bluuebunny/crossref_metadata_2025 huggingface dataset:
Secondary Amplifier Sampling Component Design of an X-ray Framing Detector Based on a Streak Tube
Authors: Zhang Jing-jin , Xu Yu-wei , Zong Fang-ke , Niu Li-hong , Lei Bao-guo , Yang Qin-lao , Cai Hou-zhi
Published: March 2023

The development of inertial confinement fusion (ICF) experiments necessitates the diagnostic instrument to have multiple frames with a high spatial and temporal resolution for the two-dimensional detection of the hot spot at the implosion end of the ICF. The existing sampling two-dimensional imaging technology in the world has superior performance; however, its subsequent development requires a streak tube with large lateral magnification. In this work, an electron beam separation device was designed and developed for the first time. The device can be used without changing the structure of the streak tube. It can be combined directly with the corresponding device and matched with a special control circuit. Based on the original transverse magnification, 1.77 times the secondary amplification can be achieved, which is conducive to expanding the recording range of the technology. The experimental results showed that the static spatial resolution of the streak tube after the inclusion of the device can still reach 10 lp/mm.
Compact UWB MIMO Antenna for 5G Millimeter-Wave Applications
Authors: Abbas Mohamed Atef , Allam Abdelmegid , Gaafar Abdelhamid , Elhennawy Hadia M. , Sree Mohamed Fathy Abo
Published: March 2023

This paper presents a printed multiple-input multiple-output (MIMO) antenna with the advantages of compact size, good MIMO diversity performance and simple geometry for fifth-generation (5G) millimeter-wave (mm-Wave) applications. The antenna offers a novel Ultra-Wide Band (UWB) operation from 25 to 50 GHz, using a Defective Ground Structure (DGS) technology. Firstly, its compact size makes it suitable for integrating different telecommunication devices for various applications, with a prototype fabricated having a total size of 33 mm × 33 mm × 0.233 mm. Second, the mutual coupling between the individual elements severely impacts the diversity properties of the MIMO antenna system. An effective technique of orthogonally positioning the antenna elements to each other increased their isolation; thus, the MIMO system provides the best diversity performance. The performance of the proposed MIMO antenna was investigated in terms of S-parameters and MIMO diversity parameters to ensure its suitability for future 5G mm-Wave applications. Finally, the proposed work was verified by measurements and exhibited a good match between simulated and measured results. It achieves UWB, high isolation, low mutual coupling, and good MIMO diversity performance, making it a good candidate and seamlessly housed in 5G mm-Wave applications.
Pearson Correlation in Determination of Quality of Current Transformers
Authors: Burgund Davorin , Nikolovski Srete , Galić Dario , Maravić Nedeljko
Published: March 2023

The article elaborates on the accuracy of current transformers (CT) in interaction with temperature and frequency using Pearson’s correlation. The first part of the analysis compares the accuracy of the mathematical model of the current transformer and the result of the measurement on the real CT using the Pearson correlation calculation. The mathematical model of CT is determined by deriving the formula of the functional error with the display of the accuracy of the measured value. The accuracy of the mathematical model is affected by the accuracy of current transformer model parameters and the calibration characteristic of the ammeter used to measure the CT current. Variables that cause deviation in the accuracy of CT are temperature and frequency. The calculation shows the effects on accuracy in both cases. The second part of the analysis refers to the calculation of the partial correlation of three quantities: (1) CT accuracy, (2) temperature, and (3) frequency on a set of 160 measurements. First, the influence of temperature on the correlation of CT accuracy and frequency is proven, following the proof of the influence of frequency on the correlation of CT accuracy and temperature. In the end, the analysis is combined by comparing the measured results of the first and second part of the analysis.
Impact of Estimation Uncertainty in PMU-Based Resynchronization of Continental Europe Synchronous Areas
Authors: Costa Federica , Peretto Lorenzo , Frigo Guglielmo
Published: March 2023

Power system stability is a task that every system operator (SO) is required to achieve daily to ensure an uninterruptible power supply. Especially at the transmission level, for each SO it is of utmost importance to ensure proper exchange of information with other SOs, mainly in case of contingencies. However, in the last years, two major events led to the splitting of Continental Europe into two synchronous areas. These events were caused by anomalous conditions which involved in one case the fault of a transmission line and in the other a fire outage in proximity to high-voltage lines. This work analyzes these two events from the measurement point of view. In particular, we discuss the possible impact of estimation uncertainty on control decisions based on measurements of instantaneous frequency. For this purpose, we simulate five different configurations of phasor measurement units (PMUs), as characterized by different signal models, processing routines, and estimation accuracy in the presence of off-nominal or dynamic conditions. The objective is to establish the accuracy of the frequency estimates in transient conditions, more specifically during the resynchronization of the Continental Europe area. Based on this knowledge, it is possible to set more suitable conditions for resynchronization operations: the idea is to consider not only the frequency deviation between the two areas but also to take into account the respective measurement uncertainty. As confirmed by the analysis of the two real-world scenarios, such an approach would allow for minimizing the probability of adverse or even dangerous conditions such as dampened oscillations and inter-modulations.
Enabling Semantic-Functional Communications for Multiuser Event Transmissions via Wireless Power Transfer
Authors: Silva Pedro E. Gória , Marchetti Nicola , Nardelli Pedro H. J. , de Souza Rausley A. A.
Published: March 2023

A central concern for large-scale sensor networks and the Internet of Things (IoT) has been battery capacity and how to recharge it. Recent advances have pointed to a technique capable of collecting energy from radio frequency (RF) waves called radio frequency-based energy harvesting (RF-EH) as a solution for low-power networks where cables or even changing the battery is unfeasible. The technical literature addresses energy harvesting techniques as an isolated block by dealing with energy harvesting apart from the other aspects inherent to the transmitter and receiver. Thus, the energy spent on data transmission cannot be used together to charge the battery and decode information. As an extension to them, we propose here a method that enables the information to be recovered from the battery charge by designing a sensor network operating with a semanticfunctional communication framework. Moreover, we propose an event-driven sensor network in which batteries are recharged by applying the technique RF-EH. In order to evaluate system performance, we investigated event signaling, event detection, empty battery, and signaling success rates, as well as the Age of Information (AoI). We discuss how the main parameters are related to the system behavior based on a representative case study, also discussing the battery charge behavior. Numerical results corroborate the effectiveness of the proposed system.
Modeling Topics in DFA-Based Lemmatized Gujarati Text
Authors: Chauhan Uttam , Shah Shrusti , Shiroya Dharati , Solanki Dipti , Patel Zeel , Bhatia Jitendra , Tanwar Sudeep , Sharma Ravi , Marina Verdes , Raboaca Maria Simona
Published: March 2023

Topic modeling is a machine learning algorithm based on statistics that follows unsupervised machine learning techniques for mapping a high-dimensional corpus to a low-dimensional topical subspace, but it could be better. A topic model’s topic is expected to be interpretable as a concept, i.e., correspond to human understanding of a topic occurring in texts. While discovering corpus themes, inference constantly uses vocabulary that impacts topic quality due to its size. Inflectional forms are in the corpus. Since words frequently appear in the same sentence and are likely to have a latent topic, practically all topic models rely on co-occurrence signals between various terms in the corpus. The topics get weaker because of the abundance of distinct tokens in languages with extensive inflectional morphology. Lemmatization is often used to preempt this problem. Gujarati is one of the morphologically rich languages, as a word may have several inflectional forms. This paper proposes a deterministic finite automaton (DFA) based lemmatization technique for the Gujarati language to transform lemmas into their root words. The set of topics is then inferred from this lemmatized corpus of Gujarati text. We employ statistical divergence measurements to identify semantically less coherent (overly general) topics. The result shows that the lemmatized Gujarati corpus learns more interpretable and meaningful subjects than unlemmatized text. Finally, results show that lemmatization curtails the size of vocabulary decreases by 16% and the semantic coherence for all three measurements—Log Conditional Probability, Pointwise Mutual Information, and Normalized Pointwise Mutual Information—from −9.39 to −7.49, −6.79 to −5.18, and −0.23 to −0.17, respectively.
The Design and Development of Instrumented Toys for the Assessment of Infant Cognitive Flexibility
Authors: Ramanathan Vishal , Ariffin Mohammad Zaidi , Goh Guo Dong , Goh Guo Liang , Rikat Mohammad Adhimas , Tan Xing Xi , Yeong Wai Yee , Ortega Juan-Pablo , Leong Victoria , Campolo Domenico
Published: March 2023

The first years of an infant’s life represent a sensitive period for neurodevelopment where one can see the emergence of nascent forms of executive function (EF), which are required to support complex cognition. Few tests exist for measuring EF during infancy, and the available tests require painstaking manual coding of infant behaviour. In modern clinical and research practice, human coders collect data on EF performance by manually labelling video recordings of infant behaviour during toy or social interaction. Besides being extremely time-consuming, video annotation is known to be rater-dependent and subjective. To address these issues, starting from existing cognitive flexibility research protocols, we developed a set of instrumented toys to serve as a new type of task instrumentation and data collection tool suitable for infant use. A commercially available device comprising a barometer and an inertial measurement unit (IMU) embedded in a 3D-printed lattice structure was used to detect when and how the infant interacts with the toy. The data collected using the instrumented toys provided a rich dataset that described the sequence of toy interaction and individual toy interaction patterns, from which EF-relevant aspects of infant cognition can be inferred. Such a tool could provide an objective, reliable, and scalable method of collecting early developmental data in socially interactive contexts.
Myoelectric Pattern Recognition Using Gramian Angular Field and Convolutional Neural Networks for Muscle–Computer Interface
Authors: Fan Junjun , Wen Jiajun , Lai Zhihui
Published: March 2023

In the field of the muscle–computer interface, the most challenging task is extracting patterns from complex surface electromyography (sEMG) signals to improve the performance of myoelectric pattern recognition. To address this problem, a two-stage architecture, consisting of Gramian angular field (GAF)-based 2D representation and convolutional neural network (CNN)-based classification (GAF-CNN), is proposed. To explore discriminant channel features from sEMG signals, sEMG-GAF transformation is proposed for time sequence signal representation and feature modeling, in which the instantaneous values of multichannel sEMG signals are encoded in image form. A deep CNN model is introduced to extract high-level semantic features lying in image-form-based time sequence signals concerning instantaneous values for image classification. An insight analysis explains the rationale behind the advantages of the proposed method. Extensive experiments are conducted on benchmark publicly available sEMG datasets, i.e., NinaPro and CagpMyo, whose experimental results validate that the proposed GAF-CNN method is comparable to the state-of-the-art methods, as reported by previous work incorporating CNN models.
Assessment of Low-Density Force Myography Armband for Classification of Upper Limb Gestures
Authors: Rehman Mustafa Ur , Shah Kamran , Haq Izhar Ul , Iqbal Sajid , Ismail Mohamed A. , Selimefendigil Fatih
Published: March 2023

Using force myography (FMG) to monitor volumetric changes in limb muscles is a promising and effective alternative for controlling bio-robotic prosthetic devices. In recent years, there has been a focus on developing new methods to improve the performance of FMG technology in the control of bio-robotic devices. This study aimed to design and evaluate a novel low-density FMG (LD-FMG) armband for controlling upper limb prostheses. The study investigated the number of sensors and sampling rate for the newly developed LD-FMG band. The performance of the band was evaluated by detecting nine gestures of the hand, wrist, and forearm at varying elbow and shoulder positions. Six subjects, including both fit and amputated individuals, participated in this study and completed two experimental protocols: static and dynamic. The static protocol measured volumetric changes in forearm muscles at the fixed elbow and shoulder positions. In contrast, the dynamic protocol included continuous motion of the elbow and shoulder joints. The results showed that the number of sensors significantly impacts gesture prediction accuracy, with the best accuracy achieved on the 7-sensor FMG band arrangement. Compared to the number of sensors, the sampling rate had a lower influence on prediction accuracy. Additionally, variations in limb position greatly affect the classification accuracy of gestures. The static protocol shows an accuracy above 90% when considering nine gestures. Among dynamic results, shoulder movement shows the least classification error compared to elbow and elbow–shoulder (ES) movements.
Biosensor Development and Innovation in Healthcare and Medical Applications
Authors: Mills David K. , Nestorova Gergana G.
Published: March 2023

The pandemic necessitated a change to the historical diagnostics model [...]
Improving the Accuracy of a Biohybrid for Environmental Monitoring
Authors: Vogrin Michael , Rajewicz Wiktoria , Schmickl Thomas , Thenius Ronald
Published: March 2023

Environmental monitoring should be minimally disruptive to the ecosystems that it is embedded in. Therefore, the project Robocoenosis suggests using biohybrids that blend into ecosystems and use life forms as sensors. However, such a biohybrid has limitations regarding memory—as well as power—capacities, and can only sample a limited number of organisms. We model the biohybrid and study the degree of accuracy that can be achieved by using a limited sample. Importantly, we consider potential misclassification errors (false positives and false negatives) that lower accuracy. We suggest the method of using two algorithms and pooling their estimations as a possible way of increasing the accuracy of the biohybrid. We show in simulation that a biohybrid could improve the accuracy of its diagnosis by doing so. The model suggests that for the estimation of the population rate of spinning Daphnia, two suboptimal algorithms for spinning detection outperform one qualitatively better algorithm. Further, the method of combining two estimations reduces the number of false negatives reported by the biohybrid, which we consider important in the context of detecting environmental catastrophes. Our method could improve environmental modeling in and outside of projects such as Robocoenosis and may find use in other fields.
Speckle Measurement for Small In-Plane Vibration Using GaAs
Authors: Gao Jiongye , Zhang Bin , Feng Qibo , Shen Xu , Xue Yong , Liu Jiacheng
Published: March 2023

In this study, the measurement characteristics of speckles based on the photoinduced electromotive force (photo-emf) effect for high-frequency, small-amplitude, and in-plane vibration were theoretically and experimentally studied. The relevant theoretical models were utilized. A GaAs crystal was used as the photo-emf detector for experimental research, as well as to study the influence of the amplitude and frequency of the vibration, the imaging magnification of the measuring system, and the average speckle size of the measuring light on the first harmonic of the induced photocurrent in the experiments. The correctness of the supplemented theoretical model was verified, and a theoretical and experimental basis was provided for the feasibility of using GaAs to measure in-plane vibrations with nanoscale amplitudes.
A Smart Agricultural System Based on PLC and a Cloud Computing Web Application Using LoRa and LoRaWan
Authors: Saban Mohamed , Bekkour Mostapha , Amdaouch Ibtisam , El Gueri Jaouad , Ait Ahmed Badiaa , Chaari Mohamed Zied , Ruiz-Alzola Juan , Rosado-Muñoz Alfredo , Aghzout Otman
Published: March 2023

The increasing challenges of agricultural processes and the growing demand for food globally are driving the industrial agriculture sector to adopt the concept of ‘smart farming’. Smart farming systems, with their real-time management and high level of automation, can greatly improve productivity, food safety, and efficiency in the agri-food supply chain. This paper presents a customized smart farming system that uses a low-cost, low-power, and wide-range wireless sensor network based on Internet of Things (IoT) and Long Range (LoRa) technologies. In this system, LoRa connectivity is integrated with existing Programmable Logic Controllers (PLCs), which are commonly used in industry and farming to control multiple processes, devices, and machinery through the Simatic IOT2040. The system also includes a newly developed web-based monitoring application hosted on a cloud server, which processes data collected from the farm environment and allows for remote visualization and control of all connected devices. A Telegram bot is included for automated communication with users through this mobile messaging app. The proposed network structure has been tested, and the path loss in the wireless LoRa is evaluated.
Feasibility Study on the Discrimination of Fluor Concentration in the Liquid Scintillator Using PMT Waveform and Short-Pass Filter
Authors: Kim Na-Ri , Joo Kyung-Kwang , Lee Hyun-Gi
Published: March 2023

Neutrinos are difficult to detect because they weakly interact with matter, making their properties least known. The response of the neutrino detector depends on the optical properties of the liquid scintillator (LS). Monitoring any characteristic changes in the LS helps to understand the temporal variation of detector response. In this study, a detector filled with LS was used to study the characteristics of the neutrinos detector. We investigated a method to distinguish the concentrations of PPO and bis-MSB, which are fluors added to LS, through a photomultiplier tube (PMT) acting as an optical sensor. Conventionally, it is very challenging to discriminate the flour concentration dissolved in LS. We employed the information of pulse shape and PMT coupled with the short-pass filter. To date, no literature report on a measurement using such an experimental setup has been published. As the concentration of PPO was increased, changes in the pulse shape were observed. In addition, as the concentration of bis-MSB was increased, a decrease in the light yield was observed in the PMT equipped with the short-pass filter. This result suggests the feasibility of real-time monitoring of LS properties, which are correlated with the fluor concentration, using a PMT without extracting the LS samples from the detector during the data acquisition process.
Joint Texture Search and Histogram Redistribution for Hyperspectral Image Quality Improvement
Authors: Hu Bingliang , Chen Junyu , Wang Yihao , Li Haiwei , Zhang Geng
Published: March 2023

Due to optical noise, electrical noise, and compression error, data hyperspectral remote sensing equipment is inevitably contaminated by various noises, which seriously affect the applications of hyperspectral data. Therefore, it is of great significance to enhance hyperspectral imaging data quality. To guarantee the spectral accuracy during data processing, band-wise algorithms are not suitable for hyperspectral data. This paper proposes a quality enhancement algorithm based on texture search and histogram redistribution combined with denoising and contrast enhancement. Firstly, a texture-based search algorithm is proposed to improve the accuracy of denoising by improving the sparsity of 4D block matching clustering. Then, histogram redistribution and Poisson fusion are used to enhance spatial contrast while preserving spectral information. Synthesized noising data from public hyperspectral datasets are used to quantitatively evaluate the proposed algorithm, and multiple criteria are used to analyze the experimental results. At the same time, classification tasks were used to verify the quality of the enhanced data. The results show that the proposed algorithm is satisfactory for hyperspectral data quality improvement.
An Anti-Noise Fast Circle Detection Method Using Five-Quadrant Segmentation
Authors: Ou Yun , Deng Honggui , Liu Yang , Zhang Zeyu , Lan Xin
Published: March 2023

Extracting circle information from images has always been a basic problem in computer vision. Common circle detection algorithms have some defects, such as poor noise resistance and slow computation speed. In this paper, we propose an anti-noise fast circle detection algorithm. In order to improve the anti-noise of the algorithm, we first perform curve thinning and connection on the image after edge extraction, then suppress noise interference by the irregularity of noise edges and extract circular arcs by directional filtering. In order to reduce the invalid fitting and speed up the running speed, we propose a circle fitting algorithm with five quadrants, and improve the efficiency of the algorithm by the idea of “divide and conquer”. We compare the algorithm with RCD, CACD, WANG and AS on two open datasets. The results show that we have the best performance under noise while keeping the speed of the algorithm.
Characterization of the Ability of Low-Cost GNSS Receiver to Detect Spoofing Using Clock Bias
Authors: Truong Victor , Vervisch-Picois Alexandre , Rubio Hernan Jose , Samama Nel
Published: March 2023

The aim of this paper was to propose a method to characterize the ability of a GNSS user to detect a spoofing attack from the behavior of the clock bias. Spoofing interference is not a new issue, especially in military GNSS, although it is a new challenge for civil GNSS, since it is currently implemented and used in many everyday applications. For this reason, it is still a topical issue, especially for receivers that only have access to high-level data (PVT,CN0). To address this important issue, after conducting a study of the receiver clock polarization calculation process, this led to the development of a very basic Matlab model that emulates a spoofing attack at the computational level. Using this model, we were able to observe that the clock bias is affected by the attack. However, the amplitude of this disturbance depends on two factors: the distance between the spoofer and the target and the synchronization between the clock that generates the spoofing signal and the reference clock of the constellation. To validate this observation, more or less synchronized spoofing attacks were carried out on a fixed commercial GNSS receiver with the use of GNSS signal simulators and also with a moving target. We propose then a method to characterize the capacity of detecting a spoofing attack with the clock bias behavior. We present the application of this method for two commercial receivers of the same manufacturer from different generations.
A Taxonomic Hierarchy of Blockchain Consensus Algorithms: An Evolutionary Phylogeny Approach
Authors: Kim Heesang , Kim Dohoon
Published: March 2023

Countless endeavors have been undertaken to address the Byzantine Generals Problem, a generalization of the Two Generals Problem. The emergence of proof of work (PoW) for Bitcoin has led to various consensus algorithms diverging, and comparable existing consensus algorithms are being gradually utilized interchangeably, or only developed for each specific application domain. Our approach employs an evolutionary phylogeny method to classify blockchain consensus algorithms based on their historical development and current usage. To demonstrate the relatedness and lineage of distinct algorithms, as well as to support the recapitulation theory, which posits that the evolutionary history of its mainnets is mirrored in the development of an individual consensus algorithm, we present a taxonomy. We have created a comprehensive classification of past and present consensus algorithms that serves to organize this swift consensus algorithm evolution period. By recognizing similarities, we have compiled a list of different verified consensus algorithms and performed clustering on over 38 of these. Our new taxonomic tree presents five taxonomic ranks, including the evolutionary process and decision-making method, as a technique for analyzing correlation. Through the examination of the evolution and utilization of these algorithms, we have developed a systematic and hierarchical taxonomy that enables the grouping of consensus algorithms into distinct categories. The proposed method classifies various consensus algorithms according to taxonomic ranks and aims to reveal the direction of research on the application of blockchain consensus algorithms for each domain.
High-Performance NiO/TiO2/ZnO Photovoltaic UV Detector
Authors: Shang Guoxin , Tang Libin , Wu Gang , Yuan Shouzhang , Jia Menghan , Guo Xiaopeng , Zheng Xin , Wang Wei , Yue Biao , Teng Kar Seng
Published: March 2023

The ultraviolet (UV) photodetector has found many applications, ranging from optical communication to environmental monitoring. There has been much research interest in the development of metal oxide-based UV photodetectors. In this work, a nano-interlayer was introduced in a metal oxide-based heterojunction UV photodetector to enhance the rectification characteristics and therefore the device performance. The device, which consists of nickel oxide (NiO) and zinc oxide (ZnO) sandwiching an ultrathin dielectric layer of titanium dioxide (TiO2), was prepared by radio frequency magnetron sputtering (RFMS). After annealing, the NiO/TiO2/ZnO UV photodetector exhibited a rectification ratio of 104 under UV irradiation of 365 nm at zero bias. The device also demonstrated a high responsivity of 291 A/W and a detectivity of 6.9 × 1011 Jones at +2 V bias. Such a device structure provides a promising future for metal oxide-based heterojunction UV photodetectors in a wide range of applications.
Characterization of a Piezoelectric Acoustic Sensor Fabricated for Low-Frequency Applications: A Comparative Study of Three Methods
Authors: Campo-Valera María , Asorey-Cacheda Rafael , Rodríguez-Rodríguez Ignacio , Villó-Pérez Isidro
Published: March 2023

Piezoelectric transducers are widely used for generating acoustic energy, and choosing the right radiating element is crucial for efficient energy conversion. In recent decades, numerous studies have been conducted to characterize ceramics based on their elastic, dielectric, and electromechanical properties, which have improved our understanding of their vibrational behavior and aided in the manufacturing of piezoelectric transducers for ultrasonic applications. However, most of these studies have focused on the characterization of ceramics and transducers using electrical impedance to obtain resonance and anti-resonance frequencies. Few studies have explored other important quantities such as acoustic sensitivity using the direct comparison method. In this work, we present a comprehensive study that covers the design, manufacturing, and experimental validation of a small-sized, easy-to-assemble piezoelectric acoustic sensor for low-frequency applications, using a soft ceramic PIC255 from PI Ceramic with a diameter of 10 mm and a thickness of 5 mm. We present two methods, analytical and numerical, for sensor design, followed by experimental validation, allowing for a direct comparison of measurements with simulated results. This work provides a useful evaluation and characterization tool for future applications of ultrasonic measurement systems.
Pre-Test Manipulation by Intraperitoneal Saline Injection with or without Isoflurane Pre-Treatment Does Not Influence the Outcome of Social Test in Male Mice
Authors: Pomogyi Flóra , Török Bibiána , Zelena Dóra
Published: March 2023

Preclinical studies on rodents should follow the 3R principle minimising the suffering of the animals. To do so, some researchers use inhalation anaesthetic induction even before intraperitoneal injection. However, several studies suggested that both interventions might influence the behaviour of the animals. We aimed to test whether intraperitoneal injection alone or in combination with isoflurane anaesthesia is a preferable treatment method 30 min before a social test. Male C57BL/6 mice were studied using a behavioural test battery comparing three groups (one control group and intraperitoneal saline-treated groups with or without short isoflurane inhalation). Our results confirmed that both interventions had no profound influence on the conventionally measured parameters of social tests (interest in sociability, social discrimination memory, social interaction as well as resident–intruder test) and were not acutely stressful (measured by similar ACTH levels between the groups) not even after repeated administration (similar body weight gain during the one-week observation period). Taking into consideration the possible long-term harmful effect of isoflurane inhalation, we recommend using intraperitoneal injection without it as saline injection did not violate the 3R principle inducing only mild stress.
Research on a Variable Water Supply Temperature Strategy for a Ground-Source Heat Pump System Based on TRNSYS-GENOPT (TRNOPT) Optimization
Authors: Cao Jiaqi , Zhou Shiyu , Wang Tao , Shan Baoqi , Liu Xueping
Published: March 2023

An office building located at Jinan equipped with ground-source heat pump (GSHP) system was selected as the research object. The GSHP system model was established using TRNSYS software. With the total energy consumption of the system as the objective function, several control strategies were proposed for the optimization work of water supply temperature at the load side of the heat pump unit. Firstly, a variable water temperature control strategy was adjusted according to the load ratio of the unit. In addition, the TRNSYS-GENOPT (TRNOPT) optimization module in TRNSYS was used to find the optimal water supply temperatures for different load ratios. After simulating and comparing the system’s energy consumption under the three control strategies, we found that the total annual energy consumption under the variable water supply temperature scheme is less than that under the constant water supply temperature scheme by 10,531.41 kWh. The energy saving ratio is about 5.7%. The simulation found that the total annual energy consumption under the optimized water supply temperature based on TRNOPT is lower than that under the variable water supply temperature scheme by 1072.04 kWh, and it is lower than that under the constant water supply temperature scheme by 11,603.45 kWh. The annual energy saving ratio of the system is about 6.3%. It is concluded that the optimized water supply temperature scheme based on TRNOPT has a better energy saving effect than the first two water supply temperature schemes.
The Efficiency of Value-at-Risk Models during Extreme Market Stress in Cryptocurrencies
Authors: Likitratcharoen Danai , Chudasring Pan , Pinmanee Chakrin , Wiwattanalamphong Karawan
Published: March 2023

In recent years, the cryptocurrency market has been experiencing extreme market stress due to unexpected extreme events such as the COVID-19 pandemic, the Russia and Ukraine war, monetary policy uncertainty, and a collapse in the speculative bubble of the cryptocurrencies market. These events cause cryptocurrencies to exhibit higher market risk. As a result, a risk model can lose its accuracy according to the rapid changes in risk levels. Value-at-risk (VaR) is a widely used risk measurement tool that can be applied to various types of assets. In this study, the efficacy of three value-at-risk (VaR) models—namely, Historical Simulation VaR, Delta Normal VaR, and Monte Carlo Simulation VaR—in predicting market stress in the cryptocurrency market was examined. The sample consisted of popular cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), and Ripple (XRP). Backtesting was performed using Kupiec’s POF test, Kupiec’s TUFF test, Independence test, and Christoffersen’s Interval Forecast test. The results indicate that the Historical Simulation VaR model was the most appropriate model for the cryptocurrency market, as it demonstrated the lowest rejections. Conversely, the Delta Normal VaR and Monte Carlo Simulation VaR models consistently overestimated risk at confidence levels of 95% and 90%, respectively. Despite these results, both models were found to exhibit comparable robustness to the Historical Simulation VaR model.
Stiffening and Toughening of Asphalt Mastic Induced by Bitumen–Mineral Selective Molecular Adsorption and Nanostructural Reconstruction
Authors: Liu Zhiyang , Wang Haipeng , Gong Xiangbing , Cui Peng , Wei Hongrui
Published: March 2023

Asphalt mastic is the most important binder in asphalt mixtures and its rheology is inevitably influenced by the mineral aggregates. Due to the little consideration that has been paid to aggregates’ effects, the rheological properties of mastic films have not been accurately characterized for the present method. Therefore, this study aimed to investigate the rheological characteristics of mastic affected by mineral aggregates and reveal its fundamental mechanism of interfacial interaction. The results suggest that the aggregates increased the stiffness and toughness of mastic within the linear and nonlinear viscoelastic regions. The mastic on limestone had a higher linear viscoelastic modulus than that on basalt below 35 °C, and its ratio reached up to 1.18. However, the modulus of the mastic on basalt surpassed that on limestone by over 50 °C, and the maximum ratio reached 2.17. The mastic in contact with the limestone had a higher failure strain and failure modulus than that in contact with the basalt, the ratios of which reached 1.60 and 1.32, respectively. The macrorheological characteristics are closely related to the nanostructures and intermolecular interactions of bitumen–mineral systems. The coexistence of a stable bitumen nanostructure and an adsorbed layer on the calcite substrate provided a strong bonding energy and high resistance to external shear deformation, leading to the high stiffness and toughness of the limestone. Abundant metal ions from augite and albite diffused into the bitumen layer and destroyed its nanostructure, decreasing the stability of the mastic–basalt interface system. The non-bond energy of bitumen-calcite was 14.15% higher than that of bitumen-albite, and the ratio of shear stress of the bitumen-calcite to the bitumen-albite reached up to 6.8. Therefore, the calcite in limestone reinforced the bitumen, and the augite and albite in basalt destroyed the bitumen colloidal structure. This provides a fundamental understanding of the rheological characterization of mastic on mineral aggregates.
Deformation State Analysis and Design Method of Bottom-Confined Gravity Retaining Walls
Authors: Zhou Yuntao , Shi Shengwei , Cai Qiang , Liang Jiong
Published: March 2023

The deformation failure modes of gravity retaining walls include overturning, toppling, deep sliding, and eccentric failure. The complex eccentric failure mode, which involves the multi-stage deformation of retaining walls, suffers from a lack of mechanical models and design theories. Based on the basic theory of reinforced concrete construction, this study established a deformation calculation model of bottom-confined reinforced concrete gravity retaining walls (RC-GRWs), which are composed of tensile, compressive, and rigid zones. Then, this study defined RC-GRWs’ normal-operation, cracking, yield, and ultimate limit states, as well as the displacement of RC-GRWs in these states, according to the concrete cracking and steel reinforcement yield conditions. Accordingly, this study proposed a design method and process to determine the deformation of bottom-confined RC-GRWs. A project case shows that the calculation method for RC-GRW deformation proposed in this study can effectively assess the deformation state of in-service retaining walls and that optimized cracked retaining walls can meet the deformation-based design requirements.
Transboundary Fisheries Management in Kavango–Zambezi Transfrontier Conservation Area (KAZA-TFCA): Prospects and Dilemmas
Authors: Imbwae Imikendu , Aswani Shankar , Sauer Warwick , Hay Clinton
Published: March 2023

Inland fisheries in the Kavango–Zambezi Transfrontier Conservation Area (KAZA-TFCA) offer food security to the riverine communities across the region. They also contribute towards the attainment of the United Nations’ Sustainable Development Goals 1 and 15, which aim to alleviate poverty and maintain biodiversity conservation. Despite this significant role, the fisheries have suffered severe declines in the previous decades due to multiple factors, such as overfishing and poor legislation. Furthermore, climate change is exerting pressure by altering the ecology and productivity of the river systems. The unprecedented challenges of the COVID-19 pandemic have further constrained management efforts. Attempts to address these challenges have pointed towards transboundary fisheries management as a silver bullet in moving towards sustainable fisheries management. However, the implementation of this strategy in the region has encountered numerous roadblocks, thereby subjecting the river ecosystem to a wider environmental threat, with dire consequences on livelihoods. This paper reviews existing management and governance structures together with key informant interviews to elicit primary and secondary data essential for management at the regional level. The study identifies conflicting regulations, and inadequate policies and institutions across the region as major bottlenecks affecting the successful implementation of transboundary fisheries management. Finally, the paper offers some suggestions for the improvement of fisheries management in the region.
Mechanical Properties of the Combination of Surrounding Rock and Primary Support under Impacting Load
Authors: Ye Zhouyuan , Zhang Liling , Zhou Zhihua , Pan Kun , Liu Xubin , Liu Wenhui , Wu Qiuhong
Published: March 2023

The SHPB experimental device was adopted to carry out the uniaxial impact tests in studying the failure characteristics of the combination of surrounding rocks and primary support for the tunnel, so as to explore the key factors affecting its dynamic strength and deforming properties. To simplify the practice model of the tunnel construction, the specimens combined by the surrounding rock and primary support were simulated by the concentric annular sandstone and cement mortar in this paper. The results indicate that the thickness of the primary support, the thickness of the surrounding rock and the proportion of the thickness of the primary support are the key factors affecting the dynamic strength of the combination of the surrounding rock and the primary support. The influence of dynamic loads on tunnel construction can be weakened by adjusting the above three factors, so that to improve the supporting capacity. The research has a significant reference for the designing of tunnel engineering under complex loads.
A Local Control Strategy for Voltage Fluctuation Suppression in a Flexible Interconnected Distribution Station Area Based on Soft Open Point
Authors: Yang Zhichun , Yang Fan , Min Huaidong , Shen Yu , Tang Xu , Hong Yun , Qin Liang
Published: March 2023

Fluctuations in the output power of a high penetration distributed generation (DG) will result in severe voltage fluctuations/violations in the power distribution station area (DSA). This paper proposes the use of Soft Open Point (SOP) for DSA interconnection to alleviate voltage violations and frequent fluctuations. Firstly, the typical topology of a flexible interconnected DSA is introduced, and then the correlation between active/reactive power fluctuations and voltage fluctuations in DSA is analyzed, according to which a local control strategy is proposed to achieve voltage control by relying only on the local electrical information of the nodes connected to the SOP. Finally, the effectiveness of the proposed control strategy is verified by simulation case. The simulation results show that the voltage fluctuation caused by the DG output power fluctuation can be suppressed within tens of milliseconds through the control strategy. This strategy greatly reduces the communication burden and has good real-time performance. It can be used as an SOP control strategy under conditions of communication failure or to realize a plug-and-play SOP, which has significant engineering importance for solving the voltage violations and frequent fluctuations in DSA and improving the consumption capacity of DGs.
Benchmark Approach for Efficiency Improvement in Green Supply Chain Management with DEA Models
Authors: Zaare Tajabadi Farzad , Daneshvar Sahand
Published: March 2023

Nowadays, concerns about environmental issues are increasing. Therefore, companies and producers are under pressure from government rules and regulations on one hand, and on the other hand, maintaining customer satisfaction concerning cares about the environment. Green supply chain management (GSCM) is a procedure to increase efficiency and decrease environmental effects for companies that collaborate with customers and suppliers. According to GSCM, there is some research about applying green aspects of purchasing, design, manufacture, distribution, packaging, marketing, and reverse logistics of supply chains to improve their company’s performance regarding environmental issues. Moreover, recently, DEA as a nonparametric model is used to evaluate the efficiency and performance of supply chains as decision-making units (DMUs). However, previous studies on efficiency improvement in GSCM did not investigate the effect of some economic and environmental factors together such as service level, emissions (CO2), and size of the supply chains (arcs) on the efficiency of the whole supply system. These factors are essential as they can affect the manager’s ability to distinguish the true performance of a green supply chain. Thus, evaluating the efficiency of GSCM by DEA models and imposing the green principles to find out the efficient ones for increasing management performance is vital. Fulfilling the mentioned research gap, this paper developed a benchmark approach to verifying efficient DMUs and potential efficient DMUs which may improve costs and efforts to become efficient. In the case study, the benchmarks and potentially efficient DMUs are found by DEA standard models and slight adjustment is conducted for potentially efficient DMUs to change their status to efficient DMUs. Moreover, the effect of some green principles on the efficiency value of DMUs is verified using Tobit regression before and after the mentioned modification. A set of realistic results provided for the priority of potential DMUs modification confirmed the applicability of the proposed procedure.
Multimodal Transportation Route Optimization of Cold Chain Container in Time-Varying Network Considering Carbon Emissions
Authors: Liu Shouchen
Published: March 2023

To solve the multimodal transport route optimization problem considering carbon emission, the vehicle speed has time-varying characteristics, and the customer has a time window limit. The carbon emission of multimodal transport system is affected by the energy consumption of transport vehicles in the time-varying network. The time-varying network is uncertain, and carbon emissions may continue to rise after a gradual decline. Based on this, this study established the sum of the carbon emission cost, transportation cost, penalty cost for exceeding the time window, and the damage cost of the cold chain cargo as the objective function. A route optimization model of cold chain container multimodal transportation was established. Static and dynamic optimization scenarios were designed and a hummingbird evolutionary genetic algorithm was used to solve the model. The effectiveness of the model and the practical value of the study are verified by the empirical analysis of the multimodal transport network of the Yangtze River Delta economic group. Results show that the dynamic model of the time-varying network can more truly reflect the transportation of the multimodal transport network and meet the efficiency requirements for the cold chain container multimodal transport. This study aims to solve the time-varying network under cold chain route optimization of container intermodal transportation, provides new insights for related businesses and a theoretical basis for reasonable multimodal transport route decisions.
Job Design for Human and Organisational Sustainability in the Context of Emerging Technologies
Authors: Stankevičiūtė Živilė
Published: March 2023

This Special Issue of Sustainability is dedicated to highlighting theoretical knowledge and recent empirical evidence on the arrangement and implementation of job design, while addressing the context of emerging technologies and striving to ensure human and organisational sustainability [...]
Measuring the Struggle of Small-Scale Businesses in the COVID-19 Environment
Authors: Fauzi Rosmadi , Mohd Yusoff Mariney , Roslan Abd Rahman , Ahmad Rozlan Siti Nadira , Marzuki Muhammad Fathi , Said Mohd Muslim , Jusoff Kamaruzaman
Published: March 2023

Small-scale business owners are among those who were very affected when the COVID-19 pandemic hit the country. This group supports their families solely through daily sales profits. However, when the country began implementing the Movement Control Order (MCO) from March 2020 to May 2020, it was difficult for this group to survive. Some of them were unable to do business at all, while the majority experienced a significant decline in sales. This study aims to measure the ability of small-scale business owners to survive before and after the COVID-19 period. GIS software (ArcMap 10.4.1) was used to map the ability of small-scale business owners to survive before and after the pandemic. Data were obtained through face-to-face questionnaires conducted throughout the state of Kedah. Findings indicated that several districts have successfully managed the pandemic for economic survival. This was achieved through a well-collaborated effort by the government and NGOs to strengthen certain aspects of these small-scale business and revive their growth. Future work should focus on studying the actions taken by successful business owners in coping with the pandemic environment.