bluuebunny/crossref_metadata_2025 huggingface dataset:
Terahertz Kerr Effect of Liquids
Authors: Zhang Minghao , Xiao Wen , Zhang Cunlin , Zhang Liangliang
Published: December 2022

In recent years, tremendous advancements have been made in various technologies such as far-infrared, low-frequency Raman, and two-dimensional (2D) Raman terahertz (THz) spectroscopies. A coherent method has emerged from numerous experimental and theoretical investigations of molecular dynamics in liquids by comparing linear and non-linear spectroscopic techniques. Intermolecular hydrogen bond vibration, molecular reorientation motion, and interaction between molecule/ionic solute and hydrogen bonds have been demonstrated to occur in the THz region, which are closely related to their physical/chemical properties and structural dynamics. However, precise probing of various modes of motion is difficult because of the complexity of the collective and cooperative motion of molecules and spectral overlap of related modes. With the development of THz science and technology, current state-of-the-art THz sources can generate pulsed electric fields with peak intensities of the order of microvolts per centimeter (MV/cm). Such strong fields enable the use of THz waves as the light source for non-linear polarization of the medium and in turn leads to the development of the emerging THz Kerr effect (TKE) technique. Many low-frequency molecular motions, such as the collective directional motion of molecules and cooperative motion under the constraint of weak intermolecular interactions, are resonantly excited by an intense THz electric field. Thus, the TKE technique provides an interesting prospect for investigating low-frequency dynamics of different media. In view of this, this paper first summarizes the research work on TKE spectroscopy by taking a solid material without low-frequency molecular motions as an example. Starting from the principle of TKE technology and its application in investigating the properties of solid matter, we have explored the low-frequency molecular dynamics of liquid water and aqueous solutions using TKE. Liquid water is a core of life and possesses many extraordinary physical and biochemical properties. The hydrogen bond network plays a crucial role in these properties and is the main reason for its various kinetic and thermodynamic properties, which differ from those of other liquids. However, the structure of the hydrogen bond network between water and solutes is not well known. Therefore, evaluating the hydrogen bond-related kinetic properties of liquid water is important.
Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things
Authors: Kim Jin-Woo , Kim Jaehee , Lee Jaeho
Published: December 2022

The IEEE 802.15.4 standard is recognized as one of the most successful for short-range low-rate wireless communications and is used in Internet of Things (IoT) applications. To improve the performance of wireless networks, interest in protocols that rely on interaction between different layers has increased. Cross-layer design has become an issue in wireless communication systems as it can improve the capacity of wireless networks by optimizing cooperation between multiple layers that constitute network systems. Power efficiency and network scalability must be addressed to spread IoT. In multi-hop networks, many devices share wireless media and are geographically distributed; consequently, efficient medium access control (MAC) and routing protocols are required to mitigate interference and improve reliability. Cross-layer design is a novel network design approach to support flexible layer techniques in IoT. We propose a cross-layer protocol for the MAC layer and routing layer to satisfy the requirements of various networks. The proposed scheme enables scalable and reliable mesh networking using the IEEE 802.15.4 standard and provides robust connectivity and efficient path discovery procedures. It also proposes a novel address-allocation technique to improve address-allocation methods that cannot support large sensor networks. Simulation results indicate that the proposed scheme could improve reliability and reduce end-to-end delay.
Research on Inherent Frequency and Vibration Characteristics of Sandwich Piezoelectric Ceramic Transducer
Authors: Lu Yuren , Xu Chunguang , Pan Qinxue , Yu Quanpeng , Xiao Dingguo
Published: December 2022

Great progress has been made in the field of ultrasonic processing in recent years, and piezoelectric ceramic transducers have been widely used as drive sources. In this paper, a sandwich piezoelectric ceramic transducer is designed, and the vibration of each part of the transducer is analyzed by elastic mechanics and piezoelectric theory. According to its mechanical and electrical boundary conditions, the vibration model of the piezoelectric transducer was established. Based on the equivalent elastic modulus method for simplifying the pre-stressed bolts into a one-dimensional transducer vibration model, the relationship between the one-dimensional axial response frequency of the transducer and the length of each component was obtained. Based on the half wavelength theory, a transducer with the vibration node in the crystal stack and an inherent frequency of 15 kHz was designed and fabricated. In order to verify the natural frequency and vibration characteristics of the piezoelectric transducer, a laser vibration measurement system was built in this study. The vibration characteristics of the transducer under different parameters such as voltage and frequency were analyzed, and the accuracy of the vibration model was verified. The vibration states of the end surface of the transducer and the radial surface were evaluated at the first-order inherent frequency and second-order inherent frequency. The results show that the equivalent simplified model established in this study can effectively design the inherent frequency of the transducer, and the operation at the first-order inherent frequency meets the one-dimensional assumptions of this study. The transducer operating conditions measured in this study also provide a more detailed reference for ultrasonic processing applications.
Electro-Optical Sampling of Single-Cycle THz Fields with Single-Photon Detectors
Authors: Shields Taylor , Dada Adetunmise C. , Hirsch Lennart , Yoon Seungjin , Weaver Jonathan M. R. , Faccio Daniele , Caspani Lucia , Peccianti Marco , Clerici Matteo
Published: December 2022

Electro-optical sampling of Terahertz fields with ultrashort pulsed probes is a well-established approach for directly measuring the electric field of THz radiation. This technique usually relies on balanced detection to record the optical phase shift brought by THz-induced birefringence. The sensitivity of electro-optical sampling is, therefore, limited by the shot noise of the probe pulse, and improvements could be achieved using quantum metrology approaches using, e.g., NOON states for Heisenberg-limited phase estimation. We report on our experiments on THz electro-optical sampling using single-photon detectors and a weak squeezed vacuum field as the optical probe. Our approach achieves field sensitivity limited by the probe state statistical properties using phase-locked single-photon detectors and paves the way for further studies targeting quantum-enhanced THz sensing.
Three-Dimensional Localization of Buried Polyethylene Pipes Using Acoustic Method
Authors: Xerri William , Saracco Gineth , Ribodetti Alessandra , Zomero Laurent , Picon Philippe
Published: December 2022

Localization of buried polyethylene pipes is an important issue for network managers. This study focuses on an acoustic method, which consists of vibrating the pipe and observing the signal with a receiver placed on the ground surface. This method provides an estimate of the path of the pipe but gives no information on the depth. We developed a multi-sensor method based on the principle of vibrating the pipe, which allows estimating the depth while being non-invasive and non-destructive and without a priori information on the propagation medium. These sensors are positioned perpendicular to the pipe. We developed a new estimator to estimate the depth and the propagation velocity in the medium, which is an important variable in our problem. This estimator is based on the MUSIC algorithm and is adapted to our choice of modeling. In this paper, two models of travel times in typical situations are presented. The first one represents the case where all sensors can be placed inside the trench (on the ground surface) in which the pipe is buried. The second one represents the case where sensors are placed inside and outside the trench. These travel time models aim to provide a fast result to allow the method to be used by field agents. They are compared with a full wavefield modeling by finite differences.
State of Health Prediction of Lithium-Ion Battery Based on Deep Dilated Convolution
Authors: Fu Pengyu , Chu Liang , Li Jihao , Guo Zhiqi , Hu Jincheng , Hou Zhuoran
Published: December 2022

A battery’s charging data include the timing information with respect to the charge. However, the existing State of Health (SOH) prediction methods rarely consider this information. This paper proposes a dilated convolution-based SOH prediction model to verify the influence of charging timing information on SOH prediction results. The model uses holes to fill in the standard convolutional kernel in order to expand the receptive field without adding parameters, thereby obtaining a wider range of charging timing information. Experimental data from six batteries of the same battery type were used to verify the model’s effectiveness under different experimental conditions. The proposed method is able to accurately predict the battery SOH value in any range of voltage input through cross-validation, and the SDE (standard deviation of the error) is at least 0.28% lower than other methods. In addition, the influence of the position and length of the range of input voltage on the model’s prediction ability is studied as well. The results of our analysis show that the proposed method is robust to different sampling positions and different sampling lengths of input data, which solves the problem of the original data being difficult to obtain due to the uncertainty of charging–discharging behaviour in actual operation.
Experimental Investigation and CFD Analysis of Pressure Drop in an ORC Boiler for a WHRS Implementation
Authors: Paz Concepción , Suárez Eduardo , Cabarcos Adrián , Díaz Antonio
Published: December 2022

Waste heat dissipated in the exhaust system of a combustion engine represents a major source of energy to be recovered and converted into useful work. The Waste Heat Recovery System (WHRS) based in an Organic Rankine Cycle (ORC) is an approach for recovering energy from heat sources, achieving a significant reduction in fuel consumption and, as a result, exhaust emissions. This paper studies pressure drop in an ORC shell-and-tubes boiler for a WHRS implementation experimentally and with computational simulations based on a 1-dimensional heat transfer model coupled with 3D calculations. An experimental database is developed, using ethanol in a pressure range of 10–15 absolute bar as working fluid, with mass fluxes inside the tubes in the range of 349.31 kg/s-m2 and 523.97 kg/s-m2, and inlet temperatures in the range of 60 °C and 80 °C. Thus, the friction factor of different regions of the boiler were estimated using both CFD simulations, experimental data, and bibliographic correlations. Simulations of operating points and the results of the experimental test bench showed good agreement in pressure drop results, with a mean absolute error of 15.47%, without a significant increment in the computational cost.
Rapid Localization and Mapping Method Based on Adaptive Particle Filters
Authors: Charroud Anas , El Moutaouakil Karim , Yahyaouy Ali , Onyekpe Uche , Palade Vasile , Huda Md Nazmul
Published: December 2022

With the development of autonomous vehicles, localization and mapping technologies have become crucial to equip the vehicle with the appropriate knowledge for its operation. In this paper, we extend our previous work by prepossessing a localization and mapping architecture for autonomous vehicles that do not rely on GPS, particularly in environments such as tunnels, under bridges, urban canyons, and dense tree canopies. The proposed approach is of two parts. Firstly, a K-means algorithm is employed to extract features from LiDAR scenes to create a local map of each scan. Then, we concatenate the local maps to create a global map of the environment and facilitate data association between frames. Secondly, the main localization task is performed by an adaptive particle filter that works in four steps: (a) generation of particles around an initial state (provided by the GPS); (b) updating the particle positions by providing the motion (translation and rotation) of the vehicle using an inertial measurement device; (c) selection of the best candidate particles by observing at each timestamp the match rate (also called particle weight) of the local map (with the real-time distances to the objects) and the distances of the particles to the corresponding chunks of the global map; (d) averaging the selected particles to derive the estimated position, and, finally, using a resampling method on the particles to ensure the reliability of the position estimation. The performance of the newly proposed technique is investigated on different sequences of the Kitti and Pandaset raw data with different environmental setups, weather conditions, and seasonal changes. The obtained results validate the performance of the proposed approach in terms of speed and representativeness of the feature extraction for real-time localization in comparison with other state-of-the-art methods.
Modelling of Backscattering off Filaments Using the Code IPF-FD3D for the Interpretation of Doppler Backscattering Data
Authors: Yashin Alexander , Teplova Natalia , Zadvitskiy Georgiy , Ponomarenko Anna
Published: December 2022

Filaments or blobs are well known to strongly contribute to particle and energy losses both in L- and H-mode, making them an important plasma characteristic to investigate. They are plasma structures narrowly localized across a magnetic field and stretched along magnetic field lines. In toroidal devices, their development is observed to take place in the peripheral plasma. Filament characteristics have been studied extensively over the years using various diagnostic techniques. One such diagnostic is the Doppler backscattering (DBS) method employed at the spherical tokamak Globus-M/M2. It has been observed that the DBS signal reacts to the backscattering from filaments. However, the DBS data have proven difficult to analyze, which is why modelling was undertaken using the code IPF-FD3D to understand what kind of information can be extrapolated from the signals. A circular filament was thoroughly investigated in slab geometry with a variety of characteristics studied. Apart from that, the motion of the filaments in the poloidal and radial directions was analyzed. Additionally, other shapes of filaments were presented in this work. Modelling for the real geometry of the Globus-M/M2 tokamak was performed.
AES Based White Box Cryptography in Digital Signature Verification
Authors: Shukla Piyush Kumar , Aljaedi Amer , Pareek Piyush Kumar , Alharbi Adel R. , Jamal Sajjad Shaukat
Published: December 2022

According to the standard paradigm, white box cryptographic primitives are used to block black box attacks and protect sensitive information. This is performed to safeguard the protected information and keys against black box assaults. An adversary in such a setting is aware of the method and can analyze many system inputs and outputs, but is blind to the specifics of how a critical instantiation primitive is implemented. This is the focus of white-box solutions, which are designed to withstand attacks that come from the execution environment. This is significant because an attacker may obtain unrestricted access to the program’s execution in this environment. The purpose of this article is to assess the efficiency of white-box implementations in terms of security. Our contribution is twofold: first, we explore the practical implementations of white-box approaches, and second, we analyze the theoretical foundations upon which these implementations are built. First, a research proposal is crafted that details white-box applications of DES and AES encryption algorithms. To begin, this preparation is necessary. The research effort planned for this project also includes cryptanalysis of these techniques. Once the general cryptanalysis results have been examined, the white-box design approaches will be covered. We have decided to launch an investigation into creating a theoretical model for white box, since no prior formal definitions have been offered, and suggested implementations have not been accompanied by any assurance of security. This is due to the fact that no formal definition of “white box” has ever been provided. In this way lies the explanation for why this is the situation. We define WBC to encompass the security requirements of WBC specified over a white box cryptography technology and a security concept by studying formal models of obfuscation and shown security. This definition is the product of extensive investigation. This state-of-the-art theoretical model provides a setting in which to investigate the security of white-box implementations, leading to a wide range of positive and negative conclusions. As a result, this paper includes the results of a Digital Signature Algorithm (DSA) study which may be put to use in the real world with signature verification. Possible future applications of White Box Cryptography (WBC) research findings are discussed in light of these purposes and areas of investigation.
A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health
Authors: Mei Peng , Karimi Hamid Reza , Chen Fei , Yang Shichun , Huang Cong , Qiu Song
Published: December 2022

The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles’ (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.
An End-to-End Automated License Plate Recognition System Using YOLO Based Vehicle and License Plate Detection with Vehicle Classification
Authors: Al-batat Reda , Angelopoulou Anastassia , Premkumar Smera , Hemanth Jude , Kapetanios Epameinondas
Published: December 2022

An accurate and robust Automatic License Plate Recognition (ALPR) method proves surprising versatility in an Intelligent Transportation and Surveillance (ITS) system. However, most of the existing approaches often use prior knowledge or fixed pre-and-post processing rules and are thus limited by poor generalization in complex real-life conditions. In this paper, we leverage a YOLO-based end-to-end generic ALPR pipeline for vehicle detection (VD), license plate (LP) detection and recognition without exploiting prior knowledge or additional steps in inference. We assess the whole ALPR pipeline, starting from vehicle detection to the LP recognition stage, including a vehicle classifier for emergency vehicles and heavy trucks. We used YOLO v2 in the initial stage of the pipeline and remaining stages are based on the state-of-the-art YOLO v4 detector with various data augmentation and generation techniques to obtain LP recognition accuracy on par with current proposed methods. To evaluate our approach, we used five public datasets from different regions, and we achieved an average recognition accuracy of 90.3% while maintaining an acceptable frames per second (FPS) on a low-end GPU.
Remote Heart Rate Prediction in Virtual Reality Head-Mounted Displays Using Machine Learning Techniques
Authors: Pagano Tiago Palma , dos Santos Lucas Lisboa , Santos Victor Rocha , Sá Paulo H. Miranda , Bonfim Yasmin da Silva , Paranhos José Vinicius Dantas , Ortega Lucas Lemos , Nascimento Lian F. Santana , Santos Alexandre , Rönnau Maikel Maciel , Winkler Ingrid , Nascimento Erick G. Sperandio
Published: December 2022

Head-mounted displays are virtual reality devices that may be equipped with sensors and cameras to measure a patient’s heart rate through facial regions. Heart rate is an essential body signal that can be used to remotely monitor users in a variety of situations. There is currently no study that predicts heart rate using only highlighted facial regions; thus, an adaptation is required for beats per minute predictions. Likewise, there are no datasets containing only the eye and lower face regions, necessitating the development of a simulation mechanism. This work aims to remotely estimate heart rate from facial regions that can be captured by the cameras of a head-mounted display using state-of-the-art EVM-CNN and Meta-rPPG techniques. We developed a region of interest extractor to simulate a dataset from a head-mounted display device using stabilizer and video magnification techniques. Then, we combined support vector machine and FaceMash to determine the regions of interest and adapted photoplethysmography and beats per minute signal predictions to work with the other techniques. We observed an improvement of 188.88% for the EVM and 55.93% for the Meta-rPPG. In addition, both models were able to predict heart rate using only facial regions as input. Moreover, the adapted technique Meta-rPPG outperformed the original work, whereas the EVM adaptation produced comparable results for the photoplethysmography signal.
A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning
Authors: Hayashi Victor Takashi , Ruggiero Wilson Vicente , Estrella Júlio Cezar , Filho Artino Quintino , Pita Matheus Ancelmo , Arakaki Reginaldo , Ribeiro Cairo , Trazzi Bruno , Bulla Romeo
Published: December 2022

Robust, fault tolerant, and available systems are fundamental for the adoption of Internet of Things (IoT) in critical domains, such as finance, health, and safety. The IoT infrastructure is often used to collect a large amount of data to meet the business demands of Smart Cities, Industry 4.0, and Smart Home, but there is a opportunity to use these data to intrinsically monitor an IoT system in an autonomous way. A Test Driven Development (TDD) approach for automatic module assessment for ESP32 and ESP8266 IoT development devices based on unsupervised Machine Learning (ML) is proposed to monitor IoT device status. A framework consisting of business drivers, non-functional requirements, engineering view, dynamic system evaluation, and recommendations phases is proposed to be used with the TDD development tool. The proposal is evaluated in academic and smart home study cases with 25 devices, consisting of 15 different firmware versions collected in one week, with a total of over 550,000 IoT status readings. The K-Means algorithm was applied to free memory available, internal temperature, and Wi-Fi level metrics to automatically monitor the IoT devices under development to identify device constraints violation and provide insights for monitoring frequency configuration of different firmware versions. To the best of the authors’ knowledge, it is the first TDD approach for IoT module automatic assessment which uses machine learning based on the real testbed data. The IoT status monitoring and the Python scripts for model training and inference with K-Means algorithm are available under a Creative Commons license.
Comprehensive Understanding of Foot Development in Children Using Capacitive Textile Sensors
Authors: De Guzman Sarah , Lowe Andrew , Williams Cylie , Kalra Anubha , Anand Gautam
Published: December 2022

Knowledge of foot growth can provide information on the occurrence of children’s growth spurts and an indication of the time to buy new shoes. Podiatrists still do not have enough evidence as to whether footwear influences the structural development of the feet and associated locomotor behaviours. Parents are only willing to buy an inexpensive brand, because children’s shoes are deemed expendable due to their rapid foot growth. Consumers are not fully aware of footwear literacy; thus, views of consumers on children’s shoes are left unchallenged. This study aims to embed knitted smart textile sensors in children’s shoes to sense the growth and development of a child’s feet—specifically foot length. Two prototype configurations were evaluated on 30 children, who each inserted their feet for ten seconds inside the instrumented shoes. Capacitance readings were related to the proximity of their toes to the sensor and validated against foot length and shoe size. A linear regression model of capacitance readings and foot length was developed. This regression model was found to be statistically significant (p-value = 0.01, standard error = 0.08). Results of this study indicate that knitted textile sensors can be implemented inside shoes to get a comprehensive understanding of foot development in children.
Comparison of Amplitude Measurements on Borehole Geophone and DAS Data
Authors: Zulic Sana , Sidenko Evgenii , Yurikov Alexey , Tertyshnikov Konstantin , Bona Andrej , Pevzner Roman
Published: December 2022

DAS and geophones are the two most popular sensors for borehole seismic acquisition. As such, it is important to get a good understanding of how these two types of sensors compare to each other. The natural measurand for the techniques is different; millivolts are approximately proportional to particle velocities for geophones vs. changes in the phase of light linked to the changes in strain on the sensing fibre. This paper focuses on the experimental comparison of absolute values of these measurands derived from a VSP survey acquired in Curtin GeoLab training well. We describe the acquisition setup for the walk-away VSP acquired with DAS and geophones, allowing the direct comparison and the workflow, which we can use to represent the data in strain rate. Albeit this is unlikely to be universal, we find that the absolute values are similar for this experimental setup.
Interactions between Hazelnut (Corylus avellana L.) Protein and Phenolics and In Vitro Gastrointestinal Digestibility
Authors: Ceylan Fatma Duygu , Yılmaz Hilal , Adrar Nabil , Günal Köroğlu Deniz , Gultekin Subasi Busra , Capanoglu Esra
Published: December 2022

In this study, we investigated the formation of protein–phenolic complexes from dephenolized hazelnut meal protein isolates (dHPI) and hazelnut skin phenolic extracts (HSE) and their effects on the bioaccessibility of both hazelnut proteins and phenolics. The dHPI–HSE complexes were of considerable size and were dependent on HSE concentration due to aggregation. Although catechin was the main component of HSE, it did not cause aggregation, except for a slight rise in particle size. According to fluorescence quenching, the hazelnut protein–phenolic extract complex had a linear Stern–Volmer plot expressing static quenching between 0–0.5 mM concentration; the interaction was mainly dependent on hydrogen bonding and van der Waals forces (ΔH < 0 and ΔS < 0), and the reaction was spontaneous (ΔG < 0). According to Fourier transform infrared (FTIR) spectroscopy results, higher phenolic extract concentration caused an increase in irregular structures in hazelnut protein, while the lowest catechin and phenolic concentration altered the regular structure. Skin extracts did not alter the digestibility of dephenolized proteins, but dephenolization reduced the degree of hydrolysis by pancreatin. The formation of the protein–phenolic complex had a beneficial effect on the bioaccessibility of hazelnut skin phenols, predominantly those on the galloylated form of the catechins, such as gallocatechin gallate and epigallocatechin gallate. Thus, the bioaccessibility and antioxidant activity analysis results showed that protein–phenolic complexes obtained from hazelnut meal and skin may promote the transition of phenolic compounds from the gastrointestinal tract without degradation.
Validation of a Rapid Ultrasound-Assisted Extraction Coupled with Anion Exchange Chromatography Method for the Determination of D-Psicose in Raisin Matrices
Authors: Zulkarnaen Fahmi Maulana , Setyaningsih Widiastuti , Palma Miguel
Published: December 2022

D-psicose is a rare sugar, used as an alternative to the natural sweetener that provides 70% of sucrose’s sweetness, with low-calorie absorption in the human body. Considering the health-beneficial effect and high availability of D-psicose in raisins (the world’s most consumed dried fruit), it is of interest to establish a reliable analytical method to determine D-psicose content in these matrices. Herein, a new method for determining D-psicose content in raisins, using ultrasound-assisted extraction in conjunction with anion exchange chromatography with pulsed amperometric detection (UAE-HPAEC-PAD) systems, has been developed and validated. The stability of D-psicose and its precursor was priorly assessed by applying a specific ultrasound power (100 W) and pulse duty cycle (0.5 s−1), with varying extraction temperatures (10, 25, 40, 55, 70, and 85 °C) and times (5, 10, 15, 20, and 25 min). The method was validated with high linearity (R2 > 0.999), accuracy (89.78–101.06%), and precision (4.8% intra-day and 4.34% inter-day). A number of raisin products were checked during the method applicability assessment. A concentration of 520 mg kg−1 was found in a specimen of commercial raisin matrix.
Immunomodulators Containing Epicor, Colostrum, Vitamin D, Zinc, Lactobacilli and Bifidobacterium Reduce Respiratory Exacerbations in Children and Adults with Chronic Pulmonary Diseases
Authors: Lazova Snezhina , Yanev Nikolay , Kolarova-Yaneva Nadia , Velikova Tsvetelina
Published: December 2022

(1) Background: A number of studies have demonstrated the connection between developing or exacerbating chronic respiratory diseases in adults and children. However, still, few studies focus on reducing exacerbations via immunomodulation. (2) Methods: In this pilot study, a total of 25 pediatric and adult patients with bronchial asthma (BA) and chronic obstructive pulmonary disease (COPD)/persistent bacterial bronchitis (PBB) were included, administered over-the-counter (OTC) immunomodulators and followed up for 6 or 12 months. (3) Results: We observed a decrease in the frequency of exacerbations with slight improvements in functional respiratory indicators in adults on their second and third visits and a reduced number of exacerbations and improved spirometry indices in children with BA, although exacerbations requiring hospital admission remained at a similar rate. (4) Conclusions: We confirmed that the number of exacerbations of underlying chronic respiratory disease in adults and children could be reduced after the administration of OTC immunomodulators, probably by optimizing the immune resistance to common viral infections.
Key Factors for Assessing Small and Rural Municipalities’ Readiness for Smart City Implementation
Authors: Mashau Nkhangweni Lawrence , Kroeze Jan Hendrik , Howard Grant Royd
Published: December 2022

The concept of a ‘smart city’ is now commonly used to describe the use of various digital and other technologies, to collect data to provide information that can be used to manage resources efficiently and provide a better standard of living for the citizens. Small and rural municipalities in developing countries face growing challenges in managing their resources, to provide basic services such as housing, education, transport, health, water and electricity to the citizens. This research is aimed at identifying key factors that could assist small and rural municipalities to assess their readiness for smart city implementation. A systematic literature review methodology was employed to provide an exhaustive summary of substantiated research relevant to the research aim and to synthesize key factors that are crucial to assess small and rural municipalities’ readiness. This study was conducted between January and July 2022. As a contribution, this study identified and synthesized key factors to be addressed when determining a municipality’s readiness to become a smart city. Through the use of ATLAS.ti to analyze the data, the study identifies the following key factors: infrastructure, the environment, skills, innovation, technology, the economy, citizens, culture, finance, the public sector, private organizations, data, management, policies, internet connectivity, education and energy. Advancing these key elements should also help small and rural municipalities, who are not yet ready to actuate a smart city, to prepare for such an endeavor, in order to address service delivery issues, the economy and the quality of life of their citizens.
Exploring the Market Requirements for Smart and Traditional Ageing Housing Units: A Mixed Methods Approach
Authors: Li Rita Yi Man , Shi Miao , Abankwa Derek Asante , Xu Yishuang , Richter Amy , Ng Kelvin Tsun Wai , Song Lingxi
Published: December 2022

The world’s population is getting older these days. Frailty, a gerontologic health condition associated with ageing, has serious consequences. One crucial remedy for the elderly population is the development of ageing-in-place infrastructures. To better understand the market requirements for ageing housing units, the causes of downsizing and the governmental measures to ameliorate the situation, face-to-face in-depth individual and focus group interviews were conducted in this study. Elderly residents of two significant ageing-in-place institutions in Hong Kong, along with their caregivers, were interviewed. The method of methodological triangulation was used to combine interviews, records, and communication tools to increase the reliability and trustworthiness of the findings. The provision of facilities for the elderly has successfully established a pathway for creating and making housing spaces available to families who need larger homes, while the elderly typically downsize from larger homes and relieve their financial needs. It is also found that a digital divide exists; some respondents suggested that they do not know about computers and do not use smart facilities in their homes.
Biomechanical, Anthropometric and Psychological Determinants of Barbell Bench Press Strength
Authors: Van Every Derrick W. , Coleman Max , Plotkin Daniel L. , Zambrano Hugo , Van Hooren Bas , Larsen Stian , Nuckols Greg , Vigotsky Andrew D. , Schoenfeld Brad J.
Published: December 2022

The purpose of this study was to improve our understanding of the relative contributions of biomechanical, anthropometric, and psychological factors in explaining maximal bench press (BP) strength in a heterogeneous, resistance-trained sample. Eighteen college-aged participants reported to the laboratory for three visits. The first visit consisted of psychometric testing. The second visit assessed participants’ anthropometrics, additional psychometric outcomes, and bench press one repetition maximum (1RM). Participants performed isometric dynamometry testing for horizontal shoulder adduction and elbow extension at a predicted sticking point joint position. Multiple linear regression was used to examine the relationships between the biomechanical, anthropometric, and psychological variables and BP 1RM. Our primary multiple linear regression accounted for 43% of the variance in BP strength (F(3,14) = 5.34, p = 0.01; R2 = 0.53; adjusted R2 = 0.43). The sum of peak isometric net joint moments from the shoulder and elbow had the greatest standardized effect (0.59), followed by lean body mass (0.27) and self-efficacy (0.17). The variance in BP 1RM can be similarly captured (R2 = 0.48) by a single principal component containing anthropometric, biomechanics, and psychological variables. Pearson correlations with BP strength were generally greater among anthropometric and biomechanical variables as compared to psychological variables. These data suggest that BP strength among a heterogeneous, resistance-trained population is explained by multiple factors and is more strongly associated with physical than psychological variables.
Review and Prospects of Enterprise Human Resource Management Effectiveness: Bibliometric Analysis Based on Chinese-Language and English-Language Journals
Authors: Yang Xiaowei , Yan Shumin , He Jiang , Dong Junjie
Published: December 2022

The effectiveness of enterprise human resource management is an important topic of human resource management in academic studies, and it is the key for enterprises to achieve long-term sustainable development. Up to now, fruitful research achievements have been achieved, but there is still a lack of a systematic literature review. Based on 218 research results collected in the CNKI and SSCI databases of the WOS from 1975 to 2021, this paper systematically reviews the enterprise human resource management effectiveness from the aspects of evolution, topic overview, and content sorting by using bibliometrics with Citespace software and a systematic literature review. This paper expounds the concept of enterprise human resource management effectiveness, analyzes the existing research results, and finds out where the research gap is. Based on this, this paper puts forward the prospect of future research topics from the aspects of measurement methods, influencing factors, and results, which not only helps to systematically reveal the overview of the existing research in the field of enterprise human resource management effectiveness, but also provides direction for further research.
Through the Lens of Workers’ Motivation: Does It Relate to Work–Family Relationship Perceptions?
Authors: Lopes Sílvia , Sabino Ana , Dias Paulo C. , Rodrigues Anabela , Chambel Maria José , Cesário Francisco
Published: December 2022

Workers’ motivations and the work–family relationship are two subjects that have been capturing the attention of researchers and practitioners. However, to date, little is known about the link between the two subjects. Thus, this research aimed to investigate the relationships among each type of motivation conceptualized in self-determination theory and work–family conflict, work–family enrichment, and work–family balance. In addition, the current study intended to investigate the relationships among work–family conflict, work–family enrichment, and Work–family balance. Data were collected through a self-administered questionnaire. To test the hypotheses, the PROCESS macro was used. The results suggested that intrinsic motivation is negatively associated with work–family conflict and positively associated with work–family enrichment and work–family balance. Additionally, identified regulations seem to be positively associated with work–family enrichment and work–family balance. However, contrary to expectations, this study revealed a positive relationship between introjected regulation and work–family enrichment and work–family balance. Concerning external regulation (material and social) and amotivation, globally, the findings were consistent with the hypotheses, i.e., the higher the external regulation and amotivation, the higher work–family conflict and the lower the work–family enrichment and work–family balance. Moreover, the higher the work–family conflict, the lower the work–family balance, and the higher the work–family enrichment, the higher the work–family balance. Theoretical and practical implications are discussed.
The Influence of Social Preference and Governments’ Strong Reciprocity on Agricultural Green Production Networks under Intensive Management in China
Authors: Feng Lin , Xu Lingyan , Zhou Zhuoyun , Du Jianguo , Wang Dandan
Published: December 2022

This paper focuses on the sustainable development path of agricultural production in China under the pattern of intensive management, which aims to promote the agricultural green production networks consisting of new agricultural operators and traditional farmers. Based on these, this paper explores the evolution of agricultural green production networks through analyzing three interactive relationships among new agricultural operators and traditional farmers and constructing evolutionary game models on complex networks considering social preference and governments’ strong reciprocity, respectively. Then, the evolutionary stability strategies of these six evolutionary game models are analyzed, and the simulation of the evolution process of agricultural green production networks in different scenarios by MATLAB are realized. The results show that: (1) The evolutionary results of agricultural green production networks are positively correlated with the extra net profit of agricultural production operators. (2) If the extra net profit is positive, traditional farmers are more likely to adopt stable strategy of agricultural green production than new agricultural operators, while a few new agricultural operators would like to adopt the strategy of agricultural green production even though the extra net profit is low or negative. (3) The effect of social preference and governments’ strong reciprocity shows heterogeneity on the emergence of agricultural green production networks. When the net profit is enhanced, agricultural production operators with competitive preference would adopt the strategy of agricultural green production more quickly, as well as those agricultural production operators with social preference as governments’ strong reciprocity strengthened. As such, this paper suggests that it should be necessary to improve the net profit of agricultural green production by reducing costs and increasing benefits, encouraging agricultural production operators to cooperate in the agricultural green production networks to learn and share their green production experience.
Automatic ROI Setting Method Based on LSC for a Traffic Congestion Area
Authors: He Yang , Jin Lisheng , Wang Huanhuan , Huo Zhen , Wang Guangqi , Sun Xinyu
Published: December 2022

Congested regions in videos put forward higher requirements for target detection algorithms, and the key detection of congested regions provides optimization directions for improving the accuracy of detection algorithms. In order to make the target detection algorithm pay more attention to the congested area, an automatic selection method of a traffic congestion area based on surveillance videos is proposed. Firstly, the image is segmented with superpixels, and a superpixel boundary map is extracted. Then, the mean filtering method is used to process the superpixel boundary map, and a fixed threshold is used to filter pixels with high texture complexity. Finally, a maximin method is used to extract the traffic congestion area. Monitoring data of night and rainy days were collected to expand the UA-DETRAC data set, and experiments were carried out on the extended data set. The results show that the proposed method can realize automatic setting of the congestion area under various weather conditions, such as full light, night and rainy days.
Transit-Oriented Development in Saudi Arabia: Riyadh as a Case Study
Authors: Almatar Khalid Mohammed
Published: December 2022

Transit-oriented development (TOD) in Saudi Arabia is becoming a significant priority for the government and developers to create a sustainable and quality living environment. TOD is an integrated transport and urban planning method that aims to reduce car use and urban sprawl, increase the use of public transport, and enhance sustainable mobility. To meet the global goals as per the Paris Accord, Saudi Arabia’s policymakers must prioritize the integration of TOD in urban planning. This study was carried out with the main aim of identifying the environmental, social, and economic benefits of implementing TOD in Riyadh, Saudi Arabia. A mixed-study research method was used, and data were collected using a questionnaire survey and semi-structured interviews. The quantitative data were analyzed using SPSS version 21, and qualitative data were analyzed using NVivo software. The findings of this study show that TOD in Riyadh City would positively impact economic, environmental, and social aspects. TOD would reduce travel time, allow its people to have an active lifestyle, and reduce congestion. TOD would help reduce mental health disorders and improve physical activity. TOD would positively impact the environment of Riyadh City and assist in reducing greenhouse gases. Overall, the study results provide a reliable perspective on the benefits of TOD. Most participants assumed that the implementation of TOD in Riyadh City would increase automobile mobility, provide more employment opportunities, and reduce travel time, positively impacting the environment and economy of Riyadh City.