Role of Acupuncture in the Treatment of Drug Addiction

This review systematically assessed the clinical evidence for and against acupuncture as a treatment for drug addiction. The existing scientific rationale and possible mechanisms for the effectiveness of acupuncture on drug addiction were also evaluated. We used computerized literature searches in English and Chinese and examined texts written before these computerized databases existed. We also used search terms of treatment and neurobiology for drug abuse and dependence. Acupuncture showed evidence for relevant neurobiological mechanisms in the treatment of drug addiction. Although positive findings regarding the use of acupuncture to treat drug dependence have been reported by many clinical studies, the data do not allow us to make conclusions that acupuncture was an effective treatment for drug addiction, given that many studies reviewed here were hampered by small numbers of patients, insufficient reporting of randomization and allocation concealment methods, and strength of the inference. However, considering the potential of acupuncture demonstrated in the included studies, further rigorous randomized controlled trials with long follow-up are warranted

Green innovation for the ecological footprints of tourism in China. Fresh evidence from ARDL approach

This study’s objective is to analyze ecological footprints that exist among China’s economic growth, energy consumption, carbon dioxide emissions, and the revenue that is generated from tourism in other countries. The years 1995 through 2020 are the focus of this particular research endeavor. The relationship between tourism and carbon emissions has been discovered by a large number of researchers; nevertheless, the findings have been inconsistent and do not give a clear picture of the situation. We can only hope that the results of the study will improve the existing body of knowledge on tourism and the quality of the surrounding environment. Throughout the whole of this investigation, the autoregressive distributed lagged (ARDL) model was used to explore both long-run and short-run estimations. A dynamic ordinary least squares (DOLS) model was used in the study to arrive at long-term estimations that could be relied upon. Even though money from tourism does not have a substantial influence on the quality of the environment in China, growth and increasing energy usage are primary donors to carbon emissions in the nation. ARDL model’s long-term projections were shown to be correct by the DOLS approach, which offered this validation. The results of the research provide fresh insights into the body of knowledge that has been accumulated on the subject of the linkage between tourism and the natural environment. Because the receipts from tourism do not have any significant negative exteriority toward the environment, energy usage is an important element of environmental degradation and policymakers should prioritize the development of the tourism sector over energy-focused manufacturing activities to maintain the growth of the nation in the upper quartiles. This is because tourismdoes not have any significant negative externalities on the environment. Sustainable tourism minimizes environmental and cultural damage while boosting profits. Developing the appropriate technology, physical infrastructure, and human capital requires money, time, and effort

QUOIN: Incentive Mechanisms for Crowd Sensing Networks

Crowd sensing networks play a critical role in big data generation where a large number of mobile devices collect various kinds of data with large-volume features. Although which information should be collected is essential for the success of crowd-sensing applications, few research efforts have been made so far. On the other hand, an efficient incentive mechanism is required to encourage all crowd-sensing participants, including data collectors, service providers, and service consumers, to join the networks. In this article, we propose a new incentive mechanism called QUOIN, which simultaneously ensures Quality and Usability Of INformation for crowd-sensing application requirements. We apply a Stackelberg game model to the proposed mechanism to guarantee each participant achieves a satisfactory level of profits. Performance of QUOIN is evaluated with a case study, and experimental results demonstrate that it is efficient and effective in collecting valuable information for crowd-sensing applications

A Green TDMA Scheduling Algorithm for Prolonging Lifetime in Wireless Sensor Networks

Fast data collection is one of the most important research issues for Wireless Sensor Networks (WSNs). In this paper, a TMDA based energy consumption balancing algorithm is proposed for the general k-hop WSNs, where one data packet is collected in one cycle. The optimal k that achieves the longest network life is obtained through our theoretical analysis. Required time slots, maximum energy consumption and residual network energy are all thoroughly analyzed in this paper. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed algorithm in terms of energy efficiency and time slot scheduling

A cooperative-based model for smart-sensing tasks in fog computing

OAPA Fog Computing is currently receiving a great deal of focused attention. Fog Computing can be viewed as an extension of cloud computing that services the edges of networks. A cooperative relationship among applications to collect data in a city is a fundamental research topic in Fog Computing (FC). When considering the Green Cloud (GC), people or vehicles with smart-sensor devices can be viewed as users in FC and can forward sensing data to the data center (DC). In a traditional sensing process, rewards are paid according to the distances between the users and the platform, which can be seen as the existing solution. Because users with smart-sensing devices tend to participate in tasks with high rewards, the number of users in suburban regions is smaller, and data collection is sparse and cannot satisfy the demands of the tasks. However, there are many users in urban regions, which makes data collection costly and of low quality. In this paper, a cooperative-based model for smartphone tasks, named a Cooperative-based Model for Smart-Sensing Tasks (CMST), is proposed to promote the quality of data collection in FC networks. In the CMST scheme, we develop an allocation method focused on improving the rewards in suburban regions. The rewards to each user with a smart sensor are distributed according to the region density. Moreover, for each task there is a cooperative relationship among the users; they cooperate with one another to reach the volume of data that the platform requires. Extensive experiments show that our scheme improves the overall data-coverage factor by 14.997% to 31.46%, and the platform cost can be reduced by 35.882

An incentive game based evolutionary model for crowd sensing networks

Crowd sensing networks can be used for large scale sensing of the physical world or other information service by leveraging the available sensors on the phones. The collector hopes to collect as much as sensed data at relatively low cost. However, the sensing participants want to earn much money at low cost. This paper examines the evolutionary process among participants sensing networks and proposes an evolutionary game model to depict collaborative game phenomenon in the crowd sensing networks based on the principles of game theory in economics. A effectively incentive mechanism is established through corrected the penalty function of the game model accordance with the cooperation rates of the participant, and corrected the game times in accordance with it’s payoff. The collector controls the process of game by adjusting the price function. We find that the proposed incentive game based evolutionary model can help decision makers simulate evolutionary process under various scenarios. The crowd sensing networks structure significantly influence cooperation ratio and the total number of participant involved in the game, and the distribution of population with different game strategy. Through evolutionary game model, the manager can select an optimal price to facilitate the system reach equilibrium state quickly, and get the number of participants involved in the game. The incentive game based evolutionary model in crowd sensing networks provides valuable decision-making support to managers

RMER: Reliable and Energy-Efficient Data Collection for Large-Scale Wireless Sensor Networks

We propose a novel event data collection approach named reliability and multipath encounter routing (RMER) for meeting reliability and energy efficiency requirements. The contributions of the RMER approach are as follows. 1) Fewer monitor nodes are selected in hotspot areas that are close to the Sink, and more monitor nodes are selected in nonhotspot areas, which can lead to increased network lifetime and event detection reliability. 2) The RMER approach sends data to the Sink by converging multipath routes of event monitoring nodes into a one-path route to aggregate data. Thus, energy consumption can be greatly reduced, thereby enabling further increased network lifetime. Both theoretical and experimental simulation results show that RMER applied to event detection outperforms other solutions. Our results clearly indicate that RMER increases energy efficiency by 51% and network lifetime by 23% over other solutions while guaranteeing event detection reliability