Influence of green technology, green energy consumption, energy efficiency, trade, economic development and FDI on climate change in South Asia.

Climate change policy has several potential risks. The purpose of this study is to investigate the impact of green technology development, green energy consumption, energy efficiency, foreign direct investment, economic growth, and trade (imports and exports) on greenhouse gas (GHG) emissions in South Asia from 1981 to 2018. We employed Breusch Pagan LM, bias-corrected scaled LM, and Pesaran CD as part of a series of techniques that can assist in resolving the problem of cross-sectional dependence. First and second generation unit root tests are used to assess the stationarity of the series, Pedroni and Kao tests are used to test co-integration. The long-term associations are examined using fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOLS) for robustness. The results revealed that trade, growth rate, and exports significantly increase GHG emissions. This accepted the leakage phenomenon. The results also demonstrated that green technology development, green energy consumption, energy efficiency, and imports all have a significant negative correlation with GHG emissions. Imports, advanced technical processes, a transition from non-green energy to green energy consumption, and energy efficiency are thus critical components in executing climate change legislation. These findings highlight the profound importance of green technology development and green energy for ecologically sustainable development in the South Asian countries and act as a crucial resource for other nations throughout the world when it comes to ecological security. This research recommends the consumption of environmentally friendly and energy-efficient technologies in order to mitigate climate change and the government's implementation of the most recent policies to neutralize GHG emissions in order to achieve sustainable development.

Risk assessment of ICU patients through deep learning technique: A big data approach.

Background: Intensive Care Unit (ICU) patients are exposed to various medications, especially during infusion, and the amount of infusion drugs and the rate of their application may negatively affect their health status. A deep learning model can monitor a patient's continuous reaction to tranquillizer therapy, analyze the treatment plans of experts to avoid severe situations such as reverse medication associations, work with a convenient mediator, and change the treatment plans of specialists as needed. Methods: Generally, patients' treatment histories are linked together via a period grouping connection, which is usually burdened by missing information. Displaying time-succession via Repetitive Neural Organization (RNO) is the best available solution. However, it's possible that a patient's treatment may be prolonged, which RNN may not be able to demonstrate in this manner. Results: We propose the use of the LSTM-RNN driven by heterogeneous medicine events to predict the patient's outcome, as well as the Regular Language Handling and Gaussian Cycle, which can handle boisterous, deficient, inadequate, heterogeneous, and unevenly tested prescription records of patients while addressing the missing value issue using a piece-based Gaussian cycle. Conclusions: We emphasize the semantic relevance of every medication event and the grouping of drug events on patients in our study. We will focus specifically on LSTM-RNN and Phased LSTM-RNN for showing treatment results and information attribution using bit-based Gaussian cycles. We worked on Staged LSTM-RNN.

QoS-Aware Cost Minimization Strategy for AMI Applications in Smart Grid Using Cloud Computing.

Cloud computing coupled with Internet of Things technology provides a wide range of cloud services such as memory, storage, computational processing, network bandwidth, and database application to the end users on demand over the Internet. More specifically, cloud computing provides efficient services such as "pay as per usage". However, Utility providers in Smart Grid are facing challenges in the design and implementation of such architecture in order to minimize the cost of underlying hardware, software, and network services. In Smart Grid, smart meters generate a large volume of different traffics, due to which efficient utilization of available resources such as buffer, storage, limited processing, and bandwidth is required in a cost-effective manner in the underlying network infrastructure. In such context, this article introduces a QoS-aware Hybrid Queue Scheduling (HQS) model that can be seen over the IoT-based network integrated with cloud environment for different advanced metering infrastructure (AMI) application traffic, which have different QoS levels in the Smart Grid network. The proposed optimization model supports, classifies, and prioritizes the AMI application traffic. The main objective is to reduce the cost of buffer, processing power, and network bandwidth utilized by AMI applications in the cloud environment. For this, we developed a simulation model in the CloudSim simulator that uses a simple mathematical model in order to achieve the objective function. During the simulations, the effects of various numbers of cloudlets on the cost of virtual machine resources such as RAM, CPU processing, and available bandwidth have been investigated in cloud computing. The obtained simulation results exhibited that our proposed model successfully competes with the previous schemes in terms of minimizing the processing, memory, and bandwidth cost by a significant margin. Moreover, the simulation results confirmed that the proposed optimization model behaves as expected and is realistic for AMI application traffic in the Smart Grid network using cloud computing.

Assessment of framingham cardiovascular disease risk among militaries in the Kingdom of Saudi Arabia.

OBJECTIVES: To measure the risk factors for cardiovascular disease (CVD) among militaries in the Kingdom of Saudi Arabia and to assess their Framingham CVD risk. METHODS: A nationwide survey included 10,500 active military personnel selected by multistage stratified random sampling representing various ranks in the army forces of 5 regions. The study used the World Health Organization STEPwise approach to chronic disease risk factor surveillance (STEPS) in the design of data collection tool. Data included demographic and health behavior information; physical assessment; and anthropometric, random blood glucose, serum cholesterol, and triglycerides measurements. RESULTS: The response rate was 97.4%. The results showed that 9.1% of the sample population had 10% or higher Framingham 10-year office-based CVD risk score, with a mean of 4.5. The risk varied by region, armed force, crowding index, waist-hip ratio, total cholesterol, and triglycerides. Multivariate analysis identified crowding index, physical inactivity, and military rank as independent predictors, apart from Framingham predictors. CONCLUSION: The prevalence of CVD risk factors is high among militaries in the Kingdom of Saudi Arabia, with an associated high 10-year CVD Framingham risk. The military health services must implement intervention programs to reduce these risks, with follow-up of the participants with identified CVD risk.

Dental caries experience, oral health status and treatment needs of dental patients with autism.

OBJECTIVES: Autism is a lifelong neurodevelopmental disorder. The aims of this study were to investigate whether children with autism have higher caries prevalence, higher periodontal problems, or more treatment needs than children of a control group of non-autistic patients, and to provide baseline data to enable comparison and future planning of dental services to autistic children. MATERIAL AND METHODS: 61 patients with autism aged 6-16 years (45 males and 16 females) attending Dubai and Sharjah Autism Centers were selected for the study. The control group consisted of 61 non-autistic patients chosen from relatives or friends of autistic patients in an attempt to have matched age, sex and socioeconomic status. Each patient received a complete oral and periodontal examination, assessment of caries prevalence, and caries severity. Other conditions assessed were dental plaque, gingivitis, restorations and treatment needs. Chi-square and Fisher's exact test of significance were used to compare groups. RESULTS: The autism group had a male-to-female ratio of 2.8:1. Compared to controls, children with autism had significantly higher decayed, missing or filled teeth than unaffected patients and significantly needed more restorative dental treatment. The restorative index (RI) and Met Need Index (MNI) for the autistic children were 0.02 and 0.3, respectively. The majority of the autistic children either having poor 59.0% (36/61) or fair 37.8% (23/61) oral hygiene compared with healthy control subjects. Likewise, 97.0% (59/61) of the autistic children had gingivitis. CONCLUSIONS: Children with autism exhibited a higher caries prevalence, poor oral hygiene and extensive unmet needs for dental treatment than non-autistic healthy control group. Thus oral health program that emphasizes prevention should be considered of particular importance for children and young people with autism.

Dental caries experience, oral health status and treatment needs of dental patients with autism

OBJECTIVES: Autism is a lifelong neurodevelopmental disorder. The aims of this study were to investigate whether children with autism have higher caries prevalence, higher periodontal problems, or more treatment needs than children of a control group of non-autistic patients, and to provide baseline data to enable comparison and future planning of dental services to autistic children. MATERIAL AND METHODS: 61 patients with autism aged 6-16 years (45 males and 16 females) attending Dubai and Sharjah Autism Centers were selected for the study. The control group consisted of 61 non-autistic patients chosen from relatives or friends of autistic patients in an attempt to have matched age, sex and socioeconomic status. Each patient received a complete oral and periodontal examination, assessment of caries prevalence, and caries severity. Other conditions assessed were dental plaque, gingivitis, restorations and treatment needs. Chi-square and Fisher's exact test of significance were used to compare groups. RESULTS: The autism group had a male-to-female ratio of 2.8:1. Compared to controls, children with autism had significantly higher decayed, missing or filled teeth than unaffected patients and significantly needed more restorative dental treatment. The restorative index (RI) and Met Need Index (MNI) for the autistic children were 0.02 and 0.3, respectively. The majority of the autistic children either having poor 59.0 percent (36/61) or fair 37.8 percent (23/61) oral hygiene compared with healthy control subjects. Likewise, 97.0 percent (59/61) of the autistic children had gingivitis. CONCLUSIONS: Children with autism exhibited a higher caries prevalence, poor oral hygiene and extensive unmet needs for dental treatment than non-autistic healthy control group. Thus oral health program that emphasizes prevention should be considered of particular importance for children and young people with autism.