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In this paper, an enhanced discriminative feature learning (EDFL) method is proposed to address single sample per person (SSPP) face recognition. With a separate auxiliary dataset, EDFL integrates Fisher discriminative learning and domain adaptation into a unified framework. The separate auxiliary dataset and the gallery/probe dataset are from two different domains (named source and target domains respectively) and have different data distributions. EDFL is modeled to transfer the discriminative knowledge learned from the source domain to the target domain for classification. Since the gallery set with SSPP contains scarce number of samples, it is hard to accurately represent the data distribution of the target domain, which hinders the adaptation effect. To overcome this problem, the generalized domain adaption (GDA) method is proposed to realize good overall domain adaptation when one domain contains limited samples. GDA considers the both global and local domain adaptation effect at the same time. Further, to guarantee that the learned domain adaptation components are optimal for discriminative learning, the domain adaptation and Fisher discriminant model learning are unified into a single framework and an efficient algorithm is designed to optimize them. The effectiveness of the proposed approach is demonstrated by extensive evaluation and comparison with some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background and Purpose: Telehealth bring significant benefits including improved quality of healthcare, efficiency and cost containment, especially for chronic patients and the elderly. China is the second largest country of investment in telemedicine systems, but the acceptance and behavioral intentions of the technology are still low in the elderly. The objective of this study is to explain the micro-mechanism that determines the behavioral intentions to use telemedicine systems from the perspective of elderly patients based on an extended Technology Acceptance Model.Methods: A sample consisting of 436 elderlies selected through multistage cluster sampling from four cities in mainland China. The empirical study was conducted to examine the proposed model by two aspects: measurement model and structural model.Results: The study determined that medical service satisfaction (t = 6.770, β = 0.332), ease of use (t = 5.200, β = 0.179), information quality (t = 12.540, β = 0.639) had a significant impact on the elderly patients' acceptance to telehealth, and the acceptance had a significant impact (t = 14.356, β = 0.697) on the elderly patients' behavior intentions of telehealth. The results also show that the variable of acceptance has significant mediating effects among the theoretical model.Conclusions: This study confirms the applicability of the extended Technology Acceptance Model in the behavioral intentions among elderly people in China using telehealth systems. The results indicate that relationship between telehealth systems and physical medical services are mutually reinforcing rather than alternative. The study will help technology developers better understand the behavioral characteristics of the elderly and encourage healthcare providers to better understand the true need of telehealth systems. These findings provide valuable information to telehealth system developers, governments, investors, and hospitals to promote the use of this technology by elderly patients. [ABSTRACT FROM AUTHOR]

Collecting samples is one of the main difficulties for face recognition, for example, in most of the real-world applications such as law enhancement, e-passport, and ID card identification, it is customary to collect a single sample per person (SSPP). Unfortunately, in such SSPP scenario, many presented face recognition methods suffer serious performance drop or fail due to their inability to learn the discriminative information of a person from a single sample. To address the SSPP problem, in this paper, we propose a multiple feature subspaces analysis (MFSA) approach, which takes advantage of facial symmetry. First, we divide each enrolled face into two halves about the bilateral symmetry axis and further partition every half into several local face patches. Second, we cluster all the patches into multiple groups according to their locations at the half face and formulate SSPP as a MFSA problem by learning a feature subspace for each group, so that the confusion between inter-class and intra-class variations of face patches is removed and more discriminative features can be extracted from each subspace. To recognize a target person, a k-NN classifier is employed in each subspace to predict the label of a face patch and majority voting strategy is used to identify the unlabeled subject. Compared with the state-of-the-art methods, MFSA is effortless and efficient in implementing, but achieves either better or competitive performance when recognizing face images taken in both constrained and unconstrained environment. [ABSTRACT FROM AUTHOR]

As a growing number of low-resolution (LR) face images are captured by surveillance cameras, LR face recognition has been a hot issue for recent years. Previous efforts on LR face recognition typically assume each subject has multiple high-resolution (HR) training samples. However, this assumption may not hold in some special cases such as law-enforcement where only a single HR sample per person exists in the training set. For LR face recognition in SSPP scenario, it often suffers from overfitting and singular matrix problems. In this paper, we are the first to investigate LR face recognition with single sample per person, and propose a cluster-based regularized simultaneous discriminant analysis (C-RSDA) method based on SDA. C-RSDA regularizes the between-class and within-class scatter matrices respectively with inter-cluster and intra-cluster scatter matrices, where the cluster-based scatter matrices are computed from unsupervised clustering. With the cluster-based scatter matrices, not only the singularity problem is resolved, but overfitting problem is overcomed as more variations are exploited from the limited training samples. Thus, the proposed C-RSDA enhances the discriminative power of the feature subspace. We extensively evaluate C-RSDA on recognizing LR face images captured in both controlled and uncontrolled environments. The encouraging experimental results demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Perishable food supply chain required special attention concerning "timely delivery supervision" that may have quality, safety and environmental effects throughout with transportation duration aspects which leads to create economic differences of product value. Consequently, the development of food quality and safety has achieved significant importance. Therefore, the development of perishable food supply chain transportation network needed to more explored to fill the gap of timely supervision. Respectively, the multi-objective conceptual simulation model is developed to optimize the cost, environmental effect, quality and safety, and the appropriate transportation facility at field level connected with perishable food supply chain transportation system. In this study, perishable food lifetime variations are significantly structured as the Weibull random approach, and product perishability is supposed to be influenced by transport refrigerator application, which is assumed as the decision variable. Apart from, multiple vehicle types and multiple perishable food types are taken. The perishable food supply chain case is examined and, the associations and interrelations of all aspects of sustainability are studied, which is known as the multi-objective model of sustainability. According to the findings, by emphasising the cost factors for highly perishable commodities, the supply chain's environmental impact may increase by 108%, and the social impact may rise by 45% for transportation networks with field refrigeration facilities. Additionally, a 23% increase in economic awareness could increase supply chain structure design sustainability by 137%. Environmental impact and the unpredictability of perishable foods are also seen as important variables that might have a variety of effects on the supply chain's structure and operation. This study contributes the cold supply chain's sustainable development objectives while enhancing the quality and safety of perishable products. The results propose that policy makers can significantly decrease the environmental and social effects of the supply chain even without considerably compromising the cost aspect. • To prevent environmental effects on food quality and safety, perishable items must be delivered quickly. • Sustainable transportation approach reduces waste and losses associated with perishable supplies. • By applying the right sustainable perishable food supply chain model, environmental effects can be reduced. • The choice and implementation of best plan depends on the analysis of the regional supply chain. [ABSTRACT FROM AUTHOR]

Details about the movement of trucks on postal express lines are investigated to improve the performances of mail distribution. A mixed driving pattern of trucks is introduced to minimize the transportation cost of a postal express line network with a service level requirement. We formulate this problem as a mixed p meeting depots location with shipment scheduling problem and build a MINLP model. A two-level tabu search procedure based on shipment grouping method is developed. Through a series of computational experiments and sensitivity analysis on different instances, some managerial insights of the network under mixed driving pattern are revealed. [ABSTRACT FROM AUTHOR]

Demand for emergency resources is usually uncertain and varies quickly in anti-bioterrorism system. Besides, emergency resources which had been allocated to the epidemic areas in the early rescue cycle will affect the demand later. In this article, an integrated and dynamic optimisation model with time-varying demand based on the epidemic diffusion rule is constructed. The heuristic algorithm coupled with the MATLAB mathematical programming solver is adopted to solve the optimisation model. In what follows, the application of the optimisation model as well as a short sensitivity analysis of the key parameters in the time-varying demand forecast model is presented. The results show that both the model and the solution algorithm are useful in practice, and both objectives of inventory level and emergency rescue cost can be controlled effectively. Thus, it can provide some guidelines for decision makers when coping with emergency rescue problem with uncertain demand, and offers an excellent reference when issues pertain to bioterrorism. [ABSTRACT FROM AUTHOR]

This article considers a supply chain for a single product involving one retailer and two independent suppliers, when the main supplier might fail to supply the products, the backup supplier can always supply the products at a higher price. The retailer could use the backup supplier as a regular provider or a stand-by source by reserving some products at the supplier. A backup agreement with penalty scheme is constructed between the retailer and the backup supplier to mitigate the supply disruptions and the demand uncertainty. The expected profit functions and the optimal decisions of the two players are derived through a sequential optimisation process. Then, the sensitivity of two players’ expected profits to various input factors is examined through numerical examples. The impacts of the disruption probability and the demand uncertainty on the backup agreement are also investigated, which could provide guideline for how to use each sourcing method. [ABSTRACT FROM PUBLISHER]

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot. [ABSTRACT FROM AUTHOR]

Objective We aimed to investigate the feasibility and method of constructing a traditional Chinese medicine (TCM) curative effect prediction model based on the data of Kechuanting acupoint plastering therapy in the treatment of bronchial asthma (asthma). Methods Data from the Chronic Disease Management Research Platform of 303 patients with asthma who were treated with Kechuanting acupoint plastering therapy for 6 weeks in the Department of Acupuncture and Rehabilitation of Jiangsu Hospital of Traditional Chinese Medicine from June to August 2018 to 2021 were selected. We used Phyton 3.10 for statistical analysis. After data preprocessing, the influencing factors were used to build models by Logistic regression, support vector machine, K-means clustering algorithm, Bayes algorithm, random forest method and Light gradient boosting machine (LightGBM) respectively, with the improvement of asthma control test score (ACT), forced expiratory volume in one second (FEV1) and exhaled nitric oxide (FeNO) as the outcome indicators. Then, the models were compared and analyzed. Subsequently, the superior model was used to establish the efficacy prediction model and verify its stability to obtain the accuracy rate and eliminate the relatively important factors. Results The accuracy rate of the Kechuanting acupoint plastering therapy curative effect prediction model established by the LightGBM model was more than 70%. Five important factors were selected, including allergic history, tabacco and alcohol abuse, plastering duration, ACT before treatment, and FeNO before treatment. According to the classification analysis and the relationship between the important factors and the outcome indicators, Kechuanting acupoint plastering therapy significantly improved the ACT of patients with no history of allergy, no tabacco and alcohol abuse, and poor ACT:5-15 points (P < 0.05). Furthermore, Kechuanting acupoint plastering therapy improved FeNO more significantly in patients with more than 3 years of treatment than those with no more than 3 years (P < 0.05). However, Kechuanting acupoint plastering therapy only improved FeNO in a few patients with poor asthma control levels (P < 0.05) and severe airway inflammation (FeNO>50x10-9) (P>0.05). Conclusion Acupoint plastering application has a significant effect on improving the control level of asthma, but its effect on improving airway inflammation is limited. It is feasible to use data from the chronic disease management research platform to construct the prediction model. After optimization and testing, the predictive model established based on the data of this study may provide an effective evaluation tool for targeted clinical treatment. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis is the second most deadly infectious disease after COVID-19 and the 13th leading cause of death worldwide. Among the 30 countries with a high burden of TB, China ranks third in the estimated number of TB cases. China is in the top four of 75 countries with a deficit in funding for TB strategic plans. To reduce costs and improve the effectiveness of TB treatment in China, the NHSA developed an innovative BP method. This study aimed to simulate the effects of this payment approach on different stakeholders, reduce the economic burden on TB patients, improve the quality of medical services, facilitate policy optimization, and offer a model for health care payment reforms that can be referenced by other regions throughout the world. Methods: We developed a simulation model based on a decision tree analysis to project the expected effects of the payment method on the potential financial impacts on different stakeholders. Our analysis mainly focused on comparing changes in health care costs before and after receiving BPs for TB patients with Medicare in the pilot areas. The data that were used for the analysis included the TB service claim records for 2019–2021 from the health insurance agency, TB prevalence data from the local Centre for Disease Control, and health care facilities' revenue and expenditure data from the Statistic Yearbook. A Monte Carlo randomized simulation model was used to estimate the results. Results: After adopting the innovative BP method, for each TB patient per year, the total annual expenditure was estimated to decrease from $2,523.28 to $2,088.89, which is a reduction of $434.39 (17.22%). The TB patient out-of-pocket expenditure was expected to decrease from $1,249.02 to $1,034.00, which is a reduction of $215.02 (17.22%). The health care provider's revenue decreased from $2,523.28 to $2,308.26, but the health care provider/institution's revenue-expenditure ratio increased from -6.09% to 9.50%. Conclusions: This study highlights the potential of BPs to improve medical outcomes and control the costs associated with TB treatment. It demonstrates its feasibility and advantages in enhancing the coordination and sustainability of medical services, thus offering valuable insights for global health care payment reform. [ABSTRACT FROM AUTHOR]

This study advances the Health Belief Model (HBM) within the context of medical tourism, exploring the intricate interplay between destination image, health beliefs, psychological distance, and the behavioral intentions of potential medical tourists. Our investigation reveals that destination image significantly influences perceived barriers and severity of risks, which, along with perceived susceptibility and benefits, strongly predicts tourists’ visitation intentions. Notably, the study revealed the dual roles of psychological distance: as a mediator between destination image and tourist preferences, and as a moderator affecting the impact of destination image on perceived risks. These findings underscore the importance of psychological factors in medical tourism decisions, suggesting that addressing psychological distance can enhance marketing strategies and improve patient outcomes. [ABSTRACT FROM AUTHOR]

• The psychological factors are determinants for delivery drivers to adopt electric trucks. • It is necessary to integrate technology anxiety into the extended UTAUT model. • Expanded the literature on UTAUT in the context of electric trucks. [ABSTRACT FROM AUTHOR]

• Public's reactions toward Changsheng vaccine scandal on the Internet in China. • 215,184 unqualified DTP vaccines were reported to be used for child vaccination. • Over 80% of comments are negative and express weak confidence to China-made vaccines. • Vaccine anxiety influenced by low-satisfaction on vaccine makers and regulation. The public acceptance and implementation of vaccination programs is essential to prevent infectious diseases. However, vaccine adverse events may cause public panic and eventually lead to an increasing number of populations who were hesitant or refuse to participate in these vaccination programs. In 2018, the Changsheng vaccine crisis broke out in mainland China, and 252,600 unqualified DTP vaccines were reported to be used for child vaccination. In this study, we observed media and public reactions toward the vaccine crisis. This study conducted Internet surveillance by four mainstream indicators from July 15th to August 7th, including social media (WeChat, Sina Weibo), online news and Baidu search index. We also analyzed the emotional perceptions of people in crisis through an online questionnaire survey. During the crisis, huge number of articles emerged on Internet, 125,882,894 articles (including forwarding) on WeChat friends circle, 1,877,660 Sina Weibo posts, 648,265 online news and 4,986,521 Baidu search indexes. Most of these articles were negative and expressed the public's weak confidence to the China-made vaccines. Public confidence in vaccines was undermined by the actions of the manufacturer and the government. The DTP vaccine crisis led to panic about immunization and eroded trust in the immunization program and in the government. Restoring public confidence in Chinese-made vaccines will take a long time, and meticulous management in vaccine production, and strict government regulation will help to alleviate public anxiety about vaccine safety and ultimately restore confidence. [ABSTRACT FROM AUTHOR]

The dynamical behaviors of an isolated population model involving delay-dependent parameters are investigated. It is shown that the positive equilibrium switches from being stable to unstable and then back to stable as the delay increases, and the Hopf bifurcation occurs finite times between the two critical values of stability changes which can be analytically determined. Moreover, the bifurcating periodic solutions are expressed analytically in an approximate form by the perturbation approach and Floquet technique. The direction and stability of the bifurcating periodic solutions are also determined. Finally, the validity of the results is shown by the consistency with the numerical simulations. [ABSTRACT FROM AUTHOR]

Abstract: Iris biometry has been widely used to recognize an individual in a natural and intuitive way. Conventional iris recognition systems transfer iris images to rectangle using polar coordinate after accurate segmentation, and have performed very well on accurate data. However, bovine iris images are usually irregular with respect to inactive participant, and the conventional methods cannot achieve high accuracy and true rotation invariance. In this paper, a new scheme is proposed based on scale invariant feature transform (SIFT) and bag-of-features. Firstly, region-based active contour is used to detect the inner boundary. Secondly, SIFT method is applied to detect the keypoints in the iris image, and points located in pupil region are removed. Then, feature vocabulary is constructed, and histogram representation for each iris image is generated. Finally, histogram distance is adopted for the matching test. Experimental results are provided to show the effectiveness and potential of developed noncooperative iris recognition. [Copyright &y& Elsevier]

Abstract: This paper studies a buy-back contract between a buyer and a backup supplier when the buyer’s main supplier experiences disruptions. The expected profit functions and the optimal decisions of the contract players are derived through a sequential optimization process. The common properties of the contract as well as the differences under the demand uncertainty and the main supplier’s recurrent supply uncertainty are explored through comparative studies and numerical examples. The study contributes to the literature by providing a better understanding of the impacts of demand and supply uncertainties and by shedding insights on the value of a backup supply. [Copyright &y& Elsevier]

Abstract: This paper investigates the issue of channel coordination for a supply chain facing stochastic demand that is sensitive to both sales effort and retail price. In the standard newsvendor setting, the returns policy and the revenue sharing contract have been shown to be able to align incentives of the supply chain’s members so that the decentralized supply chain behaves as well as the integrated one. When the demand is influenced by both retail price and retailer sales effort, none of the above traditional contracts can coordinate the supply chain. To resolve this issue, we explore a variety of other contract types including joint return policy with revenue sharing contract, return policy with sales rebate and penalty (SRP) contract, and revenue sharing contract with SRP. We find that only the properly designed returns policy with SRP contract is able to achieve channel coordination and lead to a Pareto improving win–win situation for supply chain members. We then provide analytical method to determine the contract parameters and finally we use a numerical example to illustrate the findings and gain more insights. [Copyright &y& Elsevier]

• Examined determinants for Chinese taxi drivers to adopt electric vehicles for living. • Extended the UTAUT2 framework in the context of adopting electric vehicles by taxis drivers. • Incorporated the construct Satisfaction with Incentive Policies in the UTAUT2 framework. • Governments should investigate taxi driver's satisfaction with different incentive policies. • Electric vehicle manufacturers should enhance test-driving experience to taxi drivers. Replacing combustion vehicles with electric vehicles has been promoted by the Chinese government as a viable policy to reduce fuel consumption and greenhouse gas emissions. The purpose of this study is to examine factors that influence Chinese taxi drivers' adoption of electric vehicles for living. This study takes the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as the theoretical framework and extends it to examine the influence of taxi drivers' satisfaction over government's invectives on their adoption of electric vehicles for living (i.e., adoption intention and use behavior). A questionnaire-based survey was conducted in Shenzhen and Guangzhou from November 2018 to April 2019, and 725 valid samples were collected. The model has good explanatory power, and its predictive validity accounts for about 89% of the variance in adoption intention and 50% in use behavior. The statistical results supported that such constructs as Satisfaction with Incentive Policies, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit, are influencing factors on taxi drivers' intention of using electric vehicles (i.e., behavior intention). These constructs, together with Social Influence but excluding Habit, are also influential to the actual use of electric vehicles by taxi drivers for living (i.e., use behavior). This study expands the existing theoretical modeling for characterizing the adoption of electrical vehicles. The study suggests policy makers further investigate taxi driver's satisfaction with different incentive policies, and electric vehicle manufacturers focus on improving hedonic motivation for taxi drivers, e.g., providing test-drive experiences. [ABSTRACT FROM AUTHOR]

Numerous low-resolution (LR) face images are captured by a growing number of surveillance cameras nowadays. In some particular applications, such as suspect identification, it is required to recognize an LR face image captured by the surveillance camera using only one high-resolution (HR) profile face image on the ID card. This leads to LR face recognition with single sample per person (SSPP), which is more challenging than conventional LR face recognition or SSPP face recognition. To address this tough problem, we propose a Boosted Coupled Marginal Fisher Analysis (CMFA) approach, which unites domain adaptation and coupled mappings. An auxiliary database containing multiple HR and LR samples is introduced to explore more discriminative information, and locality preserving domain adaption (LPDA) is designed to realize good domain adaptation between SSPP training set (target domain) and auxiliary database (source domain). We perform LPDA on HR and LR images in both domains, then in the domain adaptation space we apply CMFA to learn the discriminative coupled mappings for classification. The learned coupled mappings embed knowledge from the auxiliary dataset, thus their discriminative ability is superior. We extensively evaluate the proposed method on FERET, LFW and SCface database, the promising results demonstrate its effectiveness on LR face recognition with SSPP. [ABSTRACT FROM AUTHOR]

The frequent occurrence of food safety incidents is not only to be hazardous to the health of consumers, but also impacts the market of food consumption. Integrating the consumers' willingness to pay for traceability and the difference between suppliers' traceability, this paper provides four sourcing strategies for the retailer. Then, we investigate the sourcing decision of the retailer considering consumers' willingness to pay for traceability, the portion of consumers with traceability consciousness, probability of food safety incident, and additional penalty incurred by food safety incident. We find that increasing consumers' willingness to pay for traceability or expanding the portion of consumers with traceability consciousness may incur unintended consequence, such as an increase in sourcing quantity of non-traceable food product, while efforts on strengthening inspection and penalizing the retailer always lead to more supply of traceable food in the market. [ABSTRACT FROM AUTHOR]

In this paper, we characterize the humanitarian objectives of emergency resource allocation and distribution in disaster response operations. We formulate the humanitarian principles as three objective functions, i.e., lifesaving utility, delay cost and fairness. An integrated multi-objective optimization model that combines resource allocation with emergency distribution is developed, where a time space network is used to incorporate the frequent information and decision updates in a rolling horizon approach. The proposed model is shown to be a convex quadratic network flow problem, for which we design an efficient Variational Inequality algorithm. Computational results are reported to illustrate the performance of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we concentrate on solving a convex time space network flow problem with decomposable structures. We first describe the convex time space network flow optimization model, and transform it into an equivalent variational inequality problem. Then, after exploring the decomposable structure of primal decision variables, we propose a novel decomposable self-adaptive projection-based prediction–correction algorithm (DSPPCA) to solve the model, and then further provide its convergent theory. Finally, we report the computational performances through computational experiments. Numerical results reveal that DSPPCA not only can enhance the accuracy and convergence rate significantly, but also can be a powerful search algorithm for convex optimization problems with decomposable structures of decision variables. [Copyright &y& Elsevier]

Abstract: It is known that a consensus problem on any connected complete Riemannian manifold can be transformed into the one on its strongly convex balls via the compression–decompression along geodesics. From the viewpoint of interior metrics, this paper mainly provides a consensus protocol for strongly convex geodesic balls, in which the communication can be switching and directed. With the aid of nonsmooth analysis tools on Riemannian manifolds, our analysis shows that all dynamical points involved can achieve consensus in finite time. Meanwhile, the corresponding global algorithm is given, with its application to the consensus problem of rotation attitudes, as well as a case simulation, to demonstrate and verify our proposed techniques. [Copyright &y& Elsevier]

Abstract: In view of the fact that prototypes of Riemannian manifolds range from Euclidean surfaces to Lie groups, operator spaces, etc., the paper explores common consensus schemes for connected complete Riemannian manifolds. After studying the restriction exponential map, we introduce a local consensus protocol with switching communication topologies for the manifolds via the isometric mapping, based on one of the Euclidean consensus protocols. Equipped with the compression–decompression functions, we extend the local consensus protocol, and put forth a global consensus algorithm for any such manifolds. These consensus schemes can provide common solutions to the consensus problems arising from various nonlinear spaces or abstract spaces belonging to connected complete Riemannian manifolds. In particular, they are simple and concise, and can converge in finite time. Simulation on the unit sphere is provided to verify our consensus schemes, and to demonstrate the usage of our proposed techniques. [Copyright &y& Elsevier]