Background: The aim of the study was to compare the accuracy and efficacy of automated three-dimensional (3D) software-aided computed tomography (CT) volumetry and manual CT volumetry in calculating the graft weight of living-donor livers for liver transplant (LT) with actual graft obtained during surgery. Materials and Methods: A total of 27 volunteer donors for LT were evaluated and operated at an LT Center from 2014 to 2017. Triple-phase contrast-enhanced computed tomography was done for all the donors and their graft liver volumes were calculated manually and by an automated postprocessing software. The residual liver volume was also calculated automatically with remnant percentage of total volume. Donor hepatectomies were performed as per the standard procedure. Graft volume requirement is assessed preoperatively based on body weight and Graft Recipient Weight Ratio >0.8 of the recipient. The correlation of graft weight calculated by these two methods was done against actual graft weights (AGWs) during surgery to evaluate the exact accuracy and efficacy of these preoperative measurements. Results: The average total liver volume (TLV) by the automated 3D method was 1207.22 cc (range 785–1596 cc), while the average TLV by the manual method was 1286 cc (range 904.7–1590 cc). The mean graft volume calculated by 3D-automated method and manual volumetry was 581.96 cc and 610.70 cc, respectively, while AGW calculated was 562.55 g. The mean difference between graft volume calculated by 3D-automated volumetry method and AGW was 19.40 g, while the mean difference between graft volume calculated by the manual volumetry method and AGW was 48.14 g. Conclusion: Manual volumetry and 3D-automated volumetry methods were both efficient and precise in measuring and predicting the graft volumes (TLV, standard liver volume, and future liver remnant) in living donor LT.

Background: Wide variations in the soil health indicators were observed among different cropping systems. The soil texture under various sites selected in the present study varied from sandy loam. However, sandy loam was observed as the most dominant texture both under cereal and vegetables based on cropping systems. Soil reaction across various sites under present study was neutral to slightly alkaline in arid regions. Methods: A total of 90 surface (0-15 cm) and subsurface (15-30 cm) soil samples collected randomly from vegetable and cereal-based cropping systems. After collecting soil samples, these were air dried and analyzed for physical, chemical and biological properties. The study determined the level of availability of nutrients and knew the fertility status of studied areas. Result: The results show that higher salt accumulation (EC) was observed under vegetable-based cropping systems as compared to those of cereal-based. Organic carbon was medium to high and the available N, P and K contents were in the low to medium category. Organic carbon and available N-P and K contents were higher under the vegetable-based cropping systems than cereal-based. DTPA Fe, Mn, Zn and Cu were observed efficiently. Microbial biomass carbon, microbial biomass nitrogen, potentially mineralizable nitrogen and soil respiration were higher in vegetable-based cropping systems. Higher soil quality index was observed under the vegetable-based cropping system as compared to the cereal based cropping system.

Comprehensive assessment of groundwater quality in mining-affected regions is crucial to sustainably manage water resources and protect public health and ecosystems. This study investigated the hydrogeochemical characteristics and water quality of 18 dug wells in the Korba basin, Chhattisgarh, India, an area heavily impacted by coal mining activities. Water samples were collected over three seasons (pre-monsoon, monsoon, and post- monsoon) and analyzed to determine physicochemical parameters, major ions, trace elements, and carbon content. Results revealed very high total dissolved solids concentrations ranging from 315 to 19,738 mg L- 1 . Nitrate levels surpassed the Bureau of Indian Standard (BIS) limit of 45 mg L- 1 in over 50% of samples, reaching a maximum of 200 mg L- 1 . Fluoride concentrations in all samples exceeded the BIS limit (1.5 mg L- 1 ), ranging from 1.5 to 15.2 mg L- 1 . The predominant water type was Ca-Mg-HCO3, primarily influenced by rock-water interactions. Factor analysis indicated that both geogenic and anthropogenic processes influence pollution levels. Pollutant concentrations exhibited seasonal variations, generally peaking during the monsoon period. Temporal analysis from over six years revealed increasing trends for most parameters, indicating deteriorating water quality. Based on Water Quality Index values, all samples were classified as unsuitable for drinking, while assessments of irrigation water quality using various indices indicated that 61.11% of samples were suitable for agricultural use. The findings provide data to inform decision-making and public health protection in this heavily industrialized region and emphasize the urgent need for sustainable water resource management and pollution prevention strategies in the Korba basin to align with UN Sustainable Development Goals 3 (good health and well-being) and 6 (clean water and sanitation).

Several million tons of coal are extracted which discharge the contaminated mineral water into the environment in the coal mines located in the Korba basin (Chhattisgarh, India) chosen as the study area. The aim of this work is to describe: (i) the physico-chemical characteristics (pH, EC, TDS, DO, RP, CC (carbonate carbon), OC (organic carbon) F-, Cl-, NO3-, SO42-, SiO44-, PO43-, Na+, K+, Mg2+, Ca2+, Al, As, Sb, Fe, Mn, Zn, Cd, Pb, and Hg) of the coal mine water samples, (ii) spatial, seasonal (i.e. PrM (pre-monsoon), M (monsoon) and PtM (post monsoon) and temporal (over the period of 2012-2017) variations, (iii) sources of contaminants from fourteen coal mines, (iv) suitability of the water for drinking and other uses, and (v) health impact of the mine water in view of developing remedial approaches.The mine water is neutral in nature with high TDS (total dissolved solid) values ranging from 620 to 13711 mg L-1 due to mainly high carbon content. The concertation of all species lies between 519-11432 mg L-1 with maximum value of OC. The most dominating species were OC, CC, Cl-, NO3-, SO42-, Na, and Ca. Species, that is, F-, OC, Al, Fe, As, Sb, Cd, Pb, and Hg occurred beyond their limits. Their higher concentration in the PtM is observed. Water quality index (WQI) values ranged from 17.52 to 95.68 in the PtM period. 42.85%, 21.42%, and 7.14% of water samples represent "excellent water", "good water" and "poor water" respectively in the PtM period. In addition, the fertilizer and trace element concentrations in the water samples were compared with the limit values determined for usability as drinking water. Accordingly, it was concluded that it is not suitable for use as drinking water in terms of TDS, F-, and NO3- concentrations. In addition, AsT (total arsenic), F- and NO3- pollutants were detected in the water samples, and it was observed that F- and NO3- ions did not have a carcinogenic effect. A health risk assessment of As has been made, and it is found that adults and children have a low risk of developing cancer from the exposure. However, arsenic has high non-carcinogenic and potentially harmful effects.

Breast cancer is a leading factor behind cancer-caused fatalities in women all around the world. Over the past few years, the use of mammographic images for breast cancer analysis has become increasingly popular due to its noninvasive nature. In this study, we applied optimization techniques to enhance the precision of mammographic image-based breast cancer analysis. DenseNet169 and Bi-LSTM were employed for feature extraction. These are advanced machine learning models capable of automatically extracting sophisticated features from images. We also applied particle swarm optimization (PSO) for feature selection, which is a metaheuristic optimization technique that can efficiently select the most informative features for classification. To construct our model, we used one input layer, three dense layers, and one output layer. We evaluated performance of our model by measuring several metrics such as accuracy, f1-score, support, recall, precision, confusion matrix, and specificity. Outcomes of our study demonstrated that our model was able to accurately distinguish cases of breast cancer with a remarkable precision of 99.23

Besides the enhancement of the Internet of Things (IoT) distributed environment, anomalous activities are also escalating rapidly. Therefore, improving the trustworthiness of distributed networks is required for the extensive adoption of IoT infrastructure. Establishing a security mechanism in IoT networks is a challenging task as communication links are lossy and connected devices are resource-dependent. Conventional security techniques such as intrusion detection systems (IDS) are insufficient to shelter the IoT-distributed environment due to less computational capacity, restricted upgraded devices, and mismatched protocols. This paper proposes a novel machine learning-based trustworthy model for IoT attack detection. The proposed system combines the capability of Ada-boost and Gradient-boost to classify anomalous activities with low computational capacity proficiently and within a minimum time frame. Experiments were conducted on Distributed Smart Space Orchestration System (DS2OS) IoT dataset to assess the significance of the novel attack detection model. The demonstration shows that the proposed model obtains 98.28

Breast cancer (BC) is one of the leading causes of death among women worldwide, as it has emerged as the most commonly diagnosed malignancy in women.Early detection and effective treatment of BC can help save women's lives.Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques.This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set.The novelty of the proposed framework lies in the integration of various techniques, where the fusion of deep learning (DL), traditional machine learning (ML) techniques, and enhanced classification models have been deployed using the curated dataset.The analysis outcome proves that the proposed enhanced RF (ERF), enhanced DT (EDT) and enhanced LR (ELR) models for BC detection outperformed most of the existing models with impressive results.

A field experiment was conducted at the Agronomy Research Farm of Nirwan University, Jaipur (Rajasthan) during the Rabi season of 2023-24. The experiment consisted of ten treatments with different combinations of phosphorus and sulphur. The results revealed that the combined application of phosphorus and sulphur significantly enhanced the growth and yield parameters of Indian mustard compared to the control. The highest plant height (152.91 cm), dry matter accumulation (436.47 g m-2), number of siliquae per plant (340.58), number of seeds per siliqua (13.45), seed yield (1897.89 kg ha⁻¹), stover yield (4723.33 kg ha⁻¹), and biological yield (6621.22 kg ha⁻¹) were obtained with the application of 45 kg ha⁻¹ phosphorus and 40 kg ha⁻¹ sulphur. The significant improvements in growth and yield attributes can be attributed to enhanced metabolic activity, better cell division, and increased photosynthetic efficiency resulting from phosphorus and sulphur availability. Consequently, the application of 45 kg ha⁻¹ phosphorus and 40 kg ha⁻¹ sulphur was found to significantly improve overall productivity of Indian mustard.

The rapid expansion and increasing complexity of Internet of Things (IoT) networks have led to a heightened need for effective and adaptable anomaly detection techniques. The vast variety of devices, communication protocols over 5G and other networks, and data types present in diverse IoT environments poses significant challenges for traditional centralized methods. With the rapid increase of users in 5G networks will require drastic security measures in IoT. This paper proposes an edge-assisted federated learning approach for detecting anomalies in heterogeneous IoT networks, enabling robust and efficient performance across a wide range of devices and scenarios. Our proposed method combines the advantages of federated learning and edge computing, allowing IoT devices to collaboratively train a shared machine learning model while keeping their data local. This approach not only preserves privacy but also reduces communication overhead and latency, providing a scalable solution for large-scale IoT deployments. By incorporating edge computing, our method ensures that data processing occurs closer to the source, further improving efficiency and reducing the reliance on centralized cloud resources. We present a thorough evaluation of our edge-assisted federated learning approach, comparing it to traditional centralized techniques as well as other distributed learning methods. The results demonstrate that our approach achieves superior performance in detecting anomalies in diverse IoT environments while maintaining low latency, communication overhead over network, and energy consumption. Additionally, we showcase the adaptability of our method to various IoT network configurations and device capabilities, highlighting its potential as a versatile solution for real-world IoT anomaly detection challenges.

One of most prevalent types of cancer and the main reason of fatality for women is breast cancer. Mammography images of the breast are used by radiologists to search for indications of potential tumor development, such as breast masses, tissue lumps that may be the result of cancer cells, and micro-calcifications, which are tiny calcium deposits that collect around aberrant tissue. Machine learning algorithms, which learn from historical data and can anticipate the category of fresh input, are used to classify benign and malignant tumors. Pre-processing, feature extraction, selection, and classification are the four steps in which the breast detection system is implemented in this paper. This paper introduces the Random Forest classifier which employs feature selection and transfer learning to identify and classify breast cancer in histopathological images. The suggested approach classifies benign and malignant cells by feeding features extracted from pictures into a fully connected layer using VGG-16 and Densenet 121. This classification is an excellent attempt that successfully detects using feature extraction and selection.

The pace of energy use has significantly grown during the previous several years. In order to reduce energy consumption and demand, energy management systems (EMS) are required in households, workplaces, structures, industries, etc. Newly developing technologies like artificial intelligence (AI), the Internet of Things (IoT), big data, machine learning (ML), deep learning (DL), etc., may assist with this. This helps the users to achieve a very new, sustainable, and advanced life experiences in their homes. This paper aims to discuss smart home energy consumption and weather conditions which affects the demand and consumption of energy in any particular environment. In this research work, a smart home dataset which has different parameters of energy consumption and weather conditions is taken from the online repositories. This dataset is preprocessed using different machine learning techniques. After the preprocessing, the best suited model for the predictive modeling of the energy consumption in smart homes is obtained. A comparative analysis is carried out to find the best techniques among the existing techniques with the batter results and less error rate. This paper aims to perform the predictive modeling of the energy consumption dataset and find out the best suited technique with less error rate.

A brain tumor is a serious malignant condition caused by unregulated as well as aberrant cell partitioning. Recent advances in deep learning have aided the healthcare business, particularly, diagnostic imaging for the diagnosis of numerous disorders. The most frequent and widely utilized machine learning model for image recognition is probably task CNN. Similarly, in our study, we categorize brain MRI scanning images using CNN and data augmentation and image processing techniques. We compared the performance of the scratch CNN model with that of pretrained VGG-16 models using transfer learning. Even though the investigation is carried out on a small dataset, the results indicate that our model’s accuracy is quite successful and has extremely low complexity rates, achieving 100 percent accuracy compared to 96 percent accuracy for VGG-16. Compared to existing pretrained methods, our model uses much less processing resources and produces substantially greater accuracy.

In smart homes, the management of energy is gaining huge significance among researchers in recent times. This paper presents a system for predicting power utilization and scheduling household appliances in smart homes. The system utilizes a combination of Grey Wolf optimization (GWO), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) to improve energy management. The GWO algorithm is used to enhance the performance of the CNN-LSTM model. GWO is an optimization algorithm inspired by the hunting behaviour of grey wolves. It helps in finding optimal solutions for complex problems by mimicking the social hierarchy and hunting mechanisms of wolves. The fusion of CNN and LSTM serves as a pattern finding strategy for energy management. CNN is effective in extracting spatial features from data, while LSTM can capture temporal dependencies. By combining these two approaches, the model can analyze energy consumption patterns and make accurate predictions. To evaluate the performance of the proposed model, the paper uses three error metrics: Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). The reported values of RMSE, MSE, and MAE are 0.6213, 0.3860, and 0.2808, respectively. These metrics indicate the accuracy of the model’s predictions, with lower values indicating better performance. Furthermore, this paper compares the proposed approach with the existing baseline models to access its superiority. According to the results, the proposed model outperforms the existing approaches in terms of prediction accuracy, as it achieves lower errors, compared to the baseline models. In summary, the proposed GWO-based CNN-LSTM network demonstrates improved prediction accuracy compared to the existing approaches, as indicated by the evolution metrics.

Cities across the globe are installing sensors, actuators and other devices, to become safer, greener, sustainable, and efficient with the hope of improving the urban interests of people. Sensing and collection of records are at the heart of any smart infrastructure, which can display itself and act on its own intelligently. Using sensors to screen public infrastructures, including bridges, roads, and homes, presents cognizance that enables more efficient use of resources based on the facts amassed by those sensors. As smart sensors, actuators, etc., play a critical role in the smart infrastructure, this chapter explores the smart sensors and actuators in IoT-enabled smart cities. As the domain of smart cities is emerging in the present days with a huge number of research opportunities for the researchers, also data collection and sensing play their role at the heart of the infrastructure. This chapter will critically explore the role and importance of Smart sensors and actuators and their applications, challenges, and opportunities, followed by various future trends in the domain of the smart city.

An understanding of nutritional quality response to different nutrient management practices is important to counter the widespread deficiencies of nutrients among humans.This study aimed to evaluate the effect of chemical fertilizers, lime, organic farming and natural farming practices on the yield and quality of maize ( L.) The experiment consisted of 11 treatments 100% NPK , 100% NPK+FYM Zea mays viz., (Farmyard manure) , 100% NPK + lime , organic farming practices , NFS (Natural Farming System)-cow ; NFS-Crossbred cow , NFS-Desi buffalo , organic farming practices + 25% NPK , NFS-cow + 25% NPK , NFS-Crossbred cow + 25% NPK , NFS-buffalo + 25% NPK ) The Desi 100% NPK+ FYM @ 10 t ha recorded highest maize grain equivalent yield (42.25 q ha ), reducing sugars (1.01%), non-reducing sugars -1 -1 (0.64%), crude protein (9.88%) and ash content (1.34%).Organic farming recorded highest total carbohydrate (72.65%) as well as starch content (68.68%).The calcium content in maize grains was highest in 100% NPK+ lime, whereas, phosphorus, magnesium, zinc and iron content were highest in 100% NPK+ FYM.The study concluded that higher crop yield and better nutritional quality can be achieved with a balanced application of NPK fertilizers along with FYM and lime, and organic manures plays a significant role in enhancing carbohydrate and starch content of maize grains.

An active research area where the experts from the medical field are trying to envisage the problem with more accuracy is diabetes prediction. Surveys conducted by WHO have shown a remarkable increase in the diabetic patients. Diabetes generally remains in dormant mode and it boosts the other diseases if patients are diagnosed with some other disease such as damage to the kidney vessels, problems in retina of the eye, and cardiac problem; if unidentified, it can create metabolic disorders and too many complications in the body. The main objective of our study is to draw a comparative study of different classifiers and feature selection methods to predict the diabetes with greater accuracy. In this paper, we have studied multilayer perceptron, decision trees, K-nearest neighbour, and random forest classifiers and few feature selection techniques were applied on the classifiers to detect the diabetes at an early stage. Raw data is subjected to preprocessing techniques, thus removing outliers and imputing missing values by mean and then in the end hyperparameters optimization. Experiments were conducted on PIMA Indians diabetes dataset using Weka 3.9 and the accuracy achieved for multilayer perceptron is 77.60%, for decision trees is 76.07%, for K-nearest neighbour is 78.58%, and for random forest is 79.8%, which is by far the best accuracy for random forest classifier.

Delayed gastric emptying (DGE) is the most common complications after Whipple pancreaticoduodenectomy (WPD). Braun enteroenterostomy (BEE) is a useful technique to divert bile from the stomach. We recently started doing binding pancreaticogastrostomy (BPG) for pancreatic reconstruction after WPD, and the most frequent complication was DGE. The aim was to study the effect of Braun enteroenterostomy on delayed gastric emptying in binding pancreaticogastrostomy following Whipple pancreaticoduodenectomy. The study included all patients who underwent BEE in BPG following WPD from February 2014 till May 2016 at a tertiary care center. Braun enteroenterostomy was constructed approximately 25 cm distal to the gastrojejunostomy by a side-to-side hand-sewn or stapled anastomosis. Delayed gastric emptying was defined as per International Study Group of Pancreatic Surgery (ISGPS) definition. All patient data including patient demographics, type of procedure performed, complications, mortality, hospital stay, postoperative interventions, or re-operations were documented. There were 13 (30.95%) patients with DGE A and 4 (9.52%) patients with DGE B, and no patients had DGE C. Hence, there were only 4 patients (9.52%) with clinically significant DGE. Addition of Braun enteroenterostomy reduces delayed gastric emptying after Whipple pancreaticoduodenectomy with binding pancreaticogastrostomy.