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Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

Deficit irrigation (DI) has been emerging as an important technique for enhancing crop water productivity (WP). However, advantage of DI under varying nitrogen (N) application rates remains unclear. Field experiments were conducted during 2012–2014 to investigate the impacts of six irrigation levels[FI (full irrigation), DI 10 , DI 20 , DI 30 , DI 40 and DI 50 , with irrigation amount of 100, 90, 80, 70, 60 and 50% of ETc, respectively) and four N application rates (N 0 , N 10 , N 20 and N 30 , with 0, 10, 20 and 30 kg N ha −1 , respectively) on WP, yield, quality, and net economic return of peanut in hot arid region of India. We used Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to obtain the optimal combination of irrigation and N rates. Both irrigation level and nitrogen dose had significant effects on yield and quality parameters examined in the study. Relative to FI, DI 40 and DI 50 significantly reduced yield (40.2–62.1%), economic benefit (70.8–118.5%), WP (8.2–33.0%), and kernel oil content (7.5–11.9%), but DI 20 increased WP by 17.1% with only marginal reduction in economic benefit (2.6%), and yield (3.2%). Compared to N 0 , the N 30 had 1.7, 1.1, and 1.6-folds increased yield, oil content in the kernel, and WP, respectively. Among all treatments, DI 0 N 30 had the greatest yield and net return; DI 20 N 30 had greatest WP and oil content in the kernel. TOPSIS analysis showed that DI 20 N 30 was optimal in balancing of WP, yield, net return, and quality of peanut in northwestern arid India. The results have direct implications for improving irrigation water and N management for irrigated crops in arid regions.

The workspace is often a critical parameter for optimum design of parallel manipulators. Workspace shape and area are two important considerations under this. In this paper, 5-R and 3-RRR planar parallel manipulators having symmetric link lengths are considered for workspace analysis. Here, symmetric means that the lengths of the first and second links of the legs are the same in all branches. Workspace analysis for such manipulators is normally done in a nondimensional way. The determination of the workspace area is one of the important parameters in the optimum design of a manipulator, and the determination of the area in terms of nondimensional parameters is extremely difficult in the case of 3-DOF and higher-DOF manipulators. In this paper, a geometric method is presented to determine different workspace shapes and areas. Based on this, all possible shapes of workspace are presented for both 5-R and 3-RRR planar parallel manipulators. For each case, a geometrical relationship between the link lengths is determined. The geometric approach gives a closed-form expression for the area calculation, which is not possible when adopting a nondimensional approach. In addition, this approach provides relationships between workspace shape and area and link lengths.

Nitrogen (N) fertilization plays a pivotal role in physiomorphological attributes and yield formation of field-grown cotton (Gossypium hirsutum L.), but little is known of its interaction with irrigation levels. Therefore, this study was conducted with an objective of evaluating the impact of irrigation and nitrogen levels on growth attributes and nitrogen use efficiency of Bt cotton (Gossypium spp.) in the hot arid region. The experiment consisted of a factorial arrangement of three irrigation levels (200, 400, and 600 mm) and four nitrogen rates (0, 75, 150, and 225 kg ha–1) in a split-plot design with three replications. Nitrogen fertilization and irrigation levels influenced cotton growth attributes and yield. The highest leaf area index, dry matter accumulation, crop growth rate, and relative growth rate were achieved at 225 kg N ha–1 and irrigation level 600 mm as compared to other experimental treatments. Similarly, nitrogen uptake and content by seed, lint, and stalk and total nitrogen uptake recorded maximum at 225 kg N ha–1 and irrigation level 600 mm. Interestingly, the treatment of 600 mm of irrigation and 150 kg N ha–1 displayed significant increase in nitrogen use efficiency indices such as agronomic efficiency of nitrogen (AEN) and recovery efficiency of nitrogen (REN), while partial factor productivity of nitrogen (PFPN) and internal nitrogen use efficiency (iNUE) were significantly higher with application of 600 mm of irrigation and nitrogen application rate of 75 kg ha–1. Application of 600 mm of irrigation along with 225 kg N ha–1 resulted in significant increase in gross return, net return, and B:C ratio than any other treatment combinations. So, application of 600 mm of irrigation along with 225 kg N ha–1 could be recommended for achieving higher growth and yield, as well as profitability of Bt cotton under hot arid region and similar agroecologies

Background: The leading global cause of death is coronary artery disease (CAD), necessitating early and precise diagnosis. Intravascular ultrasound (IVUS) is a sophisticated imaging technique that provides detailed visualization of coronary arteries. However, the methods for segmenting walls in the IVUS scan into internal wall structures and quantifying plaque are still evolving. This study explores the use of transformers and attention-based models to improve diagnostic accuracy for wall segmentation in IVUS scans. Thus, the objective is to explore the application of transformer models for wall segmentation in IVUS scans to assess their inherent biases in artificial intelligence systems for improving diagnostic accuracy. Methods: By employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we pinpointed and examined the top strategies for coronary wall segmentation using transformer-based techniques, assessing their traits, scientific soundness, and clinical relevancy. Coronary artery wall thickness is determined by using the boundaries (inner: lumen-intima and outer: media-adventitia) through cross-sectional IVUS scans. Additionally, it is the first to investigate biases in deep learning (DL) systems that are associated with IVUS scan wall segmentation. Finally, the study incorporates explainable AI (XAI) concepts into the DL structure for IVUS scan wall segmentation. Findings: Because of its capacity to automatically extract features at numerous scales in encoders, rebuild segmented pictures via decoders, and fuse variations through skip connections, the UNet and transformer-based model stands out as an efficient technique for segmenting coronary walls in IVUS scans. Conclusions: The investigation underscores a deficiency in incentives for embracing XAI and pruned AI (PAI) models, with no UNet systems attaining a bias-free configuration. Shifting from theoretical study to practical usage is crucial to bolstering clinical evaluation and deployment.

Skin lesion classification plays a crucial role in dermatology, aiding in the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. In this study, we present an attention-enabled ensemble-based deep learning technique, a powerful, novel, and generalized method for extracting features for the classification of skin lesions. This technique holds significant promise in enhancing diagnostic accuracy by using seven pre-trained TL models for classification. Six ensemble-based DL (EBDL) models were created using stacking, softmax voting, and weighted average techniques. Furthermore, we investigated the attention mechanism as an effective paradigm and created seven attention-enabled transfer learning (aeTL) models before branching out to construct three attention-enabled ensemble-based DL (aeEBDL) models to create a reliable, adaptive, and generalized paradigm. The mean accuracy of the TL models is 95.30%, and the use of an ensemble-based paradigm increased it by 4.22%, to 99.52%. The aeTL models’ performance was superior to the TL models in accuracy by 3.01%, and aeEBDL models outperformed aeTL models by 1.29%. Statistical tests show significant p-value and Kappa coefficient along with a 99.6% reliability index for the aeEBDL models. The approach is highly effective and generalized for the classification of skin lesions.

Water deficiency is one of the most severe abiotic stresses in rainfed dry lands and limits crop productivity. Exogenous applications of salicylic acid (SA) have been applied to mitigate the adverse effects of water-deficit stresses, but the relative efficacy of different derivatives of SA in enhancing water-deficit tolerance along with the underlying physio-biochemical mechanism and yield of crops is not well documented. Field experiments were conducted to ascertain the relative efficacy of exogenous application of three plant bioregulators (PBRs) [SA, thiosalicylic acid and 5-sulfosalicylic acid (SSA)], each at three concentrations (0.5, 1.0 and 1.5 mM), on the growth, physio-biochemical characteristics and yield of cluster bean under rainfed conditions. Based on a 2-year field experiment, the application of PBRs enhanced yield (from 8 to 16%). The yield enhancement with the application of PBRs was associated with elevated water content (from 9 to 17%), membrane stability (from 12 to 18%) and antioxidant enzyme activity (from 12 to 33%) and reduced lipid peroxidation (from −15 to −34%) in leaves. The effects of PBRs were conditionally type and concentration dependent. The application of SSA at a rate of 1 mM was more effective in enhancing water-deficit tolerance and improving the yield of cluster bean under water shortage conditions. This study provides empirical evidence of the potential for the application of SA and its derivatives to enhance crop yields under drought conditions. The results have direct implications for sustainable crop production for similar regions of the world facing water deficits.

Watershed Development is a very common phenomenon in the river basins in India due to its dynamic and continuously changing nature, which are interconnected via. Land use/land cover (LULC) change and water poverty scenario over time. In the present study, the samples were chosen from seven sampled villages for the Water Poverty Index (WPI) in the upper Tons River Basin. Among them, Ghunwara and Maihar Village exhibit the highest and lowest WPI, i.e., 98.1 and 62.91 out of 100, respectively. This indicates that villages with a high WPI face challenges in their water requirements, regardless of the seasonal river serving the basin area. Conversely, villages with a low WPI can satisfy their water needs solely from the basin. The present analysis of the Upper Tons River Basin suggests that Land Use and Land Cover (LULC) will undergo influences or adjustments at various stages, ultimately affecting agricultural land in the impact region. It also becomes evident that areas with limited land use and land cover (LULC) extensions exhibit lower Water Productivity Index (WPI), primarily due to their reliance on agricultural land. It is observed that alterations, reductions, or modifications in LULC lead to changes in multiple aspects of agricultural land, resulting in noticeable variations in various metrics. The present paper not only evaluates the land use in the Upper Tons River Basin spanning from 2001 to 2021 but also highlights the changing patterns that impact water resources and their utilization capacity. Furthermore, the study estimates the influence of reducing specific features on the distribution of WPI and other LULC parameters. The Upper Tons River Basin faces challenges such as unfavorable rainfall patterns and inadequate planning for irrigation at the fundamental and local levels. Additionally, its geographical location in a rainfed area negatively affects the WPI.

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

Renal stones are a common affliction presenting in an acute setting. We report a case of asymptomatic renal stone in an elderly gentleman presenting initially as a discharging lumbar sinus managed by subcapsular nephrectomy and radical excision of the fistula tract. Nephrocutaneous fistula is most commonly associated with tuberculosis, xanthogranulomatous pyelonephritis, and rarely with complicated calyceal stones, and its occurrence with asymptomatic pelvic stones is rare. We present the points in favor of radical open surgery in the management of such patients.