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This paper proposes a comprehensive steady-state analysis for a front-end Modular Multi-Parallel Rectifiers (MMR) system with a shared DC-link. A generalised mathematical representation for n number of parallel rectifiers is proposed. The MMR is analysed for both Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). In addition, the CCM and DCM boundary of the MMR is identified mathematically. A 60kW Simulink model for the MMR is utilised to confirm the proposed steady-state mathematical modelling equations.

Background Primary iliopsoas abscess is extremely rare in neonates and overlooked easily. It is potentially curable with surgical drainage and broad-spectrum antibiotics if diagnosed early. Case presentation A 13-day-old neonate was presented with a swelling and bluish discoloration on the left thigh and groin. There was a restriction of movement of the left lower limb, and he developed high grade fever later on, during hospital stay. In spite of a great dilemma, we could finally reach the diagnosis of primary iliopsoas abscess, performed surgical drainage, and controlled ongoing sepsis. At 3 months follow-up, the patient was doing well and there was no asymmetry in appearance of movements. Conclusion High index of suspicion, vigilant clinical examinations, and targeted laboratory investigations with imaging studies are of paramount importance in establishing its diagnosis. This case highlights the diagnostic difficulties and re-evaluates the representative features of neonatal iliopsoas abscess and its management.

Introduction. Frontotemporal dementia (FTD) is a common cause of cognitive impairment, behavioral changes and language deficits. in this study we assessed the clinical and FDG-PET characteristics of our patients and compared them with available international and Indian data on FTD. Methods. All patients were evaluated with a neuropsychological battery followed by 18F-FDG-PET scan, in addition to all necessary dementia work-up. Results. 15 FTD patients (m:f = 11:4) had a mean age of presentation of 67.4 (8.6) years, with a mean interval of 1.7 (0.7) years from the symptom onset to diagnosis. those with 10 or less years of education had an earlier presentation. a positive family history was present in 20%. The mean MMSE score was 18.8 (4.6). Disinhibition was the most common symptom seen in 60%. Apathy was less frequently noted. FDG-PET scan showed predominantly anterior cingulate and anterior temporal hypometabolism, with asymmetry in 67%. 2 cases diagnosed as fvAD had a revised diagnosis of bvFTD after FDG-PET scan. 1 patient of nfvPPA was mute at presentation with a history of agrammatism with hypometabolism in left inferior frontal and superior temporal regions. Conclusions. There were significant variations compared to international/Western literature, with later age of presentation, shorter interval from onset to presentation, lower MMSE scores, with disinhibition rather than apathy as the commonest symptom. FDG-PET showed similar areas of involvement but with less extensive hypometabolism compared to other studies with a lesser frequency of asymmetry. FDG-PET scan is a useful adjunct for evaluation of FTD patients.

Super-resolution fluorescence imaging that surpasses the classical optical resolution limit is widely utilized for resolving the spatial organization of biological structures at molecular length scales. In one example, single-molecule localization microscopy, the lateral positions of single molecules can be determined more precisely than the diffraction limit if the camera collects individual photons separately. Using several schemes that introduce engineered optical aberrations in the imaging optics, super-resolution along the optical axis (perpendicular to the sample plane) has been achieved, and single-molecule localization microscopy has been successfully applied for the study of 3D biological structures. Nonetheless, the achievable axial localization accuracy is typically three to five times worse than the lateral localization accuracy. Only a few exceptional methods based on interferometry exist that reach nanometer 3D super-resolution, but they involve enormous technical complexity and restricted sample preparations that inhibit their widespread application. We developed metal-induced energy transfer imaging for localizing fluorophores along the axial direction with nanometer accuracy, using only a conventional fluorescence lifetime imaging microscope. In metal-induced energy transfer, experimentally measured fluorescence lifetime values increase linearly with axial distance in the range of 0-100 nm, making it possible to calculate their axial position using a theoretical model. If graphene is used instead of the metal (graphene-induced energy transfer), the same range of lifetime values occurs over a shorter axial distance (~25 nm), meaning that it is possible to get very accurate axial information at the scale of a membrane bilayer or a molecular complex in a membrane. Here, we provide a step-by-step protocol for metal- and graphene-induced energy transfer imaging in single molecules, supported lipid bilayer and live-cell membranes. Depending on the sample preparation time, the complete duration of the protocol is 1-3 d.

Hybrid devices consisting of graphene or transition metal dichalcogenides (TMDs) and semiconductor quantum dots (QDs) were widely studied for potential photodetector and photovoltaic applications, while for photodetector applications, high internal quantum efficiency (IQE) is required for photovoltaic applications and enhanced carrier diffusion length is also desirable. Here, we reported the electrical measurements on hybrid field-effect optoelectronic devices consisting of compact QD monolayer at controlled separations from single-layer graphene, and the structure is characterized by high IQE and large enhancement of minority carrier diffusion length. While the IQE ranges from 10.2% to 18.2% depending on QD-graphene separation, ds, the carrier diffusion length, LD, estimated from scanning photocurrent microscopy (SPCM) measurements, could be enhanced by a factor of 5-8 as compared to that of pristine graphene. IQE and LD could be tuned by varying back gate voltage and controlling the extent of charge separation from the proximal QD layer due to photoexcitation. The obtained IQE values were remarkably high, considering that only a single QD layer was used, and the parameters could be further enhanced in such devices significantly by stacking multiple layers of QDs. Our results could have significant implications for utilizing these hybrid devices as photodetectors and active photovoltaic materials with high efficiency.

The next decade is likely to witness considerable growth in the application of plug-in electric vehicles (PEVs). However, uncoordinated PEV charging in low-voltage distribution networks may cause grid issues, such as transformer overload and voltage unbalance (VU). Therefore, it is essential to consider a more suitable methodology of voltage regulation for the consumers placed at the end of the feeder when the network is predominantly resistive. With this background, this article investigates the capability of dynamic PEV load transfer within the three phases of a distribution network to propose a new hybrid PEV coordination approach. In addition to the online centralized battery charging and var discharging, this approach performs local voltage profiles improvement by switching PEV at selected single-phase residential houses such that a PEV can be switched from a heavily loaded phase to a (relatively) lightly loaded phase. This results in better voltage regulations and VU reductions. The impacts of uncoordinated and proposed genetic algorithm (GA) coordinated PEV charging on VU and node voltage profiles and losses are simulated for a real unbalanced Western Australian distribution network in the Perth solar city over 24 h.

Neuroblastoma can arise from extra-adrenal sites in the paraspinal sympathetic chain, including the presacral region, where they must be differentiated from an immature or malignant neural lesion a...

Aims: The major objectives of the study are to elucidate the distributive characters of operating farms in the selected locale of the study, to delineate a micro level policy so that the constraints of the operating farms can be analyzed and intervention programmes can be operationally described, to estimate and analyze the nature and direction of interaction among the independent and dependent variables. Study Design: The locale was selected by purposive as well as simple random sampling techniques and the respondents following rice cultivation had been interacted and were selected by the simple random sampling method. Place of Study: Village Rasulpur of Memari-1 block of Purba Bardhaman district in West Bengal was purposively selected for the study. Methodology: In this study 50 respondents following rice cultivation have been interacted and are selected by the simple random sampling method. A preliminary interview schedule has been administered to understand the knowledge, perception and attitude of the people towards climate changes concept, communication and extension system, farm enterprises, challenges faced during rice production. The collected data had been put into multivariate analysis. Statistical Package for the Social Sciences V20.0 (SPSS) of IBM was used for correlation analysis, multiple regression analysis, step-down regression analysis, path analysis and factor analysis. Results: Independent variables economic land (x7), group interaction (x11), innovation proneness (x13) and market orientation (x14) have been found to exert strong and determining contribution to estimate dependent variable self-consumption of rice production (y) and the set of economic and ecological variables as selected for the study. Conclusion: It can be concluded that the dynamics of self-sufficiency of food grains and entrepreneurship, here in case of rice enterprises, are dominantly relying on the entrepreneurs’ behavioural characters, the group interaction they are experiencing, the economic land they are possessing, the electricity consumption level, and the fuel use efficiency, innovation proneness and market interaction.

The study was conducted on Rice and Poultry entrepreneurs in Memari-1 block, Purba Barddhaman district of West Bengal, India. This has been conducted to assess their socio-personal, agro-economical and techno-managerial skills. Several entrepreneurial aspects are considered in this study to portrait a well discernible picture of their entrepreneurship. The acquired data and other information was critically examined with several statistical tools like range, mean, standard deviation, variance, correlation coefficient, stepwise regression analysis, multiple regression analysis, factor analysis and cluster analysis. Total 18 variables were considered for the study in which 15 of them are independent variables and 3 of them dependent variables. Correlation coefficient is significant for the independent variables in case of one dependent variable i.e. produce marketed (y3). After performing Stepwise Regression analysis several satisfactory conclusions were drawn for the study which well described the current neo agricultural scenario in rural Bengal. It has been recorded that when the economic land is more, then the marketed surplus becomes lower as because small holdings entrepreneurs concentrate more in the production phase. Marketed surplus has a positive relationship with the Education level. It has been recorded that Marketed surplus has a positive relationship with electricity consumption also. Having a higher marketed surplus indicate more advancement and betterment in terms of entrepreneurial management and thus fuel use efficiency becomes more prominent and fuel consumption is reduced. It has been recorded that when the innovation proneness and market interaction is higher for the respondents, the marketed surplus is also higher.

Thermal power stations are now considered as the foremost global concern as these are responsible to produce detrimental effects on the environment. Environmental Impact Assessment (EIA) is a process of evaluating the likely environmental impact so proposed project or development, taking into account inter-related socio-economic, cultural and human-health impacts, both beneficial and adverse. In this study, the perception of certain(60)farmers in 5 villages of Kolaghat block of Purba Medinipur, West Bengal, India are recorded and analyzed on the basis of their knowledge and views about thermal power, environment and about their individual enterprises. There are 10 independent variables-ages (x1), education (x2), family Member (x3), income (x4), homestead land (x5), total Land (x6), land under Boroj (x7), input cost (x8), and livestock number (x9), distance of villages from Kolaghat Thermal Power Station (x10) and 1 dependable variables- 1) perceived impacts of Kolaghat Thermal Power Station on livestock(y3).It has been found that toxic elements and fly ash from Kolaghat Thermal Power Station have several consequences upon local environment, human, agriculture and livestock. It has also revealed that Kolaghat Thermal Power Station hampers the social ecology of the local areas. The study has further suggested several recommendations to cope with adverse environmental and ecological condition.

Subacute sclerosing panencephalitis (SSPE) is a rare and progressive inflammatory disease of central nervous system due to aberrant measles virus with an outcome that is nearly always fatal. In acute fulminant SSPE, the disease rapidly evolves leading to death within 3 months of the diagnosis. We report here four cases of fulminant SSPE with atypical presentations, two of them presented at very early age with history of congenital measles infection in first case and gait abnormality as initial symptom in second case; acute disseminated encephalomyelitis (ADEM) with refractory seizures in third case, unilateral myoclonus with hemiparesis in fourth case at the onset of disease, respectively. The typical periodic electroencephalographic (EEG) complexes, elevated cerebrospinal fluid (CSF), and serum antimeasles antibodies in our patients led to the diagnosis of SSPE. A high index of clinical suspicion in fulminant type with awareness of atypical features, EEG, and CSF studies are of paramount importance in establishing its diagnosis.

Thermal power stations are now considered as the foremost global concern as these are responsible to produce detrimental effects on the environment. Environmental Impact Assessment (EIA) is a process of evaluating the likely environmental impact so proposed project or development, taking into account inter-related socio-economic, cultural and human-health impacts, both beneficial and adverse. In this study, the perception of certain(60) farmers in 5 villages of Kolaghat block of Purba Medinipur,West Bengal, India are recorded and analyzed on the basis of their knowledge and views about thermal power, environment and about their individual enterprises. There are 10 independent variables-ages (x1), education (x2), family Member (x3), income (x4), homestead land (x5), total Land (x6), land under Boroj (x7), input cost (x8), and livestock number (x9), distance of villages from Kolaghat thermal power plant (x10) and 1 dependable variables- 1) perceived impacts of Kolaghat thermal power plant on human health(y1).It has been found that toxic elements and fly ash from Kolaghat thermal power plant have several consequences upon local environment, human, agriculture and livestock. It has also revealed that Kolaghat thermal power plant hampers the social ecology of the local areas. The study has further suggested several recommendations to cope with adverse environmental and ecological condition.

BACKGROUND Medical Council of India has mandated the CISP (Curriculum Implementation Support Program) training of every medical faculty. However, due to time constraints and limited sessions of CISP programs, there is a section of faculties which has been trained only in the basic or the revised basic course but yet to be trained in the CISP workshop as well as there are faculties who have been trained in CISP after a long gap of MCI basic/revised basic course workshop (BCW/RBCW). In view of these variedly distributed faculty programs, this study was conducted with an intent to document the perceptions of medical faculties regarding these programs by analysing their views and challenges faced in implementation of CBME to search a novel solution to improve the scheduling and proforma of faculty development programs. METHODS A pre validated questionnaire consisting of components of CISP and BCW/RBCW was emailed to the teaching faculties of IQ City Medical College via google forms. Consenting faculties responded. Their results were analysed by inbuilt google statistics and was cross verified with SPSS 20.0. RESULTS The results show that among those who participated in the study, 28.2% faculties with CISP training are better suited to implement the new curriculum having a better knowledge and perception of CBME. Those faculties who have been trained only in 43.6% BCW/RBCW lack uniformity in knowledge and perception about CBME and there are ambiguities in their perceptions about CBME with mixed results. The substantial number of faculties not trained in any of the teachers training (28.2%) program completely lacked the perception about the modern modalities of teaching learning as well as competency based curriculum and its implementational techniques. CONCLUSIONS In view of the above results, a new combined (3 day) program may be suggested combining the content of both RBCW and CISP programs for training of new medical faculties.

Identification of human activity considering social interactions and group dynamics non-intrusively has been one of the fundamental problems and a challenging area of research. In real life, it is required for designing human-centric applications like assisted living, health care, and creating a smart home environment. As human beings spend 90% of time indoors, such a system will be helpful to monitor the behavioral anomalies of the inhabitants. Existing approaches have used intrusive or invasive methods like camera or wearable devices. In this work, we present a device-free, non-invasive, and non-intrusive sensing framework called HumanSense using an array of heterogeneous sensor grid for human activity monitoring. The sensor grids, comprising the ultrasonic and sound sensors, have been deployed for collective sensing combining a person’s physical activity and verbal interaction information. The proposed system senses a stream of events when the occupant(s) perform different physical activities categorized as atomic and group activities like sitting, standing, and walking. Simultaneously, it also tracks person-person verbal interactions such as monologue and discussion. Both information are then integrated into a single framework to understand the overall behavioral scenario of the indoor environment. The experimental results have shown that HumanSense can detect different activities with accuracy more than 90% and also improves overall identification accuracy compared to existing works. Our developed system can be further evolved into ready-to-deploy smart sensing panels which can be effective for human activity monitoring in an indoor environment.

Anastomotic leak (AL) is a major morbid complication after colorectal surgery. There are only few articles to find the predictors of leak after colorectal anastomoses that have been published in the literature. The aim of this study is to identify different predictors for colorectal AL in Indian set up. Historical data were collected from the hospital record of patients from June 2005 to April 2014. In prospective period, from May 2014 to February 2016, colorectal surgery patients were recruited to collect the data. Various preoperative, intraoperative, and postoperative factors for AL were analyzed using Student’s t test and Chi square test. The p value less than 0.05 was considered statistically significant for all results. The AL rate was 9.1%. The predictors for AL were comorbidities like cerebrovascular accident (CVA) (p

Virtual power plants (VPPs) are becoming critical parts of energy systems to increase renewable energy integration and to reduce the cost of electricity. To maximize the benefits to customers and VPP owners, the consumers' engagement is important for adding flexibility to the electricity load of the VPP. In this paper, the impact of customer contributions into a VPP energy management system through gamification is studied. To this aim, the contribution of customers within a realistic VPP of 67 dwellings in Western Australia is modelled. This model is included in a robust optimized procedure to maximize the profit of a VPP owner over a year. In this platform the uncertainties associated with renewable energy generation and market electricity are considered to find the optimum solution for the worst case scenario of uncertainties. The simulation results show that the gamified customer involvement has positive impacts on increasing the profit of the VPP.

The number of synchronous generators is anticipated to be reduced in the future power systems. Factors such as favorable government policies and market's preference are driving enormous deployment of renewables, while there is very little interest in replacing coal fired power stations by the same type of power generators. These have culminated in the increased deployment of asynchronous generators in power systems. While these types of generators are environmentally friendly, they perform poorly in maintaining power system strength. This paper investigates the impact of photovoltaic (PV) generator on fault level as an indicator of system strength of a network. Various technological options for system strength improvement are discussed. It shows that system fault level can be supported by independent control of synchronous condenser's voltage and reactive power control. The paper also formulates an analytical control approach and demonstrates its suitability on a modified 35-bus utility system.

In this paper, a hybrid power system is designed based on a game theory approach consisting of different components like wind turbines, PV panels and batteries in grid-connected mode. Nowadays, as energy problems and atmospheric pollution are increasing rapidly, green energy production is playing a vital contribution. The Game Theory-based approach Nash equilibrium has been used to achieve desired objective functions and to perform analysis to reach towards the goals. Game theoretical models of the noncooperative and cooperative game are considered, and wind turbines, PV panels and batteries are taken as players and the life-cycle income is chosen as payoffs. To obtain Nash equilibriums, a multi-objective particle swarm optimization algorithm has been developed in MATLAB. The sensitivity analysis is performed to validate the stability of Nash equilibriums for the price of electricity and discount rates. In the end, the outcomes of the proposed model are compared with recent research work to check the feasibility and validate the results.

This paper proposes a democratic current sharing controller for Modular Multi-parallel Rectifiers (MMR) system operating in Continuous Conduction Mode (CCM). A generalised small-signal AC model for n-number of parallel units is derived. Also, State-Space equations and transfer functions for the system are provided in this paper. A cascaded loops controller for the parallel units is utilised to ensure that the rectifiers' currents are balanced equally. A 60 kW system is modelled in MATLAB/Simulink to verify the proposed controller.

Frequency stability of most modern power systems has significantly deteriorated in the recent past due to the rapid growth of inverter interfaced renewable energy generation systems. Energy storage systems are being identified as a promising remedy to overcome such adverse effects. This paper investigates the dynamic response of a power system that has high renewable energy penetration and is also compensated by a large-scale energy storage system. The system dynamic response with a ternary pumped hydro storage, a liquid air storage and a battery energy storage system are studied and compared in this paper. Dynamic models for each storage system and their time domain responses are evaluated using a modified IEEE 14 bus test system. The paper produces an accurate methodology to simplify dynamic models and study the frequency response characteristics of large-scale ESS. Results show that the BESS has very fast frequency response compared to other two large-scale ESS. Fast and slow responsive hybrid model containing large-scale ESS may provide promising solution for storage-based grid ancillary services.

Distribution of keys in classical cryptography is one of the most significant affairs to deal with. The computational hardness is the fundamental basis of the security of these keys. However, in the era of quantum computing, quantum computers can break down these keys with their substantially more computation capability than normal computers. For instance, a quantum computer can easily break down RSA or ECC in polynomial time. In order to make the keys quantum resistant, Quantum Key Distribution (QKD) is developed to enforce security of the classical cryptographic keys from the attack of quantum computers. By using quantum mechanics, QKD can reinforce the durability of the keys of classical cryptography, which were practically unbreakable during the pre-quantum era. Thus, an extensive study is required to understand the importance of QKD to make the classical cryptographic key distributions secure against both classical and quantum computers. Therefore, in this paper, we discuss trends and limitations of key management protocols in classical cryptography, and demonstrates a relative study of different QKD protocols. In addition, we highlight the security implementation aspects of QKD, which lead to the solution of threats occurring in a quantum computing scenario, such that the cryptographic keys can be quantum resistant.

The cellular network standard is gradually stepping towards the 6th Generation (6G). In 6G, the pioneering and exclusive features, such as creating connectivity even in space and under water, are attracting Governments, organizations and researchers to spend time, money, effort extensively in this area. In the direction of intelligent network management and distributed secured systems, Artificial Intelligence (AI) and blockchain are going to form the backbone of 6G, respectively. However, there is a need for the study of the 6g architecture and technology, such that researchers can identify the scopes of improvement in 6G. Therefore, in this survey, we discuss the primary requirements of 6G along with its overall architecture and technological aspects. We also highlight crucial challenges and future research directions in 6G networks, which can lead to the successful practical implementation of 6G, as per the objective of its introduction in next generation cellular networks.

To enhance the wireless network performance, several new wireless standards, such as IEEE 802.11ac/ax, are introduced, where the objective is to increase the throughput in the network. However, Transmission Control Protocol (TCP) congestion degrades the network performance due to limited network capacity. The standard congestion control mechanism focuses primarily on acknowledgment (ACK) packets, which may result in inappropriate handling of congestion, and lead to the increase in packet drop rate and packet transmission delay. As a consequence, overall network efficiency is degraded. In this paper, we propose Intelligent Congestion control (ICon), a learning-based congestion control mechanism, that uses Deep Neural Network (DNN) to smartly predict the occurrence of congestion based on the present channel condition in the network and the last congestion window size (old window size). We use Signal-to-Noise Ratio (SNR) as the measurement of channel condition, and thus the impact of signal quality on the volume of transmitted packet is implicitly introduced in the prediction of congestion in ICon. For the generation of the dataset to train the proposed DNN, an intensive simulation is used. Through simulation, we analyze the performance of ICon, where it is noted that the proposed scheme significantly improves overall network performance.

Aim: The study aimed to evaluate the correlation of CT chest images with the clinical presentation in COVID-19 infection. Methodology: 50 confirmed COVID-19 patients those who were hospitalized were included in the study. Patient history, laboratory results, demographic data, severity, time course of symptoms was recorded. The duration of onset of the symptoms to initial CT scan, fever recordings including duration of onset of fever to CT scanning and white blood counts were recorded along with the CT Scores of each patient. Results: The CT scores were found to have significant correlation with age (r=0.348, p value= 0.046), duration of onset to CT scanning (r=0.654, p value= 0.001), duration of fever onset to CT scanning (r=0.679, p value= 0.001) and white blood cell count (r=0.398, p value= 0.034). Conclusion: We found correlation between the CT scores and Age, duration of onset to Ct scanning, duration of fever onset to Ct scanning and white blood cell count.

Corticosteroid therapy has become an important modality of treatment for diseases in which rapid control of immunoinflammatory processes is required. However, one of the serious, but less known adverse effect of this therapy is cardiac arrhythmias. This includes both tachyarrhythmias and bradyarrhythmias. Corticosteroid use may also be associated with electrolyte imbalances like hypokalaemia by its mineralocorticoid activity. Those side effects are mainly seen with high-dose intravenous methyl-prednisolone or oral pulse dose prednisolone therapy. Here we report our experience in a child with warm idiopathic autoimmune haemolytic anaemia who developed sinus bradyarrhythmias and treatment refractory hypokalaemia during low-dose steroid therapy with reduction in heart rate by 60% of baseline.

Acute disseminated encephalomyelitis (ADEM) is a monophasic demyelinating disorder of central nervous system occurring in children with a wide range of clinical manifestations after infection or vaccination. There are few case reports in literature, describing atypical presentations of ADEM with fever of unknown origin, autonomic dysfunction, complex movement disorders such as myoclonus, dystonia and chorea, acute psychosis and myocarditis. Here, we report four cases of ADEM with atypical features like uniocular blindness, myelin oligodendrocyte glycoprotein antibodies negative multiphasic disseminated encephalomyelitis, ADEM mimicking Guillain-Barre syndrome at presentation and isolated spinal ADEM. Treatment with high-dose steroids elicited an excellent neurological outcome in all patients. A high index of clinical suspicion along-with awareness of atypical features, magnetic resonance imaging and cerebrospinal fluid studies are of paramount importance in establishing ADEM diagnosis and initiation of early treatment for better outcome.

This paper presents a comprehensive steady-state analysis for active front-end Modular Multi-parallel Rectifiers (MMR) including parasitic elements. Generalised mathematical representations for the system’s gain and conduction loss effect are provided. In addition, the impact of the inductor losses and the firing angle on the system’s performance is investigated. A60kW Simulink model is utilised to validate the proposed mathematical model and the system characteristics and operating modes.

We present a theoretical study on probe field propagation through a three-level Ξ type system formed in an atomic vapour medium in presence of a strong control field. The model is based on density matrix method and is solved analytically, without neglecting any term in any power of probe and control Rabi frequencies, to find out the probe response under Doppler free condition as well as at room temperature. The probe response for a four-level Ξ type system is also determined analytically. Electromagnetically induced transparency is formed in the simulated probe response for both the level schemes. We also present the switching of probe field propagation from subluminal to superluminal as a function of probe detuning for different control transitions, one of which is to a Rydberg state. The switching window becomes asymmetric under detuned control field with the asymmetry depending on the wavelength mismatching factor between the control field and the probe field. Under strong probe regime probe propagation switches from superluminal to subluminal on resonance when the wavelength mismatch between the probe and the control field is increased, an effect which can be explained with the help of the analytical model that is free from any perturbative treatment.

The operation of multiple microgrids (MGs) in coordination with distribution system enables high penetration of locally available distributed energy resources (DERs). This approach enhances the reliability and resiliency of the power supply significantly. Also, the overall cost of energy gets reduced because of the integration of cost-free power from photovoltaic panels and wind turbines. The most effective utilization of DERs can be achieved through networked MGs. However, the implementation of the concepts of networked MGs requires extensive research. This paper presents a comprehensive literature review of the most important research works on networked MGs. Major benefits and challenges related to this new and highly exploring area have been analyzed. Also, some of the most important research areas related to networked MGs have been highlighted and discussed as the future perspectives.

This study introduces a bidirectional power sharing scheme between an ac electric utility and a dc microgrid. These two are connected together through an interlinking converter (IC) and a dual active bridge (DAB). The proposed control scheme allows predefined amount of power to be supplied from the ac to dc side or the other way around. The DAB is connected at the dc side of the IC and it controls the bidirectional power flow. The dc microgrid contains dc-dc converters, where boost converters are used with the sources and buck converters are used to supply loads. These dc-dc converters and the DAB are controlled by state feedback with integral control in a linearised discrete domain. The DAB linearisation and control law are presented in detail. The dc microgrid is controlled by conventional V-P droop where the power from/to the ac side is considered as negative/positive load. Detailed simulation results are presented to validate the proposal. Also some hardware results are included that support the findings.

Pathogenic infection is one of the major causes of death in newborns. Antibiotic based therapies are still the major mode of treatment for infection. Increased usage of antibiotics leads to selective evolution of microorganisms and causes diseases in adulthood. Attempts to develop alternatives to antibiotics did not yield much success. A recent viable trend is to identify novel probiotics that could alleviate problems associated with over usage of antibiotics. We screened three different Lactobacillus species to establish their efficacy in neonates in protecting against Salmonella challenge. The methodologies employed are metagenomics, metabonomics, transcriptional profiling, molecular assays and behavioral studies. Among the three probiotics used, only Lactobacillus rhamnosus GG (LGG) treatment of the neonates resulted in rescuing of 80% of the Salmonella-infected mice. We have shown that LGG (MTCC #1408) can prevent Salmonella mediated infection in neonates. In the current report, results from histopathology, gene expression, neutrophil infiltration, metabolite and metataxonomic profiling, and protein level data suggested that LGG treatment of the neonates enhanced anti-inflammatory cytokine expression and increased the gut barrier function. The current report establishes the potential use of LGG in clinical intervention of infectious diseases.

The myriad technological applications of infrared radiation sensors make the search for ultra-sensitive detectors extremely crucial. Materials such as bismuth telluride (Bi2Te3), having a small bulk band gap of 0.17 eV, are ideal infrared detectors. However, due to the high recombination rate of photo-generated charge carriers in the bulk, the electrical response under optical illumination is typically very weak in these materials. We have circumnavigated this by sensitizing graphene with Bi2Te3 nano-wires. These hybrid devices show an ultra-high sensitivity of ∼106 A W−1, under incident electromagnetic radiation from 940 nm to 1720 nm. The theoretical limit of the noise equivalent power and specific detectivity in these devices are ∼10−18 W Hz−1/2 and ∼1011 Jones respectively, which are comparable to those of some of the best known detectors.

Engineering the properties of layered metal dichalcogenides (LMDs) requires stringent control of their morphology. Herein, using a scalable one-step solvothermal technique, we report the synthesis of SnSe2 under two different conditions, leading to the formation of nanoflakes and nanoflowers. The use of oleic acid in the reaction leads to the formation of nanoflowers, and the presence of ethanol in the reaction medium leads to the formation of nanoflakes. Ab initio density functional theory calculations rationalise this observation, revealing a stronger adsorption of ethanol on the {0001} facet compared to the acid. Furthermore, these SnSe2 nanoflakes, when integrated with graphene, also respond to incident electromagnetic radiation, from the visible to near infrared regime, with a specific detectivity of ∼5 × 1010 Jones, which is comparable to that of the best available photodetectors, making them suitable for use in various technological applications.

With increasing photovoltaic (PV) penetration in low-voltage (LV) distribution networks, voltage violation during the peak PV generation period is one of the main power quality concerns. To minimize the PV power curtailment caused by voltage violations, this paper proposes a new approach utilizing one dynamic voltage restorer (DVR) at the secondary of an existing urban distribution transformer as a continuous voltage compensator with an accompanying control algorithm. The voltage compensation algorithm controls the DVR in real-time to prevent over-voltage and under-voltage at all network nodes, thereby maximizing PV injection and preventing equipment damage. The controller uses the equivalent line impedance of network to estimate the average voltage of load points without the need for any communication links for measuring load voltages. In this approach, the fixed tap changer of the distribution transformer is also optimally adjusted to minimize the DVR rating using a proposed offline optimization method. Simulation results on IEEE LV test feeder prove the ability of the DVR in maintaining all network node voltages within the allowable range in peak demand and peak PV generation periods with the benefit of injecting only a small amount of active and reactive power to the system, in contrast to alternative algorithms.

Demand for converter-fed distributed energy resources are rapidly growing in microgrids and these energy sources are mainly responsible for sharing loads in islanded mode of operation. In high voltage lines, sharing real and reactive load power requirements can be achieved by conventional droop control methods, nevertheless strong coupling between active and reactive power in medium voltage lines with low X/R ratio degrades rating-based load sharing among converters. A transformation matrix which incorporates the line impedance effect in droop parameter calculation by modifying droop coefficients can be used to overcome this drawback. However, inclusion of power decoupling term in reference voltage calculation, has found to be more powerful to overcome coupling effects in load power sharing. By utilizing lesser steady state frequency deviation of angle droop in comparison with frequency droop, this paper adopts angle droop in real power loop. Angle droop concept along with power decoupling factor has an added advantage of improved reactive power sharing with surplus accuracy in real power distribution, which in turn, allows harmonic power distribution based on converter rating under different load conditions. Moreover, discrete state feedback control together with third harmonic injection-based pulse width modulation scheme provides additional robustness to individual converters.

The inculcation of efficient forecasting and prediction models may assist the government in implementing better design strategies to prevent the spread of the virus. However, most of the machine learning methods along with SEIR models have failed to predict the outcome valid for a longer duration.

The gait pattern varies from person to person. However, the gait of a normal healthy human differs substantially from that of an individual with an abnormal gait. The gait abnormalities could arise from various underlying health conditions such as rheumatoid arthritis, injuries, etc. The gaits of individuals could be studied to differentiate healthy gaits from abnormal gaits which can help to identify underline health conditions and for preventive medication. In this work, for the identification of abnormal gait, we have used wearable sensors, comprising of tri-axial accelerometer and tri-axial gyroscope that track’s the motion signatures produced while walking. We have collected such motion signatures of healthy persons and persons with walking abnormalities over a period of time. Later performed classification on collected data using different machine learning algorithms to segregate the cases with abnormality which can be used for facilitating remote detection of gait abnormalities using wearable sensors. With our approach, we have successfully classified different cases with an accuracy of over 90% . In the future, implementing such models in the real-world scenario could be very beneficial for the detection of gait diseases remotely with the help of smart devices like smartphones without clinical intervention.

Human activity recognition has been a topic of discussion for long. Proper identification would lead to better automation, surveillance, and more comfortable living. Although single person activity is comparatively easier to detect and significant research have been done recently, the challenge lies in identification of group activity. Existing research for identification of group activity is either computer vision-based or wearable device-based. However, computer vision might breach privacy and heavy in computation whereas wearable devices are based on people’s will and might be discomforting as well. Therefore, in our proposed work we have used linear data, i.e., distance of the person from the sensor to predict group activity with high accuracy, at the same time it is non-invasive, and is automated. The proposed work has been conducted using customized multiple ultrasonic sensor grids on 10 individuals in a closed lab environment. The evaluation of experimental results shows an accuracy of 95.48% in group activity identification using neural networks.

The objective of multi-label image classification is to recognise several objects that appear within a single image. In the current paper, we consider the task of multi-label food recognition, where the images contain foods for which the labels in the training set are noisy, as they are annotated by inexperienced annotators. We now propose that a noise adaptation layer should be appended to a pretrained baseline model, in order to make it possible to learn from these noisy labels. From the baseline model, predictions are made on the training set and a confusion matrix is created from these predictions and the noisy labels. This confusion matrix is used to initialise the weights of the noise layer and the full model is retrained on the training set. The final predictions for the testing set are made from the baseline model, after its weights have been readjusted by the noise layer. We show that the final model significantly improves performance on noisy datasets.

The intriguing electronic properties of graphene and the strong light-matter interaction in layered transition-metal dichalcogenide (TMDC) make them a natural partner for hybrid devices, not only for optoelectronic device applications, but also to understand the conversion of light to electricity in this atomic scale prototype of a donor-acceptor complex. Here, we describe graphene-on-${\mathrm{WS}}_{2}$ binary heterostructure FET device, displaying gate-tunable persistent photoconductivity. Our time-dependent photovoltage relaxation experiments suggest that the charge-transfer time scale in this heterostructure is dependent on the input optical power, contrary to what is observed for the bare TMDC and is orders of magnitude slower than that observed for various other vdW hybrids. The optoelectronic responsivity of this device at low optical power is found to be as high as ${10}^{10}\phantom{\rule{0.1em}{0ex}}\mathrm{V/W}$, and thus shows the potential to be one of the most sensitive visible range photodetectors, while the gate tunability of the persistent photoconductivity can be utilized in the memory device applications. We identify that the photoresponse is the outcome of a photogating mechanism, due to the exciton dissociation under optical excitation, followed by the trapping of holes in ${\mathrm{WS}}_{2}$ and subsequent electron transfer to graphene.

The Mediterranean diet (MD) is regarded as a healthy eating pattern with beneficial effects both for the decrease of the risk for non-communicable diseases and also for body weight reduction. In the current manuscript, we propose an automated smartphone application which monitors and evaluates the user&rsquo, s adherence to MD using images of the food and drinks that they consume. We define a set of rules for automatic adherence estimation, which focuses on the main MD food groups. We use a combination of a convolutional neural network (CNN) and a graph convolutional network to detect the types of foods and quantities from the users&rsquo, food images and the defined set of rules to evaluate the adherence to MD. Our experiments show that our system outperforms a basic CNN in terms of recognizing food items and estimating quantity and yields comparable results as experienced dietitians when it comes to overall MD adherence estimation. As the system is novel, these results are promising, however, there is room for improvement of the accuracy by gathering and training with more data and certain refinements can be performed such as re-defining the set of rules to also be able to be used for sub-groups of MD (e.g., vegetarian type of MD).

The study of walking pattern also known as gait is affected by several underlying musculoskeletal and neurological factors. All humans have contrasting gait pattern however the pattern lies within a predictable range for all human with no underlying health disorders affecting gait. However, deviation from a regular distribution range can indicate underlying health conditions. The gait could be regularly and inexpensively monitored in real-time with help of smartphones embedded sensors such as tri-axial accelerometers and tri-axial gyroscopes as smartphones are widely available. The advancements in computational power and artificial intelligence led to the development of several supervised machine learning algorithms which could be used to detect certain patterns associated with different gait conditions. However, the machine learning has widely been used to study different gait diseases, with this paper we have proposed a more efficient motion tracking approach and a new algorithm utilizing Dynamic Time Warping for an optimized locomotor disorder detection with affordable, practically applicable and globally deployable setup to monitor gait. In this experiment, we have tracked motion of 30 healthy and gait deformed human subjects with the proposed motion tracking approach and utilizing the proposed algorithm, we have achieved 35.95 times faster classification with up to 95.20% detection accuracy.

This paper proposes a peer-to-grid (P2G) energy trading combined with peer-to-peer (P2P) energy trading scheme based on a cooperative game theoretical technique to optimize sizes of the generation resources and battery, and achieve maximum payoff from a networked microgrid. The selected architecture consists of two microgrids in which both microgrids contain solar panels, wind turbines, and batteries to meet the requirements of the load. In the first stage, a game theory technique based on particle swarm optimization (PSO) method is used to find the optimum sizes of the generation resources and batteries considering the conventional P2G combined with P2P energy trading. In the second stage, considering two energy trading scenarios including P2G and P2G combined with P2P capability, the maximum payoffs of both microgrids are optimized and compared.

The fundamental origin of low-frequency noise in graphene field effect transistors (GFETs) has been widely explored but a generic engineering strategy towards low noise GFETs is lacking. Here, we systematically study and eliminate dominant sources of electrical noise to achieve ultralow noise GFETs. We find that in edge contacted, high-quality hexagonal boron nitride (hBN) encapsulated GFETs, the inclusion of a graphite bottom gate and long (≳1.2 μm) channel-contact distance significantly reduces noise as compared to global Si/SiO2 gated devices. From the scaling of the remaining noise with channel area and its temperature dependence, we attribute this to the traps in hBN. To further screen the charge traps in hBN, we place few layers of MoS2 between graphene and hBN, and demonstrate that the noise is as low as ∼5.2 × 10−9 μm2 Hz−1 (corresponding to minimum Hooge parameter ∼5.2 × 10−6) in GFETs at room temperature, which is an order of magnitude lower than the earlier reported values.

Short guard interval (SGI) is a new feature of the physical (PHY) layer in High Throughput Wireless Local Area Networks (HT-WLANs), like IEEE 802.11n/ac, where the value of the guard interval is set to 400ns instead of general 800ns. By reducing the overhead of extra idle time between transmissions of two successive symbols, SGI helps to enhance the average system throughput, compared to the guard interval of 800ns. However, the signal quality of the channel and high data traffic can affect the performance of SGI in a network. In a congested network scenario, channel interference and load of data transmission are increased; and consequently, inter-symbol interference becomes higher, which increases the rate of packet losses and thus, reduces average throughput in the case of SGI. In this paper, we design Thompson sampling based online learning mechanism, Smart Selector of Guard Interval (S2-GI), to select guard interval dynamically by considering the present channel condition. We analyze the performance of S2-GI through a thorough simulation in IEEE 802.11ac WLAN and we observe that S2-GI can enhance the network performance.

In today's world, the Internet of Things i.e IoT has possessed a great impact in our life. Almost everything from electrical to electronic gadgets have become a part of IoT due to which the data production and consumption have increased. Cloud computing due to its centralized architecture is not enough to handle this exponential growth of data it suffers from and thus certain limitations. Due to the drawbacks of cloud computing, a decentralized computing paradigm called fog computing was coined where computation of data used to happen near to the network edge. But due to some limitations of fog computing a similar type but an advancement over fog came into existence called edge computing which could resolve all the major drawbacks and challenges of cloud and fog to a large extent. Certain issues regarding high latency, high response time, high bandwidth cost, high energy consumption and high security threat are now resolved by edge computing to a large extent with lots of popular emerging fields and applications running on this concept. So the overall journey of this paper focuses on what exactly edge computing is and the need of edge computing in this growing world of data and how edge computing introduces the technology world with some of the emerging applications being widely used nowadays.

Background With the consequences of inadequate dosing ranging from increased bleeding risk to excessive drug costs and undesirable administration regimens, the antihemophilic factors are uniquely suited to dose individualization. However, existing options for individualization are limited and exist outside the flow of care. We developed clinical decision support (CDS) software that is integrated with our electronic health record (EHR) and designed to streamline the process for our hematology providers. Objectives The aim of this study is to develop and examine the usability of a CDS tool for antihemophilic factor dose individualization. Methods Our development strategy was based on the features associated with successful CDS tools and driven by a formal requirements analysis. The back-end code was based on algorithms developed for manual individualization and unit tested with 23,000 simulated patient profiles created from the range of patient-derived pharmacokinetic parameter estimates defined in children and adults. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users were recruited to participate in traditional usability testing under an institutional review board approved protocol. Results CDS software was developed to systematically walk the point-of-care clinician through dose individualization after seamlessly importing the requisite patient data from the EHR. Classical and population pharmacokinetic approaches were incorporated with clearly displayed estimates of reliability and uncertainty. Users can perform simulations for prophylaxis and acute bleeds by providing two of four therapeutic targets. Testers were highly satisfied with our CDS and quickly became proficient with the tool. Conclusion With early and broad stakeholder engagement, we developed a CDS tool for hematology provider that affords seamless transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent dose selection.

There have been several reports of plasmonically enhanced graphene photodetectors in the visible and the near infrared regime but rarely in the ultraviolet. In a previous work, we have reported that a graphene-silver hybrid structure shows a high photoresponsivity of 13 A/W at 270 nm. Here, we consider the likely mechanisms that underlie this strong photoresponse. We investigate the role of the plasmonic layer and examine the response using silver and gold nanoparticles of similar dimensions and spatial arrangement. The effect on local doping, strain, and absorption properties of the hybrid is also probed by photocurrent measurements and Raman and UV-visible spectroscopy. We find that the local doping from the silver nanoparticles is stronger than that from gold and correlates with a measured photosensitivity that is larger in devices with a higher contact area between the plasmonic nanomaterials and the graphene layer.

In an urban area, the degree of ambient noise and air pollution play a vital role in determining the quality of human life. The impact of these two pollutants is increasing day-by-day due to rapid urbanization. Although, creating real-time pollution maps and forecasting of pollution levels have been studied extensively, the contextual, spatio-temporal correlation between air and noise pollution has not been investigated thoroughly. This correlation is important to identify the characteristics of an urban area. In this paper, we have highlighted some aspects that are useful to identify a context from different pollutant data. To collect the noise data, we have developed an android based application “AudREC” that uses the inbuilt mobile micro-phone sensor. Moreover, for measuring air pollutants, we have used a ready-made “Flow” device that senses PM2.5 and CO 2 , etc. The initial outdoor experiments show the feasibility of the platform for recognizing contexts from air and noise pollution information.

Doping of organic semiconductors enhances the performance of optoelectronic devices. Although p-type doping is well studied and successfully deployed in optoelectronic devices, air stable ntype doping was still elusive. We succeeded with n-type doping of organic semiconductors using molecular dopant N-DMBI under ambient conditions. Strikingly, n-type doping accounts for a gigantic increase of the photoconductivity of doped thin films. Electrical and optical properties of the n-doped molecular semiconductor were investigated by temperature dependent conductivity, electron paramagnetic resonance (EPR), and flash-photolysis time-resolved microwave conductivity (FP-TRMC) measurements. A significant reduction and saturation in activation energy with increasing doping level clearly suggests the formation of an impurity band and enhancement in carrier density. Computational studies reveal the formation of a charge transfer complex mediated by hydrogen abstraction as the rate-determining step for the doping mechanism. The colossal enhancement of photoconductivity induced by n-doping is a significant step toward optoelectronic devices made of molecular semiconductors.