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Children without obvious disabilities (hearing loss/low intellectual capacity) may have language skill development issues due to specific language impairment (SLI), a communication disorder. The SLI has a significant impact on a child's speaking, listening, reading, and writing abilities. SLI is typically known as development language disorder, developmental dysphasia, or language delay. Recently, machine learning as well as deep learning techniques have been quite effective in predicting the early stage of SLI, analyzing the disorder severity, and predicting the treatment efficiency. Existing approaches primarily exploited auditory indicators to diagnose communication disorders, frequently leaving out hidden information acquired in the temporal domain. To overcome this drawback, an optimized Bidirectional Long Short Term Memory (BiLSTM) architecture is presented in this paper to handle the speech dynamics. The Improved Hybrid Aquila Optimizer and Flow Directional algorithm known as IHAOFDA is integrated with the BiLSTM architecture to optimize the hyperparameters of the BiLSTM structure. When assessed using the information from the SLI children in the Laboratory of Artificial Neural Network Applications (LANNA) dataset, the proposed model performs better. The IHAOFDA-optimized BiLSTM architecture improves accuracy in classifying different severity levels such as mild, moderate, and severe. [ABSTRACT FROM AUTHOR]

Among the most common paroxysmal neurological conditions is epilepsy. When spontaneous combustion occurs seizure is a defining feature. An epileptic seizure is caused by a brain syndrome called epilepsy. The electroencephalogram test is useful for detecting epileptic seizures and diagnosing epilepsy because it contains significant physiological data that can reflect human brain activity. The EEG signal (EEGS) is used for capturing the signals from the brain, which helps in the localization of the epileptogenic region and thereby plays a vital role in successful surgery. The signals, both focal and non-focal are attained in the epileptogenic area and normal region respectively. The localization of epileptic seizures with the help of a focal signal is necessary while detecting seizures. Hence, the present article provides a detailed analysis of EEG readings. The Signals with and without focus are decomposed by elliptical mode decomposition-discrete wavelet transform (EPMD-DWT). A combination of the EPMD-DWT decomposition method by log-energy entropy gives an efficient accuracy in comparison to other entropy in distinguishing the Focal from Non-specific signals. The extracted features are subjected to support vector machine algorithm (SVMA) and K nearest-neighbour (K-NN) classifiers whose performance will be calculated and verified for accuracy, sensitivity, and specificity. In the end, it will be shown that K-NN produces the highest accuracy when compared to SVMA classifier. The EEGS categorized into focused and non-focal signals were carried out through the K-NN method whose performance was calculated and verified in terms of their specificity, sensitivity, and accuracy. It was also inferred that with an increase in data at every point, the performance parameters were enhanced and later got saturated after a certain specific point. Further, the K-NN classifier obtained the highest accuracy of 75%, a sensitivity of 77.78%, and a specificity of 72.73% while the SVMA classifier obtained an accuracy of 58.33%, a sensitivity of 60.87%, and a specificity of 56.76%. Thus it can be stated that the K-NN classifier provided the highest accuracy when related to SVMA classifier. [ABSTRACT FROM AUTHOR]

The Digital Library on Green Mobility (DLGM) aims to provide a platform for sharing ideas, knowledge, and documents among stakeholders of various organisations and institutions involved in Low Carbon Transport in India.1 It offers full-text access to national and state-level policies, regulations, reports, articles, books, standards, case studies, etc., on green mobility. Following a case study approach, the study highlights the process of developing a knowledge-sharing platform and the success of DLGM in meeting critical objectives. The usage trend and user behaviour of DLGM were also analysed to draw inferences to improve website usability. We briefly discuss how different insights from DLGM can be obtained to benefit a diverse set of stakeholders, such as policymakers, practitioners, private companies, and researchers, for evidence-based gap identification, decision-making, and way forward in various green mobility topics. The study contributes to the original findings based on users' and website accessibility surveys undertaken by authors. [ABSTRACT FROM AUTHOR]

Aim: The aim of this paper is the efficient prediction of demand for Electronics Items in a Retail Store during festive seasons by adopting Novel ResNet Algorithm and its performance comparison over Deep Belief Network. Materials and Methods: At different stages, the Deep Belief Network and Novel ResNet algorithms were iterated in order to predict the accuracy percentage of accidents that occurred. Two sample groups are considered and tested, and G-power is a computation that includes two groups, alpha (0.05), and Power (80%). Results: It was observed that the Novel ResNet algorithm obtains an accuracy of 83.16% and the Deep Belief Network has 77.24%. This DBN appears to have a better significance of P=0.016 than the Novel ResNet, that is p<0.05 using the independent T-test sample for the analysis. Conclusion: This study contains analyses that target the sparsity in the income facts with the aid of converting a wide variety of product attributes. The result proves that the Novel ResNet Algorithm approaches predicting the retail store prediction during the festival season. [ABSTRACT FROM AUTHOR]

Aim: The research is about the Novel Deep Belief Network (NDBN) approach for Retail Store Sales Prediction during peak demand seasons and its performance comparison over K-Nearest Neighbour Technique (KNN). Materials and Methods: Deep Belief Network (N=10) and K-Nearest Neighbour algorithm (N=10) samples were considered based on the clinc calc online sample size calculator for predicting the accidents that happened in terms of accuracy. Two sample groups are taken into consideration and tested, G-power is the calculation that contains two different groups, alpha (0.05), power (80%), and environment ratio. Results: The Novel Deep Belief Network algorithm achieved 84.53% accuracy and K-Nearest Neighbour has 74.24%. This NDBN appears to have significance of p equal to 0.02 for the K-Nearest Neighbour, that is p less than 0.05 using independent sample T-test analysis. From the result, it proves that the Deep Belief Network approaches predict the retail sales store prediction. [ABSTRACT FROM AUTHOR]

Aim: The aim of this paper is the efficient prediction of sales during festival times in an electronic showroom using a Deep Belief Network compared to AlexNet with improved accuracy. Materials and Methods: Deep Belief Network (N=10) and AlexNet algorithm (N=10) is the iteration for different times in predicting the accuracy percentage for accidents that happened. Two sample groups are considered and tested, G-power is a calculation that contains two different groups, alpha (0.05), and power (80%). Results: It was observed that the Deep Belief Network algorithm obtains an accuracy of 83.63% and the Novel Deep Belief Network has 74.12%. This NDBN appears to have a better significance of p=0.035 than the ResNet, that is p<0.05 using independent T-test analysis. Conclusion: The result proves that the Novel Deep Belief Network approaches to predicting the best retail sales store prediction have higher accuracy than the AlexNet algorithm. [ABSTRACT FROM AUTHOR]

Aim: The aim of this paper is to implement a Novel Convolutional Neural Network based has model for Sales forecast in an Electronic Retail Store during Festive seasons and a comparison of prediction accuracy with a Deep Belief Network. Materials and Methods: Deep Belief Network (N=10) and Novel Convolutional Neural Network algorithm (N=10), n is iterated at different times for predicting the accuracy percentage of accidents that happened. Two sample groups are taken into consideration and tested, G-power is a calculation that contains two different groups, alpha (0.05), and power (80%). Results: It was observed that the Deep Belief Network algorithm obtains an accuracy of 77.14% and the Novel Convolutional Neural Network has 84.86%. This Deep Belief Network appears to have a significance of p=0.019 than the Novel Convolutional Neural Network, that is p<0.05 using an independent sample forT-test analysis. Conclusion: The Deep Belief Network technique appears to have more significance than the Novel Convolutional Neural Network algorithm. The analysis generally works in a variety of end-use industries, and the results demonstrate that this strategy is important. The result proves that the Novel Convolutional Neural Network approaches to predict the retail sales store prediction. [ABSTRACT FROM AUTHOR]

Aim: The aim of the study is to analyze the death ratio of covid patients using Novel Multiple Logistic Regression and linear regression which comes under supervised learning. Materials and Method: Accuracy is analyzed for a covid dataset of size 239 places. Analyzingthe death ratio of covid patients is performed by a Novel Multiple Logistic Regression of sample size (N=35) and Linear Regression of sample size (N=35), obtained using the G-power value of 80%. These are supervised learning algorithms. Result: Novel Multiple Logistic Regression accuracy is 96% which is comparatively higher than LR with an accuracy of 86%. The significance value is determined as p=0.030 (p<0.05) for accuracy. Conclusion: Novel Multiple Logistic Regression performs better in finding accuracy when compared to Linear Regression. [ABSTRACT FROM AUTHOR]

Aim: The idea of this study is to analyze and improve the death ratio accuracy of covid patients with Novel Multiple Logistic Regression(MLR)and Lasso regression. Both these algorithms fall under supervised learning techniques. Materials and Method: Accuracy is analyzed for covid dataset of size 239 places. Analyzingthe death ratio of covid patients is performed by a Novel Multiple Logistic Regression of sample size (N=35) and Lasso regression of sample size (N=35), obtained using the Gpower value 80%. These are Supervised learning algorithms. Result: Novel Multiple Logistic Regression accuracy is 96% which is comparatively higher than LAS with accuracy of 66%. The significance is determined as p=0.029 (p<0.05) for obtaining accuracy. Conclusion: NovelMultiple Logistic Regression performs better in determining accuracy than Lasso Regression. [ABSTRACT FROM AUTHOR]

Aims: In the study, seven Plant Growth Promoting Rhizobacteria (PGPR) Azotobacter species were screened against three strains of Fusarium verticillioides to test its antifungal activity. Azotobacter strains were tested for the degradation of fumonisin produced by F. verticillioides. Secondary metabolites were isolated and characterized from the Azotobacter strains for the first time. Methods and Results: Potential seven Azotobacter species antifungal activity was tested following the dual culture assay against three strains of Fusarium verticillioides namely FVM‐42, FVM‐86 and MTCC156 estimating the substantial zone of inhibition. Azotobacter species AZT‐31 and AZT‐50 strains significantly inhibited the growth of F. verticillioides recording drastic growth enhancement of maize under in‐vitro conditions by calculating the infection incidence, vigour index and germination percentage. As confirmation, dereplication studies were conducted for the reconfirmation of Azotobacter strains by isolating from rhizoplane. Azotobacter strains played a key role in the degradation of fumonisin produced by F. verticillioides reporting 98% degradation at 2 h of incubation with the pathogen. Furthermore, in the study first time, we have tried to isolate and characterize the secondary metabolites from the Azotobacter strains exhibiting six compounds from the species AZT‐31 (2) and AZT‐50 (4). Preliminary in‐vitro experiments were carried out using the compounds extracted to check the reduction of infection incidence (90%) and increase in germination percentage upto 50 to 70% when compared to the test pathogen. Conclusion: Azotobacter strains referred as PGPR on influencing the growth of plant by producing certain substances that act as stimulators on inhibiting the growth of the pathogen. Significance and Impact of the study: The future perspective would be the production of an active combination of carboxamide compound and Azotobacter species for preventively controlling the phytopathogenic fungi of plants and crops and also towards the treatment of seeds. [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "A novel data privacy-preserving protocol for multi-data users by using genetic algorithm" published in Soft Computing. The publisher has retracted the article due to concerns about compromised editorial handling and peer review process, inappropriate references, and the article not being in scope of the journal or the guest-edited issue. The publisher no longer has confidence in the results and conclusions of the article. One of the authors disagrees with the retraction, while the other author has not responded to correspondence. Springer Nature, the publisher, remains neutral with regard to jurisdictional claims and institutional affiliations. [Extracted from the article]

This document is a retraction note for an article titled "Hybrid Context Aware Recommendation System for E-Health Care by merkle hash tree from cloud using evolutionary algorithm." The publisher has retracted the article due to concerns about compromised editorial handling and peer review process, inappropriate references, and the article not being in scope of the journal or guest-edited issue. The publisher no longer has confidence in the results and conclusions of the article. One of the authors disagrees with the retraction, while the other author has not responded to correspondence. The original article can be found online. [Extracted from the article]

• Developed predictive models for rail wheel temperature forecasting using Single wheel test rig. • Utilized fibre sensing technology and machine learning for accurate prediction. • Pre-processed data with cleansing, normalization, and feature selection for improved performance. • Applied Linear Regression, Decision Tree, and Random Forest algorithms. • Assessed model effectiveness using metrics like Mean Squared Error and R-squared, revealing insights into strengths and limitations. The focus of this research is the development of predictive models for temperature forecasting of rail wheel contact temperature through data collection from experimental setup with Single wheel test rig. Fibre sensing technology and the implementation of machine learning techniques are used. Our approach involves utilizing a dataset containing crucial variables such as time, speed, weight, and sensor readings in order to accurately predict temperature changes. To achieve this, we employ a thorough preprocessing methodology that includes data cleansing, normalization, and feature selection, followed by the implementation of various machine learning algorithms for regression tasks. The effectiveness of each model is evaluated using metrics like Mean Squared Error and R-squared. Experimental results reveal significant findings, including a Linear Regression model with an R-squared value of 0.9176, indicating it accounts for 91.76% of temperature variation. Furthermore, Decision Tree and Random Forest models exhibit remarkable accuracy, achieving R-squared values of 0.999997 and 0.999995 respectively. Through extensive analysis and discussion, we gain insights into the strengths and limitations of different models, ultimately identifying the most optimal approach for temperature prediction. This research serves to advance temperature forecasting methodologies in the field of railway ultimately contributing to improved safety, efficiency, and decision-making processes. [ABSTRACT FROM AUTHOR]

In recent decades agricultural decision-making system has played a vital role in the field monitoring process. For these emerging technologies like the Internet of Things (IoT), artificial intelligence (AI), and wireless sensors are utilized for precise data extraction and analysis. However numerous techniques are developed for increasing agricultural production and enhancing operational efficiency. But still, they possess various challenges like lack of accuracy, increased power utilization, and costs. Insects and pathogens cause plant diseases that reduce productivity if not diagnosed at a proper time. Thus this paper develops Deep Q Rapidly-exploring Random tree-based Adaptive Fire hawk (DQRR-AFH) for diagnosing leaf diseases and monitoring them. It comprises various phases including data acquisition; Image processing, segmentation, feature extraction, and classification. Further, the classification is performed by exploring random trees, and the hyperparameters are tuned via the Adaptive Fire Hawk Optimizer algorithm is utilized to enhance the efficiency of the model. The proposed method monitors the soil moisture content and prevents cotton leaf diseases by spraying chemicals on the plants. Multiple cotton leaf images are obtained to verify the performance with various metrics. Compared to conventional methods such as WL-CNN, ECPRC, and DT, the proposed model achieved exceptional performance with an accuracy of 98.88%, precision of 97%, and an F1-score of 99.21%. [ABSTRACT FROM AUTHOR]

Recent technological advancements in information and communication technologies introduced smart ways of handling various aspects of life. Smart devices and applications are now an integral part of our daily life; however, the use of smart devices also introduced various physical and psychological health issues in modern societies. One of the most common health care issues prevalent among almost all age groups is diabetes mellitus. This work aims to propose an artificial intelligence-based intelligent system for earlier prediction of the disease using Ridge-Adaline Stochastic Gradient Descent Classifier (RASGD). The proposed scheme RASGD improves the regularization of the classification model by using weight decay methods, namely least absolute shrinkage and selection operator and ridge regression methods. To minimize the cost function of the classifier, the RASGD adopts an unconstrained optimization model. Further, to increase the convergence speed of the classifier, the Adaline Stochastic Gradient Descent Classifier is integrated with ridge regression. Finally, to validate the effectiveness of the intelligent system, the results of the proposed scheme have been compared with state-of-the-art machine learning algorithms such as support vector machine and logistic regression methods. The RASGD intelligent system attains an accuracy of 92%, which is better than the other selected classifiers. [ABSTRACT FROM AUTHOR]

Privacy preservation permits doctors to outsource the huge encrypted reports to the cloud and permits the authenticated patients to have a safe search over the reports without leaking the private information. The doctors in our proposed have used the merkle hash tree for storing the reports of all the patients in the hospital. The existing schemes have used many types of trees like binary tree, red–black tree, spanning tree, B+ tree, etc., for the index generation purpose. Since the security is less and the searching time is high for the above said trees, we have proposed the index generation phase based on the merkle hash tree based on the evolutionary algorithm and it takes less time for searching and highly secure for storing the patient reports. The evolutionary algorithm is used for breeding the new data's through crossover as well as mutation operations to give confinement to new children. When the patient submits the search request for specialized doctor, based on the patient disease our protocol will recommend the specialized doctors and send the recommended doctors information to the patients who have the highest rating in the online social networks. After receiving the recommended results, the patient can have the treatment via online booking appointment, video call or in person based on the appointment booked. After completely cured, the patients can rate the doctors based on the medicine satisfaction, doctors' fees and doctor's response over the call. In this mechanism, we have used the hybrid context aware recommendation system collaborative filtering for rating the doctors based on their performance. After rating the doctors, our protocol has measured the accuracy based on the predicted rating and the true rating. This kind of accuracy metrics is used for ranking the good doctors in the top rank for the patient use. Our proposed work Hybrid Context Aware Recommendation System for E-Health Care (HCARS-EHC) is implemented, and the implementation results of HCARS-EHC illustrate that our protocol is efficient based on the privacy preservation, recommendation and ranking with less computation and communication complexity. [ABSTRACT FROM AUTHOR]

The competitive strength of a nation's economy depends upon the availability and access to research infrastructure which includes both hardware (that is, equipment) and software, largely catering to the scientific fraternity in the country. Scientific equipment is a vital element in research infrastructure; its access and availability enables scientists to efficiently carry out research and development. Therefore, it is of utmost importance to recognize the significance of scientific equipment in research and development infrastructure. It is significant to recognize that scientific equipment and research infrastructure are intimately associated with policy frameworks that facilitate and enable procurement, maintenance, and disposal of scientific equipment, and management systems for providing information on accessibility, sharing of equipment, and trained manpower. The present article highlights the creation and operation of a web-based database that catalogues scientific equipment located at various academic and research institutions across the country. Such inventorization of institutional record of scientific equipment is necessary for facilitation of access and resource sharing which will result in optimum utilization of equipment. It is hoped that the findings of the study contained in this article would be of immense use to the government, researchers, and academicians including industry, leading to policy actions, appropriate incentive structures for strengthening and fostering the innovation ecosystem in the country. [ABSTRACT FROM AUTHOR]

In this paper, rough, fuzzy and soft set approaches have been integrated to develop a multi-class classification model to assist the farmers in taking decision on crop cultivation for a given agriculture land. The model is divided into three major sections, namely weight calculation of variables, conversion of continuous data to fuzzified values and classification rule generation. Dominance-based rough set approach is used for the calculation of relative weights of variables. Fuzzy proximity relation is applied to convert the continuous data into fuzzified values. Bijective soft set approach is used to generate classification rules for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi. The developed model has been tested with agriculture dataset which showed 92% accuracy for the validation dataset and proved to be confident and robust for agriculture development. Further, the performance of the proposed model is compared with three popular classifiers such as naïve Bayes, support vector machine and J48. The obtained experimental results showed high predictive performance, and the potential of the proposed model is compared with the other classifiers. [ABSTRACT FROM AUTHOR]

Upper-division physics students spend much of their time solving problems. In addition to their basic skills and background, their epistemic framing can form an important part of their ability to learn physics from these problems. Encouraging students to move toward productive framing may help them solve problems. Thus, an instructor should understand the specifics of how students have framed a problem and understand how her interaction with the students will impact that framing. In this study, we investigate epistemic framing of students in problem solving situations where math is applied to physics. To analyze the frames and changes in frames, we develop and use a two axis framework involving conceptual and algorithmic physics and math. We examine student and instructor framing and the interactions of these frames over a range of problems in an upper-division electromagnetic field course. Within interactions, students and instructors generally follow each others' leads in framing. [ABSTRACT FROM AUTHOR]

In this paper, a predictive mathematical model is proposed to identify the best alternatives from the given set of alternatives characterized by multiple criteria. An objective function is developed to find the ranking index of the alternatives. A new Comprehensive-Technique for Order Preference by Similarity to Ideal Solution (C-TOPSIS) method is proposed which combines the comprehensive weights of the criteria with TOPSIS method. The proposed predictive mathematical model generates a ranking of the alternatives. An experimental study has been carried out by taking agricultural data set of rice paddy crop to demonstrate and validate the developed model. The results show significant correlation between the ranks obtained by the proposed model and the ranks obtained from the average yield per hectare. Also the results of the proposed method outperform the results of the other ranking methods, namely VIKOR and ELECTRE, particularly in the real world example. Thus, the developed predictive mathematical model seems to provide better results for the given alternatives and can also be used for other decision-making problems. [ABSTRACT FROM AUTHOR]

In this research paper, the proposed work is to put forward a Novel Data Privacy-Preserving Protocol (NDPPP) for Multi-data Users by using genetic algorithm. The data owner outsources the files in the encrypted format to the cloud. The data users can efficiently download the encrypted files from the cloud service provider without any loss of data. To provide this facility, there are certain existing mechanisms in the literature. But the existing mechanisms will result high computation complexity. By means of minimizing the computation complexity, a NDPPP for multi-data users is proposed using genetic algorithm in this research work. Genetic algorithms are usually used to produce high-quality resolutions for optimization. A new trapdoor function is proposed to preserve the data privacy by using our Data Privacy-Preserving Protocol. With the aim of avoiding numerous attacks, a secure authentication protocol is moreover developed between the trusted third party and data user. Additionally, the security is improved. Provided the leakage of data, loss of data as well as the data modification can also be avoided. The proposed work is implemented, and the implementation results of NDPPP illustrate that our protocol is efficient through computation and communication complexity. Moreover, the proposed work NDPPP is secure against various attacks like impersonation attack, eavesdropping, man-in-the-middle attack as well as replay attack. [ABSTRACT FROM AUTHOR]

In the present competitive environment, a farmer needs better education, business expertise and good knowledge of technologies and tools to be successful in agriculture. Farmers usually select crop for cultivation according to their traditional knowledge and past experience in farming, but a farmer's predictions may go wrong due to natural disaster. Thus, decision-making tool need to be developed to help farmers to take decision on crop cultivation. In this paper, decision-making tool was developed for selecting the suitable crop that can be cultivated in a given agricultural land. In the present study, 26 input variables were identified and categorized into six broad heads of main variables such as soil, water, season, input, support and infrastructure. Each main variable has several sub-variables. The priority weights for the variables were determined using the dominance-based rough set approach. In order to convert sub-variable sequences to main variable sequences, evaluation scores of each main variable were calculated by applying the weights of sub-variables and by using simple additive method. Finally, the evaluation scores were applied to Johnson's reduct algorithm and classification rules were generated. The developed tool predicts each site in the datasets into one of the three crops such as paddy, groundnut and sugarcane. In order to validate the performance of the tool, the same datasets were predicted again by agriculture experts. The results obtained from the tool showed 92% agreement with the results obtained from the experts. Thus, the tool is a feasible tool for cultivating the suitable crops in the agricultural sites. [ABSTRACT FROM AUTHOR]

A hybrid soft decision model has been developed in this paper to take decision on agriculture crop that can be cultivated in a given experimental land by integrating few soft computing techniques. The proposed model comprises of three parts, namely weight calculation, classification and prediction. Twenty-seven input criteria were categorized into seven broad criteria, namely soil (11 sub-criteria), water (2 sub-criteria), season (no sub-criterion), input (6 sub-criteria), support (2 sub-criteria), facilities (3 sub-criteria) and risk (2 sub-criteria). In the proposed model, relative weights of main criteria were calculated using Shannon’s Entropy method and relative weights of sub-criteria in each main criterion were calculated using rough set approach. As VIKOR method is effective in sorting the alternatives, it is used to determine the ranking index of main criteria in this study. A soft decision system was constructed from the results of rough set method, VIKOR method and Shannon’s Entropy method. Classification rules were generated for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi based on the soft decision system using bijective soft set approach. The developed model predicts each site in the validation dataset into one of the five crops. The performance of the proposed model has been sanity checked by agriculture experts. [ABSTRACT FROM AUTHOR]

Introduction: In type 2 diabetes mellitus, lipid abnormalities are almost the rule and is associated with a cluster of interrelated plasma lipid and lipoprotein abnormalities that are all recognized as major risk factors for coronary artery disease and other macro vascular complications. Present study aimed to assess the lipid profile in type 2 diabetic individuals in comparison with non-diabetic individuals. Materials and methods: An observational study was conducted at outpatient and Inpatient department in BLDEU'S Shri. B.M. Patil Medical College Hospital and Research Centre. The sample size was 250 of which 125 were type 2 diabetes mellitus patients who were studied as cases and 125 non diabetics were taken as controls and there lipid profile were estimated and the results obtained were statistically computed. Results: In the present study the results obtained were in cases (type 2 diabetes mellitus) values were as follows-LDL 117.99 ± 49.28, TC 196.77 ± 73.6, TG 186.05 ± 128.32, HDL 38.72 ± 12.5, VLDL 34.06 ± 19.65. These values were much higher as compared to controls (p<0.05). Conclusion: It was observed that in type 2 diabetes mellitus patient's lipid profile is significantly altered as compared to non-diabetic patients and hence regular monitoring of lipid profiles in such patients is warranted. [ABSTRACT FROM AUTHOR]

This article reports a portable optoelectronic instrumentation system for real-time monitoring ofEscherichia colicultivation. The minimally invasive sensor offers continuous measurement of the pH, dissolved oxygen, optical density, and auto-fluorescence. The analytical figures of merit, including the stability, response time, reproducibility, and long-term sensitivity, were evaluated before application for monitoring bacterial cell cultivation. The results were compared with values obtained with commercially-available instrumentation and were similar and comparable. The absolute error was between ±0.30 pH units and mg/L for dissolved oxygen. This minimally invasive, simple, and inexpensive optical system is suitable for monitoring online bacterial cell growth in a single transparent container. [ABSTRACT FROM PUBLISHER]

India being one of the major producers of fish contributes 5.5 percent of global fish production and ranks second in the world after China. The production of aquaculture mainly depends on the quality of land selected for aqua farming. Neural Network algorithms have been applied to classify the aquaculture sites based on 6 input variables viz., water, soil, support, infrastructure, input and risk factor. An artificial neural network (ANN) consists of huge number of interconnected elements called neurons that work together to solve a specific problem. An Artificial Neural network can be used for classification, prediction, pattern recognition etc., through a learning process. In this paper, the models were constructed using three Neural Network algorithms viz., Back Propagation Network (BPN), Radial Basis Function (RBF) and Linear Vector Quantization (LVQ). The models classify each aquaculture site into 3 classes viz., suitable, moderate and unsuitable. From the results of the three models, it has been found that Radial Basis Function model not only gives accurate results but also time taken for training the dataset is less when compared with the other two Neural Network models. The results obtained from the neural network models were validated with the results of the fuzzy model. [ABSTRACT FROM AUTHOR]

Agriculture crop selection cannot be formulated from one criterion but from multiple criteria. A list of criteria for crop selection was identified through literature survey and agricultural experts. The identified criteria were grouped into seven main criteria namely, soil, water, season, input, support, facilities and threats. In this paper, Mahalanobis Taguchi system based tool was developed for identification of useful set of criteria which is a subset of the original criteria, for taking decision on crop selection in a given agriculture land. The combination of Mahalanobis distance and Taguchi method is used for identification of important criteria. Matlab software was used to develop the tool. After entering the values for each main criteria in the tool, it will process the value and identify the useful sub-criteria under each main criteria for selecting the suitable crop in a given agriculture land. Instead of considering all criteria, one can use these useful set of criteria under each main criteria for taking decision on crop selection in agriculture. [ABSTRACT FROM AUTHOR]

The paper presents an optical based measurement system to monitor the fermentation process of food samples, such as raw milk, pasteurized milk, curd, grape juice and batter. The microbial growth during the fermentation and its effects in changing the parameters such as pH, dissolved oxygen, optical density and fluorescence are observed continuously by using minimal invasive optical sensing system. The sensing membranes for pH and dissolved oxygen are prepared using sol-gel technique and attached at an inner wall of a single-cell transparent container. The opto-electronic system is constructed to hold a single-cell transparent container along with light sources, detectors, signal processing circuits, computational and display unit. The performance characteristics, such as stability, response time and reproducibility are verified before it has been applied for food fermentation analysis. The developed optical sensor system shows the maximum relative error rate of 3.98% and a minimum of 0.21% for the pH measurement and for the dissolved oxygen measurement the relative error rate is observed between 0.36% and 3.75%. The results are found comparable and the proposed simple and cost effective system may be considered to monitor the food grades at the household level. [ABSTRACT FROM AUTHOR]

In India, continuous production of electricity and sweet/potable water from Solar power and desalination plant plays a major role in the industries. Particularly in Carbon black industry, Solar power adopts Solar field collector combined with thermal storage system and steam Boiler, Turbine & Generator (BTG) for electricity production and desalination plant adopts Reverse osmosis (RO) for sweet/potable water production which cannot be used for long hours of power generation and consistency of energy supply for industrial processes and power generation cannot be ensured. This paper presents an overview of enhanced technology for Solar power and Desalination plant for Carbon black industry making it continuous production of electricity and sweet/potable water. The conventional technology can be replaced with this proposed technique in the existing and upcoming industries. [ABSTRACT FROM AUTHOR]

The paper presents a hand-held optical sensor for simultaneous and minimal invasive measurement of pH and dissolved oxygen using sol–gel based sensing membranes which are attached internally in any see-through container. The sensing system shows the stable results over the period of time and possess the characteristics such as, minimum response time and repeatability. The disposable membranes are prepared using simple procedures and are very thin in size that can be attached the inner surfaces of transparent columns such as, measuring jar, conical flask, cuvettes and bio reactors. The pH sensor can be applied to the range between 3 pH and 9 pH, however, it shows good sensitivity between 4 pH and 9 pH with the response time of less than 10 s. The dissolved oxygen is measured in the range from 2 mg/L to 9 mg/L. It is found that, the opto electronic system posses the features, such as in-situ, simple, cost efficient, handheld and field deployable. [ABSTRACT FROM AUTHOR]

Background Evaluation of the combinatorial anticancer effects of curcumin/5-fluorouracil loaded thiolated chitosan nanoparticles (CRC-TCS-NPs/5-FU-TCS-NPs) on colon cancer cells and the analysis of pharmacokinetics and biodistribution of CRC-TCS-NPs/5-FU-TCS-NPs in a mouse model. Methods CRC-TCS-NPs/5-FU-TCS-NPs were developed by ionic cross-linking. The in vitro combinatorial anticancer effect of the nanomedicine was proven by different assays. Further the pharmacokinetics and biodistribution analyses were performed in Swiss Albino mouse using HPLC. Results The 5-FU-TCS-NPs (size: 150 ± 40 nm, zeta potential: + 48.2 ± 5 mV) and CRC-TCS-NPs (size: 150 ± 20 nm, zeta potential: + 35.7 ± 3 mV) were proven to be compatible with blood. The in vitro drug release studies at pH 4.5 and 7.4 showed a sustained release profile over a period of 4 days, where both the systems exhibited a higher release in acidic pH. The in vitro combinatorial anticancer effects in colon cancer (HT29) cells using MTT, live/dead, mitochondrial membrane potential and cell cycle analysis measurements confirmed the enhanced anticancer effects (2.5 to 3 fold). The pharmacokinetic studies confirmed the improved plasma concentrations of 5-FU and CRC up to 72 h, unlike bare CRC and 5-FU. Conclusions To conclude, the combination of 5-FU-TCS-NPs and CRC-TCS-NPs showed enhanced anticancer effects on colon cancer cells in vitro and improved the bioavailability of the drugs in vivo. [ABSTRACT FROM AUTHOR]

Barks of various plants have been reported to possess both in vitro and in vivo antioxidant activity. In this study, antioxidant activity of the extract from the barks of Dillenia indica was evaluated by various in vitro methods. The bark of D. indica was extracted with 70% aqueous acetone. The total phenolic content was determined by Folin-Ciocalteu method and the antioxidant activity was assayed through various in vitro methods such as antioxidant capacity by phosphomolybdenum method, radical scavenging activity using α, α-diphenyl-β-picrylhydrazyl method, hydroxyl radical (•OH) scavenging activity by deoxyribose method, and superoxide anion (O2•-) scavenging activity by phenazine methosulphate/NADH-nitroblue tetrazolium system. The total phenolic content of the extract as tannic acid equivalents was 54%. The total antioxidant capacity of the extract was found to be 3.12 mmoles/g as equivalent to ascorbic acid at 50 ppm concentration. At 25 ppm concentration, the radical scavenging activity of butylated hydroxyanisole and extract showed 90.9% and 91.0%, respectively. The •OH scavenging activity of the extract was shown to be 53.9% at 100 ppm concentration. At a concentration of 50 μg, the O2•- scavenging activity of the extract was 31.7% as compared to 47.7% by gallic acid. These results indicated that Dillenia indica barks contained large amount of phenolics and possessed potent antioxidant property. [ABSTRACT FROM AUTHOR]

Abstract: The main objective of this work was to develop nanoparticles (NPs) of a mucoadhesive polymer based on chitosan for biomedical applications. Here, we developed thiolated chitosan (TCS) using thioglycolic acid (TGA) and chitosan in the presence of 1-ethyl-3-3(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC) as catalyst. The prepared TCS was characterized using FT-IR and the degree of thiol substitution was found out by Ellman''s method. The TCS nanoparticles (TCS-NPs) were developed using ionic cross-linking reaction with pentasodium tripolyphosphate (TPP). The prepared TCS-NPs were characterized by DLS, AFM, FT-IR, TG/DTA, etc. In vitro cytocompatibility and cell uptake studies were also carried out. These studies suggest that the prepared NPs show less toxicity towards normal and cancer cells and they are easily taken up by both the normal and cancer cells. So the prepared TCS-NPs could be used for drug and gene delivery applications. [Copyright &y& Elsevier]

Abstract: Changes in total phenolics, antioxidant activity (AOX), carotenoids, capsaicin and ascorbic acid were monitored during three maturity stages in 10 genotypes of sweet pepper. In an attempt to explain the variations during maturity stages (green, intermediate and red/yellow), the data was expressed both on fresh and dry weight basis. All the antioxidant constituents (phenolics, ascorbic acid and carotenoids) and AOX, when expressed on fresh weight basis in general, showed an overall increasing trend during maturity in all the genotypes studied. On dry weight basis, phenolic content declined in majority of the genotypes during maturity to red stage. This decline was significant () in Parker, Torkel, HA-1038 and Flamingo. Genotype Flamingo and Golden Summer had the highest phenolic content of 852.0mg 100g−1 and 720.5mg 100g−1, at their final red and yellow maturity stages, respectively. With maturation, most of the cultivars showed a declining trend with regard to capsaicin content while total carotenoids and β-carotene content increased significantly. Anupam was a promising genotype in terms of both total carotenoids and β-carotene content. Ascorbic acid content declined progressively with advancing maturity. Genotype HA-1038 had the maximum content (3030mg 100g−1 dwb) at the green stage. AOX in general, increased with maturity and registered a 1.30–1.95fold increase from green to red stage. The study proposes the nutritional significance of consuming sweet peppers at the red maturity stage because of enhanced functional properties. Overall genotype Flamingo and Anupam represent superior genotypes for both nutrition and germplasm improvement. [Copyright &y& Elsevier]

Abstract: Cultivars and growing conditions seem to play an important role in affecting the metabolism of antioxidant components and antioxidant capacity. Ten cultivars of red sweet peppers grown over two consecutive years were compared with regard to ascorbic acid, total reducing content, β-carotene, total antioxidant activity and free radical scavenging activity. Cultivar Flamingo had the highest ascorbic acid content followed by cultivars Bomby and Parker. All cultivars fulfilled 100% RDA requirement for vitamin C. Torkel and Mazurka excelled in terms of β-carotene. Flamingo had the highest total reducing content and antioxidant activity. There was no effect of harvest year on antioxidant activity; however, ascorbic acid, total reducing content (mainly phenolics) and β-carotene differed significantly. A weak correlation was observed between total reducing content and antioxidant activity as measured by ferric reducing antioxidant power (FRAP) and free radical (1,1-diphenyl-2-picrylhydrazyl, or DPPH) scavenging assays. [Copyright &y& Elsevier]

Panic disorder is a recurrent and disabling illness. It is believed that Cognitive Behavioral Therapy (CBT) has a long-term protective effect for this disorder. This would offer CBT considerable advantage over medication management of panic disorder, as patients often relapse when they are tapered off their medications. This is a review of the literature about the long-term effectiveness of CBT. We searched for follow-up studies of panic disorder using CBT. Of the 78 citations produced in the initial search, most had major methodological flaws, including ignoring losses to follow-up, not accounting for interval treatment, and unclear reporting. Three papers met strict methodological criteria, and two of these demonstrated a modest protective effect of CBT in panic disorder patients. We make recommendations for well-designed studies involving comparisons of medications and cognitive behavior therapy. Depression and Anxiety 17:58–64, 2003. © 2003 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation. [ABSTRACT FROM AUTHOR]

Among the 194 Fusarium verticillioides isolates screened from 127 cereal samples, 176 were fumonisin producers and others were non-producers. Representative nine Fusarium verticillioides strains along with one reference standard strain MTCC156 were selected to study their morphological, pathological and mycotoxicological variations by conventional and molecular approaches. Fusarium verticillioides strains FVM86, FVM146, FV200 and FVS3 showed significant pathogenicity and also in pigmentation production but varied in fumonisin production. Fusarium verticillioides strain FVP19 recorded variations in all the assays. Fusarium verticillioides strain FVM42 showed drastic phenotypic variation and it also produced fumonisin. Genetic variation among the strains was independent of geographic area of origin but depended on their ability to produce fumonisin. The strains were independent in their cultural characteristics, pigmentation production, pathogenicity assays, fumonisin production and in their genetic variability without having any correlation. [ABSTRACT FROM AUTHOR]

Many studies have investigated students’ epistemological framing when solving physics problems. Framing supports students’ problem solving as they decide what knowledge to employ and the necessary steps to solve the problem. Students may frame the same problem differently and take alternative paths to a correct solution. When students work in group settings, they share and discuss their framing to decide how to proceed in problem solving as a whole group. In this study, we investigate how groups of students negotiate their framing and frame shifts in group problem solving. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is an emerging paradigm that decreases the computational burden of mobiles by task offloading. MEC is regarded as an effective method to offer computing capacities in close proximities to mobile users. The major issue in MEC is how to offload the heterogeneous task of mobile apps effectively from the user equipment to the MEC host. Some of the existing techniques contain feeble adaptability to new circumstance due to minimum sample effectiveness and require complete retraining. The purpose of MEC is to efficiently solve offloading issues such as network load and latency. In this paper, a deep belief network is proposed for solving the offloading issue in the clusters of numerous service node and several dependencies for a mobile task in huge-scale heterogeneous mobile edge computing. The deep belief network's weight is tuned optimally using Adaptive Harris Hawks optimization (AHHO) algorithm for solving the offloading issue optimally in the MEC environment. An AHHO algorithm is employed to improve the search performances of the standard algorithm. The limitations of the standard algorithm are poor stability among exploitation as well as exploration. Hence, two schemes such as the Gaussian mutation scheme and the cuckoo search are combined so as to form an adaptive Harris hawks optimization to enhance this stability. The task offloading issues are implemented using Google cluster trace and iFogSim. Furthermore, the simulation results depict that the offloading scheme based on the deep belief network-based adaptive Harris Hawks optimization approach has better results with respect to measures such as load balancing, energy consumption, average execution time, and latency than any other approach. [ABSTRACT FROM AUTHOR]