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Aerodynamic parameter estimation is critical in the aviation sector, especially in design and development programs of defense-military aircraft. In this paper, new results of the application of Artificial Neural Networks (ANN) to the field of aircraft parameter estimation are presented. The performances of Feedforward Neural Network (FFNN) with Backpropagation and FFNN with Backpropagation using Recursive Least Square (RLS) are investigated for aerodynamic parameter estimation. The methods are validated on flight data simulated using MATLAB implementations. The normalized Lyapunov energy functional has been used to derive the convergence conditions for both the ANN-based estimation algorithms. The estimation results are compared on the basis of performance metrics and computation time. The performance of FFNN-RLS has been observed to be approximately 10% better than FFNN-BPN. Simulation results from both algorithms have been found to be highly satisfactory and pave the way for further applications to real flight test data.

Accurate estimation of aerodynamic parameters of an aircraft is a matter of great importance in aviation industry as well in aircraft design and development programs. In this paper, Gaussian sum extended information filter (GSEIF) is studied for aircraft parameter estimation. The convergence condition for the GSEIF has been obtained using the normalized Lyapunov energy functional by proposing a nonlinear observer for the filter and studying its asymptotic behavior, this is a novel approach for deriving stability results for corresponding nonlinear filter. The performance of extended information filter (EIF) and GSEIF is evaluated for aircraft parameter estimation using simulated flight data generated using the MATLAB implementations. The estimation results using GSEIF are better than that obtained using EIF, especially when the initial state is not known (well), which shows that the proposed technique is really feasible for aerodynamic parameter estimation.

Accurate estimation of aerodynamic parameters of an aircraft is a matter of great importance in aviation industry as well in aircraft design and development programs. In this paper, Gaussian Sum Extended Information Filter (GSEIF) is studied for aircraft parameter estimation. The convergence condition for the GSEIF has been obtained using the normalized Lyapunov energy functional by proposing a nonlinear observer for the filter and studying its asymptotic behaviour, this is a novel approach for deriving stability results for corresponding nonlinear filter. The performance of Extended Information Filter (EIF) and GSEIF is evaluated for aircraft parameter estimation using simulated flight data generated using the MATLAB implementations. The estimation results using GSEIF are better than that obtained using EIF, especially when the initial state is not fully known, showing that the proposed technique is particularly feasible for aerodynamic parameter estimation.

Chemical characterization and source apportionment of PM10 and PM2.5 were carried out for two different elevations (lower elevation (LE) ~ 5–10 m and higher elevation (HE) ~ 30–45 m) at four different locations representing urban background, city center, upwind, and downwind of the Delhi city during January 2017–March 2017. The 24-h average PM10 and PM2.5 concentrations were varied between 135.2–258.7 and 79.3–120.9 µg/m3, respectively. The average PM10 and PM2.5 concentrations were found significantly higher at LE than HE. The PM samples were analyzed for ions, elements and carbonaceous matter (EC/OC), and their concentrations (except S, V, As, Ni, Sb, Sr, Ga, elements associated with industrial combustion activities, and NO3−, attributed to high nitrate formation potential at HE) were observed higher in LE than HE at all the study locations. The chemical mass balance model was applied to quantify the source contributions to PM10 and PM2.5 mass at two different elevations. Model identified vehicular emission (diesel, PM10 ~ 8.8–21.7% and PM2.5 ~ 10.5–24.4% and gasoline, PM10 ~ 4.8–15.6% and PM2.5 ~ 6.7–14.8%), industrial residual oil combustion (PM10 ~ 8.8–23.5% and PM2.5 ~ 3.2–10.4%), road dust (PM10 ~ 13.6–22.3% and PM2.5 ~ 8.8–17.8%), soil dust (PM10 ~ 33.8–41.1% and PM2.5 ~ 5.8–8.3%), secondary nitrate (PM10 ~ 6.1–16.2% and PM2.5 ~ 13.4–20.2%), secondary sulfate (PM10 ~ 7.1–12.3% and PM2.5 ~ 10.6–16.7%), and biomass burning (PM10 ~ 6.8–21.8% and PM2.5 ~ 4.9–38.7%) as the main sources of PM10 and PM2.5 mass at both the elevations at all the study sites. The contribution of industrial residual oil combustion, vehicular emission, and secondary nitrate to PM10 and PM2.5 mass was found relatively higher in HE than LE. Results also revealed that biomass burning contributed significantly to PM pollution in the outskirts of Delhi than inside the city. Further, potential source contribution function analysis revealed that there may not be a long-range transport of PM emitted from biomass burning in the upwind region of Delhi during the study period. Shifting to Indian BS VI vehicles and fuel, switching to cleaner fuel in slum households, strict compliance on industries, and regular vacuum cleaning of roads will reduce the severe air quality problem in Delhi.

Child safety is an important issue across the world as crimes against children are rapidly rising. Tracking and monitoring the child can help assure the safety of children and any necessary action can be taken if there is cause for worry. This paper proposes a system for monitoring and tracking children, which includes tracking the child’s movement to and from school using a wearable device and a companion application on the parents’ smartphone. This system gives us the location using a geofence and basic health information of the child.

Human fall detection is a major challenge in the healthcare domain. It is estimated that over a third of the population aged 65 years and above, fall each year, making it the most prominent cause of non-fatal injury in their age group. This hinders their sense of independence and increases the need for constant monitoring of their activities. Progress of technology helps facilitate implementation of wearable remote monitoring systems to help detect a fall and implement necessary measures to ensure appropriate medical assistance is provided. This paper outlines the implementation of such a system which helps in the detection of a fall and the notification of the required caretaker via an SMS containing the location of the fallen person, so as to provide timely medical attention.

Integrated GPUs (iGPUs) are ubiquitous in today's client devices such as laptops and desktops. Examples include Intel's HD or Iris Graphics and AMD's APUs. An iGPU resides on the same chip as the CPU, which is in contrast to a conventional discrete GPU that would typically be connected over the PCI-E bus. Much like discrete GPUs, iGPUs are also capable of general purpose computation in addition to traditional graphics roles. Further, iGPUs have some interesting differences compared to traditional GPUs such as a cache-coherent memory hierarchy and a shared last level cache with the CPU. Despite their wide spread use, they are not studied very extensively. To the best of our knowledge, this paper introduces the first open source trace generation and microarchitectural simulation framework for Intel's integrated GPUs. We characterise the performance of Intel's Skylake and Kabylake GPUs through detailed microbenchmarks, and use the performance evaluations to guide our models and validate the simulator.

Near Field Communication (NFC) is a short range, standard based wireless communication technology that allows two-way interaction between two NFC-enabled devices. This paper explains how the technology can be used to meet the challenges in the healthcare management. This technology is user-friendly and commercially viable and can be easily used by a nontechnical user also. NFC technology is available in the form of NFC cards, tags, key chains. Each NFC tag has a unique number which can be used for identification of patients in a hospital. The NFC tag can be used as an electronic health record to store the patient's medical history, prescriptions and real-time sensor data. The current health monitoring methods to measure the various health parameters are tedious and time consuming. The use of NFC along with wireless sensors intends to help the hospital staff to quickly access and monitor the patient's health record and health status respectively. The NFC technology provides ways to speed up the processes in hospitals: the doctors equipped with NFC-enabled tablets, computers can view all the information about a patient within seconds, without any issue of data being mixed. NFC provides accuracy and efficiency in healthcare as well as it is economically feasible.

The objective of this consensus paper is to provide practical guidance on why and how aripiprazole, with its distinct pharmacological and side effect profile, should be used for treatment of acute bipolar mania.An advisory panel of UK healthcare professionals, with extensive experience of prescribing aripiprazole for acute bipolar mania, met to discuss its use in this setting.The panel agreed that aripiprazole is effective in treating bipolar mania when prescribed and dosed appropriately, in both the short and long term, as monotherapy or in combination with a mood stabilizer. Unlike other atypical agents, aripiprazole has antimanic effects that are not associated with sedation, which is beneficial for patients, particularly in the long term. If rapid tranquillization is required when initiating aripiprazole in acutely disturbed patients, short-term coprescription of a benzodiazepine is recommended. Most side effects associated with aripiprazole occur within the first 1-3 weeks and are usually transient and easily treatable. Aripiprazole poses low risk of metabolic side effects, sexual dysfunction, and anhedonia, which can facilitate treatment adherence and help improve clinical outcomes.Aripiprazole is an effective first-line treatment for acute bipolar mania with a favorable safety/tolerability profile.

Background: There is evidence that insight and social judgements are impaired in schizophrenia. The influence of these factors on the decision to treat compulsorily in schizophrenia is poorly understood. Aims: To investigate the contribution of insight, social knowledge, and working memory to the determination to treat coercively in schizophrenia. Method: Insight rating scale, social knowledge questionnaire and working memory tests were administered to detained patients with schizophrenia. Results were compared with those of a control group of voluntary in-patients with schizophrenia. Results: Detained patients scored worse on insight and social knowledge, yet there was no significant correlation between these scores. There was no significant difference in severity of psychopathology between the experimental and control groups. Results for working memory were inconclusive. Conclusion: Insight and social knowledge are significantly, but independently, associated with the determination to treat coercively in schizophrenia. This suggests that insight and social knowledge are distinct skills. The contribution of working memory remains unclear.

With the advent of advances in electronics, demand from customer for more functionality of circuits, system designers are using advanced packaging technique. For consumer application, this is working well. However when it comes to mission critical system such as missiles, torpedoes, submarine and aerospace product, circuit failure in any form results in failure in strategy planned. This paper brings out technical method which can detect electrical arcing taking place in circuits; thereby necessary corrective measure can be initiated to stop catastrophic damage. Experiments have been carried out to identify spectral characteristics of electrical arcing and this data can be used for automated intelligent detection technique. An arc generation and characterizing facility has been designed and used. From the experiment carried out, it is seen the spectrum is a strong characteristics of unintentional arcing which cannot be detected. Even a small amount of arcing which takes place in the beginning of the process can be detected by observing the spectrum. From the results brought out in the paper, it is a powerful technique which when integrated with system development can give solution to major problem encountered due to arcing.

Target tracking is a very important field of research as it has wide applications in defence, civilian, industrial and medical applications. Improved target state estimate without lag by feedback in Kaiman smoother is an approach that uses information from a batch of measurements in recursive estimation by providing a feedback in the Kaiman smoother. This approach provides an estimate without delay while using measurements from multiple time frames and improves the target state estimation accuracy. In this paper this method of batch processing and providing estimates without delay, has been used to track a target that moves in coordinated turns with an unknown turn rate. The results are compared with conventional Kaiman filter. The Monte Carlo simulation results with the Improved target state estimate without lag by feedback in Kaiman smoother approach shows that the estimation accuracy can be significantly improved compared to ordinary Kaiman filter based estimates.

A retrospective case note study explored readmissions to an acute psychiatric in-patient unit within six months of discharge. The study aimed to calculate a hospital readmission rate, to investigate the timing of readmissions, and to identify risk factors associated with readmission. The readmission rate was 27% with the majority of readmissions occurring within three months after discharge, suggesting the need for investigation of such early readmissions. The three factors found to predict readmission were: discharge against medical advice, number of previous admissions, and living alone or with family rather than in care. Implications for hospital service planning are considered.

Introduction People with schizophrenia are at increased risk of developing metabolic disturbances. This risk may be further exacerbated by the use of antipsychotic agents. Research is still ongoing to determine the metabolic impact of antipsychotics on glucose regulation. In this case report we review some of the possible mechanisms of action of antipsychotic medication on glucose regulation. Case presentation We present the case of a 50-year-old man diagnosed with paranoid schizophrenia who developed type 2 diabetes mellitus whilst on treatment with second generation antipsychotics (SGA). His diabetes was controlled by a combination of antidiabetic drugs that were associated with his psychotropic treatment. Due to deterioration in his mental state, the patient was admitted on two occasions to a psychiatric unit during which his prescribed medication (olanzapine and risperidone) was discontinued and changed to aripiprazole. On both occasions, the patient suffered hypoglycaemic episodes and his antidiabetic treatment had to be adjusted accordingly. The patient did not require any antidiabetic treatment whilst on aripiprazole during the follow up period. Conclusion Clinicians face regular dilemmas in trying to find the right balance between achieving control over a patient's mental illness and reducing any adverse effects associated with the prescribed medication. In patients receiving concomitant antidiabetic therapy, caution should be exercised when changing from one SGA to another. Whilst more longitudinal data are required, a trial of alternative SGAs, including aripiprazole in those developing type 2 diabetes and impaired glucose tolerance may be a worthwhile therapeutic option.