bluuebunny/crossref_metadata_2025 huggingface dataset:
Glyphosate Sensor Based on Nanostructured Water-Gated CuO Field-Effect Transistor
Authors: Ogurcovs Andrejs , Kadiwala Kevon , Sledevskis Eriks , Krasovska Marina , Mizers Valdis
Published: November 2022

This research presents a comparative analysis of water-gated thin film transistors based on a copper oxide (CuO) semiconductor in the form of a smooth film and a nanostructured surface. A smooth CuO film was deposited through reactive magnetron sputtering followed by annealing in atmosphere at a temperature of 280 ∘C. Copper oxide nanostructures were obtained by hydrothermal synthesis on a preliminary magnetron sputtered 2 nm thick CuO precursor followed by annealing at 280 ∘C. An X-ray diffraction (XRD) analysis of the samples revealed the presence of a tenorite (CuO) phase with a predominant orientation of (002). Scanning electron microscopy (SEM) and atomic force microscopy (AFM) studies of the samples revealed a highly developed surface with crystallites having a monoclinic syngony and dimensions of 15–20 nm in thickness, 150 nm in length, and 100 nm in height relative to a 2.5 nm height for the CuO crystallites of the smooth film. Electric measurements of the studied devices revealed typical current–voltage characteristics of semiconductors with predominant hole conductivity. The maximum ON/OFF ratio at a rain-source voltage of 0.4 volts and −1.2 volts on the gate for a smooth film was 102, and for a nanostructured transistor, it was 103. However, a much stronger saturation of the channel was observed for the nanostructured channel than for the smooth film. A test solution containing glyphosate dissolved in deionized water in three different concentrations of 5, 10, and 15 μmol/L was used during the experiments. The principle of operation was based on the preliminary saturation of the solution with Cu ions, followed by the formation of a metal–organic complex alongside glyphate. The glyphosate contents in the analyte led to a decrease in the conductivity of the transistor on the axis of the smooth film. In turn, the opposite effect was observed on the nanostructured surface, i.e., an increase in conductivity was noted upon the introduction of an analyte. Despite this, the overall sensitivity of the nanostructured device was twice as high as that of the device with a thin film channel. The relative changes in the field-effect transistor (FET) conductivity at maximum glyphosate concentrations of 15 μmol/L reached 19.42% for the nanostructured CuO film and 3.3% for the smooth film.
Assessing Cyber Risks of an INS Using the MITRE ATT&CK Framework
Authors: Oruc Aybars , Amro Ahmed , Gkioulos Vasileios
Published: November 2022

Shipping performed by contemporary vessels is the backbone of global trade. Modern vessels are equipped with many computerized systems to enhance safety and operational efficiency. One such system developed is the integrated navigation system (INS), which combines information and functions for the bridge team onboard. An INS comprises many marine components involving cyber threats and vulnerabilities. This study aims to assess the cyber risks of such components. To this end, a methodology considering the MITRE ATT&CK framework, which provides adversarial tactics, techniques, and mitigation measures, was applied by modifying for cyber risks at sea. We assessed cyber risks of 25 components on the bridge by implementing the extended methodology in this study. As a result of the assessment, we found 1850 risks. We classified our results as 1805 low, 32 medium, 9 high, and 4 critical levels for 22 components. Three components did not include any cyber risks. Scientists, ship operators, and product developers could use the findings to protect navigation systems onboard from potential cyber threats and vulnerabilities.
Land-Use and Land-Cover Classification in Semi-Arid Areas from Medium-Resolution Remote-Sensing Imagery: A Deep Learning Approach
Authors: Ali Kamran , Johnson Brian A.
Published: November 2022

Detailed Land-Use and Land-Cover (LULC) information is of pivotal importance in, e.g., urban/rural planning, disaster management, and climate change adaptation. Recently, Deep Learning (DL) has emerged as a paradigm shift for LULC classification. To date, little research has focused on using DL methods for LULC mapping in semi-arid regions, and none that we are aware of have compared the use of different Sentinel-2 image band combinations for mapping LULC in semi-arid landscapes with deep Convolutional Neural Network (CNN) models. Sentinel-2 multispectral image bands have varying spatial resolutions, and there is often high spectral similarity of different LULC features in semi-arid regions; therefore, selection of suitable Sentinel-2 bands could be an important factor for LULC mapping in these areas. Our study contributes to the remote sensing literature by testing different Sentinel-2 bands, as well as the transferability of well-optimized CNNs, for semi-arid LULC classification in semi-arid regions. We first trained a CNN model in one semi-arid study site (Gujranwala city, Gujranwala Saddar and Wazirabadtownships, Pakistan), and then applied the pre-trained model to map LULC in two additional semi-arid study sites (Lahore and Faisalabad city, Pakistan). Two different composite images were compared: (i) a four-band composite with 10 m spatial resolution image bands (Near-Infrared (NIR), green, blue, and red bands), and (ii) a ten-band composite made by adding two Short Wave Infrared (SWIR) bands and four vegetation red-edge bands to the four-band composite. Experimental results corroborate the validity of the proposed CNN architecture. Notably, the four-band CNN model has shown robustness in semi-arid regions, where spatially and spectrally confusing land-covers are present.
Design and Development of a Tri-Axial Turning Dynamometer Utilizing Cross-Beam Type Force Transducer for Fine-Turning Cutting Force Measurement
Authors: Rizal Muhammad , Ghani Jaharah A. , Mubarak Amir Zaki
Published: November 2022

The main focus of this work was the design and development of a cross-beam force transducer for use in the construction of a tri-axial dynamometer. This dynamometer would be able to measure the cutting force along all three axes simultaneously during turning operations. The force transducer was built on the concept of the Maltese cross-beam, but it had been modified and improved so that it had a higher sensitivity and reduced the amount of interference error or cross-talk error that it produced. An investigation into the distribution of strain, as well as the determination of sensor locations within the transducer construction was carried out by means of finite element analysis. In order to develop a prototype of a turning dynamometer, a number of piezoresistive strain gauges were utilized in the transducer. In order to determine sensitivity, linearity, hysteresis, and repeatability, calibration tests were performed in three directions that were perpendicular to one another. To investigate the dynamic properties and capabilities of the dynamometer for use in turning applications, both modal analysis and actual turning tests were performed. The results of the experiments demonstrated that the newly developed turning dynamometer is a realistic approach for measuring cutting force in machining without reliability and accuracy.
Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning
Authors: Pastor Francisco , Lin-Yang Da-hui , Gómez-de-Gabriel Jesús M. , García-Cerezo Alfonso J.
Published: November 2022

There are physical Human–Robot Interaction (pHRI) applications where the robot has to grab the human body, such as rescue or assistive robotics. Being able to precisely estimate the grasping location when grabbing a human limb is crucial to perform a safe manipulation of the human. Computer vision methods provide pre-grasp information with strong constraints imposed by the field environments. Force-based compliant control, after grasping, limits the amount of applied strength. On the other hand, valuable tactile and proprioceptive information can be obtained from the pHRI gripper, which can be used to better know the features of the human and the contact state between the human and the robot. This paper presents a novel dataset of tactile and kinesthetic data obtained from a robot gripper that grabs a human forearm. The dataset is collected with a three-fingered gripper with two underactuated fingers and a fixed finger with a high-resolution tactile sensor. A palpation procedure is performed to record the shape of the forearm and to recognize the bones and muscles in different sections. Moreover, an application for the use of the database is included. In particular, a fusion approach is used to estimate the actual grasped forearm section using both kinesthetic and tactile information on a regression deep-learning neural network. First, tactile and kinesthetic data are trained separately with Long Short-Term Memory (LSTM) neural networks, considering the data are sequential. Then, the outputs are fed to a Fusion neural network to enhance the estimation. The experiments conducted show good results in training both sources separately, with superior performance when the fusion approach is considered.
Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay
Authors: Khadem Heydar , Nemat Hoda , Elliott Jackie , Benaissa Mohammed
Published: November 2022

People with diabetes mellitus (DM) are at elevated risk of in-hospital mortality from coronavirus disease-2019 (COVID-19). This vulnerability has spurred efforts to pinpoint distinctive characteristics of COVID-19 patients with DM. In this context, the present article develops ML models equipped with interpretation modules for inpatient mortality risk assessments of COVID-19 patients with DM. To this end, a cohort of 156 hospitalised COVID-19 patients with pre-existing DM is studied. For creating risk assessment platforms, this work explores a pool of historical, on-admission, and during-admission data that are DM-related or, according to preliminary investigations, are exclusively attributed to the COVID-19 susceptibility of DM patients. First, a set of careful pre-modelling steps are executed on the clinical data, including cleaning, pre-processing, subdivision, and feature elimination. Subsequently, standard machine learning (ML) modelling analysis is performed on the cured data. Initially, a classifier is tasked with forecasting COVID-19 fatality from selected features. The model undergoes thorough evaluation analysis. The results achieved substantiate the efficacy of the undertaken data curation and modelling steps. Afterwards, SHapley Additive exPlanations (SHAP) technique is assigned to interpret the generated mortality risk prediction model by rating the predictors’ global and local influence on the model’s outputs. These interpretations advance the comprehensibility of the analysis by explaining the formation of outcomes and, in this way, foster the adoption of the proposed methodologies. Next, a clustering algorithm demarcates patients into four separate groups based on their SHAP values, providing a practical risk stratification method. Finally, a re-evaluation analysis is performed to verify the robustness of the proposed framework.
Efficient Integrity-Tree Structure for Convolutional Neural Networks through Frequent Counter Overflow Prevention in Secure Memories
Authors: Kim Jesung , Lee Wonyoung , Hong Jeongkyu , Kim Soontae
Published: November 2022

Advancements in convolutional neural network (CNN) have resulted in remarkable success in various computing fields. However, the need to protect data against external security attacks has become increasingly important because inference process in CNNs exploit sensitive data. Secure Memory is a hardware-based protection technique that can protect the sensitive data of CNNs. However, naively applying secure memory to a CNN application causes significant performance and energy overhead. Furthermore, ensuring secure memory becomes more difficult in environments that require area efficiency and low-power execution, such as the Internet of Things (IoT). In this paper, we investigated memory access patterns for CNN workloads and analyzed their effects on secure memory performance. According to our observations, most CNN workloads intensively write to narrow memory regions, which can cause a considerable number of counter overflows. On average, 87.6% of total writes occur in 6.8% of the allocated memory space; in the extreme case, 93.9% of total writes occur in 1.4% of the allocated memory space. Based on our observations, we propose an efficient integrity-tree structure called Countermark-tree that is suitable for CNN workloads. The proposed technique reduces overall energy consumption by 48%, shows a performance improvement of 11.2% compared to VAULT-128, and requires a similar integrity-tree size to VAULT-64, a state-of-the-art technique.
A Modified Prandtl–Ishlinskii Hysteresis Model for Modeling and Compensating Asymmetric Hysteresis of Piezo-Actuated Flexure-Based Systems
Authors: Zhou Chao , Yuan Meng , Feng Chen , Ang Wei Tech
Published: November 2022

Piezo-actuated flexure-based systems are widely used in applications with high accuracy requirements, but the intrinsic hysteresis has a detrimental effect on the performance which should be compensated. Conventional models were presented to model this undesired effect using additional dead-zone operators. This paper presents a new approach using two sets of operators with a distributed compensator to model and compensate for the asymmetric system hysteresis based on inversion calculation with a simplified digitized representation. The experimental results validate the effectiveness of the proposed model in modeling and compensating the asymmetric system hysteresis.
One-Class Convolutional Neural Networks for Water-Level Anomaly Detection
Authors: Nicholaus Isack Thomas , Lee Jun-Seoung , Kang Dae-Ki
Published: November 2022

Companies that own water systems to provide water storage and distribution services always strive to enhance and efficiently distribute water to different places for various purposes. However, these water systems are likely to face problems ranging from leakage to destruction of infrastructures, leading to economic and life losses. Thus, apprehending the nature of abnormalities that may interrupt or aggravate the service or cause the destruction is at the core of their business model. Normally, companies use sensor networks to monitor these systems and record operational data including any fluctuations in water levels considered abnormalities. Detecting abnormalities allows water companies to enhance the service’s sustainability, quality, and affordability. This study investigates a 2D-CNN-based method for detecting water-level abnormalities as time-series anomaly pattern detection in the One-Class Classification (OCC) problem. Moreover, since abnormal data are usually scarce or unavailable, we explored a cheap method to generate synthetic temporal data and use them as a target class in addition to the normal data to train the CNN model for feature extraction and classification. These settings allow us to train a model to learn relevant pattern representations of the given classes in a binary classification fashion using cross-entropy loss. The ultimate goal of these investigations is to determine if any 2D-CNN-based model can be trained from scratch or if transfer learning of any pre-trained CNN model can be partially trained and used as the base network for one-class classification. The evaluation of the proposed One-Class CNN and previous approaches have shown that our approach has outperformed several state-of-the-art approaches by a significant margin. Additionally, in this paper, we mention two interesting findings: using synthetic data as the pseudo-class is a promising direction, and transfer learning should be dealt with considering that underfitting can happen because the transferred model is too complicated for training data.
Affective Computing Based on Morphological Features of Photoplethysmography for Patients with Hypertension
Authors: Yu Sung-Nien , Lin I-Mei , Wang San-Yu , Hou Yi-Cheng , Yao Sheng-Po , Lee Chun-Ying , Chang Chai-Jan , Chu Chih-Sheng , Lin Tsung-Hsien
Published: November 2022

Negative and positive emotions are the risk and protective factors for the cause and prognosis of hypertension. This study aimed to use five photoplethysmography (PPG) waveform indices and affective computing (AC) to discriminate the emotional states in patients with hypertension. Forty-three patients with essential hypertension were measured for blood pressure and PPG signals under baseline and four emotional conditions (neutral, anger, happiness, and sadness), and the PPG signals were transformed into the mean standard deviation of five PPG waveform indices. A support vector machine was used as a classifier. The performance of the classifier was verified by using resubstitution and six-fold cross-validation (CV) methods. Feature selectors, including full search and genetic algorithm (GA), were used to select effective feature combinations. Traditional statistical analyses only differentiated between the emotional states and baseline, whereas AC achieved 100% accuracy in distinguishing between the emotional states and baseline by using the resubstitution method. AC showed high accuracy rates when used with 10 waveform features in distinguishing the records into two, three, and four classes by applying a six-fold CV. The GA feature selector further boosted the accuracy to 78.97%, 74.22%, and 67.35% in two-, three-, and four-class differentiation, respectively. The proposed AC achieved high accuracy in categorizing PPG records into distinct emotional states with features extracted from only five waveform indices. The results demonstrated the effectiveness of the five indices and the proposed AC in patients with hypertension.
From Materials to Technique: A Complete Non-Invasive Investigation of a Group of Six Ukiyo-E Japanese Woodblock Prints of the Oriental Art Museum E. Chiossone (Genoa, Italy)
Authors: Gargano Marco , Longoni Margherita , Pesce Valeria , Palandri Maria Chiara , Canepari Aurora , Ludwig Nicola , Bruni Silvia
Published: November 2022

In the present work, a complete non-invasive scientific investigation of six Utagawa Kunisada’s woodblock prints (nishiki-e) belonging to the Oriental Art Museum “E. Chiossone” (Genoa, Italy), was performed in situ. The campaign started with high resolution multiband imaging (visible, multiband fluorescence, near infrared) followed by reflectance transformation imaging (RTI) to characterize and highlight the peculiar printing techniques and the condition of the support. Then fiber optics reflectance spectroscopy (FORS), spectrofluorimetry, Raman and reflectance Fourier-transform infrared (FTIR) spectroscopies were successfully applied in synergy for the investigation of the printing materials (pigments, binders, support). The results obtained represent a set of very important information for these never-before-studied works of art, useful to the different professionals involved: historians, conservators and curators. The materials identified were completely in agreement with those traditionally used in the Edo period in the 19th century, while the computational imaging technique RTI gave an additional amount of information in terms of surface characterization that could not be overlooked when studying these works of art. RTI data were further processed to enhance the texture visualization.
Sport for Social Cohesion: Transferring from the Pitch to the Community?
Authors: Moustakas Louis
Published: November 2022

European sport policies and programmes have increasingly focused on promoting social cohesion. Often presented as a multi-dimensional concept, social cohesion is considered the ‘glue’ that holds societies together and is seen as essential to addressing common challenges. However, the term remains contested, and it is not always clear how programmes conceptualize or support social cohesion. Thus, this paper explores how three European sport programmes conceptualize and foster social cohesion. Findings are generated from a thematic analysis of interviews, group discussions, observations and documents. The themes developed show how organizations adopt an individual-centerd view of social cohesion, focusing mainly on social relations, tolerance and mutual help. In turn, this translates to an individual-focused practice of social cohesion, emphasizing personal skills, behaviors, and social relations, with the transfer of social cohesion to the broader community left mostly in participants’ hands. Due to a number of systemic barriers, programmes struggle to implement more holistic and structural approaches. As such, if we want to facilitate a move towards more structural or interventionist approaches, we as researchers must play an active role in questioning, challenging, and reshaping the systems that underpin sport-based social interventions.
The Link between Family Violence and Animal Cruelty: A Scoping Review
Authors: Tomlinson Camie A. , Murphy Jennifer L. , Matijczak Angela , Califano Allegra , Santos Jiaxin , McDonald Shelby E.
Published: November 2022

There is some evidence that family violence (intimate partner violence, child maltreatment, elder abuse) co-occurs with animal cruelty (i.e., threats to and/or actual harm of an animal), which is often referred to as “the link.” The aim of this scoping review was to comprehensively search the literature to determine the extent of empirical evidence that supports the co-occurrence of family violence and animal cruelty and that provides prevalence rates of the co-occurrence. We searched eight electronic databases (e.g., Academic Search Complete, PsycArticles, PubMed) for peer-reviewed articles published until September 2021. Articles were eligible for inclusion if they were written in English and included the empirical study of at least one form of family violence and animal cruelty. We identified 61 articles for inclusion. The majority of articles (n = 48) focused on co-occurring IPV and animal cruelty, and 20 articles examined child maltreatment and animal cruelty. No articles examining elder abuse and animal cruelty were found. Prevalence rates of “the link” ranged from <1% to >80%. Findings regarding the association between family violence and animal cruelty varied. Some studies found that family violence was significantly associated with animal cruelty (or vice versa), but there was also evidence that the association was not statistically significant. Associations between family violence and animal cruelty were not significant in most studies that adjusted for sociodemographic factors. This suggests that sociodemographic factors (e.g., exposure to multiple forms of violence, and income) may explain the co-occurrence of family violence and animal cruelty. Based on the results of our scoping review, we recommend that caution should be taken regarding assertions of “the link” without further research to better understand the co-occurrence of family violence and animal cruelty and the factors and mechanisms that influence their co-occurrence.
Modeling of Acoustic Vibration Theory Based on a Micro Thin Plate System and Its Control Experiment Verification
Authors: Jiao Xiaodong , Tao Jin , Sun Hao , Sun Qinglin
Published: November 2022

As a novel control method, acoustic manipulation technology shows extraordinary talents in culturing of tissue and cell, microchip processing and research on material chemistry, which is closely relevant to the vibration modes and the driving signals of the acoustic system. In this paper, bringing up reasonable assumptions, from the perspective of vibration force analysis of a thin plate, the response function of the forced vibration thin plate is derived combining with the Green’s function. Simultaneously, the effective vibration frequencies of micro thin plate are determined. Using the finite element simulation software Comsol 5.6 building thin plate geometry in 2D, the vibration modes of a thin plate are numerically analyzed from the top view and the side view. Additionally, an experimental platform is established, and the vibration experiments of a square micro thin plate (5 cm × 5 cm × 0.625 mm) driven by a central acoustic source is conducted. By comparison, the corresponding experimental results are in good agreement with simulations. Furthermore, single particle motion control is also realized based on the presented platform, and the underlying mechanism is the effects of nodes and anti-nodes on particle motion. The vibrating platform here will become an effective manipulation tool for many scientific fields with the advantage of micro size, good compatibility, and multipurpose.
Research on the Firm Spatial Distribution and Influencing Factors of the Service-Oriented Digital Industry in Yangtze River Delta
Authors: Zhang Jing , Fu Yuhan , Zhang Baifa
Published: November 2022

Exploring the spatial distribution and influencing factors of firms’ location choices in the service-oriented digital industry is of great significance for achieving a new round of rapid development for the digital economy in the context of a post-epidemic situation. Based on the micro perspective of firms, this paper explores the spatial distribution and influencing factors of the location choice of the service-oriented digital industry in the Yangtze River Delta of China. The research shows that the number of the service-oriented digital industry firms varies greatly among cities in the Yangtze River Delta region of China, showing an obvious core–periphery structure. There are many firms in municipalities directly under the central government and provincial capitals, which are the core areas; the number of firms in non-provincial capital cities is small and they are marginal areas. The influencing factors include: per capita GDP, industrial structure upgrading, whether it is a municipality directly under the Central Government or a provincial capital city, whether it has opened high-speed rail, information transmission, number of employees in the computer service industry, number of patent applications, Internet infrastructure level, and scientific fiscal expenditure have a significant positive impact on its location choice, while the per capita total social consumer goods have a significant negative impact on its location choice.
Effect of Characteristics of Shared Housing in Single-Person Households on Housing Satisfaction and Shared Housing Performance
Authors: Baek Juhwa , Kim Seiyong
Published: November 2022

This study quantitatively evaluated South Korean residents’ housing satisfaction and factors affecting shared housing and made recommendations for policy establishment for the supply and revitalization of shared housing in South Korea. Using an online and offline survey, the study analyzed the relationship between the satisfaction with shared housing, intention to reside again in shared housing, and intention to recommend shared housing to others among young people living in shared housing in Seoul. The results showed that shared housing characteristics significantly affected the housing satisfaction but not the intention to reside again and intention to recommend. Housing satisfaction significantly affected the intention to reside again and the intention to recommend. Residents of public-supply shared housing showed no significant correlation between the shared housing characteristics and housing satisfaction; those of private-supply shared housing showed increased housing satisfaction. Housing satisfaction did not significantly affect the intention to reside again among people in public-supply shared housing; however, it affected the intention to reside again among those in private-supply shared housing. The physical location and environment and community factors did not significantly affect overall housing satisfaction in public-supply shared housing but significantly affected the housing satisfaction and intention to reside again in private-supply shared housing. These results support the need for regulation and policy to guide housing adjustments and facilitate lifestyles, the need for diversification in housing types, and the importance of uniform management and operations of public-run units.
Cognitive Dissonance and Public Compliance, and Their Impact on Business Performance in Hotel Industry
Authors: Xi Wen , Baymuminova Nigora , Zhang Yi-Wei , Xu Shi-Nyu
Published: November 2022

In this study, we investigate whether cognitive dissonance and compliance with collective rationality affect hotel CEO management activities. The study surveyed a wide range of hotel employees, from new recruits to CEOs, within 5-star hotels in Seoul to analyze perceptions of organizational members. A canonical correlation analysis was used to empirically investigate the correlations and differences among constitutional concepts. The study also used regression analysis to analyze the influential relationship between variables. The study found that despite the differences in individual beliefs, compliance to collective beliefs increased when individuals complied and received compensation despite their individual differences. The performance perceptions of financial and non-financial management improved at that time. Some research conducted on the Cognitive Dissonance also demonstrates that individuals with cognitive dissonance modify their behavior and cognition to reduce dissonance. It is true that an individual’s opinion differs from that of the group, but adding a cognitive factor that an individual is compensated by participating in and respecting the group’s beliefs leads to public compliance with those beliefs. Due to the strong public cognition and beliefs within the organization, the individual attempts to keep his or her cognitive and belief systems consistent, but complies with them regardless of his or her cognitive and belief systems. Furthermore, it suggests that managers can improve their performance by compensating people for conforming to group rules, since management performance is the end goal of management, and public compliance affects it significantly.
Inundation Depth Shape Phenotypic Variability of Phragmites australis in Liaohe Estuary Wetland, Northeast China
Authors: Cui Panpan , Su Fangli , Zhou Fang
Published: November 2022

Inundation shifts plant growth, species diversity and ecosystem stability, but it remains unclear how inundation depth shapes the phenotypic variability of clonal plants in an estuary wetland. To reveal the response of clonal plant populations to inundation depth, we calculated the variation of Phragmites australis using shoot height, leaf length, leaf width, leaf biomass and panicle length in the Liaohe estuary wetland. Reproductive allocation was defined by the ratio of panicle length to shoot height. Linear regression showed that shoot height, leaf length, leaf width, leaf biomass and panicle length were negatively correlated with inundation depth, while the ratio of panicle length to shoot height was negatively correlated with inundation depth (p < 0.0001). Based on data regarding the statistical differences of plant phenotypic traits among P. australis, we recognized populations had generated variation differentiation. Compared with other functional traits, the coefficient of variation of leaf-related traits were at a high level. Therefore, leaf parameters would be the most suitable, and they increased the area and weight to support the action of plants during floods. Multivariate statistical analysis suggested that P. australis populations in the Liaohe estuary wetland were divided into two phenotypic clusters, consistent with geographical distance and morphological similarity. Our results provide a novel perspective on the ecological strategy of cloned plants under inundation change and offer theories for the conservation and restoration of estuarine wetland ecosystems.
A Systematic Literature Review of Quantitative Studies Assessing the Relationship between Water and Conflict on the African Continent
Authors: Mack Elizabeth A. , Marcantonio Richard A. , Bunting Erin , Ross Amanda , Zimmer Andrew , Zulu Leo C. , Fernández Edna Liliana Gómez , Herndon Jay , Henebry Geoffrey M.
Published: November 2022

Since the seminal 2012 Special Issue of the Journal of Peace Research about climate change and conflict, at least 35 review papers on the topic have been published. To our knowledge, none of these reviews focused on water and conflict specifically. In order to address this research gap, the present article conducts a systematic review of scholarship examining the linkages between water and conflict, focusing on quantitative studies using secondary data sources. This review focuses on the African continent given projections about the intersection between water issues and conflict in this region, as well as the popularity of this portion of the world in studies of climate change and conflict. We discuss the findings of papers reviewed and propose six avenues for future research. As research about this topic advances, it will require attention to nuances in data processing, integration, and modeling across spatial and temporal scales, if the outcomes of this body of scholarship are to be leveraged to guide the decision making of governing bodies.
Climate Change Education in Primary and Lower Secondary Education: Systematic Review Results
Authors: Nepraš Karel , Strejčková Tereza , Kroufek Roman
Published: November 2022

Global climate change is rightly receiving increasing attention, including in the field of education. Climate change education (CCE) is becoming one of the key areas of education in general and it is therefore not surprising that more attention is also being paid to climate education research. This review study presents an analysis of papers focusing on climate education for ISCED (International Standard Classification of Education) 1 and 2 students. The selection of the analyzed sources follows the PRISMA 2020 statement; out of 850 identified records, 43 made it into the analysis. Articles that met each of the following criteria were included in the analysis: (i) the impact of the article meets the inclusion in the Web of Science Core Collection service, (ii) the article was published in the period 2001–2020, (iii) the central theme of the article is CCE, (iv) the target audience is ISCED 1, ISCED 2 students or their teachers, (v) the article has a research character with the presentation of methodology and results, or it is a review study that includes an analysis of the records meeting the criteria described above. The quantitative description of the results shows the diversity in journals publishing studies, the exponential increase in their number in recent years, and the hegemony of the United States in this field of research. The qualitative content analysis shows that the most frequently addressed topics focus on knowledge, behavior, actions, experiences, and attitudes. The results show that as the urgency of climate change increases, so do the number and diversity of research studies on climate education for the target group. Most of these studies focus on the mitigation behaviors and willingness to act of students.
Association between Sugar Intake and an ECG Parameter: A Case Study on Young Athletes
Authors: Šarčević Zoran , Lužanin Zorana , Tepavčević Andreja
Published: November 2022

In many recent studies, high sugar intake has been shown to have harmful effects on the cardiovascular system, especially in children. This study aimed to check the association between sugar intake and T-peak to T-end interval, an electrocardiogram (ECG) parameter associated with a higher risk of early afterdepolarizations and ventricular arrhythmias in young athletes. The study participants were 96 young athletes aged 7–15, 34 girls and 62 boys trained in various sports. ECGs were performed on all participants and analyzed for the duration of the period from the peak of the T-wave to the end of the T-wave. The data on years of training and training hours per week, type of sport, age, height, and weight were taken, as well as the child’s sugar intake per day. The cut-off was a minimum of 30 g of concentrated sugar in sweets or drinks. The group of children that consumed sugar had, on average, a significantly longer duration of the T-peak to T-end (TpTe) interval. There is an association between sugar intake and the duration of the T-peak to T-end (TpTe) interval. In our case study, it was demonstrated that the benefit of playing sports cannot compensate for the harm of overconsumption of sugar, so an important public health target would be to lower sugar intake in children’s diets (particularly in the diets of young athletes). It would be necessary to determine interventions and activities that would affect the reduction in sugar intake from early childhood. Aside from the fact that overconsumption of sugar is associated with human health issues, there are many environmental benefits of reducing sugar production and consumption.
Energy-Saving Operation Strategy for Hotels Considering the Impact of COVID-19 in the Context of Carbon Neutrality
Authors: Wu Yutong , Xin Bin , Zhu Hongyu , Ye Zifei
Published: November 2022

With the advent of the post-epidemic era, the energy consumption characteristics of hotels have changed, which has an important impact on urban energy conservation. In order to contribute to the goal of carbon neutrality, this study discusses the energy-saving operation strategy of hotels considering the impact of the COVID-19 epidemic. Based on the energy consumption characteristics of large public buildings, this paper analyzes the energy consumption distribution and operation characteristics of hotel buildings in detail. By collecting energy consumption data from five typical large hotel buildings in a tourist city in southern China from 2018 to 2022, the impact of COVID-19 on hotel energy consumption and hotel business characteristics was discussed in detail. Combined with the economic development characteristic in the post-epidemic era, this paper explores the energy-saving strategies that hotels can adopt in the context of normalized epidemic prevention and control and obtains the optimal path of low-carbon economic operation of hotel buildings. This study reveals the energy consumption characteristics and energy-saving potential of hotel buildings, and provides enlightenment for hotel management and low-carbon development in the post-epidemic era.
A Multicriteria Decision Framework for Solar Power Plant Location Selection Problem with Pythagorean Fuzzy Data: A Case Study on Green Energy in Turkey
Authors: Mirzaei Nima
Published: November 2022

Turkey is one of the most suitable countries for solar power plants, owing to its ideal location in terms of receiving solar radiation; accordingly, plans are in place to expand its solar power plant system to fulfill the increasing energy demand. In this study, a combination of multicriteria decision-making and fuzzy logic was used to evaluate potential locations (cities in southern Turkey) to install new solar power plants subject to different criteria of an uncertain nature. The proposed methodology has several attractive features, which are described throughout this study. The criteria were selected based on the literature and the opinions of experts. In addition, a new criterion (capacity of existing solar power plants) was added to achieve more precise results. Ten criteria and eighteen cities were selected to form the decision matrix for the problem. First, the weight of each criterion was computed by stepwise weight assessment ratio analysis (SWARA). Then, the TOPSIS approach was extended to the Pythagorean fuzzy form in ranking the locations of the decision matrix as a new solution procedure. The results show that the best candidate city to install a new solar power plant is Antalya, followed by Karaman and Malatya as the second and third best candidates, respectively. Finally, to measure the impact of the changes in the weight of the criteria, a sensitivity analysis was conducted. Multiple scenarios were considered, and the results indicated that Antalya was the best alternative in most of the scenarios.
Smart Evaluation of Sustainability of Photovoltaic Projects in the Context of Carbon Neutrality Target
Authors: Ding Wei , Zhao Xuguang , Meng Weigao , Wang Haichao
Published: November 2022

To support the sustainable development of photovoltaic (PV) projects in the context of the carbon neutrality aim, a scientific and reliable evaluation technique is crucial. In this research, an AdaBoost-LS-WSVM intelligent evaluation model built on the Spark platform is suggested to increase evaluation accuracy and timeliness. Firstly, the sustainability evaluation index system of PV projects is constructed from five levels: geographic resource sustainability, technical sustainability, economic sustainability, social sustainability, and environmental sustainability in the context of the carbon neutrality target. Then, the AdaBoost-LS-WSVM intelligent evaluation model with Spark as the platform is constructed, and the wavelet kernel function is applied to the LSSVM model to form the LS-WSVM regression model with stronger nonlinear fitting ability. The learning and training of training samples are completed by the AdaBoost model, and multiple weak LS-WSVM regressors are weighted to get a strong LS-WSVM regressor. The regression model is used for assessing the sustainability of PV projects on Spark Big Data runtime platform. Lastly, the scientific accuracy and reliability of the proposed model is confirmed by a case study, which facilitates a timely and effective assessment of the sustainability of PV projects in the context of carbon neutrality target and can provide scientific and reasonable decision support for the construction of a sustainable development model of PV projects.
Mountain Outdoor Tourism and Tibetan Mountain Guides’ Place Identity: The Case of Mt. Siguniang Town
Authors: Liu Yong , He Jinhao , Chen Qianyu , Fu Bing , Dong Erwei
Published: November 2022

This paper aimed to investigate the factors influencing the place identity of Tibetan mountain guides, in Mt. Siguniang Town, in the context of the development of mountain outdoor tourism. Based on Breakwell’s Place Identity Process Theory of distinctiveness, continuity, self-esteem, and self-efficacy, this paper analyzed data, collected by semi-structured interviews and the observation method, through content analysis and interpretivism. The research analysis provided the following conclusions: (1) The influencing factors on self-efficacy are economic income, family reunion, and community development; (2) The influencing factors on self-esteem are external evaluation, host–guest interaction, professional identity, and self-identity; (3) The influencing factors on continuity are career change, host–guest interaction, and personal growth; (4) The influencing factors on distinctiveness are sense of belonging and natural environment; (5) Among the factors, only influencing factors on distinctiveness had no significant association with mountain outdoor tourism. Factors regarding the other three dimensions were correlated with mountain outdoor tourism. The findings of this study could contribute to further research in mountain outdoor tourism and place identity, as well as provide references for local management and tourism development.
Detection and In-Depth Analysis of Causes of Delay in Construction Projects: Synergy between Machine Learning and Expert Knowledge
Authors: Ivanović Marija Z. , Nedeljković Đorđe , Stojadinović Zoran , Marinković Dejan , Ivanišević Nenad , Simić Nevena
Published: November 2022

Due to numerous reasons, construction projects often fail to achieve the planned duration. Detecting causes of delays (CoD) is the first step in eliminating or mitigating potential delays in future projects. The goal of research is unbiased CoD detection at a single project level, with the ultimate goal to discover the root causes of delay. The existing approach is based on expert knowledge which is used to create CoD lists for projects in general or groups of similar projects. When applied to a single project, it is burdened with bias, as shown on a case project returning low Spearman Rank correlation values. This research introduces a Delay Root causes Extraction and Analysis Model—DREAM. The proposed model combines expert knowledge, machine learning techniques, and Minutes of Meetings (MoM) as an unutilized extensive source of information. In the first phase, DREAM outputs a CoD list based on occurring frequency in MoM with satisfactory recall values, significantly reducing expert-induced subjectivism. In the second phase, enabled by MoM dates, DREAM adds another dimension to delay analysis—temporal CoD distribution. By analyzing corresponding informative charts, experts can understand the nature of delays and discover the root CoD, allowing intelligent decision making on future projects.
Design a Robust Logistics Network with an Artificial Physarum Swarm Algorithm
Authors: Cai Zhengying , Yang Yuanyuan , Zhang Xiangling , Zhou Yan
Published: November 2022

The robust optimization of logistics networks can improve the ability to provide sustainable service and business sustainability after uncertain disruptions. The existing works on the robust design of logistics networks insisted that it is very difficult to build a robust network topology, and this kind of optimization problem is an NP-hard problem that cannot be easily solved. In nature, Physarum often needs to build a robust and efficient topological network to complete the foraging process. Recently, some researchers used Physarum to build a robust transportation network in professional biological laboratories and received a good performance. Inspired by the foraging behavior of natural Physarum, we proposed a novel artificial Physarum swarm system to optimize the logistics network robustness just on a personal computer. In our study, first, the robustness optimization problem of a logistics network is described as a topology optimization model based on graph theory, and four robustness indicators are proposed to build a multi-objective robustness function of logistics network topology, including the relative robustness, the betweenness robustness, the edge robustness and the closeness robustness. Second, an artificial Physarum swarm system is developed to simulate the foraging behavior of a natural Physarum swarm to solve this kind of complex robust optimization problem. The proposed artificial Physarum swarm system can search for optimal solutions by expansion and contraction operations and the exchange of information with each other through a self-learning experience and neighbor-learning experiences. The plasmodium of Physarum forms the edges, and the external food sources simulate the logistics nodes. Third, an experimental example is designed on the basis of Mexico City to verify the proposed method, and the results reveal that the artificial Physarum swarm system can help us effectively improve the logistics network robustness under disruptions and receive a better performance than natural Physarum. The article may be helpful for both theory and practice to explore the robust optimization in logistics operation and provide engineers with an opportunity to resist logistics disruptions and risk loss by a novel artificial intelligence tool.