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Medical image machines serve as a valuable tool to monitor and diagnose a variety of diseases. However, manual and centralized interpretation are both error-prone and time-consuming due to malicious attacks. Numerous diagnostic algorithms have been developed to improve precision and prevent poisoning attacks by integrating symptoms, test methods, and imaging data. But in today's digital technology world, it is necessary to have a global cloud-based diagnostic artificial intelligence model that is efficient in diagnosis and preventing poisoning attacks and might be used for multiple purposes. We propose the Healthcare Federated Ensemble Internet of Learning Cloud Doctor System (FDEIoL) model, which integrates different Internet of Things (IoT) devices to provide precise and accurate interpretation without poisoning attack problems, thereby facilitating IoT-enabled remote patient monitoring for smart healthcare systems. Furthermore, the FDEIoL system model uses a federated ensemble learning strategy to provide an automatic, up-to-date global prediction model based on input local models from the medical specialist. This assures biomedical security by safeguarding patient data and preserving the integrity of diagnostic processes. The FDEIoL system model utilizes local model feature selection to discriminate between malicious and non-malicious local models, and ensemble strategies use positive and negative samples to optimize the performance of the test dataset, enhancing its capability for remote patient monitoring. The FDEIoL system model achieved an exceptional accuracy rate of 99.24% on the Chest X-ray dataset and 99.0% on the MRI dataset of brain tumors compared to centralized models, demonstrating its ability for precision diagnosis in IoT-enabled healthcare systems. [ABSTRACT FROM AUTHOR]

In the twenty-first century, global warming has become one of the severe challenges. Around one-fourth of the total carbon footprint is contributed by transport vehicles. It is crucial to provide alternative solutions to unsustainable transportation systems. This study compares the present travel modes of students of the Capital University of Science and Technology, Islamabad, with the proposed sustainable travel mode. A campus survey (N = 565) was conducted to understand travel choices, travel times, origin and destinations, and student preferences, considering university buses and private vehicles. A comparison was made between the travel time analysis of university bus users and non-bus users. A sustainability analysis has been conducted to evaluate the effects of the present travel modes on students and the proposed university bus transport system. Five main factors were compared: fuel consumption and associated fuel costs, carbon and vehicular emissions, traffic noise pollution, traffic congestion, and accident ratio. The results show that 38% of students commute by private cars and 18% by motorbikes. Around 25% of the students avoid university buses due to long travel times, and 30% prefer because their parents ask them. On average, university buses take 56.8 min, and non-bus transports take 38.8 min for one-way travel. If all students travel by university busses, they could be 86% more sustainable in fuel costs and carbon emissions than non-bus travel modes. These results give useful policy implications for providing sustainable travel alternatives to educational institutions. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) data has a wide range of valuable spectral information for numerous tasks. HSI data encounters challenges such as small training samples, scarcity, and redundant information. Researchers have introduced various research works to address these challenges. Convolution Neural Network (CNN) has gained significant success in the field of HSI classification. CNN's primary focus is to extract low-level features from HSI data, and it has a limited ability to detect long-range dependencies due to the confined filter size. In contrast, vision transformers exhibit great success in the HSI classification field due to the use of attention mechanisms to learn the long-range dependencies. As mentioned earlier, the primary issue with these models is that they require sufficient labeled training data. To address this challenge, we proposed a spectral-spatial feature extractor group attention transformer that consists of a multiscale feature extractor to extract low-level or shallow features. For high-level semantic feature extraction, we proposed a group attention mechanism. Our proposed model is evaluated using four publicly available HSI datasets, which are Indian Pines, Pavia University, Salinas, and the KSC dataset. Our proposed approach achieved the best classification results in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient. As mentioned earlier, the proposed approach utilized only 5%, 1%, 1%, and 10% of the training samples from the publicly available four datasets. [ABSTRACT FROM AUTHOR]

Kadanwari is a major gas-producing field in Pakistan's Lower Indus Basin (LIB), extensively explored for optimized production. However, the reservoir sands of the Lower Goru Formation (LGF), deposited in a complex river-dominated delta, bear severe variability and hinder accurate facies approximation for optimal production. Furthermore, the regionally extended NNW-SSE directed horst and graben structures significantly compartmentalized these reservoir facies. The main E-sand interval is analyzed for its geological properties, depositional environment, and distribution. The integration of various approaches, including seismic interpretation, attribute extraction, well-based facies modeling, and petrophysical evaluation, proved significant in evaluating the heterogeneous and tectonically influenced E-sands. The discontinuity attribute substantially highlighted the structural style and aided in analyzing the geometries of faults. The low values of the frequency attribute (< 10 Hz) signified the entrapped gas-bearing sands along the faulted zones. The high responses of instantaneous amplitude and sweetness profoundly illuminated the gas-significant deposits throughout the field in association with the well-identified gas-prone sand facies. The outcomes of the neutron-density crossplot depicted gas-bearing sands having low density (< 2.3 g/cc) and good porosity (12%) with the assessment of various cements. The facies modeling distinguished between clean and intermixed sand-shale reservoir zones. Petrophysical analysis revealed a net pay of 14 m within E-sand having gas saturation of about 68%. The adopted approach is robust and efficient, employing a limited data set for developing well-associated seismic responses for potential zone delineation within structural arrangements. The techniques can be optimistic about the LGF's complex potential sands demarcation throughout the Indus Basin. [ABSTRACT FROM AUTHOR]

The synthesis of water dispersible phosphor materials is important due to the possibility of their applications in sensors, bioimaging, magnetic resonance imaging (MRI), etc. Surfactants are in use for the controlled size synthesis of nanomaterials as well as for tuning the dispersibility of colloidal particles in different solvents. In this study, we exploited cetrimonium bromide (CTAB) with pre-added lanthanide ions or with "so called modified counterions" for the synthesis of water dispersible phosphor materials. Colloidal GdF3:Ce3+(5%), Eu3+(x%) (where x is 1, 3, 5 and 7% Eu+3) and GdF3:Ce3+(5%), Eu3+(5%)/SiO2 phosphor nanocomposites were synthesized. Initially, cationic CTAB system was altered by pre-adding selected lanthanides required for synthesizing phosphors as well as silica–phosphor nanocomposite. Later, the same modified CTABs were used for single step synthesis of nanoparticles as well as nanocomposites. The elemental and structural characterizations were performed using x-ray photoelectron spectroscopy (XPS), and powder x-ray diffraction (XRD) techniques. The size and morphology of the samples were checked using a transmission electron microscope (TEM). The photoluminescence (PL) properties were explored using a spectrofluorometer, and the results established the formation of phosphor nanocomposite. The method expanded the possibilities of size and shape control in for phosphor materials. Furthermore, the study offered a way to decrease the use of lanthanide while keeping the phosphor's overall properties intact. The results shown that this study can pave an alternate way for the synthesis of nano-sized structures of GdF3: Ce3+(5%) Eu3+(x%) and also for size and shape control in GdF3: Ce3+(5%) Eu3+(x%)/SiO2 type nanocomposites. [ABSTRACT FROM AUTHOR]

Soil contamination with cadmium (Cd) is problem nowadays due to industrialization. A pot experiment was conducted to assess the exogenous effect of calcium nitrate (CaNO3) on morphological traits, photosynthetic efficiency, oxidative stress indicators, secondary metabolites and enzymatic antioxidative capacity in two chickpea (chickpea-brittle and chickpea-2008) cultivars under Cd stress (0 and 200 μM). Results revealed that the Cd stress caused a significant (p < 0.05) reduction in biomass production, photosynthetic pigments, flavonoids and phenolics whereas increase in the activities of various antioxidative enzymes like superoxidase dismutase (SOD), peroxidase (POD), catalase (CAT) and ascorbate peroxidase (APX) as well as malondialdehyde (MDA) and H2O2 content. Cd-administered plants accumulated proline and total soluble proteins in substantial amount. However, results also show that chickpea-brittle showed more tolerance to the chickpea-2008 under the same levels of Cd in the soil. The leaves may be treated with Ca(NO3)2 to reduce toxic effects of Cd, which enhances biomass through increased plant growth along elevated pigments of photosynthetic, activities of antioxidant enzymes and levels of secondary metabolites, in comparison to untreated plants. Additionally, fortifying the Ca(NO3)2 application also decreases the levels of oxidative damaged to membrane-bounded organelles in the plants. Our findings suggested that Ca(NO3)2 spray can mitigate Cd toxicity and leads to increased chickpea growth and yield under Cd-contaminated conditions depicting by decreasing oxidative stress within cell. It was concluded that foliar application of Ca(NO3)2 may alleviate toxic effects of Cd in studied chickpea cultivars through enhanced plant growth, high content of photosynthetic pigments and low oxidative stress under Cd treatments. [ABSTRACT FROM AUTHOR]