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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.

The current scenario is to achieve dimensional accuracy and surface finish of the casting components which is identified to be a difficult task. It can be achieved through lost foam casting process. This casting process eliminate the difficulties in design considerations of casting products. Lost foam casting process has provided the quality of components through flexible manner. It’s being used to create complex metal pieces with less production time. In this experimental investigation, aluminium 6082 and silicon is fabricated through lost foam casting method. During casting process, the different input constraints like pouring temperature, slurry viscosity and cooling time were considered. The tensile strength is evaluated for the fabricated specimens. Taguchi technique is applied to determine the optimal factor. The contribution of factor is confirmed through variance analysis. The effect of factor and its correlation were studied through interaction plot.

Introduction: Luxatio erecta femoris or inferior hip dislocation is the rarest type of hip dislocation. It can be caused by a severe trauma to the hip and the mechanism of injury is often very complex. Very few cases have been reported in the literature, and often associated with other musculoskeletal injuries. Overall about 3026 cases were reported till recently. Case Presentations:We came across five cases of Luxatio erecta femoris from our six year study period from 2015 to 2020. All five cases were males (mean age-28.4 years, age range 20–40 years). All of them were isolated injuries (pure dislocation without any associated femur, acetabular, or pelvic fractures). We will present the details of these cases along with review of literature, trying to unravel the mechanism of such an injuries. Conclusion:With the increase in high energy road traffic accidents, the incidence of luxatio erecta femoris in adults has increased. We could identify all those injuries and treated it with excellent clinical outcome. This type of rare dislocations if properly identified, we can manage it without much delay and very minimal complications. Keywords:Luxatio erecta femoris, dislocation, trauma.

Introduction: Hydatid disease of the bone is a parasitic infestation in the form of cystic echinococcosis by a tapeworm echinococcus granulosus. Its manifestation in the bone is relatively low and has the ability to mimic other skeletal pathologies. Case Report: We came across a 30-year-old male farmer with complaints of sero-purulent discharge from a sinus tract wound in the axilla for the past 3 years. He had been previously evaluated elsewhere with blood investigations, pus culture/sensitivity tests, MRI, and CT scans. Blood reports and culture/sensitivity tests were inconclusive and imaging tests were suggestive of a sinus tract originating from the right scapula. He was incorrectly diagnosed as a case of Tuberculosis of the Scapula and started on anti-tubercular medications for 9 months which was ineffective. To confirm the diagnosis, a biopsy was taken from the scapular lesion through a posterior approach. Histological tests revealed a cystic lesion composed of trilaminar membrane consisting of dead and degenerating scolices consistent with “Hydatid Cyst of Bone.” Conclusion: Hydatid disease of the bone has an inconsistent clinical picture which makes diagnosing this rare disease even more difficult. A high suspicion for hydatid infestation in bone pathologies could help in diagnosing the disease at the earliest.

The study taken up during 2021 aimed at assessing the extent of financial inclusion and its determinants among the farmers in two aspirational districts (Raichur and Yadgir) of Karnataka and selected 30 respondents per block from two block per district making a sample size of 120. The overall extent of financial inclusion was moderate. The level of awareness of majority of the respondents on financial products and services was moderate to high, but majority of them reported difficulty in accessing them. The level of usage was found to be low, whereas satisfaction level was moderate. The determinants study revealed that age, distance to financial institute and education (Illiteracy, Primary) were negatively related to extent of financial inclusion. Operational land holding and annual income were observed to be positive and significant. Frequency of visit to financial institute was positive and significant contributor to extent of financial inclusion. The results are clear cut indication for tailored plans, polices and strategies for farmers’ financial inclusion.

Introduction: Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) imaging is the mainstay of breast MRI techniques in characterising breast masses. Diffusion-weighted Imaging (DWI) is an adjunct MRI technique to differentiate between benign and malignant breast masses. Aim: To evaluate the diagnostic efficacy of breast MRI by combining DCE-MRI and DWI to differentiate benign from malignant breast masses and compare it with histopathology. Materials and Methods: The present cross-sectional study was conducted in the Department of Radiology and Imaging, Bharat Scans private limited, Chennai, India, from July 2013 to April 2015. A total of 51 patients with suspicious breast masses detected by mammography and/or ultrasonography were evaluated by DCE-MRI and DWI using General Electric (GE) 3 Tesla Magnetic Resonance Imaging. The results were compared with histopathology. Sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Values (NPV) were calculated for DCE-MRI and for the combined method using DCE-MRI with DWI. Statistical analysis was done using Statistical Package for the Social Sciences (SPSS) software version 17.0 and Open Epi software. A p-value

Press-brake bending is one of the important sheet metal forming processes widely used to fabricate different angles and channels in automotive applications. Plastic deformation of material during bending generates elastic stress waves known as acoustic emission (AE), enabling online monitoring of the bending process. The analysis of these stress waves helps in process characterization and aids in the identification of defects. The non-homogeneous plastic deformation that occurs during bending leads to the development of residual stress (RS) in the bend regions after forming. The locked-in residual stresses that remain after bending are measured using the x-ray diffraction method. The present study aims to use the AE technique for online bend monitoring of austenitic stainless steel (SS) of type 304L and correlate the AE parameters acquired during bending with the final locked-in RS present in the bend regions. Toward this, SS 304L sheets were bent to various bend angles, viz. 45°, 60°, 90°, 108°, 120°, and 135°, along the rolling direction and perpendicular to the rolling direction. Among the many AE parameters derived from the analysis of AE signals, it is shown that normalized cumulative counts and energy increase with bend angle. The increase in AE is well correlated with the RS that develops due to plastic deformation. The observed correlations between AE parameters acquired during bending and RS measured after bending are discussed.

The present scenario indefinitely needs certain developments in the field of renewable energy as an effective replacement of conventional energy sources. Reflection loss in solar cell is one of the reasons for reduction in power conversion efficiency which can be controlled through antireflective coatings on solar cell surface. This current research focuses on the development of MoSe2 nano-crystalline structure as an effective antireflective material for attaining enhanced light trapping ability. Electrospraying technique was taken into account for the deposition of thin films over the solar cell surface. Transition metal chalcogenide MoSe2 was deposited under argon atmosphere with the coating time of 30–120 min. The impact of thin film MoSe2 coating on solar cell surface was determined through optical, electrical, morphological and thermal studies. The thickness of optimal MoSe2 coating was found to be 761 nm through Atomic Force Microscopy technique. The maximum optical transmittance of 87.6 % was achieved at 90 min of coating (D3) within the spectrum of 300 to 800 nm wavelength. The minimum electrical resistivity of 90 min coated MoSe2 thin film coating over multicrystalline silicon solar cell was measured as 3.93 × 10− 3 Ω-cm. The enhanced power conversion efficiency of MoSe2 coated solar cell under open and closed conditions were found to be 17.13 and 18.67 % especially for D3 solar cell sample, which facilitates maximum transmission of incident photons into the solar cell. From the observed results, it is evident that MoSe2 nanostructure was found to be promising antireflection coating material for multicrystalline silicon solar cell.

In this paper, an endeavour is made to study the feasibility of natural coagulants like Cicer arietinum, Dolichos lablab, Maize seeds, Moringa oleifera seeds, Okra seeds, Orange peel, Peanut seeds, Roselle seeds, Sapodilla seeds and Strychonos potatorum seeds on the reduction of phosphorous content from Industrial wastewater. The batch coagulation test was done to identify the optimum coagulant dosage, pH level at which the removal efficiency is high, mixing time and floc settling time required for the removal of phosphorous in the wastewater. A chart was plotted between the removal efficiency of all the chosen coagulants corresponding to the dosage, pH, mixing time and settling time. The removal percentage of phosphorous is more for Moringa oleifera seeds with 93%, followed by Strychonos potatorum seeds with 89% and Sapodilla seeds with 83% demonstrates the minimum phosphorous removal from the wastewater as 37%. Moringa oleifera seeds can be utilised as an effective coagulant for the removal of phosphorous from the wastewater. The maximum percentage removal efficiency of phosphorous was achieved by Moringa oleifera seeds for the coagulant dosage 5.8 mL, pH of 7, mixing time of 30 minutes, settling time of 30 minute and the final phosphorus concentration of 306.1 mg/L was achieved. Also, the corresponding values of pH, optimum coagulant dosage, mixing time and settling time for the phosphorus removal vary according to each chosen coagulants. Based on this study, a model was developed in accordance with the coagulation dosage and concentration of phosphorus. The overall regression accuracy of the Langmuir hypothesis model was identified as r2 = 0.94.

The research aims to improve the productivity of freshwater yield by increasing the operating time of the single basin multi slope solar still (SBMSSS). It is achieved by the integration of the SBM...

The high trap-state density and low conductivity in compact TiO2 (c-TiO2) layer limits the power conversion efficiency (PCE) of organic-inorganic hybrid perovskite solar cells (HPSCs). Metal doping in c-TiO2 has been proven to be a successful strategy for enhancing the PCE of HPSCs. Herein, Zr is incorporated in the c-TiO2 layer with different doping concentrations, and its impact on the photovoltaic performance of methylammonium lead iodide (MAPbI3) HPSCs is investigated. Inclusion of Zr enhances the charge carrier collection at TiO2/perovskite junction as well as conductivity of TiO2 layer. It is demonstrated that Zr-doping reduces the dark current significantly by suppressing non-geminated recombinations, leakage path, and electron trap-states. As a result, the HPSCs with optimum Zr-doping (10 vol%) exhibits open-circuit voltage (VOC) of 1.076 V, short-circuit current density (JSC) of 23.57 mA cm−2, and PCE of 18.16% under one-sun illumination conditions. UV absorption confirms the increase in bandgap upon Zr-doping that leads to favorable band alignment with the perovskite layer. Further, the engineered solar cells are probed for current density-voltage (J-V) hysteresis, operational stability, leakage current, intensity-dependence behavior, and electrochemical impedance spectroscopy (EIS).

Aluminium 7075 alloy was reinforced with silicon nitride (Si3N4) in the range of 4%, 8% and 12% were fabricated using stir casting route. Casted specimens are heat-treated at various conditions to eliminate the Porosity and X-ray computed tomography and image analyzer techniques were used to measure the Porosity in casting. Further, the casted specimens were tested for chemical composition using Spectro analysis; micro image and micro-hardness are conducted. Dispersion of Si3N4 in Al alloy was observed using a scanning electron microscope.

The bedridden and physically disabled person is unable to move from their bed and they need a caretaker to take care of them or to assist them in this work. They are in the situation of spending more money on the caretaker. Thus, a design solution is proposed to help bedridden patients in their defecation activities. This design helps people, by giving them ease to pass their excreta by sitting on a specially designed bed. The control of the machine lies with the person lying on the bed. On switching ON the machine, the headrest rises and the leg rest lowers. This gives a sitting comfortable position for the patient to carry out the defecation. Along with that movement, the door on the bed opens and the basin set-up fixed under the cot moves sideward till it covers the bed opening. After the completion of the process, the person confirms and makes the next move by pressing the actuation button. When it is pressed, the flush is done and the waste is then transferred to the outlet which is connected to the toilet drainage. The machine has an inlet where the water supply enters the basin. After the flushing of the toilet, a forced water supply from the machine sprays the water to clean the patient’s back. The whole mechanisms are to be controlled by an Arduino controller board and limit switches.

This experimental work explains and compares the effect of preheating in Premixed Charge Compression Ignition technique(PCCI) by using three different fuels such as diesel, citronella oil – diesel blend (B20), cedarwood oil – diesel blend (B20). This experiment have been carried in a single cylinder diesel engine by incorporating a premixing chamber with port fuel injector in the intake port, and placing the preheater before to prechamber near to the surge tank in the intake manifold, the injector , preheater is controlled through ardino micro controller Experiments results showed that there is an increase in brake thermal efficiency with citronella oil – diesel blend (B20), cedarwood oil – diesel blend (B20), and also there is a simultaneous decrease in hydrocarbon (HC) and carbon monoxide (CO) emissions due the preheating effect. The performance results indicated that there is a drop in volumetric efficiency due do preheating effect of the premixed fuels, penalting in increased fuel consumption. NOx is lower for diesel fuel since it contains lower oxygen molecule than citronella, cedarwood diesel blends. So Neat diesel has the lower emission characteristics than the other premixed fuels in PCCI technique. And the effect of fuel distribution inside the premixing chamber is shown by using ANSYS-FLUENT.