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This Paper is intended to explore research done on identifying research done on the identifying the face recognition of users and play music according to the mood of the users by using AI and ML

A method for low-distortion (low-dissipation, low-dispersion) information propagation in swarm-type networks with suppression of high-frequency noise is presented. Information propagation in current neighbor-based networks, where each agent seeks to achieve a consensus with its neighbors, is diffusion-like, dissipative, and dispersive and does not reflect the wave-like (superfluidic) behavior seen in nature. However, pure wave-like neighbor-based networks have two challenges: i) It requires additional communication for sharing information about time derivatives and ii) it can lead to information decoherence through noise at high frequencies. The main contribution of this work is to show that delayed self-reinforcement (DSR) by the agents using prior information (e.g., using short-term memory) can lead to the wave-like information propagation at low-frequencies as seen in nature without the need for additional information sharing between the agents. Moreover, it is shown that the DSR can be designed to enable suppression of high-frequency noise transmission while limiting the dissipation and dispersion of (lower-frequency) information content leading to similar (cohesive) behavior of agents. In addition to explaining noise-suppressed wave-like information transfer in natural systems, the result impacts the design of noise-suppressing cohesive algorithms for engineered networks.

Introduction: Incompetent valves of deep, superficial or perforating veins lead to varicose veins. Dilated, elongated or tortuous subcutaneous veins of lower legs are a manifestation of increase in venous pressure. This increase in venous pressure results from the reflux of blood due to incompetence. The diseased Great Saphenous Vein (GSV) is removed by High Saphenous Ligation and Stripping (HLS) surgery. Aims and Objectives: To study the clinical and socio-demographic profile, precipitating factors/ risk factors, complications and clinical outcomes of management of varicose veins. Material and Methods: Data collected from 150 patients with varicose veins by appropriate history taking and clinical examination, relevant radiological and laboratory investigations and follow-up of cases to study the clinical outcome. Results: Study was conducted with 150 patients. Most common age group affected with primary varicose veins was between 41-60 yrs of age with male predominance (64.7%). Most of the cases in present study were engaged in work involving long standing hours i.e. farming (34%) and housewives (28%). Most common presenting complaints were oedema (88%), dilated veins (74%) and pain (71.3%). Conservative management was tried in all cases while Trendelenburg operation and SPJ ligation was done in 26.7% and 9.3% cases. Stripping and PERF ligation was done in 28.7% and 25.3% cases respectively. A total of 24.7% were lost to follow up. By the end of 6 month, quality of life improved in 70% cases. By the end of 6 month, symptoms improved in 72.5% cases while it disappeared in 26.5% cases. Conclusion: Males have been found to have larger incidence than females. They are mostly affected in their fourth and fifth decade of life. Ulceration and pain are the frequent findings in this condition. Compression stocking has beneficial effects if prolonged standing cannot be avoided during work hours. Trendelenburg operation with flush ligation with subfascial or extra fascial stripping is the definitive management and shows significant clinical improvement.

Wastewater-based epidemiology (WBE), got its start with polio and ascended in acceptance with COVID-19 pandemic has reaffirmed its potential to sound the alarm on both old opponents and new threats like monkeypox, which has been stated a public health emergency by the World Health Organization [Nelson 2022; Taylor 2022]. The revelations in our sewage have offered evidence for why developing and maintaining the infrastructure necessary for wastewater-data analysis, modeling, and visualization should be an urgent public health priority. Critical to success for WBE is the capability to rapidly translate data into results that are visualized and presented such that public health officials can make timely decisions, where the officials are typically accustomed to clinical data, and rarely have data science, microbiology, genetic sequencing, or environmental science experience. Science gateways uniquely equip research communities with shared data, applications, and resources, usually in a graphical user interface, that can be further customized to meet the requirements of a specific project. In the current study, we present the design of a science gateway-based WBE data analysis and visualization framework for accomplishing Centers for Disease Control and Prevention (CDC) standard weekly (or short-term) pandemic/disease reporting and an effective investigation report. This framework incorporates heterogeneous datasets that come in many varied formats from different organizations like sewer-specific information from the water resources department, wastewater treatment geographical details from wastewater treatment plants (WWTPs), clinical information from the health department, sequencing data and PCR data. The challenge is to clean up the data, check whether the values make sense or might have errors included to create profiles with information for CDC and in a further step to the public. Currently, we have developed a set of data analytic scripts and visualizations via Python, which can be started manually on command line. The logical workflow and the different data sources are complex to integrate. Thus, we have designed a framework based on Galaxy to automate steps and support the researchers in the project to access data and analysis without needing to use the command line. The design uses access via RESTful APIs or via FTP to different resources where appropriate. To demonstrate the feasibility of the proposed framework, a case study is presented with wastewater data collected from OCT 2020 to AUG 2021 in one of the world’s largest WWTP, Stickney WWTP, Chicago, USA. The Stickney WWTP serves 2 million residents in the central part of Chicago as well as 46 other communities within a 260-square-mile-area. Once deployed, the proposed framework can be immediately utilized by WBE scientists and commercial vendors as an on-demand science gateway for providing COVID-19 WBE services. This can also be further customized for adding new services for opioids, polio, monkeypox, etc.

The detection of strategically important geospatial objects in remote sensing images is one of the important tasks for interpreting satellite images. Identification and mapping of objects such as tank, airplane, roads, buildings, etc. from remotely sensed images has gained much research and exploration in both defense and civilian applications. Detection and identification of buildings become more challenging when analyzed in forest neighborhood environment. The presence of dense trees in the surroundings increases the complexity and the rates of both false positives and false negatives. In the current study, an integration of image pan-sharpening (Gram-Schmidt) and sub-pixel image classification (Linear Mixture Model) algorithm is used for identification and mapping of building objects in two high resolution remote sensing satellite imagery (IKONOS and Worldview-2). Building objects having large, medium and small geometric size are selected from both the satellite imagery in strategically challenged forest neighborhood. The capability of pan-sharpening and sub-pixel classification techniques are jointly used for detection of building objects. The accuracy of target detection has been evaluated qualitatively (visual interpretation) and quantitatively (statistical analysis). Results on selected datasets show that the proposed combination of pan-sharpening and sub-pixel classification offers higher correctness in identification of building than other contemporary techniques. Although, significant improvement is noticed in results obtained for both the satellite imagery. Irrespective of having lower spatial resolution, after pan-sharpening, IKONOS datasets are found to be slightly more accurate in building identification in comparison to Worldview-2 datasets.

This letter addresses the rapid transport of flexible objects without increasing deformation, from one position to another, using a decentralized robotic team, rather than using a single large robot. However, current decentralized control theories do not ensure that the robot responses will remain cohesive (i.e., all robots move in a similar manner) during the transport process. This can lead to potentially large distortion, internal stress and damage when transporting sensitive objects. The main contribution of this work is to use an accelerated-gradient-based approach for faster transport without increasing the object deformation. Alternatively, for the same transport time, the approach allows the reduction of the object deformation. An advantage is that the proposed gradient-based approach can be implemented using an accelerated delayed self reinforcement (A-DSR) approach that requires only local force sensing by each robot, without additional inter-robot communication. Comparative experimental results, with and without A-DSR, show reduction in deformations by 60% for the same transport time, and a reduction of 59% in transport time for the same level of object deformation.

UAV technology when aided with the unique data acquisition strategies, preprocessing techniques and analytical abilities of an established domain of remote sensing provide more affordable, customized and user-friendly option of “UAV-Remote Sensing”. This extended branch of remote sensing flourishes in both the mapping and measurement, if implemented in the ordered fashion to ensure remote sensing grade data. The current study integrates the potential of UAV technology to the high-resolution data classification approach of object-based image analysis. Department of Civil Engineering, Indian Institute of Technology-Roorkee, India, is selected as study area. In the first part of the study, a detailed UAV survey followed by UAV data processing was carried out to capture the VHR orthorectified image of the selected study area. In the second step, a comparative assessment of nearest neighbor (NN) and rule-based classifications were performed. Orthorectified image was segmented using a multi-resolution segmentation. The overall accuracy for NN and rule-based classifier were 95.13% and 93.87%, respectively. Detailed assessment of user accuracy and producer accuracy described that tree, road, solar panel and waterbody were more accurately classified with NN classifier, whereas building, grass land, open land and vehicle were more accurately classified with rule-based classifier.

Classification of remotely sensed imagery with the integration of spatial context should be a constructive way of improved accuracy in image classification. An efficient spatial–spectral classification approach with colorimetric edge preservation of spatial–spectral modeling is processed. This research paper consists of three phases first, and the multispectral imagery [Captured by unmanned aerial vehicles (UAV)] is classified using a per-pixel classification method, i.e., support vector machine (SVM). Then, the classified image is constituted as various probability thematic maps and an edge preservation using chromaticity mapping is conducted on each probability map, with the principal component analysis of the multispectral imagery (Captured by UAVs) using as the color or gray-guidance imagery. Finally, from the edge preservation probability images, the classes of all the pixels are specified with the maximum probabilities. Significantly, the accuracy assessment of image classification improved in a short computational time.

This study performs a comparative evaluation of Frequency Ratio (FR), Analytic Hierarchy Process (AHP), and Fuzzy AHP (FAHP) modeling techniques for forest fire susceptibility mapping in Pauri Garhwal, Uttarakhand, India. Locations of past forest fire events reported from November 2002 to July 2019 were collected from the Uttarakhand Forest Department and Forest Survey of India and combined with the ground observations obtained from the manual survey. Then, the locations were categorized into two groups of 70% (10,500 locations) and 30% (4500 locations), randomly, for training and validation purposes, respectively. Forest fire susceptibility mapping was performed on the basis of fourteen different topographic, biological, human-induced and climatic criteria such as Digital Elevation Model, Slope, Aspect, Curvature, Normalized Difference Vegetation Index, Normalized Difference Moisture Index, Topographic Wetness Index, Soil, Distance to Settlement, Distance to Road, Distance to Drainage, Rainfall, Temperature, and Wind Speed. The Receiver Operating Characteristic curve and the Area Under the Curve (AUC) were implemented for validation of the three achieved Forest Fire Susceptibility Maps. The AUC plot evaluation revealed that FAHP has a maximum prediction accuracy of 83.47%, followed by AHP (81.75%) and FR (77.21%). Thus, the map produced by FAHP exhibits the most satisfactory properties. Results and findings of this study will help in developing more efficient fire management strategies in both the open and the protected forest areas (Rajaji and Jim Corbett National Park) of the district.

The current study intended to geospatially analyze the potentiality and site suitability of geo-ecotourism in West Bengal, India. The state of West Bengal is a great platform for diverse tourism and has enormous potential to cultivate geo-ecotourism, as has come up in recent years. The current effort throws some valuable light on the possibility of turning the many geologically, geomorphologically and ecologically significant tourist spots of West Bengal into geo-ecotourism sites, aided with geospatial techniques. The study deals with the qualitative and quantitative investigation of the potentiality of the whole state by dividing it into several geo-ecotourism zones, based on its physiographic setting and Land Use Land Cover (LULC) features, using satellite image data. The application of geospatial technology combined with Remote Sensing (RS) and Geographic Information System (GIS) was employed for this geospatial analysis to portray the potential zones using cartographic and statistical techniques. Furthermore, nine criteria were selected to run the Analytic Hierarchy Process (AHP) method to determine the site suitability for geo-ecotourism. The present submission attempts to record the mapping and analysis of geo-ecotourism of West Bengal employing a secondary database, an expert’s opinions and primary observations, with the application of the AHP method and GIS. The outcomes of the study were found to be very significant, as they indicate a proviso for geo-ecotourism development in the state and will contribute to the formation of location-specific planning and the sustainable management of geo-ecotourism.

Membrane interfaces are vital for various cellular processes, and their involvement in neurodegenerative disorders such as Alzheimer's and Parkinson's disease has taken precedence in recent years. The amyloidogenic proteins associated with neurodegenerative diseases interact with the neuronal membrane through various means, which has implications for both the onset and progression of the disease. The parameters that regulate the interaction between the membrane and the amyloids remain poorly understood. The review focuses on the various aspects of membrane interactions of amyloids, particularly amyloid-β (Aβ) peptides and Tau involved in Alzheimer's and α-synuclein involved in Parkinson's disease. The genetic, cell biological, biochemical, and biophysical studies that form the basis for our current understanding of the membrane interactions of Aβ peptides, Tau, and α-synuclein are discussed.

Agriculture is the major source of livelihood in rural areas and is considered the backbone of the Indian economy. In Sikkim, agriculture is being practiced by 80% of the rural population, and having no other major livelihood options has created immense pressure on the farmers and agricultural land. Agriculture sector is under great stress as the farmers are being confronted by various challenges in Sikkim Himalaya in recent years, such as land degradation, climate change and socio-economic problems. Despite the number of indigenous agriculture management methods being practised in Sikkim Himalaya, the agricultural production system is weakening. In this context, this paper presents an analysis of challenges faced by indigenous communities, local farmers and potential sustainable strategies for their management in Rani Khola watershed of Sikkim Himalaya. Data and information were collected by field observation, questionnaire surveys of 300 households, key informant interviews and focus group discussions conducted during 2017–18. Data processing and analysis were carried out with a combination of techniques, such as the application of remote sensing (RS), geographic information system (GIS)-based data processing and descriptive statistics. Major challenges identified in the watershed are water scarcity (80%), climate change (88%), soil erosion and runoff (72%), higher investment cost (100%), lack of irrigation facilities (77%), fragmentation and size of landholdings (100), human–wildlife conflict (59%) and pests and disease (60%). Some possibilities and innovations that could address these problems are the use and retaining of various indigenous soil and water conservation (SWC) measures, diversified farming systems, community involvement in the government development process, better irrigation facilities, strengthening the local economy, coordinated planning between stakeholders and development of market feedback mechanism within the system.

Amyloid-beta (Aβ) aggregation mediated neuronal membrane deformation, although poorly understood, is implicated in Alzheimer’s Disease (AD). Particularly, whether Aβ aggregation can induce neuronal demyelination remains unknown. Here we show that Aβ-40 binds and induces extensive tubulation in the myelin membrane in vitro. The binding of Aβ-40 depends predominantly on the lipid packing defect densities and electrostatic interactions and results in rigidification of the myelin membrane in the early time scales. Furthermore, elongation of Aβ-40 into higher oligomeric and fibrillar species leads to eventual fluidization of the myelin membrane followed by extensive membrane tubulation observed in the late phase. Taken together, our results capture mechanistic insights into snapshots of temporal dynamics of Aβ-40 - myelin membrane interaction and demonstrate how short timescale, local phenomena of binding, and fibril mediated load generation manifests into long timescale, global phenomena of myelin tubulation and demonstrates the ability of Aβ-40 to demyelinate.

This study uses remote sensing, GIS and MCDA techniques for studying the availability of groundwater resources and their suitability for urban residential planning in Bhopal, the capital city of Madhya Pradesh, India. The first part of our research involves integration of nine different thematic layers in AHP to identify GWPZ. The study reveals that 13.51% of the area has very high, 13.77% of the area has high, 12.05% has medium-to-high, 27.61% of area has medium groundwater potential, while the remaining 33.05% has low groundwater potential. In the second part of the research, the resultant GWPZ is combined with eight different thematic layers for identifying suitable urban residential development sites. Urban residential site suitability map shows that only 16.97% of the area has very high suitability, 20.71% area has high suitability, 25.81% of the area has moderate suitability and 23.56% has low suitability, while the remaining 12.94% area is non-suitable.

Background The Leprosy Post-Exposure Prophylaxis (LPEP) program explored the feasibility and impact of contact tracing and the provision of single dose rifampicin (SDR) to eligible contacts of newly diagnosed leprosy patients in Brazil, India, Indonesia, Myanmar, Nepal, Sri Lanka and Tanzania. As the impact of the programme is difficult to establish in the short term, we apply mathematical modelling to predict its long-term impact on the leprosy incidence. Methodology The individual-based model SIMCOLEP was calibrated and validated to the historic leprosy incidence data in the study areas. For each area, we assessed two scenarios: 1) continuation of existing routine activities as in 2014; and 2) routine activities combined with LPEP starting in 2015. The number of contacts per index patient screened varied from 1 to 36 between areas. Projections were made until 2040. Principal findings In all areas, the LPEP program increased the number of detected cases in the first year(s) of the programme as compared to the routine programme, followed by a faster reduction afterwards with increasing benefit over time. LPEP could accelerate the reduction of the leprosy incidence by up to six years as compared to the routine programme. The impact of LPEP varied by area due to differences in the number of contacts per index patient included and differences in leprosy epidemiology and routine control programme. Conclusions The LPEP program contributes significantly to the reduction of the leprosy incidence and could potentially accelerate the interruption of transmission. It would be advisable to include contact tracing/screening and SDR in routine leprosy programmes., Author summary The Leprosy Post-Exposure Prophylaxis (LPEP) program explored the feasibility and impact of contact tracing and the provision of SDR to eligible contacts of newly diagnosed leprosy patients in states or districts of Brazil, India, Indonesia, Myanmar, Nepal, Sri Lanka and Tanzania. This study investigated the long-term impact of the LPEP program on the leprosy new case detection rate (NCDR). Our results show that LPEP could reduce the NCDR beyond the impact of the routine leprosy control programme and that many new cases could be prevented. The benefit of LPEP increases gradually over time. LPEP could accelerate the time of reaching predicted NCDR levels of 2040 under routine program by up to six years. Furthermore, we highlighted how the impact varies between countries due to differences in the number of contacts per index patient screened and differences in leprosy epidemiology and national control programme. Generally, including both household contacts and neighbours (> 20 contacts per index patient) would yield the highest impact.

Background Leprosy is a chronic bacterial infection caused by Mycobacterium leprae, which may lead to physical disability, stigma, and discrimination. The chronicity of the disease and disabilities are the prime contributors to the disease burden of leprosy. The current figures of the disease burden in the 2017 global burden of disease study, however, are considered to be under-estimated. In this study, we aimed to systematically review the literature and perform individual patient data meta-analysis to estimate new disability weights for leprosy, using Health-Related Quality of Life (HRQOL) data. Methodology/principal findings The search strategy included all major databases with no restriction on language, setting, study design, or year of publication. Studies on human populations that have been affected by leprosy and recorded the HRQOL with the Short form tool, were included. A consortium was formed with authors who could share the anonymous individual-level data of their study. Mean disability weight estimates, sorted by the grade of leprosy disability as defined by WHO, were estimated for individual participant data and pooled using multivariate random-effects meta-analysis. Eight out of 14 studies from the review were included in the meta-analysis due to the availability of individual-level data (667 individuals). The overall estimated disability weight for grade 2 disability was 0.26 (95%CI: 0.18–0.34). For grade 1 disability the estimated weight was 0.19 (95%CI: 0.13–0.26) and for grade 0 disability it was 0.13 (95%CI: 0.06–0.19). The revised disability weight for grade 2 leprosy disability is four times higher than the published GBD 2017 weights for leprosy and the grade 1 disability weight is nearly twenty times higher. Conclusions/significance The global burden of leprosy is grossly underestimated. Revision of the current disability weights and inclusion of disability caused in individuals with grade 0 leprosy disability will contribute towards a more precise estimation of the global burden of leprosy., Author summary Leprosy is a chronic, disabling disease that causes various kinds of disability in the affected person. This includes physical impairment, activity limitation, and participation restriction. However, the published global burden of disease estimates for leprosy is considered to be a gross under-estimation. Disability weights form an integral component in the calculation of the burden estimates. But the methodology for calculation of the weights focuses only on physical impairment and lacks the perspective of the patient. In this study, we systematically reviewed the literature and performed an individual patient data meta-analysis for revising the disability weights for leprosy using domain scores from health-related quality of life instruments. The domains of these instruments cover all aspects of disability from a patient’s perspective. We found that the revised weights were considerably higher than the current weights, and were more reflective of the actual disability caused by leprosy. We also found that for individuals without any severe disability due to leprosy (grade 0), they still experience comparable suffering. Revision of the current disability weights and inclusion of the disability caused in grade 0 individuals will contribute towards better estimation of the global burden of leprosy.

COVID-19 is now one of the most leading causes of death in the United States (US). Systemic health, social and economic disparities have put the minorities and economically poor communities at a higher risk than others. There is an immediate requirement to develop a reliable measure of county-level vulnerabilities that can capture the heterogeneity of vulnerable communities. This study reports a COVID-19 Vulnerability Index (C19VI) for identifying and mapping vulnerable counties. We proposed a Random Forest machine learning-based vulnerability model using CDC’s sociodemographic and COVID-19-specific themes. An innovative ‘COVID-19 Impact Assessment’ algorithm was also developed for evaluating severity of the pandemic and to train the vulnerability model. Developed C19VI was statistically validated and compared with the CDC COVID-19 Community Vulnerability Index (CCVI). Finally, using C19VI and the census data, we explored racial inequalities and economic disparities in COVID-19 health outcomes. Our index indicates that 575 counties (45 million people) fall into the ‘very high’ vulnerability class, 765 counties (66 million people) in the ‘high’ vulnerability class, and 1435 counties (204 million people) in the ‘moderate’ or ‘low’ vulnerability class. Only 367 counties (20 million people) were found as ‘very low’ vulnerable areas. Furthermore, C19VI reveals that 524 counties with a racial minority population higher than 13% and 420 counties with poverty higher than 20% are in the ‘very high’ or ‘high’ vulnerability classes. The C19VI aims at helping public health officials and disaster management agencies to develop effective mitigation strategies especially for the disproportionately impacted communities., Graphical abstract Unlabelled Image

India has the highest burden of leprosy in the world. Following a recent WHO guideline, the Indian National Leprosy Programme is introducing post-exposure prophylaxis with single-dose rifampicin (SDR-PEP) in all high-endemic districts of the country. The aim of this study is to estimate the long-term cost-effectiveness of SDR-PEP in different leprosy disability burden situations. We used a stochastic individual-based model (SIMCOLEP) to simulate the leprosy new case detection rate trend and the impact of implementing contact screening and SDR-PEP from 2016 to 2040 (25 years) in the Union Territory of Dadra Nagar Haveli (DNH) in India. Effects of the intervention were expressed as disability adjusted life years (DALY) averted under three assumption of disability prevention: 1) all grade 1 disability (G1D) cases prevented; 2) G1D cases prevented in PB cases only; 3) no disability prevented. Costs were US$ 2.9 per contact. Costs and effects were discounted at 3%. The incremental cost per DALY averted by SDR-PEP was US$ 210, US$ 447, and US$ 5,673 in the 25th year under assumption 1, 2, and 3, respectively. If prevention of G1D was assumed, the probability of cost-effectiveness was 1.0 at the threshold of US$ 2,000, which is equivalent to the GDP per capita of India. The probability of cost-effectiveness was 0.6, if no disability prevention was assumed. The cost per new leprosy case averted was US$ 2,873. Contact listing, screening and the provision of SDR-PEP is a cost-effective strategy in leprosy control in both the short (5 years) and long term (25 years). The cost-effectiveness depends on the extent to which disability can be prevented. As the intervention becomes increasingly cost-effective in the long term, we recommend a long-term commitment for its implementation., Author summary Leprosy is an infectious disease caused by the Mycobacterium leprae and is one of the major causes of preventable disability in low- and middle-income countries. Disabled people face lifelong socioeconomic challenges and are often excluded from society. It brings people affected by leprosy in a vicious cycle of poverty and disability. This cycle needs to be curbed through scientific evidence on health economic aspects of leprosy. Unfortunately, there is not much literature available on cost or cost-effectiveness of leprosy prevention. New prevention methods such as post-exposure chemoprophylaxis are now available, providing opportunity to collect up-to-date health economic evidence to support health policy and planning. Here, we present a cost-effectiveness study of contact screening in combination of post-exposure prophylaxis with single-dose rifampicin in the Union Territory of Dadra Nagar Haveli in India. We compare the intervention with the continuation of the routine leprosy control programme. As leprosy is moving towards elimination, evidence from this study will contribute to an investment case for leprosy elimination.