bluuebunny/crossref_metadata_2025 huggingface dataset:
Lightweight and Efficient Dynamic Cluster Head Election Routing Protocol for Wireless Sensor Networks
Authors: Yagoub Mudathir F. S. , Khalifa Othman O. , Abdelmaboud Abdelzahir , Korotaev Valery , Kozlov Sergei A. , Rodrigues Joel J. P. C.
Published: August 2021

Wireless Sensor Networks (WSNs) have gained great significance from researchers and industry due to their wide applications. Energy and resource conservation challenges are facing the WSNs. Nevertheless, clustering techniques offer many solutions to address the WSN issues, such as energy efficiency, service redundancy, routing delay, scalability, and making WSNs more efficient. Unfortunately, the WSNs are still immature, and suffering in several aspects. This paper aims to solve some of the downsides in existing routing protocols for WSNs; a Lightweight and Efficient Dynamic Cluster Head Election routing protocol (LEDCHE-WSN) is proposed. The proposed routing algorithm comprises two integrated methods, electing the optimum cluster head, and organizing the re-clustering process dynamically. Furthermore, the proposed protocol improves on others present in the literature by combining the random and periodic electing method in the same round, and the random method starts first at the beginning of each round/cycle. Moreover, both random and periodic electing methods are preceded by checking the remaining power to skip the dead nodes and continue in the same way periodically with the rest of the nodes in the round. Additionally, the proposed protocol is distinguished by deleting dead nodes from the network topology list during the re-clustering process to address the black holes and routing delay problems. Finally, the proposed algorithm’s mathematical modeling and analysis are introduced. The experimental results reveal the proposed protocol outperforms the LEACH protocol by approximately 32% and the FBCFP protocol by 8%, in terms of power consumption and network lifetime. In terms of Mean Package Delay, LEDCHE-WSN improves the LEACH protocol by 42% and the FBCFP protocol by 15%, and regarding Loss Ratio, it improves the LEACH protocol by approximately 46% and FBCFP protocol by 25%.
Implementation of a Deep Learning Algorithm Based on Vertical Ground Reaction Force Time–Frequency Features for the Detection and Severity Classification of Parkinson’s Disease
Authors: Setiawan Febryan , Lin Che-Wei
Published: August 2021

Conventional approaches to diagnosing Parkinson’s disease (PD) and rating its severity level are based on medical specialists’ clinical assessment of symptoms, which are subjective and can be inaccurate. These techniques are not very reliable, particularly in the early stages of the disease. A novel detection and severity classification algorithm using deep learning approaches was developed in this research to classify the PD severity level based on vertical ground reaction force (vGRF) signals. Different variations in force patterns generated by the irregularity in vGRF signals due to the gait abnormalities of PD patients can indicate their severity. The main purpose of this research is to aid physicians in detecting early stages of PD, planning efficient treatment, and monitoring disease progression. The detection algorithm comprises preprocessing, feature transformation, and classification processes. In preprocessing, the vGRF signal is divided into 10, 15, and 30 s successive time windows. In the feature transformation process, the time domain vGRF signal in windows with varying time lengths is modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, principal component analysis (PCA) is used for feature enhancement. Finally, different types of convolutional neural networks (CNNs) are employed as deep learning classifiers for classification. The algorithm performance was evaluated using k-fold cross-validation (kfoldCV). The best average accuracy of the proposed detection algorithm in classifying the PD severity stage classification was 96.52% using ResNet-50 with vGRF data from the PhysioNet database. The proposed detection algorithm can effectively differentiate gait patterns based on time–frequency spectrograms of vGRF signals associated with different PD severity levels.
Multi-Headed Conv-LSTM Network for Heart Rate Estimation during Daily Living Activities
Authors: Wilkosz Michał , Szczęsna Agnieszka
Published: August 2021

Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during physical activity under free-living conditions. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. Because of their generalization capabilities, deep learning methods can be a major direction in the search for a heart rate estimation solution based on signals from wearable devices. A novel multi-headed convolutional neural network model enriched with long short-term memory cells (MH Conv-LSTM DeepPPG) was proposed for the estimation of heart rate based on signals measured by a wrist-worn wearable device, such as PPG and acceleration signals. For the PPG-DaLiA dataset, the proposed solution improves the performance of previously proposed methods. An experimental approach was used to develop the final network architecture. The average mean absolute error (MAE) of the final solution was 6.28 bpm and Pearson’s correlation coefficient between the estimated and true heart rate values was 0.85.
Damage Detection on a Beam with Multiple Cracks: A Simplified Method Based on Relative Frequency Shifts
Authors: Gillich Gilbert-Rainer , Maia Nuno M. M. , Wahab Magd Abdel , Tufisi Cristian , Korka Zoltan-Iosif , Gillich Nicoleta , Pop Marius Vasile
Published: August 2021

Identifying cracks in the incipient state is essential to prevent the failure of engineering structures. Detection methods relying on the analysis of the changes in modal parameters are widely used because of the advantages they present. In our previous research, we found that eigenfrequencies were capable of indicating the position and depth of damage when sufficient vibration modes were considered. The damage indicator we developed was based on the relative frequency shifts (RFS). To calculate the RFSs for various positions and depths of a crack, we established a mathematical relation that involved the squared modal curvatures in the healthy state and the deflection of the healthy and damaged beam under dead mass, respectively. In this study, we propose to calculate the RFS for beams with several cracks by applying the superposition principle. We demonstrate that this is possible if the cracks are far enough from each other. In fact, if the cracks are close to each other, the superposition method does not work and we distinguish two cases: (i) when the cracks affect the same beam face, the frequency drop is less than the sum of the individual frequency drops, and (ii) on the contrary, cracks on opposite sides cause a decrease in frequency, which is greater than the sum of the frequency drop due to individual damage. When the RFS curves are known, crack assessment becomes an optimization problem, the cost function being the distance between the measured RFSs and all possible RFSs for several vibration modes. Thus, the RFS constitutes a benchmark that characterizes damage using only the eigenfrequencies. We can accurately locate multiple cracks and estimate their severity through experiments and thus prove the reliability of the proposed method.
Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings
Authors: Najeh Taoufik , Lundberg Jan , Kerrouche Abdelfateh
Published: August 2021

The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.
LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices
Authors: Sanchez-Iborra Ramon
Published: August 2021

The penetration of wearable devices in our daily lives is unstoppable. Although they are very popular, so far, these elements provide a limited range of services that are mostly focused on monitoring tasks such as fitness, activity, or health tracking. Besides, given their hardware and power constraints, wearable units are dependent on a master device, e.g., a smartphone, to make decisions or send the collected data to the cloud. However, a new wave of both communication and artificial intelligence (AI)-based technologies fuels the evolution of wearables to an upper level. Concretely, they are the low-power wide-area network (LPWAN) and tiny machine-learning (TinyML) technologies. This paper reviews and discusses these solutions, and explores the major implications and challenges of this technological transformation. Finally, the results of an experimental study are presented, analyzing (i) the long-range connectivity gained by a wearable device in a university campus scenario, thanks to the integration of LPWAN communications, and (ii) how complex the intelligence embedded in this wearable unit can be. This study shows the interesting characteristics brought by these state-of-the-art paradigms, concluding that a wide variety of novel services and applications will be supported by the next generation of wearables.
Training Load in Different Age Category Soccer Players and Relationship to Different Pitch Size Small-Sided Games
Authors: Santos Fernando J. , Figueiredo Teresa P. , Filho Dalton M. Pessôa , Verardi Carlos E. L. , Macedo Anderson G. , Ferreira Cátia C. , Espada Mário C.
Published: August 2021

This study sought to evaluate the training load in different age category soccer players associated with distinct pitch size small-sided games (SSGs). Twenty-four soccer players (eight in each age category: U-12, U-15, and U-23) performed three consecutive 4 vs. 4 ball possession SSGs (SSG1: 16 × 24 m; SSG2: 20 × 30 m; and SSG3: 24 × 36 m) all with 3 min duration and 3 min rest. Subjects carried ultra-wideband-based position-tracking system devices (WIMU PRO, RealTrack System). Total distance covered increased from SSG1 to SSG3 in all age categories and predominantly in running speeds below 12 km·h−1. Moreover, distance covered in 12–18 km·h−1 running speed was different in all performed SSGs and age categories. Residual or null values were observed at 18–21 km·h−1 or above running speed, namely in U-12, the only age category where metabolic power and high metabolic load distance differences occurred throughout the performed SSGs. Edwards’ TRIMP differences between age categories was only observed in SSG2 (U-12 < U-15). The design of SSGs must consider that the training load of the players differs according to their age category and metabolic assessment should be considered in parallel to external load evaluation in SSGs. Wearable technology represents a fundamental support in soccer.
Texture Recognition Based on Perception Data from a Bionic Tactile Sensor
Authors: Huang Shiyao , Wu Hao
Published: August 2021

Texture recognition is important for robots to discern the characteristics of the object surface and adjust grasping and manipulation strategies accordingly. It is still challenging to develop texture classification approaches that are accurate and do not require high computational costs. In this work, we adopt a bionic tactile sensor to collect vibration data while sliding against materials of interest. Under a fixed contact pressure and speed, a total of 1000 sets of vibration data from ten different materials were collected. With the tactile perception data, four types of texture recognition algorithms are proposed. Three machine learning algorithms, including support vector machine, random forest, and K-nearest neighbor, are established for texture recognition. The test accuracy of those three methods are 95%, 94%, 94%, respectively. In the detection process of machine learning algorithms, the asamoto and polyester are easy to be confused with each other. A convolutional neural network is established to further increase the test accuracy to 98.5%. The three machine learning models and convolutional neural network demonstrate high accuracy and excellent robustness.
Scrutinising Multidimensional Challenges in the Maloti-Drakensberg (Lesotho/South Africa)
Authors: Delves Jess , Clark V. , Schneiderbauer Stefan , Barker Nigel , Szarzynski Jörg , Tondini Stefano , Vidal João , Membretti Andrea
Published: August 2021

The Maloti-Drakensberg (MD) is the largest and highest-elevation mountain system in southern Africa. Covering 40,000 km2 and reaching 3500 m, the MD provides a range of ecosystem services (ES) to the entire southern African region—benefitting diverse users and extending well beyond the mountains. Rapid socioecological change threatens the provision of ES and presents multidimensional challenges to sustainable development. However, the continued land degradation and persisting socioeconomic problems indicate that development policy has not been effective in tackling these issues. In this paper, a multidisciplinary literature review forms the basis of a discussion which takes an ES framing to scrutinise the multidimensional social, political, economic and cultural issues in the study area. Three critical management systems are presented, and their associated ES are discussed, namely, water transfer, rangelands and conservation and tourism. In particular, the diversity of ES uses and values in the MD is considered. The results reveal the main drivers of continued unsustainable development and highlight important information gaps.
A Preliminary Geothermal Prospectivity Mapping Based on Integrated GIS, Remote-Sensing, and Geophysical Techniques around Northeastern Nigeria
Authors: Yusuf Abubakar , San Lim Hwee , Abir Ismail Ahmad
Published: August 2021

Spatial mapping of potential geothermal areas is an effective tool for a preliminary investigation and the development of a clean and renewable energy source around the globe. Specific locations within the Earth’s crust display some manifestations of sub-surface geothermal occurrences, such as hot springs, a volcanic plug, mud volcanoes, and hydrothermal alterations, that need to be investigated further. The present area of investigations also reveals some of these manifestations. However, no attempt was made to examine the prospectivity of this terrain using the efficient GIS-based multicriteria evaluation (MCE) within the scope of the Analytic hierarchy process (AHP). The integration of remote sensing, Geographic information system (GIS), and other geophysical methods (Magnetic and gravity) was performed to map the promising geothermal areas. Multiple input data sets such as aero-magnetic, aero-gravity, aero-radiometric, digital elevation model (DEM), geological map, and Landsat-8 Operational Land Imager (OLI) data were selected, processed, and use to generate five thematic layers, which include heat flow, temperature gradients, integrated lineaments, residual gravity, and lithology maps. The five thematic layers were standardized and synthesized into a geothermal prospectivity map. The respective ranks and weight of the thematic layers and their classes were assigned based on expert opinion and knowledge of the local geology. This research aims to apply an efficient method to evaluate the factors influencing the geothermal energy prospects, identify and map prospective geothermal regions, and, finally, create a geothermal prospectivity model.
Bullying, Cyberbullying and the Overlap: What Does Age Have to Do with It?
Authors: Pichel Rafael , Foody Mairéad , O’Higgins Norman James , Feijóo Sandra , Varela Jesús , Rial Antonio
Published: August 2021

School bullying and cyberbullying represent the most common forms of victimization during childhood and adolescence in many countries across the globe. Although they can be studied as distinct phenomena with their own defining characteristics, there is evidence to suggest that they are related and often co-occur. The present research aimed to estimate the rates of school bullying and cyberbullying, studied their evolution by age, and analyzed any possible overlap between the two. An empirical study was carried out with a large sample of children and adolescents in Galicia, Spain (N = 2083), where 10–17 year olds were presented with The European Bullying Intervention Project Questionnaire and European Cyberbullying Intervention Project Questionnaire. School bullying was found to be more prevalent than cyberbullying, with 25.1% involved as victims and 14.3% as bully-victims, while the cyberbullying rates were 9.4% for victims and 5.8% for bully-victims. Perpetration rates were similar for school and cyberbullying (4.4% and 4.3% respectively). The overlap between both phenomena adds to the evidence for a whole-community approach to tackling all types of bullying and victimization experiences, as opposed to each in silo. The clear age differences in bullying behaviours also suggest the appropriateness of tailoring anti-bullying programs to target specific age groups.
Sustainable Workplace: Impact of Authentic Leadership on Change-Oriented Organizational Citizenship Behavior and the Moderating Role of Perceived Employees’ Calling
Authors: Jang Eunmi
Published: August 2021

To be sustainable in the current rapidly changing business environment, organizations must strive to adapt and respond to a new environment. Employees are the key performers of organizational change. Furthermore, change-oriented organizational citizenship behavior (OCB) is essential for them to positively accept and implement organizational change. Additionally, the leader’s role is crucial to promoting such change-oriented OCB. In this regard, this study investigates the effect of authentic leadership on change-oriented OCB, demonstrating that the vocational calling of employees strengthens such positive influences. Based on the self-determination theory (SDT), this study examines that the moderating effect between authentic leadership on change-oriented OCB increases when employees have a higher perception of calling for work than lower. This study uses a two-wave data set gathered from 485 currently working employees in South Korea. The empirical analysis is revealed below. First, authentic leadership has a positive effect on direct OCB. Second, employees’ perception of calling has a positive effect on direct change-oriented OCB. Third, the higher the level of employees’ perception of calling, the greater the effect of authentic leadership on change-oriented OCB. The most significant theoretical contribution of the study is that it is the first to determine that calling acts as a moderating factor between authentic leadership and change-oriented OCB. The fact that the positive effect of authentic leadership on change-oriented OCB increases when there is a high calling implies that employees are more likely to conduct change-oriented OCB when they perceive a high level of calling. Based on this result, this study explains the method and reason for maximizing change-oriented OCB through authentic leadership.
What Causes Waterlogging?—Explore the Urban Waterlogging Control Scheme through System Dynamics Simulation
Authors: Chen Weike , Dong Jing , Yan Chaohua , Dong Hui , Liu Ping
Published: August 2021

It is a common phenomenon in cities that waterlogging affects people’s normal life. It is of great significance for targeted transformation and upgrading to identify the risk factors of urban waterlogging. This paper collected the waterlogging data of Tianjin in China, analyzed the coupling mechanism among waterlogging risk factors of urban drainage systems, and then selected the system dynamics theory and the Vensim software as the analysis tools due to the mixing characteristic and the limited availability of data. After that, the sensitive factors were identified by model simulation and sensitivity analysis, and the prominent impact of urban expansion on waterlogging risk was discussed. Then, through the comparison of the three simulation scenarios, it was found that, compared with the urban development focus shifting strategy, the strategies of sponge city reconstruction and management optimization could achieve the risk control goal within a shorter time. On this basis, two kinds of governance schemes with strong operability were put forward, which were the data governance strategy and the sponge city reconstruction strategy of giving priority to old urban areas. According to the simulation results, a city can reverse the increasing trend of waterlogging risk within ten years under the appropriate scheme. Furthermore, the paper puts forward the strategic reimagining of the rural revitalization strategy and the ecological restoration strategy for the long-term sustainable development transformation of Tianjin.
Scale Characteristics and Optimization of Park Green Space in Megacities Based on the Fractal Measurement Model: A Case Study of Beijing, Shanghai, Guangzhou, and Shenzhen
Authors: Li Zhen , Zhao Wanmin , Nie Miaoyao
Published: August 2021

This paper applies fractal theory to research of green space in megacity parks due to the lack of a sufficient qualitative description of the scale structure of park green space, a quantifiable evaluation system, and operable planning methods in traditional studies. Taking Beijing, Shanghai, Guangzhou, and Shenzhen as examples, GIS spatial analysis technology and the Zipf model are used to calculate the fractal dimension (q), the goodness of fit (R2), and the degree of difference (C) to deeply interpret the connotation of indicators and conduct a comparative analysis between cities to reveal fractal characteristics and laws. The research results show that (1) the fractal dimension is related to the complexity of the park green space system; (2) the fractal dimension characterizes the hierarchical iteration of the park green space to a certain extent and reflects the internal order of the scale distribution; (3) the scale distribution of green space in megacity parks deviates from the ideal pyramid configuration; and (4) there are various factors affecting the scale structure of park green space, such as natural base conditions, urban spatial structure, and the continuation of historical genes working together. On this basis, a series of targeted optimization strategies are proposed.
A New Formulation to Estimate the Elastic Modulus of Recycled Concrete Based on Regression and ANN
Authors: Felix Emerson Felipe , Possan Edna , Carrazedo Rogério
Published: August 2021

A new formulation to estimate the elastic modulus of concrete containing recycled coarse aggregate is proposed in this work using artificial neural networks (ANN) and nonlinear regression. Up to six predictors variables were used to training 243 ANN. The models were generated based on results obtained from experimental campaigns. Feedforward neural network and Levenberg–Marquardt back propagation algorithm were used for training the ANN. The best ANN was found with the architecture 6-4-2-1 (input -1st hidden layer -2nd hidden layer -output), attaining a root-mean-square error of 2.4 GPa associated with a coefficient of determination of 0.91. Once the ANN model was established, 46,656 concrete samples were created. These were employed to formulate the model using nonlinear regression. The developed model showed a highly efficient performance to predict the elastic modulus. Lastly, considering the parametric study conducted, the results pointed out that the approach can be applied to predict the concrete elastic modulus and can indicate better mix proportions for concretes containing natural and/or recycled coarse aggregates, enabling its use as a simulation tool in the development of engineering projects focused on durability and sustainability.
The Measurement and Influencing Factors of Total Factor Productivity in the Chinese Rural Distribution Industry
Authors: Ding Chaoxun , Zhang Ruidan
Published: August 2021

Total factor productivity (TFP) is critical to the sustainable development of the rural distribution industry. Improvements in productivity of the rural distribution industry can promote the high-quality development of the Chinese distribution industry. Studying the characteristics and influencing factors of total factor productivity in regard to the rural distribution industry in China is significant for promoting the transformation and development of the rural distribution industry. This paper uses the DEA–Malmquist Index to measure the total factor productivity (TFP) of the Chinese rural distribution industry and its decomposition index, and uses a panel data model to empirically study its influencing factors. The results show that, from 2008 to 2018, the TFP of the Chinese rural distribution industry showed a trend of rising first and then fluctuating and declining, with an average annual growth rate of 2.93%; the fluctuation direction of the TFP of the rural distribution industry in the eastern and western regions of China is basically the same, which has had a reverse change relationship with the central and northeast regions for many years. The industrial structure, urbanization rate, rural informatization rate, and conditions of the transportation facilities have significant impacts on the TFP of the rural distribution industry, among which the informatization rate has the greatest positive impact.
Climate Change and Food Security: The Impact of Some Key Variables on Wheat Yield in Kazakhstan
Authors: Shmelev Stanislav E. , Salnikov Vitaliy , Turulina Galina , Polyakova Svetlana , Tazhibayeva Tamara , Schnitzler Tobias , Shmeleva Irina A.
Published: August 2021

In such drought-prone regions as Kazakhstan, research on regional drought characteristics and their formation conditions is of paramount importance for actions to mitigate drought risks caused by climate change. This paper presents the results of research on the spatio-temporal patterns of atmospheric droughts as one of the most important factors hindering the formation of crop yields. The influence of several potential predictors characterizing teleconnection in the coupled “atmosphere–ocean” system and cosmic-geophysical factors affecting their formation is analyzed. The spatial relationships between atmospheric aridity at the individual stations of the investigated area and the wheat yield in Kazakhstan as well as its relationships with potential predictors were determined using econometric methods. High correlation was shown between wheat yield fluctuations and Multivariate El-Niño–Southern Oscillation (ENSO), galactic cosmic radiation, solar activity, and atmospheric drought expressed through the soil moisture index, which in turn depends on precipitation levels and temperatures. The model could be modified further so that the individual components could be forecasted into the future using various time series in an ARIMA model. The resulting integration of these forecasts would allow the prediction of wheat yields in the future. The obtained results can be used in the process of creating effective mechanisms for adaptation to climate change and droughts based on their early diagnosis.
Global Fisheries Management and Community Interest
Authors: Zhang Lei
Published: August 2021

The conservation of fishery resources is a common interest of the international community. In the 1990s, the global fisheries management system was formed based on the 1982 United Nations Convention on the Law of the Sea (LOSC). In order to achieve sustainable fisheries management, the international community also adopted a series of legally binding and non-binding policy instruments for the implementation of the global fisheries regime. The regional institutional framework was strengthened and expanded to offer broad coverage worldwide. Based on the analysis of the global fisheries management system, the article concludes that the current legal and policy instruments collectively provide a comprehensive framework for global fisheries management, but there still exist limits in addressing the challenges of fishery resources today. More effective implementation of the current legal system through better cooperation among States, as well as efficient coordination within and between national, regional and global institutions, is required.
Effect of Intake Air Temperature and Premixed Ratio on Combustion and Exhaust Emissions in a Partial HCCI-DI Diesel Engine
Authors: Teoh Yew Heng , Huspi Hishammudin Afifi , How Heoy Geok , Sher Farooq , Din Zia Ud , Le Thanh Danh , Nguyen Huu Tho
Published: August 2021

Homogeneous charge compression ignition (HCCI) is considered an advanced combustion method for internal combustion engines that offers simultaneous reductions in oxides of nitrogen (NOx) emissions and increased fuel efficiency. The present study examines the influence of intake air temperature (IAT) and premixed diesel fuel on fuel self-ignition characteristics in a light-duty compression ignition engine. Partial HCCI was achieved by port injection of the diesel fuel through air-assisted injection while sustaining direct diesel fuel injection into the cylinder for initiating combustion. The self-ignition of diesel fuel under such a set-up was studied with variations in premixed ratios (0–0.60) and inlet temperatures (40–100 °C) under a constant 1600 rpm engine speed with 20 Nm load. Variations in performance, emissions and combustion characteristics with premixed fuel and inlet air heating were analysed in comparison with those recorded without. Heat release rate profiles determined from recorded in-cylinder pressure depicted evident multiple-stage ignitions (up to three-stage ignition in several cases) in this study. Compared with the premixed ratio, the inlet air temperature had a greater effect on low-temperature reaction and HCCI combustion timing. Nonetheless, an increase in the premixed ratio was found to be influential in reducing nitric oxides emissions.
Sanitation Upgrading as Climate Action: Lessons for Local Government from a Community Informal Settlement Project in Cape Town
Authors: Peirson Alma Elisabeth , Ziervogel Gina
Published: August 2021

In light of the increasing call for climate action, there is a growing body of literature studying the ways in which informal settlements in the Global South are adapting to the impacts of climate change. In these particularly vulnerable communities where the existing infrastructural vulnerabilities faced by residents are exacerbated by the hazards of climate change, multi-level approaches involving more inclusive forms of governance are needed for the implementation of climate action. Drawing from the case of a sanitation upgrading project in the informal settlement of Murray, located in Philippi, Cape Town, this paper adds to current understandings of multi-level rapid climate action in informal settlements by endeavouring to address two gaps in this body of literature. Firstly, this paper demonstrates a link between climate change and sanitation which has received little attention by showing that improving sanitation infrastructure makes communities more resilient to extreme weather events associated with climate change. Secondly, the paper addresses how and by whom rapid climate action can be implemented in complex socio-institutional contexts such as informal settlements where the impacts of climate change are felt particularly strongly. This paper identifies what enabled and constrained climate action in the Murray informal settlement in an attempt to provide lessons for local government from the case of the sanitation upgrading project. Bottom-up initiation of multi-level climate action is dependent on fragile partnerships which require the support and involvement of a skilled and dedicated local government. Nevertheless, co-operative and transparent engagements across levels hold the potential to contribute to transformative adaptation through the establishment of new partnerships and forms of governance which recognise community groups as legitimate stakeholders and acknowledge the importance of lived experiences and mentalities.
Cyclone Aila and Post-Disaster Housing Assistance in Bangladesh
Authors: Saha Sebak Kumar , Ballard Chris
Published: August 2021

This paper examines the processes and outcomes of a post-disaster housing assistance program delivered by an NGO, Islamic Relief Bangladesh (ISRB), in one of the villages worst affected by Cyclone Aila in Bangladesh in 2009. The findings, based on both qualitative and quantitative data collected in the field, reveal that the selection of the most suitable beneficiaries for housing aid was subject to undue influence from both elected and unelected leaders. A broad tendency on the part of ISRB to deliver houses through a top-down approach left little room for meaningful consultation with local people. The recipients thus felt excluded and became passive recipients. Although ISRB adopted a ‘build back better’ principle in the delivery of the housing assistance, the capacity of the new houses to withstand a severe cyclone remains limited. Despite some unhappiness with the process through which the housing was delivered, and dissatisfaction with certain aspects of the houses, most recipients were satisfied overall as they would have been unable to build houses of the same quality by themselves due to their poverty. These findings should be of direct value for relevant government agencies, NGOs, and donor agencies in the future delivery of more successful outcomes in post-disaster contexts in Bangladesh and elsewhere.
Characteristics of Indoor Flood Fatalities: Evidence from Greece
Authors: Diakakis Michalis , Papagiannaki Katerina
Published: August 2021

Despite the important advances in flood forecasting and protection, floods remain one of the most lethal types of natural hazards. Previous works have explored several factors influencing the risks of flooding to human life and health. However, there is limited research and understanding on indoor flood fatalities and the circumstances under which they occur. This study explores victim-, building-, and situation-related characteristics in order to provide a better understanding of the conditions that lead to flood-related indoor deaths, exploiting a fatality database developed for Greece (1960–2020). The correlation analysis showed that indoor victims, compared with outdoor ones, tend to be older individuals, with high percentages of disabilities. A significant statistical association of the building material, roof type, and distance from the river with the building collapse was also found. The profile of the buildings in which flood fatality occurred was further compared with that of neighboring non-fatal buildings that were inhabited during the flood events. The statistical results indicated that the buildings with a fatality occurrence are mostly single-storey structures, made from masonry as the main building material. The findings have practical implications in risk communication and mitigation in terms of identifying the specific populations, circumstances, settings, and mechanisms that lead to dangerous indoor situations during flooding events.
Food Behavior Changes during the COVID-19 Pandemic: Statistical Analysis of Consumer Survey Data from Bosnia and Herzegovina
Authors: Ben Hassen Tarek , El Bilali Hamid , Allahyari Mohammad S. , Karabašević Darjan , Radosavac Adriana , Berjan Sinisa , Vaško Željko , Radanov Pavle , Obhođaš Ibrahim
Published: August 2021

Bosnia and Herzegovina has responded to the COVID-19 pandemic by implementing quarantine and other social distancing measures. These measures, however, have mental, social, and economic consequences on diet and food behaviors. The Bosnian case is particularly pertinent since the country has one of the highest global mortality rates relative to its population. In addition, its health system is fragmented and under-resourced. Consequently, in this study, we analyze the effects of the COVID-19 pandemic on Bosnians’ behaviors relating to food. It is based on an online survey performed during October–November 2020 with 3133 adult respondents. The statistical analysis encompassed descriptive statistics (means, percentages, and frequencies), as well as cluster analysis. The results reveal that the pandemic transformed consumers’ shopping behaviors and food sourcing, reducing shopping frequency and increasing food bought on each trip. Furthermore, there was an increase in the consumption of local food items due to food safety concerns. Additionally, the pandemic improved the awareness of Bosnians towards food with a decrease in food waste. Regarding diets, Bosnians tried to have healthier diets during the pandemic. Therefore, COVID-19 might have opened a ‘window of opportunity’ to promote the shift towards more sustainable food consumption and lifestyles.
Voronovskaja-Type Quantitative Results for Differences of Positive Linear Operators
Authors: Acu Ana Maria , Başcanbaz-Tunca Gülen , Raşa Ioan
Published: August 2021

We consider positive linear operators having the same fundamental functions and different functionals in front of them. For differences involving such operators, we obtain Voronovskaja-type quantitative results. Applications illustrating the theoretical aspects are presented.
Dynamics of Plane Waves in the Fractional Nonlinear Schrödinger Equation with Long-Range Dispersion
Authors: Duo Siwei , Lakoba Taras I. , Zhang Yanzhi
Published: August 2021

We analytically and numerically investigate the stability and dynamics of the plane wave solutions of the fractional nonlinear Schrödinger (NLS) equation, where the long-range dispersion is described by the fractional Laplacian (−Δ)α/2. The linear stability analysis shows that plane wave solutions in the defocusing NLS are always stable if the power α∈[1,2] but unstable for α∈(0,1). In the focusing case, they can be linearly unstable for any α∈(0,2]. We then apply the split-step Fourier spectral (SSFS) method to simulate the nonlinear stage of the plane waves dynamics. In agreement with earlier studies of solitary wave solutions of the fractional focusing NLS, we find that as α∈(1,2] decreases, the solution evolves towards an increasingly localized pulse existing on the background of a “sea” of small-amplitude dispersive waves. Such a highly localized pulse has a broad spectrum, most of whose modes are excited in the nonlinear stage of the pulse evolution and are not predicted by the linear stability analysis. For α≤1, we always find the solution to undergo collapse. We also show, for the first time to our knowledge, that for initial conditions with nonzero group velocities (traveling plane waves), an onset of collapse is delayed compared to that for a standing plane wave initial condition. For defocusing fractional NLS, even though we find traveling plane waves to be linearly unstable for α<1, we have never observed collapse. As a by-product of our numerical studies, we derive a stability condition on the time step of the SSFS to guarantee that this method is free from numerical instabilities.
A Certain Structure of Bipolar Fuzzy Subrings
Authors: Alolaiyan Hanan , Mateen Muhammad Haris , Pamucar Dragan , Mahmmod Muhammad Khalid , Arslan Farrukh
Published: August 2021

The role of symmetry in ring theory is universally recognized. The most directly definable universal relation in a symmetric set theory is isomorphism. This article develops a certain structure of bipolar fuzzy subrings, including bipolar fuzzy quotient ring, bipolar fuzzy ring homomorphism, and bipolar fuzzy ring isomorphism. We define (α,β)-cut of bipolar fuzzy set and investigate the algebraic attributions of this phenomenon. We also define the support set of bipolar fuzzy set and prove various important properties relating to this concept. Additionally, we define bipolar fuzzy homomorphism by using the notion of natural ring homomorphism. We also establish a bipolar fuzzy homomorphism between bipolar fuzzy subring of the quotient ring and bipolar fuzzy subring of this ring. We constituted a significant relationship between two bipolar fuzzy subrings of quotient rings under a given bipolar fuzzy surjective homomorphism. We present the construction of an induced bipolar fuzzy isomorphism between two related bipolar fuzzy subrings. Moreover, to discuss the symmetry between two bipolar fuzzy subrings, we present three fundamental theorems of bipolar fuzzy isomorphism.
Integral Equations: Theories, Approximations, and Applications
Authors: Noeiaghdam Samad , Sidorov Denis
Published: August 2021

Linear and nonlinear integral equations of the first and second kinds have many applications in engineering and real life problems [...]
Quark Self-Energy and Condensates in NJL Model with External Magnetic Field
Authors: Liu Juan , Du Yilun , Shi Song
Published: August 2021

In a one-flavor NJL model with a finite temperature, chemical potential, and external magnetic field, the self-energy of the quark propagator contains more condensates besides the vacuum condensate. We use Fierz identity to identify the self-energy and propose a self-consistent analysis to simplify it. It turns out that these condensates are related to the chiral separation effect and spin magnetic moment.
Exploiting Biomedical Sensors for a Home Monitoring System for Paediatric Patients with Congenital Heart Disease
Authors: Donati Massimiliano , Panicacci Silvia , Ruiu Alessio , Dalmiani Stefano , Festa Pierluigi , Ait-Ali Lamia , Mastorci Francesca , Pingitore Alessandro , Pennè Wanda , Fanucci Luca , Saponara Sergio
Published: August 2021

Congenital heart disease, the most frequent malformation at birth, is usually not fatal but leads to multiple hospitalisations and outpatient visits, with negative impact on the quality of life and psychological profile not only of children but also of their families. In this paper, we describe the entire architecture of a system for remotely monitoring paediatric/neonatal patients with congenital heart disease, with the final aim of improving quality of life of the whole family and reducing hospital admissions. The interesting vital parameters for the disease are ECG, heart rate, oxygen saturation, body temperature and body weight. They are collected at home using some biomedical sensors specifically selected and calibrated for the paediatric field. These data are then sent to the smart hub, which proceeds with the synchronisation to the remote e-Health care center. Here, the doctors can log and evaluate the patient’s parameters. Preliminary results underline the sensor suitability for children and infants and good usability and data management of the smart-hub technology (E@syCare). In the clinical trial, some patients from the U.O.C. Paediatric and Adult Congenital Cardiology- Monasterio Foundation are enrolled. They receive a home monitoring kit according to the group they belong to. The trial aims to evaluate the effects of the system on quality of life. Psychological data are collected through questionnaires filled in by parents/caregivers in self-administration via the gateway at the beginning and at the end of the study. Results highlight an overall improvement in well-being and sleep quality, with a consequent reduction in anxious and stressful situations during daily life thanks to telemonitoring. At the same time, users reported a good level of usability, ease of data transmission and management of the devices.
Electrically Conductive Textile Materials—Application in Flexible Sensors and Antennas
Authors: Krifa Mourad
Published: August 2021

This paper reviews some prominent applications and approaches to developing smart fabrics for wearable technology. The importance of flexible and electrically conductive textiles in the emerging body-centric sensing and wireless communication systems is highlighted. Examples of applications are discussed with a focus on a range of textile-based sensors and antennas. Developments in alternative materials and structures for producing flexible and conductive textiles are reviewed, including inherently conductive polymers, carbon-based materials, and nano-enhanced composite fibers and fibrous structures.
Effect of Polystyrene Microplastics on Rice Seed Germination and Antioxidant Enzyme Activity
Authors: Zhang Qiuge , Zhao Mengsai , Meng Fansong , Xiao Yongli , Dai Wei , Luan Yaning
Published: August 2021

The accumulation and distribution of microplastics (MPs) in agricultural soils, including rice fields, is well studied. However, only a few studies have investigated the uptake of MPs by rice plants and the consequential toxic effects of MPs under solid-phase culture conditions. Hence, in this study, we explored the effects of different concentrations of polystyrene MPs (PS-MPs, with a size of 200 nm) on rice seed germination, root growth, antioxidant enzyme activity, and transcriptome. PS-MPs exhibited no significant effect on the germination of rice seeds (p > 0.05). However, PS-MPs significantly promoted root length (10 mg L−1; p < 0.05), and significantly reduced antioxidant enzyme activity (1000 mg L−1; p < 0.05). Staining with 3,3-diaminobenzidine and nitrotetrazolium blue chloride further revealed significant accumulation of reactive oxygen species in the roots of rice treated with PS-MPs. In addition, transcriptome data analysis revealed that PS-MPs induce the expression of genes related to antioxidant enzyme activity in plant roots. Specifically, genes related to flavonoid and flavonol biosynthesis were upregulated, whereas those involved in linolenic acid and nitrogen metabolism were downregulated. These results enhance our understanding of the responses of agricultural crops to MP toxicity.