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Context: The rice-wheat (RW) system, spanning 13.5 million hectares in South Asia, is crucial for food security and livelihoods. However, intensive conventional tillage-based practices have harmed soil and environmental health, decreased productivity trends and increased greenhouse gas emissions. Objective: This study aims to develop resilient, climate-smart cropping systems within the RW system, focusing on soil and crop productivity, economic viability, and reduced greenhouse gas (GHG) emissions. Methods: Over eight years, the study evaluated diverse parameters compared to farmer practices (FP) in seven scenarios (Sc), including one representing FP (Sc1) and six based on conservation agriculture (CA) principles. The study assessed system crop productivity, economic returns, soil quality (organic carbon; OC, nitrogen; N, phosphorus; P, potassium; K contents, bulk density; BD, soil aggregation, infiltration rates, microbial counts, and earthworm density), and GHG emissions. Results: CA-based scenarios (Sc2 to Sc7) showed improved soil quality, lower bulk density, enhanced soil aggregation, and increased infiltration rates compared to Sc1. In the 0–15 cm layer, surface soil organic carbon (OC) and C stock were 63.7 % and 49.6 % higher, respectively, in CA-based scenarios. Additionally, available N, P and K contents in the surface layer increased by 10.2 %, 28.6 %, and 21.8 % under CA-based scenarios. Adoption of CA in intensified maize-based scenarios (Sc4 and Sc5) led to the increased system and economic yields, higher soil quality index (SQI), reduced GHG emissions and increased C stock compared to Sc1. Implications: The study highlights that Conservation Agriculture (CA) practices and diversified crop rotations can address issues like falling crop productivity, reduced economic returns, soil degradation, and increasing environmental impacts in northwestern India's traditional rice-wheat system. However, widespread adoption requires government policies, including C credit payments and guaranteed markets with supportive pricing.

The aim of this article is to gain a better knowledge of the non-Newtonian features of Casson fluid flow under torsional motion through two concentric cylinders in a porous medium. This study is crucial because it has the ability to control the flow characteristics of Casson fluids in porous media using rotating cylinders for the optimization of industrial processes. The flow-narrating differential equations are converted into a set of nonlinear differential equations. To obtain numerical results to flow problem, a computational model is developed. Numerical solutions are acquired employing the fourth-order exactness programme (Bvp4c), and findings are validated by comparing them to the Runge-Kutta fourth-order approach. Consequences of various distinct parameters for fluid flow and heat transfer are interpreted in graphical and tabular form. It is found that both fluid velocity and temperature increase with growing inputs of outer angular velocity (0.3≤Ω2≤0.9). Skin friction coefficient (CfsTa) is observed to decline in the case when both cylinders are rotating in same direction (Ω1=0.7andΩ2=0.7) with enhanced inputs of Hartmann number (0.5≤Ha≤2.0), while Nusselt number (Nus) profiles are enhanced with rising inputs of the Forchheimer parameter (0.2≤Fr≤0.8) when both cylinders are rotating in opposite direction (Ω1=0.7andΩ2≤−0.7).

For the characterization of fiber-reinforced soil under static loading conditions, there is a wealth of literature. However, study on the dynamic behavior of fiber-reinforced soil is very limited. Now that earthquakes are occurring frequently around the world, the dynamic soil analysis has become important for all classes of geotechnical engineering problems. Through numerical simulation of triaxial specimens, the current study explores the behaviour of fiber-reinforced cohesionless soil. The numerical model was validated using existing laboratory triaxial compression testing literature. The stress–strain response of fiber-reinforced sand has been investigated using static and cyclic triaxial testing, as well as other combinations of fiber contents. Fiber-reinforced sand is tested for bulk modulus, shear modulus, and damping values. Effects of fiber contents on static and dynamic stress–strain response of fiber-reinforced soil are highlighted. It has been observed from results that, with an increase in fiber content modulus of elasticity, bulk modulus and shear modulus values increase while damping coefficients decrease for the same. It is believed that the highlighted numerical approach will be an alternative to laboratory experiments to determine the dynamic properties of fiber-reinforced soil.

Background: Lung cancer ranks as the second-most common cancer globally and remains the leading cause of cancer-related deaths. Despite a global decline in mortality due to reduced smoking, its incidence is rising in India. Aims and Objectives: This study retrospectively examines the clinical profiles of lung cancer patients at their initial presentation and treatment acceptance. Materials and Methods: This retrospective analysis used a database of 185 lung cancer patients reported at the Regional Cancer Centre, Tirunelveli, from January 2021 to December 2022. Clinical records of confirmed lung cancer cases were reviewed, focusing on demographic details, smoking history, symptoms, radiographic findings, histopathology, and clinical staging. Most patients underwent fiberoptic bronchoscopy or fine-needle aspiration biopsy. Patients were classified based on morphology and assessed using the modified Karnofsky Performance Scale and Eastern Cooperative Oncology Group (ECOG). Results: Most patients were male (74.5%), with a median age of 61 years. Most had an ECOG performance status of 0–2 (82.7%). Smoking was prevalent, with 53% of patients being current smokers. The most prominent symptoms at initial presentation were cough, dyspnea, fatigue, and weight loss. Adenocarcinoma was the predominant pathological type (47%). The right lung was more frequently affected, and most patients had comorbid conditions. Paclitaxel plus carboplatin was the primary first-line chemotherapy for most patients. A significant proportion (68.1%) were diagnosed at Stage IV, with the lung being the most common site of metastasis (46.8%). Conclusion: Most cases are diagnosed at advanced metastatic stages due to delayed diagnosis, often being initially misdiagnosed as tuberculosis.

The rice-wheat cropping system (RWCS) provides the world’s population with staple foods, and it is crucial to maintain global food demand and security. Food systems are a complex ecosystem and sustain many feedback mechanisms. Crop residue management is one of those feedback mechanisms that was assessed under conservation agriculture, and a decomposition study was analyzed for the rice-wheat cropping system using rice, wheat, and maize crop residue for decomposition rate and nutrients release under agricultural practices (zero till, raised beds). Different zero tillage techniques in Samastipur demonstrated an accelerated decomposition trend, which was especially noticeable in the straw from wheat and rice. At the same time, permanent bed systems showed a relatively larger residue mass, especially in the case of wheat and maize straw. Permanent bed systems (wheat-maize system), particularly those using wheat straw, held the largest amounts of residue mass when the total residue mass throughout the sites was taken into account. Samastipur showed higher nutrient release for all the rice residue in wheat, rice residue in maize, and wheat residue in rice except maize residue in rice as compared to the Karnal sites. Decomposition kinetics, modeled via a first-order exponential decay function, showed high correlations (R2: 0.941 to 0.996) across treatment methods. The research underscores the significant effect of agroecological factors on residue decomposition and nutrient release, irrespective of residue type, highlighting the importance of tailored residue management practices for enhanced nutrient cycling and sustainability. These findings contribute to the optimization of residue management strategies in RWCS, promoting sustainable agriculture practices in the face of climate change and increasing food security demands.

This study evaluates the impact of conservation agriculture (CA) on soil carbon sequestration and crop productivity in the Indo-Gangetic Plains, focusing on short-term effects over 3 and 5 years. Conducted at two distinct sites, Karnal and Samastipur, the research compares zero tillage, permanent raised beds, and conventional tillage systems across diverse cropping patterns. Initial findings after 3 years showed no significant differences in carbon and nitrogen stocks at Karnal, while Samastipur’s maize-mustard-mungbean rotation on permanent raised beds showed increased carbon stocks. Notably, after 5 years, significant differences in soil carbon stocks emerged at both sites, with improved organic matter input indicated by coarse particulate organic matter (cPOM) formation. The study confirms the potential of POXC and POC as early indicators for carbon sequestration in CA systems, highlighting the role of CA practices in enhancing soil health and crop productivity sustainably.

This study investigated fluoride contamination in groundwater and associated health risks in the Badin district of Pakistan. Fifty-seven groundwater samples were analyzed for fluoride, turbidity, iron, and total dissolved solids (TDS). Pollution indices and health risk models were employed to assess contamination levels and potential health impacts. Results showed that 47 % of samples exceeded the WHO fluoride limit of 1.5 mg/L, with a mean concentration of 1.92 mg/L. Spatial analysis revealed high contamination in northern and southern areas. Health risk assessments indicated that children, particularly females, faced the highest risk of fluorosis. TDS, turbidity, and iron levels also exceeded WHO limits in significant portions of the samples. This investigation uniquely combines multiple pollution indicators, spatial analysis, and age-specific health risk assessments, presenting vibrant insights for targeted interventions, policy development, and resource allocation to address this critical public health issue in fluoride-endemic regions.

Abstract Linseed or flaxseed, native to the Indian subcontinent, had undergone domestication, edaphic selection and evolutionary processes that may have resulted in huge genetic variability in Indian genotypes. To understand the hitherto unexplored genetic diversity for sustainable flaxseed production amid challenges of climate fluctuation and identify trait-specific high-yielding genotypes, 2576 unique linseed accessions were comprehensively evaluated for 36 traits for up to six environments representing two major agroecological zones in India. A wide range of variability was recorded for days to initiation of flowering (42.86–114.99), plant height (43.31–122.88 cm), capsules/plant (64.62–375.87), seed size (6.06–14.44 cm2), thousand seed weight (2.80–11.86 g), seed yield (2.93–17.28 g/plant), oil content (30.14–45.96%) and fatty acid profile especially the key constituent omega-3 fatty acid (25.4–65.88%). Most of the traits such as plant height, flowering time, seed yield, seed and capsule size showed a high or moderately high level of variance coupled with high broad sense heritability indicating precise capturing of less heritable quantitative traits. The infraspecific classification of the tested collection revealed the seed/oil type (2498 accessions) as the dominant morphotype over dual-purpose/fiber flax (78 accessions) in the conserved collection. Correlation analysis indicated a significant positive association between flowering time, plant height, days to maturity and oil content. Trait-specific superior genotypes for earliness (50% flowering in 11 g), capsules/plant (> 350), oil content (> 45%) and fatty acid composition (> 65% alpha-linolenic acid) were identified to aid genetic improvement of linseed and to broaden the narrow genetic base.

Objectives This article presents a systematic review of recent advancements in telemedicine architectures for continuous monitoring, providing a comprehensive overview of the evolving software engineering practices underpinning these systems. The review aims to illuminate the critical role of telemedicine in delivering healthcare services, especially during global health crises, and to emphasize the importance of effectiveness, security, interoperability, and scalability in these systems. Methods A systematic review methodology was employed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. As the primary research method, the PubMed, IEEE Xplore, and Scopus databases were searched to identify articles relevant to telemedicine architectures for continuous monitoring. Seventeen articles were selected for analysis, and a methodical approach was employed to investigate and synthesize the findings. Results The review identified a notable trend towards the integration of emerging technologies into telemedicine architectures. Key areas of focus include interoperability, security, and scalability. Innovations such as cognitive radio technology, behavior-based control architectures, Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) standards, cloud computing, decentralized systems, and blockchain technology are addressing challenges in remote healthcare delivery and continuous monitoring. Conclusions This review highlights major advancements in telemedicine architectures, emphasizing the integration of advanced technologies to improve interoperability, security, and scalability. The findings underscore the successful application of cognitive radio technology, behavior-based control, HL7 FHIR standards, cloud computing, decentralized systems, and blockchain in advancing remote healthcare delivery.

Abstract Agricultural field experiments are costly and time-consuming, and often struggling to capture spatial and temporal variability. Mechanistic crop growth models offer a solution to understand intricate crop-soil-weather system, aiding farm-level management decisions throughout the growing season. The objective of this study was to calibrate and the Crop Environment Resource Synthesis CERES-Maize (DSSAT v 4.8) model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based maize system. The model was also used to investigate the relationship between, temperature, nitrate and ammoniacal concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on maize yields. Using field data from 2019 and 2020, the DSSAT-CERES-Maize model was calibrated for plant growth stages, leaf area index-LAI, biomass, and yield. Data from 2021 were used to evaluate the model's performance. The treatments consisted of four nitrogen management options, viz., N0 (without nitrogen), N150 (150 kg N/ha through urea), GS (Green seeker-based urea application) and USG (urea super granules @150kg N/ha) in two contrasting tillage systems, i.e., CA-based zero tillage-ZT and conventional tillage-CT. The model accurately simulated maize cultivar’s anthesis and physiological maturity, with observed value falling within 5% of the model’s predictions range. LAI predictions by the model aligned well with measured values (RMSE 0.57 and nRMSE 10.33%), with a 14.6% prediction error at 60 days. The simulated grain yields generally matched with measured values (with prediction error ranging from 0 to 3%), except for plots without nitrogen application, where the model overestimated yields by 9–16%. The study also demonstrated the model's ability to accurately capture soil nitrate–N levels (RMSE 12.63 kg/ha and nRMSE 12.84%). The study concludes that the DSSAT-CERES-Maize model accurately assessed the impacts of tillage and nitrogen management practices on maize crop’s growth, yield, and soil nitrogen dynamics. By providing reliable simulations during the growing season, this modelling approach can facilitate better planning and more efficient resource management. Future research should focus on expanding the model's capabilities and improving its predictions further.