bluuebunny/crossref_metadata_2025 huggingface dataset:
Association analysis of genetic variants in the ghrelin and tumor necrosis factor α genes and the risk for non-Hodgkin’s lymphoma in Kuwaitis
Authors: Alrashid Maryam H. , Al-Serri Ahmad , Alshemmari Salem H. , Geo Jeethu Anu , Al-Bustan Suzanne A.
Published: May 2021

BACKGROUND: Non-Hodgkin’s lymphoma (NHL) is the most common hematological malignancy in the world. Many etiologic factors have been implicated in the risk of developing NHL, including genetic susceptibility and obesity. Single-nucleotide polymorphisms (SNPs) in Ghrelin (GHRL), an anti-inflammatory hormone, and tumor necrosis factor α (TNF-α), an inflammatory cytokine, have been independently associated with the risk for obesity and NHL. OBJECTIVE: To investigate the association between SNPs in GHRL and TNF-α and the risk for NHL and obesity in Kuwaitis. METHODS: We recruited 154 Kuwaiti NHL patients and 217 controls. Genotyping was performed for rs1629816 (GHRL promoter region), rs35684 (GHRL 3’ untranslated region), and rs1800629 (TNF-α promoter region). Logistic regression analysis was performed to assess the association of the investigated SNPs with NHL and the relationship between the selected SNPs with BMI in each group separately. RESULTS: We show that rs1629816 GG was associated with an increased risk for NHL in our sample (p= 0.0003, OR 1.82; CI: 1.31–2.54). None of the investigated SNPs were associated with obesity, nor was obesity found to be associated with the risk for NHL. CONCLUSIONS: Our study demonstrates an association between rs1629816, a SNP in the GHRL regulatory region, and NHL in Kuwaitis.
Auto-sharing parameters for transfer learning based on multi-objective optimization
Authors: Liu Hailin , Gu Fangqing , Lin Zixian
Published: May 2021

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.
Sudoku Ripeto
Authors: Palomo Miguel G.
Published: May 2021

We present a variant of Sudoku called Sudoku Ripeto. It seems to be the first to admit any combination of repeated symbols, and includes Sudoku as a proper subset. We present other Sudoku Ripeto families, each with a different repetition pattern. We define Sudoku Ripeto squares and puzzles, prove several solving rules that generalize those for Sudoku, and give sufficient conditions to flexibly solve puzzles with rules only, without search.
Altered Vergence Eye Movements and Pupil Response of Patients with Alzheimer’s Disease and Mild Cognitive Impairment During an Oddball Task
Authors: Jiménez Elizabeth Carolina , Sierra-Marcos Alba , Romeo August , Hashemi Amin , Leonovych Oleksii , Bustos Valenzuela Patricia , Solé Puig Maria , Supèr Hans
Published: May 2021

Background: Alzheimer’s disease (AD) is characterized by progressive deterioration of cognitive functions and may be preceded by mild cognitive impairment (MCI). Evidence shows changes in pupil and vergence responses related to cognitive processing of visual information. Objective: Here we test the hypothesis that MCI and AD are associated with specific patterns in vergence and pupil responses. Methods: We employed a visual oddball task. In the distractor condition (80%of the trials), a blue stimulus was presented whereas in the target condition (20%of trials) it was red. Participants (23 Controls, 33 MCI patients, and 18 AD patients) were instructed to press a button when a target appeared. Results: Participants briefly converged their eyes 200 ms after stimulus presentation. In controls, this transient peak response was followed by a delay response to targets but not to distractor stimuli. In the patient groups, delay responses to distractors were noticed. Consequently, the differential vergence response was strong in the control group, weak in the MCI group, and absent in the AD group. Pupils started to dilate 500–600 ms after the appearance of a target but slightly contracted after the presentation of a distractor. This differential pupil response was strongest in the AD group. Conclusion: Our findings support the idea of a role of vergence and pupil responses in attention and reveal altered responses in MCI and AD patients. Further studies should assess the value of vergence and pupil measurements as an objective support tool for early diagnosis of AD.
Assessment of the Psychological Burden Among Family Caregivers of People Living with Alzheimer’s Disease Using the Zarit Burden Interview
Authors: Yin Xuejun , Xie Qixing , Huang Lieyu , Liu Liming , Armstrong Elizabeth , Zhen Miaomiao , Ni Jingnian , Shi Jing , Tian Jingzhou , Cheng Wei
Published: May 2021

Background: In China, family caregivers play a major role in caring for people living with Alzheimer’s disease (PLWAD), but little is known about the burden this creates. Objective: This study aimed to investigate the burden among family caregivers of PLWAD and the factors influenced it. Methods: Family caregivers of PLWAD were recruited from a hospital in China from January 2018 to July 2018. All data were collected online using the Chinese version of the Zarit Burden Interview (ZBI), and the participants’ sociodemographic and caregiving details were obtained. T-tests and Kruskal-Wallis H (K) tests were used to compare ZBI scores between groups. Factors related to the caregiver psychological burden were analyzed using multiple linear regression analysis. Results: A total of 300 participants were assessed, of which 213 (71.00%) were female. More than half of the caregivers were the patient’s daughter (51.0%, n = 153). The average ZBI score of the caregivers was 43.05 (13.42). The level of burden was influenced by age, the relationship of the caregiver to the patient, the severity of AD, the caregiver’s retirement status, the income level of the caregiver, and the caring time. Regression analysis showed that retired caregivers were more likely to have higher levels of burden and that burden increased with AD severity. Conclusion: Most family caregivers of PLWAD have a considerable caregiver psychological burden. The findings increase the understanding of factors that influence family caregiver burden, and pave the way for potential interventions, such as social support and caregiver empowerment, to reduce their burden.
A new model development for demand forecasting of supply chain from data fusion perspective
Authors: Wang Zhonghuai , Cheng Guoping , Xiong Yu
Published: May 2021

In the increasingly competitive society, the supply chain, as the third profit center of the enterprise, makes scholars and industry-related with supply chain more and more interesting in the study of relevant issues. Exiting studies believed that an application of predictive analytics could be a tremendous impact on supply chain management. The uncertainty of supply chain demand and bullwhip effect is a challenge in the supply chain. Data fusion can effectively reduce the uncertainty of demand and the amplification effect. In this study, a new conceptual model was established on the traditional supply chain based on data fusion. Results show that the conceptual model refers to data fusion for solving the uncertain and inconsistent multi-source data by Bayesian estimation and to providing reasonable decision information for supply chain managers.
A decision-making model of railway tourist dedicated train operation plan
Authors: Liang Taixin , Liu Shifeng
Published: May 2021

The rapid increase of China’s railway mileage and the effective release of transport capacity provide a basis for the development of tourist-dedicated train. Considering the decision-making between the railway enterprises and the local government on whether the tourist-dedicated train stops at the local station, the length of stay and the market price, the model of its operation plan is designed to solve algorithm problem and provide foundation for railway enterprises to improve the efficiency of operation. Based on the results of experiments, we found that the price sensitivity of consumers is negatively correlated with the pricing and income of tourist-dedicated train. The higher the sensitivity of prices is, the lower the fares and profits of the tourist railways service will realize. In addition, the initial scale of tourism demand of each region along the railway has a direct impact on whether to build a stop in the area. A larger demand scale will increase the possibility of the tourist-dedicated train stopping. In this case, to develop the Mid-and High Tier tourist-dedicated train, the close contact and strategic cooperation with local governments before its opening should be traded-off and considered as important factors for the China National Rail.
Distribution optimization of electric vehicle load space based on interval type-II fuzzy logic algorithm
Authors: Zhang Zhongwei
Published: May 2021

The disorderly charging behavior of a mobile compound, large-scale electric vehicles in time and space will not only cause the phenomenon of “compound the peak” of the electric load, increase of the peak-valley difference of the grid, and may cause partial overcharge and line congestion and other issues. Relying on the Interval Type-II fuzzy logic algorithm, the two optimization algorithm of interval type 2 fuzzy logic algorithm and genetic algorithm are compared from the space point of view, based on the research on the optimization problem of electric vehicle charging load space allocation, through the results of calculation examples in this paper. Practical results have verified the effectiveness and feasibility of the algorithm, and the interval two fuzzy logic algorithm has high practicability.
An improved AHP based fuzzy evaluation model for ship collision risk
Authors: Luo Jie , Wang Deling , Gao Yuan
Published: May 2021

In the research of ship collision avoidance and automatic collision avoidance decision-making system, collision risk level is especially important. To evaluate the risk of ship collision, the nature of the risk level of ship collision is analyzed. Based on the fuzzy comprehensive evaluation model based on improved AHP, the concepts of space collision risk and time collision risk are proposed, and the models for space collision risk, time collision risk and collision risk level are established at the same time. The model developed and perfected the concept of ship collision risk, and are more logical, laid a solid foundation for subsequent comprehensive evaluation.
Time-space evolution of the goods export network of the Belt and Road countries: The view of weighted complex network
Authors: Liu Min , Xue Weixian , He Lisong , Yan Xue
Published: May 2021

The weighted complex network is utilized to analyze the evolution of the overall structural features of the goods export network and the role transitions of each country in the network. The research suggests: 1. The network of exports of the Belt and Road countries has transformed from multi-core pattern into one extreme along with multi-core pattern; 2. China, South Korea, Russia, Singapore and Italy are the highest-ranking countries in the network. Among these countries, the influence of China is on the rise, South Korea South Korea’s influence remains basically unchanged., however, Russia, Singapore and Italy are on the decline; 3. The leading edge of Asia-Pacific block in the network has been enhanced year by year. Not only has the trade volume within the block increased to 50% of the whole network, but the trade export to other three blocks has significant increasement. The total volume of trade in European block increased greatly and its block mode has transformed from external to universal. The trade volume of the former Soviet Union block along with the West Asia-Africa block increased significantly as well, but there is still a large gap compared with the European block and Asia-Pacific block.
Value co-creation mechanisms of multi-agent participation in crowdsourcing innovation: A grounded theory study
Authors: Qingliang Meng , Yi Hang , Xiaojun Chen , Qiaoyi Cao
Published: May 2021

As a new business model, crowdsourcing innovation is widely used for enterprises to complete innovation tasks by the external crowds. The enterprises, the external crowds, and the crowdsourcing platform make an alliance and co-create value to achieve the multi-win goal. Based on the operation process analysis of the third-party platform crowdsourcing innovation model, this paper proposes that the essence of crowdsourcing innovation is the value co-creation of multi-agent participation. Based on grounded theory, this paper constructs the theoretical model of the realization mechanism of multi-agent participation value co-creation in crowdsourcing innovation from the three parties’ perspective. This research finds that value co-creation factors include six factors: task attributes, incentive mechanisms, trust mechanisms, the platform supports, participation motivation, and diversities. The process of value co-creation includes resource integration and interaction. The results of value co-creation include three elements: innovation value, knowledge value, and relationship value.
Analyzing influencing factors of crash injury severity incorporating FARS data
Authors: Zhang Zhijian , Jiang Yubin , Chen Zhijun , Xiong Yubing
Published: May 2021

The purpose of this study is to deeply analyze the influencing factors of drivers’ traffic accident casualties and reduce the occurrence of casualties. From the FARS database of the National Highway Safety Administration (NHTSA), 93248 traffic accident data were extracted as analysis samples. On this basis, the Bayesian network and multinomial logit model are established. The constructed model was tested from the perspective of model prediction accuracy and variables importance. Bayesian networks are used to analyze the interrelationships among influencing factors, and multinomial logit models are used to compare and evaluate the impact of different variables on the injury severity under different circumstances. The results show that: the prediction accuracy of the Bayesian network model and multinomial logit model is 64.57% and 65.97%, respectively. The Bayesian network reference analyses indicate that injury severity is affected by the crash factors, and there are various interactions between the various factors. The multinomial logit model analyses indicate that the factors including drivers’ age, female driver, rural roads, drunk driving, drug driving, crash time, side collision accident, etc. could significantly increase injury severity. Airbags are more effective in reducing fatal crash than injury crash. The probability of accidents caused by drug driving drivers is greater than drunk driving, drunk driving drivers are 1.79 times and 2.34 times more likely to suffer an injury severity and fatal injury severity in crashes as compared to a no injury severity, respectively, and drug driving is 1.93 times and 2.6 times, respectively. Seat belts may avoid 92.2% of fatalities. Roadside guardrail reduces the incidence of fatal crash better than injury crash. Fatal injuries severity and injury severity are 1.124 times and 1.141 times more likely to occur during the 0 : 00 to 6 : 00 as compared to no injuries, respectively, etc.
Profit distribution of liner alliance based on shapley value
Authors: Guo Bin , Hao Shengyue , Cao Guangmei , Gao Honghu
Published: May 2021

Profit distribution plays an important role in the sustainable and stable development of liner alliances, this paper tries to solve the profit distribution issues in the liner alliance based on Shapley Value Method. Meanwhile, seeing that there is little consideration from the customer satisfaction, this paper establishes a new model by revising Shapley Value Method to distribute the profit of liner alliances from the perspectives of suppliers and customers and carry out verification through case analysis. The profit distribution method proposed in the paper is helpful to the reasonable profit distribution of liner alliance. It ensures the continuity and stability of liner alliance and provides a scientific decision-making basis for the profit distribution of liner alliance.
Stochastic programming model and algorithm for flight schedule optimization
Authors: Zhang Ying , Yinge A. , Wang Bing , Tian Wen , Wen Tao
Published: May 2021

To solve the strategic flight schedule optimization problem for multiple airport and multiple operation days, a two-stage stochastic programming model is established. Flight schedule optimization is made in the first stage of the model, and the tactical flight delay decision is made in the second stage with consideration of the impact of the uncertain operational airport capacity at the tactical stage. The arrival and departure service rates are also optimized as decision variables in the second stage so as to simulate the actual operation characteristics of the airports and more accurately estimate the operation delay at the tactical stage. The model’s objective function considers the minimum deviation of flight scheduled time in the strategic stage and the expected delay in the tactical stage. A hybrid evolutionary algorithm is designed to solve the model by two-stage decomposition. The model is simulated and verified using the flight plan data of Beijing Capital Airport and Guangzhou Baiyun Airport. The effect of the model in optimizing flight scheduled time and reducing the mean value of operational delay in the tactical phase under different capacity scenarios is analyzed, and the effectiveness of the model and the hybrid evolutionary algorithm is verified.
A fuzzy analytic hierarchy process for risk evaluation of urban rail transit PPP projects
Authors: Feng Yuting , Guo Xuemeng , Wei Bolu , Chen Bingyao
Published: May 2021

The Public-Private Partnership (PPP mode) has been widely used in the field of transportation infrastructure. It can relieve the debt pressure of local governments and improve the efficiency of resource allocation. However, due to the long life cycle of urban rail transit PPP projects and numerous participants, the potential risks arising from the coordination of multi-party relations have become a major obstacle to the development of urban rail transit PPP mode. Based on the perspective of the PPP project company (SPV), this paper adopts the method of fuzzy analytic hierarchy process, takes Shenzhen Metro Line 4 project as the research object and constructs the risk index system from nine dimensions to evaluate the risks of PPP project. Political risk, legal risk and project promotion risk are the most important risk indicators, while relationship risk and force majeure risk take the lowest proportion. According to the above results, the risk management system, risk sharing scheme and countermeasures in key risk areas are proposed. The study not only applies the fuzzy analytic hierarchy process to the research of urban rail transit risks, but also provides a systematic risk assessment basis and prevention and control reference for the smooth implementation of PPP projects.
Two-echelon imperfect production fuzzy supply chain model for reliability dependent demand with probabilistic deterioration and rework
Authors: Adak Sudip , Mahapatra G.S.
Published: May 2021

This paper develops a fuzzy two-layer supply chain for manufacturer and retailer with defective and non-defective types of products. The manufacturer produces up to a specific time, including faulty and non-defective items, and after the screening, the non-defective item sends to the retailer. The retailer’s strategy is to do the screening of items received from the manufacturer; subsequently, the perfect quality items are used to fulfill the customer’s demand, and the defective items are reworked. The retailer considers that customer demand is time and reliability dependent. The supply chain considers probabilistic deterioration for the manufacturer and retailers along with the strategies such as production rate, unit production cost, cost of idle time of manufacturer, screening, rework, etc. The optimum average profit of the integrated model is evaluated for both the cases crisp and fuzzy environments. Managerial insights and the effect of changes in the parameters’ values on the optimal inventory policy under fuzziness are presented.
Case-based reasoning adaptation based on fuzzy gravitational search algorithm for disaster emergency plan
Authors: Yu Xiaobing , Yu Xianrui , Zhang Xueying
Published: May 2021

Disasters can result in substantial destructive damages to the world. Emergency plan is vital to deal with these disasters. It is still difficult for the traditional CBR to generate emergency plans to meet requirements of rapid responses. An integrated system including Case-based reasoning (CBR) and gravitational search algorithm (GSA) is proposed to generate the disaster emergency plan. Fuzzy GSA (FGSA) is developed to enhance the convergence ability and accomplish the case adaptation in CBR. The proposed algorithm dynamically updates the main parameters of GSA by introducing a fuzzy system. The FGSA-CBR system is proposed, in which fitness function is defined based on the effectiveness of disaster emergency management. The comparison results have revealed that the proposed algorithm has good performances compared with the original GSA and other algorithms. A gas leakage accident is taken as an empirical study. The results have demonstrated that the FGSA-CBR has good performances when generating the disaster emergency plan. The combination of CBR and FGSA can realize the case adaptation, which provides a useful approach to the real applications.
Modeling pricing decision problem based on interval type-2 fuzzy theory
Authors: Pei Huili , Li Hongliang , Liu Yankui
Published: May 2021

In practical decision-making problems, decision makers are often affected by uncertain parameters because the exact distributions of uncertain parameters are usually difficult to determine. In order to deal with this issue, the major contribution in this paper is to propose a new type of type-2 fuzzy variable called level interval type-2 fuzzy variable from the perspective of level-sets, which is a useful tool in modeling distribution uncertainty. With our level interval type-2 fuzzy variable, we give a method for constructing a parametric level interval (PLI) type-2 fuzzy variable from a nominal possibility distribution by introducing the horizontal perturbation parameters. The proposed horizontal perturbation around the nominal distribution is different from the vertical perturbation discussed in the literature. In order to facilitate the modeling in practical decision-making problems, for a level interval type-2 fuzzy variable, we define its selection variable whose distribution can be determined via its level-sets. The numerical characteristics like expected value and second order moments are important indices in practical optimization and decision-making problems. With this consideration, we establish the analytical expressions about the expected values and second order moments of the selection variables of PLI type-2 trapezoidal, normal and log-normal fuzzy variables. Furthermore, in order to derive the analytical expressions about the numerical characteristics of the selection variable for the sums of the common PLI type-2 fuzzy variables, we discuss the arithmetic about the sums of common PLI type-2 fuzzy variables. Finally, we apply the proposed optimization method to a pricing decision problem to demonstrate the efficiency of our new method. The computational results show that even a small perturbation of the nominal possibility distribution can affect the quality of solutions.
GINS: A Global intensifier-based N-Gram sentiment dictionary
Authors: Zargari Hamed , Zahedi Morteza , Rahimi Marziea
Published: May 2021

Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.
Minimum attribute reduction algorithm based on quick extraction and multi-strategy social spider optimization
Authors: Wei Qianjin , Wang Chengxian , Wen Yimin
Published: May 2021

Intelligent optimization algorithm combined with rough set theory to solve minimum attribute reduction (MAR) is time consuming due to repeated evaluations of the same position. The algorithm also finds in poor solution quality because individuals are not fully explored in space. This study proposed an algorithm based on quick extraction and multi-strategy social spider optimization (QSSOAR). First, a similarity constraint strategy was called to constrain the initial state of the population. In the iterative process, an adaptive opposition-based learning (AOBL) was used to enlarge the search space. To obtain a reduction with fewer attributes, the dynamic redundancy detection (DRD) strategy was applied to remove redundant attributes in the reduction result. Furthermore, the quick extraction strategy was introduced to avoid multiple repeated computations in this paper. By combining an array with key-value pairs, the corresponding value can be obtained by simple comparison. The proposed algorithm and four representative algorithms were compared on nine UCI datasets. The results show that the proposed algorithm performs well in reduction ability, running time, and convergence speed. Meanwhile, the results confirm the superiority of the algorithm in solving MAR.
Decision fusion method for fault diagnosis based on closeness and Dempster-Shafer theory
Authors: Gao Xiue , Jiang Panling , Xie Wenxue , Chen Yufeng , Zhou Shengbin , Chen Bo
Published: May 2021

Decision fusion is an effective way to resolve the conflict of diagnosis results. Aiming at the problem that Dempster-Shafer (DS) theory deals with the high conflict of evidence and produces wrong results, a decision fusion algorithm for fault diagnosis based on closeness and DS theory is proposed. Firstly, the relevant concepts of DS theory are introduced, and the normal distribution membership function is used as the evidence closeness. Secondly, the harmonic average is introduced, and the weight of each evidence is established according to the product of closeness of each evidence and its harmonic average. Thirdly, the weight of conflicting evidence is regularized, and the final decision fusion result is obtained by using the Dempster’s rule. Lastly, the simulation and application examples are designed. Simulation and application results show that the method can effectively reduce the impact of diagnostic information conflicts and improve the accuracy of decision fusion. What’s more, the method considers the overall average distribution of evidence in the identification framework, it can reduce evidence conflicts while preserving important diagnostic information.
Enhanced context-aware recommendation using topic modeling and particle swarm optimization
Authors: Gasmi Ibtissem , Azizi Mohamed Walid , Seridi-Bouchelaghem Hassina , Azizi Nabiha , Belhaouari Samir Brahim
Published: May 2021

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.
A partner selection problem for complex product of manufacturing enterprises in supply chain
Authors: Li Congdong , Yu Yinyun , Xu Wei , Sun Jianzhu
Published: May 2021

In order to better meet customer needs and respond to market demands more quickly, mounting number of manufacturing companies have begun to bid farewell to the traditional unitary manufacturing model. The collaborative manufacturing model has become a widely adopted manufacturing model for manufacturing companies. Aiming at the problem of partner selection for collaborative manufacturing of complex products in a collaborative supply chain environment, this paper proposes a multi-objective decision-making model that comprehensively considers the maximization of the matching degree of manufacturing capacity and the profits of supply chain, and gives the modeling process and application steps in detail. The method first uses fuzzy theory to evaluate the manufacturing capabilities of candidate collaborative manufacturing partners. Secondly, Vector Space Model (VSM) is used to calculate the matching degree of manufacturing capacity and manufacturing demand. Then, the paper studied the profit of the supply chain under the “non-cooperative” mechanism and the “revenue sharing” mechanism. Furthermore, the decision-making model is established. Finally, a simulation was carried out by taking complex product manufacturing of Gree enterprise as an example. The research results show the feasibility and effectiveness of the method.
Emergency evacuation with unbalanced utilization of exits at platform level: A simulation study
Authors: Chen Hongxu , Huang Xingjian , Li Huan , Zhang Haibo
Published: May 2021

Urban rail transit station has a large number of people gathering and relatively closed space, which has become a high incidence of emergencies. In the process of emergency evacuation, due to the unbalanced utilization rate of exits, the evacuation time is prolonged, which seriously threatens the safety of people in the station. In order to find out the reasons for the imbalance of the utilization rate of the exit, and put forward the solution, balance the utilization rate of each exit, improve the evacuation efficiency. In this paper, a subway station platform in Southwest China is taken as an example to establish a three-dimensional simulation model by using anylogic software. Through in-depth analysis, it is found that the reason for the unbalanced utilization of the platform floor exit is that the number of evacuation people at each exit does not match the evacuation capacity. According to the analysis results, the optimization model is put forward. Through comparative analysis, it is found that the optimization model can effectively balance the export utilization rate. Under the current conditions of off peak passenger flow, peak passenger flow and long-term peak passenger flow, the evacuation time can be saved by more than 20%.
Parking lot allocation model considering conversion between dynamic and static traffic
Authors: Xie Zhouhu , Wu Xianyu , Guo Jingxue , Zhan Zhenxi
Published: May 2021

Nowadays, parking spaces are scarce resources in urban cities. Travelers often spend too much time looking for available parking spaces, which increases travel time of travelers and results in additional traffic congestion. With the innovation and application of intelligent parking technology, parking spaces can be booked in the system in advance through mobile phone, which will greatly reduce the time for drivers to cruise and search for parking spaces. Targeted at the serious waste of parking resources, traffic congestion caused by too intensive parking demand in time and space, a parking allocation model considering the ability of dynamic and static traffic conversion is established with the goal of minimizing the total travel time of the travelers. Based on the dynamic traffic distribution model, considering the constraints of capacity of the road link, parking lot entrance and the number of the parking spaces in parking lot, the dynamic and static traffic is combined by considering the parking lot connection section as a conversion link to be added into the traffic network. And the solution method based on particle swarm optimization is proposed. Experimental results on a case (Beijing Chaoyang Joy City and surrounding parking lots) show that our parking allocation model works satisfactorily by effectively reducing the travel time of travelers and increasing customer arrivals in shopping centers. From the point of view of traffic managers, the model can make the parking occupancy of all parking lots more balanced, which indicates that the model can help to better coordinate the available parking resources. In summary, the model proposed in this paper can not only divert the flow beyond the capacity of the road facilities at the connection of the parking lot, but also balance the utilization rate of the surrounding parking resources and reduce the dynamic traffic pressure, it is a proper way to develop the sustainable transportation.
Improved differentiation classification of variable precision artificial intelligence higher education management
Authors: Dong Chao , Guo Yan
Published: May 2021

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.
Subcutaneous Immunoglobulin Therapy with IgPro20 in Patients with Stiff Person Syndrome and Primary Immunodeficiency Disease
Authors: Francis Olivia L. , Joshi Avni , Prince Ty , Krishnaswamy Guha , Patel Niraj C.
Published: May 2021

Stiff Person Syndrome (SPS), a rare autoimmune neurologic disorder characterized by fluctuating muscle spasms and rigidity, is mediated by autoantibodies to glutamic acid decarboxylase (GAD) antibodies. Symptoms of SPS have been shown to improve after administration of intravenous immunoglobulin (IVIG) however, there is a paucity of information regarding use of SCIg in SPS. Four patients with Stiff Person Syndrome were treated with SCIgPro20 for a period between 31 to 101 months. Most reactions were local and mild. All patients reported improvement in spasticity, and 2 patients reported improvement in seizure frequency. SCIgPro20 was well tolerated in patients with SPS and was associated with improvement in symptoms.
Evading the Immune System: Immune Modulation and Immune Matching in Cell Replacement Therapies for Parkinson’s Disease
Authors: Morizane Asuka , Takahashi Jun
Published: May 2021

Stem cell-based therapies for Parkinson’s disease are now being applied clinically. Notably, studies have shown that controlling the graft-induced immune response improves the results. In this mini-review, we concisely summarize current approaches used for this control. We focus on four modes of stem cell-based therapies: autologous transplantation, allogeneic transplantation with human leukocyte antigen-matching and allogeneic transplantation without, and finally the application of “universal” pluripotent stem cells. We also discuss immuno-suppressive treatments and the monitoring of immune reactions in the brain.
Robust SURE estimates of profitability in the Egyptian insurance market
Authors: Youssef Ahmed H. , Kamel Amr R. , Abonazel Mohamed R.
Published: May 2021

This paper proposes three robust estimators (M-estimation, S-estimation, and MM-estimation) for handling the problem of outlier values in seemingly unrelated regression equations (SURE) models. The SURE model is one of regression multivariate cases, which have especially assumption, i.e., correlation between errors on the multivariate linear models; by considering multiple regression equations that are linked by contemporaneously correlated disturbances. Moreover, the effects of outliers may permeate through the system of equations; the primary aim of SURE which is to achieve efficiency in estimation, but this is questionable. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we study and compare the performance of robust estimations with the traditional non-robust (ordinary least squares and Zellner) estimations based on a real dataset of the Egyptian insurance market during the financial year from 1999 to 2018. In our study, we selected the three most important insurance companies in Egypt operating in the same field of insurance activity (personal and property insurance). The effect of some important indicators (exogenous variables) issued by insurance corporations on the net profit has been studied. The results showed that robust estimators greatly improved the efficiency of the SURE estimation, and the best robust estimation is MM-estimation. Moreover, the selected exogenous variables in our study have a significant effect on the net profit in the Egyptian insurance market.
Labor figures for Mexico’s municipalities: Small Area Estimation
Authors: Orozco Edgar Vielma , Rivera José Vences , Mata Gustavo Aguilar
Published: May 2021

Labor figures for Mexico’s municipalities were estimated during 2018’s first quarter by using Small Area Estimation (SAE) techniques with the incorporation of a spatial component – given there is no recent information source with such a level of geographic disaggregation. To achieve this, combined information from different sources was used to build statistical models in which the Economically Active Population, the Employed Population and the Informal Employed Population were taken as variables object of estimation – this information was taken from the National Survey of Occupation and Employment (ENOE for its acronym in Spanish). Auxiliary variables were selected from population censuses, administrative records, and population projections. The results were contrasted with those calculated by applying the percentage structures of 2010 Population and Housing Census to the figures provided by ENOE at a federal entity level, and with the data in this survey (obtained by direct estimation for those municipalities which had a sufficient sample with acceptable coefficients of variation). It is observed that the results obtained by Small Area Estimation are plausible and register coefficients of variation below 10 percent.
A path planning method of 6-DOF robot for mirror therapy based on A* algorithm
Authors: Xu Zhenyu , Guo Shuai , Zhang Leigang
Published: May 2021

BACKGROUND: With the population aging, post-stroke patients suffering from hemiplegia are also rapidly increasing. It is essential to provide valid rehabilitation methods for hemiplegia patients. Mirror therapy is an effective rehabilitation method and is widely applied in many rehabilitation robots. OBJECTIVE: The aim of this paper is to present a path planning method to guarantee the robot’s motion performance during mirror therapy. METHODS: The kinematic framework of the proposed rehabilitation system is detailed, then the reference motion path of the manipulator is calculated according to kinematic transformation. The concept of manipulability is introduced to describe the motion performance of the manipulator. Based on the above work, a path planning method based on A* algorithm is proposed to quantitatively analyze and optimize the motion performance of the manipulator. RESULTS: Preliminary experiments with the proposed rehabilitation system are conducted to verify the proposed path planning method. The characteristics of the proposed method are analyzed through two typical situations. The results showed that the proposed method can build a new path for manipulator, which can ensure the robot’s motion performance and is highly consistent with the reference path. CONCLUSION: The results showed that the manipulator could achieve the task with acceptable error, which indicates the potential of the proposed path planning method for mirror therapy.
Effects of lower trunk movement in flat-back syndrome during stair climbing: A technical note
Authors: Liu Yaoyao , Yoo Won-Gyu
Published: May 2021

OBJECTIVE: This study investigated the differences in trunk sway during stair climbing between people with normal spinal alignment and people with flat-back syndrome. METHODS: Twelve male volunteers with flat-back syndrome (global angle < 20 degrees) and 12 male volunteers with normal spinal alignment (global angle between 20 degrees and 30 degrees) were enrolled. An accelerator was attached to the third lumbar spine and the sway of each participant’s trunk was measured during stair climbing. RESULT: Participants with flat-back syndrome showed significant differences in vector, anteroposterior sway, and vertical sway of the trunk during stair climbing (p< 0.05). However, mediolateral sway of the trunk and gait time did not significantly differ between groups (p> 0.05). CONCLUSION: Our findings can be used as baseline data for prevention of back pain. Furthermore, increased trunk sway can cause increased energy usage, leading to inefficient gait. Further research is needed to prevent this problem.
The effect of the COVID-19 Pandemic on anxiety, depression, and musculoskeletal system complaints in healthcare workers
Authors: Arca Muhammed , Dönmezdil Süleyman , Durmaz Elif Dilara
Published: May 2021

BACKGROUND: COVID-19 increased psychological and physical complaints, including anxiety, depression, and pain. OBJECTIVE: We aimed to examine anxiety, depression, and musculoskeletal complaints that may occur in healthcare workers during the COVID-19 pandemic. METHODS: Data were cross-sectional, collected by the online questionnaire method consisted of demographic questions, Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9), Nordic Musculoskeletal System Query (NMQ), and Visual Analogue Scale (VAS). RESULTS: Among the groups, the number of doctors in the frontline group, the fear of being infected, the number of tests performed, and personal protective equipment were higher than the second-line group (p < 0.05). It was found that 73.4% of the participants had anxiety, 83.5% had depression, and 94.9% had musculoskeletal pain. It was observed that there was no statistically significant difference between the applied scale scores between the groups (p > 0.05). About musculoskeletal system complaints of the participants, neck pain presence was determined as 73.4% in the last 12 months, and upper back pain was the highest with 61.4% in the last week. Neck (p < 0.005) and upper back pain (p = 0.04) parameters in the last seven days of the NMQ scale applied to the participants were found to be statistically significantly higher in the contact group. CONCLUSIONS: Anxiety, depression, and musculoskeletal pain of healthcare workers were found to be high. While neck and back pain was the most common in musculoskeletal complaints, it was found that these pains were higher in the frontline group. In this regard, healthcare professionals can be given psychological support and exercise training.
On demonetization short term effects: Psychosocial risks in tea garden workers
Authors: Roy Priyanka , Grandi Annalisa , Pira Enrico
Published: May 2021

BACKGROUND: In November 2016, the government of India declared 86%of the total money in circulation as demonetized. This policy was brought into effect overnight, and it had great macro socioeconomic impact not only on the economy of the country but on the common people, especially the socioeconomically challenged. While several researchers have focused on, and continue to investigate, the effects of demonetization on the economy, its impact on the psychosocial health of workers has not yet been studied. OBJECTIVE: To provide an exploratory investigation of the psychosocial consequences of demonetization on the workers in Indian tea gardens. METHODS: A qualitative research approach was employed. Face-to-face interviews were conducted with seven key informants (clinicians and executives/managers), and 36 tea garden workers were involved in six focus groups. Collected data were analyzed using the Template Analysis technique. RESULTS: From the data analysis, five main themes emerged concerning the psychosocial factors involved in demonetization effects: socioeconomic changes, organizational consequences, workplace interpersonal relationships, work-family interface, and psychophysical symptoms. CONCLUSIONS: This preliminary study highlighted the significant impact that demonetization had on tea gardens at both the organizational and individual levels.