bluuebunny/crossref_metadata_2025 huggingface dataset:
Achieving Resilience: Resilient Price and Quality Strategies of Fresh Food Dual-Channel Supply Chain Considering the Disruption
Authors: Li Zhinan , Liu Qinming , Ye Chunming , Dong Ming , Zheng Yihan
Published: May 2022

In the face of demand disruptions, dual-channel supply chains (SCs) that lack resilience may be more vulnerable. Reaching moderate SC resilience through coordination is essential for dealing with disruptions. This paper investigates the operation management of a dual-channel fresh-food SC (FSC) under disruption. The centralized and decentralized decision models propose joint quality efforts based on the consideration of quality preference and loss. From the perspective of SC resilience, we analyze how SC members can optimally make price, quality, and quantity decisions resiliently and robustly under the disruption of quality preference. The results show that (1) no matter the kind of decision model, considering quality preference disruptions can significantly increase the SC profit; (2) there is a resilience range in decisions with the influence of the disruption cost. The original optimal decisions in the resilience range are robust and sustain SC performance without change; and (3) the disruption significantly impacts offline channel retailers, who are at a disadvantage when competing with online channels. A centralized decision model can achieve higher profits and quality levels in response to demand disruptions. This paper extends the concept of resilience to the FSC and provides suggestions for fresh-food enterprises to conduct quality efforts and cope with demand interruption.
Determination and Comparative Analysis of Critical Velocity for Five Objects of Railway Vehicle Class
Authors: Zboinski Krzysztof , Golofit-Stawinska Milena
Published: May 2022

The purpose of this article is to determine the critical velocities for five railway vehicle objects and to find possible generalizations of these results. It is done in the context of increasing the safety of travel on modern railways. The article discusses four methods of determining these velocities, and then the results obtained, mainly using one of the methods, are presented in tabular and graphical form. The chosen method is supplemented with the second one when higher accuracy or certainty is necessary. Finally, the results are discussed and the similarities and differences between them for different groups of railway vehicle class objects are shown.
Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction
Authors: Alshboul Odey , Shehadeh Ali , Almasabha Ghassan , Almuflih Ali Saeed
Published: May 2022

Accurate building construction cost prediction is critical, especially for sustainable projects (i.e., green buildings). Green building construction contracts are relatively new to the construction industry, where stakeholders have limited experience in contract cost estimation. Unlike conventional building construction, green buildings are designed to utilize new technologies to reduce their operations’ environmental and societal impacts. Consequently, green buildings’ construction bidding and awarding processes have become more complicated due to difficulties forecasting the initial construction costs and setting integrated selection criteria for the winning bidders. Thus, robust green building cost prediction modeling is essential to provide stakeholders with an initial construction cost benchmark to enhance decision-making. The current study presents machine learning-based algorithms, including extreme gradient boosting (XGBOOST), deep neural network (DNN), and random forest (RF), to predict green building costs. The proposed models are designed to consider the influence of soft and hard cost-related attributes. Evaluation metrics (i.e., MAE, MSE, MAPE, and R2) are applied to evaluate and compare the developed algorithms’ accuracy. XGBOOST provided the highest accuracy of 0.96 compared to 0.91 for the DNN, followed by RF with an accuracy of 0.87. The proposed machine learning models can be utilized as a decision support tool for construction project managers and practitioners to advance automation as a coherent field of research within the green construction industry.
Price Sensitivity of Fish Fed with Insect Meal: An Analysis on Italian Consumers
Authors: Arru Brunella , Furesi Roberto , Pulina Pietro , Madau Fabio A.
Published: May 2022

The importance of aquaculture in reducing pressure on wild stocks in the seas and meeting the demand for fish worldwide has increased greatly in recent years. However, sustainability of the sector can be jeopardized by the incessant use of fish meal as the main source of feed. For this reason, replacing, even partially, fish meal with other feeds such as insect meal is essential to make this sector more sustainable. However, this transition requires consumer acceptance of this innovation, which comes through price, one of the most powerful marketing tools affecting the evaluation of product alternatives (e.g., fish fed with traditional feed or insects) and the final purchase decision. The objective of this study is to explore the acceptable price and the limits of price thresholds of fish fed with insect meal using a direct measure of assessing consumers’ willingness to pay. In particular, the study uses the Price Sensitivity Meter (PSM) of Van Westendorp to evaluate the reaction of Italian consumers to the price of European sea bass (Dicentrarchus labrax) hypothetically fed with insect meal. The results showed a wide acceptable price range, an indifferent price point very close to the price of traditionally fed fish, and a high price stress factor. Consumers have shown considerable price sensitivity, which does not allow the additional costs arising from the use of this sustainable alternative feed to be passed on to them. Consumers with great subject knowledge showed major willingness to pay. The contribution of our study lies in providing detailed insights into the possible prices that consumers are willing to pay for sea bass fed with insect meal and thus on the product’s perceived quality, offering several implications for academics, practitioners, and policymakers.
From Responsibility to Requirement: COVID, Cars, and the Future of Corporate Social Responsibility in Canada
Authors: Billedeau David Benjamin , Wilson Jeffrey , Samuel Naima
Published: May 2022

The COVID-19 pandemic has caused significant impacts to the automotive manufacturing industry. Despite substantial financial uncertainty, disruptions to supply chains, and shutdowns of manufacturing operations, automotive firms supported crisis response efforts throughout the course of the pandemic. Drawing on interviews with all the consumer automotive manufacturing companies in Canada (Ford, General Motors, Honda, Stellantis, and Toyota) as well as the two largest global automotive parts suppliers operating in Canada (Linamar and Magna), we investigated whether voluntary corporate responses to COVID-19 will shape long-term corporate social responsibility programs or simply constitute one-off crisis management actions. Ultimately, we argue that while Canada’s pandemic response efforts have benefitted from the voluntary involvement of automotive manufacturing companies, the limited coordination between stakeholders underscores the need for greater public sector oversight of the relationship between society and the private sector. To ensure preparedness for meeting new challenges, such as climate change, we call for the era of voluntary corporate social responsibility programs to yield to a period of corporate social requirements.
Study on Soda–Ethanol Delignification of Pine Sawdust for a Biorefinery
Authors: Imlauer Vedoya Camila María , Area María Cristina , Raffaeli Natalia , Felissia Fernando Esteban
Published: May 2022

The soda–ethanol process was conceived as a sulfur-free pulping process, which may also be an alternative to conventional alkaline pulping, such as kraft or soda–AQ in the biorefinery context. An in-depth study using two experimental designs was conducted to establish the viability of soda–ethanol delignification of pine sawdust. At first, a simple factorial design involving the ethanol–water ratio (ethanol:water) and the alkaline load (AL, % over dry wood, odw) was applied to define the levels of these variables and their eventual interaction. Then, a 32 experimental design was performed to evaluate the ability of the process concerning the pulping of pine sawdust. The tested conditions were carefully selected to screen a broad range of cooking times (60, 100, and 140 min) and alkaline loads (19.0, 23.3, and 27.6 %odw) to obtain pulps with different extents of delignification (residual lignin contents). Finally, the kraft, soda–AQ, and soda–ethanol treatments were compared. Soda–ethanol pulping was shown to be a suitable delignification stage for a biorefinery scheme of Pinus elliottii and Pinus taeda sawdust. It has many advantages over traditional processes regarding its environmental impact, harmless chemicals, and selectivity. The tested conditions were similar to those frequently used in conventional pulping at an industrial scale, suggesting the technical feasibility of the soda–ethanol process for pine sawdust processing.
Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach
Authors: Pena Alejandro , Tejada Juan C. , Gonzalez-Ruiz Juan David , Gongora Mario
Published: May 2022

Given the challenges in reducing greenhouse gases (GHG), one of the sectors that have attracted the most attention in the Sustainable Development Agenda 2030 (SDA-2030) is the agricultural sector. In this context, one of the crops that has had the most remarkable development worldwide has been oil-palm cultivation, thanks to its high productive potential and being one of the most efficient sources of palmitic acid production. However, despite the significant presence of oil palm in the food sector, oil-palm crops have not been exempt from criticism, as its cultivation has developed mainly in areas of ecological conservation around the world. This criticism has been extended to other crops in the context of the Sustainable Development Goals (SDG) due to insecticides and fertilisers required to treat phytosanitary events in the field. To reduce this problem, researchers have used unmanned aerial vehicles (UAVs) to capture multi-spectral aerial images (MAIs) to assess fields’ plant vigour and detect phytosanitary events early using vegetation indices (VIs). However, detecting phytosanitary events in the early stages still suggests a technological challenge. Thus, to improve the environmental and financial sustainability of oil-palm crops, this paper proposes a hybrid deep-learning model (stacked–convolutional) for risk characterisation derived from a phytosanitary event, as suggested by lethal wilt (LW). For this purpose, the proposed model integrates a Lagrangian dispersion model of the backward-Gaussian-puff-tracking type into its convolutional structure, which allows describing the evolution of LW in the field for stages before a temporal reference scenario. The results show that the proposed model allowed the characterisation of the risk derived from a phytosanitary event, (PE) such as lethal wilt (LW), in the field, promoting improvement in agricultural environmental and financial sustainability activities through the integration of financial-risk concepts. This improved risk management will lead to lower projected losses due to a natural reduction in insecticides and fertilisers, allowing a balance between development and sustainability for this type of crop from the RSPO standards.
PestOn: An Ontology to Make Pesticides Information Easily Accessible and Interoperable
Authors: Medici Marco , Dooley Damion , Canavari Maurizio
Published: May 2022

Globally, present regulations treat pesticide use with a light touch, leaving users with scarce reporting requirements in the field. However, numerous initiatives have been undertaken to reduce risks from pesticide product use and provide the public with sufficient information. Nevertheless, food chain actors are not required to disclose much information on hazards, with many undervalued safety aspects. This situation has resulted in information gaps concerning the production, authorization, use, and impact of pesticide products for both consumers and regulatory stakeholders. Often, the public cannot directly access relevant information about pesticides with respect to retail products and their farm origins. National authorities have poor legal tools to efficiently carry out complete investigations and take action to mitigate pesticide externalities. We created the ontology PestOn to bridge these gaps and directly access pesticide product information, making existing data more useful and improving information flow in food value chains. This demonstration project shows how to integrate various existing ontologies to maximize interoperability with related information on the semantic web. As a semantic tool, it can help address food quality, food safety, and information disclosure challenges, opening up several opportunities for food value chain actors and the public. In its first version, the ontology PestOn accounts for more than 16,000 pesticide products that were authorized in Italy during the last 50 years and retrieved from the public pesticide register. The ontology includes information about active ingredients contained in pesticide products, roles, hazards, production companies, authorization status, and regulatory dates. These pieces of information can support agri-food stakeholders in classifying information in the domain of pesticide products and their active ingredients, while reducing unnecessary repetition in research. PestOn can support the addition of food attributes in the domains of human health, resource depletion, and eco-social impact, turning the spotlight on each possible improper use of pesticide products.
The Importance of Social Influencer-Generated Contents for User Cognition and Emotional Attachment: An Information Relevance Perspective
Authors: Zhang Xiuping , Choi Jaewon
Published: May 2022

It has become a marketing trend for marketers to use influencers to advertise and sell products because influencers can affect the attitudes and decision-making of other social media users. Most previous research on influencer marketing has concentrated on its effectiveness as a promotional tool. In contrast, there have been limited studies on the influencer-social media user relationship. The relationship that influencers have with other social media users is the foundation for the success of influencer marketing. Therefore, it is critical to investigate the factors that affect the influencers’ relationships with other users. In accordance with the concept of information relevance, this study presents a model relating the various attributes of influencer-generated content with emotional attachment and information quality to examine the relationship between influencers and other social media users. The findings of a survey of 280 respondents indicate that the interestingness, novelty, reliability, and understandability of influencer-generated content can effectively increase users’ emotional attachment to influencers. Reliability and understandability can also have a significant positive impact on information quality. This eventually inclines social media users to follow or recommend influencers to others, which can increase the popularity of influencers. This study helps researchers and marketers advance their understanding of influencers’ relationships with other social media users and offers management-related recommendations for influencers and marketers.
Diffusion Characteristics and Driving Factors of the Smart Tourism City Policy—Event History Analysis
Authors: Ji Yingchao , Yin Jie
Published: May 2022

It is of great significance to explore the spatial-temporal characteristics and analyze the driving factors of the diffusion of smart tourism city policy, which promotes the adoption of smart tourism city policy and the sustainable development of tourism. We aimed to explore the diffusion law and influencing factors of smart tourism city so as to provide reference for the construction of smart tourism city. By employing the 249 cases in China from 2012 to 2019, we revealed the spatial-temporal characteristics and driving factors influencing the diffusion of smart tourism city policy by employing the event history analysis method. The results reveal that the diffusion of smart tourism city policy presents the typical S-shaped curve in cumulative adoptions over time. Furthermore, the diffusion of smart tourism city policy presents the spatial distribution characteristic of the Hu Line, which spreads from the eastern coastal areas to the central inland areas. Moreover, there are multiple driving sources for the diffusion of smart tourism city policy, among which economic lift force, intellectual support force, technological pull force and demand impetus force are the important driving sources for the policy diffusion.
Path Selection and Optimization of Chinese Manufacturing Industry Participating in Global Value Chain Reconstruction
Authors: Cai Qingqing , Li Li
Published: May 2022

This paper focuses on the path of China’s participation in global value chain reconstruction and concludes three ways to reconstruct the global value chain: embedding in the global value chain, reconstructing the national value chain, and leading the regional value chain. Based on the value-added accounting system and the latest statistics of the TiVA database, we construct an index system for the path selection of global value chain reconstruction and put forward a more suitable path for different manufacturing industries in China. According to the VRCA index and ranking of each type of manufacturing industry, our study concludes that: transportation equipment manufacturing tends to embed in global value chains; textiles, clothing, leather, and related manufacturing; wood products, paper products, and printing; chemical and non-metallic mineral products; base metals and metal products; computer, electronic, and electrical equipment manufacturing; machinery and equipment manufacturing; and other manufacturing industries tend to dominate the regional value chains; and food and beverage manufacturing and tobacco industries tend to restructure national value chains. Finally, our paper gives suggestions and prospects for path upgrading; promoting the integrated development of e-commerce and the manufacturing industry can enhance the competitive advantages of China’s manufacturing industry and achieve path upgrading and optimization. Furthermore, the two-way nesting of the “Belt and Road” regional value chain and global value chain can help China’s manufacturing industry eliminate the dilemma of low-end lock-in and upgrade from the original low-end dependent embedding mode to the middle high-end hub embedding mode.
A Comprehensive Review on Sustainable Natural Fiber in Cementitious Composites: The Date Palm Fiber Case
Authors: Adamu Musa , Alanazi Fayez , Ibrahim Yasser E. , Alanazi Hani , Khed Veerendrakumar C.
Published: May 2022

The use of natural fibers in cementitious composites continue gaining acceptability and applicability due to the shortcomings and disadvantages of synthetic fiber; this is because natural fibers have advantages of sustainability, eco-friendliness, and economy. Biodegradable natural fibers, being low density and lightweight, with typical values of strength-to-weight ratio, aspect ratio, elastic modulus, and strength, may be competitive for substituting synthetic fibers such as glass and carbon. Indeed, natural fibers are mostly non-irritating for the skin and typically pose no troubles or issues for breathing, which is not the case with many synthetic fibers. Date palm fiber (DPF) is a natural fiber obtained as waste material from a date palm tree. In many countries, with large date production, DPF is easily available as a process by-product, with a low processing cost. Being sustainable and environmentally friendly, DPF is continuously gaining acceptability as fiber material in different composites such as concrete, mortar, gypsum composites, clay composites, and bricks. Based on the most available literature reviewed, DPF reinforced composites have been found to be a good insulation material, with higher thermal properties, thereby reducing energy consumption which consequently saves the running and maintenance cost of the building. DPF reinforced composites were reported to have higher energy absorption capacity, ductility, and bending resistance, leading to delaying crack propagation and preventing catastrophic failures of structures such as beams and slabs. Additionally, due to its lower density, DPF reinforced composites have the advantage for usage in areas prone to seismic effects, and when used for buildings, the overall weight of the building is expected to reduce hence reduction in foundation cost. The major setback of using DPF in composites is the reduction in the compressive strength of the composites and the durability performance of the composites. Therefore, for effective usage of DPF in composites to derive the maximum benefits, there is a need to devise a method of mitigating its negative effects on the compressive strength and durability performance of the Composites; this is a future study that needs to be explored for better performance of DPF in cementitious and other materials composites.
Feasibility of Using Coal Ash for the Production of Sustainable Bricks
Authors: Mukhtar Ahmad , Qazi Asad Ullah , Khan Qasim Shaukat , Munir Muhammad Junaid , Kazmi Syed Minhaj Saleem , Hameed Asif
Published: May 2022

In this research study, environmentally friendly unburnt coal ash (CA) bricks were investigated as an alternative to conventional burnt clay bricks. In this research study, various physical and mechanical properties of unburnt CA bricks were investigated. The unburnt CA bricks were prepared by using 60% CA and 10% lime by weight. In these unburnt CA bricks, varying cement contents (5%, 10%, and 15%), sand contents (10% and 15%), and quarry dust contents (5% and 10%) by weight were used. A forming pressure of 29 MPa was applied through an automatic pressure control system either for 3 s or 6 s. The prepared bricks were moist cured for 28 days. The experimental results exhibited that unburnt CA bricks with 10% cement, 10% sand, and 10% quarry dust subjected to forming pressure for 3 s exhibited the highest compressive strength of 19 MPa and flexural strength of 2.1 MPa. The unburnt CA bricks exhibited reduced water absorption, reduced efflorescence, and lower weight per unit area than the conventional clay bricks. A cost comparison of unburnt CA bricks and clay bricks exhibited that unburnt CA bricks are cost-effective compared to clay bricks.
The Balanced Allocation of Medical and Health Resources in Urban Areas of China from the Perspective of Sustainable Development: A Case Study of Nanjing
Authors: Wu Fang , Chen Wei , Lin Lingling , Ren Xu , Qu Yingna
Published: May 2022

The balanced allocation of medical and health resources is an important basis for the sustainable development of health undertakings. In recent years, China has made remarkable achievements in the medical and health services, but there is still a phenomenon of unbalanced allocation of medical and health resources among different regions, which has become an urgent problem to be solved in deepening the reform of the medical and health system during the 14th Five-Year Plan period. From the perspective of people’s needs for health, this study analyzed the equity and efficiency of urban medical and health resources allocation in China by using the Theil index method and DEA method. Meanwhile, the authors used the coupling coordination degree model to construct a balanced development model with equity and efficiency as subsystems, taking the city of Nanjing as an example to analyze its balanced allocation of medical and health resources from 2008 to 2019. In general, taking Nanjing as an example, it shows that the balanced allocation of medical and health resources in Chinese cities is good, but in geographical dimension, the level of balanced allocation is low, and there are still significant differences in the equity and efficiency of allocation among regions. In the future, the government can strengthen the rationality of regional planning, appropriately increasing health investment and medical supply, considering both equity and efficiency to further realize the balanced allocation of medical and health resources and improve the sustainability of urban medical service system. The main contribution of this paper lies in that, from the perspective of sustainable development, the evaluation system is integrated to measure the equity and efficiency respectively, and the balanced development model is used to investigate the allocation of urban medical and health resources. The research results can provide reference for optimizing resources allocation and promoting the sustainable development of medical and health undertakings.
Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows
Authors: Wang Yong , Zhe Jiayi , Wang Xiuwen , Sun Yaoyao , Wang Haizhong
Published: May 2022

Dynamic customer demands impose new challenges for vehicle routing optimization with time windows, in which customer demands appear dynamically within the working periods of depots. The delivery routes should be adjusted for the new customer demands as soon as possible when new customer demands emerge. This study investigates a collaborative multidepot vehicle routing problem with dynamic customer demands and time windows (CMVRPDCDTW) by considering resource sharing and dynamic customer demands. Resource sharing of multidepot across multiple service periods can maximize logistics resource utilization and improve the operating efficiency of delivery logistics networks. A bi-objective optimization model is constructed to optimize the vehicle routes while minimizing the total operating cost and number of vehicles. A hybrid algorithm composed of the improved k-medoids clustering algorithm and improved multiobjective particle swarm optimization based on the dynamic insertion strategy (IMOPSO-DIS) algorithm is designed to find near-optimal solutions for the proposed problem. The improved k-medoids clustering algorithm assigns customers to depots in terms of specific distances to obtain the clustering units, whereas the IMOPSO-DIS algorithm optimizes vehicle routes for each clustering unit by updating the external archive. The elite learning strategy and dynamic insertion strategy are applied to maintain the diversity of the swarm and enhance the search ability in the dynamic environment. The experiment results with 26 instances show that the performance of IMOPSO-DIS is superior to the performance of multiobjective particle swarm optimization, nondominated sorting genetic algorithm-II, and multiobjective evolutionary algorithm. A case study in Chongqing City, China is implemented, and the related results are analyzed. This study provides efficient optimization strategies to solve CMVRPDCDTW. The results reveal a 32.5% reduction in total operating costs and savings of 29 delivery vehicles after optimization. It can also improve the intelligence level of the distribution logistics network, promote the sustainable development of urban logistics and transportation systems, and has meaningful implications for enterprises and government to provide theoretical and decision supports in economic and social development.
Micro-Mobility User Pattern Analysis and Station Location in Thessaloniki
Authors: Ayfantopoulou Georgia , Salanova Grau Josep Maria , Maleas Zisis , Siomos Alexandros
Published: May 2022

In recent years, European countries have been trying to cultivate electrical scooters (e-scooters) as an alternative form of micro-mobility. The purchase and maintenance costs of private e-scooters are expensive so cities have been collaborating with companies to construct an e-scooter rental network. This paper aims to interpret the usage patterns of e-scooters and suggest optimal station locations for charging and parking. The dataset depicts a three-month time period (August, September, and May of the year 2020/2021) of 26,374 trips, in which there were no pandemic limitations. The results are based on two main features: the initial–final position of the e-scooter (the total distance is unknown) and the travel time. Most e-scooter rides refer to leisure trips but there is a portion of users that ride e-scooters for casual trips or commutes to and from work. Here, an electrical scooter network that covers the demand of the city of Thessaloniki is suggested. The implementation plan indicates three stages of construction up to the completion of the network. The construction operation is responsible for the creation and development of a sustainable network by adding approximately 20 stations at every stage. The capacity of the stations fluctuates between 3 and 20 parking spaces, which should accommodate e-scooters and preserve free spaces for potential riders. According to the characteristics of the city of Thessaloniki, its coverage requires the construction of 67 carefully distributed stations.
Corporate Social Responsibility in Social SMEs: Discourses of Prosocial Behavior in Individual, Organizational, and Societal Levels
Authors: Myyryläinen Heidi , Torkkeli Lasse
Published: May 2022

The past decades have seen an increase in studies on social entrepreneurship, yet its theorization remains underdeveloped. This is especially the case for clarifying how the social mission inherent in social enterprises is related to the social responsibility of traditional businesses, usually understood through corporate social responsibility (CSR) activities. The relation between social entrepreneurship and CSR is not unequivocal, as from a theoretical perspective these constructs should be distinct, yet their boundaries both in theory and in practice are still unclear. The literature suggests that it is their social mission that defines social enterprises whereas, for other types of small- and medium-sized enterprises (SMEs), CSR activities would be instrumental and motivated by profit maximization. Until now, it has been unclear what the role of CSR in activities and behavior in social enterprises is, which is a notable research gap since social entrepreneurship is an emerging domain of study in business research and practice. This study contributes by illustrating how CSR manifests through prosocial behavior across different levels in social SMEs, thus shedding light on how social entrepreneurs view their motivations towards others-oriented behavior in SMEs. We study the discourses of entrepreneurs who manage mission-driven businesses and social enterprises in Finland, Sweden, Estonia, and Latvia. The perspective can also have implications for SMEs and their strategic positioning of social entrepreneurship and CSR. Viewing social entrepreneurship, CSR, and prosocial motivation as sociopsychological and contextual, constructivist processes sheds light on the multifaceted nature of these phenomena. This discourse study presents a model of how individual, group, organization and societal prosocial motivations co-exist.
Rotational Cryptanalysis on ChaCha Stream Cipher
Authors: Barbero Stefano , Bazzanella Danilo , Bellini Emanuele
Published: May 2022

In this paper we consider the ChaCha20 stream cipher in the related-key scenario and we study how to obtain rotational-XOR pairs with nonzero probability after the application of the first quarter round. The ChaCha20 input can be viewed as a 4×4 matrix of 32-bit words, where the first row of the matrix is fixed to a constant value, the second two rows represent the key, and the fourth some initialization values. Under some reasonable independence assumptions and a suitable selection of the input, we show that the aforementioned probability is about 2−251.7857, a value greater than 2−256, which is the one expected from a random permutation. We also investigate the existence of constants, different from the ones used in the first row of the ChaCha20 input, for which the rotational-XOR probability increases, representing a potential weakness in variants of the ChaCha20 stream cipher. So far, to our knowledge, this is the first analysis of the ChaCha20 stream cipher from a rotational-XOR perspective.
A New Result in Form of Finite Triple Sums for a Series from Ramanujan’s Notebooks
Authors: Vigren Erik , Dieckmann Andreas
Published: May 2022

We consider a function g(r,x,u) with x,u∈ℂ and r∈ℕ, which, over a symmetric domain, equals the sum of an infinite series as noted in the 16th Entry of Chapter 3 in Ramanujan’s second notebook. The function attracted new attention since it was established to be closely connected to the theory of labelled trees. However, to the best of our knowledge, a closed-form solution allowing, e.g., the rapid computation of g(r,x,u) in Mathematica without explicit use of recursions has been lacking until now. Our proposed formula transforms the part depending on the variable u into a more symmetric form, which then appears inside a finite triple sum consisting of binomials and Stirling numbers of the second kind.
Solving the Sylvester-Transpose Matrix Equation under the Semi-Tensor Product
Authors: Jaiprasert Janthip , Chansangiam Pattrawut
Published: May 2022

This paper investigates the Sylvester-transpose matrix equation A⋉X+XT⋉B=C, where all mentioned matrices are over an arbitrary field. Here, ⋉ is the semi-tensor product, which is a generalization of the usual matrix product defined for matrices of arbitrary dimensions. For matrices of compatible dimensions, we investigate criteria for the equation to have a solution, a unique solution, or infinitely many solutions. These conditions rely on ranks and linear dependence. Moreover, we find suitable matrix partitions so that the matrix equation can be transformed into a linear system involving the usual matrix product. Our work includes the studies of the equation A⋉X=C, the equation X⋉B=C, and the classical Sylvester-transpose matrix equation.
TACDFSL: Task Adaptive Cross Domain Few-Shot Learning
Authors: Zhang Qi , Jiang Yingluo , Wen Zhijie
Published: May 2022

Cross Domain Few-Shot Learning (CDFSL) has attracted the attention of many scholars since it is closer to reality. The domain shift between the source domain and the target domain is a crucial problem for CDFSL. The essence of domain shift is the marginal distribution difference between two domains which is implicit and unknown. So the empirical marginal distribution measurement is proposed, that is, WDMDS (Wasserstein Distance for Measuring Domain Shift) and MMDMDS (Maximum Mean Discrepancy for Measuring Domain Shift). Besides this, pre-training a feature extractor and fine-tuning a classifier are used in order to have a good generalization in CDFSL. Since the feature obtained by the feature extractor is high-dimensional and left-biased, the adaptive feature distribution transformation is proposed, to make the feature distribution of each sample be approximately Gaussian distribution. This approximate symmetric distribution improves image classification accuracy by 3% on average. In addition, the applicability of different classifiers for CDFSL is investigated, and the classification model should be selected based on the empirical marginal distribution difference between the two domains. The Task Adaptive Cross Domain Few-Shot Learning (TACDFSL) is proposed based on the above ideas. TACDFSL improves image classification accuracy by 3–9%.
Visualising Departures from Symmetry and Bowker’s X2 Statistic
Authors: Beh Eric J. , Lombardo Rosaria
Published: May 2022

Sometimes, the same categorical variable is studied over different time periods or across different cohorts at the same time. One may consider, for example, a study of voting behaviour of different age groups across different elections, or the study of the same variable exposed to a child and a parent. For such studies, it is interesting to investigate how similar, or different, the variable is between the two time points or cohorts and so a study of the departure from symmetry of the variable is important. In this paper, we present a method of visualising any departures from symmetry using correspondence analysis. Typically, correspondence analysis uses Pearson’s chi-squared statistic as the foundation for all of its numerical and visual features. In the case of studying the symmetry of a variable, Bowker’s chi-squared statistic, presented in 1948, provides a simple numerical means of assessing symmetry. Therefore, this paper shall discuss how a correspondence analysis can be performed to study the symmetry (or lack thereof) of a categorical variable when Bowker’s statistic is considered. The technique presented here provides an extension to the approach developed by Michael Greenacre in 2000.
An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
Authors: Zhang Zhaohui , Min Xiaofei , Chen Yue
Published: May 2022

Adaptive control of traffic engineering (TE) based on 5G network function virtualization (NFV) authorizes the efficient and dynamic network resource allocation, whose utilization is increasingly wide and will become more widespread. In this paper, we first devise an adaptive control scheme for data-driven traffic migration engineering (TME) on the 5G virtual network. The proposed TME technology focuses on a 5G enhancing mobile broadband (eMBB) network application scenario and takes the network operating expenditure (OPEX) as the main research target. Firstly, we predict the network traffic of the virtual network through the constructed traffic predicted mathematical model. Then, based on the triangle inequality violation (TIV) theorem, some local network traffic is adaptively migrated when the predicted link traffic exceeds the peak rate. Consequently, the migrations of logical links in the virtual network layer are completed. Finally, our experiments show that the proposed protocol can effectively improve the key performance indicators (KPIs) of the reconfigured network, such as throughput, delay and energy consumption. Furthermore, the Fridman and Holm statistical hypothesis tests are also used to analyze the simulation data, which proves that the proposed approximate TME algorithm has statistical significance.
Approximating Fixed Points of Relatively Nonexpansive Mappings via Thakur Iteration
Authors: Pragadeeswarar V. , Gopi R. , Sen M. De la
Published: May 2022

The study of symmetry is a major tool in the nonlinear analysis. The symmetricity of distance function in a metric space plays important role in proving the existence of a fixed point for a self mapping. In this work, we approximate a fixed point of noncyclic relatively nonexpansive mappings by using a three-step Thakur iterative scheme in uniformly convex Banach spaces. We also provide a numerical example where the Thakur iterative scheme is faster than some well known iterative schemes such as Picard, Mann, and Ishikawa iteration. Finally, we provide a stronger version of our proposed theorem via von Neumann sequences.
Tsallis and Other Generalised Entropy Forms Subject to Dirichlet Mixture Priors
Authors: Ferreira Johannes T. , Botha Tanita , Bekker Andriette
Published: May 2022

Entropy indicates a measure of information contained in a complex system, and its estimation continues to receive ongoing focus in the case of multivariate data, particularly that on the unit simplex. Oftentimes the Dirichlet distribution is employed as choice of prior in a Bayesian framework conjugate to the popular multinomial likelihood with K distinct classes, where consideration of Shannon- and Tsallis entropy is of interest for insight detection within the data on the simplex. However, this prior choice only accounts for negatively correlated data, therefore this paper incorporates previously unconsidered mixtures of Dirichlet distributions as potential priors for the multinomial likelihood which addresses the drawback of negative correlation. The power sum functional, as the product moment of the mixture of Dirichlet distributions, is of direct interest in the multivariate case to conveniently access the Tsallis- and other generalized entropies that is incorporated within an estimation perspective of the posterior distribution using real economic data. A prior selection method is implemented to suggest a suitable prior for the consideration of the practitioner; empowering the user in future for consideration of suitable priors incorporating entropy within the estimation environment as well as having the option of certain mixture of Dirichlet distributions that may require positive correlation.
Research on Prediction Method of Gear Pump Remaining Useful Life Based on DCAE and Bi-LSTM
Authors: Wang Chenyang , Jiang Wanlu , Yue Yi , Zhang Shuqing
Published: May 2022

As a hydraulic pump is the power source of a hydraulic system, predicting its remaining useful life (RUL) can effectively improve the operating efficiency of the hydraulic system and reduce the incidence of failure. This paper presents a scheme for predicting the RUL of a hydraulic pump (gear pump) through a combination of a deep convolutional autoencoder (DCAE) and a bidirectional long short-term memory (Bi-LSTM) network. The vibration data were characterized by the DCAE, and a health indicator (HI) was constructed and modeled to determine the degradation state of the gear pump. The DCAE is a typical symmetric neural network, which can effectively extract characteristics from the data by using the symmetry of the encoding network and decoding network. After processing the original vibration data segment, health indicators were entered as a label into the RUL prediction model based on the Bi-LSTM network, and model training was carried out to achieve the RUL prediction of the gear pump. To verify the validity of the methodology, a gear pump accelerated life experiment was carried out, and whole life cycle data were obtained for method validation. The results show that the constructed HI can effectively characterize the degenerative state of the gear pump, and the proposed RUL prediction method can effectively predict the degeneration trend of the gear pump.
TEXT Analysis on Ocean Engineering Equipment Industry Policies in China between 2010 and 2020
Authors: Ren Jiajia , Ge Shilun
Published: May 2022

The ocean engineering equipment industry is the foundation for the implementation of maritime strategy. China’s national departments at all levels have developed relevant ocean engineering equipment industry policies to promote the rapid development of the industry. By using 56 industrial policies issued between 2010 and 2020 as the research sample, we conducted an in-depth assessment of the external structural characteristics and structure of the main cooperation network for such policies using descriptive statistics and social network analysis. Based on a symmetric analysis method, the two-dimensional matrix of cooperation breadth and cooperation depth, together with the measurement of the issuing subject’s centrality, was used to analyze the evolution of the subject’s role in the network. The research shows that the development of China’s ocean engineering equipment industry policies can be divided into three stages, and there are the following problems during the development of policies: (1) some policies and regulations are imperfect; (2) the network of cooperation among joint issuers is limited; and (3) some policies are issued by multiple government departments, but there is a lack of specialized and unified management from an absolute core department. Based on the above problems, we present some suggestions for policy optimization at the end of this paper.
A Complementary Split-Ring Resonator (CSRR)-Based 2D Displacement Sensor
Authors: Ren Kun , Zhu Pengwen , Sun Taotao , Wang Junchao , Wang Dawei , Liu Jun , Zhao Wensheng
Published: May 2022

In this paper, a two-dimensional (2D) displacement sensor based on the diagonal symmetry complementary split-ring resonator (CSRR) structure is proposed. The one-dimensional (1D) displacement sensor is initially developed by etching a ring on the ground plane, and a triangle metal patch is used as the mover. When the mover is attached, a CSRR is formed, thereby inducing a resonant frequency. The displacement of the mover can be retrieved by the variation in the resonant frequency. Then, the structure is extended for designing a 2D displacement sensor. To further reduce the error caused by the lateral displacement, the shape of the sensor is further optimized using the particle swarm algorithm. Finally, the structure of the CSRR displacement sensor is simulated and analyzed, and a physical prototype is made. The measured results are consistent with the theoretical analysis, and its sensitivity is 160 MHz/mm.
A Novel Undeniable (t, n)-Threshold Signature with Cheater Identification
Authors: Tseng Yi-Fan , Lin Yan-Bin
Published: May 2022

A digital signature is one of the most widely used cryptographic primitives in asymmetry cryptography. According to the security requirements in different symmetry or asymmetry network models, various digital signatures have been developed in the literature. To protect the right of the signer, Chaum and Antrepen first introduced the concept of an undeniable signature, where interactive protocols are needed for the verification process. Besides, a signer can, also, perform a disavowal protocol to prove that they did not sign the message. On the other hand, threshold cryptography is, usually, used to protect the system from a single point of failure. In a (t,n)-threshold signature scheme, as long as t people in the group of n people participate, the signature can be smoothly signed. By combining these two features, an undeniable threshold signature enjoys the advantages from both sides. After our survey, we found that the existing undeniable threshold signature schemes are either insecure or apply impractical assumptions. Thus, in this manuscript, we aim at designing a novel and provably secure undeniable threshold signature scheme. The proposed scheme is formally proven to be unforgeable and invisible. Besides, our scheme supports cheater identification, which allows one to find the cheater, when a signing protocol fails. Moreover, the proposed scheme can be performed without the help of trusted third parties or secure cryptographic modules, which would be more practical when our scheme is deployed in real-world applications.
(Quantum) Time-Memory-Data Tradeoff Attacks on the SNOW-V Stream Cipher
Authors: Li Sijia , Liao Zhiyi , Wu Zhengyang , Wu Zheng , Ding Lin
Published: May 2022

Symmetric cryptosystems (i.e., stream ciphers and block ciphers) have always played an important part in securing the various generations of 3GPP (3rd Generation Partnership Project) mobile telephony systems. The SNOW-V stream cipher, published in September 2019, is the most recent member of the well-known SNOW family of ciphers. It is designed to provide confidentiality and integrity for 5G communications. There have been no time-memory-data tradeoff (TMDTO) attacks on the cipher published so far. By combining with the BSW sampling technique, we propose TMDTO attacks on SNOW-V. The results show that the attacker can mount a TMDTO attack, where none of the online time complexity, the memory complexity and the offline time complexity are bigger than 2256, if the keystream sequences generated by the secret key, together with different IVs, are provided to the attacker. Furthermore, we analyze the security of SNOW-V against quantum TMDTO attacks, and the results show that a quantum TMDTO attack offers, strictly, better online time complexity than Grover’s algorithm, when the available memory space is bigger than 2170.67. These results are helpful in evaluating the security of SNOW-V against (quantum) TMDTO attacks.