Search Results (33)

Climate-induced migration has emerged as a major concern in India and Bangladesh, due to their geographical vulnerability and socioeconomic conditions. Coastal areas, such as the Sundarbans and the Ganges–Brahmaputra Delta, face relentless threats due to rising sea levels, cyclones, and floods. These factors force millions to relocate, resulting in rural–urban transitions and cross-border movements that worsen urban challenges and socioeconomic vulnerabilities. For this, a systematic literature review of the Scopus database was undertaken using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A detailed review analysis of 65 papers was carried out. The study highlighted key climatic and non-climatic drivers of migration, including natural disasters, resource depletion, poverty, and poor governance. Despite existing adaptation strategies, such as early warning systems, micro-insurance, and climate-resilient practices, gaps remain in addressing long-term resilience and legal recognition for climate migrants. The research emphasizes the need for a holistic, multi-stakeholder approach, integrating adaptive infrastructure, sustainable livelihoods, and international cooperation. Recommendations include bridging research gaps, increasing community participation, and implementing global frameworks, like the Fund for Responding to Loss and Damage. Addressing climate migration through fair, inclusive measures is essential for building resilience and ensuring long-term development in the region. Full article

Emerging and reemerging fungal infections are very common in nosocomial and non-nosocomial settings in people having poor immunogenic profiles either due to hematopoietic stem cell transplants or are using immunomodulators to treat chronic inflammatory disease or autoimmune disorders, undergoing cancer therapy or suffering from an immune weakening disease like HIV. The refractory behavior of opportunistic fungi has necessitated the discovery of unconventional antifungals. The emergence of black fungus infection during COVID-19 also triggered the antifungal discovery program. Natural products are one of the alternative sources of antifungals. Endophytic fungi reside and co-evolve within their host plants and, therefore, offer a unique bioresource of novel chemical scaffolds with an array of bioactivities. Hence, immense possibilities exist that these unique chemical scaffolds expressed by the endophytic fungi may play a crucial role in overcoming the burgeoning antimicrobial resistance. These chemical scaffolds so expressed by these endophytic fungi comprise an array of chemical classes beginning from cyclic peptides, sesquiterpenoids, phenols, anthraquinones, coumarins, etc. In this study, endophytic fungi reported in the last six years (2018–2023) have been explored to document the antifungal entities they produce. Approximately 244 antifungal metabolites have been documented in this period by different groups of fungi existing as endophytes. Various aspects of these antifungal metabolites, such as antifungal potential and their chemical structures, have been presented. Yet another unique aspect of this review is the exploration of volatile antifungal compounds produced by these endophytic fungi. Further strategies like epigenetic modifications by chemical as well as biological methods and OSMAC to induce the silent gene clusters have also been presented to generate unprecedented bioactive compounds from these endophytic fungi. Full article

This paper presents a mathematical model for stacks of direct methanol fuel cells (DMFCs) using an optimised method. In order to reduce the sum of squared errors (SSE) in calculating the polarisation profile, the suggested technique makes use of simulated experimental data. Given that DMFC is one of the viable fuel cell choices, developing an appropriate model is essential for cost reduction. However, resolving this issue has proven difficult due to its complex and highly nonlinear character, particularly when adjusting the DMFC model to various operating temperatures. By combining the algorithm and the objective function, the current work introduces a novel method called LSHADE (ELSHADE) for determining the parameters of the DMFC model. This technique seeks to accurately identify DMFCs’ characteristics. The ELSHADE method consists of two stages, the first of which is controlled by a reliable mutation process and the latter by a chaotic approach. The study also recommends an improved Newton–Raphson (INR) approach to deal with the chaotic nature of the I-V curve equation. The findings show that, when used on actual experimental data, the ELSHADE-INR technique outperforms existing algorithms in a variety of statistical metrics for accurately identifying global solutions. Full article

To assist in the speed and accuracy of designing brick-based DNA nanostructures, we introduce a lightweight software suite 3DNA that can be used to generate complex structures. Currently, implementation of this fabrication strategy involves working with generalized, typically commercial CAD software, ad-hoc sequence-generating scripts, and visualization software, which must often be integrated together with an experimental lab setup for handling the hundreds or thousands of constituent DNA sequences. 3DNA encapsulates the solutions to these challenges in one package by providing a customized, easy-to-use molecular canvas and back-end functionality to assist in both visualization and sequence design. The primary motivation behind this software is enabling broader use of the brick-based method for constructing rigid, 3D DNA-based nanostructures, first introduced in 2012. 3DNA is developed to provide a streamlined, real-time workflow for designing and implementing this type of 3D nanostructure by integrating different visualization and design modules. Due to its cross-platform nature, it can be used on the most popular desktop environments, i.e., Windows, Mac OS X, and various flavors of Linux. 3DNA utilizes toolbar-based navigation to create a user-friendly GUI and includes a customized feature to analyze the constituent DNA sequences. Finally, the oligonucleotide sequences themselves can either be created on the fly by a random sequence generator, or selected from a pre-existing set of sequences making up a larger molecular canvas. Full article

The air quality of the Himalayan region of India is deteriorating due to the increasing load of particulate matter that is emitted from various local and regional sources, as well as to the transit of dust-related pollutants from the Indo-Gangetic Plain (IGP) and surrounding areas. In this study, the mineralogical characteristics of coarse mode particulate matter (PM₁₀) was analyzed using the X-ray diffraction (XRD) technique from January to December 2019 over Nainital (29.39° N, 79.45° E; altitude: 1958 m above mean sea level), a central Himalayan region of India. XRD analysis of PM₁₀ samples showed the presence of clay minerals, crystalline silicate minerals, carbonate minerals, and asbestiform minerals. It was shown that quartz minerals with significant levels of crystallinity were present in all the samples. Other minerals that are contributing to the soil dust were also observed in the analysis (CaFe₂O₄, CaCO₃, CaMg(CO₃)₂, calcium ammonium silicate hydrate (C-A-S-H), gypsum, kaolinite, illite, augite, and montmorillonite). The minerals ammonium sulphate, hematite, and magnetite were also found in the samples and are suggested to be from biogenic and anthropogenic activities, including biomass burning, fuel combustion, vehicle exhaust, construction activities, etc. This study indicated that the majority of the minerals in PM₁₀ that were present in this Himalayan region are from soil/crustal dust. Full article

It is challenging to differentiate between tuberculous peritonitis and peritoneal carcinomatosis due to their insidious nature and intersecting symptoms. Computed tomography (CT) is the modality of choice in evaluating diffuse peritoneal disease. We conducted an ambispective analysis of patients suspected as having tuberculous peritonitis or peritoneal tuberculosis between Jan 2020 to Dec 2021. The study aimed to identify the clinical and radiological features differentiating the two entities. We included 44 cases of tuberculous peritonitis and 45 cases of peritoneal carcinomatosis, with a median age of 31.5 (23.5–40) and 52 (46–61) years, respectively (p ≤ 0.001). Fever, past history of tuberculosis, and loss of weight were significantly associated with tuberculous peritonitis (p ≤ 0.001, p = 0.038 and p = 0.001). Pain in the abdomen and history of malignancy were significantly associated with peritoneal carcinomatosis (p = 0.038 and p ≤ 0.001). Ascites was the most common radiological finding. Loculated ascites, splenomegaly and conglomeration of lymph nodes predicted tuberculous peritonitis significantly (p ≤ 0.001, p = 0.010, p = 0.038). Focal liver lesion(s) and nodular omental involvement were significantly associated with peritoneal carcinomatosis (p = 0.011, p = 0.029). The use of clinical features in conjunction with radiological findings provide better diagnostic yields because of overlapping imaging findings. Full article

Background: Health-related quality of life is rapidly becoming recognized as an important indicator of how a disease affects patient lives and for evaluating the quality of care, especially for chronic conditions such as chronic kidney disease (CKD). Objectives: This study is an attempt to assess the quality of life in patients with chronic kidney disease at MMIMSR and also identify characteristics that may be associated with their worsening quality of life. Materials and Methods: This cross-sectional investigation was conducted at the in-patient department (IPD) of the MMIMSR hospital. This study included 105 CKD patients and used a systematic random sampling method for quantitative analysis. This study utilized a 36-item short-form SF-36 (v1.3) questionnaire to assess HRQoL in CKD patients. Descriptive statistics were employed at the baseline. Chi square and ANOVA were used to draw comparisons between two groups or more than two groups, respectively. Logistic regression analysis was utilized to identify the potential QoL determinants. A p value of 0.05 or lower was used to determine statistical significance. Results: Among a total of 105 participants, the mean (±standard deviation) age was found to be 54.53 ± 13.47 years; 48 were male patients, and 57 were female patients. Diabetes Mellitus (61.9%), hypertension (56.2%), chronic glomerulonephritis (7.6%), chronic pyelonephritis (6.7%), and polycystic kidney disease (5.7%) were identified to be the most frequent disorders associated with CKD. The current study also demonstrated that the HRQoL score domains such as symptom problem list, the effect of kidney disease, and the burden of kidney disease decline significantly and progressively as the patient advances into higher stages of CKD (p = 0.005). A similar pattern was observed in work status, sleep, and general health (p < 0.005). Additionally, a statistically significant difference was noted for cognitive function, quality of social interaction, overall health, dialysis staff encouragement, patient satisfaction, social support, physical functioning, role of physical health, pain, emotional well-being, role of emotional health, social functioning, and energy fatigue (p < 0.005). The mean difference for PCS and MCS based on CKD stages was found to be statistically significant (p < 0.005). The PCS and MCS showed a positive correlation with GFR (r = 0.521), and Hb (r = 0.378), GFR (r = 0.836), and Hb (r = 0.488), respectively. Conclusions: The findings of this study demonstrated that a significant decrease in HRQoL was observed among CKD patients, with a progressive deterioration of HRQoL dimensions as the patient advances to end-stage renal disease. This study also revealed that CKD imposes various restrictions on patients’ day-to-day lives, particularly in terms of their physical and mental functioning, even in the initial stages of the disease. Full article

The development of highly nutritious bakery products with optimum utilization of food waste is a major challenge for the food industry. The optimum utilization of food waste for the sustainable development goal of the country is important for the growth of the nation. The aim of the present work is to prepare value-added composite flour-mixed bread from waste fruit and vegetables. The composite flour was prepared in four formulations of peel and pomace with wheat flour (PPWF), as PPWF1, PPWF2, PPWF3, and PPWF4. Composite flour was blended with a mix of vegetable and fruit pomace powders and whole wheat flour. Indian gooseberry pomace powder, apple pomace powder, bottle gourd peel powder, and potato peel powder were used with whole wheat flour to make pomace and whole wheat flour compositions such as PPWF1, PPWF2, PPWF3, and PPWF4. Out of these four flours, PPWF3 contained a good amount of fiber 8.16%, crude protein 3.18%, total phenolic content 14.48%, moisture 9.5%, vitamin C 13.64 mg/100 g, and total phenolic compound 14.48 (mg/GAE/g), which are maximum and acceptable range values as compared to the other three composite flours and the control group flour. PPWF3 is used as a partial replacement ratio for wheat flour due to its high phenolic content, vitamin C content, and richness in fibers. This composite flour is used to make bread dough, and two samples, G1 and G2, are made, out of which G2 offers better nutritional, functional, and sensory evaluations in comparison with refined wheat bread, which is taken as a control group. Thus, such utilization of food waste in bread making can generate value from waste and improve the nutritional attributes of bread, which may improve an individual’s health. Full article

In improving PV system performance, the parameters associated with electrical photovoltaic equivalent models play a pivotal role. However, due to the increased mathematical complexities and non-linear traits of PV cells, the precise prediction of these parameters is a challenging task. To estimate the parameters associated with PV models, a reliable, robust, and accurate optimization technique is needed. This paper introduces a new algorithm, Rat Swarm Optimizer (RSO), for obtaining the optimum PV cell and module parameters. The proposed method maintains an adequate balance between the exploration and exploitation phases to overcome premature particle issues. The results obtained using RSO are compared with those of other algorithms, i.e., Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), and Grasshopper Optimization (GOA), in this work. The modified one-diode model (MODM) and modified two-diode model (MTDM) are used to analyze the parameters of the mono-crystalline PV cell using the suggested RSO. The obtained findings imply that the parameters estimated by the suggested RSO are more accurate than those calculated by the other algorithms taken into consideration in the paper. The statistical results are compared, and it is clear that RSO is a very accurate, fast, and dependable approach for the parameter estimation of PV cells. Full article

The term “soft computing” refers to a system that can work with varying degrees of uncertainty and approximations in real-life complex problems using various techniques such as Fuzzy Logic, Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). Owing to the low-cost and high-performance digital processors today, the use of soft computing techniques has become more prevalent. The main focus of this paper is to study the use of soft computing in the prediction and diagnosis of heart diseases, which are considered one of the major causes of fatalities in modern-day humans. The heart is a major human organ that can be affected by various conditions such as high blood pressure, diabetes, and heart failure. The main cause of heart failure is the narrowing of the blood vessels due to excess cholesterol deposits in the coronary arteries. The objective of this study is to review and compare the various soft computing techniques that are used for the prediction, diagnosis, failure, detection, identification, and classification of heart disease. In this paper, a comprehensive list of recent soft computing techniques in heart condition monitoring is reviewed and compared with an experiment with specific applications to developing countries including South Asian countries. The relevant experimental outcomes demonstrate the benefits of soft computing in medical services with a high accuracy of 99.4% from Fuzzy Logic and Convolutional Neural Networks, with comparable results from other competing state-of-the-art soft computing models. Full article

This manuscript presents a thorough review of unitized regenerative fuel cells (URFCs) and their importance in Remote Area Power Supply (RAPS). In RAPS systems that utilize solar and hydrogen power, which typically include photovoltaic modules, a proton exchange membrane (PEM) electrolyzer, hydrogen gas storage, and PEM fuel cells, the cost of these systems is currently higher compared to conventional RAPS systems that employ diesel generators or batteries. URFCs offer a potential solution to reduce the expenses of solar hydrogen renewable energy systems in RAPS by combining the functionalities of the electrolyzer and fuel cell into a single unit, thereby eliminating the need to purchase separate and costly electrolyzer and fuel cell units. URFCs are particularly well-suited for RAPS applications because the electrolyzer and fuel cell do not need to operate simultaneously. In electrolyzer mode, URFCs function similarly to stand-alone electrolyzers. However, in fuel cell mode, the performance of URFCs is inferior to that of stand-alone fuel cells. The presented review summarizes the past, present, and future of URFCs with details on the operating modes of URFCs, limitations and technical challenges, and applications. Solar hydrogen renewable energy applications in RAPS and challenges facing solar hydrogen renewable energy in the RAPS is discussed in detail. Full article

The characterization of Finsler spaces with Ricci curvature is an ancient and cumbersome one. In this paper, we have derived an expression of Ricci curvature for the homogeneous generalized Matsumoto change. Moreover, we have deduced the expression of Ricci curvature for the aforementioned space with vanishing the S-curvature. These findings contribute significantly to understanding the complex nature of Finsler spaces and their curvature properties. Full article

The transcription factor encoded by the PAX2 gene plays a significant role in the development of the urogenital tract, eyes, ears, and central nervous system. Heterozygous mutations in the PAX2 gene cause renal coloboma syndrome, a rare autosomal dominant disorder characterized by optic nerve coloboma and renal anomalies. In this study, two siblings with chronic kidney disease (CKD) receiving regular dialysis therapy were investigated. DNA sequencing was performed on blood samples from both patients, which revealed four novel heterozygous variations in the PAX2 gene in both patients. Sequencing analysis showed a C to G transversion at position c.352 of the PAX2 gene in a heterozygous state. Full article

This article presents a cost-effective and reliable solution for meeting the energy demands of remote areas through the integration of multiple renewable energy sources. The proposed system aims to reduce dependence on fossil fuels and promote sustainable development by utilizing accessible energy resources in a self-contained microgrid. Using the Hybrid Optimization Model for Electric Renewable (HOMER) software, the study examined the optimal combination of energy sources and storage technologies for an integrated hybrid renewable energy system (IHRES) in the Patiala location of Punjab. The total life cycle cost (TLCC) is the main objective of this manuscript. The HOMER result is taken as a reference, and the results are compared with the optimization hybrid algorithm (PSORSA). From this, it is clear that the proposed algorithm has less TLCC as compared to others. Two combinations of energy sources and storage technologies were considered, namely solar photovoltaic (PV)/battery and solar PV/fuel cell (FC). The results showed that the solar PV/FC combination is more cost-effective, reliable, and efficient than the solar PV/battery combination. Additionally, the IHRES strategy was found to be more economically viable than the single energy source system, with lower total life cycle costs and greater reliability and efficiency. Overall, the proposed IHRES model offers a promising solution for meeting energy demands in remote areas while reducing dependence on fossil fuels and promoting sustainable development. Full article

This study presents the source apportionment of coarse-mode particulate matter (PM₁₀) extracted by 3 receptor models (PCA/APCS, UNMIX, and PMF) at semi-urban sites of the Indian Himalayan region (IHR) during August 2018–December 2019. In this study, water-soluble inorganic ionic species (WSIIS), water-soluble organic carbon (WSOC), carbon fractions (organic carbon (OC) and elemental carbon (EC)), and trace elements of PM₁₀ were analyzed over the IHR. Nainital (62 ± 39 µg m⁻³) had the highest annual average mass concentration of PM₁₀ (average ± standard deviation at 1 σ), followed by Mohal Kullu (58 ± 32 µg m⁻³) and Darjeeling (54 ± 18 µg m⁻³). The annual total ∑WSIIS concentration order was as follows: Darjeeling (14.02 ± 10.01 µg m⁻³) > Mohal-Kullu (13.75 ± 10.21 µg m⁻³) > Nainital (10.20 ± 6.30 µg m⁻³), contributing to 15–30% of the PM₁₀ mass. The dominant secondary ions (NH₄⁺, SO₄²⁻, and NO₃⁻) suggest that the study sites were strongly influenced by anthropogenic sources from regional and long-range transport. Principal component analysis (PCA) with an absolute principal component score (APCS), UNMIX, and Positive Matrix Factorization (PMF) were used for source identification of PM₁₀ at the study sites of the IHR. All three models showed relatively similar results of source profiles for all study sites except their source number and percentage contribution. Overall, soil dust (SD), secondary aerosols (SAs), combustion (biomass burning (BB) + fossil fuel combustion (FFC): BB+FFC), and vehicular emissions (VEs) are the major sources of PM₁₀ identified by these models at all study sites. Air mass backward trajectories illustrated that PM₁₀, mainly attributed to dust-related aerosols, was transported from the Thar Desert, Indo-Gangetic Plain (IGP), and northwestern region of India (i.e., Punjab and Haryana) and Afghanistan to the IHR. Transported agricultural or residual burning plumes from the IGP and nearby areas significantly contribute to the concentration of carbonaceous aerosols (CAs) at study sites. Full article