bluuebunny/crossref_metadata_2025 huggingface dataset:
Developing a Method to Estimate the Downstream Metabolite Signals from Hyperpolarized [1-13C]Pyruvate
Authors: Hsieh Ching-Yi , Sung Cheng-Hsuan , Shen Yi-Liang (Eric) , Lai Ying-Chieh , Lu Kuan-Ying , Lin Gigin
Published: July 2022

Hyperpolarized carbon-13 MRI has the advantage of allowing the study of glycolytic flow in vivo or in vitro dynamically in real-time. The apparent exchange rate constant of a metabolite dynamic signal reflects the metabolite changes of a disease. Downstream metabolites can have a low signal-to-noise ratio (SNR), causing apparent exchange rate constant inconsistencies. Thus, we developed a method that estimates a more accurate metabolite signal. This method utilizes a kinetic model and background noise to estimate metabolite signals. Simulations and in vitro studies with photon-irradiated and control groups were used to evaluate the procedure. Simulated and in vitro exchange rate constants estimated using our method were compared with the raw signal values. In vitro data were also compared to the Area-Under-Curve (AUC) of the cell medium in 13C Nuclear Magnetic Resonance (NMR). In the simulations and in vitro experiments, our technique minimized metabolite signal fluctuations and maintained reliable apparent exchange rate constants. In addition, the apparent exchange rate constants of the metabolites showed differences between the irradiation and control groups after using our method. Comparing the in vitro results obtained using our method and NMR, both solutions showed consistency when uncertainty was considered, demonstrating that our method can accurately measure metabolite signals and show how glycolytic flow changes. The method enhanced the signals of the metabolites and clarified the metabolic phenotyping of tumor cells, which could benefit personalized health care and patient stratification in the future.
Design, Implementation and Experimental Investigation of a Pedestrian Street Crossing Assistance System Based on Visible Light Communications
Authors: Căilean Alin-Mihai , Beguni Cătălin , Avătămăniței Sebastian-Andrei , Dimian Mihai , Popa Valentin
Published: July 2022

In urban areas, pedestrians are the road users category that is the most exposed to road accident fatalities. In this context, the present article proposes a totally new architecture, which aims to increase the safety of pedestrians on the crosswalk. The first component of the design is a pedestrian detection system, which identifies the user’s presence in the region of the crosswalk and determines the future street crossing action possibility or the presence of a pedestrian engaged in street crossing. The second component of the system is the visible light communications part, which is used to transmit this information toward the approaching vehicles. The proposed architecture has been implemented at a regular scale and experimentally evaluated in outdoor conditions. The experimental results showed a 100% overall pedestrian detection rate. On the other hand, the VLC system showed a communication distance between 5 and 40 m when using a standard LED light crosswalk sign as a VLC emitter, while maintaining a bit error ratio between 10−7 and 10−5. These results demonstrate the fact that the VLC technology is now able to be used in real applications, making the transition from a high potential technology to a confirmed technology. As far as we know, this is the first article presenting such a pedestrian street crossing assistance system.
A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition
Authors: Zhao Zhenxiao , Zhang Lei , Shang Huiliang
Published: July 2022

Falls pose a great danger to social development, especially to the elderly population. When a fall occurs, the body’s center of gravity moves from a high position to a low position, and the magnitude of change varies among body parts. Most existing fall recognition methods based on deep learning have not yet considered the differences between the movement and the change in amplitude of each body part. Besides, some problems exist such as complicated design, slow detection speed, and lack of timeliness. To alleviate these problems, a lightweight subgraph-based deep learning method utilizing skeleton information for fall recognition is proposed in this paper. The skeleton information of the human body is extracted by OpenPose, and an end-to-end lightweight subgraph-based network is designed. Sub-graph division and sub-graph attention modules are introduced to add a larger perceptual field while maintaining its lightweight characteristics. A multi-scale temporal convolution module is also designed to extract and fuse multi-scale temporal features, which enriches the feature representation. The proposed method is evaluated on a partial fall dataset collected in NTU and on two public datasets, and outperforms existing methods. It indicates that the proposed method is accurate and lightweight, which means it is suitable for real-time detection and rapid response to falls.
Development of an Inductive Rain Gauge
Authors: Clemens Christoph , Jobst Annette , Radschun Mario , Himmel Jörg , Kanoun Olfa , Quirmbach Markus
Published: July 2022

Measuring weather data in an urban environment is an important task on the journey towards smart cities. Heavy rain can cause flooding in cities and prevent emergency services from reaching their destination because roads or underpasses are blocked. In order to provide a high-resolution site-specific overview in urban areas during heavy rainfall, a dense measurement network is necessary. To achieve this, a smart low-cost rain gauge is needed. In this paper, the current status of the development of an inductive rain gauge is presented. The sensor is based on the eddy current principle and evaluates the frequency of an electrical resonant circuit. For this purpose, a coil is placed under a metal plate. When raindrops hit the plate, it starts to oscillate, which changes the distance to the coil accordingly and causes changes in the frequency of the resonant circuit. Since the sensor is cost-effective, operates self-sufficiently in terms of energy and transmits data wirelessly via LoRaWAN, it can be used flexibly. This enables dense, area-wide coverage over the urban area of interest. The first experimental investigations show a correlation between the size of the rain droplets and the frequency change. Small droplets cause a shift of about 8 kHz and larger droplets of up to 40 kHz. The results prove that raindrops can be detected and categorized using this measurement principle. These data will be used as a basis for future work on calculating precipitation.
Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy
Authors: Agustiansyah Patiyus , Nurmaini Siti , Nuranna Laila , Irfannuddin Irfannuddin , Sanif Rizal , Legiran Legiran , Rachmatullah Muhammad Naufal , Florina Gavira Olipa , Sapitri Ade Iriani , Darmawahyuni Annisa
Published: July 2022

Precancerous screening using visual inspection with acetic acid (VIA) is suggested by the World Health Organization (WHO) for low–middle-income countries (LMICs). However, because of the limited number of gynecological oncologist clinicians in LMICs, VIA screening is primarily performed by general clinicians, nurses, or midwives (called medical workers). However, not being able to recognize the significant pathophysiology of human papilloma virus (HPV) infection in terms of the columnar epithelial-cell, squamous epithelial-cell, and white-spot regions with abnormal blood vessels may be further aggravated by VIA screening, which achieves a wide range of sensitivity (49–98%) and specificity (75–91%); this might lead to a false result and high interobserver variances. Hence, the automated detection of the columnar area (CA), subepithelial region of the squamocolumnar junction (SCJ), and acetowhite (AW) lesions is needed to support an accurate diagnosis. This study proposes a mask-RCNN architecture to simultaneously segment, classify, and detect CA and AW lesions. We conducted several experiments using 262 images of VIA+ cervicograms, and 222 images of VIA−cervicograms. The proposed model provided a satisfactory intersection over union performance for the CA of about 63.60%, and AW lesions of about 73.98%. The dice similarity coefficient performance was about 75.67% for the CA and about 80.49% for the AW lesion. It also performed well in cervical-cancer precursor-lesion detection, with a mean average precision of about 86.90% for the CA and of about 100% for the AW lesion, while also achieving 100% sensitivity and 92% specificity. Our proposed model with the instance segmentation approach can segment, detect, and classify cervical-cancer precursor lesions with satisfying performance only from a VIA cervicogram.
EP-ADTA: Edge Prediction-Based Adaptive Data Transfer Algorithm for Underwater Wireless Sensor Networks (UWSNs)
Authors: Wang Bin , Ben Kerong , Lin Haitao , Zuo Mingjiu , Zhang Fengchen
Published: July 2022

The underwater wireless sensor network is an important component of the underwater three-dimensional monitoring system. Due to the high bit error rate, high delay, low bandwidth, limited energy, and high dynamic of underwater networks, it is very difficult to realize efficient and reliable data transmission. Therefore, this paper posits that it is not enough to design the routing algorithm only from the perspective of the transmission environment; the comprehensive design of the data transmission algorithm should also be combined with the application. An edge prediction-based adaptive data transmission algorithm (EP-ADTA) is proposed that can dynamically adapt to the needs of underwater monitoring applications and the changes in the transmission environment. EP-ADTA uses the end–edge–cloud architecture to define the underwater wireless sensor networks. The algorithm uses communication nodes as the agents, realizes the monitoring data prediction and compression according to the edge prediction, dynamically selects the transmission route, and controls the data transmission accuracy based on reinforcement learning. The simulation results show that EP-ADTA can meet the accuracy requirements of underwater monitoring applications, dynamically adapt to the changes in the transmission environment, and ensure efficient and reliable data transmission in underwater wireless sensor networks.
A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease
Authors: Deb Ranadeep , An Sizhe , Bhat Ganapati , Shill Holly , Ogras Umit Y.
Published: July 2022

Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist’s subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018–2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community’s growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.
A Novel Deep-Learning Method with Channel Attention Mechanism for Underwater Target Recognition
Authors: Xue Lingzhi , Zeng Xiangyang , Jin Anqi
Published: July 2022

The core of underwater acoustic recognition is to extract the spectral features of targets. The running speed and track of the targets usually result in a Doppler shift, which poses significant challenges for recognizing targets with different Doppler frequencies. This paper proposes deep learning with a channel attention mechanism approach for underwater acoustic recognition. It is based on three crucial designs. Feature structures can obtain high-dimensional underwater acoustic data. The feature extraction model is the most important. First, we develop a ResNet to extract the deep abstraction spectral features of the targets. Then, the channel attention mechanism is introduced in the camResNet to enhance the energy of stable spectral features of residual convolution. This is conducive to subtly represent the inherent characteristics of the targets. Moreover, a feature classification approach based on one-dimensional convolution is applied to recognize targets. We evaluate our approach on challenging data containing four kinds of underwater acoustic targets with different working conditions. Our experiments show that the proposed approach achieves the best recognition accuracy (98.2%) compared with the other approaches. Moreover, the proposed approach is better than the ResNet with a widely used channel attention mechanism for data with different working conditions.
An Efficient Method for Solving Router Placement Problem in Wireless Mesh Networks Using Multi-Verse Optimizer Algorithm
Authors: Binh Le Huu , Truong Tung Khac
Published: July 2022

Wireless Mesh Networks (WMNs) are increasingly being used in a variety of applications. To fully utilize the network resources of WMNs, it is critical to design a topology that provides the best client coverage and network connectivity. This issue is solved by determining the best solution for the mesh router placement problem in WMN (MRP-WMN). Because the MRP-WMN is known to be NP-hard, it is typically solved using approximation algorithms. This is also why we are conducting this work. We present an efficient method for solving the MRP-WMN using the Multi-Verse Optimizer algorithm (MVO). A new objective function for the MRP-WMN is also proposed, which takes into account two important performance metrics, connected client ratio and connected router ratio. Experiment results show that when the MVO algorithm is applied to the MRP-WMN problem, the connected client ratio increases by 15.1%, 11.5%, and 5.9% on average, and the path loss reduces by 1.3, 0.9, and 0.6 dB when compared to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), respectively.
Evaluating the Forest Ecosystem through a Semi-Autonomous Quadruped Robot and a Hexacopter UAV
Authors: Idrissi Moad , Hussain Ambreen , Barua Bidushi , Osman Ahmed , Abozariba Raouf , Aneiba Adel , Asyhari Taufiq
Published: July 2022

Accurate and timely monitoring is imperative to the resilience of forests for economic growth and climate regulation. In the UK, forest management depends on citizen science to perform tedious and time-consuming data collection tasks. In this study, an unmanned aerial vehicle (UAV) equipped with a light sensor and positioning capabilities is deployed to perform aerial surveying and to observe a series of forest health indicators (FHIs) which are inaccessible from the ground. However, many FHIs such as burrows and deadwood can only be observed from under the tree canopy. Hence, we take the initiative of employing a quadruped robot with an integrated camera as well as an external sensing platform (ESP) equipped with light and infrared cameras, computing, communication and power modules to observe these FHIs from the ground. The forest-monitoring time can be extended by reducing computation and conserving energy. Therefore, we analysed different versions of the YOLO object-detection algorithm in terms of accuracy, deployment and usability by the EXP to accomplish an extensive low-latency detection. In addition, we constructed a series of new datasets to train the YOLOv5x and YOLOv5s for recognising FHIs. Our results reveal that YOLOv5s is lightweight and easy to train for FHI detection while performing close to real-time, cost-effective and autonomous forest monitoring.
A Timestamp-Independent Haptic–Visual Synchronization Method for Haptic-Based Interaction System
Authors: Xu Yiwen , Huang Liangtao , Zhao Tiesong , Fang Ying , Lin Liqun
Published: July 2022

The booming haptic data significantly improve the users’ immersion during multimedia interaction. As a result, the study of a Haptic-based Interaction System has attracted the attention of the multimedia community. To construct such a system, a challenging task is the synchronization of multiple sensorial signals that is critical to the user experience. Despite audio-visual synchronization efforts, there is still a lack of a haptic-aware multimedia synchronization model. In this work, we propose a timestamp-independent synchronization for haptic–visual signal transmission. First, we exploit the sequential correlations during delivery and playback of a haptic–visual communication system. Second, we develop a key sample extraction of haptic signals based on the force feedback characteristics and a key frame extraction of visual signals based on deep-object detection. Third, we combine the key samples and frames to synchronize the corresponding haptic–visual signals. Without timestamps in the signal flow, the proposed method is still effective and more robust in complicated network conditions. Subjective evaluation also shows a significant improvement of user experience with the proposed method.
A Low-Cost and Efficient Indoor Fusion Localization Method
Authors: Yan Suqing , Wu Chunping , Deng Honggao , Luo Xiaonan , Ji Yuanfa , Xiao Jianming
Published: July 2022

Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.
Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey
Authors: Shi Yongjie , Ying Xianghua , Yang Jinfa
Published: July 2022

Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the monitored system and prediction of unknown risks. Thanks to the development of deep learning, the ability to analyze temporal signals collected by sensors has been greatly improved. However, models trained in the source domain do not perform well in the target domain due to the presence of domain gaps. In recent years, many researchers have used deep unsupervised domain adaptation techniques to address the domain gap between signals collected by sensors in different scenarios, i.e., using labeled data in the source domain and unlabeled data in the target domain to improve the performance of models in the target domain. This survey first summarizes the background of recent research on unsupervised domain adaptation with time series sensor data, the types of sensors used, the domain gap between the source and target domains, and commonly used datasets. Then, the paper classifies and compares different unsupervised domain adaptation methods according to the way of adaptation and summarizes different adaptation settings based on the number of source and target domains. Finally, this survey discusses the challenges of the current research and provides an outlook on future work. This survey systematically reviews and summarizes recent research on unsupervised domain adaptation for time series sensor data to provide the reader with a systematic understanding of the field.
Feasibility of a Planar Coil-Based Inductive-Capacitive Water Level Sensor with a Quality-Detection Feature: An Experimental Study
Authors: Areekath Lakshmi , Lodha Gaurav , Kumar Sahana Subham , George Boby , Philip Ligy , Mukhopadhyay Subhas Chandra
Published: July 2022

This paper presents a new water-level-sensing mechanism based on planar coils fabricated on a printed circuit board (PCB). In addition to level, the sensor detects any relative increase in conductivity compared to that of clean water, which is an indicator of its quality. The sensing mechanism utilizes the eddy current induced in the water column, the corresponding change in the coil inductance, and the change in the turn-to-turn capacitance of the coil in the presence of water. Although several level sensors are available, there is none that gives the level and quality information using a single sensing element. Since both water quantity and quality measurements are fundamental in realizing efficient water and wastewater management, obtaining these two parameters from the same sensor is very beneficial. A scalable, planar coil-based sensor that helps achieve this goal is designed, fabricated, and tested in a laboratory setting. The results illustrate that the reactance of the sensor coil measured at a frequency (1 kHz for the prototype) much lower than the self-resonance of the coil gives reliable information about the level of water, while the measurement made at resonance, using an inductance-to-digital converter, is a clear indicator of its conductivity and, hence, quality.
Application of PSO-BPNN-PID Controller in Nutrient Solution EC Precise Control System: Applied Research
Authors: Wang Yongtao , Liu Jian , Li Rong , Suo Xinyu , Lu Enhui
Published: July 2022

In this paper, we present a nutrient solution control system, designing a nutrient solution electrical conductivity (EC) sensing system composed of multiple long-range radio (LoRa) slave nodes, narrow-band Internet of Things (NB-IoT) master nodes, and a host computer, building a nutrient solution EC control model and using the particle swarm optimization (PSO) algorithm to optimize the initial weights of a back-propagation neural network (BPNN). In addition, the optimized best weights are put into the BPNN to adjust the proportional–integral–derivative (PID) control parameters Kp, Ki, and Kd so that the system performance index can be optimized. Under the same initial conditions, we input EC = 2 mS/cm and use the particle swarm optimization BP neural network PID (PSO-BPNN-PID) to control the EC target value of the nutrient solution. The optimized scale factors were Kp = 81, Ki = 0.095, and Kd = 0.044; the steady state time was about 43 s, the overshoot was about 0.14%, and the EC value was stable at 1.9997 mS/cm–2.0027 mS/cm. Compared with the BP neural network PID (BPNN-PID) and the traditional PID control approach, the results show that PSO-BPNN-PID had a faster response speed and higher accuracy. Furthermore, we input 1 mS/cm, 1.5 mS/cm, 2 mS/cm, and 2.5 mS/cm, respectively, and simulated and verified the PSO-BPNN-PID system model. The results showed that the fluctuation range of EC was 0.003 mS/cm~0.119 mS/cm, the steady-state time was 40 s~60 s, and the overshoot was 0.3%~0.14%, which can meet the requirements of the rapid and accurate integration of water and fertilizer in agricultural production.
Customizable Stochastic High-Fidelity Model of the Sensors and Camera Onboard a Fixed Wing Autonomous Aircraft
Authors: Gallo Eduardo , Barrientos Antonio
Published: July 2022

The navigation systems of autonomous aircraft rely on the readings provided by a suite of onboard sensors to estimate the aircraft state. In the case of fixed wing vehicles, the sensor suite is usually composed by triads of accelerometers, gyroscopes, and magnetometers, a Global Navigation Satellite System (GNSS) receiver, and an air data system (Pitot tube, air vanes, thermometer, and barometer), and it is often complemented by one or more digital cameras. An accurate representation of the behavior and error sources of each of these sensors, together with the images generated by the cameras, is indispensable for the design, development, and testing of inertial, visual, or visual–inertial navigation algorithms. This article presents realistic and customizable models for each of these sensors; a ready-to-use C++ implementation is released as open-source code so non-experts in the field can easily generate realistic results. The pseudo-random models provide a time-stamped series of the errors generated by each sensor based on performance values and operating frequencies obtainable from the sensor’s data sheets. If in addition, the simulated true pose (position plus attitude) of the aircraft is provided, the camera model generates realistic images of the Earth’s surface that resemble those taken with a real camera from the same pose.
Low-Complexity Multiple Transform Selection Combining Multi-Type Tree Partition Algorithm for Versatile Video Coding
Authors: He Liqiang , Xiong Shuhua , Yang Ruolan , He Xiaohai , Chen Honggang
Published: July 2022

Despite the fact that Versatile Video Coding (VVC) achieves a superior coding performance to High-Efficiency Video Coding (HEVC), it takes a lot of time to encode video sequences due to the high computational complexity of the tools. Among these tools, Multiple Transform Selection (MTS) require the best of several transforms to be obtained using the Rate-Distortion Optimization (RDO) process, which increases the time spent video encoding, meaning that VVC is not suited to real-time sensor application networks. In this paper, a low-complexity multiple transform selection, combined with the multi-type tree partition algorithm, is proposed to address the above issue. First, to skip the MTS process, we introduce a method to estimate the Rate-Distortion (RD) cost of the last Coding Unit (CU) based on the relationship between the RD costs of transform candidates and the correlation between Sub-Coding Units’ (sub-CUs’) information entropy under binary splitting. When the sum of the RD costs of sub-CUs is greater than or equal to their parent CU, the RD checking of MTS will be skipped. Second, we make full use of the coding information of neighboring CUs to terminate MTS early. The experimental results show that, compared with the VVC, the proposed method achieves a 26.40% reduction in time, with a 0.13% increase in Bjøontegaard Delta Bitrate (BDBR).
Covert Performance for Integrated Satellite Multiple Terrestrial Relay Networks with Partial Relay Selection
Authors: Wu Zeke , Liu Rui , Shuai Haifeng , Zhu Shibing , Li Changqing
Published: July 2022

Integrated satellite multiple terrestrial relay network (ISMTRN) is a new network architecture that combines satellite communication with terrestrial communication. It both utilizes the advantages of the two systems and overcomes their shortcomings. However, security issues inevitably arise in the ISMTRN resulting from the broad coverage of the satellite beams and the openness of wireless communication. One of the promising methods to achieve secure transmission is covert communication technology, which has been a hot discussion topic in recent years. In this paper, we investigate the performance of covert communication in the ISMTRN with partial relay selection. Particularly, when the satellite transmits its signal to the user, we consider the scenario that the selected relay opportunistically sends covert information to the destination. Furthermore, the closed-form error detection probability and average covert communication rate are derived. Finally, numerical simulation results are provided to reveal the impact of critical parameters on system covert performance.
The Narrative of a Line of Research from a Feminist Perspective: Fiber Optic Sensors and Actor–Network Theory (ANT)
Authors: Aliende Urtasun Ana , Garrido Julian J.
Published: July 2022

The narrative of a line of research from a feminist perspective involves describing the situated practices that contribute to understanding inequality in the construction of the social relationships that build links between human and nonhuman materialities. This paper aims to link feminist epistemology and actor–network theory to the expert network that produces, sustains and manages the design of the chemical zone of a fiber optic sensor (FOS) and of new smart materials, tracing the path of a university research group with important scientific publications that has also developed a line of work in the field of science, technology and society (STS). We are interested in the generalized division of labor and the role of science and technology in the creation of symbols in our culture, particularly in the field of expert knowledge, where research carried out by men and women constructs trajectories of academic success that are predominantly male. The transversal incorporation of gender analysis into the production of FOSs helps reveal the processes that have, up to this point, excluded women from prominent positions in publications.
Numerical Investigation of the Influence of Fatigue Testing Frequency on the Fracture and Crack Propagation Rate of Additive-Manufactured AlSi10Mg and Ti-6Al-4V Alloys
Authors: Awd Mustafa , Walther Frank
Published: July 2022

Advances in machine systems and scanning technologies have increased the use of selective laser melted materials in industrial applications, resulting in almost full-density products. Inconsistent mechanical behavior of components under cyclic stress is caused by microstructure and porosity created during powder melting. The extended finite element method, XFEM, was used to imitate crack propagation utilizing an arbitrary fracture route to study fatigue crack growth in additively produced fatigue specimens. The influence of loading level and testing frequency on fatigue life was studied using fracture energy rate curves. Micro-computed tomography (µ-CT) scans offer 2D images in angular increments. There are several ways to reduce the number of faces and vertices. Opensource software was used to isolate the cylindrical shell from interior pores and create finite element models from µ-CT projections. All simulations were on supposedly cylindrical fatigue specimens made by selective laser melting (SLM) based on previous experimental results of the authors. Crack propagation rate curves were utilized to evaluate the effects of loading level and testing frequency. At larger loads, the fracture area increases abruptly at 3E3 cycles, then stabilizes at 4E4 cycles in Al alloys in comparison to Ti-6Al-4V alloys. Crack propagation rate curves may be used to determine Paris constants based on the applied stresses.
Partial Order as Decision Support between Statistics and Multicriteria Decision Analyses
Authors: Carlsen Lars , Bruggemann Rainer
Published: July 2022

Evaluation by ranking/rating of data based on a multitude of indicators typically calls for multi-criteria decision analyses (MCDA) methods. MCDA methods often, in addition to indicator values, require further information, typically subjective. This paper presents a partial-order methodology as an alternative to analyze multi-indicator systems (MIS) based on indicator values that are simultaneously included in the analyses. A non-technical introduction of main concepts of partial order is given, along with a discussion of the location of partial order between statistics and MCDA. The paper visualizes examples of a ‘simple’ partial ordering of a series of chemicals to explain, in this case, unexpected behavior. Further, a generalized method to deal with qualitative inputs of stakeholders/decision makers is suggested, as well as how to disclose peculiar elements/outliers. The paper finishes by introducing formal concept analysis (FCA), which is a variety of partial ordering that allows exploration and thus the generation of implications between the indicators. In the conclusion and outlook section, take-home comments as well as pros and cons in relation to partial ordering are discussed.
Food Safety Management System (FSMS) Model with Application of the PDCA Cycle and Risk Assessment as Requirements of the ISO 22000:2018 Standard
Authors: Stoyanova Antoaneta , Marinova Velichka , Stoilov Daniel , Kirechev Damyan
Published: July 2022

The management strategy requires a shift to change-oriented management. These management approaches are process- and activity-oriented and are based on the assumption that the future is difficult to predict and ineffective for modeling. The aim of this study is to present a model of food safety management using a process approach based on the PDCA cycle set in the international standard ISO 22000:2018 by supplementing the regulatory requirements for food safety management. After analyzing the aspects of food safety management, a model is proposed for risk analysis and assessment at the operational and organisational level. In this study, the FMEA method for risk assessment of storage of foods of plant origin was used. The research can be useful for producers and traders in the planning and development of food safety management systems according to the requirements of the ISO 22000:2018 standard. The implementation of documented rules for compliance with the requirements of the international standard is aimed at the management and control of processes at the operational and organisational level in the activities of companies. Process management and data analysis is a direction to improve activities aimed at minimizing food safety risks.
Comparing the Robustness of the Structural after Measurement (SAM) Approach to Structural Equation Modeling (SEM) against Local Model Misspecifications with Alternative Estimation Approaches
Authors: Robitzsch Alexander
Published: July 2022

Structural equation models (SEM), or confirmatory factor analysis as a special case, contain model parameters at the measurement part and the structural part. In most social-science SEM applications, all parameters are simultaneously estimated in a one-step approach (e.g., with maximum likelihood estimation). In a recent article, Rosseel and Loh (2022, Psychol. Methods) proposed a two-step structural after measurement (SAM) approach to SEM that estimates the parameters of the measurement model in the first step and the parameters of the structural model in the second step. Rosseel and Loh claimed that SAM is more robust to local model misspecifications (i.e., cross loadings and residual correlations) than one-step maximum likelihood estimation. In this article, it is demonstrated with analytical derivations and simulation studies that SAM is generally not more robust to misspecifications than one-step estimation approaches. Alternative estimation methods are proposed that provide more robustness to misspecifications. SAM suffers from finite-sample bias that depends on the size of factor reliability and factor correlations. A bootstrap-bias-corrected LSAM estimate provides less biased estimates in finite samples. Nevertheless, we argue in the discussion section that applied researchers should nevertheless adopt SAM because robustness to local misspecifications is an irrelevant property when applying SAM. Parameter estimates in a structural model are of interest because intentionally misspecified SEMs frequently offer clearly interpretable factors. In contrast, SEMs with some empirically driven model modifications will result in biased estimates of the structural parameters because the meaning of factors is unintentionally changed.
Factors Influencing the Use of Geospatial Technology with LiDAR for Road Design: Case of Malaysia
Authors: Hatta Antah Fazilah , Khoiry Muhamad Azry , Abdul Maulud Khairul Nizam , Ibrahim Ahmad Nazrul Hakimi
Published: July 2022

This study seeks a better understanding of the acceptance of geospatial technology with Light Detention and Ranging (LIDAR) in road design in a developing country, Malaysia. Existing surveying measurement methods to provide quick, accurate, and reliable information are unsuccessful in producing an expected result, especially in large areas. In addition, topographic data cannot be observed well with the conventional total station method in areas under thick canopies, which is challenging to identify road areas at risk to the environment, such as slope failure. Geospatial surveying technology by LiDAR helps in measuring fields over a wide area and provides a broader spatial extent. At the same time, the laser capability of airborne LiDAR, which penetrates the canopy, helps give accurate readings on the terrain. However, the use of LiDAR geospatial technology for use in road design is still insufficient to date. Thus, this study is developed to identify the factors that influence the use of LiDAR in road design among engineers. Factors identified are barriers, motivation, and strategy. Barrier factors consist of lack of knowledge, risk, cost, and human aspects that slow down the development of LiDAR use. On the other hand, motivational factors consist of encouraging engineers to obtain knowledge about LiDAR and to use it more widely. Meanwhile, a strategy factor form increases LiDAR measurement methods through activities or work procedures. The finding shows that barriers and strategy factors are the significant factors that affect the acceptance of LiDAR among engineers. However, motivational factors have no significant effect to engineers in accepting the use of LiDAR. The advantages of this study and its limitations are also discussed. Finally, this study also provides compilation of few suggestions pertaining this topic to improve future research.
Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks
Authors: Mansour Hassnen Shakir , Mutar Mohammed Hasan , Aziz Izzatdin Abdul , Mostafa Salama A. , Mahdin Hairulnizam , Abbas Ali Hashim , Hassan Mustafa Hamid , Abdulsattar Nejood Faisal , Jubair Mohammed Ahmed
Published: July 2022

In recent years, unmanned aerial vehicles (UAVs) have become the trend for different types of research and applications. UAVs can accomplish some technical and risky tasks while still being safe, mobile, and inexpensive to operate. However, UAVs need flying ad-hoc networks (FANET) to operate in inaccessible or infrastructure-less areas. Subsequently, in many military and civil applications, the UAVs are connected ad hoc. FANET-based UAV systems have been developed for search and rescue, wildlife surveys, real-time monitoring, and delivery services. Maintaining the reliability and connectivity among UAV nodes in FANET becomes challenging because of the UAV movement, environmental conditions, energy efficiency, etc. Energy-aware routing protocols have become essential for developing advanced and effective FANETs. This paper presents a proposed Cross-Layer and Energy-Aware Ad-hoc On-demand Distance Vector (CLEA-AODV) routing protocol for improving FANET performance. The CLEA-AODV protocol is mainly divided into three sections: routing with AODV protocol, Glow Swarm Optimization (GSO)-based Cluster Head Selection, and Cooperative Medium Access Control (MAC). The cross-layer approach is implemented on the network layer and the data layer. The major parameters considered to evaluate the performance of the FANET are Packet Success Rate (PSR), Throughput (TP), End-to-End (E2E) delay, and packet drop ratio (PDR). The Network Simulator version 2 (NS2) is used to implement the CLEA-AODV protocol and evaluate the network performance. The results are compared with the standard AODV, Self-Organization Clustering-GSO (SOC-GSO), and Energy Efficient Neuro-Fuzzy Cluster-based Topology Construction with Meta-Heuristic Route Planning (EENFC-MRP) protocols. The results show that the CLEA-AODV surpasses these protocols in terms of PSR, TP, E2E delay, and PDR.
Informal Settlement Resilience Upgrading-Approaches and Applications from a Cross-Country Perspective in Three Selected Metropolitan Regions of Southeast Asia
Authors: Du Juan , Greiving Stefan , Yap David Leonides T.
Published: July 2022

Managing climate change is synonymous to managing cities and their growth. To shoulder the challenge of climate change adaptation, informal settlement upgrading in the global south has amounted to the importance of being attuned with the growth of its city and region at large. Changing the paradigm of on-site upgrading to being community-driven and city-led with domestic funding unlocks potentials for community resilience building, especially in countries that strive for inclusive growth. This research looks into informal settlement development dynamics and its resilience stance in conjunction of the metropolitan growth in three Southeast Asian countries. Greater Manila Area, Bangkok Metropolitan Region and Hanoi Capital Region serve as the backdrop for this investigation. The research mainly addresses informal settlement upgrading roles, mechanism and approaches for resilience building in these three metropolises, meanwhile also unveiling their city-regional development needs. The methodological approach of this study is highly participatory, demonstrating a hybrid of multi-spectrum stakeholder workshops, online surveys (due to COVID), expert interviews, project interim reports and correspondence with the local expert team in the three countries, etc. The paper attempts at providing a cross-country appraisal of the central strategies of informal settlement upgrading, related institutional constellations and upgrading applications along with the three metropolises’ urban development. This attempt accentuates the pressing needs of mitigating multi-facet vulnerability of informal communities, who are the most adversely affected by climate change and rampant urbanization. Further, this research will also reveal the mindset change of how decision-makers and the public contemplate upgrading objectives, e.g., recasting secure tenure instruments.
Who Prefers Regional Products? A Systematic Literature Review of Consumer Characteristics and Attitudes in Short Food Supply Chains
Authors: Csordás Adrián , Lengyel Péter , Füzesi István
Published: July 2022

The present work is a selection of empirical studies focusing on the characteristics and attitudes of Short Food Supply Chain (SFSC) consumers. Using a systematic literature review approach (PRISMA), we identified five different aspects of the SFSC within the publications: producer participation, swot, state intervention, attitude and “other”. Based on the findings of studies from the academic literature, the results are quite mixed. Though the number of SFSC-related empirical studies has risen in recent years, there is a lack of SFSC-related data, even in the European Union (EU), where a sustainable agriculture and food system must play a crucial role in the implementation of the Green Deal. Overall, it is hard to name those features that, without any doubt, affect the willingness of consumers to purchase from an SFSC. The studies mostly remarked on age and education; however, even these findings cannot be generalized. Therefore, some consumers of non-global food supply chains could be characterized very well, but these observations could differ in diverse cases because of local factors.
Use of Latent Dirichlet Allocation and Structural Equation Modeling in Determining the Factors for Continuance Intention of Knowledge Payment Platform
Authors: Xu Heng , Zhang Menglu , Zeng Jun , Hao Huihui , Lin Hao-Chiang Koong , Xiao Mengyun
Published: July 2022

Knowledge payment is a new type of E-learning that has developed in the era of social media. With the influence of the COVID-19 epidemic, the knowledge payment market is developing rapidly. Exploring the influencing factors of users’ continuance intention is beneficial for the sustainable development of knowledge payment platforms. Our study took “Himalayan FM” as an example and included two studies: Study 1 used latent dirichlet allocation (LDA) to explore the main factors affecting the users’ willingness to continue use, through mining user comment data on the knowledge payment platform; Study 2 constructed the conceptual model by integrating the technology acceptance model (TAM) and IS success model (IS) and carried out empirical analysis by SPSS and AMOS using the data that were collected through the questionnaire. The results show that: (1) perceived usefulness, user satisfaction, and spokesperson identity have a direct positive impact on users’ willingness to continuous use, while perceived cost has a direct negative impact on users’ willingness to continue use; (2) perceived ease of use, content quality, and system quality of knowledge payment platforms impacted user satisfaction directly, then affected users’ willingness to continue use indirectly; (3) users’ perceived enjoyment, membership experience, auditory experience, and other factors also directly impacted user satisfaction, affecting users’ willingness to continue use indirectly. This study effectively expands the factors influencing knowledge payment users’ willingness to continue use and provides a useful reference for the sustainable development of knowledge payment platforms.
China’s Low-Carbon Cities Pilot Promotes Sustainable Carbon Emission Reduction: Evidence from Quasi-Natural Experiments
Authors: Jiang Botao , He Zhisong , Xue Wei , Yang Cheng , Zhu Hanbo , Hua Yifei , Lu Bin
Published: July 2022

Cities are critical agents to promote carbon emission reduction, and are also a key part of China achieving carbon peaking by 2030 and carbon neutrality by 2060. This study used a time-varying difference-in-difference (DID) method to provide quasi-natural experimental evidence based on the data of 284 prefecture-level cities in China. We robustly found that the low-carbon city pilot (LCCP) policy has a significant effect on carbon emissions’ reduction. The carbon emissions of pilot cities were reduced by about 1.63 percentage points compared to non-pilot cities. In addition, this study generates several intriguing findings: (1) The carbon emission reduction effect of the LCCP is more significant for cities in the eastern areas and cities with high economic development. (2) The LCCP policy is sustainable and has a lagging effect. The carbon emissions of pilot areas with one lag period and two lag periods were reduced by 1.76% and 1.90%, respectively, which means that the LCCP led to greater carbon reductions over time. (3) We prove the existence of the mediating effect of electricity consumption. The LCCP policy reduced carbon emissions by 3.72% by affecting per capita electricity consumption. (4) Cities in a state of negative decoupling between carbon emissions and economic growth gradually transformed into a state of enhanced decoupling, which shows that the carbon emissions of low-carbon pilot cities were effectively controlled with the economic growth. The conclusion of this study evaluates the current achievements of the LCCP policy and provides an empirical reference for the further formulation of environmental policies.