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The changing climate scenarios harshen the biotic stresses including boosting up the population of insect/pest and disease, uplifting weed growth, declining soil beneficial microbes, threaten pollinator, and boosting up abiotic stresses including harsh drought/waterlogging, extremisms in temperature, salinity/alkalinity, abrupt rainfall pattern)) and ulitamtely  affect the plant in multiple ways. This nexus review paper will cover four significant points viz (1) the possible impacts of climate change; as the world already facing the problem of food security, in such crucial period, climatic change severely affects all four dimensions of food security (from production to consumption) and will lead to malnutrition/malnourishment faced by low-income peoples. (2) How some major crops (wheat, cotton, rice, maize, and sugarcane) are affected by stress and their consequent loss. (3) How to develop a strategic work to limit crucial factors, like their significant role in climate-smart breeding, developing resilience to stresses, and idiotypic breeding. Additionally, there is an essence of improving food security, as much of our food is wasted before consumption for instance post-harvest losses. (4) Role of biotechnology and genetic engineering in adaptive introgression of the gene or developing plant transgenic against pests. As millions of dollars are invested in innovation and research to cope with future climate change stresses on a plant, hence community base adaptation of innovation is also considered an important factor in crop improvements. Because of such crucial predictions about the future impacts of climate change on agriculture, we must adopt measures to evolve crop.

Optimizing sensor node coverage remains a central challenge in wireless sensor networks (WSNs), where premature convergence and suboptimal solutions in traditional optimization methods often lead to coverage gaps and uneven node distribution. To address these issues, this paper presents a novel velocity-scaled adaptive search factor particle swarm optimization (VASF-PSO) algorithm that integrates dynamic mechanisms to enhance population diversity, guide the search process more effectively, and reduce uncovered areas. The proposed algorithm is evaluated through extensive simulations across multiple WSN deployment scenarios with varying node densities, sensing ranges, and monitoring area sizes. Comparative results demonstrate that the approach consistently outperforms several widely used metaheuristic algorithms, achieving faster convergence, better global exploration, and significantly improved coverage performance. On average, the proposed method yields up to 14.71% higher coverage rates than baseline techniques. These findings underscore the algorithm’s robustness and suitability for efficient and scalable WSN deployments.

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.

This study investigates the possible environmental effects of economic openness, such as economic growth, foreign direct investment (FDI) inflows, and trade liberalization in South Asian Association for Regional Cooperation (SAARC) countries. The study employed panel autoregressive lag distribution (ARDL) model to evaluate the environmental effects of economic openness; causality test was also conducted to confirm short- and long-run causality among the variables under discussion. The results show that trade, FDI, capital, and economic growth in the long run have a positive correlation with environmental degradation in SAARC countries while FDI, capital, and trade inflows have a negative relation with CO 2 emissions in the short run. Furthermore, economic growth by creating new job opportunities improved emissions also in the short run. FDI, trade, capital, and GDP have long-run causality with CO 2 emissions. Bidirectional causality was found between GDP and CO 2 emissions, unidirectional causality was also running from FDI inflows to economic growth, unidirectional causality running from capital to FDI and trade to capital. Finally, trade and economic growth also have unidirectional causality in the short run. This study concludes, therefore, that SAARC countries should invest in green energy and promote green trade liberalization.

The COVID-19 outbreak caused a massive setback to the stability of financial system due to emergence of several other risks with COVID, which significantly influenced the continuity of profitable banking operations. Therefore, this study aims to see that how differently the liquidity risk and credit risk influenced the banking profitability during Covid-19 (Q12020 to Q42021) than before COVID (Q12018 to Q42019). The study employs pooled OLS, and OLS fixed & random effects models, to analyze the panel data on a sample of 37 banks currently operating in Pakistan. The results depict that liquidity risk has a positive and significant relationship with return on assets and return on equity, but insignificant relationship with net interest margin. Credit risk has a negative and significant relationship with return on assets, return on equity, and net interest margin. The study also applies quantile regression to address the normality issue in data. The quantile regression results are consistent with pooled OLS, and OLS fixed and random effects results. The study makes valuable suggestions for regulators, policymakers, and others users of financial institutional data. The current study will help to set policies for efficient management of LR and CR.

Rheumatoid arthritis (RA) is an autoimmune disease affecting the joints and other extra-articular organs. RA has the symptoms of inflammation, joint dysfunction, and reduction in life expectancy. The main causes of RA are family history, immunogenicity, smoking, and genetic factors. Among the genetic factors, DNA damage response pathway genes are primarily involved in repairing damage caused by smoking and other carcinogens. Studies have reported an increased DNA damage frequency in RA patients. The present study is designed to illuminate the association between the DNA damage response pathway genes (PARP1, TREX1, ATM, and TP53) and RA in the Pakistani population. Methods For this purpose, 500 RA patients and 500 age/gender-matched controls were collected and DNA/RNA was extracted. The genotype frequency of selected SNPs [PARP1 (Val76Ala), ATM (Pro1054Arg), TP53 (Ala138Val), and TREX (Tyr177Tyr)] was measured using the Tetra-ARMS PCR. Expression analysis of selected genes was measured using quantitative PCR. Statistical analysis showed a significantly increased frequency of mutant allele Val76Ala (p < 0.0001), Pro1054Arg (p < 0.0001), Ala138Val (p < 0.0001), and Tyr177Tyr (p < 0.0001) in RA patients compared to controls. Linkage disequilibrium showed a strong linkage disequilibrium between selected SNPs in RA patients compared to controls. Quantitative PCR showed a significant downregulation of PARP1 (p < 0.0001), ATM (p < 0.0001), TP53 (p < 0.0001), and TREX (p < 0.0001) in RA patients. ROC curve analysis showed a good diagnostic value for selected genes in RA patients. The present study showed that increased mutant genotype frequency and expression deregulation of DNA damage response pathway genes was linked with significant increased risk of RA. This study showed that DNA damage response pathway genes can act as efficient/specific diagnostic markers for said disease. Furthermore, these findings also lay a solid foundation for further research based on targeted metabolomics/genomics, which may lead to the development of more effective treatment strategies in the future.

Quality control is paramount in product manufacturing as it ensures consistent production to meet customer expectations, regulatory requirements and maintain a company’s reputation and profitability. Distance measures within fuzzy sets serve as powerful tools for quality control, allowing for data comparison and identification of potential defects or outliers within a system. This study aims to develop a hybrid concept by combining a Cubic Intuitionistic Fuzzy Set (CIFS) with Soft Set (SS) and extending it to Cubic Intuitionistic Fuzzy Hypersoft Set (CIFHSS). CIFHSS enables handling multiple distinct attributes at the sub-attribute level within a cubic set environment. The concept includes operations like internal, partial internal, external, complement, direct sum, and product. Additionally, six distance metrics are defined within CIFHSS and applied to establish a quality control management system for industrial applications. The versatility of CIFHSS in quality control management stems from its ability to capture and model uncertainty, vagueness, and imprecision in data. This makes it an effective tool for decision-making, risk analysis, and process optimization across a wide range of industrial applications.

Risk evaluation has always been of great interest for individuals wanting to invest in various businesses, especially in the marketing and product sale centres. A finely detailed evaluation of the risk factor can lead to better returns in terms of investment in a particular business. Considering this idea, this paper aims to evaluate the risk factor of investing in different nature of products in a supermarket for a better proportioning of investment based on the product’s sales. This is achieved using novel Picture fuzzy Hypersoft Graphs. Picture Fuzzy Hypersoft set (PFHSs) is employed in this technique, a hybrid structure of Picture Fuzzy set and Hypersoft Set. These structures work best for evaluating uncertainty using membership, non-membership, neutral, and multi-argument functions, making them ideal for Risk Evaluation studies. Also, the concept of the PFHS graph with the help of the PFHS set is introduced with some operations like the cartesian product, composition, union, direct product, and lexicographic product. This method presented in the paper provides new insight into product sale risk analysis with a pictorial representation of its associated factors.

[This corrects the article DOI: 10.1371/journal.pone.0308356.].