A vehicular network based intelligent transport system for smart cities using machine learning algorithms.

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. Internet traffic analysis is a thriving field of study due to the myriad ways in which it may be put to practical use. In the intelligent Internet-of-Vehicles (IOVs), traffic congestion can be predicted and identified using cutting-edge technologies. Using tree-based decision-tree, random-forest, extra-tree, and XGBoost machine learning (ML) strategies, this research proposes an intelligent-transport-system for the IOVs-based vehicular network traffic in a smart city set-up. The suggested system uses ensemble learning and averages the selection of crucial features to give high detection accuracy at minimal computational costs, as demonstrated by the simulation results. For IOV-based vehicular network traffic, the tree-based ML approaches with feature-selection (FS) outperformed those without FS. When contrasted to the lowest KNN accuracy of 96.6% and the highest SVM accuracy of 98.01%, the Stacking approach demonstrates superior accuracy as 99.05%.

Design of optimal Elman Recurrent Neural Network based prediction approach for biofuel production.

Renewable sources like biofuels have gained significant attention to meet the rising demands of energy supply. Biofuels find useful in several domains of energy generation such as electricity, power, or transportation. Due to the environmental benefits of biofuel, it has gained significant attention in the automotive fuel market. Since the handiness of biofuels become essential, effective models are required to handle and predict the biofuel production in realtime. Deep learning techniques have become a significant technique to model and optimize bioprocesses. In this view, this study designs a new optimal Elman Recurrent Neural Network (OERNN) based prediction model for biofuel prediction, called OERNN-BPP. The OERNN-BPP technique pre-processes the raw data by the use of empirical mode decomposition and fine to coarse reconstruction model. In addition, ERNN model is applied to predict the productivity of biofuel. In order to improve the predictive performance of the ERNN model, a hyperparameter optimization process takes place using political optimizer (PO). The PO is used to optimally select the hyper parameters of the ERNN such as learning rate, batch size, momentum, and weight decay. On the benchmark dataset, a sizable number of simulations are run, and the outcomes are examined from several angles. The simulation results demonstrated the suggested model's advantage over more current methods for estimating the output of biofuels.

Concomitant pulmonary disease is common among patients with extrapulmonary TB.

BACKGROUND: Microbiologic screening of extrapulmonary TB (EPTB) patients could inform recommendations for aerosol precautions and close contact prophylaxis. However, this is currently not routinely recommended in India. Therefore, we estimated the proportion of Indian patients with EPTB with microbiologic evidence of pulmonary TB (PTB).METHODS: We characterized baseline clinical, radiological and sputum microbiologic data of 885 adult and pediatric TB patients in Chennai and Pune, India, between March 2014 and November 2018.RESULTS: Of 277 patients with EPTB, enhanced screening led to the identification of 124 (45%) with concomitant PTB, including 53 (19%) who reported a cough >2 weeks; 158 (63%) had an abnormal CXR and 51 (19%) had a positive sputum for TB. Of 70 participants with a normal CXR and without any cough, 14 (20%) had a positive sputum for TB. Overall, the incremental yield of enhanced screening of patients with EPTB to identify concomitant PTB disease was 14% (95% CI 12-16).CONCLUSIONS: A high proportion of patients classified as EPTB in India have concomitant PTB. Our results support the need for improved symptom and CXR screening, and recommends routine sputum TB microbiology screening of all Indian patients with EPTB.

Respiratory health status is associated with treatment outcomes in pulmonary tuberculosis.

The association between respiratory impairment and tuberculosis (TB) treatment outcomes is not clear. We prospectively evaluated respiratory health status, measured using the Saint George's Respiratory Questionnaire (SGRQ), in a cohort of new adult pulmonary TB cases during and up to 18 months following treatment in India. Associations between total SGRQ scores and poor treatment outcomes of failure, recurrence and all-cause death were measured using multivariable Poisson regression. We enrolled 455 participants contributing 619 person-years at risk; 39 failed treatment, 23 had recurrence and 16 died. The median age was 38 years (interquartile range 26-49); 147 (32%) ever smoked. SGRQ scores at treatment initiation were predictive of death during treatment (14% higher risk per 4-point increase in baseline SGRQ scores, 95%CI 2-28, P = 0.01). Improvement in SGRQ scores during treatment was associated with a lower risk of failure (1% lower risk for every per cent improvement during treatment, 95%CI 1-2, P = 0.05). Clinically relevant worsening in SGRQ scores following successful treatment was associated with a higher risk of recurrence (15% higher risk per 4-point increase scores, 95%CI 4-27, P = 0.004). Impaired respiratory health status was associated with poor TB treatment outcomes. The SGRQ may be used to monitor treatment response and predict the risk of death in pulmonary TB. .

Isoniazid preventive treatment in children in two districts of South India: does practice follow policy?

SETTING: Two districts of Tamil Nadu, India OBJECTIVES: To determine the proportion of household contacts aged <6 years of patients with tuberculosis (TB) with positive sputum microscopy results who initiated and completed isoniazid preventive treatment (IPT), and to determine reasons for non-initiation and non-completion of IPT. DESIGN: Household visits were conducted on a random sample of adult patients registered during January-June 2012 to identify household contacts aged <6 years. RESULTS: Among 271 children living with 691 index patients, 218 (80%) were evaluated and 9 (4%) were diagnosed with TB. Of 209 remaining contacts, 70 (33%) started IPT and 16 (22.9%) completed a full course of IPT. Of 139 contacts who did not start IPT, five developed TB disease. Reasons for non-initiation of IPT included no home visit by the field staff (19%) and no education about IPT (61%). Reasons for non-completion included isoniazid not provided (52%) and long duration of treatment (28%). CONCLUSION: This study shows that Revised National TB Programme guidance was not being followed and IPT implementation was poor. Poor IPT uptake represents a missed opportunity to prevent future TB cases. Provision of IPT may be improved through training, improved logistics and enhanced supervision and monitoring.

Low power adder based digital filter for QRS detector.

Most of the Biomedical applications use dedicated processors for the implementation of complex signal processing. Among them, sensor network is also a type, which has the constraint of low power consumption. Since the processing elements are the most copiously used operations in the signal processors, the power consumption of this has the major impact on the system level application. In this paper, we introduce low power concept of transistor stacking to reduce leakage power; and new architectures based on stacking to implement the full adder and its significance at the digital filter level for QRS detector are implemented. The proposed concept has lesser leakage power at the adder as well as filter level with trade-off in other quality metrics of the design. This enabled the design to be dealt with as the low-power corner and can be made adaptable to any level of hierarchical abstractions as per the requirement of the application. The proposed architectures are designed, modeled at RTL level using the Verilog-HDL, and synthesized in Synopsys Design Compiler by mapping the design to 65 nm technology library standard cells.

Perceptual criteria and attributes used for evaluation of clothing by women using wheelchairs.

The apparel and clothing attributes important to persons with disabilities are poorly understood. The purpose of this study was to identify apparel attributes of personal value to persons using a wheelchair and used to evaluate clothing. Four focus group interviews on three categories of clothing, formal wear, casual wear, and undergarments, were conducted with 19 female wheelchair users. Participants discussed clothing generally and after viewing garments designed especially for people with disabilities. Analysis of the content of the interviews yielded 49 phrases or words that could be categorized into Physical Appearance, Physical Performance, Expressiveness, and Extrinsic Attributes. Participants were concerned most about Physical Appearance for which criteria are observable attributes of the garment. Attributes of value in order of importance are styling of garment, fabric, garment construction, and color. The Physical Performance criteria include in order of attention benefits of physical aspects such as convenience, performance of garment, fabric, and effect of care. Expressiveness criteria evoked participants' feelings and reaction to use. Extrinsic Attribute criteria are those used to evaluate and make purchase decisions and are of less importance. These 19 persons with disability value casual clothing which had regular features. Results describe useful information on the criteria disabled persons use to evaluate clothing for use.