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Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection and treatment of pneumonia are essential for avoiding complications and enhancing clinical results. We can reduce mortality, improve healthcare efficiency, and contribute to the global battle against a disease that has plagued humanity for centuries by devising and deploying effective detection methods. Detecting pneumonia is not only a medical necessity but also a humanitarian imperative and a technological frontier. Chest X-rays are a frequently used imaging modality for diagnosing pneumonia. This paper examines in detail a cutting-edge method for detecting pneumonia implemented on the Vision Transformer (ViT) architecture on a public dataset of chest X-rays available on Kaggle. To acquire global context and spatial relationships from chest X-ray images, the proposed framework deploys the ViT model, which integrates self-attention mechanisms and transformer architecture. According to our experimentation with the proposed Vision Transformer-based framework, it achieves a higher accuracy of 97.61%, sensitivity of 95%, and specificity of 98% in detecting pneumonia from chest X-rays. The ViT model is preferable for capturing global context, comprehending spatial relationships, and processing images that have different resolutions. The framework establishes its efficacy as a robust pneumonia detection solution by surpassing convolutional neural network (CNN) based architectures.

Groundwater samples were collected from 30 sampling sites throughout the Subarnarekha River Basin for source apportionment and risk assessment studies. The concentrations of As, Ba, Cd, Cr, Co, Cu, Fe, Mn, Mo, Ni, Se, Sr, V and Zn were determined using inductively coupled plasma-mass spectrometry (ICP-MS). The results demonstrated that concentrations of the metals showed significant spatial variation with some of the metals like As, Mn, Fe, Cu and Se exceeding the drinking water standards at some locations. Principal component analysis (PCA) outcome of four factors that together explained 84.99 % of the variance with > 1 initial eigenvalue indicated that both innate and anthropogenic activities are contributing factors as source of metal in groundwater of Subarnarekha River Basin. Risk of metals on human health was then evaluated using hazard quotients (HQ) and cancer risk by ingestion for adult and child, and it was indicated that Mn was the most important pollutant leading to non-carcinogenic concerns. The carcinogenic risk of As for adult and child was within the acceptable cancer risk value of 1 × 10 −4 . The largest contributors to chronic risks were Mn, Co and As. Considering the geometric mean concentration of metals, the hazard index (HI) for adult was above unity. Considering all the locations, the HI varied from 0.18 to 11.34 and 0.15 to 9.71 for adult and child, respectively, suggesting that the metals posed hazard by oral intake considering the drinking water pathway.

Brain tumors are a significant challenge to human health as they impair the proper functioning of the brain and the general quality of life, thus requiring clinical intervention through early and accurate diagnosis. Although current state-of-the-art deep learning methods have achieved remarkable progress, there is still a gap in the representation learning of tumor-specific spatial characteristics and the robustness of the classification model on heterogeneous data. In this paper, we introduce a novel Pyramidal Attention-Based bi-partitioned T Network (PABT-Net) that combines the hierarchical pyramidal attention mechanism and T-block based bi-partitioned feature extraction, and a self-convolutional dilated neural classifier as the final task. Such an architecture increases the discriminability of the space and decreases the false forecasting by adaptively focusing on informative areas in brain MRI images. The model was thoroughly tested on three benchmark datasets, Figshare Brain Tumor Dataset, Sartaj Brain MRI Dataset, and Br35H Brain Tumor Dataset, containing 7023 images labeled in four tumor classes: glioma, meningioma, no tumor, and pituitary tumor. It attained an overall classification accuracy of 99.12%, a mean cross-validation accuracy of 98.77%, a Jaccard similarity index of 0.986, and a Cohen’s Kappa value of 0.987, indicating superb generalization and clinical stability. The model’s effectiveness is also confirmed by tumor-wise classification accuracies: 96.75%, 98.46%, and 99.57% in glioma, meningioma, and pituitary tumors, respectively. Comparative experiments with the state-of-the-art models, including VGG19, MobileNet, and NASNet, were carried out, and ablation studies proved the effectiveness of NASNet incorporation. To capture more prominent spatial-temporal patterns, we investigated hybrid networks, including NASNet with ANN, CNN, LSTM, and CNN-LSTM variants. The framework implements a strict nine-fold cross-validation procedure. It integrates a broad range of measures in its evaluation, including precision, recall, specificity, F1-score, AUC, confusion matrices, and the ROC analysis, consistent across distributions. In general, the PABT-Net model has high potential to be a clinically deployable, interpretable, state-of-the-art automated brain tumor classification model.

Worldwide, pneumonia is the leading cause of infant mortality. Experienced radiologists use chest X-rays to diagnose pneumonia and other respiratory diseases. The diagnostic procedure's complexity causes radiologists to disagree with the decision. Early diagnosis is the only feasible strategy for mitigating the disease's impact on the patent. Computer-aided diagnostics improve the accuracy of diagnosis. Recent studies established that Quaternion neural networks classify and predict better than real-valued neural networks, especially when dealing with multi-dimensional or multi-channel input. The attention mechanism has been derived from the human brain's visual and cognitive ability in which it focuses on some portion of the image and ignores the rest portion of the image. The attention mechanism maximizes the usage of the image's relevant aspects, hence boosting classification accuracy. In the current work, we propose a QCSA network (Quaternion Channel-Spatial Attention Network) by combining the spatial and channel attention mechanism with Quaternion residual network to classify chest X-Ray images for Pneumonia detection. We used a Kaggle X-ray dataset. The suggested architecture achieved 94.53% accuracy and 0.89 AUC. We have also shown that performance improves by integrating the attention mechanism in QCNN. Our results indicate that our approach to detecting pneumonia is promising.

Dual topological materials are unique topological phases that host coexisting surface states of different topological nature on the same or on different material facets. Here, we show that Bi 2 TeI is a dual topological insulator. It exhibits band inversions at two time reversal symmetry points of the bulk band, which classify it as a weak topological insulator with metallic states on its ‘side’ surfaces. The mirror symmetry of the crystal structure concurrently classifies it as a topological crystalline insulator. We investigated Bi 2 TeI spectroscopically to show the existence of both two-dimensional Dirac surface states, which are susceptible to mirror symmetry breaking, and one-dimensional channels that reside along the step edges. Their mutual coexistence on the step edge, where both facets join, is facilitated by momentum and energy segregation. Our observation of a dual topological insulator should stimulate investigations of other dual topology classes with distinct surface manifestations coexisting at their boundaries. Bi 2 TeI is identified as a dual topological insulator. It is a weak topological insulator with metallic states at the (010) surfaces and a topological crystalline insulator at the (001) surfaces.

Groundwater samples were collected from 22 locations for 3 seasons in mining areas of Singhbhum copper belt. The concentrations of Al, As, Ba, Co, Cu, Fe, Mn, Ni, V and Zn were evaluated using inductively coupled plasma mass spectrometry to assess the metal pollution using conventional heavy metal pollution index (HPI) and multivariate indexing approaches. Considering all the seasons, metal concentrations exceeded the drinking water standards for Fe and Mn for most of the locations and Al, Cu, Ni and Zn at some of the locations. An assessment using principal component analysis suggested that metals in groundwater in the area are derived from both geogenic and anthropogenic sources. The PCA-based index (PMI) was computed using the factor scores, which does not need any permissible limits or standards for its calculation. A classification of the index is also done using the terciles (3 quantiles) to demarcate the locations into “low”, “moderate” and “high” pollution classes. Marked differences could be identified between the results of HPI and the PCA-based index, of which the latter was found to be robust and more suitable for the study.

The goal of image cryptosystems is to protect image transmission when there are network adversaries present. To ensure secrecy, images are subject to encryption to produce unintelligible cipher images; the techniques used for this process differ significantly from those applied to text data. The majority of the cryptosystems consider complicated or confusion–diffusion architectures that change and permute the values of the pixels. These frequently entail binary operations like bitwise , addition–subtraction, DNA operations, etc, and are carried out utilizing chaotic maps, each having certain limitations. This paper employs a non-linear binary function applicable to traditional and DNA techniques for color image cryptosystems. In the proposed method, each of the color components of the image follows some steps which start with inter-channel mixing and mix rows, taking the values from the initial vector and mixing it. Arnold’s cat map algorithm is then applied to shuffle the pixels, and a novel spiral mixing of the pixels is applied in four different directions to prevent the differential attack. Finally, encoding, substitution, and decoding based on DNA is carried out. A multiple collapse chaotic map is used to derive initialization vectors, rule maps, and DNA substitution maps which are used in encoding and decoding processes. Experimental results and analysis show that the proposed cryptosystem results in cipher images that are resistant to different types of cryptographic attacks, thereby providing a secure color image encryption and decryption scheme.

Hydogrochemical investigation of groundwater resources of Paragraph district has been carried out to assess the solute acquisition processes and water quality for domestic and irrigation uses. Fifty-five groundwater samples were collected and analyzed for pH, electrical conductivity, total dissolved solids, hardness, major anions (F-, Cl-, NO3, HCO3-, SO42-) and cations (Ca2+, Mg2+, Na+, K+). Study results reveal that groundwater of the area is alkaline in nature and HCO3-, Cl-, Mg2+, Na+ and Ca2+ are the major contributing ions to the dissolved solids. The hydrogeochemical data suggest that weathering of rock forming minerals along with secondary contributions from agricultural and anthropogenic sources are mainly controlling the groundwater composition of Pratapgarh district. Alkaline earth metals (Ca2++Mg2+) exceed alkalis (Na++K+) and weak acid (HCO3-) dominate over strong acids (Cl-+SO42-) in majority of the groundwater samples. Ca-Mg-HCO3 and Ca-Mg-Cl-HCO3 are the dominant hydrogeochemical facies in the groundwater of the area. The computed saturation indices demonstrate oversaturated condition with respect to dolomite and calcite and undersaturated with gypsum and fluorite. A comparison of groundwater quality parameters in relation to specified limits for drinking water shows that concentrations of TDS, F-, NO3- and total hardness exceed the desirable limits in many water samples. Quality assessment for irrigation uses reveal that the groundwater is good for irrigation. However, values of salinity, sodium adsorption ratio (SAR), residual sodium carbonate (RSC), %Na and Kelley index are exceeding the prescribed limit at some sites, demanding adequate drainage and water management plan for the area. Copyright 2014 Geological Society of India

Metal toxicity in groundwater surrounding coal mines is a major concern because it may pose a significant risk to human health of the local populace. The present study investigated Al, As, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, Sr, and Zn concentrations in the groundwater of Umaria coalfields during the post-monsoon season and pre-monsoon season. The study was carried out to investigate the spatial and temporal variation of the metals in the groundwater along with statistical source identification of the metals and assessment of human health risks due to intake of the metals through the groundwater. The metals of concern were Al, Fe and Mn, which exceeded the Indian drinking water quality standards in 26%, 38% and 12% of samples in the post-monsoon season and 38%, 40% and 14% of samples in the pre-monsoon season. A marked decrease in metal concentrations in the post-monsoon season was also observed, which may be attributed to the dilution effect associated with the heavy rainfall during the monsoon season. Principal component analysis used to identify contamination sources of the metals indicated geogenic attributes, coal mining activities and vehicular load as the sources of the metals in the groundwater. The human health risk assessment suggested considerable risk to the local populace using the groundwater for drinking purposes. The probable health risk, as suggested by the Hazard Index, depicted a higher risk to the child population as opposed to the adults. The Hazard Index for the child population was greater than unity in 60% and 76% of the samples in the post- and pre-monsoon seasons, respectively, suggesting a significant risk of metal exposure from groundwater intake. The study also suggested that ingestion was the primary exposure pathway and risk due to dermal exposure was trivial. The carcinogenic risk due to As and Cr were within the acceptable limits except for one location each for As and Cr. The present study suggests a potential non-carcinogenic human health risk due to groundwater intake; hence, the study area needs routine groundwater quality monitoring.