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Irrigation of crops with domestic wastewater (DW) is a common practice in developing countries like India. However, domestic wastewater irrigation poses a risk of migration of toxic heavy metals to edible parts of crops, which requires serious measures to prevent their uptake. In this study, the effect of DW irrigation in comparison with Sarbal Lake water (SLW) and borewell water (BW) on soil characteristics and cultivated saffron (Crocus sativus L.) was investigated. For this purpose, samples of water, soil, and saffron (corm, petal, and stigma) were collected from the suburban area of Pampore, Srinagar district, Jammu and Kashmir, India. The results showed that DW irrigation had the maximum significant (p

The detection of the most salient parts of images as objects in salient object detection tasks mimics human behavior, which is useful for a variety of computer vision applications. In this paper, the Local and Global Saliency Driven Dual-Channel Pulse Coupled Neural Network (LGSD-DCPCNN) model is used to provide a novel strategy for saliency detection. To achieve visually homogeneous sections and save computation costs, the input image is first subjected to superpixel segmentation. The global and local saliency maps are then created using the segmented image’s position, color, and textural properties. The LGSD-DCPCNN network is activated using these saliency maps to extract visually consistent features from the input maps to provide the final saliency map. An extensive qualitative and quantitative performance study is undertaken to assess the efficacy of the proposed method. When compared to state-of-the-art approaches, the experimental results show a considerable improvement in the detection of salient regions. Quantitative analysis of the proposed method reveals a significant improvement in the area under the ROC curve (AUC) score, F-measure score, and mean absolute error (MAE) score. The qualitative analysis describes the proposed algorithm’s ability to detect multiple salient objects accurately while maintaining significant border preservation.

Motivation: Machine learning (ML)-based stroke risk stratification systems have typically focused on conventional risk factors (CRF) (AtheroRisk-conventional). Besides CRF, carotid ultrasound image phenotypes (CUSIP) have shown to be powerful phenotypes risk stratification. This is the first ML study of its kind that integrates CUSIP and CRF for risk stratification (AtheroRisk-integrated) and compares against AtheroRisk-conventional. Methods: Two types of ML-based setups called (i) AtheroRisk-integrated and (ii) AtheroRisk-conventional were developed using random forest (RF) classifiers. AtheroRisk-conventional uses a feature set of 13 CRF such as age, gender, hemoglobin A1c, fasting blood sugar, low-density lipoprotein, and high-density lipoprotein (HDL) cholesterol, total cholesterol (TC), a ratio of TC and HDL, hypertension, smoking, family history, triglyceride, and ultrasound-based carotid plaque score. AtheroRisk-integrated system uses the feature set of 38 features with a combination of 13 CRF and 25 CUSIP features (6 types of current CUSIP, 6 types of 10-year CUSIP, 12 types of quadratic CUSIP (harmonics), and age-adjusted grayscale median). Logistic regression approach was used to select the significant features on which the RF classifier was trained. The performance of both ML systems was evaluated by area-under-the-curve (AUC) statistics computed using a leave-one-out cross-validation protocol. Results: Left and right common carotid arteries of 202 Japanese patients were retrospectively examined to obtain 404 ultrasound scans. RF classifier showed higher improvement in AUC (~57%) for leave-one-out cross-validation protocol. Using RF classifier, AUC statistics for AtheroRisk-integrated system was higher (AUC = 0.99,p-value

Diabetes and atherosclerosis are the predominant causes of stroke and cardiovascular disease (CVD) both in low- and high-income countries. This is due to the lack of appropriate medical care or high medical costs. Low-cost 10-year preventive screening can be used for deciding an effective therapy to reduce the effects of atherosclerosis in diabetes patients. American College of Cardiology (ACC)/American Heart Association (AHA) recommended the use of 10-year risk calculators, before advising therapy. Conventional risk calculators are suboptimal in certain groups of patients because their stratification depends on (a) current blood biomarkers and (b) clinical phenotypes, such as age, hypertension, ethnicity, and sex. The focus of this review is on risk assessment using innovative composite risk scores that use conventional blood biomarkers combined with vascular image-based phenotypes. AtheroEdge™ tool is beneficial for low-moderate to high-moderate and low-risk to high-risk patients for the current and 10-year risk assessment that outperforms conventional risk calculators. The preventive screening tool that combines the image-based phenotypes with conventional risk factors can improve the 10-year cardiovascular/stroke risk assessment.

The choice of gate metal technology for junctionless transistors needs to have diverse characteristics as metals have distinct work functions and hence, they show incompatibility while tailoring threshold of the device. In such a scenario, bimetallic stacked gate can be a promising candidate to present wide range of tunable work functions required for nano-regime junctionless transistors. This paper explores the electronic phenomena occurring at metal-metal interface and the impact of Platinum (Pt)/Titanium (Ti) bimetallic stacked gate-based work function tunability on the RF and thermal performances of p-type window-based Silicon on Insulator Junctionless Transistor (SOI JLT) using numerical simulator SILVACO ATLAS. The parameters considered for performance evaluation are ON-state current (I_{ON}), OFF-state current (I_{OFF}), I_{ON}/I_{OFF} ratio, transconductance (g_m),\linebreak cutoff frequency (f_T), Transconductance Frequency Product (TFP), Intrinsic Gate Delay (IGD), intrinsic gain (A_V), and Global Device Temperature (GDT). The g_m, f_T, TFP, A_V and GDT improve for modified over conventional in the ON state at higher work function, while IGD improves at lower work function. The improvements of 11.7% and 2.21% are obtained in maximum g_m and f_T, respectively, for modified transistor over conventional. The findings suggest that bimetallic stacked gate modified SOIJLT is a better option than conventional for low-power RF application.

A binary comparator architecture is proposed in this work for static logic to achieve both low-power and high-performance operations. It also presents a detailed timing performance and power analysis of various state-of-the-art comparator designs. The main advantages of this design are its high speed and power efficiency maintained over a wide range of operands size, which is useful at low-input data activity environments. The proposed circuit design uses minimum fan-in and fan-out logic gates for achieving high speed and low power dissipation. Utilizing a 2-bit binary comparator circuit with minimum fan-in and fan-out of logic gates (NAND-NOR), the architecture of a parallel binary comparator is proposed for higher input operands by using a low radix multiplexer and priority encoder. Further, to decrease the size of the multiplexer and priority encoder by two times, a general architecture is also proposed by using a 4-bit binary comparator to reduce its complexity. The proposed circuits are optimized in terms of the power consumption and delay, which are due to low load capacitance, low leakages, and reduced dynamic power dissipation. Each of the proposed circuits has its own merits in terms of speed, power consumption, Power-Delay Product (PDP). Its synthesis is done on 180 nm as well as 90 nm CMOS technology using the Cadence tool. The physical layout of the proposed architecture using a 90 nm CMOS process (GPDK process) is also obtained.

Background: The hands of health-care workers are the primary mode of transmission of the pathogens to patients, so hand hygiene is considered to be the most important and least expensive means of preventing nosocomial infections. Aim: The aim of this study is to assess and compare the antimicrobial efficacy of different hand sanitizers. Subjects and Methods: An in vitro study was conducted in the Department of Public Health Dentistry in the Dental College, Delhi NCR, to evaluate the antimicrobial efficacy of Green Apple (Himalaya), Lemon (Lifebuoy), Aloe vera (Tempo) hand sanitizers and compared them with the antimicrobial efficacy of sterillium hand sanitizers which is considered as the gold standard in hand sterilization. Twenty five participants were divided into five groups. The Gloves worn by the participants were swabbed after oral prophylaxis using different hand sanitizers. The inoculation of the sample was done and incubated for 48 h at 37°C, and colony-forming units were counted. The data obtained were compiled systematically using Microsoft Excel and were analyzed using the SPSS version 19.0. Data were statistically analyzed using descriptive statistics to assess the mean, standard deviation, standard error of the mean, and ANOVA and post hoc test for group-wise comparisons. A value of P < 0.05 was considered statistically significant. Results: Sterillium group showed the highest antimicrobial effectiveness followed by lemon group (71.4 ± 11.80), Green Apple group (112 ± 13.78), and A. vera group (156.4 ± 34.37), respectively, against all the organisms in the study. Conclusion: Sterillium possessed maximum antimicrobial effect against all the Gram-positive as well as Gram-negative bacteria as compared to lemon, green apple, and A. vera hand sanitizers.

Context: Edentulism is a handicap that affects the quality of life and nutrition. Tooth loss leads to functional impairment at the level of the orofacial system, significantly interfering with chewing, swallowing and speech, as well as to temporomandibular joint dysfunction, disorders of intermaxillary relation, and facial physiognomy. Aim: The aim of the study was to explore variables that influence self-reported oral health and denture satisfaction in partially and completely edentulous patients using oral health impact profile for edentulous (OHIP-EDENT). Materials and Methods: A hospital-based study was carried out on the patient's age ranged from 30 to 70 years in the dental college who had been treated with complete dentures and removable partial denture using convenience sampling method. Prevalidated questionnaire consisting of OHIP-EDENT was self-administered to all the study participants. The information about the appetite, perceived general and oral health, and satisfaction from the dentures was recorded using a 4-point Likert scale. Data were collected, entered, and analyzed using SPSS version 20.0. One-way ANOVA was performed to test for comparison between different denture groups. P < 0.05 shows statistically significant difference. Results: When the mean of denture satisfaction level was calculated between different denture group and different domains of OHIP-EDENT, there was no difference found in level of denture satisfaction between different denture groups, i.e., overall denture satisfaction mean in Group 1 was 2.18 ± 0.32, Group 2 has 2.23 ± 0.36, Group 3 has the mean of 2.18 ± 0.42, Group 4 has 2.23 ± 0.67, and P value was 0.810 which shows nonsignificant difference. Conclusion: The results of the present study suggest that the poor self-perceived satisfaction among denture wearers affect their quality of life.