bluuebunny/crossref_metadata_2025 huggingface dataset:
Development and Validation of a Questionnaire to Measure Digital Skills of Chinese Undergraduates
Authors: Fan Cunying , Wang Juan
Published: March 2022

To keep sustainable development in a digital society, it is essential for young people to possess certain digital skills. Levels of the digital skills of Chinese undergraduates take a certain role in the process of educational digitalization promoted by the Chinese government, becoming an important concern for Chinese universities and policymakers. However, few valid and reliable instruments are available for the assessment of the digital skills of undergraduates in China. Thus, developing, and testing the reliability and validity of a questionnaire to measure the digital skills of Chinese undergraduates are necessary. Based on previous literature and situations in the Chinese educational context, this study developed and validated a questionnaire to assess the digital skills of Chinese undergraduates. Through factor analysis approaches, the internal factor structure of the questionnaire was explored, and its reliability and validity were verified. Development and validation of a questionnaire, in which 6 factors were extracted, were described: access to and management of digital content, use of digital means, communication of digital content, creation of digital content, digital empathy, and digital safety. The questionnaire was applied to the first sample of 222 undergraduates and the second sample of 231 undergraduates selected randomly from a university located in the east of China. An exploratory factor analysis (EFA) through SPSS 26.0 with the first sample was used to determine the internal factor structure of the whole questionnaire, which showed the expected congruency between items and dimensions. Then a confirmatory factor analysis (CFA) through Mplus 8.3 with the second sample was utilized to check model-data fit of the questionnaire, which showed a good fit between them. Convergent validity and discriminant validity of the questionnaire were also verified. The resulting questionnaire emerged as a useful tool for carrying out nationwide studies on digital skills in higher education generally or in different disciplines specifically in China in the future.
Technical Performance Prediction and Employment Potential of Solar PV Systems in Cold Countries
Authors: Agyekum Ephraim Bonah , Mehmood Usman , Kamel Salah , Shouran Mokhtar , Elgamli Elmazeg , Adebayo Tomiwa Sunday
Published: March 2022

Power distribution to decentralized and remote communities secluded from centralized grid connections has always been a problem for utilities and governments worldwide. This situation is even more critical for the isolated communities in Russia due to the vast nature of the country. Therefore, the Russian government is formulating and implementing several strategies to develop its renewable energy sector. However, very little information is available on the possible performance of solar photovoltaic (PV) modules under Russian weather conditions for all year round. Thus, this study has been designed to fill that research gap by assessing the performance ratio (PR), degradation, energy loss prediction, and employment potential of PV modules in the Sverdlovsk region of Russia using the PVsyst simulation model. A side-by-side comparison of the fixed tilted plane and tracking horizontal axis East–West were analyzed. According to the results, the annual production probability (P) for the fixed PV module for a P50, P75, and P90 is 39.68 MWh, 37.72 MWh, and 35.94 MWh, respectively, with a variability of 2.91 MWh. In the case of the tracking PV module, the annual production probability for the P50, P75, and P90 is 43.18 MWh, 41.05 MWh, and 39.12 MWh, respectively, with a variability of 3.17 MWh. A PR of 82.3% and 82.6% is obtained for the fixed and tracking systems, respectively, while the PV array losses for the fixed and tracking orientations are 15.1% and 14.9%, respectively. The months of May to August recorded the highest array losses due to the high temperatures that are usually recorded within that period.
The Role of Actor Networks in Enabling Agroecological Innovation: Lessons from Laos
Authors: Castella Jean-Christophe , Lestrelin Guillaume , Phimmasone Sisavath , Tran Quoc Hoa , Lienhard Pascal
Published: March 2022

In this paper, we use conceptual insights from the actor–network theory (ANT) to explore the role of agroecological innovation systems (AeISs) in the reconfiguration of agricultural practices toward sustainability. AeISs are actor networks involving a diversity of individuals (e.g., farmers, traders, experts) and organizations (e.g., cooperatives, rural development agencies, teaching and research institutions) that mainstream agroecology principles and practices to enhance agroecosystems’ resilience. Their composition and structure affect the way different agents of change interact, as well as how they access, exchange, and use knowledge as they drive the adoption of specific technologies. We document seven AeISs that were active between 2005 and 2020 in the northern uplands of Laos. Within the framework of these initiatives, action research was conducted for understanding the processes underpinning diverse technical, organizational, and institutional innovations to foster an agroecological transition. Building on a comparative analysis of AeIS, we consider how agency was distributed among collectives as they reorganized in time. Our discussion highlights the importance of configuring, enlarging, and nurturing spaces in which actors are empowered to adjust and adapt, as well as to think and act collectively in complexity. Lastly, what counts in the innovation is the underlying networking process itself, i.e., the process through which all actors of the AeIS interact and exchange. Changes in the networking processes come with a changing conception of knowledge. Moving from knowledge to knowing (i.e., knowledge in the making), AeISs no longer only promote products or technologies, but also collective intelligence based on an ethic of care.
Does Job Satisfaction Differ at Different Levels of Employees? Measurement of Job Satisfaction among the Levels of Sugar Industrial Employees
Authors: Issa Gazi Md. Abu , Islam Md. Aminul , Sobhani Farid Ahammad , Dhar Bablu Kumar
Published: March 2022

Purpose: The main purpose of the present study is to find comparative job satisfaction scores between different levels of employees (foremen and workers). Design/Methodology/Approach: Researcher has selected sugar industrial sector of Bangladesh as research ground. On the basis of stratified random sampling, 300 respondents have been selected from 5 (five) state-run sugar mills. This study is absolutely based on primary sources of information. To measure job satisfaction, the Job Satisfaction Index (JSI) developed by Brayfield and Rothe (1951) has been used in this study. This study conducted a face-to-face interview with a scheduled of questionnaire for data collection and the core value of workers satisfaction and dissatisfaction that they feel from working in different sugar mills in Bangladesh are placed here. Data were analyzed using SPSS (Statistical Package for Social Science). Findings: The research exposed that a pointedly higher percentage of the foreman was gratified with their existent job but not statistically significant. The study also revealed that some job-related factors have the significant contribution on job satisfaction than those of personal factors for both groups of respondents. Originality/ Value: This paper investigated the impact of job facets and personal factors on job satisfaction of the employees. This study used primary data sources. The study suggested that pay, recognition for good work, participation in decision making, promotion, and good relationship with colleagues were more important as determinants of job satisfaction, so superfluous prominence should be given on these for humanizing workplace pleasure of the workers and foremen of sugar industry.
Flexible Photovoltaic System on Non-Conventional Surfaces: A Techno-Economic Analysis
Authors: Esmaeili Shayan Mostafa , Najafi Gholamhassan , Ghobadian Barat , Gorjian Shiva , Mazlan Mohamed , Samami Mehdi , Shabanzadeh Alireza
Published: March 2022

Renewable energy policies emphasize both the utilization of renewable energy sources and the improvement of energy efficiency. Over the past decade, built-in photovoltaic (BIPV) technologies have mostly focused on using photovoltaic ideas and have been shown to aid buildings that partially meet their load as sustainable solar energy generating technologies. It is challenging to install conventional photovoltaic systems on curved facades. In this research, elastic solar panels assisted by flexible photovoltaic systems (FPVs) were developed, fabricated, and analyzed on a 1 m2 scale. A flexible structure on a flat, hemispherical, and cylindrical substrate was studied in real terms. Using the LabVIEW application, warm and dry climate data has been recognized and transmitted online. The results showed that when installed on the silo and biogas interfaces, the fill factor was 88% and 84%, respectively. Annual energy production on the flat surface was 810 kWh, on the cylindrical surface was 960 kWh, and on the hemisphere surface was 1000 kWh, respectively. The economic results indicate that the net present value (NPV) at a flat surface is USD 697.52, with an internal rate of return (IRR) of 34.81% and a capital return term of 8.58 years. Cylindrical surfaces and hemispheres each see an increase of USD 955.18. The investment yield returned 39.29% and 40.47% for cylindrical and hemispheres structures. A 20% increase in fixed investment in the flat system increased IRR by 21.3%, while this increase was 25.59% in the cylindrical system and 24.58% in the hemisphere. Research innovation is filling the gap on the use of flexible solar panels on curved and unconventional surfaces.
Sustainable Entrepreneurship: Good Deeds, Business, Social and Environmental Responsibility in a Market Experiment
Authors: Biggeri Mario , Colucci Domenico , Doni Nicola , Valori Vincenzo
Published: March 2022

We study how commitment of entrepreneurs to sustainability practices might effectively improve the social and environmental impact of market competition. To this end we devised a market experiment in which profit maximization and socially and environmentally concerned behavior were both potential goals of producers. Our subject pool included two distinct types of students having different prosocial attitudes. The two types adopted significantly different strategies in the treatment group, where producers could contribute to a positive externality, whereas they behaved similarly in the control group, where the only objective was profit maximization. Subjects who were ex-ante more prosocial chose to produce with more focus on the positive externality than their counterparts. However, they failed to actually deliver a larger social impact as a consequence of the market outcome. We conclude that producers often commit to social responsibility, even though well-meaning conducts do not necessarily beget equally good outcomes.
Leisure Constraint Negotiation Strategies among Serious Leisure Participants in Swimming: Experiences of Facility Use Restriction Due to COVID-19
Authors: Kim Jongsoon , Oh Saesook , Yeon Boonhong
Published: March 2022

The purpose of this study was to explore the leisure constraint negotiation strategies adopted by the people participating in swimming as a serious leisure activity in the context of COVID-19 by using qualitative research methods. The study was conducted over five months, from September 2020 to January 2021, via the use of snowball sampling. Six unmarried employees in their 30s living in Seoul and Incheon, Korea, were the participants of this study. Data collection was conducted through in-depth interviews, two or three times among each participant. For data analysis, all the recorded contents stated by the participants were transcribed. The results are as follows: first, among the leisure constraint negotiation strategies, the cognitive constraint negotiation strategies were “supernatural attitude toward swimming” and “positive attitude toward the post-COVID-19 period”; and second, among the leisure constraint negotiation strategies, behavioral constraint negotiation strategies were derived as “substitution of activity and motivation” and “participating facilities and crowding management.” Based on these results, it can be concluded that the special leisure experiences and leisure constraint negotiation strategies only for swimming within the framework of facility use restrictions were caused due to COVID-19 and further suggested ways to improve the practical management of leisure and sports activities based on the facilities.
Cluster Commercial Credit and Total Factor Productivity of the Manufacturing Sector
Authors: Liu Tinghua , Kou Fengjuan , Liu Xiao , Elahi Ehsan
Published: March 2022

The study estimates the impact of cluster commercial credit on manufacturing total factor productivity from the perspective of county-level clusters. The data were collected from 1998 to 2015 from the Chinese Industrial Enterprise Database. Various econometrics methods were used to approach the study objectives. The study found that cluster commercial credit can significantly increase the total factor productivity of manufacturing. Through the analysis of the theoretical mechanism, it was found that improving the level of innovation and improving the efficiency of the capital allocation are important ways for cluster commercial credit to positively promote the total factor productivity of the manufacturing sector. The analysis of heterogeneity found that cluster commercial credit promoted the total factor productivity of state-owned enterprises, large-scale enterprises, capital-intensive industries, high-tech industries, and enterprises in coastal areas to a great extent. By constructing a quasi-natural experiment, using the method of multi-period double difference and PSM-DID to solve the problem of cluster commercial credit endogeneity, it passed the parallel trend, placebo, and other tests. Finally, this article further describes the relationship between corporate commercial credit and corporate total factor productivity from the enterprise-to-enterprise level and found that the use of commercial credit by enterprises has a significant and positive role in promoting corporate total factor productivity. The research results of this paper provide a reference for promoting the high-quality development of the country’s economy from the perspective of informal finance.
Spatial Spillover Effect of Carbon Emissions and Its Influencing Factors in the Yellow River Basin
Authors: Gong Wei-Feng , Fan Zhen-Yue , Wang Chuan-Hui , Wang Li-Ping , Li Wen-Wen
Published: March 2022

The high-quality development of the Yellow River Basin is the focus of China’s development. A spatial lag model and a spatial error model were constructed. The mechanism of spatial spillover effects of economic growth, industrial structure, urbanization level on carbon emissions of all provinces in the Yellow River Basin were analyzed. The results show that: (1) There are obvious spatial spillover effects and spatial agglomeration characteristics of provincial carbon emissions. The carbon emissions of Shandong, Shanxi, Shaanxi, Henan, Inner Mongolia, Sichuan show a high–high agglomeration feature, while the carbon emissions of Gansu, Qinghai and Ningxia show a low–low agglomeration feature. (2) The relationship between carbon emissions and economic growth in the whole Yellow River Basin shows a “U” shaped EKC curve, while the relationship between carbon emissions and economic growth in the Yangtze River Basin shows an inverted “U” shaped EKC curve, and the two aspects are in stark contrast. The population size, industrial structure and urbanization level can promote carbon emissions, while technology plays a role in curbing carbon emissions in the Yellow River Basin. The measures to reduce carbon emissions should be achieved in terms of regional joint prevention and control, transformation of economic growth, optimization of industrial structure, and strict implementation of differentiated emission reduction policies.
Use of Vegetable Residues and Cover Crops in the Cultivation of Maize Grown in Different Tillage Systems
Authors: Chețan Felicia , Chețan Cornel , Bogdan Ileana , Moraru Paula Ioana , Pop Adrian Ioan , Rusu Teodor
Published: March 2022

The purpose of research on alternative variants of soil tillage systems related to fertilization, vegetal residues, and cover crops, in the case of the maize crop, is to elaborate and promote certain integrated technologies on conservation agriculture. This paper presents the results of a study conducted in the Transylvanian Plain during 2019–2021, regarding the influence of certain technological and climatic factors on the yield and quality of maize. The objective of the research was to focus on how vegetable residues and cover crops can be integrated into the optimization of the fertilization system of conservation agriculture. A multifactorial experiment was carried out based on the formula A × B × C × D − R: 4 × 2 × 3 × 3 − 2, where A represents the soil tillage system (a1 conventional tillage with moldboard plow; a2 minimum tillage with chisel; a3 minimum tillage with disk; a4 no tillage); B represents the maize hybrid (b1 Turda 332; b2 Turda 344); C represents the vegetable residues and cover crops (c1 vegetable residues 2.5 t ha−1 + 350 kg ha−1 NPK; c2 vegetable residues 2.5 t ha−1 + cover crops mustard; c3 vegetable residues 2.5 t ha−1 + gulle 10 t ha−1); D represents the year (d1 2019; d2 2020; d3 2021); and R represents the replicates. The results emphasized the fact that for the soil conditions from the area taken into account (Chernozem), for maize, a minimum tillage with chisel during autumn + disk harrow in spring + sowing can be considered as an alternative to the conventional tillage system. Yield and quality of maize can be improved and optimized by combined fertilization: vegetable residues and cover crops being supported with different sources of mineral NPK.
The Effectiveness of Centralized Payment Network Advertisements on Digital Branding during the COVID-19 Crisis
Authors: Sakas Damianos P. , Kamperos Ioannis Dimitrios G. , Reklitis Dimitrios P. , Giannakopoulos Nikolaos T. , Nasiopoulos Dimitrios K. , Terzi Marina C. , Kanellos Nikos
Published: March 2022

Crises are always challenging for banking systems. In the case of COVID-19, centralized payment networks and FinTech companies’ websites have been affected by user behavior globally. As a result, there is ample opportunity for marketing managers and professionals to focus on big data from FinTech websites. This can contribute to a better understanding of the variables impacting their brand name and how to manage risk during crisis periods. This research is divided into three stages. The first stage presents the web analytics and the data retrieved from the FinTech platforms. The second stage illustrates the statistical analysis and the fuzzy cognitive mapping (FCM) performed. In the final stage, an agent-based model is outlined in order to simulate and forecast a company’s brand name visibility and user behavior. The results of this study suggest that, during crises, centralized payment networks (CPNs) and FinTech companies with high organic traffic tend to convert new visitors to actual “customers”.
The Economic and Welfare Effects of Food Waste Reduction on a Food-Production-Driven Rural Region
Authors: Friman Aino , Hyytiä Nina
Published: March 2022

Food waste is economically and ecologically unsustainable; the benefits of food waste reduction are indisputable. Yet knowledge of the economic trade-offs and knock-on effects of such reduction is deficient. This study examines the economic effects of food waste reduction in a rural region that is a nationally important producer of agricultural and food products in Finland. We built a detailed social accounting matrix to trace the transactions among the economic agents. Five different simulations of food waste reduction were run by applying a computable general equilibrium model. In the simulations, households and food services halved their food waste. The results indicated that food waste reduction is economically worthwhile in terms of regional investments and gross domestic product at market prices. However, the reduction induced economic trade-offs and welfare redistribution. The value added to the agriculture and food industries and the welfare of agricultural households decreased, albeit that the simulated compensations alleviated the effects. In the long run, falling agricultural wages and factor incomes entail closedowns and, finally, decrease local food production. This aspect is worth considering in terms of policy planning under the principle of just transition of the European Green Deal.
The Evolution of Food Security: Where Are We Now, Where Should We Go Next?
Authors: Akbari Morteza , Foroudi Pantea , Shahmoradi Mohana , Padash Hamid , Parizi Zahra Shahabaldini , Khosravani Ala , Ataei Pouria , Cuomo Maria Teresa
Published: March 2022

Food security is one of the most challenging topics globally; however, the concept of food security has taken on additional dimensions that are general and are less detailed. This study aims to identify the intellectual structure of food security research and the changes in this structure. This paper analyzed 3169 documents from the Web of Science database through a bibliometric review. A review of the published documents shows an increasing trend over the past 46 years. In accordance with co-occurrence analysis, 125 keywords were grouped into five clusters: food security and sustainable development; food security and socioeconomic factors; food security policy and governance; coping strategies for poverty, inequality, and hunger; and modern food security management. This study identifies four streams within food security research: sustainability and environmental, socioeconomic, cultural, and political factors. The paper concludes that even though food security might investigate many significant areas, other new dimensions should still be considered. Further gaps in the literature emerge that present avenues for future research directions. Reviewing indicators and techniques of food security assessment and identifying high-risk groups should be a priority.
State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing
Authors: McAfee Marion , Kariminejad Mandana , Weinert Albert , Huq Saif , Stigter Johannes D. , Tormey David
Published: March 2022

State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given the sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables that cannot be measured directly (‘soft sensing’) or for which only noisy, intermittent, delayed, indirect, or unreliable measurements are available, perhaps from multiple sources (‘sensor fusion’). In this paper, we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes across sectors including industrial robotics, material synthesis and processing, semiconductor, and additive manufacturing. It is shown that state estimation algorithms can play a key role in manufacturing systems for accurately monitoring and controlling processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and controlling distributed manufacturing systems.
Effect of Sowing Rate and Maturity on the Yield and Nutritive Value of Triticale–Field Pea Forage Crops
Authors: Piltz John W. , Rodham Craig A.
Published: March 2022

Experiments were conducted over three years at Murrumburrah, in southern NSW, Australia to assess the yield and nutritive value of triticale–pea forage crops. Field pea (Pisum sativum L. cvv. Parafield and Morgan) were sown at 40 or 80 kg ha−1 in mixtures with triticale (x Triticosecale Wittmack cv. Tobruk) at 15, 30 or 45 kg ha−1 and sampled when the triticale was at the boot, anthesis, and milk stage of maturity, though lodging forced the third harvest at Murrumburrah to be abandoned. The yield, botanical composition, and forage nutritive value was determined. The yield was dependent on seasonal rainfall conditions ranging from an average of 58,326 kg ha−1 dry matter (DM) in 2009 to 19,914 kg ha−1 in 2010. The pea content was higher in Morgan compared to Parafield crops (486.4 vs. 384.8 g kg−1), and those sown at 80 kg ha−1 compared to 40 kg ha−1 (485.3 vs. 385.8 g kg−1). The crude protein (CP) content was higher when pea were sown at 80 kg ha−1 compared to 40 kg ha−1 (124.9 vs. 114.4 g kg−1 DM). Digestibility declined from 704.3 to 639.9 g kg−1 between the boot and milk harvests. Some yield, digestibility, and crude protein differences occurred due to pea variety, or due to triticale or pea sowing rates; however, these were infrequent, and the effect was minor.
A Two-Stage Planning Optimization Study of an Integrated Energy System Considering Uncertainty
Authors: Tang Lijun , Gou Xiaolong , Liang Junyu , Yang Yang , Yuan Xingyu , Yang Jiaquan , Yan Yuting , Wang Dada , Wang Yongli , Chen Xin , Yuan Bo , Tao Siyi
Published: March 2022

In the context of a rapidly evolving integrated energy system (IES), taking into account the uncertainty of the renewable energy output can make integrated energy system planning more realistic. In view of this, this paper proposes an integrated energy system planning approach that takes uncertainty into account. Firstly, in order to accurately describe the renewable energy output scenarios, this paper describes the IES model and introduces the scenario analysis methods used. Secondly, an integrated energy system equipment output model is constructed, the corresponding constraints and objective functions are set, an improved particle swarm-ant colony optimization algorithm is used for the solution, and a planning solution strategy considering uncertainty is proposed. Finally, the above conclusions are verified by the actual case data of a park, and the results show that the method proposed in this paper is economical and reasonable.
Citizen Science and the Sustainable Development Goals: Building Social and Technical Capacity through Data Collection in the Upper Blue Nile Basin, Ethiopia
Authors: Rigler Genevieve , Dokou Zoi , Khadim Fahad Khan , Sinshaw Berhanu G. , Eshete Daniel G. , Aseres Muludel , Amera Wendale , Zhou Wangchi , Wang Xingyu , Moges Mamaru , Azage Muluken , Li Baikun , Holzer Elizabeth , Tilahun Seifu , Bagtzoglou Amvrossios , Anagnostou Emmanouil
Published: March 2022

Engaging youth and women in data-scarce, least developed countries (LDCs) is gaining attention in the Sustainable Development Goal (SDG) arena, as is using citizen science as a multi-faceted mechanism for data collection, engendering personal empowerment and agency. Involving these populations in citizen science is a powerful synergy that simultaneously addresses the Leave-No-One-Behind promise in the United Nations’ 2030 Agenda, yet most citizen science takes place in the Global North, and attention to LDCs is needed. This article highlights a four-year, four-location, hydrology-focused, interdisciplinary citizen science initiative (CSI) in the Upper Blue Nile Basin, Ethiopia. Through a systematic evaluation, we explore scientific applications of the hydrologic data, as well as the social dimensions in the CSI, towards building a social and technical capacity that supports the SDGs at the local and international scale. In the CSI, Ethiopian high school students received training from local university faculty and graduate students, collecting river stage and groundwater level measurements, and farmers conducted soil resistivity measurements using a novel sensor technology developed for the study area. We found the datasets to be ample for use to locally validate regional groundwater models and seasonal forecasts on soil moisture and streamflow. We conducted written interviews with the students, which revealed their ability to perceive benefits of engagement in the CSI, as well as recognize their increased individual technical capacity. An analysis of the hydrological data demonstrates the readiness of the datasets to be used for evaluating water-related interventions that facilitate the SDGs, broadly, by building synergies between individuals and institutions. As such, we map how both the hydrologic data and experiences of the citizen scientists support the SDGs at the Goal and Target-level, while forging new social and technical pathways.
Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm
Authors: Masich Igor S. , Kulachenko Margarita A. , Stanimirović Predrag S. , Popov Aleksey M. , Tovbis Elena M. , Stupina Alena A. , Kazakovtsev Lev A.
Published: March 2022

The formation of patterns is one of the main stages in logical data analysis. Fuzzy approaches to pattern generation in logical analysis of data allow the pattern to cover not only objects of the target class, but also a certain proportion of objects of the opposite class. In this case, pattern search is an optimization problem with the maximum coverage of the target class as an objective function, and some allowed coverage of the opposite class as a constraint. We propose a more flexible and symmetric optimization model which does not impose a strict restriction on the pattern coverage of the opposite class observations. Instead, our model converts such a restriction (purity restriction) into an additional criterion. Both, coverage of the target class and the opposite class are two objective functions of the optimization problem. The search for a balance of these criteria is the essence of the proposed optimization method. We propose a modified evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve this problem. The new algorithm uses pattern formation as an approximation of the Pareto set and considers the solution’s representation in logical analysis of data and the informativeness of patterns. We have tested our approach on two applied medical problems of classification under conditions of sample asymmetry: one class significantly dominated the other. The classification results were comparable and, in some cases, better than the results of commonly used machine learning algorithms in terms of accuracy, without losing the interpretability.
Symmetric Properties of Routh–Hurwitz and Schur–Cohn Stability Criteria
Authors: Zahreddine Ziad
Published: March 2022

It is often noticed in the literature that some key results on the stability of discrete-time systems of difference equations are obtained from their corresponding results on the stability of continuous-time systems of differential equations using suitable conformal mappings or bilinear transformations. Such observations lead to the search for a unified approach to the study of root distribution for real and complex polynomials, with respect to the left-half plane for continuous-time systems (Routh–Hurwitz stability) and with respect to the unit disc for discrete-time systems (Schur–Cohn stability). This paper is a further contribution toward this objective. We present, in a systematic way, the similarities, and yet, the differences between these two types of stability, and we highlight the symmetry that exists between them. We also illustrate how results on the stability of continuous-time systems are conveyed to the stability of discrete-time systems through the proposed techniques. It should be mentioned that the results on Schur–Cohn stability are known to be harder to obtain than Routh–Hurwitz stability ones, giving more credibility to the proposed approach.
Coupled Fixed Points for Hardy–Rogers Type of Maps and Their Applications in the Investigations of Market Equilibrium in Duopoly Markets for Non-Differentiable, Nonlinear Response Functions
Authors: Kabaivanov Stanimir , Zhelinski Vasil , Zlatanov Boyan
Published: March 2022

In this paper we generalize Hardy–Rogers maps in the context of coupled fixed points. We comment on the symmetry of some of the coefficients involved in the Hardy–Rogers condition, and thus, we deduce a simpler formula. We generalize, with the help of the obtained main theorem, some known results about existence and uniqueness of market equilibrium in duopoly markets. As a consequence, we ascertain that the equilibrium production should be equal for both market participants provided that they have symmetric response functions. With the help of the main theorem, we investigate and enrich some recent results regarding market equilibrium in duopoly markets. We define a generalized response function that includes production and surpluses. Finally, we illustrate a possible application of the main result in the investigation of market equilibrium when the payoff functions are non-differentiable.
Narrowband Active Noise Control Using Decimated Controller for Disturbance with Close Frequencies
Authors: An Fengyan , Liu Bilong
Published: March 2022

In this paper, multi-channel active noise control systems subjected to narrowband disturbances with close frequencies are investigated. Instead of controlling each frequency separately, a mixed-reference signal is assumed and thus a transversal controller is utilized. First, the convergent behaviors of a generalized FxLMS-based algorithm are theoretically analyzed in the mean sense, from which the influence of the controller structure on the convergence rate is revealed. A novel narrowband algorithm is then proposed, in which a decimated transversal controller is used to alleviate the computational burden. Simulations based on a 4 × 8 active-noise-control system are carried out to verify the proposed method. The results show that a good convergence rate can be obtained, and the computational complexity can also be greatly reduced.
Geometric Classification of Warped Products Isometrically Immersed into Conformal Sasakian Space Froms
Authors: Fan Xiaoming , Li Yanlin , Majeed Prince , Lone Mehraj Ahmad , Sharma Sandeep
Published: March 2022

Warped products play important roles in differential geometry, general relativity, and symmetry science. In this paper, we study the warped product pointwise semi-slant submanifolds that are isometrically immersed into conformal Sasakian space form. We show that there does not exist any proper warped product pointwise semi-slant submanifolds in conformal Sasakian manifolds. We derived some geometric inequalities for squared norm of second fundamental form from a warped product pointwise semi-slant submanifold into a conformal Sasakian manifolds.
Genetic-Algorithm-Inspired Difficulty Adjustment for Proof-of-Work Blockchains
Authors: Chin Zi Hau , Yap Timothy Tzen Vun , Tan Ian Kim Teck
Published: March 2022

In blockchains, the principle of proof-of-work (PoW) is used to compute a complex mathematical problem. The computation complexity is governed by the difficulty, adjusted periodically to control the rate at which new blocks are created. The network hash rate determines this, a phenomenon of symmetry, as the difficulty also increases when the hash rate increases. If the hash rate grows or declines exponentially, the block creation interval cannot be maintained. A genetic algorithm (GA) is proposed as an additional mechanism to the existing difficulty adjustment algorithm for optimizing the blockchain parameters. The study was conducted with four scenarios in mind, including a default scenario that simulates a regular blockchain. All the scenarios with the GA were able to achieve a lower standard deviation of the average block time and difficulty compared to the default blockchain network without GA. The scenario of a fixed difficulty adjustment interval with GA was able to reduce the standard deviation of the average block time by 80.1%, from 497.1 to 98.9, and achieved a moderate median block propagation time of 6.81 s and a stale block rate of 6.67%.
Decentralized Multi-Robot Collision Avoidance: A Systematic Review from 2015 to 2021
Authors: Raibail Mehak , Rahman Abdul Hadi Abd , AL-Anizy Ghassan Jasim , Nasrudin Mohammad Faidzul , Nadzir Mohd Shahrul Mohd , Noraini Nor Mohd Razif , Yee Tan Siok
Published: March 2022

An exploration task can be performed by a team of mobile robots more efficiently than human counterparts. They can access and give live updates for hard-to-reach areas such as a disaster site or a sewer. However, they face some issues hindering them from optimal path planning due to the symmetrical shape of the environments. Multiple robots are expected to explore more areas in less time while solving robot localization and collision-avoidance issues. When deploying a multi-robot system, it is ensured that the hardware parts do not collide with each other or the surroundings, especially in symmetric environments. Two types of methods are used for collision avoidance: centralized and decentralized. The decentralized approach has mainly been used in recent times, as it is computationally less expensive. This article aims to conduct a systematic literature review of different collision-avoidance strategies and analyze the performance of innovative collision-avoidance techniques. Different methods such as Reinforcement Learning (RL), Model Predictive Control (MPC), Altruistic Coordination, and other approaches followed by selected studies are also discussed. A total of 17 studies are included in this review, extracted from seven databases. Two experimental designs are studied: empty/open space and confined indoor space. Our analysis observed that most of the studies focused on empty/open space scenarios and verified the proposed model only through simulation. ORCA is the primary method, against which all the state-of-the-art techniques are evaluated. This article provides a comparison between different methods used for multi-robot collision avoidance. It discusses if the methods used are focused on safety or path planning. It also sheds light on the limitations of the studies included and possible future directions.
Semantic SLAM Based on Deep Learning in Endocavity Environment
Authors: Wu Haibin , Zhao Jianbo , Xu Kaiyang , Zhang Yan , Xu Ruotong , Wang Aili , Iwahori Yuji
Published: March 2022

Traditional endoscopic treatment methods restrict the surgeon’s field of view. New approaches to laparoscopic visualization have emerged due to the advent of robot-assisted surgical techniques. Lumen simultaneous localization and mapping (SLAM) technology can use the image sequence taken by the endoscope to estimate the pose of the endoscope and reconstruct the lumen scene in minimally invasive surgery. This technology gives the surgeon better visual perception and is the basis for the development of surgical navigation systems as well as medical augmented reality. However, the movement of surgical instruments in the internal cavity can interfere with the SLAM algorithm, and the feature points extracted from the surgical instruments may cause errors. Therefore, we propose a modified endocavity SLAM method combined with deep learning semantic segmentation that introduces a convolution neural network based on U-Net architecture with a symmetric encoder–decoder structure in the visual odometry with the goals of solving the binary segmentation problem between surgical instruments and the lumen background and distinguishing dynamic feature points. Its segmentation performance is improved by using pretrained encoders on the network model to obtain more accurate pixel-level instrument segmentation. In this setting, the semantic segmentation is used to reject the feature points on the surgical instruments and reduce the impact caused by dynamic surgical instruments. This can provide more stable and accurate mapping results compared to ordinary SLAM systems.
Skorokhod Reflection Problem for Delayed Brownian Motion with Applications to Fractional Queues
Authors: Ascione Giacomo , Leonenko Nikolai , Pirozzi Enrica
Published: March 2022

Several queueing systems in heavy traffic regimes are shown to admit a diffusive approximation in terms of the Reflected Brownian Motion. The latter is defined by solving the Skorokhod reflection problem on the trajectories of a standard Brownian motion. In recent years, fractional queueing systems have been introduced to model a class of queueing systems with heavy-tailed interarrival and service times. In this paper, we consider a subdiffusive approximation for such processes in the heavy traffic regime. To do this, we introduce the Delayed Reflected Brownian Motion by either solving the Skorohod reflection problem on the trajectories of the delayed Brownian motion or by composing the Reflected Brownian Motion with an inverse stable subordinator. The heavy traffic limit is achieved via the continuous mapping theorem. As a further interesting consequence, we obtain a simulation algorithm for the Delayed Reflected Brownian Motion via a continuous-time random walk approximation.
Protecting the Intellectual Property of Speaker Recognition Model by Black-Box Watermarking in the Frequency Domain
Authors: Wang Yumin , Wu Hanzhou
Published: March 2022

Benefiting from the rapid development of computer hardware and big data, deep neural networks (DNNs) have been widely applied in commercial speaker recognition systems, achieving a kind of symmetry between “machine-learning-as-a-service” providers and consumers. However, this symmetry is threatened by attackers whose goal is to illegally steal and use the service. It is necessary to protect these DNN models from symmetry breaking, i.e., intellectual property (IP) infringement, which motivated the authors to present a black-box watermarking method for IP protection of the speaker recognition model in this paper. The proposed method enables verification of the ownership of the target marked model by querying the model with a set of carefully crafted trigger audio samples, without knowing the internal details of the model. To achieve this goal, the proposed method marks the host model by training it with normal audio samples and carefully crafted trigger audio samples. The trigger audio samples are constructed by adding a trigger signal in the frequency domain of normal audio samples, which enables the trigger audio samples to not only resist against malicious attack but also avoid introducing noticeable distortion. In order to not impair the performance of the speaker recognition model on its original task, a new label is assigned to all the trigger audio samples. The experimental results show that the proposed black-box DNN watermarking method can not only reliably protect the intellectual property of the speaker recognition model but also maintain the performance of the speaker recognition model on its original task, which verifies the superiority and maintains the symmetry between “machine-learning-as-a-service” providers and consumers.
Moving Forward by Shaking Sideways
Authors: Thiffeault Jean-Luc
Published: March 2022

We investigate a simple model for a self-propelled swimmer, which consists of a fluctuating force acting at a point on a rigid body. The rigid body is subject to Newton’s equations with linear friction, corresponding to drag in a viscous fluid. The force has zero time average, so net motion is challenging. We show that the swimmer can inch forward by shaking from side to side and exploiting friction coupled with nonlinearity. For large enough forcing amplitude it can reverse direction and swim backward.
2D and 3D Visualization for the Static Bifurcations and Nonlinear Oscillations of a Self-Excited System with Time-Delayed Controller
Authors: Saeed Nasser A. , Awrejcewicz Jan , Alkashif Mohamed A. , Mohamed Mohamed S.
Published: March 2022

This research focuses on the nonlinear vibration control of a self-excited single-degree-of-freedom system. The integral resonant controller (IRC) is introduced to stabilize the unstable motion and suppress nonlinear oscillations of the considered system. The nonlinear dynamical equations that govern the vibratory behaviors of the proposed closed-loop control system are investigated using perturbation analysis, where loop delays have been included in the studied model. The system bifurcation behaviors have been visualized in both the two and three-dimensional spaces, and corresponding dynamical behaviors have been explored numerically using the bifurcation diagrams, Poincaré map, time-response, zero-one chaotic test algorithm, and frequency spectrum. The obtained analytical investigations revealed that the uncontrolled system can oscillate with one of four vibration modes depending on the excitation frequency, which are mono-stable periodic motion, bi-stable periodic motion, periodic and quasi-period motion, and quasi-periodic motion only. In addition, it is found that the existence of time delays in the control loop can either improve or degrade the control performance. Therefore, an objective function has been introduced to design the optimum control parameters. Based on the derived objective function, it is found that the performance of the proposed control strategy is proportional to the product of the control and feedback gains and inversely proportional to the internal loop feedback gain when the loop delays are neglected. Moreover, it is reported that the controller performance is a periodic function of the total sum of the loop delays. Accordingly, the optimal operating conditions of the time-delayed integral resonant controller have been explained. Finally, numerical validations for all obtained analytical results have been performed, where an excellent correspondence between the analytical and numerical investigations has been demonstrated.
A New Meta-Heuristics Data Clustering Algorithm Based on Tabu Search and Adaptive Search Memory
Authors: Alotaibi Youseef
Published: March 2022

Clustering is a popular data analysis and data mining problem. Symmetry can be considered as a pre-attentive feature, which can improve shapes and objects, as well as reconstruction and recognition. The symmetry-based clustering methods search for clusters that are symmetric with respect to their centers. Furthermore, the K-means (K-M) algorithm can be considered as one of the most common clustering methods. It can be operated more quickly in most conditions, as it is easily implemented. However, it is sensitively initialized and it can be easily trapped in local targets. The Tabu Search (TS) algorithm is a stochastic global optimization technique, while Adaptive Search Memory (ASM) is an important component of TS. ASM is a combination of different memory structures that save statistics about search space and gives TS needed heuristic data to explore search space economically. Thus, a new meta-heuristics algorithm called (MHTSASM) is proposed in this paper for data clustering, which is based on TS and K-M. It uses TS to make economic exploration for data with the help of ASM. It starts with a random initial solution. It obtains neighbors of the current solution called trial solutions and updates memory elements for each iteration. The intensification and diversification strategies are used to enhance the search process. The proposed MHTSASM algorithm performance is compared with multiple clustering techniques based on both optimization and meta-heuristics. The experimental results indicate the superiority of the MHTSASM algorithm compared with other multiple clustering algorithms.