bluuebunny/crossref_metadata_2025 huggingface dataset:
Environmental Impacts of the Brazilian Egg Industry: Life Cycle Assessment of the Battery Cage Production System
Authors: Maciel Fabiane de Fátima , Gates Richard Stephen , Tinôco Ilda de Fátima Ferreira , Pelletier Nathan , Ibarburu-Blanc Maro A. , Renato Natalia dos Santos , Sousa Fernanda Campos de , Andrade Rafaella Resende , Silva Guilherme Moreira de Melo , Becciolini Valentina
Published: March 2024

Brazil stands as one of the world’s leading producers of animal protein, ranking sixth in global egg production. However, estimated growth in production demand, along with environmental impacts, represents a potential threat to the sustainability of the food system. Methods for assessing and quantifying the environmental impacts generated by Brazilian egg production remain scarce, lacking current reports on comparative effects or guiding standards. The objective of this study was to conduct a life cycle assessment from cradle to farm gate, adhering to ISO 14040 and 14044 standards, for the battery cage egg production systems and associated products in Brazil, with the aim of supporting and promoting sustainability improvements in the Brazilian egg industry. The entire life cycle modeling and process sustainability analysis were executed using the openLCA software, integrated with the Ecoinvent database. Emissions related to egg production yielded results of 65.06 kg SO2 eq., 27.74 kg N eq., 3086.71 kg CO2 eq., 75,152.66 CTUe, 2.75 × 10−5 CFC-11 eq., and 10,044.68 kg MJ eq. per ton of eggs produced. These findings can serve as comparative benchmarks for future studies and for analyzing data across different egg production systems in Brazil.
Episomal Vectors for Stable Production of Recombinant Proteins and Engineered Antibodies
Authors: Fallahee Ian , Hawiger Daniel
Published: March 2024

There is tremendous interest in the production of recombinant proteins, particularly bispecific antibodies and antibody–drug conjugates for research and therapeutic use. Here, we demonstrate a highly versatile plasmid system that allows the rapid generation of stable Expi293 cell pools by episomal retention of transfected DNA. By linking protein expression to puromycin resistance through an attenuated internal ribosome entry site, we achieve stable cell pools producing proteins of interest. In addition, split intein–split puromycin-mediated selection of two separate protein expression cassettes allows the stable production of bispecific antibody-like molecules or antibodies with distinct C-terminal heavy chain modifications, such as an antigen on one chain and a sortase tag on the other chain. We also use this novel expression system to generate stable Expi293 cell pools that secrete sortase A Δ59 variant Srt4M. Using these reagents, we prepared a site-specific drug-to-antibody ratio of 1 antibody–siRNA conjugate. We anticipate the simple, robust, and rapid stable protein expression systems described here being useful for a wide variety of applications.
A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
Authors: Wang Baoyi , Feng Yaowei , Zhang Shaomin
Published: March 2024

In the advanced metering infrastructure (AMI), impersonation, eavesdropping, man-in-the-middle and other attacks occur in the process of communication between entities through public channels, which will lead to the leakage of user privacy or the incorrect issuance of control instructions, resulting in economic losses and even power system operation failures. In view of this situation, we design a lightweight key agreement scheme based on a chaotic map for the AMI. We use the chaotic map to replace the time-consuming bilinear pairing and elliptic curve method and establish a secure communication channel between legal entities. In addition, we also design a multicast key generation mechanism for message transmission in AMI. The security analysis proves the security of the proposed scheme in the random oracle model, which can meet the security characteristics of anonymity and forward secrecy, and can effectively resist common attacks such as impersonation, replay and man-in-the-middle. The performance analysis results show that the proposed scheme requires lower computational and communication costs than related schemes, so it is more suitable for AMI scenarios with limited resources.
Progressive Adoption of RINA in IoT Networks: Enhancing Scalability and Network Management via SDN Integration
Authors: Sarabia-Jácome David , Giménez-Antón Sergio , Liatifis Athanasios , Grasa Eduard , Catalán Marisa , Pliatsios Dimitrios
Published: March 2024

Thousands of devices are connected to the Internet as part of the Internet of Things (IoT) ecosystems. The next generation of IoT networks is expected to support this growing number of Intelligent IoT devices and tactile Internet solutions to provide real-time applications. In view of this, IoT networks require innovative network architectures that offer scalability, security, and adaptability. The Recursive InterNetwork Architecture (RINA) is a clean slate network architecture that provides a scalable, secure, and flexible framework for interconnecting computers. SDN technology is becoming a de facto solution to overcome network requirements, making RINA adoption difficult. This paper presents an architecture for integrating RINA with SDN technologies to lower the barriers of adopting RINA in IoT environments. The architecture relies on a RINA-based distributed application facility (DAF), a RINA southbound driver (SBI), and the RINA L2VPN. The RINA-based DAF manages RINA nodes along the edge–fog–cloud continuum. The SBI driver SDN enables the hybrid centralized management of SDN switches and RINA nodes. Meanwhile, the RINA L2VPN allows seamless communication between edge nodes and the cloud to facilitate the data exchange between network functions (NFs). Such integration has enabled a progressive deployment of RINA in current IoT networks without affecting their operations and performance.
DC Voltage Induces Quadratic Optical Nonlinearity in Ion-Exchanged Glasses at Room Temperature
Authors: Scherbak Sergey , Kan Gennadiy , Tagantsev Dmitry , Lipovskii Andrey
Published: March 2024

We demonstrate that applying DC voltage at room temperature to an ion-exchanged glass induces quadratic optical nonlinearity in a subsurface region of the glass. We associate this with the EFISH (Electric-Field-Induced Second Harmonic) effect due to the Maxwell–Wagner charge accumulation in the subsurface region of the glass, in which a conductivity gradient forms as a result of the ion exchange processing. The second harmonic (SH) signal from the soda–lime glass subjected to potassium-for-sodium ion exchange is comparable with one from the same glass after thermal poling. The signal linearly increases with the duration of the ion exchange. The lower mobility of the potassium ions results in a higher SH signal from the potassium-for-sodium exchanged glass than that from the silver-for-sodium ion-exchanged one. This phenomenon is resistant to thermal annealing: only a 500 °C anneal caused noticeable degradation of the SH signal after “charging” the specimen. The phenomenon found is of interest for characterizing graded conductivity regions and providing and controlling second-order optical nonlinearity in transparent isotropic media.
Study on the Influence of Deep Soil Liquefaction on the Seismic Response of Subway Stations
Authors: Shi Ming , Tao Lianjin , Wang Zhigang
Published: March 2024

Subway systems are a crucial component of urban public transportation, especially in terms of safety during seismic events. Soil liquefaction triggered by earthquakes is one of the key factors that can lead to underground structural damage. This study investigates the impact of deep soil liquefaction on the response of subway station structures during seismic activity, aiming to provide evidence and suggestions for earthquake-resistant measures in underground constructions. The advanced finite element software PLAXIS was utilized for dynamic numerical simulations. Non-linear dynamic analysis methods were employed to construct models of subway stations and the surrounding soil layers, including soil–structure interactions. The UBC3D-PLM liquefaction constitutive model was applied to describe the liquefaction behavior of soil layers, while the HS constitutive model was used to depict the dynamic characteristics of non-liquefied soil layers. The study examined the influence of deep soil liquefaction on the dynamic response of subway station structures under different seismic waves. The findings indicate that deep soil liquefaction significantly increases the vertical displacement and acceleration responses of subway stations compared to non-liquefied conditions. The liquefaction behavior of deep soil layers leads to increased horizontal effective stress on both sides of the structure, thereby increasing the horizontal deformation of the structure and posing a potential threat to the safety and functionality of subway stations. This research employed detailed numerical simulation methods, incorporating the non-linear characteristics of deep soil layer liquefaction, providing an analytical framework based on regulatory standards for evaluating the impact of deep soil liquefaction on the seismic responses of subway stations. Compared to traditional studies, this paper significantly enhances simulation precision and practical applicability. Results from this research indicate that deep soil layer liquefaction poses a non-negligible risk to the structural safety of subway stations during earthquakes. Therefore, the issue of deep soil liquefaction should receive increased attention in engineering design and construction, with effective prevention and mitigation measures being implemented.
Enhancing Industrial Digitalisation through an Adaptable Component for Bridging Semantic Interoperability Gaps
Authors: Carvajal-Flores Diego F. , Abril-Jiménez Patricia , Buhid Eduardo , Fico Giuseppe , Cabrera Umpiérrez María Fernanda
Published: March 2024

Digital transformation has a great effect on industries but also in the perception of their products with high consequences on society as a whole, as driver for innovation. However, there are still many barriers to the adoption of digital technologies, especially in small and medium-sized enterprises (SMEs). In the present study we propose a new software component that facilitates the adoption of industrial reference architectures (RAs) based on the semantic interoperability concept. Among these RAs, FIWARE has become a key enabler for flexible and resource effective digitalisation of shop floor processes. The proposed software component aims to enable full interoperability between these more widely used technologies and industrial standards and protocols such as OPC UA. As a result, a flexible, open, and adaptable implementation assists the automatic translation of messages from different standards enabling semantic interoperability of shop floor processes. The component aims to enable industrial SMEs to bring current barriers to digitalisation towards a more human-centred, resilient, and sustainable industry and society.
Margin and Shared Proxies: Advanced Proxy Anchor Loss for Out-of-Domain Intent Classification
Authors: Park Junhyeong , Kim Byeonghun , Han Sangkwon , Ji Seungbin , Rhee Jongtae
Published: March 2024

Out-of-Domain (OOD) intent classification is an important task for a dialog system, as it allows for appropriate responses to be generated. Previous studies aiming to solve the OOD intent classification task have generally adopted metric learning methods to generate decision boundaries in the embedding space. However, these existing methods struggle to capture the high-dimensional semantic features of data, as they learn decision boundary using scalar distances. They also use generated OOD samples for learning. However, such OOD samples are biased, and they cannot include all real-world OOD intents, thus representing a limitation. In the current paper, we attempt to overcome these challenges by using Advanced Proxy-Anchor loss, which introduces a margin proxy and shared proxy. First, to generate a decision boundary that has the high-dimensional semantic features of training data, we use a margin proxy for learnable embedding vectors. Next, the shared proxy, which is shared by all In-Domain (IND) samples, is introduced to make it possible to learn the discriminative feature between IND intents and OOD intent, ultimately leading to the improved classification of OOD samples. We conduct evaluations of the proposed method using three benchmark datasets. The experimental results demonstrate that our method achieved an improved performance compared to the methods described in previous studies.
Study on the Vertical Stability of Drilling Wellbore under Optimized Constraints
Authors: Pan Ruixue , Liu Jimin , Cheng Hua , Fan Haixu
Published: March 2024

With the development of coal resource extraction and wellbore construction proceeding towards deeper depths, the stability of drilling wellbore structures has become increasingly severe, even posing a barrier to the use of drilling method technology in deep wellbore construction. To address this issue, this study raised an optimized constraints method involving pre-throwing cement slurry to the bottom before wellbore decent, altering bottom constraints. Firstly, the critical depth and instability criterion of this optimized method was derived by catastrophe theory. Subsequently, the role of single-factor and multi-factor sensitivity analyses on critical depth was discussed. The engineering effects of optimized constraint methods were contrasted and examined in several drilling projects. Finally, the characteristic values of real engineering were computed using numerical techniques and ABAQUS2020 software, and the efficacy of optimization approaches was examined and validated. The results revealed that the critical depth increased by 41.39 ± 5%. The influence factors described in order of the degree were the counterweight water height, the elastic modulus, the thickness of the wellbore, and the self-weight of the wellbore, sequentially. The conclusion on structural stability between the numerical calculation solution and theoretical calculation solution was completely the same. The optimized constraints method can effectively improve the stability of the wellbore structure.
SFFNet: Staged Feature Fusion Network of Connecting Convolutional Neural Networks and Graph Convolutional Neural Networks for Hyperspectral Image Classification
Authors: Li Hao , Xiong Xiaorui , Liu Chaoxian , Ma Yong , Zeng Shan , Li Yaqin
Published: March 2024

The immense representation power of deep learning frameworks has kept them in the spotlight in hyperspectral image (HSI) classification. Graph Convolutional Neural Networks (GCNs) can be used to compensate for the lack of spatial information in Convolutional Neural Networks (CNNs). However, most GCNs construct graph data structures based on pixel points, which requires the construction of neighborhood matrices on all data. Meanwhile, the setting of GCNs to construct similarity relations based on spatial structure is not fully applicable to HSIs. To make the network more compatible with HSIs, we propose a staged feature fusion model called SFFNet, a neural network framework connecting CNN and GCN models. The CNN performs the first stage of feature extraction, assisted by adding neighboring features and overcoming the defects of local convolution; then, the GCN performs the second stage for classification, and the graph data structure is constructed based on spectral similarity, optimizing the original connectivity relationships. In addition, the framework enables the batch training of the GCN by using the extracted spectral features as nodes, which greatly reduces the hardware requirements. The experimental results on three publicly available benchmark hyperspectral datasets show that our proposed framework outperforms other relevant deep learning models, with an overall classification accuracy of over 97%.
Numerical Analysis of Ground Surcharge Effects on Deformation Characteristics in Shield Tunnel Linings
Authors: Wei Lixin , Yang Chunshan , Chen Weijie , Liu Liying , Su Dong
Published: March 2024

To investigate the deformation characteristics of shield tunnel linings under ground surcharge, finite element software was employed to create a detailed three-dimensional model of the staggered assembly of the shield tunnel lining. This model includes components such as precast concrete segments, reinforcements, and joints (comprising bent bolts, washers, and bolt sleeves). Additionally, the model accounts for interface frictions between segments and the interactions between different rings. The reliability of the numerical model was verified based on the results of a full-scale model test. Additionally, the model accounts for interface frictions between segments and the interactions between different rings. Changes in tunnel convergence, joint tensioning, bolt stresses, reinforcement stresses, and concrete crack development were systematically analyzed. The results indicate the following: (1) the deformation mode of the lining structure under ground surcharge resembles a “transverse ellipse”. Joints located near the haunch opened along the outer arc, while those near the vault and bottom opened along the inner arc. The restraining effect of the bolts on joints opening in the inner arc was greater than that on the outer arc. Notably, when the opening of the inner arc reached 4.9 mm, the bolt stress escalated to the yield strength of 640 Mpa. (2) Under larger loads, the lining structure’s joints are susceptible to greater deformation, resulting in the tensile yielding of local reinforcement within these joints. (3) Cracks predominantly occur near the haunch, vault, and bottom of the lining structure, with the central angle of crack distribution ranging between 70° and 85°.
Visual Clue Guidance and Consistency Matching Framework for Multimodal Named Entity Recognition
Authors: He Li , Wang Qingxiang , Liu Jie , Duan Jianyong , Wang Hao
Published: March 2024

The goal of multimodal named entity recognition (MNER) is to detect entity spans in given image–text pairs and classify them into corresponding entity types. Despite the success of existing works that leverage cross-modal attention mechanisms to integrate textual and visual representations, we observe three key issues. Firstly, models are prone to misguidance when fusing unrelated text and images. Secondly, most existing visual features are not enhanced or filtered. Finally, due to the independent encoding strategies employed for text and images, a noticeable semantic gap exists between them. To address these challenges, we propose a framework called visual clue guidance and consistency matching (GMF). To tackle the first issue, we introduce a visual clue guidance (VCG) module designed to hierarchically extract visual information from multiple scales. This information is utilized as an injectable visual clue guidance sequence to steer text representations for error-insensitive prediction decisions. Furthermore, by incorporating a cross-scale attention (CSA) module, we successfully mitigate interference across scales, enhancing the image’s capability to capture details. To address the third issue of semantic disparity between text and images, we employ a consistency matching (CM) module based on the idea of multimodal contrastive learning, facilitating the collaborative learning of multimodal data. To validate the effectiveness of our proposed framework, we conducted comprehensive experimental studies, including extensive comparative experiments, ablation studies, and case studies, on two widely used benchmark datasets, demonstrating the efficacy of the framework.
Prediction of Utility Tunnel Performance in a Soft Foundation during an Operation Period Based on Deep Learning
Authors: Gao Wei , Ge Shuangshuang , Gao Yangqinchu , Yuan Shuo
Published: March 2024

The underground utility tunnel in a soft foundation is generally affected by the serious disturbance of the vehicle load during the operation period. Therefore, in this study, for the typical utility tunnel engineering in Suqian City of Jiangsu Province, China, field tests were conducted to monitor the performance of the utility tunnel structure in a soft foundation affected by the ground traffic loads during the operation period. Based on the test results, the datasets whose number is 15,376, composed of the five main disturbance factors (four vehicle operating load parameters and one operating time parameter), and the corresponding two main structure responses (displacement and stress) have been constructed. Based on the obtained datasets, using the proposed new deep learning model called WO-DBN, in which the seven hyperparameters of a deep belief network (DBN) are determined by the whale optimization algorithm (WOA), the safety responses of the utility tunnel structure have been predicted. The results show that for the prediction results, the average absolute error for the displacement is 0.1604, and for the stress, it is 12.3726, which are not significant and can meet the requirement of the real engineering. Therefore, the deep learning model can accurately predict the performance of the utility tunnel structure under a vehicle load and other disturbances, and the model has good applicability.
Model-Based Analysis of the Accuracy of Tracking Control and Energy Efficiency of a Six-Wheeled Skid-Steered Robot
Authors: Trojnacki Maciej
Published: March 2024

This article concerns the modeling and motion control of a mobile robot with six independently driven and non-steered wheels. The main research issue is analyzing the influence of the structure of the control system and wheel track on the control accuracy and energy efficiency during robot motion on horizontal paved ground. For this purpose, the kinematic relationships for the robot are discussed and a simplified dynamics model for control applications is developed. The robot’s dynamics model takes into account the most important phenomena of the wheel interaction with the paved ground, including slip. In addition, it is supplemented with a model of the robot’s drive units. Two versions of the control system were adopted for analysis, i.e., with the wheels’ controller only and additionally equipped with a pose controller. Simulation studies were carried out for the developed robot dynamics model and the analyzed versions of the control system in order to investigate the influence of the track width of the wheels and the structure of the control system on motion accuracy and energy efficiency. In order to quantitatively compare the results for the analyzed solutions, quality indices were introduced. The results of the simulation research indicate the influence of the track width of the wheels on the accuracy of motion when using the wheels’ controller, as well as its impact on energy efficiency. Moreover, they show that it is possible to significantly improve the accuracy of motion by using an additional pose controller, which allows limiting the impact of the non-optimal geometric parameters of the robot and the slip of the wheels on trajectory tracking errors. However, the addition of the pose controller does not significantly affect the energy efficiency during the robot’s motion, which may be even worse in this case.
Microwave Oven Application for the Preparation and Sterilization of Microbiological Culture Media: A Feasible Method with An Adapted Water Bath and Perforable Cap
Authors: Terrones-Fernandez Ines , Rodero-De-Lamo Lourdes , López Asunción , Peiró Sara , Asensio Daniel , Castilla Robert , Gamez-Montero Pedro Javier , Piqué Nuria
Published: March 2024

There is currently increased interest in the use of alternatives to autoclaved culture media, in order to maintain the properties of the media, while saving energy and time. In this study, we assess a new system for culture media preparation, using a conventional microwave with a water bath and a glass bottle with a rubber cap that allows depressurization. Sterilization, using the proposed system (1000 W, 3 to 20 min), was compared with autoclaving for the preparation of tryptone soy agar (TSA), tryptone soy broth (TSB), Sabouraud 4% dextrose agar (SDA), and violet red bile glucose agar (VRBG). Microwave exposure for 7 min yielded sterile TSA plates. The productivity of both sterilization methods was assessed using the pour plate method, and significant increases in the growth of certain micro-organisms after using a microwave were observed for every culture medium, especially those that were sterilized by boiling (VRBG). The kinetics of microbial destruction showed that Escherichia coli and Bacillus subtilis spores were destroyed after 3 and 7 min in a microwave, respectively, while three decimal reductions were obtained for Geobacillus stearothermophilus spores after 15 min in an autoclave. This new sterilization method could be a feasible, rapid, and economical method to prepare microbiological media, with a quality similar to that obtained through autoclaving.
Study on Relay Contact Bounce Based on the Adaptive Weight Rotation Template Matching Algorithm
Authors: Zhao Wenze , Yan Jiaxing , Wang Xin , Li Wenhua , Yang Xinglin , Wang Weiming
Published: March 2024

In order to analyze the relay action process from an imaging perspective and further investigate the bounce phenomenon of relay contacts during the contact process, this paper utilizes a high-speed shooting platform to capture images of relay action. In light of the situation where the stationary contact in the image is inclined and continuously changing, a rotation template matching algorithm based on adaptive weight is proposed. The algorithm identifies and obtains the inclination angle of the stationary contact, enabling the study of the relay contact bounce process. By extracting contact bounce distance data from the images, a bounce process curve is plotted. Combined with the analysis of the contact bounce process, the reasons for the bounce are explored. The results indicate that the proposed rotation template matching algorithm can accurately identify stationary contacts and their angles at different angles. By analyzing the contact status and bounce process of the relay contacts in conjunction with the relay structure, parameters such as the bounce time, bounce height, and time required to reach the maximum distance can be calculated. Additionally, the main reason for contact bounce in the relay studied in this paper is the limitation imposed on the continued movement of the stationary contact by the presence of the relay brackets when the kinetic energy of the contact is too high. This phenomenon occurs during the first vibration peak in the vibration process after the moving contact contacts the stationary contact. The research results provide a reference for further studying the relay contact bounce process, optimizing relay structure, and suppressing contact bounce.
A Comprehensive Review of the Nutritional Composition and Toxicological Profile of Date Seed Coffee (Phoenix dactylifera)
Authors: Kiesler Raphaela , Franke Heike , Lachenmeier Dirk W.
Published: March 2024

Approximately 8 million tons of dates (Phoenix dactylifera) are produced globally each year. The seeds of the fruit, which make up 10–15% of its weight, are typically discarded. Date seed coffee is a sustainable food system innovation rooted in the traditions of high date-producing regions. Dating back to the late 19th century, date seed coffee has evolved from a historical coffee substitute to a modern caffeine-free alternative. Date seed coffee has a long history of consumption in the European Union (EU). This indicates that it may not require novel food authorization. The composition of date seeds is evaluated in this review and a toxicological risk assessment for date seed coffee is conducted. Subchronic studies show that consuming date seed or date seed coffee has no adverse effects. Therefore, currently unavailable chronic toxicity, carcinogenicity, and reproductive toxicity studies may be unnecessary. However, for a comprehensive evaluation, it is recommended to conduct an in vitro mutagenicity test. This review provides information on the safety of date seed coffee and highlights the need for further research.
A Novel Identification Approach Using RFECV–Optuna–XGBoost for Assessing Surrounding Rock Grade of Tunnel Boring Machine Based on Tunneling Parameters
Authors: Shi Kebin , Shi Renyi , Fu Tao , Lu Zhipeng , Zhang Jianming
Published: March 2024

In order to solve the problem of the poor adaptability of the TBM digging process to changes in geological conditions, a new TBM digging model is proposed. An ensemble learning prediction model based on XGBoost, combined with Optuna for hyperparameter optimization, enables the real-time identification of surrounding rock grades. Firstly, an original dataset was established based on the TBM tunneling parameters under different surrounding rock grades based on the KS tunnel. Subsequently, the RF–RFECV was employed for feature selection and six features were selected as the optimal feature subset according to the importance measure of random forest features and used to construct the XGBoost identification model. Furthermore, the Optuna framework was utilized to optimize the hyperparameters of XGBoost and validated by applying the established TBM dataset of the KS Tunnel. In order to verify the applicability and efficiency of the proposed model in surrounding rock grade identification, the prediction results of five commonly used machine learning models, Optuna–XGBoost, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Decision Tree (DT), XGBoost, and PSO–XGBoost, were compared and analyzed. The main conclusions are as follows: the feature selection method based on RF–RFECV improved the accuracy by 8.26%. Among the optimal feature subset, T was the most essential feature for the model’s input, while PR was the least important. The Optuna–XGBoost model proposed in this paper had higher accuracy (0.9833), precision (0.9803), recall (0.9813), and F1 score (0.9807) than other models and could be used as an effective means for the lithological identification of surrounding rock grade.
Assessing Training Methods for Advanced Driver Assistance Systems and Autonomous Vehicle Functions: Impact on User Mental Models and Performance
Authors: Murtaza Mohsin , Cheng Chi-Tsun , Fard Mohammad , Zeleznikow John
Published: March 2024

Understanding the complexities of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technologies is critical for road safety, especially concerning their adoption by drivers. Effective training is a crucial element in ensuring the safe and competent operation of these technologies. This study emphasises the critical role of training methodologies in shaping drivers’ mental models, defined as an individual’s cognitive frameworks for understanding and interacting with ADAS and AV systems. Their mental models substantially influence their interactions with those technologies. A comparative analysis of text-based and video-based training methods has been conducted to assess their influence on participants’ performance and the development of their mental models of ADAS and AV functionalities. Performance is evaluated in terms of the accuracy and reaction time of the participants as they interacted with ADAS and AV functions in a driving simulation. The findings reveal that video-based training yielded better performance outcomes, more accurate mental models, and a deeper understanding of ADAS functionalities among participants. These findings are crucial for policy makers, automotive manufacturers, and educational institutions involved in driver training. They underscore the necessity of developing tailored training programs to facilitate the proficient and safe operation of increasingly complex automotive technologies.
Corrosion Monitoring Techniques in Subcritical and Supercritical Water Environments
Authors: Li Yanhui , Bai Zhouyang , Xing Limei , Zhang Qian , Ding Shaoming , Zhang Yinan , Gao Pengfei , Yu Zhihong , Xu Donghai
Published: March 2024

A series of advanced equipment exposed to sub-/supercritical water environments at high temperatures, high pressures, and extreme water chemistry with high salt and dissolved oxygen content faces serious corrosion problems. Obtaining on-site corrosion data for typical materials in harsh environments is crucial for operating and maintaining related equipment and optimizing various corrosion prediction models. First, this article introduces the advantages and disadvantages, usage scenarios, and future development potential of several in situ monitoring technologies, including ultrasonic thickness measurement, the infrared thermography method, microwave imaging, eddy current detection, and acoustic emission. Considering the importance of electrochemical corrosion data in revealing microscale and nanoscale corrosion mechanisms, in situ testing techniques such as electrical resistance probes, electrochemical corrosion potential, electrochemical impedance spectroscopy, and electrochemical noise that can be applied to sub-/supercritical water systems were systematically discussed. The testing platform and typical data obtained were discussed with thick and heavy colors to establish a mechanical prediction model for corrosion behavior. It is of great significance to promote the development of corrosion monitoring techniques, such as breaking through testing temperature limitations and broadening the industrial application scenarios and maturity.
Smart Solutions for Diet-Related Disease Management: Connected Care, Remote Health Monitoring Systems, and Integrated Insights for Advanced Evaluation
Authors: Coman Laura-Ioana , Ianculescu Marilena , Paraschiv Elena-Anca , Alexandru Adriana , Bădărău Ioana-Anca
Published: March 2024

The prevalence of diet-related diseases underscores the imperative for innovative management approaches. The deployment of smart solutions signifies a paradigmatic evolution, capitalising on advanced technologies to enhance precision and efficacy. This paper aims to present and explore smart solutions for the management of diet-related diseases, focusing on leveraging advanced technologies, such as connected care, the Internet of Medical Things (IoMT), and remote health monitoring systems (RHMS), to address the rising prevalence of diet-related diseases. This transformative approach is exemplified in case studies focusing on tailored RHMS capabilities. This paper aims to showcase the potential of three RHMS in introducing a novel evaluation method and their customisation for proactive management of conditions influenced by dietary habits. The RO-SmartAgeing System uniquely addresses age-related aspects, providing an integrated approach that considers the long-term impact of dietary choices on ageing, marking an advanced perspective in healthcare. The NeuroPredict Platform, leveraging complex neuroinformatics, enhances the understanding of connections between brain health, nutrition, and overall well-being, contributing novel insights to healthcare assessments. Focused on liver health monitoring, the HepatoConect system delivers real-time data for personalized dietary recommendations, offering a distinctive approach to disease management. By integrating cutting-edge technologies, these smart solutions transcend traditional healthcare boundaries.
Recent Advances in the Application of Agricultural Waste in Construction
Authors: Khalife Esmail , Sabouri Maryam , Kaveh Mohammad , Szymanek Mariusz
Published: March 2024

On a global scale, millions of tons of diverse agricultural residues are generated annually. Most of these wastes are burned or dumped in landfills, which causes environmental pollution. Addressing environmental issues arising from agricultural waste materials, in addition to mitigating heating and cooling expenses in the construction sector, is an interesting challenge for researchers. The utilization of agricultural wastes in different parts of construction is producing positive findings day by day, and investigating research in this field is a curiosity for researchers. This short study reviewed the most recent achievements in using agricultural wastes as a substitute or additive material for construction. Using these wastes as aggregate, ash (as a supplementary for cement), or fibers for foam concrete, insulation materials, etc. has been reviewed. This review has focused on very recent published papers. Several studies have demonstrated the effective influences of agro-waste materials in construction, like retaining the compressive strength (155 MPa) of concrete at standard levels and reducing heat losses in buildings (69% energy savings for brick insulated using wheat straw), as well as sound insulation. The use of agro-waste materials for insulation positively improved thermal conductivity, costs, and energy savings. However, some wastes did not provide a high added value, which shows that more investigations still need be performed to fill this gap in the research. Considering the global scale of agricultural waste generation and the potential benefits to both the environment and construction industry, continued research in this area is essential.
Fibromyalgia and Sedentarism: Which Came First, the Chicken or the Egg?
Authors: Quero-Calero Carmen Daniela , Otero Eduardo , Abellán-Aynés Oriol , Ortega Eduardo
Published: March 2024

Fibromyalgia is a chronic disease that causes widespread pain throughout the body, as well as fatigue and a variety of other accompanying symptoms. Physical activity is one of the most useful non-pharmacological treatments for pain and symptom reduction. Therefore, the main objective of this research was to analyse the objective levels of sedentary lifestyle and physical activity, as well as the study of heart rate variability as a predictor of health. A total of 21 women previously diagnosed with fibromyalgia (FM) and 10 healthy women of the same age range participated in the study. Accelerometry was used for the determination of physical activity and sedentary lifestyle as well as the study of heart rate variability (HRV) at rest for the assessment of cardiovascular health. The results show that participants with fibromyalgia have higher levels of sedentary lifestyles and worse cardiovascular health outcomes compared to healthy participants not diagnosed with fibromyalgia. In conclusion, it is observed that the analysis of heart rate variability is a good predictor for the determination of cardiovascular health in patients diagnosed with fibromyalgia. A debate is open to whether sedentary lifestyles and being overweight accentuate the fibromyalgia disease or whether it is the disease itself that favors increased physical inactivity, reflected in inferior cardiovascular health. Future research is needed to deepen this analysis in order to improve the quality of life of these patients.
An Experimental Study on the Seismic Performance of a Replaceable Steel Link System Acting as a Structural Fuse
Authors: Ro Kyong Min , Kim Min Sook , Lee Young Hak
Published: March 2024

This study investigated the seismic performance of reinforced concrete columns retrofitted with Replaceable Steel Links (RSLs), focusing on the effects of varying sliding slot lengths and torsional loads. The RSL system, known for its simple construction and effective seismic performance, was analyzed to assess the feasibility of substituting damaged steel links post-earthquake, using the system as a structural fuse. The experimental results highlighted the role of sliding slot lengths in delaying the initiation of shear cracks, especially under eccentric lateral loads. The RSL system exhibited notable torsional resistance, showing only a 10% decrease in maximum load capacity, even with a two-fold increase in the eccentric distance. Furthermore, with an increase in sliding slot length, the difference in cumulative energy dissipation attributable to augmented eccentric distances reached approximately 50%, indicating a notable impact of sliding slot length on the system’s ability to resist torsion. Consequently, it is recommended that the length of the sliding slot be based on the specific seismic design objectives when employing the RSL system as a structural fuse. The post-experiment inspection revealed no deformation in the steel plates, and the buckled steel links could be effortlessly replaced by loosening the high-tensile bolts in the slots. These findings demonstrate the RSL system’s efficiency as a structural fuse.
Analysis of Dynamic Mechanical Properties and Energy Consumption of Fractured Sandstone under Dynamic–Static Combination Conditions
Authors: Ping Qi , Xu Yijie , Hu Jing , Sun Shijia , Li Xiangyang , Wu Shiwei
Published: March 2024

Under dynamic loads, such as blasting, excavation, or quarrying, rocks with fissures are the first to sustain damage, leading to instability in the engineering rock mass. To investigate the kinetic properties of fractured rocks, fractured coal mine sandstone specimens underwent impact compression tests using a dynamic–static combination SHPB (split Hopkinson pressure bar) test device at different loading rates under combined dynamic and static conditions. The damage characteristics of the specimens were analyzed from an energy point of view. The results show that under the dynamic and static combined condition, when five impact loading air pressures are used for loading at different impact rates, the trends of the dynamic stress–strain curves of prefabricated fissured rock samples under various impact pressures were discovered to be similar and were mainly categorized into three main stages of elasticity, yield, and destruction; the specimen’s dynamic compressive strength increases according to a power function relationship; as the average strain rate increases, the dynamic strain increases linearly and the dynamic modulus of elasticity increases in a quadratic relationship, all of which show a significant strain rate effect. The incident energy is a power function of the loading rate. The reflected, transmitted, and absorbed energies by the sample increase with the incident energy. The degree of the sandstone specimen fragmentation gradually grows with increasing impact loading rate and incident energy, as evidenced by a decrease in the scale of the fragments. The absorbed energy in the sample is mainly used for the deformation damage of the rock, and the more intense the fragmentation of the specimen, the more absorbed energy is required.
Assessments Associated with the Diagnostics and Non-Surgical Treatment of Posterior Tibialis Tendon Dysfunction: A Systematic Review
Authors: Banwell George , Ramos-Petersen Laura , Gatt Alfred , Gijon-Nogueron Gabriel , Lopezosa-Reca Eva
Published: March 2024

Objectives: To investigate the efficacy of the assessment methods that are used to diagnose posterior tibialis tendon dysfunction (PTTD) and investigate how the results of these assessments can be used to instruct non-surgical treatments. Design: Systematic review. Data sources: Searches were conducted up to January 2023 in the PubMed, Scopus, Science Direct, Embase, Web of Science and Lilacs databases. Study eligibility criteria: Studies which included patients over 18 years of age, diagnosed with PTTD, which describe the assessments used to aid in the non-surgical treatment of PTTD in comparison with another type of assessment. All types of studies apart from protocols, letters to editor and other systematic reviews and meta-analysis were included. Study appraisal and synthesis methods: Two blinded reviewers performed screening, data extraction, and methodological quality assessments using the QUADAS-2. Results: A total of 15 observational studies were included in the review. Assessment methods included were verbal assessments, physical examinations, and imaging (magnetic resonance imaging (MRI), ultrasound (US), and radiographs). The single-heel rise test was a commonly mentioned physical examination, showing reliability in one study but contradicting in another. MRI was frequently described as the “gold standard”. However, US showed both comparable accuracy and advantages compared to MRI, such as cost-effectiveness and real-time examinations. Conclusions: There are many assessment methods described in the literature and, due to the lack of accuracy of the tests, a mixture of tests should be used to assess PTTD. The parameters from the tests can provide a diagnosis of PTTD, but do not offer detail on how they assist the non-surgical treatment of the condition.
Optimizing Large Language Models on Multi-Core CPUs: A Case Study of the BERT Model
Authors: Zhao Lanxin , Gao Wanrong , Fang Jianbin
Published: March 2024

The BERT model is regarded as the cornerstone of various pre-trained large language models that have achieved promising results in recent years. This article investigates how to optimize the BERT model in terms of fine-tuning speed and prediction accuracy, aiming to accelerate the execution of the BERT model on a multi-core processor and improve its prediction accuracy in typical downstream natural language processing tasks. Our contributions are two-fold. First, we port and parallelize the fine-tuning training of the BERT model on a multi-core shared-memory processor. We port the BERT model onto a multi-core processor platform to accelerate the fine-tuning training process of the model for downstream tasks. Second, we improve the prediction performance of typical downstream natural language processing tasks through fine-tuning the model parameters. We select five typical downstream natural language processing tasks (CoLA, SST-2, MRPC, RTE, and WNLI) and perform optimization on the multi-core platform, taking the hyperparameters of batch size, learning rate, and training epochs into account. Our experimental results show that, by increasing the number of CPUs and the number of threads, the model training time can be significantly reduced. We observe that the reduced time is primarily concentrated in the self-attention mechanism. Our further experimental results show that setting reasonable hyperparameters can improve the accuracy of the BERT model when applied to downstream tasks and that appropriately increasing the batch size under conditions of sufficient computing resources can significantly reduce training time.
The Effects of Ubiquinol Intake and Sociophysical Training on the Activation of Psychological and Infrared Camera-Measured Body Temperature Physiology and Blood Molecular Markers: A Pilot Study among Healthy Female Older Adults
Authors: Ota Makoto , Hariyama Masanori , Colman Ricki , Koshiba Mamiko
Published: March 2024

A combination of existing treatments with sensing technology may be the most appropriate approach for incurable neuropsychiatric disorders. Dietary antioxidant supplementation, exercise, and cognitive training are individually well-established treatments for neurodegeneration, Alzheimer’s disease, and other dementias. Therefore, in a double-blind randomized controlled trial, we evaluated the response of normal healthy older female subjects to coenzyme Q10 supplementation and simultaneous sociophysical training that was undertaken in a non-contact environment using infrared cameras. The current pilot study reports the results from a multivariate analysis of blood biomarkers, body surface temperature measured with infrared thermal cameras, and psychological questionnaire scores from this trial, in which 100 mg/day of supplemental ubiquinol (the reduced form of coenzyme Q10) was administered daily for one month. We found a significant positive correlation between ubiquinol supplementation and positive mood scores in the State–Trait Anxiety Inventory test (STAI-positive) and a weak inverse correlation between ubiquinol supplementation and serum interleukin 4 (IL-4), a systemic inflammatory marker. We also found a significant positive correlation between the standard deviation of body surface temperatures, detected with non-contact infrared image sensors, and both STAI-positive and serum antidiuretic hormone (ADH). The results from this small pilot study indicate the potential synergistic effects of oral ubiquinol intake and sociophysical training on neuropsychiatric health in healthy female older adults.