Search Results (112)

Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers. Full article

Despite the growing availability of very-high-resolution (VHR) remote sensing imagery, extracting fine-grained urban features and materials remains a complex task. Land use/land cover (LULC) maps generated from satellite imagery often fall short in providing the resolution needed for detailed urban studies. While hyperspectral imagery offers rich spectral information ideal for material classification, its complex acquisition process limits its use on aerial platforms such as manned aircraft and unmanned aerial vehicles (UAVs), reducing its feasibility for large-scale urban mapping. This study explores the potential of using only RGB and LiDAR data from VHR aerial imagery as an alternative for urban material classification. We introduce an end-to-end workflow that leverages a multi-head segmentation network to jointly classify roof and ground materials while also segmenting individual roof components. The workflow includes a multi-offset self-ensemble inference strategy optimized for aerial data and a post-processing step based on digital elevation models (DEMs). In addition, we present a systematic method for extracting roof parts as polygons enriched with material attributes. The study is conducted on six cities in Flanders, Belgium, covering 18 material classes—including rare categories such as green roofs, wood, and glass. The results show a 9.88% improvement in mean intersection over union (mIOU) for building and ground segmentation, and a 3.66% increase in mIOU for material segmentation compared to a baseline pyramid attention network (PAN). These findings demonstrate the potential of RGB and LiDAR data for high-resolution material segmentation in urban analysis. Full article

With rising global temperatures attributed to climate change, an increase in fire occurrences worldwide is anticipated. Therefore, a detailed examination of changing fire patterns is essential to improve our understanding of effective control strategies. This study analyzes the long-term trends of fire activity in Southern India (8–20° N, 73–85° E), utilizing MODIS active fire count data from January 2003 to December 2023. The climatological monthly mean results show that Southern India experiences heightened fire activity from December to May, reaching a peak in March. Yearly variations indicate that the highest fire counts occurred in 2021, followed by 2023, 2012, and 2018. The three most significant fire years in recent history reflect an upward trend in fire activity over the past decade, confirming insights from annual trend analysis. The correlation between inter-annual fire anomalies and different meteorological factors reveals a notable negative relationship with precipitation and soil moisture and a positive relationship with surface air temperature (SAT). Soil moisture demonstrates a stronger correlation (−0.45) than precipitation and SAT. In summary, long-term trends show a noteworthy annual increase of 3%. Additionally, monthly trends reveal interesting rising patterns in October, November, December, and January with higher significance levels. Our research supports regional climate action initiatives and policies addressing fire incidents in Southern India in light of the ongoing warming crisis. Full article

Rice is a staple food crop widely consumed across the world. It is rich in carbohydrates, quality protein, and micronutrients. The grain protein content (GPC) in rice varies considerably. Although it is generally lower than that of other major cereals, the quality of protein is superior. GPC and its components are complex quantitative traits influenced by both genetics and environmental factors. Glutelin is the major protein fraction (70–80%) in rice. Rice protein is rich in lysine, methionine, and cysteine along with other amino acids. Globally, Protein–Energy Malnutrition (PEM) is a major concern, particularly in Asia and Africa. Additionally, non-communicable diseases (NCDs) including diabetes, cancer, cardiovascular diseases, hypertension, and obesity are on the rise due to various reasons including changes in lifestyle and consumption patterns. Rice plays a very important part in the daily human diet, and therefore, substantial research efforts focus on the genetic characterization of GPC and understanding its role in the prevention of NCDs. The contribution of both rice grain and bran protein in improving human health is an established fact. The present study summarizes the different aspects of rice grain protein including its variability, composition, factors affecting it, and its industrial uses and more importantly its role in human health. Full article

Grain protein content (GPC) is gaining attention due to increasing consumer demand for nutritious foods. The present study carried out at ICAR-IIRR, Hyderabad, focused on the identification of quantitative trait loci (QTLs) linked with GPC and other quality traits. We utilized a population of 188 F₂ individuals developed from BPT 5204 (low GPC) X JAK 686 (high GPC) for QTL analysis. QTL analysis yielded four significant QTLs for GPC, three for amylose content, and multiple QTLs for other quality traits. qPC1.2, a major QTL in milled rice, was located in the marker interval RM562-RM11307 on chromosome 1 with an LOD value of 4.4. qPC1.2 explained 15.71% of the phenotypic variance (PVE). Additionally, the Interval Mapping for Epistatic QTLs (IM-EPI) method detected 332 pairs of di-genic epistatic QTLs. Fifteen QTLs exhibited a positive additive effect, indicating that the contributing allele(s) was from JAK 686. Five F₂ plants, viz., F₂-140, F₂-12, F₂-7, F₂-147, and F₂-41, exhibited a high GPC of 14.67%, 14.36%, 14.32%, 13.60%, and 13.36%, respectively. Additionally, these plants also exhibited high per-plant grain yield (~17.0–29.0 g) with desirable agronomic traits. The QTLs identified are valuable resources for developing high-grain-protein varieties with high grain yield and desirable quality traits. Full article

Background/Objectives: This study evaluated metabolites and lipid composition in the calf muscles of Type 2 diabetes mellitus (T2DM) patients and age-matched healthy controls using multi-dimensional MR spectroscopic imaging. We also explored the association between muscle metabolites, lipids, and intra-abdominal fat in T2DM. Methods: Participants included 12 T2DM patients (60.3 ± 8.6 years), 9 age-matched healthy controls (AMHC) (60.9 ± 7.8 years), and 10 young healthy controls (YHC) (28.3 ± 1.8 years). We acquired the 2D MR spectra of calf muscles using an enhanced accelerated 5D echo-planar correlated spectroscopic imaging (EP-COSI) technique and abdominal MRI with breath-hold 6-point Dixon sequence. Results: In YHC, choline levels were lower in the gastrocnemius (GAS) and soleus (SOL) muscles but higher in the tibialis anterior (TA) compared to AMHC. YHC also showed a higher unsaturation index (U.I.) of extramyocellular lipids (EMCL) in TA, intramyocellular lipids (IMCL) in GAS, carnosine in SOL, and taurine and creatine in TA. T2DM patients exhibited higher choline in TA and myo-inositol in SOL than AMHC, while triglyceride fat (TGFR2) levels in TA were lower. Correlation analyses indicated associations between IMCL U.I. and various metabolites in muscles with liver, pancreas, and abdominal fat estimates in T2DM. Conclusions: This study highlights distinct muscle metabolite and lipid composition patterns across YHC, AMHC, and T2DM subjects. Associations between IMCL U.I. and abdominal fat depots underscore the interplay between muscle metabolism and adiposity in T2DM. These findings provide new insights into metabolic changes in T2DM and emphasize the utility of advanced MR spectroscopic imaging in characterizing muscle-lipid interactions. Full article

Despite being a highly nutritious and resilient cereal, pearl millet is not popular among consumers and food industries due to the short shelf-life of flour attributed to rapid rancidity development. The biochemical mechanism underlying rancidity, a complex and quantitative trait, needs to be better understood. The present study aims to elucidate the differential accumulation of metabolites in pearl millet that impact the rancidity process. Metabolite profiling was conducted on ten pearl millet genotypes with varying levels of rancidity—comprising high, low, and medium rancid genotypes—utilizing liquid chromatography and high-resolution mass spectrometry (LC-HRMS) at different accelerated ageing conditions. Through non-targeted metabolomic analysis, crucial metabolites associated with rancidity were identified across various biochemical pathways, including fatty acids, glycerophospholipids, sphingolipids, glycerol lipids, flavonoids, alkaloids, and terpenoids. Notably, metabolites such as fatty aldehydes, fatty alcohols, fatty esters, fatty acyls, fatty esters, and fatty amides were significantly elevated in high rancid genotypes, indicating their involvement in the rancidity process. These fatty acids-related metabolites further break down into saturated and unsaturated fatty acids. Four key fatty acids—stearic, palmitic, linoleic and linolenic acid—were quantified in the ten pearl millet genotypes, confirming their role in rancidity development. This investigation promises novel insights into utilizing metabolomics to understand the biochemical processes and facilitate precision breeding for developing low-rancidity pearl millet lines. Full article

We introduce a theoretical model for the photocurrent-voltage (I-V) characteristics designed to elucidate the interfacial phenomena in photoelectrochemical cells (PECs). This model investigates the sources of voltage losses and the distribution of photocurrent across the semiconductor–electrolyte interface (SEI). It calculates the whole exchange current parameter to derive cell polarization data at the SEI and visualizes the potential drop across n-type cells. The I-V model’s simulation outcomes are utilized to distinguish between the impacts of bulk recombination and space charge region (SCR) recombination within semiconductor cells. Furthermore, we develop an advanced deep neural network model to analyze the electron–hole transfer dynamics using the I-V characteristic curve. The model’s robustness is evaluated and validated with real-time experimental data, demonstrating a high degree of concordance with observed results. Full article

The South-East Asia (SEA) Region of the World Health Organization (WHO), through a Regional Committee resolution in 2013, adopted the goal of “measles elimination and rubella control by 2020”. The goal was revised in 2019 to “measles and rubella elimination by 2023”. Countries of the Region have made significant efforts to achieve the goal. Progress has been made in the Region, with five of the 11 countries of the Region having been verified for having eliminated measles and rubella. Surveillance and immunization program performance for measles and rubella has shown an improvement since 2013. This progress has been possible due to a high level of political and programmatic commitment in the countries of the Region, as well as due to the alliances and infrastructures established for disease elimination initiatives in the past, notably for polio, being utilized effectively to implement strategies for measles and rubella elimination. The unforeseen COVID-19 pandemic had a detrimental effect on the immunization and surveillance efforts, leading to a delay in the achievement of measles and rubella elimination in the Region. Challenges to achieve the goal remain; however, efforts are ongoing in countries to not only protect the gains made so far but also to make further progress towards the goal of measles and rubella elimination. Full article

Glioblastoma (GBM) is a malignant cancer affecting the brain. As per the WHO classifications, it is a grade IV glioma and is characterized by heterogenous histopathology, high recurrence rates, and a high median age of diagnosis. Most individuals diagnosed with GBM are aged between 50 and 64 years, and the prognosis is often poor. Untreated GBM patients have a median survival of 3 months, while treatments with Temozolomide (TMZ) and radiotherapy can improve the survival to 10–14 months. Tumor recurrence is common, owing to the inefficiency of surgical resection in removing microscopic tumor formations in the brain. A crucial component of GBM-related research is understanding the tumor microenvironment (TME) and its characteristics. The various cellular interactions in the TME contribute to the higher occurrence of malignancy, resistance to treatments, and difficulty in tumor resection and preventative care. Incomplete pictures of the TME have been obtained in 2D cultures, which fail to incorporate the ECM and other crucial components. Identifying the hallmarks of the TME and developing ex vivo and in vitro models can help study patient-specific symptoms, assess challenges, and develop courses of treatment in a timely manner which is more efficient than the current methods. Microfluidic models, which incorporate 3D cultures and co-culture models with various channel patterns, are capable of stimulating tumor conditions accurately and provide better responses to therapeutics as would be seen in the patient. This facilitates a more refined understanding of the potential treatment delivery systems, resistance mechanisms, and metastatic pathways. This review collates information on the application of such microfluidics-based systems to analyze the GBM TME and highlights the use of such systems in improving patient care and treatment options. Full article

This study aimed to determine COVID-19 recovery time and identify predictors among hospitalized patients in the Dhanusha District of Madhesh Province, Nepal. This hospital-based longitudinal study involved 507 COVID-19 patients admitted to three distinct medical facilities for therapeutic intervention between April and October 2021. Data were collected for patient demography, symptoms, vital signs, oxygen saturation levels, temperatures, heart rates, respiratory rates, blood pressure measurements, and other health-related conditions. Kaplan–Meier survival curves estimated the recovery time, and a Cox proportional hazard model was used to identify the predictors of recovery time. For the total participants, mean age was 51.1 (SD = 14.9) years, 68.0% were males. Of the total patients, 49.5% recovered, and 16.8% died. The median for patient recovery was 26 days (95% CI: 25.1–26.7). Patients with severe or critical conditions were less likely to recover compared to those with milder conditions (hazard ratio (HR) = 0.34, 95% CI: 0.15–0.79; p = 0.012). In addition, an increase in oxygen saturation was associated with an elevated likelihood of recovery (HR = 1.09, 95% CI = 1.01–1.17, p = 0.018). This study underscores the need for early admission to hospital and emphasizes the targeted interventions in severe cases. Additionally, the results highlight the importance of optimizing oxygen levels in COVID-19 patient care. Full article

In the present study, composites incorporating NiO-Co₃O₄ (NC) and CuO-NiO-Co₃O₄ (CNC) as active electrode materials were produced through the hydrothermal method and their performance was investigated systematically. The composition, formation, and nanocomposite structure of the fabricated material were characterized by XRD, FTIR, and UV–Vis. The FE-SEM analysis revealed the presence of rod and spherical mixed morphologies. The prepared NC and CNC samples were utilized as supercapacitor electrodes, demonstrating specific capacitances of 262 Fg⁻¹ at a current density of 1 Ag⁻¹. Interestingly, the CNC composite displayed a notable long-term cyclic stability 84.9%, which was observed even after 5000 charge–discharge cycles. The exceptional electrochemical properties observed can be accredited to the harmonious effects of copper oxide addition, the hollow structure, and various metal oxides. This approach holds promise for the development of supercapacitor electrodes. These findings collectively indicate that the hydrothermally synthesized NC and CNC nanocomposites exhibit potential as high-performance electrodes for supercapacitor applications. Full article

Renewable energy sources are essential to address climate change, fossil fuel depletion, and stringent environmental regulations in the subsequent decades. Horizontal-axis wind turbines (HAWTs) are particularly suited to meet this demand. However, their efficiency is affected by environmental factors because they operate in open areas. Adverse weather conditions like rain reduce their aerodynamic performance. This study investigates wind turbine power prediction under rainy conditions by integrating Blade Element Momentum (BEM) theory with explainable artificial intelligence (XAI). The S809 airfoil’s aerodynamic characteristics, used in NREL wind turbines, were analyzed using ANSYS FLUENT and symbolic regression under varying rain intensities. Simulations at a Reynolds number (Re) of 1 × 10⁶ were performed using the Discrete Phase Model (DPM) and k–$\omega$ SST turbulence model, with liquid water content (LWC) values of 0 (dry), 10, 25, and 39 g/m³. The lift and drag coefficients were calculated at various angles of attack for all the conditions. The results indicated that rain led to reduced lift and increased drag. The innovative aspect of this research is the development of machine learning models predicting changes in the airfoil coefficients under rain with an $R^2$ value of 0.97. The proposed XAI framework models rain effects at a lower computational time, enabling efficient wind farm performance assessment in rainy conditions compared to conventional CFD simulations. It was found that a heavy rain LWC of 39 g/m³ could reduce power output by 5.7% to 7%. These findings highlight the impact of rain on aerodynamic performance and the importance of advanced predictive models for optimizing renewable energy generation. Full article

Proton exchange membrane fuel cells are a prime choice for substitute electricity producers. Membrane electrode assembly (MEA), bipolar electrodes, and current collectors belong to only a limited number of primary parts of the proton exchange membrane fuel cell (PEMFC). Bipolar plates are among the most famous elements in the fuel cell; they are responsible for the electrochemical reaction, as well as the flow of gases from one bipolar plate to another. A bipolar plate is to be a good electro-conducting, non-corrosive, and a high mechanical strength product. The attainability of the specification is achieved by graphite and metallic materials, each one having its own merits and demerits that are discussed in this article. Likewise, making the second pass for the flow pattern is equally important for the cell to have good performance and efficiency. The emergence of innovative and new bipolar plate designs has caused the achievement of high performance of these plates. The present review article principally focuses on the experimental study of diverse flow fields in the design of PEMFC and on the influence of various geometrical properties on the general operation of fuel cells made of PEMFC, and also on the manufacturing procedure utilized for building contemporary fuel cells. Full article

The present study examines the high-temperature (500–800 °C) oxidation behavior of Fe-10Cr-(3,5) Al alloys and studies the effect of nanocrystalline structure and Al content on their resistance to oxidation. The nanocrystalline (NC) alloy powder was synthesized via planetary ball milling. The prepared NC alloy powder was consolidated using spark plasma sintering to form NC alloys. Subsequently, an annealing of the NC alloys was performed to transform them into microcrystalline (MC) alloys. It was observed that the NC alloys exhibit superior resistance to oxidation compared to their MC counterparts at high temperatures. The superior resistance to oxidation of the NC alloys is attributed to their considerably finer grain size, which enhances the diffusion of those elements to the metal–oxide interface that forms the protective oxide layer. Conversely, the coarser grain size in MC alloys limits the diffusion of the oxide-forming components. Furthermore, the Fe-10Cr-5Al alloy showed greater resistance to oxidation than the Fe-10Cr-3Al alloy. Full article