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The local food systems and farmers' markets support the local economy, build relationships between producers and consumers, and promote social interaction along with the provision of fresh and nutritious foods. The local food systems cater to the three dimensions of sustainability viz. environment, economy, and society by providing access to fresh produce. Various models of local food systems are in operation, direct-to-consumer, direct-to-retail, farmers markets, and farm-to-school programs each with distinct features of operation. Consumers prefer local food markets as they perceive the locally grown food to be fresher and they like unique and special foods on offer at these markets. Policy support and capacity building of smallholder farmers on reducing the cost of cultivation, marketing skills, and knowledge of food safety regulations are essential. Farmers' markets are integral to local food systems and various models of farmers' markets are operational in different countries of the world. The mode of functioning and operational principles of a farmers market (Rythu Bazar) in the Telangana State of India has been described in this article. The participating smallholder farmers benefitted from higher price realization, an increase in earnings, and immediate cash receivables. However, the major constraint was inadequate storage facilities for leftover produce. The disruptions to food supply chains faced during the covid-19 pandemic have indicated that the farmer's markets were preferred by both poor and elite customers alike and thus the local food systems can provide resilience to the food systems. The SWOT analysis of the farmers' markets indicates a robust system with explicit strengths, and operational weaknesses, the opportunities are also many and with the suitable policy and affirmative action the threats can be addressed.

The market requirements for Medium Voltage (MV) metal enclosed switchgear are getting more and more stringent. Bothbuilding costs as well as the level of the transmitted and distributed electrical power have increased rapidly over the recentyears and are expected to continue to rise. This means that switchgear manufacturers must bring more and more compactand powerful systems on the market, while being simultaneously as cost effective as possible. Internal Arc Classification(IAC) of switchgear according to IEC and IEEE standards is one of the most important requirements to guaranteesafety in case of internal arc faults. Internal arcs cause a sudden pressure rise in electrical installations. This leads to anextreme pressure stress acting on switchgear compartments and switchgear rooms and could cause collapse of buildingsThis paper describes few important design rules and innovations, which were necessary to achieve best results duringinternal arc tests. Furthermore it demonstrates the effect of roof height during the internal arc test with the important casestudies. Finally the best solution to eliminate the roof effect is also proposed.

The presence of various types of pharmaceutical substances and organic pollutants in significant amounts and in traces were reported by many researchers globally. India in the past few decades has grown as one of the top five large scale producers of low-cost pharmaceutical products. More than 50% of revenue generated by Indian large scale and small-scale industries is through exports to developed countries like the United States of America. These pharmaceutical companies dispose of complex and non-biodegradable wastes into the environment, especially water resources which are either untreated or partially treated, therefore contaminating the aquatic ecosystem. These pharmaceutical products reach the ground and surface water easily through various routes such as waste water treatment plants (WTPs), pharmaceutical industries, hospital services, and through human and animal excreta and due to improper disposal of unused and expired medications. Regulatory bodies should frequently monitor the impact of these pharmaceutical products and micro pollutants on human health and aquatic ecosystems. Laws should be enforced for disposal of pharmaceutical and personal care products to prevent contamination of water bodies by employing advanced analytical detection methods like gas and liquid chromatography with mass spectrometry (GC-MS and LC-MS) to determine very low concentrations of pharmaceuticals in ng/L level. Drugs like Atenolol (β-blocker) and Amoxicillin (penicillin antibiotic) are excreted as such unmetabolized were detected in wastewater influent in study conducted in the UK. There is no or limited data in assessing the potential harm and health risks from exposure to these pharmaceutical products in water. No standardised protocol was laid to determine these pharmaceutical pollutants analytically. This present review is aimed in focusing on the pharmaceutical pollution of different water bodies by pharmaceutical products and their metabolites and highlights the importance of strict enforcement of laws governing prevention and removal of these pharmaceutical wastes based on previously published works. Keywords: Pharmaceutical products, organic pollutants, wastewater treatment plants (WTPs), water resources and drinking water.

Purpose: Brain tumour detection and classification require trained radiologists for efficient diagnosis. The proposed work aims to build a Computer Aided Diagnosis (CAD) tool to automate brain tumour detection using Machine Learning (ML) and Deep Learning (DL) techniques. background: Early detection of brain tumours using CAD systems can accelerate treatment and reduce mortality rate . In recent times, advancements in medical imaging techniques and advent growth in Artificial Intelligence (AI) techniques have improved the CAD based diagnosis of brain tumour. Therefore, the proposed CAD system uses Machine Learning (ML) and Deep Learning (DL) techniques for detection and grading the severity of brain tumours using brain Magnetic Resonance Images (MRI). Materials and Methods: Magnetic Resonance Image (MRI) collected from the publicly available Kaggle dataset is used for brain tumour detection and classification. Deep features extracted from the global pooling layer of Pretrained Resnet18 network are classified using 3 different ML Classifiers, such as Support vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT). The above classifiers are further hyperparameter optimised using Bayesian Algorithm (BA) to enhance the performance. Fusion of features extracted from shallow and deep layers of the pretrained Resnet18 network followed by BA-optimised ML classifiers is further used to enhance the detection and classification performance. The confusion matrix derived from the classifier model is used to evaluate the system's performance. Evaluation metrics, such as accuracy, sensitivity, specificity, precision, F1 score, Balance Classification Rate (BCR), Mathews Correlation Coefficient (MCC) and Kappa Coefficient (Kp), are calculated. objective: To propose a multilayer CNN and deep feature-based CAD framework for brain tumour detection and classification of 2D MRI images. Results: Maximum accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC, and Kp of 99.11 %, 98.99 %, 99.22 %, 99.09 %, 99.09 %, 99.10 %, 98.21 %, 98.21 %, respectively, were obtained for detection using fusion of shallow and deep features of Resnet18 pretrained network classified by BA optimized SVM classifier. Feature fusion performs better for classification task with accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC and Kp of 97.31 %, 97.30 %, 98.65 %, 97.37 %, 97.34 %, 97.97%, 95.99 %, 93.95 %, respectively. method: Magnetic resonance Images (MRI) collected from Publicly available Kaggle dataset is used for brain tumour detection and classification. Deep features extracted from Pretrained Resnet18 network is classified using 3 different ML Classifiers such as Support vector Machine (SVM), K-Nearest Neighbour (K-NN) and Decision Tree (DT). The above classifiers are further hyperparameter optimised using Bayesian algorithm (BA) to improve the performance. Feature fusion of shallow and deep layers of pretrained Resnet18 network followed by BA optimised ML classifiers is further used to enhance the detection and classification performance. Confusion matrix derived from the classifier model is used to evaluate the system performance. Metrics such as accuracy, sensitivity, specificity, precision, F1 score, Balance Classification Rate (BCR), Mathews Correlation Coefficient (MCC) and Kappa Coefficient ( Kp) are calculated. Conclusion: The proposed brain tumour detection and classification framework using deep feature extraction from Resnet 18 pretrained network in conjunction with feature fusion and optimised ML classifiers can improve the system performance. Henceforth, the proposed work can be used as an assistive tool to aid the radiologist in automated brain tumour analysis and treatment. other: The proposed research is the one among the first to employ feature fusion of shallow and deep layers of Resnet-18 pretrained network in conjunction with Bayesian optimised ML classifiers to improve the performance of the proposed CAD system compared to existing literature.

A field experiment was carried out during the Navarai season (2022) using rice variety ADT - 37 to assess the impact of inorganic, organic, and biological sources of nutrients on the growth and yield of rice in an Inceptisol soil of Tamil Nadu. The experimental soil was clay loam in texture and had a pH-8.10, EC-0.7 dSm-1, and available NPK of 186.20, 23.30, and 192.25 kg ha-1, respectively. The experiment comprised of nine treatments viz., T1 – Control, T2 - 100% RDF, T3 - 125% RDF , T4 - 100% RDF + Press mud @ 10 t ha-1 + 2% Zinc Solubilizing Bacteria, T5 – 100% RDF + Vermicompost @ 6 t ha-1 + 2% Zinc Solubilizing Bacteria, T6 – 100% RDF + Coirpith Compost @ 6 t ha-1 + 2% Zinc Solubilizing Bacteria, T7 – 125% RDF + Press mud @ 10 t ha-1 + 2% Zinc Solubilizing Bacteria, T8 – 125% RDF + Vermicompost @ 6 t ha-1 + 2% Zinc Solubilizing Bacteria, T9 – 125% RDF + Coirpith Compost @ 6 t ha-1 + 2% Zinc Solubilizing Bacteria. The results of the experiment revealed that application of 125% RDF + Vermicompost @ 6 t ha-1 + 2% Zinc Solubilizing Bacteria (T8) recorded higher values in growth and yield parameters viz., plant height (106.8 cm), Number of productive tillers hill-1 (14.5), Leaf area index (8.02), chlorophyll content (1.285 mg g-1) Number of panicles m-2 (585), Number of grains panicle-1 (227) Dry matter production (5720 kg ha-1), Grain yield (6293 kg ha-1) and Straw yield (8943 kg ha-1).

In construction, the real-time monitoring of the worker is necessary for ensuring safety in terms of health and accidents. The technology advancement in the sensors and wireless communication technology has inspired to implement Internet of Things (IoT) real-time monitoring in construction site. With this motivation, in this study we have proposed a system that is powered with long range (LoRa) and IEEE 802.15.4 based Zigbee communication for real-time implementation. Worker health monitoring mote, helmet detection mote, shoe detection mote, and glove detection mote are the primary components of the proposed system. In addition to this, a local server is embedded to supervise all the primary components and interconnect with gateway to log the real-time data on the cloud server for real-time implementation. As proof of concept, one of primary component i.e., health monitoring mote is implemented in the construction site to measure temperature and pulse rate of the work. In future, the remain components will be implemented in construction site, for the implementation of complete system in real-time. The current study enables to monitor the worker in the construction site and assist to respond immediately in case of emergency.