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Process discovery approaches analyze the business data to automatically uncover structured information, known as a process model. The quality of a process model is measured using quality dimensions, completeness (replay fitness), preciseness, simplicity, and generalization. Traditional process discovery algorithms usually output a single process model. A single model may not accurately capture the observed behavior and overfit the training data. We have formed the process discovery problem in a multiobjective framework that yields several candidate solutions for the end user who can pick a suitable model based on the local environmental constraints (possibly varying). We consider the Binary Differential Evolution approach in a multiobjective framework for the task of process discovery. The proposed method employs dichotomous crossover/mutation operators. The parameters are tuned using grey relational analysis combined with the Taguchi approach. We have compared the proposed approach with the well-known single-objective algorithms and state-of-the-art multiobjective evolutionary algorithm—Nondominated Sorting Genetic Algorithm (NSGA-II). Additional comparison via computing a weighted average of the quality dimensions is also undertaken. Results show that the proposed algorithm is computationally efficient and produces diversified candidate solutions that score high on the fitness functions. It is shown that the process models generated by the proposed approach are superior to or at least as good as those generated by the state-of-the-art algorithms.

Agricultural waste materials are increasingly being used as partial replacements for cement in concrete. Several experimental studies are available to evaluate the mechanical properties of plastic waste reinforced concrete but there are limited evaluations on agricultural waste material. In this study, an attempt is made to investigate the compressive strength of Corn Cob Ash (CCA) concrete at different replacement levels by implementing an Artificial Neural Network (ANN). As the percentage of CCA increases, workability, density and compressive strength decreases, hence the developed ANN model consists of 3 input parameters (cement content, CCA content, and curing ages) in the input layer, 4 hidden neurons in the hidden layer and 3 output parameters (slump, density, and compressive strength) in the output layer. Training is done by adopting Levenberg-Marquardt back-propagation algorithm by considering 80% of experimental data with log-sigmoid activation function for both hidden and output layers. The developed model has a high correlation coefficient of 0.999 for both the training and testing data sets. It has low MSE and MAPE values of 2.2768x10-5 and 1.25 for training data respectively and 3.0463x10-5 and 1.37 for testing data respectively. Hence, it is concluded that the developed model predicts the output at an average rate of 98% accuracy. The predicted 2.5% replaced CCA concrete shows the best performance at all curing ages. Therefore, this percentage level is considered as an optimum replacement level which does not much affect the hardened properties of concrete.

Conversion of the conventional electrical grid into a smart and sustainable grid involves several considerations. The primary factors, however, are renewable energy penetration, associated storage systems, and energy generation costs. This research endeavors to conduct a thorough survey and analysis of the solar irradiance on various hydropower locations in India, including Run-of-River (RoR), Run-of-River with Pondage (RoRP), Reservoir Storage (S), Multi-Purpose Storage (MP) and Pumped Storage Systems (PSS). The hydroelectric projects in rural Indian regions have been the subject of the proposed case study. As a preliminary study, the probabilistic variables like minimum, maximum, and mean solar irradiance are calculated for 252 High-Scale Hydropower Plant locations (HSHPs) using the past 40 years day ahead solar radiation data to identify the high-irradiance hydropower plant location in each state of India. This study concludes that the maximum mean solar irradiance location in each state as these sites are well suited for hybrid PV-hydro systems. The identified high-irradiance locations 40 years day ahead data sets are analyzed employing 8 machine learning models and 2 deep learning models. This analysis aims to forecast solar irradiance, serving as a crucial foundation for the initial phase of the implementation of hybrid PV-hydro. [ABSTRACT FROM AUTHOR]

Process discovery approaches analyze the business data to automatically uncover structured information, known as a process model. The quality of a process model is measured using quality dimensions, completeness (replay fitness), preciseness, simplicity, and generalization. Traditional process discovery algorithms usually output a single process model. A single model may not accurately capture the observed behavior and overfit the training data. We have formed the process discovery problem in a multiobjective framework that yields several candidate solutions for the end user who can pick a suitable model based on the local environmental constraints (possibly varying). We consider the Binary Differential Evolution approach in a multiobjective framework for the task of process discovery. The proposed method employs dichotomous crossover/mutation operators. The parameters are tuned using grey relational analysis combined with the Taguchi approach. We have compared the proposed approach with the well‐known single‐objective algorithms and state‐of‐the‐art multiobjective evolutionary algorithm—Nondominated Sorting Genetic Algorithm (NSGA‐II). Additional comparison via computing a weighted average of the quality dimensions is also undertaken. Results show that the proposed algorithm is computationally efficient and produces diversified candidate solutions that score high on the fitness functions. It is shown that the process models generated by the proposed approach are superior to or at least as good as those generated by the state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Background: The burden of mental health conditions and consequent disability impacts are felt most in low- and middle-income settings. These settings are constrained by the limited availability of resources to provide even essential aspects of mental health care (MHC). Task shifting and sharing interventions have shown promise in delivering community-based MHC across such low-resource settings. Some counseling tasks such as friendship bench interventions have been successfully shifted to laypersons. However, ethical and legal concerns regarding laypersons’ incorporation in MHC delivery systems have not been examined. Purpose: To examine the ethical and legal concerns surrounding the certification of laypersons as community-based mental health counselors. Method: We undertook an academic review of various legislations pertinent to MHC service delivery and the certification of allied health care professionals to inform on acceptable and tenable strategies toward incorporating such a task-shifted intervention. Conclusion: Scaling up the training of human resources to address access problems can be the first step in addressing the MHC access and treatment gaps. The certification of laypersons as community-based mental health counselors, although legally tenuous, can be pioneered by tertiary-level MHC institutions. This certification has sound ethical justification and is a progressive step toward realizing universal mental health coverage.

In recent times, thermoplastic waste materials are being extensively used as fine and coarse aggregates in the concrete mix as an environmentally friendly construction material. This study aims at utilizing polypropylene (PP) as a partial substitute for the conventional coarse aggregates in M30 grade concrete. The different replacement levels of coarse aggregates such as 0%, 20%, 40%, and 60% by weight were used in concrete. Sieve analysis, specific gravity, and water absorption tests were performed in all replacement levels of aggregates. The mechanical (compressive and split tensile tests) tests were conducted after 3, 14, and 28 days. The change in mechanical properties of concrete with the addition of different weight proportions of plastics was studied experimentally. Further, experimental values were predicted using the two-parameter Weibull distribution and artificial neural network (ANN)-based statistical approaches. The Levenberg–Marquardt algorithm was used in predicting the mechanical properties using the ANN. A good correlation was obtained between the experimental and predicted values with an error (%) of less than 10. The decrease in mechanical properties with the increase in replacement levels of coarse aggregates in concrete was observed in both experimental and predicted approaches. This can be attributed to poor bonding between concrete and PP aggregate owing to the development of agglomerations and the hydrophobic nature of PP. However, the strength values of 20% PP-embedded concrete specimens are closer to 0% PP-embedded concrete specimens. Therefore, 20% PP-embedded concrete specimens can be used for primary load-bearing applications to utilize plastic waste and reduce the cost of the component.

Elderly persons can get involved in the criminal justice system as victims or as perpetrators. The interaction of elderly persons with mental illness at various cross-sections of the judicial process needs thoughtful consideration. Through this review, the authors approach this less studied aspect of forensic psychiatry. Concerning the evaluation of a prisoner, three scenarios need focused consideration: evaluation for fitness to stand trial before a competent court, evaluations for an insanity defense, and fitness for sentencing. At the same time, incarcerated elderly who developed dementia or a severe mental illness at any point of time during the trial or in prison need specific approaches. In this article, the authors discuss the acts and case laws relevant to navigating these legal scenarios. We discuss existing mental health care provisions for protecting the health interests of elderly care in prison.

As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned.

Byline: Y. Smitha, C. Naveen Kumar Epidural volume extension involves injection of normal saline into the epidural space soon after an intrathecal injection, with the aim of augmenting the sensory [...]

Background: Task shifting has been recommended as a strategy to reach out to persons with mental illness and bridge the treatment gap. There is a need to explore task-shifting using existing health staff like Accredited Social Health Activists (ASHAs). Aim and Context: ASHAs are involved in ongoing community-based rehabilitation (CBR) program run with a public–private partnership over the last 5 years at Jagaluru Taluk (an administrative block) in Davanagere district (Karnataka, India). This article aims to summarize a randomized controlled trial (RCT) to examine whether CBR delivered by ASHAs is more effective than treatment as usual (TAU) control group in reducing disability associated with severe mental illness (SMI). Method: A group of proactive ASHAs is already working with us for a follow-up of persons with SMI. For the study, we would allocate areas that are currently not being covered proactively by ASHAs randomly in a 1:1 ratio via computer-generated randomization list to receive either ASHAs delivered CBR arm or TAU control group. A sample size of about 100 in each arm is enough to identify an effect size of 0.5 in total IDEAS score between the intervention and control arms with a power of 90% and an alpha of 0.05. We use the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) statement to describe the methods of the trial. Result: The study has been approved by the institute ethics committee and registered with CTRI (CTRI/2019/08/020585 dated 6th August 2019). The recruitment of subjects is ongoing. The patients will be followed up for 1 year and assessed. The trial is funded by the Indian Council of Medical Research, Government of India. Discussion: The results of the study will be helpful from a public health perspective in delivering cost-effective and replicable CBR for persons with SMI through ASHAs. If the model turns successful, this could be expanded throughout the state/country. This would go a long way in bridging the huge treatment gap.

Introduction: There is an unmet need for continuity-of-care is well known for those with severe mental disorders (SMDs) after acute care at hospitals in India. The “Sakalawara Rehabilitation Services (SRS)” functioned from March 2014 at “Sakalawara Community Mental Health Centre” (SCMHC) of “National Institute of Mental Health and Neurosciences,” Bengaluru, India in the concepts of residential care (half-way-home) with the aim to develop a replicable model.Aim: To review the inpatient records after the initial 2 years of experience in residential care at SCMHC. Methodology: Retrospective file review of inpatients at SCMHC from March 2014 to March 2016 in a semi-structured proforma designed for the study. Ethical committee of NIMHANS Bengaluru has approved the study. Results: The total number of inpatients during this period was 85. It was found that Schizophrenia spectrum disorders were the most common diagnosis among these patients. The activity of daily living and psycho-education were the most common individual interventions. The majority of families underwent structured family psycho-educational interventions. This review also demonstrated the feasibility of tele-aftercare in continuity of care after discharge of patients. Conclusion: SRS kind of residential set-up is feasible and demonstrated effectiveness in maintaining continuity of care of SMDs. There is a need for better structured and customized interventions. There is further a scope for tele (video) aftercare for those with SMDs.