At present,momentous changes not seen in a century are accelerating across the world.Our world,our times and the history are changing in ways like never before."International competition and frictions are intensi...At present,momentous changes not seen in a century are accelerating across the world.Our world,our times and the history are changing in ways like never before."International competition and frictions are intensifying,geopolitical manoeuvring has grown more pronounced,and trust and cooperation in the international community are being undermined"China,as a responsible major country.展开更多
The service recommendation mechanism as a key enabling technology that provides users with more proactive and personalized service is one of the important research topics in mobile social network （MSN）. Meanwhile, M...The service recommendation mechanism as a key enabling technology that provides users with more proactive and personalized service is one of the important research topics in mobile social network （MSN）. Meanwhile, MSN is susceptible to various types of anonymous information or hacker actions. Trust can reduce the risk of interaction with unknown entities and prevent malicious attacks. In our paper, we present a trust-based service recommendation algorithm in MSN that considers users＇ similarity and friends＇ familiarity when computing trustworthy neighbors of target users. Firstly, we use the context information and the number of co-rated items to define users＇ similarity. Then, motivated by the theory of six degrees of space, the friend familiarity is derived by graph-based method. Thus the proposed methods are further enhanced by considering users＇ context in the recommendation phase. Finally, a set of simulations are conducted to evaluate the accuracy of the algorithm. The results show that the friend familiarity and user similarity can effectively improve the recommendation performance, and the friend familiarity contributes more than the user similarity.展开更多
Large-scale mobile social networks(MSNs)facilitate communications through mobile devices.The users of these networks can use mobile devices to access,share and distribute information.With the increasing number of user...Large-scale mobile social networks(MSNs)facilitate communications through mobile devices.The users of these networks can use mobile devices to access,share and distribute information.With the increasing number of users on social networks,the large volume of shared information and its propagation has created challenges for users.One of these challenges is whether users can trust one another.Trust can play an important role in users'decision making in social networks,so that,most people share their information based on their trust on others,or make decisions by relying on information provided by other users.However,considering the subjective and perceptive nature of the concept of trust,the mapping of trust in a computational model is one of the important issues in computing systeins of social networks.Moreover,in social networks,various communities may exist regarding the relationships between users.These connections and communities can affect trust among users and its complexity.In this paper,using user characteristics on social networks,a fuzzy clustering method is proposed and the trust between users in a cluster is computed using a computational model.Moreover,through the processes of combination,transition and aggregation of trust,the trust value is calculated between users who are not directly connected.Results show the high performance of the proposed trust inference method.展开更多
With the rapid development of social network in recent years, a huge number of social information has been produced. As traditional recommender systems often face data sparsity and cold-start problem, the use of socia...With the rapid development of social network in recent years, a huge number of social information has been produced. As traditional recommender systems often face data sparsity and cold-start problem, the use of social information has attracted many researchers' attention to improve the prediction accuracy of recommender systems. Social trust and social relation have been proven useful to improve the performance of recommendation. Based on the classic collaborative filtering technique, we propose a PCCTTF recommender method that takes the rating time of users, social trust among users, and item tags into consideration, then do the item recommending. Experimental results show that the PCCTTF method has better prediction accuracy than classical collaborative filtering technique and the state-of-the-art recommender methods, and can also effectively alleviate data sparsity and cold-start problem. Furthermore, the PCCTTF method has better performance than all the compared methods while counting against shilling attacks.展开更多
Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship am...Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.展开更多
Inferring unknown social trust relations attracts increasing attention in recent years. However, social trust, as a social concept, is intrinsically dynamic, and exploiting temporal dynamics provides challenges and op...Inferring unknown social trust relations attracts increasing attention in recent years. However, social trust, as a social concept, is intrinsically dynamic, and exploiting temporal dynamics provides challenges and opportunities for social trust prediction. In this paper, we investigate social trust prediction by exploiting temporal dynamics. In particular, we model the dynamics of user preferences in two principled ways. The first one focuses on temporal weight; the second one targets temporal smoothness. By incorporating these two types of temporal dynamics into traditional matrix factorization based social trust prediction model, two extended social trust prediction models are proposed and the cor- responding algorithms to solve the models are designed too. We conduct experiments on a real-world dataset and the results dem- onstrate the effectiveness of our proposed new models. Further experiments are also conducted to understand the importance of temporal dynamics in social trust prediction.展开更多
With the quick growth of sharing economy, service sharing becomes a popular phenomenon in daily lives. However, some service providers give exaggerated information about their services on the Peer-to-Peer（P2 P） serv...With the quick growth of sharing economy, service sharing becomes a popular phenomenon in daily lives. However, some service providers give exaggerated information about their services on the Peer-to-Peer（P2 P） service sharing platforms to get more profits. How to identify a reliable service provider becomes a difficult challenge for users. In this paper, we propose a trustworthy group trust metric for P2 P service sharing（TMPSS） economy based on personal social network（PSN） of users. Deriving from Advogato group trust metric, it considers factors such as social circle similarity, preference similarity, interaction degree, ranks the reliable nodes in a target user＇s PSN, outputs an ordered set of reliable nodes, and prevents unreliable nodes from access PSN of honest users. Experimental results show that TMPSS has advantages over existing representative methods because it finds more reliable nodes, and counts against malicious nodes＇ attacks more effectively, and it is suitable for mobile transaction circumstances.展开更多
With the rapid development of social networks, there is a focus on marketing strategies and business models that are based on social media. In the academic world, scholars believe that online trust is a key factor con...With the rapid development of social networks, there is a focus on marketing strategies and business models that are based on social media. In the academic world, scholars believe that online trust is a key factor contributing to online purchasing behavior. This article explored several factors in social media trust and verified the moderating role of offline familiarity by using relevant research on online trust in conjunction with a structure equation model. The results show that independent variables such as reputation, SNS interaction, information quality, reciprocity, satisfaction and shared values have a positive influence on trust, whereas perceived similarity does not, and information quality is the most important factor. In addition, offline familiarity significantly moderates the relations between information quality, reciprocity, reputation, shared values and social media trust. This information is important to assist companies in developing an effective social network marketing strategy.展开更多
With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that consid...With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.展开更多
Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial ...Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial factor in the success of social commerce. Business factors, environment factors and social factors including twelve secondary indexes build up a social commerce trust evaluation model. Questionnaires are handed out to collect twelve secondary indexes scores as input of BP neural network and composite score of trust as output. Model simulation shows that both training samples and test samples have low level of average error and standard deviation, which certify that the model has good stability and it is a good method for evaluating social commerce trust.展开更多
The development of the Internet coincides with 90s financial crises. The net seemed to realize what Karl Polanyi defined social self-defence from the capitalism against the transformation of labour, land and money in ...The development of the Internet coincides with 90s financial crises. The net seemed to realize what Karl Polanyi defined social self-defence from the capitalism against the transformation of labour, land and money in fictitious commodities. In this paper we try to consider how Internet, and in particular social network, has modified many deep aspects of our life. With two different approaches, sociological and philosophical, we try to understand how social networks support new shapes of happiness and trust in an economic crisis context.展开更多
In the era of big data, personalized recommendation has become an important research issue in social networks as it can find and match user’s preference. In this paper, the user trust is integrated into the recommend...In the era of big data, personalized recommendation has become an important research issue in social networks as it can find and match user’s preference. In this paper, the user trust is integrated into the recommendation algorithm, by dividing the user trust into 2 parts: user score trust and user preference trust. In view of the common items in user item score matrix, the algorithm combines the number of items with the score similarity between users, and establishes an asymmetric trust relationship matrix so as to calculate the user’s score trust. For the non common score items, we use the attribute information of items and the scoring weight to calculate the user’s preference trust. Based on the user trust in social network, a new collaborative filtering recommendation algorithm is proposed. Besides, a new matrix factorization recommendation algorithm is proposed by combining the user trust with matrix factorization. We did the experiments comparing with the related algorithms on the real data sets of social network. The results show that the proposed algorithms can effectively improve the accuracy of recommendation.展开更多
The development of science and technology as well as the internet have brought changes to our daily lives.In addition,with the widespread use of social media,more and more people are using social platforms to connect ...The development of science and technology as well as the internet have brought changes to our daily lives.In addition,with the widespread use of social media,more and more people are using social platforms to connect with colleagues and serve business activities.This study takes WeChat,a specific social media platform in China,as an example to study how personal social relations influence personal consumption behaviour in the digital media era through WeChat users'daily use experience.This study adopts a mixed method.First,it tests users' perception based on cognitive and emotional factors through 122 questionnaire surveys.Then,the users'experiences from their participation in social enterprises are gathered through 10 semi-structured interviews,and subsequently,the relationship between personal relations and social enterprises are analyzed.Finally,after data collation and analysis,it can be concluded that trust is the core relationship quality and also the basis for promoting the development of social business activities.At the same time,since social business activities rely on social relations,the development of swift guanxi is conducive to the realization of repurchase behaviours in social business relations.展开更多
Globalization and developments in digital technology paved the way for online communication,mobile penetration,and social media.Digital platforms and particularly social media have become popular sources of news and o...Globalization and developments in digital technology paved the way for online communication,mobile penetration,and social media.Digital platforms and particularly social media have become popular sources of news and online interaction.Literature review so far reveals more than one billion social media users exist globally and use social media for shopping purposes.Hence,social media has become one of the most popular tools companies using for brand relationship building activities.The effect of social media on building customer commitment needs to be explored.This article aims to identify social media use among Turkish 18-40 years old in building commitment towards their favorite brands.展开更多
With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,whic...With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient workers.In this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few workers.Specifically,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each worker.Then,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate tasks.Only when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start issue.More importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker pool.Finally,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.展开更多
文摘At present,momentous changes not seen in a century are accelerating across the world.Our world,our times and the history are changing in ways like never before."International competition and frictions are intensifying,geopolitical manoeuvring has grown more pronounced,and trust and cooperation in the international community are being undermined"China,as a responsible major country.
基金Supported by the National Natural Science Foundation of China(71662014 and 61602219)the Natural Science Foundation of Jiangxi Province of China(20132BAB201050)the Science and Technology Project of Jiangxi Province Educational Department(GJJ151601)
文摘The service recommendation mechanism as a key enabling technology that provides users with more proactive and personalized service is one of the important research topics in mobile social network （MSN）. Meanwhile, MSN is susceptible to various types of anonymous information or hacker actions. Trust can reduce the risk of interaction with unknown entities and prevent malicious attacks. In our paper, we present a trust-based service recommendation algorithm in MSN that considers users＇ similarity and friends＇ familiarity when computing trustworthy neighbors of target users. Firstly, we use the context information and the number of co-rated items to define users＇ similarity. Then, motivated by the theory of six degrees of space, the friend familiarity is derived by graph-based method. Thus the proposed methods are further enhanced by considering users＇ context in the recommendation phase. Finally, a set of simulations are conducted to evaluate the accuracy of the algorithm. The results show that the friend familiarity and user similarity can effectively improve the recommendation performance, and the friend familiarity contributes more than the user similarity.
文摘Large-scale mobile social networks(MSNs)facilitate communications through mobile devices.The users of these networks can use mobile devices to access,share and distribute information.With the increasing number of users on social networks,the large volume of shared information and its propagation has created challenges for users.One of these challenges is whether users can trust one another.Trust can play an important role in users'decision making in social networks,so that,most people share their information based on their trust on others,or make decisions by relying on information provided by other users.However,considering the subjective and perceptive nature of the concept of trust,the mapping of trust in a computational model is one of the important issues in computing systeins of social networks.Moreover,in social networks,various communities may exist regarding the relationships between users.These connections and communities can affect trust among users and its complexity.In this paper,using user characteristics on social networks,a fuzzy clustering method is proposed and the trust between users in a cluster is computed using a computational model.Moreover,through the processes of combination,transition and aggregation of trust,the trust value is calculated between users who are not directly connected.Results show the high performance of the proposed trust inference method.
基金Supported by the National Natural Science Foundation of China(71662014,61602219,71861013)。
文摘With the rapid development of social network in recent years, a huge number of social information has been produced. As traditional recommender systems often face data sparsity and cold-start problem, the use of social information has attracted many researchers' attention to improve the prediction accuracy of recommender systems. Social trust and social relation have been proven useful to improve the performance of recommendation. Based on the classic collaborative filtering technique, we propose a PCCTTF recommender method that takes the rating time of users, social trust among users, and item tags into consideration, then do the item recommending. Experimental results show that the PCCTTF method has better prediction accuracy than classical collaborative filtering technique and the state-of-the-art recommender methods, and can also effectively alleviate data sparsity and cold-start problem. Furthermore, the PCCTTF method has better performance than all the compared methods while counting against shilling attacks.
基金the National Natural Science Foundation of China(71871121).
文摘Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.
基金Supported by the National Natural Science Foundation of China(61063039)Project of Guangxi Key Laboratory of Trusted Software(kx201202)
文摘Inferring unknown social trust relations attracts increasing attention in recent years. However, social trust, as a social concept, is intrinsically dynamic, and exploiting temporal dynamics provides challenges and opportunities for social trust prediction. In this paper, we investigate social trust prediction by exploiting temporal dynamics. In particular, we model the dynamics of user preferences in two principled ways. The first one focuses on temporal weight; the second one targets temporal smoothness. By incorporating these two types of temporal dynamics into traditional matrix factorization based social trust prediction model, two extended social trust prediction models are proposed and the cor- responding algorithms to solve the models are designed too. We conduct experiments on a real-world dataset and the results dem- onstrate the effectiveness of our proposed new models. Further experiments are also conducted to understand the importance of temporal dynamics in social trust prediction.
基金Supported by the National Social Science Foundation of China(17BGL201)
文摘With the quick growth of sharing economy, service sharing becomes a popular phenomenon in daily lives. However, some service providers give exaggerated information about their services on the Peer-to-Peer（P2 P） service sharing platforms to get more profits. How to identify a reliable service provider becomes a difficult challenge for users. In this paper, we propose a trustworthy group trust metric for P2 P service sharing（TMPSS） economy based on personal social network（PSN） of users. Deriving from Advogato group trust metric, it considers factors such as social circle similarity, preference similarity, interaction degree, ranks the reliable nodes in a target user＇s PSN, outputs an ordered set of reliable nodes, and prevents unreliable nodes from access PSN of honest users. Experimental results show that TMPSS has advantages over existing representative methods because it finds more reliable nodes, and counts against malicious nodes＇ attacks more effectively, and it is suitable for mobile transaction circumstances.
文摘With the rapid development of social networks, there is a focus on marketing strategies and business models that are based on social media. In the academic world, scholars believe that online trust is a key factor contributing to online purchasing behavior. This article explored several factors in social media trust and verified the moderating role of offline familiarity by using relevant research on online trust in conjunction with a structure equation model. The results show that independent variables such as reputation, SNS interaction, information quality, reciprocity, satisfaction and shared values have a positive influence on trust, whereas perceived similarity does not, and information quality is the most important factor. In addition, offline familiarity significantly moderates the relations between information quality, reciprocity, reputation, shared values and social media trust. This information is important to assist companies in developing an effective social network marketing strategy.
基金The work was supported by Humanities and Social Sciences Fund of the Ministry of Education(No.22YJA630119)the National Natural Science Foundation of China(No.71971051)Natural Science Foundation of Hebei Province(No.G2021501004).
文摘With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.
文摘Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial factor in the success of social commerce. Business factors, environment factors and social factors including twelve secondary indexes build up a social commerce trust evaluation model. Questionnaires are handed out to collect twelve secondary indexes scores as input of BP neural network and composite score of trust as output. Model simulation shows that both training samples and test samples have low level of average error and standard deviation, which certify that the model has good stability and it is a good method for evaluating social commerce trust.
文摘The development of the Internet coincides with 90s financial crises. The net seemed to realize what Karl Polanyi defined social self-defence from the capitalism against the transformation of labour, land and money in fictitious commodities. In this paper we try to consider how Internet, and in particular social network, has modified many deep aspects of our life. With two different approaches, sociological and philosophical, we try to understand how social networks support new shapes of happiness and trust in an economic crisis context.
基金This work is supported by the National Natural Science Foundation of China under Grants No. 61272186 and the Foundation of Heilongjiang Postdoctoral under Grant No. LBH-Z12068.
文摘In the era of big data, personalized recommendation has become an important research issue in social networks as it can find and match user’s preference. In this paper, the user trust is integrated into the recommendation algorithm, by dividing the user trust into 2 parts: user score trust and user preference trust. In view of the common items in user item score matrix, the algorithm combines the number of items with the score similarity between users, and establishes an asymmetric trust relationship matrix so as to calculate the user’s score trust. For the non common score items, we use the attribute information of items and the scoring weight to calculate the user’s preference trust. Based on the user trust in social network, a new collaborative filtering recommendation algorithm is proposed. Besides, a new matrix factorization recommendation algorithm is proposed by combining the user trust with matrix factorization. We did the experiments comparing with the related algorithms on the real data sets of social network. The results show that the proposed algorithms can effectively improve the accuracy of recommendation.
文摘The development of science and technology as well as the internet have brought changes to our daily lives.In addition,with the widespread use of social media,more and more people are using social platforms to connect with colleagues and serve business activities.This study takes WeChat,a specific social media platform in China,as an example to study how personal social relations influence personal consumption behaviour in the digital media era through WeChat users'daily use experience.This study adopts a mixed method.First,it tests users' perception based on cognitive and emotional factors through 122 questionnaire surveys.Then,the users'experiences from their participation in social enterprises are gathered through 10 semi-structured interviews,and subsequently,the relationship between personal relations and social enterprises are analyzed.Finally,after data collation and analysis,it can be concluded that trust is the core relationship quality and also the basis for promoting the development of social business activities.At the same time,since social business activities rely on social relations,the development of swift guanxi is conducive to the realization of repurchase behaviours in social business relations.
文摘Globalization and developments in digital technology paved the way for online communication,mobile penetration,and social media.Digital platforms and particularly social media have become popular sources of news and online interaction.Literature review so far reveals more than one billion social media users exist globally and use social media for shopping purposes.Hence,social media has become one of the most popular tools companies using for brand relationship building activities.The effect of social media on building customer commitment needs to be explored.This article aims to identify social media use among Turkish 18-40 years old in building commitment towards their favorite brands.
基金supported by the National Natural Science Foundation of China under Grant No.62072475 and No.62302062in part by the Hunan Provincial Natural Science Foundation of China under Grant Number 2023JJ40081。
文摘With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient workers.In this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few workers.Specifically,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each worker.Then,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate tasks.Only when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start issue.More importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker pool.Finally,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.
