ discomfort. Fairness is quantified by the balance in charging station coverage across the network, while driver comfort is measured by the total time spent during the charging process. Then, the CSLP is formulated as a reinforcement learning problem, and we introduce a novel model called PPO-Attention. This model incorporates an attention layer into the Proximal Policy Optimization (PPO) algorithm, enhancing the algorithm’s capacity to identify and understand the intricate interdependencies between different nodes in the network. We have conducted extensive tests on urban road networks in Europe, North America, and Asia. The results demonstrate the superior performance of our approach compared to existing baseline algorithms. On average, our method achieves a profit increase of 258.04% and reduces waiting time by 73.40%, travel time by 18.46%, and charging time by 40.10%.
Experimental and Theoretical Study on Operation Characteristics of an Oscillating Heat Pipe
Authors: Ling Yunzhi , Li Xiaozhao , Zhang Xiaosong , Liu Zhan , Zhao Peng
Published: July 2023

An oscillating heat pipe (OHP) is an effective heat transfer device for the thermal management of electronic devices. However, the heat transfer mechanism of the OHP was not fully understood due to its complicated operation characteristics. In this paper, the thermal performance of an OHP was experimentally studied. The condensation and evaporation temperature variations were monitored under different heat inputs and were then used to evaluate the OHP system operating characteristics. Thermal resistance was used as a key parameter to evaluate the thermal performance of the OHP system. The results indicated that as the heat input increased from 25 to 100 W, the average thermal resistance decreased while the stable evaporating and condensing temperatures increased. The equivalent heat transfer coefficient was derived theoretically. It showed that the reciprocal of the radial heat transfer coefficient increased with increasing liquid film thickness. Based on this result, an empirical correlation was proposed to evaluate the thermal resistance of an OHP system. This correlation was validated using both the experimental data provided in this study and the data collected from the open literature. The comparison results indicated that the proposed empirical correlation could reasonably predict the thermal resistance under different filling ratios and heat inputs.
Removal of Metals and Dyes in Water Using Low-Cost Agro-Industrial Waste Materials
Authors: Tejada-Tovar Candelaria , Villabona-Ortíz Ángel , Ortega-Toro Rodrigo
Published: July 2023

The pollution of water bodies due to the discharge of effluents without treatment is a global problem. Therefore, different technologies have been implemented for the removal of contaminants from wastewater before the final disposition. Among them, adsorption processes using residual biomasses are becoming very popular due to the low cost and high availability of adsorbents. Thus, in the present work, the synthesis of modified biochar from agro-industrial residues derived from the wheat-processing industry, as a valorization alternative of these residues, for its use in the removal of Cr (VI) and methylene blue (MB) has been analyzed. The biochar was prepared using a ramp function of 5 °C/min until 250 °C for 30 min. The adsorption tests were developed in a batch system, using 30 mg of adsorbent in 10 mL of solution. From SEM analysis, the formation of tubular cavities and porous structure was seen, caused by the basic hydrolysis with KOH. From adsorption tests, an adsorption capacity of 12.98 mg/g and 97.38% of efficiency for MB at pH 10 was noted, while for Cr (VI), it was 11.35 mg/g and 85.15% at pH 2. Freundlich’s model adjusted the adsorption equilibrium data with R2 > 0.9. The maximum adsorption capacities in the monolayer were 186,375 mg/g and 90.723 mg/g for Cr (VI) and MB, according to Langmuir’s model. From a kinetic study, it can be said that the process occurs by chemisorption through electrostatic interaction and ionic interchange between adsorbate and adsorbent.
Evaluating the Influence of Room Illumination on Camera-Based Physiological Measurements for the Assessment of Screen-Based Media
Authors: Williams Joseph , Francombe Jon , Murphy Damian
Published: July 2023

Camera-based solutions can be a convenient means of collecting physiological measurements indicative of psychological responses to stimuli. However, the low illumination playback conditions commonly associated with viewing screen-based media oppose the bright conditions recommended for accurately recording physiological data with a camera. A study was designed to determine the feasibility of obtaining physiological data, for psychological insight, in illumination conditions representative of real world viewing experiences. In this study, a novel method was applied for testing a first-of-its-kind system for measuring both heart rate and facial actions from video footage recorded with a single discretely placed camera. Results suggest that conditions representative of a bright domestic setting should be maintained when using this technology, despite this being considered a sub-optimal playback condition. Further analyses highlight that even within this bright condition, both the camera-measured facial action and heart rate data contained characteristic errors. In future research, the influence of these performance issues on psychological insights may be mitigated by reducing the temporal resolution of the heart rate measurements and ignoring fast and low-intensity facial movements.
Voice Deepfake Detection Using the Self-Supervised Pre-Training Model HuBERT
Authors: Li Lanting , Lu Tianliang , Ma Xingbang , Yuan Mengjiao , Wan Da
Published: July 2023

In recent years, voice deepfake technology has developed rapidly, but current detection methods have the problems of insufficient detection generalization and insufficient feature extraction for unknown attacks. This paper presents a forged speech detection method (HuRawNet2_modified) based on a self-supervised pre-trained model (HuBERT) to improve detection (and address the above problems). A combination of impulsive signal-dependent additive noise and additive white Gaussian noise was adopted for data boosting and augmentation, and the HuBERT model was fine-tuned on different language databases. On this basis, the size of the extracted feature maps was modified independently by the α-feature map scaling (α-FMS) method, with a modified end-to-end method using the RawNet2 model as the backbone structure. The results showed that the HuBERT model could extract features more comprehensively and accurately. The best evaluation indicators were an equal error rate (EER) of 2.89% and a minimum tandem detection cost function (min t-DCF) of 0.2182 on the database of the ASVspoof2021 LA challenge, which verified the effectiveness of the detection method proposed in this paper. Compared with the baseline systems in databases of the ASVspoof 2021 LA challenge and the FMFCC-A, the values of EER and min t-DCF decreased. The results also showed that the self-supervised pre-trained model with fine-tuning can extract acoustic features across languages. And the detection can be slightly improved when the languages of the pre-trained database, and the fine-tuned and tested database are the same.
Investigation on the Stress and Deformation Evolution Laws of Shield Tunnelling through a Mining Tunnel Structure
Authors: Du Entong , Zhou Lei , Fei Ruizhen
Published: July 2023

In the construction of a shield crossing an existing mined tunnel without load, it is imperative to develop corresponding design standards that reflect actual engineering force characteristics to ensure the successful completion of the tunnel construction. This study uses the MIDAS-GTS NX 2022 finite element software to facilitate the creation of a numerical model of a shield structure for an air-push-over mine tunnel project in Changsha, China while investigating the stress field’s evolution during shield construction and calculating the maximum positive and negative bending moments and maximum axial forces for different structures and other force states under various construction conditions. This study’s findings informed the design and construction optimisation of a shield tunnelling empty-push method. The outcomes of this numerical simulation led to several key findings: (1) The soil density exerted a significantly greater impact on the internal forces of the initial support structure than both the tunnel depth and soil Poisson’s ratio. Additionally, a sudden shift in internal forces occurred within the 300–350 mm range when the lining thickness was altered. (2) Factors such as the tunnel depth, soil density, soil Poisson’s ratio μ, and lining thickness similarly influenced the internal forces of the segment and the initial support. Notably, the backfill layer thickness significantly affected the segment’s maximum axial force, causing an abrupt change of approximately 300 mm. (3) It is essential to control the guide rail’s thickness under the shield machine equipment’s weight constraint to prevent it from becoming overly large.
Supervision System 4.0 for a Road Tanker Washing Robot Manipulator
Authors: Vicente Luís , Carreira Fernando , Campos Francisco M. , Mendes Mário J. G. C. , Calado João M. F. , Carvalho Gamboa
Published: July 2023

The washing of road tankers is currently still a manual process that requires an operator to place the washing head into the tanks. To increase productivity and operator safety, it is essential to implement automated systems with Fault Detection and Isolation (FDI) capabilities. On the other hand, the industry 4.0 paradigm promotes the use of collaborative systems that integrate with the other organization’s processes. Realizing this new vision requires Supervision, Control and Data Acquisition (SCADA) systems with FDI modules that integrate with collaborative systems and promote the digitalization of companies. This paper proposes a SCADA system for a new road tanker washing robot, aimed at integrating all systems and communication networks of the organization and future FDI modules. To this end, this paper proposes a communication architecture based on open protocols and a common database to connect SCADA to the lower and higher levels of automation. Furthermore, this paper describes the various aspects of SCADA system development, from synoptic design to validation. To support the development of the SCADA system, a Digital Twin (DT) of the road tanker washing robot was used, allowing to test and validate its functionality through this virtual prototype. The results show that the proposed SCADA system and underlying information architecture are suitable for supervision of a robotized wash operation and that the use of a Digital Twin facilitates SCADA system design and validation.
Optimization of Wheelchair-Mounted Robotic Arms’ Base Placement by Fusing Occupied Grid Map and Inverse Reachability Map
Authors: Zhong Ming , He Yuhang , Liu Yan , Han Ruiqing , Liu Yaxin
Published: July 2023

In a household setting, a wheelchair-mounted robotic arm (WMRA) can be useful for assisting elderly and disabled individuals. However, the current WMRA can only perform movement and grasping tasks through joystick remote control. This method results in low efficiency due to poor coordination between the mobile platform and the robotic arm as well as the numerous operational steps required. To improve the efficiency and success rate of the robot in task execution, this paper proposes a parking location optimization method that combines the occupied grid map (OGM) and the inverse reachability map (IRM). Firstly, the SLAM algorithm is used to collect environment information, which is then stored in the form of an occupied grid map. The robotic arm workspace is then gridded, and the inverse reachability map is calculated based on the grasping pose of the target object. Finally, the optimal position of the mobile platform is obtained by comparing the optimal location point in the inverse reachability map and the obstacle information in the occupied grid map. This process achieves base placement optimization based on the grasping pose. The experimental results demonstrate that this method reduces the user operation time by 97.31% and overall task completion time by 40.57% when executing household environment tasks compared with the joystick control, increasing the range of executable tasks compared with the algorithm of the EL-E robot and reducing task completion time by 23.48% for the same task. This paper presents a parking location optimization method that can improve the grasping efficiency of the robotic arm and achieve parking location position selection for the WMRA in a household environment.
Predictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques
Authors: Stojčić Mirko , Banjanin Milorad K. , Vasiljević Milan , Nedić Dragana , Stjepanović Aleksandar , Danilović Dejan , Puzić Goran
Published: July 2023

Delay in data transmission is one of the key performance indicators (KPIs) of a network. The planning and design value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in a Long Term Evolution (LTE) network using three ML techniques: Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that the k-NN model has been selected as the best solution in all three optimization approaches. For the RReliefF optimization algorithm, the best model has six inputs and the minimum relative error (RE) RE = 0.109. For the Backward selection via the recursive feature elimination algorithm, the best model has four inputs and RE = 0.041. Finally, for the Pareto 80/20 rule, the best model has 11 inputs and RE = 0.049. The comparative analysis of the results concludes that, according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.
Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems
Authors: Sun Ye , Chen Qing , Xie Dan , Shao Ning , Ding Wei , Dong Yuzhan
Published: July 2023

This paper puts forward a fault location method combining the improved matrix algorithm and the genetic tabu algorithm based on multi-source information in view of the limitation of existing fault location methods in active distribution networks, such as long-time consumption, low accuracy, and low tolerance performance. Firstly, the fault region location method is proposed to simplify the active distribution network model and reduce the matrix calculation dimension. Secondly, the improved matrix algorithm is proposed to locate the fault section, and a positioning result verification method is proposed to improve the tolerance performance. Finally, the genetic tabu algorithm is proposed to optimize suspicious fault sections when the verification is incorrect and obtain the positioning result. The simulation results show that the fault location method proposed in this paper locates faster and performs better in both accuracy and tolerance in different fault conditions than others.
Special Issue on “Computational Approaches for Protein Dynamics and Function”
Authors: Scaramozzino Domenico , Jernigan Robert L.
Published: July 2023

Proteins are fundamental macromolecules that sustain living organisms by performing an astonishingly wide variety of tasks [...]
Potential Health Benefits of Fruits and Vegetables II
Authors: Mazzoni Luca , Capocasa Franco , Ariza Fernández Maria Teresa
Published: July 2023

Consumer awareness regarding the significance of a well-balanced diet in preventing chronic diseases has increased significantly in recent years [...]
Reproducing High Spatiotemporal Resolution Precipitable Water Distributions Using Numerical Prediction Data
Authors: Akatsuka Shin
Published: July 2023

Water vapor is an important greenhouse gas that affects regional climatic and weather processes. Atmospheric water vapor content is highly variable spatially and temporally, and continuous quantification over a wide area is problematic. However, existing methods for measuring precipitable water (PW) have advantages and disadvantages in terms of spatiotemporal resolution. This study uses high temporal resolution numerical prediction data and high spatial resolution elevation to reproduce PW distributions with high spatiotemporal resolution. This study also focuses on the threshold for elevation correction, improving temporal resolution, and reproducing PW distributions in near real time. Results show that using the water vapor content in intervals between the ground surface and 1000-hPa isobaric surface as the threshold value for elevation correction and generating hourly numerical prediction data using the Akima spline interpolation method enabled the reproduction of hourly PW distributions for 75% of the global navigation satellite system observation stations in the target region throughout the year with a root mean square error of 3 mm or less. These results suggest that using the mean value of monthly correction coefficients for the past years enables the reproduction of PW distributions in near real time following the acquisition of numerical prediction data.
The Impact of Shipping on Air Quality in the Port Cities of the Mediterranean Area: A Review
Authors: Toscano Domenico
Published: July 2023

Shipping emissions contribute significantly to air pollution at the local and global scales and will do so even more in the future because global maritime transport volumes are projected to increase. The Mediterranean Sea contains the major routes for short sea shipping within Europe and between Europe and East Asia. For this reason, concern about maritime emissions from Mediterranean harbours has been increasing on the EU and IMO (International Maritime Organization, London, UK) agenda, also supporting the implementation of a potential Mediterranean Emission Control Area (MedECA). Many studies are concerned with the impact of ship emissions in port cities. Studies of the contributions of ship emissions to air quality at the local scale include several monitoring and modelling techniques. This article presents a detailed review of the contributions of ship emissions of NO2, SO2, PM10, and PM2.5 on air quality in the main ports in the Mediterranean area. The review extracts and summarises information from published research. The results show a certain variability that suggests the necessity of harmonisation among methods and input data in order to compare results. The analysis illustrates the effects of this pollution source on air quality in urban areas, which could be useful for implementing effective mitigation strategies.
Development and Application of a Novel Snow Peak Sighting Forecast System over Chengdu
Authors: Lu Chengwei , Chen Ting , Yang Xinyue , Tan Qinwen , Kang Xue , Zhang Tianyue , Zhou Zihang , Yang Fumo , Chen Xi , Wang Yuancheng
Published: July 2023

As air quality has improved rapidly in recent years, the public has become more interested in whether a famous snow peak, Yaomei Feng on the Tibetan Plateau, can be seen from Chengdu, a megacity located on the western plain of the Sichuan Basin, east of the plateau. Therefore, a threshold-method-based forecasting system for snow peak sighting was developed in this study. Variables from numerical models, including cloud–water mixing ratio, cloud cover over snow peak, water mixing ratio, PM2.5 concentration, and ground solar radiation, were used in the snow peak sighting forecast system. Terrain occlusion rate of each model grid was calculated. Monte Carlo simulations were applied for threshold determination. A WRF-CMAQ hindcast was conducted for 2020, owing to insufficient observation data, hindcast results on the snow peak sighting were compared with posts collected from social media. Estimations showed that the snow peak sighting forecast system performed well in reflecting the monthly trend of snow peak sightings, and the hindcast results matched the daily observations, especially from May to August. Accuracy of the snow peak sighting forecast model was 78.9%, recall value was 57.1%, and precision was 24.4%.
Variability of River Runoff in Poland and Its Connection to Solar Variability
Authors: Wrzesiński Dariusz , Sobkowiak Leszek , Mares Ileana , Dobrica Venera , Mares Constantin
Published: July 2023

The aim of this research was to determine relationships between solar activity and variability of discharges of three Central European rivers: the Vistula, Odra and Warta in Poland in the multi-annual period of 1901–2020. Changes in precipitation and air temperature at Poznań meteorological station in the same period were also analyzed. The long-term variations in river runoff were investigated both from the point of view of temporal variability in relation to climate variations in the study area, and from the point of view of linear/non-linear links to solar activity, as described by the Wolf sunspot number. The wavelet transform analysis was used to highlight the frequency-time distribution of the coherences between solar and discharge variability. It was found that most of the links between solar activity and discharges were non-linear.
Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning
Authors: Tang Ze-Yang , Hu Qi-Biao , Cui Yi-Bo , Hu Lei , Li Yi-Wen , Li Yu-Jie
Published: July 2023

This paper aims to address the issue of evaluating the operation of electric vehicle charging stations (EVCSs). Previous studies have commonly employed the method of constructing comprehensive evaluation systems, which greatly relies on manual experience for index selection and weight allocation. To overcome this limitation, this paper proposes an evaluation method based on natural language models for assessing the operation of charging stations. By utilizing the proposed SimCSEBERT model, this study analyzes the operational data, user charging data, and basic information of charging stations to predict the operational status and identify influential factors. Additionally, this study compared the evaluation accuracy and impact factor analysis accuracy of the baseline and the proposed model. The experimental results demonstrate that our model achieves a higher evaluation accuracy (operation evaluation accuracy = 0.9464; impact factor analysis accuracy = 0.9492) and effectively assesses the operation of EVCSs. Compared with traditional evaluation methods, this approach exhibits improved universality and a higher level of intelligence. It provides insights into the operation of EVCSs and user demands, allowing for the resolution of supply–demand contradictions that are caused by power supply constraints and the uneven distribution of charging demands. Furthermore, it offers guidance for more efficient and targeted strategies for the operation of charging stations.