Sensors on Internet of Things Systems for the Sustainable Development of Smart Cities: A Systematic Literature Review.

The Internet of Things (IoT) is a critical component of smart cities and a key contributor to the achievement of the United Nations Sustainable Development Goal (UNSDG) 11: Sustainable Cities and Communities. The IoT is an infrastructure that enables devices to communicate with each other over the Internet, providing critical components for smart cities, such as data collection, generation, processing, analysis, and application handling. IoT-based applications can promote sustainable urban development. Many studies demonstrate how the IoT can improve smart cities' sustainable development. This systematic literature review provides valuable insights into the utilization of the IoT in the context of smart cities, with a particular focus on its implications for sustainable urban development. Based on an analysis of 73 publications, we discuss the role of IoT in the sustainable development of smart cities, focusing on smart communities, smart transportation, disaster management, privacy and security, and emerging applications. In each domain, we have detailed the attributes of IoT sensors. In addition, we have examined various communication technologies and protocols suitable for transmitting sensor-generated data. We have also presented the methods for analyzing and integrating these data within the IoT application layer. Finally, we identify research gaps in the literature, highlighting areas that require further investigation.

Anthropogenic and climate impacts on carbon stocks of grassland ecosystems in Inner Mongolia and adjacent region.

Up to date, most studies reported that degradation is worsened in the grassland ecosystems of Inner Mongolia and adjacent regions as a result of intensified grazing. This seems to be scientific when considering the total forage or total above-ground biomass as a degradation indicator, but it does not hold true in terms of soil organic carbon density (SOCD). In this study, we quantified the changes of grassland ecosystem carbon stock in Inner Mongolia and adjacent regions from the 1980s to 2000s and identified the major drivers influencing these variations, using the National Grassland Resource Inventory and Soil Survey Dataset in 1980s and the Inventory data during 2002 to 2009 covering 624 sampling plots concerned vegetal traits and edaphic properties across the study region. The result indicated that the above-, below-ground and total vegetation biomass declined from the 1980s to 2000s by ∼ 10 %. However, total forage production increased by 6.72 % when considering livestock intake. SOCD remained stable despite a 67 % increase in grazing intensity. A generalized linear model (GLIM) analysis suggested that an increase in grazing intensity from the 1980s to 2000s could only explain 1.04 % of the total biomass change, while changes in precipitation and temperature explained 17.7 % (p < 0.05) of total vegetation biomass (TVB) change. Meanwhile, SOCD change during 1980s - 2000s could be explained 10.08 % by the soil texture (p < 0.05) and <1.6 % by changes in climate and livestock. This implies that the impacts of climate change on grassland biomass are more significant than those of grazing utilization, and SOCD was resistant to both climate change and intensified grazing. Overall, intensified grazing did not result in significant negative impacts on the grassland carbon stocks in the study region during the 1980s and 2000s. The grassland ecosystems possess a mechanism to adjust their root-shoot ratio, enabling them to maintain resilience against grazing utilization.

Predictive role of impulsivity, anxiety, and depression in the efficacy of intermittent theta burst transcranial magnetic stimulation modalities for treating methamphetamine use disorder: A randomized clinical trial.

INTRODUCTION: Methamphetamine use disorder (MUD) can cause impulsive behavior, anxiety, and depression. Stimulation of the left dorsolateral prefrontal cortex in MUD patients by intermittent theta burst repetitive transcranial magnetic stimulation (iTBS-rTMS) is effective in reducing cravings, impulsive behavior, anxiety, and depression. The purpose of this study was to explore whether these psychological factors helped to predict MUD patients' responses to iTBS-rTMS treatment. METHODS: Fifty MUD patients and sixty healthy subjects matched for general conditions were used as study subjects. The study randomly divided MUD patients into iTBS-rTMS and sham stimulation groups and received 20 sessions of real or sham iTBS-rTMS treatment, and the study collected cue-related evoked craving data before and after treatment. All subjects completed the Barratt Impulsiveness Scale (BIS-11), Self-rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS). RESULTS: The MUD patients showed significantly higher levels of impulsivity, anxiety, and depression than the healthy subjects. The MUD patients who received the real treatment had significantly lower impulsivity, anxiety, and depression scores, and better treatment effects on cravings than the sham stimulation group. The Spearman rank correlation and stepwise multiple regression analyses showed that the baseline BIS-11 and the reduction rate (RR) of BIS-11 and RR of SDS were positively correlated with the decrease in cravings in the iTBS-rTMS group. ROC curve analysis showed that RR of SDS (AUC = 91.6 %; 95 % CI = 0.804-1.000) had predictive power to iTBS- rTMS therapeutic efficacy, the cutoff value is 15.102 %. CONCLUSIONS: iTBS-rTMS had a good therapeutic effect in MUD patients and the baseline impulsivity, the improved depression and impulsivity were associated with therapeutic effect of iTBS-rTMS. The improved depression had the potential to predict the efficacy of the iTBS-rTMS modality for MUD treatment.