bluuebunny/crossref_metadata_2025 huggingface dataset:
Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images
Authors: Alohali Manal Abdullah , Aljebreen Mohammed , Nemri Nadhem , Allafi Randa , Duhayyim Mesfer Al , Ibrahim Alsaid Mohamed , Alneil Amani A. , Osman Azza Elneil
Published: June 2023

Anomaly detection in pedestrian walkways is a vital research area that uses remote sensing, which helps to optimize pedestrian traffic and enhance flow to improve pedestrian safety in intelligent transportation systems (ITS). Engineers and researchers can formulate more potential techniques and tools with the power of computer vision (CV) and machine learning (ML) for mitigating potential safety hazards and identifying anomalies (i.e., vehicles) in pedestrian walkways. The real-world challenges of scenes and dynamics of environmental complexity cannot be handled by the conventional offline learning-based vehicle detection method and shallow approach. With recent advances in deep learning (DL) and ML areas, authors have found that the image detection issue ought to be devised as a two-class classification problem. Therefore, this study presents an Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems using Federated Learning and Harris Hawks Optimizer (ADPW-FLHHO) algorithm on remote sensing images. The presented ADPW-FLHHO technique focuses on the identification and classification of anomalies, i.e., vehicles in the pedestrian walkways. To accomplish this, the ADPW-FLHHO technique uses the HybridNet model for feature vector generation. In addition, the HHO approach is implemented for the optimal hyperparameter tuning process. For anomaly detection, the ADPW-FLHHO technique uses a multi deep belief network (MDBN) model. The experimental results illustrated the promising performance of the ADPW-FLHHO technique over existing models with a maximum AUC score of 99.36%, 99.19%, and 98.90% on the University of California San Diego (UCSD) Ped1, UCSD Ped2, and avenue datasets, respectively. Therefore, the proposed model can be employed for accurate and automated anomaly detection in the ITS environment.
An Integrated Approach for Analyzing the Morphological Evolution of the Lower Reaches of the Minjiang River Based on Long-Term Remote Sensing Data
Authors: Zhou Nie , Sheng Sheng , He Li-Ying , Tian Bing-Ru , Chen Hua , Xu Chong-Yu
Published: June 2023

Understanding the evolution of river morphology is crucial for comprehending changes in water resources and implementing development projects along rivers. This study proposes an integrated approach utilizing remote sensing image data combined with deep learning and visual interpretation algorithms to analyze continuous-type changes in river morphology. This research focuses on the lower reaches of the Minjiang River in China and comprehensively analyzes the river’s morphological evolution from 1986 to 2021. The results show that the proposed method of river water identification in this study demonstrates high accuracy and effectiveness, with an F1 score and Kappa coefficient greater than 0.96 and 0.91, respectively. The morphology of the river channel remains stable in the upstream and estuarine sections of the study region while undergoing substantial alterations in the middle section. Additionally, this study also identifies several factors that significantly impact the evolution of river morphology, including reservoir construction, river sediment mining, river training measures, geological conditions, and large flood events. The findings of this study can provide some insights into the management and conservation of water resources.
Exploiting User Clustering and Fixed Power Allocation for Multi-Antenna UAV-Assisted IoT Systems
Authors: Nguyen Sang Quang , Le Anh-Tu , Le Chi-Bao , Tin Phu Tran , Kim Yong-Hwa
Published: June 2023

Internet of Things (IoT) systems cooperative with unmanned aerial vehicles (UAVs) have been put into use for more than ten years, from transportation to military surveillance, and they have been shown to be worthy of inclusion in the next wireless protocols. Therefore, this paper studies user clustering and the fixed power allocation approach by placing multi-antenna UAV-mounted relays for extended coverage areas and achieving improved performance for IoT devices. In particular, the system enables UAV-mounted relays with multiple antennas together with non-orthogonal multiple access (NOMA) to provide a potential way to enhance transmission reliability. We presented two cases of multi-antenna UAVs such as maximum ratio transmission and the best selection to highlight the benefits of the antenna-selections approach with low-cost design. In addition, the base station managed its IoT devices in practical scenarios with and without direct links. For two cases, we derive closed-form expressions of outage probability (OP) and closed-form approximation ergodic capacity (EC) generated for both devices in the main scenario. The outage and ergodic capacity performances in some scenarios are compared to confirm the benefits of the considered system. The number of antennas was found to have a crucial impact on the performances. The simulation results show that the OP for both users strongly decreases when the signal-to-noise ratio (SNR), number of antennas, and fading severity factor of Nakagami-m fading increase. The proposed scheme outperforms the orthogonal multiple access (OMA) scheme in outage performance for two users. The analytical results match Monte Carlo simulations to confirm the exactness of the derived expressions.
Modelling and Experimental Study of Power Losses in Toothed Wheels
Authors: Giannetti Guglielmo , Meli Enrico , Rindi Andrea
Published: June 2023

In recent decades, the request for more efficient performances in the aeronautical sector moved researchers to pay particular attention to all the related mechanisms and systems, especially with respect to the saving of power. In this context, the bearing modeling and design, as well as gear coupling, play a fundamental role. Moreover, the need for low power losses also concerns the study and the implementation of advanced lubrication systems, especially for high peripheral speed. With the previous aims, this paper presents a new validated model for toothed gears, added to a bearing model; with the link of these different submodels, the whole model describes the system’s dynamic behavior, taking into account the different kinds of power losses (windage losses, fluid dynamic losses, etc.) generated by the mechanical system parts (especially rolling bearings and gears). As the bearing model, the proposed model is characterized by high numerical efficiency and allows the investigation of different rolling bearings and gears with different lubrication conditions and frictions. A comparison between the experimental and simulated results is also presented in this paper. The analysis of the results is encouraging and shows a good agreement between experiments and model simulations, with particular attention to the power losses in the bearing and gears.
A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media
Authors: Butt Muhammad Junaid , Malik Ahmad Kamran , Qamar Nafees , Yar Samad , Malik Arif Jamal , Rauf Usman
Published: June 2023

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.
Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment
Authors: Kim Kyuri , Lee Jaeho
Published: June 2023

In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model’s performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising.
What MEMS Research and Development Can Learn from a Production Environment
Authors: Niekiel Malte Florian , Meyer Jana Marie , Lewitz Hanna , Kittmann Anne , Nowak Marc Alexander , Lofink Fabian , Meyners Dirk , Zollondz Jens-Hendrik
Published: June 2023

The intricate interdependency of device design and fabrication process complicates the development of microelectromechanical systems (MEMS). Commercial pressure has motivated industry to implement various tools and methods to overcome challenges and facilitate volume production. By now, these are only hesitantly being picked up and implemented in academic research. In this perspective, the applicability of these methods to research-focused MEMS development is investigated. It is found that even in the dynamics of a research endeavor, it is beneficial to adapt and apply tools and methods deduced from volume production. The key step is to change the perspective from fabricating devices to developing, maintaining and advancing the fabrication process. Tools and methods are introduced and discussed, using the development of magnetoelectric MEMS sensors within a collaborative research project as an illustrative example. This perspective provides both guidance to newcomers as well as inspiration to the well-versed experts.
Toward Energy-Efficient Routing of Multiple AGVs with Multi-Agent Reinforcement Learning
Authors: Ye Xianfeng , Deng Zhiyun , Shi Yanjun , Shen Weiming
Published: June 2023

This paper presents a multi-agent reinforcement learning (MARL) algorithm to address the scheduling and routing problems of multiple automated guided vehicles (AGVs), with the goal of minimizing overall energy consumption. The proposed algorithm is developed based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, with modifications made to the action and state space to fit the setting of AGV activities. While previous studies overlooked the energy efficiency of AGVs, this paper develops a well-designed reward function that helps to optimize the overall energy consumption required to fulfill all tasks. Moreover, we incorporate the e-greedy exploration strategy into the proposed algorithm to balance exploration and exploitation during training, which helps it converge faster and achieve better performance. The proposed MARL algorithm is equipped with carefully selected parameters that aid in avoiding obstacles, speeding up path planning, and achieving minimal energy consumption. To demonstrate the effectiveness of the proposed algorithm, three types of numerical experiments including the ϵ-greedy MADDPG, MADDPG, and Q-Learning methods were conducted. The results show that the proposed algorithm can effectively solve the multi-AGV task assignment and path planning problems, and the energy consumption results show that the planned routes can effectively improve energy efficiency.
Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation
Authors: Lueken Markus , Gramlich Michael , Leonhardt Steffen , Marx Nikolaus , Zink Matthias D.
Published: June 2023

Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications.
Multi-Task Learning Radar Transformer (MLRT): A Personal Identification and Fall Detection Network Based on IR-UWB Radar
Authors: Jiang Xikang , Zhang Lin , Li Lei
Published: June 2023

Radar-based personal identification and fall detection have received considerable attention in smart healthcare scenarios. Deep learning algorithms have been introduced to improve the performance of non-contact radar sensing applications. However, the original Transformer network is not suitable for multi-task radar-based applications to effectively extract temporal features from time-series radar signals. This article proposes the Multi-task Learning Radar Transformer (MLRT): a personal Identification and fall detection network based on IR-UWB radar. The proposed MLRT utilizes the attention mechanism of Transformer as its core to automatically extract features for personal identification and fall detection from radar time-series signals. Multi-task learning is applied to exploit the correlation between the personal identification task and the fall detection task, enhancing the performance of discrimination for both tasks. In order to suppress the impact of noise and interference, a signal processing approach is employed including DC removal and bandpass filtering, followed by clutter suppression using a RA method and Kalman filter-based trajectory estimation. An indoor radar signal dataset is generated with 11 persons under one IR-UWB radar, and the performance of MLRT is evaluated using this dataset. The measurement results show that the accuracy of MLRT improves by 8.5% and 3.6% for personal identification and fall detection, respectively, compared to state-of-the-art algorithms. The indoor radar signal dataset and the proposed MLRT source code are publicly available.
Verification of Mechanical Properties Identification Based on Impulse Excitation Technique and Mobile Device Measurements
Authors: Scislo Lukasz
Published: June 2023

The Impulse Excitation Technique (IET) is one of the most useful testing methods for evaluating or calculating some material properties. This can be useful to evaluate and confirm that the material ordered is what was delivered. In the case of unknown materials, where their properties are required by simulation software, this is also a quick way to obtain mechanical properties and thus improve the simulation quality. The main drawback of the method is the requirement for a specialized sensor and acquisition system and a well-trained engineer to prepare the setup and analyze the results. The article evaluates the possibility of using a low-cost solution in the form of a mobile device microphone as a way to obtain data, which after the Fast Fourier Transform (FFT), allows to obtain frequency response graphs and use the IET method procedure to calculate the mechanical properties of the samples. The data obtained by the mobile device are compared with the data obtained by professional sensors and data acquisition systems. The results confirm that for typical homogenous materials, the mobile phone is a cheap and reliable alternative for fast, on-the-go material quality inspections and can be introduced even in small companies and on construction sites. Additionally, this kind of approach does not require specific knowledge of sensing technology, signal treatment, or data analysis and can be performed by any assigned employee, who can receive the quality check information immediately on-site. Additionally, the presented procedure allows data collection and transfer to the cloud for future references and additional information extraction. This element is fundamental for introducing sensing technologies under the Industry 4.0 concept.
Fault Diagnosis of Rotating Machinery: A Highly Efficient and Lightweight Framework Based on a Temporal Convolutional Network and Broad Learning System
Authors: Wei Hao , Zhang Qinghua , Gu Yu
Published: June 2023

Efficient fault diagnosis of rotating machinery is essential for the safe operation of equipment in the manufacturing industry. In this study, a robust and lightweight framework consisting of two lightweight temporal convolutional network (LTCN) backbones and a broad learning system with incremental learning (IBLS) classifier called LTCN-IBLS is proposed for the fault diagnosis of rotating machinery. The two LTCN backbones extract the fault’s time–frequency and temporal features with strict time constraints. The features are fused to obtain more comprehensive and advanced fault information and input into the IBLS classifier. The IBLS classifier is employed to identify the faults and exhibits a strong nonlinear mapping ability. The contributions of the framework’s components are analyzed by ablation experiments. The framework’s performance is verified by comparing it with other state-of-the-art models using four evaluation metrics (accuracy, macro-recall (MR), macro-precision (MP), and macro-F1 score (MF)) and the number of trainable parameters on three datasets. Gaussian white noise is introduced into the datasets to evaluate the robustness of the LTCN-IBLS. The results show that our framework provides the highest mean values of the evaluation metrics (accuracy ≥ 0.9158, MP ≥ 0.9235, MR ≥ 0.9158, and MF ≥ 0.9148) and the lowest number of trainable parameters (≤0.0165 Mage), indicating its high effectiveness and strong robustness for fault diagnosis.
Unified Environment for Real Time Control of Hybrid Energy System Using Digital Twin and IoT Approach
Authors: Chalal Lamine , Saadane Allal , Rachid Ahmed
Published: June 2023

Today, climate change combined with the energy crisis is accelerating the worldwide adoption of renewable energies through incentive policies. However, due to their intermittent and unpredictable behavior, renewable energy sources need EMS (energy management systems) as well as storage infrastructure. In addition, their complexity requires the implementation of software and hardware means for data acquisition and optimization. The technologies used in these systems are constantly evolving but their current maturity level already makes it possible to design innovative approaches and tools for the operation of renewable energy systems. This work focuses on the use of Internet of Things (IoT) and Digital Twin (DT) technologies for standalone photovoltaic systems. Based on Energetic Macroscopic Representation (EMR) formalism and the Digital Twin (DT) paradigm, we propose a framework to improve energy management in real time. In this article, the digital twin is defined as the combination of the physical system and its digital model, communicating data bi-directionally. Additionally, the digital replica and IoT devices are coupled via MATLAB Simulink as a unified software environment. Experimental tests are carried out to validate the efficiency of the digital twin developed for an autonomous photovoltaic system demonstrator.
Low-Profile Broadband Dual-Polarized Dipole Antenna for Base Station Applications
Authors: Feng Hao , Li Mengyuan , Zhang Zhiyi , Fu Jiahui , Zhang Qunhao , Zhao Yulin
Published: June 2023

A low-profile broadband dual-polarized antenna is investigated for base station applications. It consists of two orthogonal dipoles, fork-shaped feeding lines, an artificial magnetic conductor (AMC), and parasitic strips. By utilizing the Brillouin dispersion diagram, the AMC is designed as the antenna reflector. It has a wide in-phase reflection bandwidth of 54.7% (1.54–2.70 GHz) and a surface-wave bound range of 0–2.65 GHz. This design effectively reduces the antenna profile by over 50% compared to traditional antennas without an AMC. For demonstration, a prototype is fabricated for 2G/3G/LTE base station applications. Good agreement between the simulations and measurements is observed. The measured −10-dB impedance bandwidth of our antenna is 55.4% (1.58–2.79 GHz), with a stable gain of 9.5 dBi and a high isolation of more than 30 dB across the impedance passband. As a result, this antenna is an excellent candidate for miniaturized base station antenna applications.
An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI
Authors: Alyoubi Esraa H. , Moria Kawthar M. , Alghamdi Jamaan S. , Tayeb Haythum O.
Published: June 2023

Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients’ lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widely to predict MCI. This study proposes optimized deep learning models for differentiating between MCI and normal control samples. In previous studies, the hippocampus region located in the brain is used extensively to diagnose MCI. The entorhinal cortex is a promising area for diagnosing MCI since severe atrophy is observed when diagnosing the disease before the shrinkage of the hippocampus. Due to the small size of the entorhinal cortex area relative to the hippocampus, limited research has been conducted on the entorhinal cortex brain region for predicting MCI. This study involves the construction of a dataset containing only the entorhinal cortex area to implement the classification system. To extract the features of the entorhinal cortex area, three different neural network architectures are optimized independently: VGG16, Inception-V3, and ResNet50. The best outcomes were achieved utilizing the convolution neural network classifier and the Inception-V3 architecture for feature extraction, with accuracy, sensitivity, specificity, and area under the curve scores of 70%, 90%, 54%, and 69%, respectively. Furthermore, the model has an acceptable balance between precision and recall, achieving an F1 score of 73%. The results of this study validate the effectiveness of our approach in predicting MCI and may contribute to diagnosing MCI through MRI.
Unbalanced Current Identification of Three-Core Power Cables Based on Phase Detection of Magnetic Fields
Authors: Liang Shangqing , Yang Mingchao , Yang Guoqing , Wang Lin , Cai Xiong , Zhou Yuanguo
Published: June 2023

Identifying unbalanced phase currents is crucial for control and fault alarm rates in power grids, especially in urban distribution networks. The zero-sequence current transformer, specifically designed for measuring unbalanced phase currents, offers advantages in measurement range, identity, and size, compared to using three separate current transformers. However, it cannot provide detailed information on the unbalance status beyond the total zero-sequence current. We present a novel method for identifying unbalanced phase currents based on phase difference detection using magnetic sensors. Our approach relies on analyzing phase difference data from two orthogonal magnetic field components generated by three-phase currents, as opposed to the amplitude data used in previous methods. This enables the differentiation of unbalance types (amplitude unbalance and phase unbalance) through specific criteria and allows for the simultaneous selection of an unbalanced phase current in the three-phase currents. In this method, the amplitude measurement range of magnetic sensors is no longer a critical factor, allowing for an easily attainable wide identification range for current line loads. This approach offers a new avenue for unbalanced phase current identification in power systems.
Being “Both”: Identifications of Second and Third Generation Brussels Muslim Youths towards the Country of Origin and the Country of Residence
Authors: Torrekens Corinne , Kavadias Dimokritos , Bensaid Nawal
Published: June 2023

In Belgium, several incidents and public debates have highlighted ethnocentric conceptions of the nation held by public opinion and prominent politicians where immigrants and particularly Muslims are marked as the unwilling “others”. In this framework, immigrants maintaining ethnic identities and transnational ties is classically seen as weakening their integration in the receiving countries. Based on results drawn from 16 focus group discussions and 50 individual semi-structured interviews conducted in Brussels with youth with a foreign background, we show that while a minority of our respondents still identified themselves primarily in reference to the country of origin of their (grand)parents, emphasizing ethnic boundaries and family heritage, most of them have developed a multiple-identity strategy for themselves, which acknowledges both their ethnic background and their belonging to Belgium and Brussels as a multicultural city. Those involved in upward social mobility strategies through higher education or better secondary schools also have more facilities to express the double-presence narrative.
Advancing Women’s and Indigenous Groups’ Educational Achievement: Changes in Outcomes following the 2017 Ley de Gratuidad in Chile
Authors: Carnes Matthew , Salas-Schweikart Raimundo
Published: June 2023

Who benefits when economic costs to the pursuit of higher education are diminished? Can such reforms advance the educational trajectory of historically marginalized groups, including women and indigenous people, or are historic social inequalities replicated in the use of new opportunities? The 2016 Ley de Gratuidad in Chile presents a unique opportunity to examine who benefits and how when the state provides a policy guaranteeing free higher education at participating universities. Given the systematic biases and cultural norms faced by women and indigenous people in Chile and the broad design of the state-led policy, the educational reform constitutes an important test of the extent to which lowering costs can facilitate education across an economically and socially diverse population. Leveraging data from two waves of a household survey, this paper finds that educational attainment, measured in the number of years of schooling achieved by respondents, is significantly higher for those who receive such a benefit, consistent with the design and intent of the policy. More surprisingly, these increases in years of education occur for women and indigenous people, even though no programmatic element emphasizes these groups. The results suggest that state financing is significantly associated with promoting higher education in the population and that women, the poor, and indigenous people are significant beneficiaries of this effect despite the social and cultural barriers in the country.
Belonging and Its Barriers: A Critical Perspective of Latiné and Mixed-Status Families’ Experiences
Authors: McKay Steven C. , Ganis Alberto , Lopez Leslie , Martinez Jennifer Aimee , Reyes Marlen , Modha Shivani
Published: June 2023

This article presents findings from a four-year collaborative research project on immigrant and mixed-status families in Santa Cruz County, California. The project employed a new model of critical community-engaged scholarship called Community Initiated Student Engaged Research (CISER) in order to gain access to and build trust with this vulnerable population. The study used an overarching theoretical framework of “belonging” to identify six key factors most consequential for belonging and/or exclusion, including access to education, economic security, legal immigration status, health services, opportunities for youth, and social networks. The findings reveal the complex and interconnected nature of these factors and demonstrate how exclusion experienced due to a lack of legal immigration status had far-reaching effects on interviewees’ job prospects and experiences of economic, health, and housing insecurity. The article highlights the importance of using an assets-based approach to draw out the myriad ways interviewees and communities create spaces, networks, and ways to promote and enhance both material and emotional forms of belonging. The CISER model and its participatory approach also provide tangible benefits for community partners and undergraduate researchers. This article contributes to the literature on immigrant experiences and critical community-engaged research while offering insights into sources of and systemic barriers to collective belonging.
Improving Automotive Air Conditioning System Performance Using Composite Nano-Lubricants and Fuzzy Modeling Optimization
Authors: Alahmer Ali , Ghoniem Rania M.
Published: June 2023

This study aims to enhance the effectiveness of automobile air conditioning (AAC) systems through the use of composite nano-lubricants and fuzzy modeling optimization techniques. Composite nano-lubricants, which consist of varied metal oxide ingredients and content ratios, are projected to surpass single-component nano-lubricants in terms of improving the performance of AAC systems. Fuzzy modeling is used to simulate the AAC system based on experimental data using three input parameters: volume concentration of nano-lubricants (%), the refrigerant charge (g), and compressor speed (rpm). The output performance of the AAC system is measured using four parameters: cooling capacity (CC) in kW, compressor work (CW) in kJ/kg, coefficient of performance (COP), and power consumption (PC) in kW. Optimization is performed using the marine predators algorithm (MPA) to identify the best values for the input control parameters. The objective function is to minimize CW, COP, and PC while simultaneously maximizing CC and COP. Results showed that the performance of the AAC system improved from 85% to 88% compared to the experimental dataset, highlighting the potential benefits of using composite nano-lubricants and fuzzy modeling optimization for improving the energy efficiency of AAC systems. Furthermore, a comprehensive comparison with ANOVA was performed to demonstrate the superiority of the fuzzy modeling approach. The results indicate that the fuzzy model outperforms ANOVA, as evidenced by a reduced root mean square error (RMSE) for all data, from 0.412 using ANOVA to 0.0572 using fuzzy. Additionally, the coefficient of determination for training increased from 0.9207 with ANOVA to 1.0 with fuzzy, further substantiating the success of the fuzzy modeling phase.
In Control or Being Controlled? Investigating the Control of Space Heating in Smart Homes
Authors: Larsen Simon Peter , Gram-Hanssen Kirsten , Madsen Line Valdorff
Published: June 2023

Low-carbon scenarios for enabling heat demand flexibility in district heating networks include smart home technology (SHT), which can automate control of heating by responding to utility signals while considering household preferences. This study empirically explores how control of space heating using SHT is performed in heating practices by occupants. The study is based on in-depth interviews and home tours with occupants living in smart homes in Denmark. The results suggest that (1) practical knowledge, (2) notions of being in control, and (3) temporal aspects of everyday life are of specific importance for how occupants perform control of space heating using SHT. Furthermore, results show how occupants act when feeling out of control. The data illustrate that control of space heating using SHT is performed in a variety of different ways, displaying the dynamic relationships between the materiality of the home, the importance of practical knowledge that occupants draw upon, and the meaning they ascribe to ‘homely’ practices. As SHT limits people’s active engagement in controlling space heating by relying on automated features, the findings presented in this paper highlight how control of space heating is more than the ability to control but concerns the dynamics of social practices performed within and outside of the home. Based on the results, the paper recommends four specific design and policy implications for future SHT solutions.
Long-Term Field Observation of the Power Generation and System Temperature of a Roof-Integrated Photovoltaic System in South Korea
Authors: Azhar Muhammad Hanif Ainun , Alhammadi Salh , Jang Seokjin , Kim Jitaek , Kim Jungtaek , Kim Woo Kyoung
Published: June 2023

A miniature house roof-integrated photovoltaic (PV) system in South Korea was monitored for 2.5 years. System performance was evaluated through power generation, solar irradiance, and system temperature. The comparison of each month’s power generation and solar irradiance revealed a parallel correlation over the entire observation period. The internal module temperature was almost always higher than the roof rear and module rear temperatures by 1–2 and 1–5 °C, respectively, while the temperature behind the PV modules was the lowest among the three temperatures, showing that the installation of PV modules as a roofing system does not affect the temperature of the roofing system. The system temperatures affected the power conversion efficiency; a maximum of 11.42% was achieved when the system temperatures were the lowest, and a minimum of 5.24% was achieved when the system temperatures were the highest. Hence, half of the anticipated generated power was lost due to the temperature fluctuation. Overall, installing PV modules as an entire roofing system is possible with this configuration due to the minimum effect on the roof temperature. However, PV system temperature control is essential for maintaining the power generation performance of the PV modules.
On-Site Experimental Study on Low-Temperature Deep Waste Heat Recovery of Actual Flue Gas from the Reformer of Hydrogen Production
Authors: Mu Lianbo , Wang Suilin , Liu Guichang , Lu Junhui , Lan Yuncheng , Zhao Liqiu , Liu Jincheng
Published: June 2023

Improving the energy-saving efficiency of flue gas deep waste heat and reducing the emission of carbon dioxide and other pollutants have been two issues that need to be paid attention to in petrochemical heating furnaces. A hydrogen production reformer with high energy consumption and high carbon emissions in the petroleum refining process affects the thermal and productive efficiency of the hydrogen production, amounts of heat from flue gas are wasted with the exhausted corrosive gas of the reformer, and latent heat is not recovered. To recover the sensible and latent heat from the exhausted gas, a new anti-corrosion, high-efficiency, and low-pressure-drop flue gas condensing heat exchanger (FGCHE) with low consumption and pressure drop was developed. The energy-saving performance was evaluated through on-site measurements and theoretical analysis. The results show that the exhausted gas temperature was reduced from 161.3~175.9 °C to 33.9~38.9 °C after using the new FGCHE to recover waste heat. The energy-saving efficiency and the utilization ratio of flue gas waste heat were 12~16.1% and 74~81.9%, respectively. The latent heat accounted for 41.3~48.1% of the total recovered heat. The exergy efficiency and the total thermal efficiency of the reformer reached 73~86.8% and 95.2~96.6%, respectively. The condensation in the flue gas reduced pollutant emissions (SO2 and NOx). This paper provides a practical application reference for the technology development of waste heat recovery and the application of an FGCHE for petrochemical heating furnaces.
Detection and Management of Freshwater Invasive Alien Species through Environmental DNA Analysis and Geographic Information Systems: A Review
Authors: Mohd Dali Mohamad Zulkarnain , Umar Roslan , Ismail Norshida , Juahir Hafizan , Mohd Nasir Muhammad Syafiq Aiman , Khaleel Aliyu Garba , Madiran Nor Ainsyafikah , Abdul Kari Zulhisyam , Wei Lee Seong , Tahiluddin Albaris B. , Téllez-Isaías Guillermo , Kamarudin Ahmad Syazni
Published: June 2023

Freshwater invasive alien species (IAS) are non-native organisms that were intentionally or unintentionally released into local water bodies and later harmed the invaded habitat by disrupting the ecological processes. Over the last few years, environmental deoxyribonucleic acid (eDNA) analyses have been used in many studies to detect IAS, with positive results. However, with the help of geographic information systems (GIS), efforts to detect the presence of IAS can be made faster and more efficiently. In this paper, we review the background of IAS in Southeast Asia and management efforts undertaken involving the input of known habitat-specific geographical parameters into GIS mapping. Via this strategy, it is possible to identify and distinguish areas that fit IAS habitat features from those that do not. eDNA analysis can later be applied to confirm the presence of IAS in detected areas, enabling further studies and actions. The presence of IAS in certain areas can be used as an indicator to assess the environmental integrity of native waterways. This combined method is likely the first approach to be applied to the detection of freshwater IAS in local water bodies. Apart from saving energy and resources, embedding GIS and eDNA into the study of IAS not only benefits the ecosystem but also assists locals and authorities in managing and taking necessary enforcement actions to curb further spread.
Analyzing the Impact of Enterprise Social Media on Employees’ Competency through the Mediating Role of Knowledge Sharing
Authors: Chen Miaojie , Babar Mehtab , Ahmed Ammar , Irfan Muhammad
Published: June 2023

The research study examines the impact of enterprise social media (ESM) on employees’ competence through the mediating role of knowledge sharing and the moderating influence of information relevance. The study was conducted with 272 respondents working in various educational institutions in Multan City, Pakistan, using a structured questionnaire to collect data. The research findings indicate that enterprise social media positively affects employees’ competencies, and knowledge sharing plays a significant role in mediating this effect. The study also suggests that information relevance moderates the relationship between ESM and employees’ competencies, indicating that the association is stronger when information relevance is high. The research is based on the social capital theory, which proposes that trust, shared vision, and network ties can enhance employee competence and knowledge sharing. This theoretical framework emphasizes the importance of building organizational social connections to promote knowledge sharing and employee competence. The study highlights the potential benefits of using ESM in organizations to enhance employees’ competencies through improved knowledge sharing. It also emphasizes the importance of information relevance in determining the effectiveness of ESM in promoting employee competence. The findings of this study have practical implications for organizations seeking to leverage ESM to improve their employees’ skills and knowledge.
Achieving Sustainability in Manufacturing through Additive Manufacturing: An Analysis of Its Enablers
Authors: Balubaid Mohammed , Alsaadi Naif
Published: June 2023

The manufacturing sector has undergone significant growth due to the integration of technologies from the Fourth Industrial Revolution. Industry 4.0 has revolutionized industrial operations, leading to increased utilization of smart and automated systems in manufacturing. Among these technologies is additive manufacturing (AM), which has been widely adopted in various industries to enhance new product development with minimal time constraints. This research aimed to identify and analyze the potential enablers of AM that support its adoption in the manufacturing sector. This study identified 15 enablers through a literature review, and they were analyzed using a grey decision-making trial and evaluation laboratory (DEMATEL)-based multicriteria decision-making technique. The results were used to develop a causal diagram that depicts the enablers in cause and effect groups. This study provides insights that will help manufacturing firms adopt AM by identifying its enablers and benefits. Overall, this study is significant as it contributes to a deeper understanding of AM technology and its potential enablers, thus facilitating its adoption in the manufacturing sector.
How Do Food Neophobia and Neophilia Moderate the Effect of Local Food Quality on Dining Satisfaction and Post-Dining Behavioral Intention in the Perspective of Sustainable Gastronomy Tourism?
Authors: Tarinc Abdullah , Aytekin Arif , Ozbek Ozlem Tekin , Ergün Gozde Seval , Keles Ali , Uslu Fatih , Keles Huseyin , Yayla Ozgur
Published: June 2023

This study examines the relationship between local food quality perception, dining satisfaction, and post-dining behavioral intention in the context of sustainable gastronomy tourism in Manavgat/Antalya. The research also analyzes the moderator roles of food neophilia and neophobia in this relationship. A face-to-face survey was conducted with the tourists who visited the region and experienced local restaurants. Accordingly, 487 participants were interviewed. In order to analyze the collected data, data survey analysis was applied, and the findings were analyzed using AMOS software (Version 24) to test the structural model. The results have shown that the core perceptions of local food and delivery quality positively affect dining satisfaction, whereas no effect on external quality has been found. Moreover, the findings have also revealed that dining satisfaction positively influences post-dining behavioral intention. In addition, food neophilia and neophobia moderate the relationship between dining satisfaction and post-dining behavioral intention. These findings have emphasized the importance of promoting local food quality to increase tourists’ dining satisfaction and their intention to participate in sustainable gastronomy tourism. Destination managers should collaborate with food producers to create a branded local food line that offers sustainable and delicious options, thereby enhancing the travel experiences of domestic and foreign tourists.
Relevant Sport Management Knowledge, Competencies, and Skills: An Umbrella Review
Authors: Guidotti Flavia , Demarie Sabrina , Ciaccioni Simone , Capranica Laura
Published: June 2023

The present umbrella review aimed to: (i) analyze review manuscripts on sport management knowledge/competencies/skills; (ii) propose a harmonized, evidence-based, competency framework for a comprehensive understanding of the intertwined relationships between knowledge, competencies, and skills in determining sport managers’ expected working performance and need for training; and (iii) provide insights for a sound implementation of educational curricula. Based on the PRIO guidelines, inclusion criteria encompassed systematic and narrative literature peer-reviewed review manuscripts relevant to sport management knowledge/competencies/skills, published between 2012 and 2022 in English. The search was performed on three databases, resulting in twenty-two retained review manuscripts representing different research topics. From 277 recorded elements, 72 knowledge/competencies/skills items were extracted. Leadership skills, Finance and administration, Marketing, and Effective communication accounted for the highest representation. Based on the identified evidence, a sport management comprehensive framework was developed including: (1) Life-long learning; (2) Necessary knowledge; (3) What is needed to be done; (4) How things get done; (5) Modulating factors; (6) Transversality within the industry; and (7) Dynamic interaction and intertwined relations. In considering the research propositions and relative recommendations for curricula implementation and future research, the present findings could foster the debate for the sustainable growth of this research area.
Research on the Policy Effects and Impact Mechanisms of the Belt and Road Initiative on China’s Forest Products Trade
Authors: Wu Tianbo , Zhou Dan , Cheng Hao , Zhang Keqiu , Jiang Yihao
Published: June 2023

The Belt and Road Initiative, as an important measure for China in terms of opening up and participating in international economic and trade cooperation, has become a new driving force for the sustainable development of China’s forest products trade. This paper takes the Belt and Road Initiative as a policy event and evaluates its policy effects on the development of China’s forest products trade from the causal level through the difference-in-differences model (DID), explores the policy effect in detail from the perspectives of product heterogeneity and regional heterogeneity, and clarifies the specific impact mechanism. The main results are as follows: (1) there is a significant policy promotion effect of the Belt and Road Initiative on the growth of the bilateral trade scale of forest products between China and the countries along the route. (2) In terms of product structure, the policy promotion effect of the Belt and Road Initiative is mainly manifested in processed wood products. (3) In terms of regional distribution, the policy promotion effects of the Belt and Road Initiative are mainly concentrated in Europe, Africa, and Asia. (4) The “logistics performance, political partnership with China, and Internet penetration” of trading countries play a significant positive mediating role in the policy effects of the Belt and Road Initiative. Therefore, in view of the significant role of the Belt and Road Initiative in promoting the development of bilateral forest products trade, China should promote more countries to participate in the joint construction of the Belt and Road and tap new momentum for the development of the forest products trade by focusing on key countries, priority areas, and key products.