bluuebunny/crossref_metadata_2025 huggingface dataset:
Theoretical and Experimental Study on Nonlinear Failure of an MEMS Accelerometer under Dual Frequency Acoustic Interference
Authors: Guo Peng , Zhang Jiayu , Feng Lihui , Cui Jianmin , Xing Chaoyang
Published: February 2021

In order to quantitatively study the interfered output of the accelerometer under an acoustic injection attack, a mathematical model for fitting and predicting the accelerometer output was proposed. With ADXL103 as an example, an acoustic injection attack experiment with amplitude sweeping and frequency sweeping was performed. In the mathematical model, the R-squared coefficient was R2 = 0.9990 in the acoustic injection attack experiment with amplitude sweeping, and R2 = 0.9888 with frequency sweeping. Based on the mathematical model, the dual frequency acoustic injection attack mode was proposed. The difference frequency signal caused by the nonlinear effect was not filtered by the low-pass filter. At a 115 dB sound pressure level, the maximum acceleration bias of the output was 4.4 m/s2 and the maximum amplitude of fluctuation was 4.97 m/s2. Two kinds of methods of prevention against acoustic injection attack were proposed, including changing the damping ratio of the accelerometer and adding a preposition low-pass filter.
A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal
Authors: Ullah Amin , Rehman Sadaqat ur , Tu Shanshan , Mehmood Raja Majid , Fawad  , Ehatisham-ul-haq Muhammad
Published: February 2021

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.
Estimating Sleep Stages Using a Head Acceleration Sensor
Authors: Yoshihi Motoki , Okada Shima , Wang Tianyi , Kitajima Toshihiro , Makikawa Masaaki
Published: February 2021

Sleep disruption from causes, such as changes in lifestyle, stress from aging, family issues, or life pressures are a growing phenomenon that can lead to serious health problems. As such, sleep disorders need to be identified and addressed early on. In recent years, studies have investigated sleep patterns through body movement information collected by wristwatch-type devices or cameras. However, these methods capture only the individual’s awake and sleep states and lack sufficient information to identify specific sleep stages. The aim of this study was to use a 3-axis accelerometer attached to an individual’s head to capture information that can identify three specific sleep stages: rapid eye movement (REM) sleep, light sleep, and deep sleep. These stages are measured by heart rate features captured by a ballistocardiogram and body movement. The sleep experiment was conducted for two nights among eight healthy adult men. According to the leave-one-out cross-validation results, the F-scores were: awake 76.6%, REM sleep 52.7%, light sleep 78.2%, and deep sleep 67.8%. The accuracy was 74.6% for the four estimates. This proposed measurement system was able to estimate the sleep stages with high accuracy simply by using the acceleration in the individual’s head.
An Optical Fiber Sensor Based on Fluorescence Lifetime for the Determination of Sulfate Ions
Authors: Ding Liyun , Gong Panfeng , Xu Bing , Ding Qingjun
Published: February 2021

A new optical fiber sensor based on the fluorescence lifetime was prepared for specific detection of sulfate ion concentration, where 1,1′-(anthracene-9,10-diylbis(methylene))bis(3-(dodecylcarbamoyl)pyridin-1-ium) acted as the sulfate fluorescent probe. The probe was immobilized in a porous cellulose acetate membrane to form the sensitive membrane by the immersion precipitation method, and polyethylene glycol 400 acted as a porogen. The sensing principle was proven, as a sulfate ion could form a complex with the probe through a hydrogen bond, which led to structural changes and fluorescence for the probe. The signals of the fluorescence lifetime data were collected by the lock-in amplifier and converted into the phase delay to realize the detection of sulfate ions. Based on the phase-modulated fluorometry, the relationship between the phase delay of the probe and the sulfate ion concentration was described in the range from 2 to 10 mM. The specificity and response time of this optical fiber sensor were also researched.
Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking
Authors: Li Zhiyuan , Peng Ershuai
Published: February 2021

With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks.
Three-Dimensional Model of the Moon with Semantic Information of Craters Based on Chang’e Data
Authors: Lu Yunfan , Hu Yifan , Xiao Jun , Liu Lupeng , Zhang Long , Wang Ying
Published: February 2021

China’s Chang’e lunar exploration project obtains digital orthophoto image (DOM) and digital elevation model (DEM) data covering the whole Moon, which are critical to lunar research. The DOM data have three resolutions (i.e., 7, 20 and 50 m), while the DEM has two resolutions (i.e., 20 and 50 m). Analysis and research on these image data effectively help humans to understand the Moon. In addition, impact craters are considered the most basic feature of the Moon’s surface. Statistics regarding the size and distribution of impact craters are essential for lunar geology. In existing works, however, the lunar surface has been reconstructed less accurately, and there is insufficient semantic information regarding the craters. In order to build a three-dimensional (3D) model of the Moon with crater information using Chang‘e data in the Chang‘e reference frame, we propose a four-step framework. First, software is implemented to annotate the lunar impact craters from Chang’e data by complying with our existing study on an auxiliary annotation method and open-source software LabelMe. Second, auxiliary annotation software is adopted to annotate six segments in the Chang’e data for an overall 25,250 impact crater targets. The existing but inaccurate craters are combined with our labeled data to generate a larger dataset of craters. This data set is analyzed and compared with the common detection data. Third, deep learning detection methods are employed to detect impact craters. To address the problem attributed to the resolution of Chang’e data being too high, a quadtree decomposition is conducted. Lastly, a geographic information system is used to map the DEM data to 3D space and annotate the semantic information of the impact craters. In brief, a 3D model of the Moon with crater information is implemented based on Chang’e data in the Chang‘e reference frame, which is of high significance.
Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-World Hazy Scenes
Authors: Li Zhan , Zhang Jianhang , Zhong Ruibin , Bhanu Bir , Chen Yuling , Zhang Qingfeng , Tang Haoqing
Published: February 2021

In this paper, a transmission-guided lightweight neural network called TGL-Net is proposed for efficient image dehazing. Unlike most current dehazing methods that produce simulated transmission maps from depth data and haze-free images, in the proposed work, guided transmission maps are computed automatically using a filter-refined dark-channel-prior (F-DCP) method from real-world hazy images as a regularizer, which facilitates network training not only on synthetic data, but also on natural images. A double-error loss function that combines the errors of a transmission map with the errors of a dehazed image is used to guide network training. The method provides a feasible solution for introducing priors obtained from traditional non-learning-based image processing techniques as a guide for training deep neural networks. Extensive experimental results demonstrate that, in terms of several reference and non-reference evaluation criteria for real-world images, the proposed method can achieve state-of-the-art performance with a much smaller network size and with significant improvements in efficiency resulting from the training guidance.
Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot
Authors: Zheng Kuisong , Wu Feng , Chen Xiaoping
Published: February 2021

This paper describes the development of a laser-based people detection and obstacle avoidance algorithm for a differential-drive robot, which is used for transporting materials along a reference path in hospital domains. Detecting humans from laser data is an important functionality for the safety of navigation in the shared workspace with people. Nevertheless, traditional methods normally utilize machine learning techniques on hand-crafted geometrical features extracted from individual clusters. Moreover, the datasets used to train the models are usually small and need to manually label every laser scan, increasing the difficulty and cost of deploying people detection algorithms in new environments. To tackle these problems, (1) we propose a novel deep learning-based method, which uses the deep neural network in a sliding window fashion to effectively classify every single point of a laser scan. (2) To increase the speed of inference without losing performance, we use a jump distance clustering method to decrease the number of points needed to be evaluated. (3) To reduce the workload of labeling data, we also propose an approach to automatically annotate datasets collected in real scenarios. In general, the proposed approach runs in real-time and performs much better than traditional methods. Secondly, conventional pure reactive obstacle avoidance algorithms can produce inefficient and oscillatory behaviors in dynamic environments, making pedestrians confused and possibly leading to dangerous reactions. To improve the legibility and naturalness of obstacle avoidance in human crowded environments, we introduce a sampling-based local path planner, similar to the method used in autonomous driving cars. The key idea is to avoid obstacles by switching lanes. We also adopt a simple rule to decrease the number of unnecessary deviations from the reference path. Experiments carried out in real-world environments confirmed the effectiveness of the proposed algorithms.
The Potential of Satellite Imagery for Surveying Whales
Authors: Höschle Caroline , Cubaynes Hannah C. , Clarke Penny J. , Humphries Grant , Borowicz Alex
Published: February 2021

The emergence of very high-resolution (VHR) satellite imagery (less than 1 m spatial resolution) is creating new opportunities within the fields of ecology and conservation biology. The advancement of sub-meter resolution imagery has provided greater confidence in the detection and identification of features on the ground, broadening the realm of possible research questions. To date, VHR imagery studies have largely focused on terrestrial environments; however, there has been incremental progress in the last two decades for using this technology to detect cetaceans. With advances in computational power and sensor resolution, the feasibility of broad-scale VHR ocean surveys using VHR satellite imagery with automated detection and classification processes has increased. Initial attempts at automated surveys are showing promising results, but further development is necessary to ensure reliability. Here we discuss the future directions in which VHR satellite imagery might be used to address urgent questions in whale conservation. We highlight the current challenges to automated detection and to extending the use of this technology to all oceans and various whale species. To achieve basin-scale marine surveys, currently not feasible with any traditional surveying methods (including boat-based and aerial surveys), future research requires a collaborative effort between biology, computation science, and engineering to overcome the present challenges to this platform’s use.
An Improved Algorithm for Measuring Nitrate Concentrations in Seawater Based on Deep-Ultraviolet Spectrophotometry: A Case Study of the Aoshan Bay Seawater and Western Pacific Seawater
Authors: Zhu Xingyue , Yu Kaixiong , Zhu Xiaofan , Su Juan , Wu Chi
Published: February 2021

Nowadays, it is still a challenge for commercial nitrate sensors to meet the requirement of high accuracy in a complex water. Based on deep-ultraviolet spectral analysis and a regression algorithm, a different measuring method for obtaining the concentration of nitrate in seawater is proposed in this paper. The system consists of a deuterium lamp, an optical fiber splitter module, a reflection probe, temperature and salinity sensors, and a deep-ultraviolet spectrometer. The regression model based on weighted average kernel partial least squares (WA-KPLS) algorithm together with corrections for temperature and salinity (TSC) is established. After that, the seawater samples from Western Pacific and Aoshan Bay in Qingdao, China with the addition of various nitrate concentrations are studied to verify the reliability and accuracy of the method. The results show that the TSC-WA-KPLS algorithm shows the best results when compared against the multiple linear regression (MLR) and ISUS (in situ ultraviolet spectrophotometer) algorithms in the temperatures range of 4–25 °C, with RMSEP of 0.67 µmol/L for Aoshan Bay seawater and 1.08 µmol/L for Western Pacific seawater. The method proposed in this paper is suitable for measuring the nitrate concentration in seawater with higher accuracy, which could find application in the development of in-situ and real-time nitrate sensors.
Predictors of Hospital Mortality in Patients with Acute Coronary Syndrome Complicated by Cardiogenic Shock
Authors: Szabó Gábor Tamás , Ágoston András , Csató Gábor , Rácz Ildikó , Bárány Tamás , Uzonyi Gábor , Szokol Miklós , Sármán Balázs , Jebelovszki Éva , Édes István Ferenc , Czuriga Dániel , Kolozsvári Rudolf , Csanádi Zoltán , Édes István , Kőszegi Zsolt
Published: February 2021

As demonstrated by earlier studies, pre-hospital triage with trans-telephonic electrocardiogram (TTECG) and direct referral for catheter therapy shows great value in the management of out-of-hospital chest pain emergencies. It does not only improve in-hospital mortality in ST-segment elevation myocardial infarction, but it has also been identified as an independent predictor of higher in-hospital survival rate. Since TTECG-facilitated triage shortens both transport time and percutaneous coronary intervention (PCI)-related procedural time intervals, it was hypothesized that even high-risk patients with acute coronary syndrome (ACS) and cardiogenic shock (CS) might also benefit from TTECG-based triage. Here, we decided to examine our database for new triage- and left ventricular (LV) function-related parameters that can influence in-hospital mortality in ACS complicated by CS. ACS patients were divided into two groups, namely, (1) hospital death patients (n = 77), and (2) hospital survivors (control, n = 210). Interestingly, TTECG-based consultation and triage of CS and ACS patients were confirmed as significant independent predictors of lower hospital mortality risk (odds ratio (OR) 0.40, confidence interval (CI) 0.21–0.76, p = 0.0049). Regarding LV function and blood chemistry, a good myocardial reperfusion after PCI (high area at risk (AAR) blush score/AAR LV segment number; OR 0.85, CI 0.78–0.98, p = 0.0178) and high glomerular filtration rate (GFR) value at the time of hospital admission (OR 0.97, CI 0.96–0.99, p = 0.0042) were the most crucial independent predictors of a decreased risk of in-hospital mortality in this model. At the same time, a prolonged time interval between symptom onset and hospital admission, successful resuscitation, and higher peak creatine kinase activity were the most important independent predictors for an increased risk of in-hospital mortality. In ACS patients with CS, (1) an early TTECG-based teleconsultation and triage, as well as (2) good myocardial perfusion after PCI and a high GFR value at the time of hospital admission, appear as major independent predictors of a lower in-hospital mortality rate.
Eight-Channel Multispectral Image Database for Saliency Prediction
Authors: Martínez-Domingo Miguel Ángel , Nieves Juan Luis , Valero Eva M.
Published: February 2021

Saliency prediction is a very important and challenging task within the computer vision community. Many models exist that try to predict the salient regions on a scene from its RGB image values. Several new models are developed, and spectral imaging techniques may potentially overcome the limitations found when using RGB images. However, the experimental study of such models based on spectral images is difficult because of the lack of available data to work with. This article presents the first eight-channel multispectral image database of outdoor urban scenes together with their gaze data recorded using an eyetracker over several observers performing different visualization tasks. Besides, the information from this database is used to study whether the complexity of the images has an impact on the saliency maps retrieved from the observers. Results show that more complex images do not correlate with higher differences in the saliency maps obtained.
Blind Source Separation Method Based on Neural Network with Bias Term and Maximum Likelihood Estimation Criterion
Authors: Liu Sheng , Wang Bangmin , Zhang Lanyong
Published: February 2021

Convergence speed and steady-state source separation performance are crucial for enable engineering applications of blind source separation methods. The modification of the loss function of the blind source separation algorithm and optimization of the algorithm to improve its performance from the perspective of neural networks (NNs) is a novel concept. In this paper, a blind source separation method, combining the maximum likelihood estimation criterion and an NN with a bias term, is proposed. The method adds L2 regularization terms for weights and biases to the loss function to improve the steady-state performance and designs a novel optimization algorithm with a dual acceleration strategy to improve the convergence speed of the algorithm. The dual acceleration strategy of the proposed optimization algorithm smooths and speeds up the originally steep, slow gradient descent in the parameter space. Compared with competing algorithms, this strategy improves the convergence speed of the algorithm by four times and the steady-state performance index by 96%. In addition, to verify the source separation performance of the algorithm more comprehensively, the simulation data with prior knowledge and the measured data without prior knowledge are used to verify the separation performance. Both simulation results and validation results based on measured data indicate that the new algorithm not only has better convergence and steady-state performance than conventional algorithms, but it is also more suitable for engineering applications.
Role of Peripheral Immune Cells in Multiple Sclerosis and Experimental Autoimmune Encephalomyelitis
Authors: Dhaiban Sarah , Al-Ani Mena , Elemam Noha Mousaad , Al-Aawad Mahmood H. , Al-Rawi Zeinab , Maghazachi Azzam A.
Published: February 2021

Multiple sclerosis (MS) is a chronic autoimmune disease that affects the myelination of the neurons present in the central nervous system (CNS). The exact etiology of MS development is unclear, but various environmental and genetic factors might play a role in initiating the disease. Experimental autoimmune encephalomyelitis (EAE) is a mouse model that is used to study the pathophysiology of MS disease as well as the effects of possible therapeutic agents. In addition, autoreactive immune cells trigger an inflammatory process upon the recognition of CNS antigens, which leads to destruction of the neurons. These include innate immune cells such as macrophages, dendritic cells, and natural killer cells. Additionally, the activation and extravasation of adaptive immune cells such as CD4+ T cells into the CNS may lead to further exacerbation of the disease. However, many studies revealed that immune cells could have either a protective or pathological role in MS. In this review, we highlight the roles of innate and adaptive immune cellular and soluble players that contribute to the pathogenesis of MS and EAE, which may be used as potential targets for therapy.
Simultaneous Chemical and Sensory Analysis of Domestic Cat Urine and Feces with Headspace Solid-Phase Microextraction and GC-MS-Olfactometry
Authors: Banik Chumki , Koziel Jacek A. , Li James Z.
Published: February 2021

The association between humans and cats (Felis catus) is well known. This domestic animal is also known for its malodorous urine and feces. The complexity of the odorous urine and feces impacts human life by triggering the human sensory organ in a negative way. The objective of this research was to identify the volatile organic chemicals (VOCs) and associated odors in cat urine and feces using gas chromatography–mass spectrometry and simultaneous sensory analysis of fresh and aged samples. The solid-phase microextraction (SPME) technique was used to preconcentrate the VOCs emitted from urine or feces samples. Twenty-one compounds were identified as emitted from fresh urine, whereas 64 compounds were emitted from fresh feces. A contrasting temporal impact was observed in the emission of VOCs for urine and feces. On aging, the emission increased to 34 detected chemicals for stale urine, whereas only 12 chemicals were detected in stale feces. Not all compounds were malodorous; some compounds had a pleasant hedonic smell to the human nose. Although trimethylamine, low-molecular-weight organic acids, and ketones were contributors to the odor to some extent, phenolic compounds and aromatic heterocyclic organic N compounds generated the most intense odors and substantially contributed to the overall malodor, as observed by this study. This work might be useful to formulate cat urine and feces odor remediation approaches to reduce odor impacts.
Knowledge, Attitudes, and Barriers towards Dietary Pulse Consumption in Women with Polycystic Ovary Syndrome Participating in a Multi-Disciplinary Lifestyle Intervention to Improve Women’s Health
Authors: Kazemi Maryam , McBreairty Laura E. , Chilibeck Philip D. , Pierson Roger A. , Chizen Donna R. , Zello Gordon A.
Published: February 2021

Pulse (beans, lentils, chickpeas, peas) consumption is low in developed countries. Pulses have the potential to benefit the management of polycystic ovary syndrome (PCOS) because they improve aspects of metabolic derangements (dyslipidaemia, insulin resistance), which contribute to reproductive disturbances (oligo-amenorrhea, hyperandrogenism). We compared changes in knowledge, attitudes, and barriers towards pulse consumption in PCOS cohorts who participated in a pulse-based or a Therapeutic Lifestyle Changes (TLC) dietary intervention. Thirty women (18–35 years old) randomised to a pulse-based diet (supplied with pulse-based meals) and 31 women in a TLC group completed pulse consumption questionnaires before and after a 16-week intervention. The pulse-diet group demonstrated increased knowledge of pulses per Canada’s Food Guide recommendations versus the TLC group post-intervention (p < 0.05). In both groups, increased scores were evident in the domain of attitude about pulses (p < 0.01). The top-ranked barrier to pulse consumption in no-/low-consumers was lack of knowledge about cooking pulses pre- and post-intervention. We attributed increased knowledge about pulse consumption in the pulse group to greater awareness through education and consuming pulse foods during the intervention. Our observations highlight the importance of multi-dimensional behavioural counselling and education to integrate healthy dietary practices for improving reproductive and sexual health in this under-studied high-risk population (ClinicalTrials.gov identifier: NCT01288638).
COVID-19 Is Examining the EU and the Member States: The Role of Attitudes and Sociodemographic Factors on Citizens’ Support towards National Policies
Authors: Martín Juan Carlos , Román Concepción
Published: February 2021

During March and April 2020, the European Union (EU) was the center of the COVID-19 pandemic. Many national governments imposed severe lockdown policies to mitigate the health crisis, but the citizens’ support to these policies was unknown. The aim of this paper was to analyze empirically how citizens in the EU have reacted towards the measures taken by the national governments. To this end, a microeconometric model (ordered probit) that explains the citizens’ satisfaction by a number of attitudes and sociodemographic factors was estimated using a wide database formed by 21,804 European citizens in 21 EU countries who responded a survey between 23 April and 1 May 2020. Our results revealed that Spaniards were the least satisfied citizens in comparison with Danes, Irelanders, Greeks, and Croats, who were the most satisfied nationals. The years of education and the social class also played a determinant role. We also found that the most important determinant was the political support to the government, and that those who were more worried by the economy and the protection of individual rights were usually more critical of the measures than those who were more worried by the health consequences.
Im/Mobility at the US–Mexico Border during the COVID-19 Pandemic
Authors: Blue Sarah A. , Devine Jennifer A. , Ruiz Matthew P. , McDaniel Kathryn , Hartsell Alisa R. , Pierce Christopher J. , Johnson Makayla , Tinglov Allison K. , Yang Mei , Wu Xiu , Moya Sara , Cross Elle , Starnes Carol Anne
Published: February 2021

In March 2020, the United States government began a series of measures designed to dramatically restrict immigration as part of its response to the global health crisis caused by the coronavirus pandemic. This included Title 42, which deported asylum seekers immediately and prevented them from applying for asylum. These measures worsened an already precarious situation at the US–Mexico border for an estimated 60,000 asylum seekers who were prevented, by the Trump administration’s ‘Remain in Mexico’ (aka MPP) policy enacted in January 2019, from remaining in the United States while they awaited their asylum hearings. In-depth interviews, participant observation, and social media analysis with humanitarian and legal advocates for asylum seekers living in a camp at the border in Matamoros, Mexico reveal that COVID-19’s impacts are not limited to public health concerns. Rather, COVID-19’s impacts center on how the Trump administration weaponized the virus to indefinitely suspend the asylum system. We argue that the Matamoros refugee camp provides a strategic vantage point to understand the repercussions of state policies of exclusion on im/mobility and survival strategies for asylum seekers. Specifically, we use the analytical lenses of the politics of im/mobility, geographies of exclusion, and asylum seeker resilience to identify how COVID-19 has shaped the im/mobility and security of the camp and its residents in unexpected ways. At the same time, our research illustrates that camp residents exercise im/mobility as a form of political visibility to contest and ameliorate their precarity as they find themselves in conditions not of their choosing.
Military Service and Offending Behaviors of Emerging Adults: A Conceptual Review
Authors: Salvatore Christopher , Taniguchi Travis
Published: February 2021

Focusing on the United States, this paper examines the impact of military service for the cohort of individuals that have experienced the social factors that characterize emerging adulthood as a unique stage in the life course. We argue that military service, as a turning point, may act differently in contemporary times compared to findings from past research. This difference is driven by changes in military service, the draft versus volunteer military service, and the prevalence of emerging adulthood. As a background, we describe emerging adulthood, examine how emerging adulthood relates to crime and deviance, explore the impact of military life on young adults, provide an overview of the demographics of military service, discuss the influence and outcomes of military life on young adults, and explore existing research linking military service and deviant and criminal behavior. We develop a theoretical model of the relationship between military service and emerging adulthood and explore the impact on criminological theory and policy.
aquaZone: An Integrative Tool for Sustainable Fish Farm Zoning
Authors: Seliger Carina , Haslauer Melanie , Unfer Günther , Schmutz Stefan
Published: February 2021

Aquaculture is the fastest-growing animal food production sector and is expected to become increasingly important to meet future food demands. As a landlocked country, Austria’s self-sufficiency rate for fish is rather low with 6% in total and 48% for freshwater fish. Therefore, and in order to enable sustainable growth of the sector while avoiding negative impacts on the aquatic ecosystem and other uses, we developed aquaZone, a decision support tool for sustainable trout farm zoning. Thereby, 30 spatially explicit criteria related to environmental prerequisites, land use, legal constraints and water quality/quantity were defined, collected and classified according to their suitability for sustainable trout production. Criteria were combined in an integrative GIS-based modelling approach in order to perform the first countrywide and spatially-explicit zonation of suitable areas for aquaculture in Austria. Thereby, 7920 suitable areas with a mean size of 8.2 ha located in 1129 out of 5011 sub-basins (23%) were identified. The decision tree assigned the highest variable importance to water temperature, slope, agricultural unit, geology, nitrate retention capacity, fish region, minimum flow and pollution risk. These results should support decision making of investors and authorities in order to avoid conflicts and stranded investments at an early stage.
Key Ratios for Long-Term Prediction of Hotel Financial Distress and Corporate Default: Survival Analysis for an Economic Stagnation
Authors: Pelaez-Verdet Antonio , Loscertales-Sanchez Pilar
Published: February 2021

Hospitality companies often face economic crises, which stress their financial structure. In 2008, Spanish hotels were jeopardized when the travelers’ flows became stagnated, in either domestic and foreign markets. Most of them overcame the crisis, but not all, in part depending on their capital structure at the moment the downturn loomed upon them. This study analyzes the financial ratios registered in 2008 by 3.341 Spanish lodging enterprises, to find out the most relevant ratios that were associated with an eventual breakdown. The analyzed ratios have been largely suggested by previous literature for anticipating financial distress; however, using survival tables and Kaplan–Meier estimates we could also find new insights about several promising variates for future research. In the end, by performing a Cox regression, we could isolate the return on capital employed (ROCE) ratio as a long-term predictor for small hotels’ bankruptcy after a market downturn. Moreover, the legal status seems to be a key predictor concerning medium-sized hotels.
Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating
Authors: Zhan Jiawang , Wang Chuang , Fang Zhiheng
Published: February 2021

The condition of joints in steel truss bridges is critical to railway operational safety. The available methods for the quantitative assessment of different types of joint damage are, however, very limited. This paper numerically investigates the feasibility of using a probabilistic neural network (PNN) and a finite element (FE) model updating technique to assess the condition of joints in steel truss bridges. A two-step identification procedure is developed to achieve damage localization and severity assessment. A series of FE models with single or multiple damages are simulated to generate the training and testing data samples and validate the effectiveness of the proposed approach. The influence of noise on the identification accuracy is also evaluated. The results show that the change rate of modal curvature (CRMC) can be used as a damage-sensitive input of the PNN and the accuracy of preliminary damage localization can exceed 90% when suitable training patterns are utilized. Damaged members can be localized in the correct substructure even with noise contamination. The FE model updating method used can effectively quantify the joint deterioration severity and is robust to noise.
Linking Critical Thinking and Knowledge Management: A Conceptual Analysis
Authors: Indrašienė Valdonė , Jegelevičienė Violeta , Merfeldaitė Odeta , Penkauskienė Daiva , Pivorienė Jolanta , Railienė Asta , Sadauskas Justinas , Valavičienė Natalija
Published: February 2021

Knowledge management and critical thinking are two broad and important phenomena for contemporary society. Their concepts are both well-discussed in the literature. However, the existing conceptual links between them have not been analyzed, and the role of critical thinking in the process of effective knowledge management has not been revealed. This article aims to fill this gap by presenting the conceptual connections between knowledge management and critical thinking. In order to reveal the inner structure of each concept and identify the conceptual connections, a critical review was conducted. The results showed the links between the concepts of knowledge management and critical thinking within three dimensions: relationships, process, and goals. In conclusion, each dimension is presented and described, with a special focus on the unexpected and deep intersections revealed between the two concepts on a personal, interpersonal, and societal level. This research may be regarded as providing the basis for further analysis of the links between these two phenomena. Increased awareness of the existence of critical thinking in knowledge management can forge new directions in organizational strategies and staff training programs.
Sustainability Indicator Selection by a Novel Triangular Intuitionistic Fuzzy Decision-Making Approach in Highway Construction Projects
Authors: Hashemi Hassan , Ghoddousi Parviz , Nasirzadeh Farnad
Published: February 2021

The construction industry has been criticized as being a non-sustainable industry that requires effective tools to monitor and improve its sustainability performance. The multiplicity of indicators of the three pillars of sustainability—economic, social, and environmental—complicates construction sustainability assessments for project managers. Therefore, prioritizing and selecting appropriate sustainability indicators (SIs) is essential prior to conducting a construction sustainability assessment. The main purpose of this research is to select the most appropriate set of SIs to address all three pillars of highway sustainability by a new group decision-making approach. The proposed approach accounts for risk attitudes of experts and entropy measures under a triangular intuitionistic fuzzy (TIF) environment, to handle the inherent uncertainty and vagueness that is present throughout the evaluation process. Furthermore, new separation measures and ranking scores are introduced to distinguish the preference order of SIs. Eventually, the approach is implemented in a case study of highway construction projects and the applicability of the approach is examined. To investigate the stability and validity of computational results, a sensitivity analysis is carried out and a comparison is made between the obtained ranking outcomes and the traditional decision-making methods.
Integrated Valuation of Nature-Based Solutions Using TESSA: Three Floodplain Restoration Studies in the Danube Catchment
Authors: Perosa Francesca , Gelhaus Marion , Zwirglmaier Veronika , Arias-Rodriguez Leonardo F. , Zingraff-Hamed Aude , Cyffka Bernd , Disse Markus
Published: February 2021

Floodplain restoration measures are among the most well-known nature-based solutions for flood risk reduction but practitioners see their limitations in comparison to technical measures when considering both their effectiveness and profitability. The aim of this study is to show the co-benefits (besides flood risk reduction) of floodplain restoration and handle them in terms of monetized ecosystem services (ES). Our work focused on six ES groups for three study areas in the Danube catchment along the Krka, Morava, and Danube rivers. ES mapping through stakeholder engagement is also considered. We applied the methodologies suggested in the Toolkit for Ecosystem Service Site-Based Assessment (TESSA) complemented with alternative methodologies (e.g., questionnaires on social media). Results show annual combined benefits of floodplain restoration in a range from 237,000 USD2019 at Krka to 3.1 million USD2019 at Morava, suggesting the utility of ES assessment. The combination of stakeholder workshops and the TESSA guidelines, as well as the newly developed methods, were all central tools to provide decision-makers with arguments to use nature-based solutions for an integrated and holistic riparian land use management.
Identification of Hotspot Segments with a Risk of Heavy-Vehicle Accidents Based on Spatial Analysis at Controlled-Access Highway
Authors: Manap Norhafizah , Borhan Muhamad Nazri , Yazid Muhamad Razuhanafi Mat , Hambali Mohd Khairul Azman , Rohan Asyraf
Published: February 2021

Significant risk factors that influence the occurrence of heavy vehicle accidents have been explored in numerous studies in order to lower injury severity in traffic accidents. It is imperative to explore road sections with a high risk of heavy vehicle accident occurrence by considering the significant consequences of such accidents for road users, despite the low number of heavy vehicles in traffic flow. To address this, this study proposes a method to predict clustering hotspots for heavy vehicle accidents on the basis of three different criteria, namely, heavy vehicle accident cases, the number of heavy vehicles involved, and accident severity index values. Moran’s I spatial autocorrelation was employed to identify the clustering for each criterion, and the Getis–Ord Gi* statistic was applied to estimate the likelihood of risk along the network. This study considers the features of hotspot points at significance levels from 0.10 to 0.01 with a 1355 m buffer radius to create segments for each criterion. The three criteria for hotspots were considered within the overlapped buffer zone. A total of 22 heavy vehicle risk segments (HVRSs) were identified and then ranked by crash rate. Overall, this study demonstrates the application of different criteria to identify accident hotspots involving a specific vehicle type, which could help in prioritizing segments with a high risk of heavy vehicle accidents, as well as providing information for HVRSs for the purpose of developing appropriate countermeasures for the identified accident hotspots.
Application of Marginal Rate of Transformation in Decision Making of Multi-Objective Reservoir Optimal Operation Scheme
Authors: Wu Yueqiu , Wang Liping , Zhang Yanke , Wu Jiajie , Ma Qiumei , Yue Lisha
Published: February 2021

For reservoirs with combined storage capacity for flood control and beneficial purposes, there tends to be potential benefit loss when the flood control limited water level is used in medium and small floods. How to find the optimal water level scheme for profit-making and pursue the optimization of comprehensive benefits has always been a difficult problem in multi-objective reservoir optimal operation. Based on the principle of the maximum benefit obtained by the product conversion curve and the isorevenue line in microeconomics, taking flood control and power generation as two products of a reservoir, a multi-objective optimal operation scheme decision-making model is established to seek the highest water level scheme that can produce the maximum comprehensive benefits of flood control and power generation. A case study of the Three Gorges reservoir in the early flood season of a dry year shows that on the one hand, under the condition of deterministic inflow, the model can work out the optimal water level and the corresponding best equilibrium point for both flood control and power generation, and it can increase the total power output by 4.48% without reducing the flood control benefits; on the other hand, it can also obtain the dynamic control area of the maximum allowable water level for power generation considering inflow forecast error, which provides a theoretical reference for determining the starting water level in medium and small floods and utilizing flood resources.
Emerging Beef Producer Organisations (POs) in the Irish Beef Sector: An Analysis of Media Coverage in the Context of Nationwide Beef Producer Protests
Authors: Javornicky Martin , Macken-Walsh Áine , Naughton Anita
Published: February 2021

International literature acknowledges benefits of the legally recognised Producer Organisations (POs). Successful leveraging of these benefits depends on two forms of cooperation: horizontal integration among the producers for more effective functioning of the POs; and vertical integration of POs with other actors in the production chain to facilitate processes of co-creation and interactive innovation. In 2016 PO legislation was first introduced in Ireland, and in 2019 Ireland’s first two beef POs emerged at a time when primary producers in the beef sector mobilised en masse, protesting against poor prices and seeking changes in supply chain relationships. Throughout this period, significant and detailed media reporting of the beef sector surrounded the protests, which takes the focus of our analysis. Building on an existing but limited literature on institutional conditions in the Irish beef industry and international accounts of factors influencing the success of POs, we analyse media coverage in order to shed light on the nature of emerging new forms of horizontal and vertical cooperation. In this regard, we focus on horizontal integration of producers into PO and associations of POs (APOs); and vertical integration of POs into Inter Branch Organisations (IBOs) and value-based supply chains (VBSCs). Our analysis shows that the media representations of the Irish beef sector evidence significant challenges to the establishment and successful operation of POs, in any form. The analysis suggests that current constellation of relations in the Irish beef sector represents an environment that is partially resistant to horizontal co-operation and significantly hostile to vertical co-operation. Interactive innovation involving different chain actors seems not to be imminent, at least in the short term, unless there are strategic public and/or private interventions introduced to support it.
Sustainable Management of Soil-Borne Bacterium Ralstonia solanacearum In Vitro and In Vivo through Fungal Metabolites of Different Trichoderma spp.
Authors: Guo Yancui , Fan Zhenyu , Yi Xiong , Zhang Yuhong , Khan Raja Asad Ali , Zhou Zhiqiang
Published: February 2021

The efficacy of traditional control measures for the management of plant pathogens is decreasing, and the resistance of these pathogens to pesticides is increasing, which poses a serious threat to global food security. The exploration of novel and efficient management measures to combat plant disease is an urgent need at this time. In this study, fungal metabolites from three Trichoderma spp. (T. harzianum, T. virens and T. koningii) were prepared on three different growth media (STP, MOF and supermalt (SuM)). The fungal metabolites were tested in vitro and in vivo from March–April 2020 under greenhouse conditions in a pot experiment utilizing completely randomized design to test their management of the bacterial wilt disease caused by R. solanacearum in tomato plants. The effect of the fungal metabolites on bacterial cell morphology was also investigated through scanning electron microscopy (SEM) analysis. In vitro investigation showed that the fungal metabolites of T. harzianum obtained on the STP medium were the most effective in inhibiting in vitro bacterial growth and produced a 17.6 mm growth inhibition zone. SEM analysis confirms the rupture of the cell walls and cell membranes of the bacterium, along with the leakage of its cell contents. Generally, fungal metabolites obtained on an STP medium showed higher activity than those obtained on the other two media, and these metabolites were then evaluated in vivo according to three application times (0 days before transplantation (DBT), 4 DBT and 8 DBT) in a greenhouse trial to examine their ability to manage R. solanacearum in tomato plants. Consistent with in vitro results, the results from the greenhouse studies showed a level of higher anti-bacterial activity of T. harzianum metabolites than they did for the metabolites of other fungi, while among the three application times, the longest time (8 DBT) was more effective in controlling bacterial wilt disease in tomato plants. Metabolites of T. harzianum applied at 8 DBT caused the maximum decrease in soil bacterial population (1.526 log cfu/g), resulting in the lowest level of disease severity (area under disease progressive curve (AUDPC) value: 400), and maximum plant freshness (with a resulting biomass of 36.7 g, a root length of 18.3 cm and a plant height of 33.0 cm). It can be concluded that T. harzianum metabolites obtained on an STP medium, when applied after 8 DBT, can suppress soil bacterial population and enhance plant growth, and thus can be used as a safe, environmentally-conscious and consumer-friendly approach to managing bacterial wilt disease in tomato plants and possibly other crops.