bluuebunny/crossref_metadata_2025 huggingface dataset:
Anatomy of Anthropically Controlled Natural Lagoons through Geophysical, Geological, and Remote Sensing Observations: The Valli Di Comacchio (NE Italy) Case Study
Authors: Bonetti Jarbas , Del Bianco Fabrizio , Schippa Leonardo , Polonia Alina , Stanghellini Giuseppe , Cenni Nicola , Draghetti Stefano , Marabini Francesco , Gasperini Luca
Published: February 2022

Newly collected morphobathymetric and seismic reflection data from the Valli di Comacchio coastal lagoons, south of the Po River delta (Northeast Italy), combined with historical, remote sensing, and geodetic data highlight a complex geological evolution during the Holocene, strongly affected by anthropic control. All data allowed us to define the present-day depositional environment of the lagoons and reconstruct their recent (late Pleistocene/Holocene) geo-history. We focused on the effects of the anthropic impacts in modifying the pristine environments created by the Holocene transgression along the Adriatic Sea coast, at the mouth of a major river. They include land reclamation works, artificial damming, channel excavations, fluvial diversions, and a recent (last decades) increase in subsidence rate due to gas and water withdrawals. Despite the development of economic activities, which promoted occupation and exploitation of this area in the last millennia, the post-Glacial evolution of the lagoons shows the important role of inherited morphological features, such as sand ridges and barriers. This complex and relatively well-documented evolution makes the Comacchio lagoons a unique example of deep connections between natural processes and long-term human controls, offering insights into the management policies of these important and delicate environments challenged by global changes.
A New Probabilistic Ellipse Imaging Method Based on Adaptive Signal Truncation for Ultrasonic Guided Wave Defect Localization on Pressure Vessels
Authors: Li Qinfei , Luo Zhi , Hu Gangyi , Zhou Shaoping
Published: February 2022

Pressure vessels are prone to defects due to environmental conditions, which may cause serious safety hazards to industrial production. The probabilistic ellipse imaging method, based on ultrasonic guided wave, is a common method for locating defects on plate-like structures. In this paper, the research showed that the accuracy of the traditional probabilistic ellipse imaging method was severely affected by the truncation length of the signal. In order to improve the defect location accuracy of the probabilistic elliptic imaging algorithm, an adaptive signal truncation method based on signal difference analysis was proposed, and a novel probabilistic elliptic imaging method was developed. Firstly, the relationship model between the signal difference coefficient (SDC) and the distance coefficient was constructed. Through this model, the distance coefficient of each group signal can be calculated, so that the adaptive truncation length for each group of signals can be determined and the truncated signals used for defect imaging. Secondly, in order to improve the robustness of the new imaging method, the relationship between the defect location accuracy and SDC thresholds were investigated and the optimal threshold was determined. The experimental results showed that the probabilistic ellipse imaging algorithm, based on the new adaptive signal truncation method, can effectively locate a single defect on a pressure vessel.
Localization and Edge-Based Segmentation of Lumbar Spine Vertebrae to Identify the Deformities Using Deep Learning Models
Authors: Mushtaq Malaika , Akram Muhammad Usman , Alghamdi Norah Saleh , Fatima Joddat , Masood Rao Farhat
Published: February 2022

The lumbar spine plays a very important role in our load transfer and mobility. Vertebrae localization and segmentation are useful in detecting spinal deformities and fractures. Understanding of automated medical imagery is of main importance to help doctors in handling the time-consuming manual or semi-manual diagnosis. Our paper presents the methods that will help clinicians to grade the severity of the disease with confidence, as the current manual diagnosis by different doctors has dissimilarity and variations in the analysis of diseases. In this paper we discuss the lumbar spine localization and segmentation which help for the analysis of lumbar spine deformities. The lumber spine is localized using YOLOv5 which is the fifth variant of the YOLO family. It is the fastest and the lightest object detector. Mean average precision (mAP) of 0.975 is achieved by YOLOv5. To diagnose the lumbar lordosis, we correlated the angles with region area that is computed from the YOLOv5 centroids and obtained 74.5% accuracy. Cropped images from YOLOv5 bounding boxes are passed through HED U-Net, which is a combination of segmentation and edge detection frameworks, to obtain the segmented vertebrae and its edges. Lumbar lordortic angles (LLAs) and lumbosacral angles (LSAs) are found after detecting the corners of vertebrae using a Harris corner detector with very small mean errors of 0.29° and 0.38°, respectively. This paper compares the different object detectors used to localize the vertebrae, the results of two methods used to diagnose the lumbar deformity, and the results with other researchers.
A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing
Authors: Yin Zhenyu , Xu Fulong , Li Yue , Fan Chao , Zhang Feiqing , Han Guangjie , Bi Yuanguo
Published: February 2022

With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.
Multi-level Hierarchical Complex Behavior Monitoring System for Dog Psychological Separation Anxiety Symptoms
Authors: Wang Huasang , Atif Othmane , Tian Jirong , Lee Jonguk , Park Daihee , Chung Yongwha
Published: February 2022

An increasing number of people own dogs due to the emotional benefits they bring to their owners. However, many owners are forced to leave their dogs at home alone, increasing the risk of developing psychological disorders such as separation anxiety, typically accompanied by complex behavioral symptoms including excessive vocalization and destructive behavior. Hence, this work proposes a multi-level hierarchical early detection system for psychological Separation Anxiety (SA) symptoms detection that automatically monitors home-alone dogs starting from the most fundamental postures, followed by atomic behaviors, and then detecting separation anxiety-related complex behaviors. Stacked Long Short-Term Memory (LSTM) is utilized at the lowest level to recognize postures using time-series data from wearable sensors. Then, the recognized postures are input into a Complex Event Processing (CEP) engine that relies on knowledge rules employing fuzzy logic (Fuzzy-CEP) for atomic behaviors level and higher complex behaviors level identification. The proposed method is evaluated utilizing data collected from eight dogs recruited based on clinical inclusion criteria. The experimental results show that our system achieves approximately an F1-score of 0.86, proving its efficiency in separation anxiety symptomatic complex behavior monitoring of a home-alone dog.
Automatic and Efficient Fall Risk Assessment Based on Machine Learning
Authors: Eichler Nadav , Raz Shmuel , Toledano-Shubi Adi , Livne Daphna , Shimshoni Ilan , Hel-Or Hagit
Published: February 2022

Automating fall risk assessment, in an efficient, non-invasive manner, specifically in the elderly population, serves as an efficient means for implementing wide screening of individuals for fall risk and determining their need for participation in fall prevention programs. We present an automated and efficient system for fall risk assessment based on a multi-depth camera human motion tracking system, which captures patients performing the well-known and validated Berg Balance Scale (BBS). Trained machine learning classifiers predict the patient’s 14 scores of the BBS by extracting spatio-temporal features from the captured human motion records. Additionally, we used machine learning tools to develop fall risk predictors that enable reducing the number of BBS tasks required to assess fall risk, from 14 to 4–6 tasks, without compromising the quality and accuracy of the BBS assessment. The reduced battery, termed Efficient-BBS (E-BBS), can be performed by physiotherapists in a traditional setting or deployed using our automated system, allowing an efficient and effective BBS evaluation. We report on a pilot study, run in a major hospital, including accuracy and statistical evaluations. We show the accuracy and confidence levels of the E-BBS, as well as the average number of BBS tasks required to reach the accuracy thresholds. The trained E-BBS system was shown to reduce the number of tasks in the BBS test by approximately 50% while maintaining 97% accuracy. The presented approach enables a wide screening of individuals for fall risk in a manner that does not require significant time or resources from the medical community. Furthermore, the technology and machine learning algorithms can be implemented on other batteries of medical tests and evaluations.
An Analysis of Respiration with the Smart Sensor SENSIRIB in Patients Undergoing Thoracic Surgery
Authors: Ceccarelli Marco , Taje Riccardo , Papuc Paula Elena , Ambrogi Vincenzo
Published: February 2022

The paper examines the problem of respiration monitoring with easily wearable instrumentation by using a smart device that is properly designed and implemented with small and light components. The practical implementation is presented both in practical aspects and from experimental results by following a properly defined method with a medical-like protocol and specific procedure of testing. The results of a statistically significant campaign of experimental tests are reported with the characteristic data from the angles and acceleration components of a sensed rib both to validate the smart device and the procedure for respiration monitoring.
Application of Thermography and Adversarial Reconstruction Anomaly Detection in Power Cast-Resin Transformer
Authors: Fanchiang Kuo-Hao , Kuo Cheng-Chien
Published: February 2022

Dry-type power transformers play a critical role in the power system. Detecting various overheating faults in the running state of the power transformer is necessary to avoid the collapse of the power system. In this paper, we propose a novel deep variational autoencoder-based anomaly detection method to recognize the overheating position in the operation of the dry-type transformer. Firstly, the thermal images of the transformer are acquired by the thermal camera and collected for training and testing datasets. Next, the variational autoencoder-based generative adversarial networks are trained to generate the normal images with different running conditions from heavy to light loading. Through the pixel-wise cosine difference between original and reconstructed images, the residual images with faulty features are obtained. Finally, we evaluate the trained model and anomaly detection method on normal and abnormal testing images to demonstrate the effeteness and performance of the proposed work. The results show that our method effectively improves the anomaly accuracy, AUROC, F1-scores and average precision, which is more effective than other anomaly detection methods. The proposed method is simple, lightweight and has less storage size. It reveals great advantages for practical applications.
Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications
Authors: Karem Rana , Ahmed Mehaseb , Newagy Fatma
Published: February 2022

One of the main targets of future 5G cellular networks is enlarging the Internet of Things (IoT) devices’ connectivity while facing the challenging requirements of the available bandwidth, power and the restricted delay limits. Unmanned aerial vehicles (UAVs) have been recently used as aerial base stations (BSs) to empower the line of sight (LoS), throughput and coverage of wireless networks. Moreover, non-orthogonal multiple access (NOMA) has become a bright multiple access technology. In this paper, NOMA is combined with UAV for establishing a high-capacity IoT uplink multi-application network, where the resource allocation problem is formulated with the objective of maximizing the system throughput while minimizing the delay of IoT applications. Moreover, power allocation was investigated to achieve fairness between users. The results show the superiority of the proposed algorithm, which achieves 31.8% delay improvement, 99.7% reliability increase and 50.8% fairness enhancement when compared to the maximum channel quality indicator (max CQI) algorithm in addition to preserving the system sum rate, spectral efficiency and complexity. Consequently, the proposed algorithm can be efficiently used in ultra-reliable low-latency communication (URLLC).
Two Models for Time-Domain Simulation of Hybrid Magnetic Bearing’s Characteristics
Authors: Wajnert Dawid , Tomczuk Bronisław
Published: February 2022

A comparison of two developed simulation models for a hybrid magnetic bearing (HMB) transient states is presented. This applies to analyses using the flux-circuit directly coupled magnetic equivalent circuit and field-circuit indirectly coupled finite element analysis. The required control system was implemented for both models. The results obtained from the simulations were compared with those obtained from measurement tests.
Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG
Authors: Mandekar Swati , Holland Abigail , Thielen Moritz , Behbahani Mehdi , Melnykowycz Mark
Published: February 2022

Wearable EEG has gained popularity in recent years driven by promising uses outside of clinics and research. The ubiquitous application of continuous EEG requires unobtrusive form-factors that are easily acceptable by the end-users. In this progression, wearable EEG systems have been moving from full scalp to forehead and recently to the ear. The aim of this study is to demonstrate that emerging ear-EEG provides similar impedance and signal properties as established forehead EEG. EEG data using eyes-open and closed alpha paradigm were acquired from ten healthy subjects using generic earpieces fitted with three custom-made electrodes and a forehead electrode (at Fpx) after impedance analysis. Inter-subject variability in in-ear electrode impedance ranged from 20 kΩ to 25 kΩ at 10 Hz. Signal quality was comparable with an SNR of 6 for in-ear and 8 for forehead electrodes. Alpha attenuation was significant during the eyes-open condition in all in-ear electrodes, and it followed the structure of power spectral density plots of forehead electrodes, with the Pearson correlation coefficient of 0.92 between in-ear locations ELE (Left Ear Superior) and ERE (Right Ear Superior) and forehead locations, Fp1 and Fp2, respectively. The results indicate that in-ear EEG is an unobtrusive alternative in terms of impedance, signal properties and information content to established forehead EEG.
Facile Synthesis of Polyaniline/Carbon-Coated Hollow Indium Oxide Nanofiber Composite with Highly Sensitive Ammonia Gas Sensor at the Room Temperature
Authors: Hong Sheng-Zhe , Huang Qing-Yi , Wu Tzong-Ming
Published: February 2022

Hollow carbon-coated In2O3 (C#In2O3) nanofibers were prepared using an efficiently combined approach of electrospinning, high-temperature calcination, and hydrothermal process. The polyaniline (PANI)/hollow C#In2O3 nanofiber composites were synthesized used hollow C#In2O3 nanofibers worked as a core through the in situ chemical oxidative polymerization. The morphology and crystalline structure of the PANI/hollow C#In2O3 nanofiber composite were identified using wide-angle X-ray diffraction and transmission electron microscopy. The gas-sensing performances of the fabricated PANI/hollow C#In2O3 nanofiber composite sensor were estimated at room temperature, and the response value of the composite sensor with an exposure of 1 ppm NH3 was 18.2, which was about 5.74 times larger than that of the pure PANI sensor. The PANI/hollow C#In2O3 nanofiber composite sensor was demonstrated to be highly sensitive to the detection of NH3 in the concentration range of 0.6~2.0 ppm, which is critical for kidney or hepatic disease detection from the human breath. This composite sensor also displayed superior repeatability and selectivity at room temperature with exposures of 1.0 and 2.0 ppm NH3. Because of the outstanding repeatability and selectivity to the detection of NH3 at 1.0 and 2.0 ppm confirmed in this investigation, the PANI/hollow C#In2O3 nanofiber composite sensor will be considered as a favorable gas-sensing material for kidney or hepatic disease detection from human breath.
A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran
Authors: Ghasemian Bahareh , Shahabi Himan , Shirzadi Ataollah , Al-Ansari Nadhir , Jaafari Abolfazl , Kress Victoria R. , Geertsema Marten , Renoud Somayeh , Ahmad Anuar
Published: February 2022

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping.
An Efficient Distributed Content Store-Based Caching Policy for Information-Centric Networking
Authors: Dinh Ngoc-Thanh , Kim Younghan
Published: February 2022

Content store (CS) is one of the main components of information-centric networking (ICN), which enables content objects to be cached and retrieved from any intermediate node in the network. However, in existing ICN designs, CS information is not exploited to coordinate content caching and content retrieval. CS of nodes in the network operates independently while Interest packets forwarding mainly uses forwarding information base (FIB). This paper highlights the importance of CS information for efficient content caching and content retrieval to improve the performance of information-centric networking, especially in resource-constrained environments like the Internet of Things. We propose an efficient caching policy to coordinate the CS of a node with its neighbor nodes in a distributed manner so that more and more popular content objects are cached in the neighborhood of the node. To exploit and coordinate CS information among nodes, we urge to enable CS information in the data plane of the network and design an efficient way for CS information transmission. Each node contributes to the objective of its neighborhood by maximizing its number of unique popular content objects being cached in its CS and not cached in the CS of its neighbors. We implement the proposed policy on top of state-of-the-art popularity-based caching schemes. Through analysis and experiments, we show that the proposed caching policy achieves a significant improvement in terms of cache hit ratio, stretch ratio, content retrieval latency, and energy efficiency significantly compared to state-of-the-art schemes.
ANN and LEFM-Based Fatigue Reliability Analysis and Truck Weight Limits of Steel Bridges after Crack Detection
Authors: Nie Lei , Wang Wei , Deng Lu , He Wei
Published: February 2022

Fatigue of steel bridges is a major concern for bridge engineers. Previous fatigue-based weight-limiting method of steel bridges is founded on the Palmgren–Miner’s rule and S-N curves, which overlook the effect of existing cracks on the fatigue life of in-service steel bridges. In the present study, based on the theory of linear elastic fracture mechanics, a framework combining the artificial neural networks and Monte Carlo simulations is proposed to analyze the fatigue reliability of steel bridges with the effects of cracks and truck weight limits considered. Using the framework, a new method of setting the gross vehicle weight limit for in-service steel bridges with cracks is proposed. The influences of four key parameters, including the average daily truck traffic, the gross vehicle weight limit, the violation rate, and the detected crack size, on the fatigue reliability of a steel bridge are analyzed quantitatively with the new framework. Results show that the suggested framework can enhance the fatigue reliability assessment process in terms of accuracy and efficiency. The method of setting gross vehicle weight limits can effectively control the fatigue failure probability to be within 2.3% according to the desired remaining service time and the detected crack size.
Effect of Temporal Sampling Interval on the Irradiance for Moon-Based Wide Field-of-View Radiometer
Authors: Zhang Yuan , Bi Shengshan , Wu Jiangtao
Published: February 2022

Moon-based Earth radiation observation can provide longer-term, continuous multi-angle measurements for the Earth’s outward radiative flux. In addition, the large distance between the Moon and Earth means that the radiation can be monitored by a non-scanning Moon-based Wide Field-of-View (MWFOV) radiometer considering the Earth as one pixel. In order to parameterize the radiometer, studying the effect of the temporal sampling interval on irradiance is of great importance. In this work, based on radiation transfer model, simulated irradiance time series from March 2000 to December 2020 were analyzed. Then, we used them to reveal the effects of the sampling interval on irradiance. The results show that the measurements of the MWFOV radiometer can reveal the variation of irradiance on hourly, daily and monthly time scales, and the high-frequency measurements can reflect the variation of scene types in the MWFOV-viewed area. In order to obtain more meaningful measurements, the radiation resolution of the MWFOV radiometer should be better than 0.5mW∙m−2 with an accuracy of 1% or better in the future actual design, and the sampling interval should be less than 1 h, which can ensure that 97% of the surface area can be sampled more than nine times per day for longwave radiation. The derived results in this study could facilitate Moon-based data processing and the determination of the sampling interval and radiation resolution of an MWFOV under a certain manufacturing cost and error limit.
Gender Dynamics in a Masculine Professional Context: The Case of the Portuguese Air Force
Authors: Santos Maria Helena , Morais Patrícia , Correia Rita B.
Published: February 2022

This article aims to understand the experiences of women working in the Portuguese Air Force, a traditionally male professional environment, and the perspectives of their male counterparts on the subject, considering women’s minority status and symbolic gender asymmetries in the military. This study draws on 16 semi-structured interviews with Air Force personnel, evenly split by gender. Findings reveal four main themes that convey awareness that the Air Force is still a masculine world, perceptions of gender dynamics in the Air Force, barriers to equality, and strategies to address the situation. Although gender equality is formally in place in the Air Force, women continue to face obstacles that hinder their career advancement and ensure that their professional experiences differ from those of their male counterparts.
Economic Sustainability, Innovation, and the ESG Factors: An Empirical Investigation
Authors: Di Simone Luca , Petracci Barbara , Piva Mariacristina
Published: February 2022

The growing attention to sustainability has generated increasing interest in its relevant determinants and a possible relationship with economic growth’s main drivers. Our paper contributes to this literature in three ways, by proposing the following empirical analysis of most innovative companies listed worldwide (909 firms over the 2013–2017 time-span): firstly, market-perceived innovation—proxied by the interaction between R&D intensity and the market-to-book ratio—has a positive impact on economic sustainability; secondly, when the three ESG pillars are considered, the social one turns out to have the highest effect on economic sustainability; thirdly, results are confirmed even when we control for context-specific conditions.
Perceived Effectiveness of Developing a Mobile System of Formative Test with Handwriting Revision to Devise an Instruction Design Based on Cognitive Apprenticeship Theory
Authors: Lo Shih-Che , Tsai Hung-Hsu
Published: February 2022

Education helps increase socioeconomic mobility and is an important way of leaving poverty according to the United Nations, especially since COVID-19 hit the world hard in early 2020. A Mobile System of Formative Tests with Handwriting Revision is proposed in the paper, called the MSFT system. The MSFT is developed from the cognitive apprenticeship theory (CAT) in instructional design. The instruction model can be utilized for higher education mathematics teaching/studying for quiz-oriented instruction inside traditional classrooms as well as for distance-learning modes. The MSFT platform provides college undergraduates and graduates an app for a handheld device, which is used to upload their answer sheets with captured photos to the cloud database server. Moreover, instructors can use the platform to revise or assess answer sheets with instructors’ handwriting through web interfaces or apps. Important features of the integrated platform for teachers are (1) grading answer sheets by handwriting, (2) correcting mistakes in the answer sheets by handwriting, (3) writing down instructors’ comments on students’ answer sheets directly, and (4) choosing examples to demonstrate during class presentation, in a single window through web applications. To evaluate MSFT performance and service level for students, a questionnaire survey was conducted for 51 students and separated into an experimental group and a control group. Results from the experiment show that learning attitudes and learning satisfaction were significantly increased with the MSFT system in the experimental group compared to the control group.
The Impact of the COVID-19 Pandemic on the General Public in Urban and Rural Areas in Southern Japan
Authors: Sasaki Keiko , Ichinose Tomohiro
Published: February 2022

Urban and rural areas are situated in contrasting physical and social settings, which influence their levels of exposure as well as their preventive behaviors in response to the COVID-19 outbreak. The study investigated whether there were differences between the areas regarding the levels of difficulty and anxiety felt by the general public during the first wave of COVID-19 in April and May 2020. We conducted an online questionnaire in Fukuoka and Kumamoto Prefecture in southern Japan via a private research company and collected a total of 913 valid responses from individuals whose conditions of employment were affected by the coronavirus outbreak. Although urban areas experienced higher case rates compared to rural areas, ordinal logistic regression analysis revealed no significant differences between urban and rural respondents concerning the level of difficulty in routine life. The daily-life contents which made them feel difficult during the first wave also did not differ largely between the contrasting areas. Urban respondents appeared to have experienced a higher level of difficulty in finding an alternative job, but how respondents found one, if successful, did not differ between urban and rural areas. The area of residence played a role in explaining the level of anxiety toward being infected, especially when the anxiety-related questions involved relationships with neighbors. Rural respondents showed a significantly higher level of anxiety toward causing neighbors trouble and being criticized if infected. Respondents who were better embedded in their communities generally felt more anxious about being infected, regardless of whether they lived in urban or rural areas. Women and respondents with children were more likely affected by abnormal situations caused by the COVID-19 outbreak. Our study highlights the prevailing impact of the COVID-19 pandemic on the general public regardless of whether in urban or rural settings, as well as the potential contribution of social ties among people to protecting communities from infectious pathogens.
Entrepreneurship Intentions Analysis of Mexican University Students Using an Artificial Neural Network to Promote Sustainable Businesses: An Interdisciplinary Perspective
Authors: López-Leyva Josué Aarón , Ponce-Camacho Miguel Ángel , Valadez-García Alfredo , Ramos-García Víctor Manuel , Mena-Ibarra Hania Nered
Published: February 2022

This paper shows an analysis of the entrepreneurship intentions patterns in the short, medium, and long term of Mexican university students. The entrepreneurship patterns analysis was carried out using a customized artificial neural network considering as inputs the self-assessment of multiple intelligences from an interdisciplinary perspective. Thus, many important findings reveal that not all multiple intelligences have a direct and proportional impact on entrepreneurship intention. In fact, Linguistic–Verbal Intelligence, Intrapersonal Intelligence, and Interpersonal intelligence are the types of intelligence that have the greatest influence on entrepreneurial intentions. In addition, the performance metrics of the ANN for classifying the entrepreneurship intentions are higher than the ones reported in the literature (i.e., accuracy ≈ 99.29%, precision ≈ 98.89%, sensitivity ≈ 99.53%, and specificity ≈ 99.01%). The paper contributes to the literature on the deep understanding of entrepreneurs’ behavior concerning the strengths and weaknesses of their multiple intelligences. Besides, this interdisciplinary empirical work contributes to improving the design of methods and techniques to strengthen entrepreneurship from the earliest stages of students’ lives and promote sustainable businesses. The most surprising finding was the minimal relationship of Naturalistic Intelligence to undertake sustainable and robust projects, which demands more detailed and in-depth analysis. Finally, some proposals are presented to improve the teaching process of entrepreneurship and sustainability, considering the artificiality, sustainability, and entrepreneurship involved in academic programs at universities.
Multi-Criteria Decision Analysis for Evaluating Transitional and Post-Mining Options—An Innovative Perspective from the EIT ReviRIS Project
Authors: Amaro Sandra Lourenço , Barbosa Sofia , Ammerer Gloria , Bruno Aina , Guimerà Jordi , Orfanoudakis Ioannis , Ostręga Anna , Mylona Evangelia , Strydom Jessica , Hitch Michael
Published: February 2022

In mine design and planning, identifying appropriate Post-Mining Land Use (PMLU) is necessary and crucial to achieving environmental quality and socioeconomic renewal. In this context, Multi-Criteria Decision Making (MCDM) methods are used to support decision-maker and stakeholder decisions. However, most studies regarding the application of MCDM methods to PMLU decisions do not favor their widespread use because they start from an already structured decisional problem. The structure they present may not apply to another PMLU decision. Therefore, the primary goal of this study is to present an innovative methodology and its corresponding framework to help decision-makers and stakeholders structure their PMLU decisions. This innovative methodology can be used from an early stage, with a low level of detail, until a later stage, with a high level of detail, and is composed of three main stages. The first stage is selecting the Transitional Post-Mining Landscape Profile, which guides the user to different Multi-Criteria Decision Analysis (MCDA) goals. The second stage is developing criteria and alternatives according to the MCDA goal, using topics representing essential dimensions that cannot be disregarded, and testing the MCDM methods. Finally, the third stage is the participatory process and final application of MCDM methods.
A Cluster-First Route-Second Constructive Heuristic Method for Emergency Logistics Scheduling in Urban Transport Networks
Authors: Yin Ruyang , Lu Peixia
Published: February 2022

Advanced strategies for emergency logistics scheduling problems in urban transport networks have been a challenging topic for centuries. This study proposed a cluster-first route-second constructive heuristic method based on the continuous approximation (CA) for ‘one-to-many’ vehicle routing to dispatch commidities after an emergency. The objective of the study is to provide a replenish schedule and routing solution from the government/provider’s end in order to minimize the total motion cost, pipeline inventory cost, and holding cost with backorder for the disaster relief operation. The developed method can turn the complicated vehicle routing problem (VRP) into a relatively simple travel salesman problem (TSP) for pre-assigned customer sets. The CA is employed to determine the optimal replenish amount and inventory level for the route serving a given location. The Christofides method is then applied to solve the TSP for the selected cluster. Two clustering methods are investigated in this research: (1) a local-based approach where clustering and routing are determined; and (2) a K-mean clustering method where points are clustered upfront by the CA solution. A case study in Miami-Dade County in Florida to dispatch fuels from the depot to 72 gas stations is presented, demonstrating the proposed approach and comparing two clustering methods. The numerical results illustrate the effectiveness of the algorithms and conclude that the local-based clustering approach may yield a lower total cost with a higher motion cost.
Sustainable Diet Optimization Targeting Dietary Water Footprint Reduction—A Country-Specific Study
Authors: Tompa Orsolya , Kiss Anna , Maillot Matthieu , Sarkadi Nagy Eszter , Temesi Ágoston , Lakner Zoltán
Published: February 2022

Food production creates 70% of the total anthropogenic water footprint, and it is the main cause of water pollution. Thus, more sustainable diets could contribute to the achievement of the Sustainable Development Goals. A linear programming-based stepwise optimization was designed to create dietary water footprint-reduced, culturally acceptable, and healthier diets in the case of Hungary based on a representative dietary survey. Optimization resulted in a considerable total dietary water footprint reduction (women: 18%; men: 28%) with a moderate dietary shift (~32%). Milk and dairies (observed: ~31.5%, optimized: ~20.5%) and meats and meat products (observed: ~28.0%, optimized: 28.9%) contributed the most to the dietary water footprint. In the water footprint–healthiness synergy, the vegetables, eggs, poultries, and fermented dairies were the most beneficial, increasing in amount, while fatty dairies, foods high in added sugar, and meat products were the most non-beneficial food sub-groups, decreasing in amount in the optimized diets. The problematic nutrients to fulfill in the optimized diets were energy, dietary fibers, sodium, vitamin D, zinc, vitamin B12, calcium, iron, and potassium at the maximum water footprint reduction. The study provides supporting evidence about the dietary water footprint–healthiness synergy for the further improvement of the national food-based dietary guideline.
Measuring Cluster-Based Spatial Access to Shopping Stores under Real-Time Travel Time
Authors: Qin Jiahui , Luo Shijia , Yi Disheng , Jiang Heping , Zhang Jing
Published: February 2022

Shopping stores are an important part of retail facilities and indispensable public facilities in a city. They are not only concentrated in shopping malls, but also distributed independently throughout the city, and often agglomerated in space. This paper attempts to measure the rationality of the spatial layout of all shopping stores in the city. Residents will visit multiple shopping stores in one trip to meet their demands. Based on this characteristic, this paper studies shopping store clusters and proposes a cluster two-step floating catchment area (C-2SFCA) method to analyze the accessibility differences of shopping stores in urban areas. Using the case of Beijing within the Fifth Ring Road, this paper implements the C-2SFCA method in a study unit of traffic analysis zones (TAZ) considering three transport modes (car, public transport, walking) with the support of real-time travel time collected from an internet map. The results show that spatial accessibility differed greatly under different transport modes and also had an uneven distribution pattern. Among these three results, the spatial variation of public transport accessibility was the highest. The results can provide references for urban planners in facility configuration and decision-making.
Early Prognostics of Lithium-Ion Battery Pack Health
Authors: Wang Jiwei , Deng Zhongwei , Peng Kaile , Deng Xinchen , Xu Lijun , Guan Guoqing , Abudula Abuliti
Published: February 2022

Accurate health prognostics of lithium-ion battery packs play a crucial role in timely maintenance and avoiding potential safety accidents in energy storage. To rapidly evaluate the health of newly developed battery packs, a method for predicting the future health of the battery pack using the aging data of the battery cells for their entire lifecycles and with the early cycling data of the battery pack is proposed. Firstly, health indicators (HIs) are extracted from the experimental data, and high correlations between the extracted HIs and the capacity are verified by the Pearson correlation analysis method. To predict the future health of the battery pack based on the HIs, degradation models of HIs are constructed by using an exponential function, long short-term memory network, and their weighted fusion. The future HIs of the battery pack are predicted according to the fusion degradation model. Then, based on the Gaussian process regression algorithm and battery pack data, a data-driven model is constructed to predict the health of the battery pack. Finally, the proposed method is validated with a series-connected battery pack with fifteen 100 Ah lithium iron phosphate battery cells. The mean absolute error and root mean square error of the health prediction of the battery pack are 7.17% and 7.81%, respectively, indicating that the proposed method has satisfactory accuracy.
Suitability Assessment of Marble, Glass Powders and Poly-Propylene Fibers for Improvement of Siwalik Clay
Authors: Ashiq Syed Zishan , Akbar Aziz , Farooq Khalid , Kazmi Syed Minhaj Saleem , Munir Muhammad Junaid
Published: February 2022

Raising of the Mangla Dam in Pakistan submerged about 15,780 acres of land, resulting in the relocation of 8020 inhabitants to a newly developed town named New City. The new site, consisting of 1300 acres, is in the sub-tropical zone and comprises badland topography. The parent soils (Siwalik clay) pose infrastructure serviceability issues, causing immense loss to property. The study aims to improve the properties of Siwalik clay (base soil) using industrial wastes like marble and glass powders (5 to 20%) and polypropylene fibers (0.25 to 1.25%) as modifiers. Laboratory tests including grain size distribution, Atterberg limits, standard Proctor compaction, unconfined compression, indirect tensile strength, swell potential, and California bearing ratio were conducted on the control and modified clay samples. The results showed that unconfined compressive strength (UCS) and swelling strains (SS) were increased by 43% and 8% at 1.57 kPa pressure with 15% replacement of marble powder. However, the addition of the 20% glass powder and 0.5% polypropylene fibers not only improved UCS by 110% and 39%, but also reduced SS by 27% and 86%, respectively. The capital construction cost of 1 km long road with modified subgrade using 15% glass powder was reduced by 16% whereas it increased for marble powder and polypropylene fibers by 22% and 17%, respectively. All modifiers had very low hazard to adjoining aqueous environment. Conclusively, glass powder and polypropylene fibers can be used as environmentally-friendly soil improvement modifiers, leading towards sustainable solutions of the serviceability problems.
Cross-Project Defect Prediction Considering Multiple Data Distribution Simultaneously
Authors: Zhao Yu , Zhu Yi , Yu Qiao , Chen Xiaoying
Published: February 2022

Software testing is the main method for finding software defects at present, and symmetric testing and other methods have been widely used, but these testing methods will cause a lot of waste of resources. Software defect prediction methods can reasonably allocate testing resources by predicting the defect tendency of software modules. Cross-project defect prediction methods have huge advantages when faced with missing datasets. However, most cross-project defect prediction methods are designed based on the settings of a single source project and a single target project. As the number of public datasets continues to grow, the number of source projects and defect information is increasing. Therefore, in the case of multi-source projects, this paper explores the problems existing when using multi-source projects for defect prediction. There are two problems. First, in practice, it is not possible to know in advance which source project is used to build the model to obtain the best prediction performance. Second, if an inappropriate source project is used in the experiment to build the model, it can lead to lower performance issues. According to the problems found in the experiment, the paper proposed a multi-source-based cross-project defect prediction method MSCPDP. Experimental results on the AEEEM dataset and PROMISE dataset show that the proposed MSCPDP method effectively solves the above two problems and outperforms most of the current state-of-art cross-project defect prediction methods on F1 and AUC. Compared with the six cross-project defect prediction methods, the F1 median is improved by 3.51%, 3.92%, 36.06%, 0.49%, 17.05%, and 9.49%, and the ACU median is improved by −3.42%, 8.78%, 0.96%, −2.21%, −7.94%, and 5.13%.
Several Isospectral and Non-Isospectral Integrable Hierarchies of Evolution Equations
Authors: Zhao Shiyin , Zhang Yufeng , Zhou Jian
Published: February 2022

By introducing a 3×3 matrix Lie algebra and employing the generalized Tu scheme, a AKNS isospectral–nonisospectral integrable hierarchy is generated by using a third-order matrix Lie algebra. Through a matrix transformation, we turn the 3×3 matrix Lie algebra into a 2×2 matrix case for which we conveniently enlarge it into two various expanding Lie algebras in order to obtain two different expanding integrable models of the isospectral–nonisospectral AKNS hierarchy by employing the integrable coupling theory. Specially, we propose a method for generating nonlinear integrable couplings for the first time, and produce a generalized KdV-Schrödinger integrable system and a nonlocal nonlinear Schrödinger equation, which indicates that we unite the KdV equation and the nonlinear Schrödinger equation as an integrable model by our method. This method presented in the paper could apply to investigate other integrable systems.
Evolutionary Game of Small and Medium-Sized Enterprises’ Accounts-Receivable Pledge Financing in the Supply Chain
Authors: Hu Haiju , Li Yakun , Tian Mao , Cai Xinjiang
Published: February 2022

Due to limited guarantees, it is difficult for small and medium-sized enterprises (SMEs) to obtain loans from banks. Supply chain accounts-receivable pledge financing (SCARPF) can help in overcoming those financing difficulties. This study developed an evolutionary game model of banks, core enterprises and SMEs in SCARPF, analyzed the evolution path and evolution rules of the model, and performed a numerical simulation. The results indicated that the result of the evolutionary game depends on the initial values of the variables. When certain conditions are met, the system will evolve to (lending, keep the contract). The higher the return rate during either normal production of SMEs, the loan interest rate or supply chain punishment, the more likely it is that banks will lend money and SMEs will keep the contract. However, the bank will only be likely to lend money, enabling SMEs to keep the contract, when the probability of core enterprises and SMEs engaging in joint loan fraud—or the proportion of the benefits that SMEs share when engaging in joint loan fraud—is reduced. The results of this study provide insights for banks, core enterprises, and SMEs in supply chain financing decisions, which is conducive to solving the financing difficulties of SMEs.
Gut Microbiota Dynamics and Uremic Toxins
Authors: Mishima Eikan , Abe Takaaki
Published: February 2022

Recent evidence has highlighted the importance of the gut microbiota in the pathophysiology of kidney diseases [...]