bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Potential Hazards Associated with South Korean Prawns and Monitoring Results Targeting Fishing Bait
Authors: Kang Gyoungsik , Woo Won-Sik , Kim Kyung-Ho , Son Ha-Jeong , Sohn Min-Young , Kong Hee Jeong , Kim Young-Ok , Kim Dong-Gyun , Kim Eun Mi , Noh Eun Soo , Park Chan-Il
Published: October 2023

This study detected two potential pathogens, Vibro parahaemolyticus, which causes acute hepatopancreatic necrosis disease (AHPND), and white spot syndrome virus (WSSV), in fishing bait in South Korea. However, their infectious nature was not confirmed, possibly due to the degradation caused by freezing/thawing or prolonged storage under frozen conditions. While infectivity was not confirmed in this study, there is still a significant risk of exposure to these aquatic products. Furthermore, fishing bait and feed should be handled with caution as they are directly exposed to water, increasing the risk of disease transmission. In Australia, cases of WSSV infection caused by imported shrimp intended for human consumption have occurred, highlighting the need for preventive measures. While freezing/thawing is a method for inactivating pathogens, there are still regulatory and realistic issues to be addressed.
Can Tick Microbiome Explain Nonlinear Relationship between Tick Abundance and Lyme Disease Incidence?
Authors: Tonk-Rügen Miray , Zając Zbigniew , Cabezas-Cruz Alejandro
Published: October 2023

Ticks (Acari: Ixodida) are hematophagous ectoparasitic arachnids that feed on the blood of vertebrate hosts, posing significant concern due to their unrivaled capacity to transmit various pathogens, which surpasses those of all other known arthropod vectors [...]
TACkling Cancer by Targeting Selective Protein Degradation
Authors: Noblejas-López María del Mar , Tébar-García David , López-Rosa Raquel , Alcaraz-Sanabria Ana , Cristóbal-Cueto Pablo , Pinedo-Serrano Alejandro , Rivas-García Lorenzo , Galán-Moya Eva M.
Published: October 2023

Targeted protein degradation has emerged as an alternative therapy against cancer, offering several advantages over traditional inhibitors. The new degrader drugs provide different therapeutic strategies: they could cross the phospholipid bilayer membrane by the addition of specific moieties to extracellular proteins. On the other hand, they could efficiently improve the degradation process by the generation of a ternary complex structure of an E3 ligase. Herein, we review the current trends in the use of TAC-based technologies (TACnologies), such as PROteolysis TArgeting Chimeras (PROTAC), PHOtochemically TArgeting Chimeras (PHOTAC), CLIck-formed Proteolysis TArgeting Chimeras (CLIPTAC), AUtophagy TArgeting Chimeras (AUTAC), AuTophagosome TEthering Compounds (ATTEC), LYsosome-TArgeting Chimeras (LYTAC), and DeUBiquitinase TArgeting Chimeras (DUBTAC), in experimental development and their progress towards clinical applications.
Cydonia oblonga-Seed-Mucilage-Based pH-Sensitive Graft Copolymer for Controlled Drug Delivery—In Vitro and In Vivo Evaluation
Authors: Sarfraz Muhammad , Tulain Ume Ruqia , Erum Alia , Malik Nadia Shamshad , Mahmood Arshad , Sumaira Sumaira , Aslam Sidra , Sandhu Mansur Abdullah , Tayyab Muhammad
Published: October 2023

The primary objective of this study was to assess the potential utility of quince seed mucilage as an excipient within a graft copolymer for the development of an oral-controlled drug delivery system. The Cydonia oblonga-mucilage-based graft copolymer was synthesized via a free radical polymerization method, employing potassium per sulfate (KPS) as the initiator and N, N-methylene bisacrylamide (MBA) as the crosslinker. Various concentrations of monomers, namely acrylic acid (AA) and methacrylic acid (MAA), were used in the graft copolymerization process. Metoprolol tartarate was then incorporated into this graft copolymer matrix, and the resultant drug delivery system was subjected to comprehensive characterization using techniques such as Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The swelling behavior of the drug delivery system was evaluated under different pH conditions, and in vitro drug release studies were conducted. Furthermore, pharmacokinetic parameters including the area under the curve (AUC), maximum plasma concentration (Cmax), time to reach Cmax (Tmax), and half-life (t1/2) were determined for metoprolol-loaded hydrogel formulations in rabbit plasma, and these results were compared with those obtained from a commercially available product. The key findings from the study include observations that higher concentrations of acrylic acid (AA) and Cydonia oblonga mucilage (CM) in the graft copolymer enhanced swelling, while the opposite trend was noted at elevated concentrations of methacrylic acid (MAA) and N, N-methylene bisacrylamide (MBA). FTIR analysis confirmed the formation of the graft copolymer and established the compatibility between the drug and the polymer. SEM imaging revealed a porous structure in the prepared formulations. Additionally, the swelling behavior and drug release profiles indicated a pH-sensitive pattern. The pharmacokinetic assessment revealed sustained release patterns of metoprolol from the hydrogel network system. Notably, the drug-loaded formulation exhibited a higher Cmax (156.48 ng/mL) compared to the marketed metoprolol product (96 ng/mL), and the AUC of the hydrogel-loaded metoprolol was 2.3 times greater than that of the marketed formulation. In conclusion, this study underscores the potential of quince seed mucilage as an intelligent material for graft-copolymer-based oral-controlled release drug delivery systems.
High-Resolution Laser in Widely Tunable Range (4.16–7.13 µm) Based on Mid-Infrared BaGa4Se7 Optical Parametric Oscillator Pumped at 1064 nm
Authors: Zhao Xinqiang , Kong Hui , Zou Jierui , Lv Guorui , Bian Jintian , Xu Haiping , Wen Kaihua , Jiang Xiuhong , Ma Yunfan
Published: October 2023

A widely tunable 4.16–7.31 μm high-resolution mid-infrared optical parametric oscillator with a type-I BaGa4Se7 (BGSe) crystal pumped by a 1064 nm laser was demonstrated for the first time. We have achieved a wavelength tuning accuracy of less than 5 nm through simultaneously performing temperature tuning and angular tuning on the BGSe (46°, 0°) Optical Parametric Oscillation (OPO). The wavelength resolution will be less than 1 nm when using commercial temperature control equipment.
A First Report of Sclerotinia sclerotiorum Causing Forsythia Twig Blight in Romania
Authors: Florea Andreea-Mihaela , Gafencu Andrei-Mihai , Lipșa Florin-Daniel , Gabur Iulian , Ulea Eugen
Published: October 2023

Sclerotinia sclerotiorum (Lib.) de Bary (1884) is a fungal plant pathogen with worldwide distribution and a varying host range from different botanical families. It can cause damage to a large variety of crops such as sunflower, soybean, dry bean, canola, some vegetables, and ornamental plants. This article reports the occurrence of twig blight on the forsythia plant from the NE region of Romania. The disease was observed on Forsythia × intermedia Zab. plants from the Arboretum Park of the Iasi University of Life Sciences (IULS), located in Iasi City, Romania. Infected tissue was investigated through morphological characteristics using Sanger sequencing. Genomic DNA was extracted from the isolate obtained from naturally infected plants, and the ribosomal internal transcribed spacer region was amplified using the ITS1, ITS2, and LSU D1 and D2. Based on the results of this study, molecular and morphological data suggest that Forsythia twig blight can be caused by S. sclerotiorum. Constant monitoring of Sclerotinia sclerotiorum across multiple hosts and time intervals will reduce potential spread and future economic losses in cultivated species.
Root Development Monitoring under Different Water Supply Levels in Processing Tomato Plants
Authors: M’hamdi Oussama , Égei Márton , Pék Zoltán , Ilahy Riadh , Nemeskéri Eszter , Helyes Lajos , Takács Sándor
Published: October 2023

Managing crop yields and optimizing water use is a global challenge, as fresh water supply decreases rapidly and demand remains high. Therefore, understanding how plants react to varying water levels is crucial for efficient water usage. This study evaluates how tomato plants adapt to varying water levels (100%, 50% of crop evapotranspiration, and non-irrigated control) over two growing seasons in 2020 and 2021. Root images were captured weekly during an 8-week monitoring period in 2020 and 6 weeks in 2021 using a non-destructive CI-600 in-situ root imager at depths between 10 and 70 cm. Under water stress, plants developed deeper, more extensive root systems to maximize water uptake, consistent with prior research. Root depth and architecture varied with soil depth and the severity of water stress. Year-to-year variations were also found, likely due to changes in irrigation levels and environmental conditions such as temperature. SPAD values were higher under control conditions, especially in the 2021 growing season, suggesting reduced chlorophyll degradation, while no significant differences were observed in chlorophyll fluorescence (Fv/Fm) between treatments, suggesting stable photosynthetic efficiency under varied water stress conditions. These findings contribute to our understanding of root zone optimization and drought-resilient cultivar breeding, contributing to more sustainable agricultural practices.
Effects of Magnesium Imbalance on Root Growth and Nutrient Absorption in Different Genotypes of Vegetable Crops
Authors: Qu Shuai , Li Huixia , Zhang Xueke , Gao Jingbo , Ma Rui , Ma Ling , Ma Jing
Published: October 2023

Magnesium (Mg) plays a crucial role in crop growth, but how Mg supply level affects root growth and nutrient absorption in vegetable crops with different genotypes has not been sufficiently investigated. In this study, the responses of tomato (Solanum lycopersicum L.) and cucumber (Cucumis sativus L.) crops to different levels of Mg supply were explored. Four levels of Mg treatment (i.e., 0.2, 1.0, 2.0, 3.0 mmol/L) were applied under hydroponic conditions, denoted as Mg0.2, Mg1, Mg2, and Mg3, respectively. The results showed that with increasing Mg levels, the plant biomass, root growth, and nutrient accumulation in both vegetable crops all increased until reaching their maximum values under the Mg2 treatment and then decreased. The total biomass per tomato plant of Mg2 treatment was 30.9%, 14.0%, and 14.0% higher than that of Mg0.2, Mg1, and Mg3 treatments, respectively, and greater increases were observed in cucumber plant biomass (by 54.3%, 17.4%, and 19.9%, respectively). Compared with the Mg0.2 treatment, the potassium (K) and calcium (Ca) contents in various plant parts of both crops remarkably decreased under the Mg3 treatment. This change was accompanied by prominently increased Mg contents in various plant parts and para-hydroxybenzoic acid and oxalic acid contents in root exudates. Irrespective of Mg level, plant biomass, root growth, nutrient accumulation, and root exudation of organic acids were all higher in tomato plants than in cucumber plants. Our findings indicate that excessive Mg supply promotes the roots to exude phenolic acids and hinders the plants from absorbing K and Ca in different genotypes of vegetable crops despite no effect on Mg absorption. A nutritional deficiency of Mg stimulates root exudation of organic acids and increases the types of exuded organic acids, which could facilitate plant adaption to Mg stress. In terms of root growth and nutrient absorption, tomato plants outperform cucumber plants under low and medium Mg levels, but the latter crop is more tolerant to Mg excess.
Essential Oils and Antagonistic Microorganisms as Eco-Friendly Alternatives for Coffee Leaf Rust Control
Authors: Santiago-Santiago Maricela , Sánchez-Viveros Gabriela , Hernández-Adame Luis , Chiquito-Contreras Cesar Josué , Salinas-Castro Alejandro , Chiquito-Contreras Roberto Gregorio , Hernández-Montiel Luis Guillermo
Published: October 2023

Coffee leaf rust (CLR) is caused by the biotrophic fungus Hemileia vastatrix Berk. & Br., a disease of economic importance, reducing coffee yield up to 60%. Currently, CLR epidemics have negatively impacted food security. Therefore, the objective of the present research study is to show a current framework of this disease and its effects on diverse areas, as well as the biological systems used for its control, mode of action, and effectiveness. The use of essential plant oils and antagonistic microorganisms to H. vastatrix are highlighted. Terpenes, terpenoids, and aromatic compounds are the main constituents of these oils, which alter the cell wall and membrane composition and modify the basic cell functions. Beneficial microorganisms inhibit urediniospore germination and reduce disease incidence and severity. The antagonistic microorganisms and essential oils of some aromatic plants have great potential in agriculture. These biological systems may have more than one mechanism of action, which reduces the possibility of the emergence of resistant strains of H. vastatrix.
Simulated Drift of Dicamba and Glyphosate on Coffee Crop
Authors: Zampiroli Renan , da Cunha João Paulo Arantes Rodrigues , de Alvarenga Cleyton Batista
Published: October 2023

Weed management in areas adjacent to coffee plantations makes herbicide drift a constant concern, especially with the use of nonselective products such as dicamba. The objective of this study was to evaluate the phytotoxic effects of the herbicide dicamba alone and mixed with glyphosate as a result of simulated drift in a coffee-producing area. The study was conducted in duplicate at two different coffee cherry development stages. The study was performed with a randomized block design and a 2 × 5 + 1 factorial scheme with four replications using two herbicide spray solutions (dicamba and dicamba + glyphosate) and five low doses (0.25; 1; 5; 10; and 20%). Additionally, a control treatment without herbicide application was also employed. In this study, we evaluated the phytotoxic damage and biometric and productive parameters. Visual damages were observed with the use of dicamba and dicamba + glyphosate doses reduced by 0.25% to 5% in the first days after application. The main symptoms were new leaf epinasty, changes in the internodal distance, and plagiotropic branch curvature. Low doses led to reduced plant height and branch length. The treatments did not reduce productivity and performance but altered the physical classifications of grains.
A Minireview of the Solid-State Electrolytes for Zinc Batteries
Authors: Yao Wangbing , Zheng Zhuoyuan , Zhou Jie , Liu Dongming , Song Jinbao , Zhu Yusong
Published: October 2023

Aqueous zinc-ion batteries (ZIBs) have gained significant recognition as highly promising rechargeable batteries for the future due to their exceptional safety, low operating costs, and environmental advantages. Nevertheless, the widespread utilization of ZIBs for energy storage has been hindered by inherent challenges associated with aqueous electrolytes, including water decomposition reactions, evaporation, and liquid leakage. Fortunately, recent advances in solid-state electrolyte research have demonstrated great potential in resolving these challenges. Moreover, the flexibility and new chemistry of solid-state electrolytes offer further opportunities for their applications in wearable electronic devices and multifunctional settings. Nonetheless, despite the growing popularity of solid-state electrolyte-based-ZIBs in recent years, the development of solid-state electrolytes is still in its early stages. Bridging the substantial gap that exists is crucial before solid-state ZIBs become a practical reality. This review presents the advancements in various types of solid-state electrolytes for ZIBs, including film separators, inorganic additives, and organic polymers. Furthermore, it discusses the performance and impact of solid-state electrolytes. Finally, it outlines future directions for the development of solid-state ZIBs.
Comparative Analysis of Machine Learning Models for Bankruptcy Prediction in the Context of Pakistani Companies
Authors: Máté Domicián , Raza Hassan , Ahmad Ishtiaq
Published: October 2023

This article presents a comparative analysis of machine learning models for business failure prediction. Bankruptcy prediction is crucial in assessing financial risks and making informed decisions for investors and regulatory bodies. Since machine learning techniques have advanced, there has been much interest in predicting bankruptcy due to their capacity to handle complex data patterns and boost prediction accuracy. In this study, we evaluated the performance of various machine learning algorithms. We collect comprehensive data comprising financial indicators and company-specific attributes relevant to the Pakistani business landscape from 2016 through 2021. The analysis includes AdaBoost, decision trees, gradient boosting, logistic regressions, naive Bayes, random forests, and support vector machines. This comparative analysis provides insights into the most suitable model for accurate bankruptcy prediction in Pakistani companies. The results contribute to the financial literature by comparing machine learning models tailored to anticipate Pakistani stock market insolvency. These findings can assist financial institutions, regulatory bodies, and investors in making more informed decisions and effectively mitigating financial risks.
Field Patch Extraction Based on High-Resolution Imaging and U2-Net++ Convolutional Neural Networks
Authors: Long Chen , Wenlong Song , Tao Sun , Yizhu Lu , Wei Jiang , Jun Liu , Hongjie Liu , Tianshi Feng , Rongjie Gui , Abbas Haider , Lingwei Meng , Shengjie Lin , Qian He
Published: October 2023

Accurate extraction of farmland boundaries is crucial for improving the efficiency of farmland surveys, achieving precise agricultural management, enhancing farmers’ production conditions, protecting the ecological environment, and promoting local economic development. Remote sensing and deep learning are feasible methods for creating large-scale farmland boundary maps. However, existing neural network models have limitations that restrict the accuracy and reliability of agricultural parcel extraction using remote sensing technology. In this study, we used high-resolution satellite images (2 m, 1 m, and 0.8 m) and the U2-Net++ model based on the RSU module, deep separable convolution, and the channel-spatial attention mechanism module to extract different types of fields. Our model exhibited significant improvements in farmland parcel extraction compared with the other models. It achieved an F1-score of 97.13%, which is a 7.36% to 17.63% improvement over older models such as U-Net and FCN and a more than 2% improvement over advanced models such as DeepLabv3+ and U2-Net. These results indicate that U2-Net++ holds the potential for widespread application in the production of large-scale farmland boundary maps.
Development of Multifunctional Detection Robot for Roller Coaster Track
Authors: Song Weike , Zhao Zhao , Zhang Kun , Wang Huajie , Sun Yifeng
Published: October 2023

Recent advances in roller coasters accelerate the creation of complex tracks to provide stimulation and excitement for humans. As the main load-bearing component, tracks are prone to damage such as loose connecting bolts, paint peeling, corroded sleeper welds, corroded butt welds, reduced track wall thickness and surface cracks under complex environments and long-term alternating loads. However, inspection of the roller coaster tracks, especially the high-altitude rolling tracks, is a crucial problem that traditional manual detection methods have difficulty solving. In addition, traditional inspection is labor-intensive, time-consuming, and provides only discrete information. Here, a concept of the multifunctional detection robot with a mechanical structure, electrical control system, camera, electromagnetic ultrasonic probes and an array of eddy current probes for detecting large roller coaster tracks is reported. By optimizing the design layout, integrating multiple systems and completing machine testing, the multifunctional roller coaster track detection robot exhibits outstanding performance in track appearance, thickness and crack detection. This study provides great potential for intelligent detection in amusement equipment, railcar, train and so on.
Artificial Neural Networks with Machine Learning Design for a Polyphasic Encoder
Authors: Alvarez-Rodríguez Sergio , Peña-Lecona Francisco G.
Published: October 2023

Artificial neural networks are a powerful tool for managing data that are difficult to process and interpret. This article presents the design and implementation of backpropagated multilayer artificial neural networks, structured with a vector input, hidden layers, and an output node, for information processing generated by an optical encoder based on the polarization of light. A machine learning technique is proposed to train the neural networks such that the system can predict with remarkable accuracy the angular position in which the rotating element of the neuro-encoder is located based on information provided by light’s phase-shifting arrangements. The proposed neural designs show excellent performance in small angular intervals, and a methodology was proposed to avoid losing this remarkable characteristic in measurements from 0 to 180° and even up to 360°. The neuro-encoder was implemented in the simulation stage to obtain performance results, where the main evaluation metric employed to assess the performance is the total error. This proposal can be useful to improve the capabilities of resolvers or other polyphasic sensors used to obtain outstanding precision and accurate data, even when working under hard and noisy industrial conditions.
Salient Object Detection Based on Optimization of Feature Computation by Neutrosophic Set Theory
Authors: Song Sensen , Li Yue , Jia Zhenhong , Shi Fei
Published: October 2023

In recent saliency detection research, too many or too few image features are used in the algorithm, and the processing of saliency map details is not satisfactory, resulting in significant degradation of the salient object detection result. To overcome the above deficiencies and achieve better object detection results, we propose a salient object detection method based on feature optimization by neutrosophic set (NS) theory in this paper. First, prior object knowledge is built using foreground and background models, which include pixel-wise and super-pixel cues. Simultaneously, the feature maps are selected and extracted for feature computation, allowing the object and background features of the image to be separated as much as possible. Second, the salient object is obtained by fusing the features decomposed by the low-rank matrix recovery model with the object prior knowledge. Finally, for salient object detection, we present a novel mathematical description of neutrosophic set theory. To reduce the uncertainty of the obtained saliency map and then obtain good saliency detection results, the new NS theory is proposed. Extensive experiments on five public datasets demonstrate that the results are competitive and superior to previous state-of-the-art methods.
Exploring the ITO/PET Extended-Gate Field-Effect Transistor (EGFET) for pH Sensing
Authors: Mouffak Z. , Adapala V.
Published: October 2023

In this project we investigated the extended-gate field-effect transistor (EGFET) structure used with ITO (Indium Tin Oxide)/PET (Polyethylene Terephthalate) sensitive films acting as the extended-gate part of an EGFET obtained from a combination of FETs from the CD4007 chip. We tested the device as a pH sensor by immersing the ITO/PET electrode in several chemical solutions of acidic and basic nature, including hydrogen peroxide, acetic acid, sulfuric acid, and ammonium hydroxide, at different concentrations. Using a Tektronix 4200A sourcemeter, we plotted the current–voltage (I–V) characteristics for the different chemical solutions, and we established a correlation to the pH changes. Results from the plotted I–V characteristics show a great dependance of the drain current (ID) on solution concentration. Furthermore, we measured the pH of each of the used solutions, and we established a relationship between the drain current and the pH value. Our results show a consistent decrease in the current with an increase in the pH value, although with different rates depending on the solution. The device showed high voltage sensitivity at 0.23 V per pH unit when tested in sulfuric acid.
Enhanced Fault Type Detection in Covered Conductors Using a Stacked Ensemble and Novel Algorithm Combination
Authors: Kabot Ondřej , Klein Lukáš , Prokop Lukáš , Walendziuk Wojciech
Published: October 2023

This study introduces an innovative approach to enhance fault detection in XLPE-covered conductors used for power distribution systems. These covered conductors are widely utilized in forested areas (natural parks) to decrease the buffer zone and increase the reliability of the distribution network. Recognizing the imperative need for precise fault detection in this context, this research employs an antenna-based method to detect a particular type of fault. The present research contains the classification of fault type detection, which was previously accomplished using a very expensive and challenging-to-install galvanic contact method, and only to a limited extent, which did not provide information about the fault type. Additionally, differentiating between types of faults in the contact method is much easier because information for each phase is available. The proposed method uses antennas and a classifier to effectively differentiate between fault types, ranging from single-phase to three-phase faults, as well as among different types of faults. This has never been done before. To bolster the accuracy, a stacking ensemble method involving the logistic regression is implemented. This approach not only advances precise fault detection but also encourages the broader adoption of covered conductors. This promises benefits such as a reduced buffer zone, improved distribution network reliability, and positive environmental outcomes through accident prevention and safe covered conductor utilization. Additionally, it is suggested that the fault type detection could lead to a decrease in false positives.
CARRT—Motion Capture Data for Robotic Human Upper Body Model
Authors: Trivedi Urvish , Alqasemi Redwan , Dubey Rajiv
Published: October 2023

In recent years, researchers have focused on analyzing humans’ daily living activities to study various performance metrics that humans subconsciously optimize while performing a particular task. In order to recreate these motions in robotic structures based on the human model, researchers developed a framework for robot motion planning which is able to use various optimization methods to replicate similar motions demonstrated by humans. As part of this process, it will be necessary to record the motions data of the human body and the objects involved in order to provide all the essential information for motion planning. This paper aims to provide a dataset of human motion performing activities of daily living that consists of detailed and accurate human whole-body motion data collected using a Vicon motion capture system. The data have been utilized to generate a subject-specific full-body model within OpenSim. Additionally, it facilitated the computation of joint angles within the OpenSim framework, which can subsequently be applied to the subject-specific robotic model developed MATLAB framework. The dataset comprises nine daily living activities and eight Range of Motion activities performed by ten healthy participants and with two repetitions of each variation of one action, resulting in 340 demonstrations of all the actions. A whole-body human motion database is made available to the public at the Center for Assistive, Rehabilitation, and Robotics Technologies (CARRT)-Motion Capture Data for Robotic Human Upper Body Model, which consists of raw motion data in .c3d format, motion data in .trc format for the OpenSim model, as well as post-processed motion data for the MATLAB-based model.
Experimental Study on Monitoring Damage Progression of Basalt-FRP Reinforced Concrete Slabs Using Acoustic Emission and Machine Learning
Authors: Zhang Tonghao , Mahdi Mohammad , Issa Mohsen , Xu Chenxi , Ozevin Didem
Published: October 2023

Basalt fiber-reinforced polymer (BFRP) reinforced concrete is a new alternative to conventional steel-reinforced concrete due to its high tensile strength and corrosion resistance characteristics. However, as BFRP is a brittle material, unexpected failure of concrete structures reinforced with BFRP may occur. In this study, the damage initiation and progression of BFRP-reinforced concrete slabs were monitored using the acoustic emission (AE) method as a structural health monitoring (SHM) solution. Two simply supported slabs were instrumented with an array of AE sensors in addition to a high-resolution camera, strain, and displacement sensors and then loaded until failure. The dominant damage mechanism was concrete cracking due to the over-reinforced design and adequate BFRP bar-concrete bonding. The AE method was evaluated in terms of identifying the damage initiation, progression from tensile to shear cracks, and the evolution of crack width. Unsupervised machine learning was applied to the AE data obtained from the first slab testing to develop the clusters of the damage mechanisms. The cluster results were validated using the k-means supervised learning model applied to the data obtained from the second slab. The accuracy of the K-NN model trained on the first slab was 99.2% in predicting three clusters (tensile crack, shear crack, and noise). Due to the limitation of a single indicator to characterize complex damage properties, a Statistical SHapley Additive exPlanation (SHAP) analysis was conducted to quantify the contribution of each AE feature to crack width. Based on the SHAP analysis, the AE duration had the highest correlation with the crack width. The cumulative duration of the AE sensor near the crack had close to 100% accuracy to track the crack width. It was concluded that the AE sensors positioned at the mid-span of slabs can be used as an effective SHM solution to monitor the initiation of tensile cracks, sudden changes in structural response due to major damage, damage evolution from tensile to shear cracks, and the progression of crack width.
Electron Temperature Measurements Using a Two-Filter Soft X-ray Array in VEST
Authors: Lee M. W. , Lim S. , Jeong W. , Kim S. , Kim J. H. , Hwang Y. S. , Sung C.
Published: October 2023

A multichannel soft X-ray (SXR) array has been developed to measure the electron temperature in the Versatile Experiment Spherical Torus (VEST). To estimate electron temperature using the two-filter method applied to SXR intensity, we designed a pinhole camera that has two photodiode arrays with different metallic filters. We also adopted a filter wheel and tested various filter parameters to find the optimal filter set. Through tests, the combination of aluminum and beryllium was found to be the most suitable for the current experimental conditions in VEST. The filtered SXR signals were acquired with a low-noise preamplifier, exhibiting sufficient signal-to-noise ratios for electron temperature estimation based on the intensity ratio of two signals obtained with different filters. The estimated electron temperature from the developed two-filter SXR array showed reasonably matched levels and consistent trends with Thomson scattering measurements. Error contribution from impurity line emission is also discussed.
Correction: Che et al. Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT). Sensors 2023, 23, 5710
Authors: Che Fuhu , Ahmed Qasim Zeeshan , Lazaridis Pavlos I. , Sureephong Pradorn , Alade Temitope
Published: October 2023

There was an error in the original publication [...]
BL-YOLOv8: An Improved Road Defect Detection Model Based on YOLOv8
Authors: Wang Xueqiu , Gao Huanbing , Jia Zemeng , Li Zijian
Published: October 2023

Road defect detection is a crucial task for promptly repairing road damage and ensuring road safety. Traditional manual detection methods are inefficient and costly. To overcome this issue, we propose an enhanced road defect detection algorithm called BL-YOLOv8, which is based on YOLOv8s. In this study, we optimized the YOLOv8s model by reconstructing its neck structure through the integration of the BiFPN concept. This optimization reduces the model’s parameters, computational load, and overall size. Furthermore, to enhance the model’s operation, we optimized the feature pyramid layer by introducing the SimSPPF module, which improves its speed. Moreover, we introduced LSK-attention, a dynamic large convolutional kernel attention mechanism, to expand the model’s receptive field and enhance the accuracy of object detection. Finally, we compared the enhanced YOLOv8 model with other existing models to validate the effectiveness of our proposed improvements. The experimental results confirmed the effective recognition of road defects by the improved YOLOv8 algorithm. In comparison to the original model, an improvement of 3.3% in average precision mAP@0.5 was observed. Moreover, a reduction of 29.92% in parameter volume and a decrease of 11.45% in computational load were achieved. This proposed approach can serve as a valuable reference for the development of automatic road defect detection methods.
Evaluating the Use of a Robot in a Hematological Intensive Care Unit: A Pilot Study
Authors: Falcone Michela , D’Onofrio Grazia , Iannacone Giuseppina , Steduto Matteo , Carella Angelo Michele , Giuliani Francesco , Ricciardi Francesco
Published: October 2023

The aim of the SYRIACA project was to test the capability of a social robot to perform specific tasks in healthcare settings, reducing infection risks for patients and caregivers. The robot was piloted in an Intensive Hematological Unit, where the patients’ and healthcare operators’ acceptability of the robot was evaluated. The robot’s functions, including logistics, surveillance, entertainment, and remote visits, were well accepted. Patients expressed interest in having multiple interactions with the robot, which testifies to its engaging potential and that it provides useful services. During remote visits, the robot reduced perceived stress among patients, alleviating feelings of isolation. The successful implementation of the robot suggests its potential to enhance safety and well-being in healthcare settings.
‘If She Can, All of You Can’: Violence as a Restoration of the Male Mandate in Vocational Education Training
Authors: Meri Esperanza , Navas Almudena A. , Mora Enrico
Published: October 2023

This paper analyses violence as a restoration of both male mandate and power in male-dominated fields, such as in the context of Vocational Educational Training specialising in Transport and Vehicle Maintenance in the Spanish city of Valencia and how women who enter it struggle against it. Our theory is based on the developments made by Judith Butler, who understands gender as a power device, and by Rita L. Segato through the concepts of male mandate and moral violence. We also analyse the resistance that is being deployed against gender normativity. To offer an account of these ideas, our research was designed on a qualitative basis following an abductive approach. We conducted eight biographical interviews, throughout the 2019–2020 academic year, with women who are linked to the automotive sector and to the VET area in question. We can state that their entering into this productive field leads to a denaturalisation of the hierarchy imposed by the male mandate and that in challenging things, it exacerbates the violent practices as a restoration of the male mandate.
Analyzing the Impact of Public Housing Privatization on Immigrant Micro-Segregation in Milan
Authors: Costarelli Igor
Published: October 2023

In several Western European countries, a significant share of social rental housing stock has been sold since the 1980s as part of government policies aimed at promoting homeownership societies. Research has shown that tenure conversion has contributed to increasing socio-spatial segregation of lower-income groups, with diverging spatial patterns of homeownership among immigrants. This paper examines the impact of recent public housing privatization schemes in Milan in relation to micro-segregation and peripheralization processes of foreign populations, which represent distinctive features of immigrant residential distribution in this city. By employing name analysis, an unconventional approach in segregation studies, I inferred the geographical origins of homebuyers and mapped their distribution across the city. The findings reveal divergent purchasing behaviors, whereby Italians predominantly acquire properties in semi-central areas currently undergoing urban regeneration. In contrast, immigrants tend to concentrate their acquisitions in peripheral post-war public housing neighborhoods or in areas predominantly inhabited by residents with similar geographical origins. This paper contributes to the existing literature on ethnic residential segregation in Southern European cities by shedding light on the underexplored role of public housing privatization policies in shaping specific residential patterns and housing outcomes among different groups.
Construction of Evaluation Index System of Harmonious Labor Relations in Chinese Enterprises: An Empirical Analysis Based on Structural Equation Model
Authors: Zhang Yi , Ni Jihui
Published: October 2023

Since entering the period of economic and social transformation, China’s labor relations conflicts have entered a period of prominence and prevalence, which has brought great challenges to ensure the sustainable development of enterprises and build a harmonious society. Constructing a scientific and reasonable evaluation index system of harmonious labor relations in enterprises to measure the harmonious degree of labor relations is of great significance for improving the management level of labor relations in enterprises, promoting sustainable development of enterprises, and building a socialist harmonious society. Based on the relevant research results of labor relations and from the perspective of employees, this paper took the ERG theory (E stands for existence needs, R stands for relatedness needs, G stands for growth needs) as the basic framework of the evaluation index system, combined with the literature research method and expert consultation method to initially design evaluation index system, used SPSS 26.0 software and principal component analysis to further determine the evaluation index system structure. Based on the structural equation model, this paper used AMOS 23.0 software to carry out first-order and second-order confirmatory factor analysis for the evaluation index system and assigned objective weights to the evaluation indicators according to standardized factor analysis path coefficients. The index system, via structure confirmation and index empowerment, is used to evaluate the harmonious degree of labor relations between the two enterprises, N and Z. It is found that the evaluation index system of enterprise harmonious labor relations constructed in this paper conforms to the theoretical hypothesis model; The index system includes salary and benefits, working conditions, communication management, interpersonal relationship, individual participation and personal development six dimension-level indexes and 18 index-level indexes; The overall labor relations of companies N and Z are relatively harmonious, but the relative satisfaction of individual development indicators at the dimension level is low, and there is still room for improvement. The research results of this paper not only enrich and improve the theoretical system of labor relations to a certain extent but also provide a new perspective for the integration of enterprise harmonious labor relations and ERG theory and provide a theoretical basis and practical guidance for enterprises to establish harmonious labor relations and promote sustainable development of enterprises.
Experimental Study on Pile Load Transfer Considering Rice Stone Filled-In Gaps between Steel Drive Pipe and Pile Casing in Karst Region
Authors: Zhu Fangcai , Yang Zhijia , Liu Qing , Zhao Yanlin , Wu Binbin , Zhang Shaolong , Chen Qi , Chen Yifan , Luo Rui
Published: October 2023

For a guarantee of perpendicularity and stiffness in piles in Karst areas, full rotary cast-in-place piles are often utilized, steel pipes are rotarily driven into a stratum, and inner-steel pile casing is positioned. With the engineering background of the bridge piles of Guinan high-speed railway in Guangxi autonomous region, the space between steel drive pipe is filled with rice stones, the load-transfer mechanism of which was studied. An apparatus was designed for pullout of the drive pipe, rice stones are replaced with coarse stones, a simplified organic glass-pipe model is put forward, another similar indoor stratigraphic model is also pre-cast, and the movement of coarse sands and load transfer is studied with two models. The quantity of sands is calculated using back analysis through reappearance and the Rhino model: the first one is estimated using a reproduction of the pullout procedure, the second is calculated through the Rhino model based on the observation of the shape of sand in caves. When the drive pipe is pulled out, some coarse sand flows into the Karst caves and becomes loose, while load is applied on the top of the pile. The movement of coarse sand develops further, and more coarse sand flows into caves close to the bottom; this leads to a reduction its frictional resistance, and the axial force of the pile increases closer to the upper position of the cave, whereas the axial force of the pile is concentrated almost constantly close to the bottom of the cave. Comparing the end resistance and the frictional resistance, coarse sand can bear pile load to a great extent.
Quantitative Investigation of Fracture Apertures during Temporary Plugging and Diverting Fracturing
Authors: Wang Yubin , Sun Baojiang , Wang Tianju , Hao Zhiwei , Wang Bo
Published: October 2023

Oil and gas resources are closely related to daily life and are an important support for the economy of a city or even a country. Hydraulic fracturing is an indispensable technique to economically develop oil and gas resources through creating complex fractures. Temporary plugging and diverting fracturing (TPDF) can generate diversion fractures perpendicular to the initial fractures and enhance the stimulated area. The aperture of the diversion fractures determines its conductivity and the oil/gas production. However, it is difficult to evaluate the aperture of the diversion fracture due to the complex physical process of hydraulic fracturing. This work established a fluid–solid fully coupled simulation model to investigate the fracture aperture influenced by various factors during TPDF. The model can simulate the propagation of the initial fracture and the diversion fracture. Various factors include the tight plug’s permeability, the tight plug’s length, Young’s modulus, rock tensile strength, in situ stress contrast, the leak-off coefficient of the fracture surface, and fluid injection rate. The results show that the aperture of the previous fracture can be enlarged, and the aperture of the diversion fracture can be decreased by the tight plug. The aperture at the diversion fracture mouth is much smaller than that along the diversion fracture. Reservoirs with low Young’s modulus values and high rock tensile strength can generate the diversion fracture with a wider aperture. Moreover, increasing the fluid injection rate can effectively increase the fracture mouth aperture. In this way, the risk of screenout can be lowered. This work is beneficial for the design of the TPDF and ensures safe construction.
Diagnostics of Early Faults in Wind Generator Bearings Using Hjorth Parameters
Authors: Santos Arthur C. , Souza Wesley A. , Barbara Gustavo V. , Castoldi Marcelo F. , Goedtel Alessandro
Published: October 2023

Machine learning techniques are a widespread approach to monitoring and diagnosing faults in electrical machines. These techniques extract information from collected signals and classify the health conditions of internal components. Among all internal components, bearings present the highest failure rate. Classifiers commonly employ vibration data acquired from electrical machines, which can indicate different levels of bearing failure severity. Given the circumstances, this work proposes a methodology for detecting early bearing failures in wind turbines, applying classifiers that rely on Hjorth parameters. The Hjorth parameters were applied to analyze vibration signals collected from experiments to distinguish states of normal functioning and states of malfunction, hence enabling the classification of distinct conditions. After the labeling stage using Hjorth parameters, classifiers were employed to provide an automatic early fault identification model, with the decision tree, random forest, support vector machine, and k-nearest neighbors methods presenting accuracy levels of over 95%. Notably, the accuracy of the classifiers was maintained even after undergoing a dimensionality reduction process. Therefore, it can be stated that Hjorth parameters provide a feasible alternative for identifying early faults in wind generators through time-series analysis.
Tourism Degrowth: Painful but Necessary
Authors: Dwyer Larry
Published: October 2023

Despite the well-known adverse effects of economic growth, the core strategic goal of a high proportion of destination managers globally continues to be the pursuit of tourism growth. Proponents of the dominant ‘growth management’ view claim that tourism’s adverse environmental effects can be solved by ongoing ‘decoupling’ of economic growth from resource use through more efficient management of tourism development, supported by improvements in technology. In contrast, ‘heterodox’ approaches, sceptical of the ability of technological change to restrict growth-induced environmental and social degradation, reject the mainstream growth ethic and its action agenda. Arguing that faith in decoupling is a fragile basis for the growth management approach, this paper argues the merits of an alternative ‘degrowth’ approach to tourism planning and management. The paper articulates the nature of ‘degrowth’, the types of policies that can support a degrowth strategy, and the challenges involved in applying a degrowth approach to the tourism industry. It is concluded that, while tourism degrowth is necessary, the approach faces formidable challenges that must be overcome if resident wellbeing is to be maintained or enhanced through tourism development over the longer term. A research agenda is identified, addressing the nature of tourism, the consumption problem, localism and downsizing, effects on resident wellbeing, business degrowth, choice of policy mix, and types of institutional change required to support tourism degrowth.