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

Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

With the unprecedented prevalence of 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%.publishedVersio

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

ActiveTrust : Secure and Trustable Routing in Wireless Sensor Networks

Wireless sensor networks (WSNs) are increasingly being deployed in security-critical applications. Because of their inherent resource-constrained characteristics, they are prone to various security attacks, and a black hole attack is a type of attack that seriously affects data collection. To conquer that challenge, an active detection-based security and trust routing scheme named ActiveTrust is proposed for WSNs. The most important innovation of ActiveTrust is that it avoids black holes through the active creation of a number of detection routes to quickly detect and obtain nodal trust and thus improve the data route security. More importantly, the generation and the distribution of detection routes are given in the ActiveTrust scheme, which can fully use the energy in non-hotspots to create as many detection routes as needed to achieve the desired security and energy efficiency. Both comprehensive theoretical analysis and experimental results indicate that the performance of the ActiveTrust scheme is better than that of the previous studies. ActiveTrust can significantly improve the data route success probability and ability against black hole attacks and can optimize network lifetime