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\"Mathematical Modeling for Intelligent Systems: Theory, Methods and Simulation aims to provide a reference for the applications of mathematical modeling using intelligent techniques in various unique industry problems in the era of Industry 4.0. Providing a thorough introduction to the field of soft computing techniques, the book covers every major technique in artificial intelligence in a clear and practical style. It also highlights current research and applications, addresses issues encountered in the development of applied systems, and describes a wide range of intelligent systems techniques, including neural networks, fuzzy logic, evolutionary strategy, and genetic algorithms. The book demonstrates concepts through simulation examples and practical experimental results. The book offers a well-balanced mathematical analysis of modelling physical systems. Summarizes basic principles in differential geometry and convex analysis as needed. The book covers a wide range of industrial and social applications, and bridges the gap between core theory and costly experiments through simulations and modelling. The focus of the book is manifold ranging from stability of fluid flows, nano fluids, drug delivery, and security of image data to Pandemic modeling etc. The book is primarily aimed at advanced undergraduates and postgraduate students studying computer science, mathematics and statistics. Researchers and professionals will also find this book useful\"-- Provided by publisher.

Background Common wheat ( Triticum aestivum L.) is one of the most widely grown and consumed cereal crops, but its complicated genome makes it difficult to study how genes affect important agronomic and yield-related traits. Genome-wide association study (GWAS) is a useful method for finding specific loci that control complex agronomic and yield-related traits. Results The present investigation revealed significant phenotypic variability across the genotypes examined for all traits. The broad sense heritability (H 2 ) for all traits ranged from 0.50 to 0.71 (Env1; 2021–2022) and 0.53 to 0.81 (Env2; 2022–2023). Using two environments’ phenotypic data, and high-throughput single-nucleotide polymorphisms (SNPs) genotypic data of 20,996 markers, we discovered 114 grain-yield-related quantitative trait loci (QTLs) and 300 associated SNP markers. Eighty-five of the identified markers were stable, consistently detected across environments (Env1 and Env2) and combined environment (CE) data, and showed a significant association with 32 different QTLs. The trait with the most associated QTLs (28) was the number of fertile tillers (NFT), with 70 markers. This was followed by 20 QTLs for each, spike length (SL) and spikelet number per spike (SPS), with 69 and 82 SNPs, respectively. Conversely, six SNPs that exhibited association with multiple traits were also identified. Twenty-nine of the total 114 identified QTLs were located in chromosomal positions where at least one marker-trait association had been previously identified. Conclusion This study has found new SNPs, and useful QTLs that may help us to understand the biological processes behind each studied trait. Further validation in various genetic backgrounds and environments is necessary to confirm the potential utility of the significant alleles found in this study for breeding wheat varieties with improved agronomic and yield-related traits.

The formation of planetesimals from centimeter-sized pebbles in protoplanetary disks faces significant barriers, including fragmentation and radial drift. We identify a previously unaccounted screening force, arising from mutual shielding of thermal gas particles between pebbles when their separation falls below the gas mean free path. This force facilitates pebble binding, overcoming key growth barriers under turbulent disk conditions. Unlike conventional mechanisms, screening forces operate independently of surface adhesion and complement streaming instability and pressure traps by enhancing aggregation in high-density regions. Our analysis predicts that screening interactions are most effective in the middle disk regions (∼0.3 to a few astronomical units), consistent with Atacama Large Millimeter/submillimeter Array observations (e.g., TW Hya) of enhanced dust concentrations. Furthermore, we find that screening-induced pebble growth from centimeter to kilometer scales can occur on timescales significantly shorter than the disk lifetime (∼105 yr). Importantly, this growth naturally terminates when particles smaller than the local gas mean free path are depleted, thereby avoiding runaway accretion. Beyond planetary science, the screening forces have potential implications for high-energy astrophysics, dusty plasmas, confined particle suspensions, and other relevant areas, suggesting a broader fundamental significance.

More than a decade, a numerous experimental and theoretical studies of thermophysical properties of nanofluids are conducted to reveal its heat transfer characteristics. Due to nanofluid unique thermal properties, it is broadly used in various applications from automobile applications to biomedical applications. Despite that various experimental and theoretical studies of nanofluids are developed, the accordance between them is very little and also it is tiresome and expensive. To predict the thermal properties in an easy way, soft computing tools are utilized. In this research work, dynamic viscosity ratio of Al2O3/H2O is predicted using machine learning techniques like multilayer perceptron and Gaussian process regression. In the proposed multilayer perceptron—artificial neural network model, varying a range of neurons in the hidden layer and using Levenberg–Marquardt as training function, it is found that 6 neurons in the hidden layer give less root mean square error value of 0.01118. Different kernel functions are opted to train the proposed Gaussian process regression model, and it is found that Matern kernel function shows the best performance with less root mean square error value of 0.018, and regression coefficient value of both the models is 0.99. This research work will reduce the experimental test run cost, and the models are accurate in prediction.

The Oxidation behavior of ternary electronic lead-free solder In–Sn–Zn alloys are determined at 500, 600, and 700 °C by thermo gravimetric assembly system (TGA) which has been hooked up with a CAHN-1000 auto electric balance for alloys x Zn  = 0.9, x In / x Sn  = 1/1, x Zn  = 0.9, x In / x Sn  = ½ and  x Zn  = 0.5,  x In / x Sn  = ½. The oxidation was carried out in the air for 50 min. The oxidation rate increases with temperature and decreases with time for the given alloy. The rate of oxidation also increases with the increase of zinc content in the alloy. It has also been observed that the alloy's oxidation rate decreases with the increase of In/Sn ratio, and the oxidation processes follow the parabolic rate law. The oxidation rate of alloy x Zn  = 0.9, x In/ x Sn  = ½ is higher at 700 °C as compared to the other two alloys.

Urbanization can alter the local climate through modifications in land-atmosphere feedback. However, a continental scale evaluation of its influence on precipitation phase remains unknown. Here, we assess the difference in the likelihood of snow dominated events (SDEs) over 7,415 urban and surrounding non-urban (buffer) regions across the continental United States. Among 4,856 urban-buffer pairs that received at least five SDEs per year, 81% of urban regions are characterized by a smaller snow probability, 99% by a lower frequency of SDEs, and 57% by faster declining trends in SDEs compared to their buffer counterparts. Notably, urban (buffer) regions with lower snow probability are often characterized by higher net incoming and sensible energy fluxes as compared to buffer (urban) regions, thus highlighting the influence of land-energy feedback on precipitation phase. Results highlight a clear imprint of urbanization on precipitation phase and underscore the need to consider these influences while projecting hydro-meteorological risks. This study shows that urban areas in the continental US are associated with decreased snowfall likelihood and frequency, in large part due to surface albedo contrasts with neighboring areas. They also see a faster decline in snow precipitation frequency with time.

HighlightsMoS2 shows enormous potential for gas sensing due to its high surface to volume ratio, position-dependent gas molecules adsorption and easy control on morphology.The recent experimental and theoretical strategies to develop NO2 chemiresistance sensors based on MoS2 are addressed.A detailed overview of the fabrication of MoS2 chemiresistance sensors in terms of devices, structure, morphology, defects, heterostructures, metal doping, and under light illumination are discussed.Nitrogen dioxide (NO2), a hazardous gas with acidic nature, is continuously being liberated in the atmosphere due to human activity. The NO2 sensors based on traditional materials have limitations of high-temperature requirements, slow recovery, and performance degradation under harsh environmental conditions. These limitations of traditional materials are forcing the scientific community to discover future alternative NO2 sensitive materials. Molybdenum disulfide (MoS2) has emerged as a potential candidate for developing next-generation NO2 gas sensors. MoS2 has a large surface area for NO2 molecules adsorption with controllable morphologies, facile integration with other materials and compatibility with internet of things (IoT) devices. The aim of this review is to provide a detailed overview of the fabrication of MoS2 chemiresistance sensors in terms of devices (resistor and transistor), layer thickness, morphology control, defect tailoring, heterostructure, metal nanoparticle doping, and through light illumination. Moreover, the experimental and theoretical aspects used in designing MoS2-based NO2 sensors are also discussed extensively. Finally, the review concludes the challenges and future perspectives to further enhance the gas-sensing performance of MoS2. Understanding and addressing these issues are expected to yield the development of highly reliable and industry standard chemiresistance NO2 gas sensors for environmental monitoring.

Evapotranspiration (ET) plays a critical role in water and energy budgets over the land surface. Eddy Covariance (EC) is the most widely used technique to measure ET at ecosystem scale, providing insights into land–atmosphere interactions and serving as a benchmark for Earth System Models (ESMs). However, ET measurements at EC flux sites suffer from a fundamental limitation: the persistent issue of surface energy budget non‐closure. It is essential to correct EC‐measured ET fluxes for energy imbalance, both for improved system understanding and diagnostic benchmarking. Here, we introduce PULSE, a new correction approach based on the concept of potential underlying water use efficiency. We implement the method at >250 flux sites globally, and evaluate its performance using a data‐driven framework and an ecosystem conductance model. We also benchmark PULSE against existing energy closure correction methods, including the Bowen ratio‐based flux correction method (OFC) and Available Energy‐based correction (AEC) method. Our results demonstrate that PULSE not only identifies and corrects ET fluxes for energy imbalance but also produces more physically consistent estimates. PULSE is simple, robust, avoids arbitrary assumptions such as Bowen ratio constancy, and is broadly applicable across diverse EC sites.

Extreme rainfall causes transient ponding on roads, which increases the risk of vehicle accidents due to hydroplaning (HP), a phenomenon characterized by reduced friction between the pavement surface and the tires of moving vehicles. Before mitigation plans are drawn, it is important to first assess the spatio-temporal patterns of hydroplaning risk (HpR). This study quantifies HpR over the entire continental USA considering the coupled role of precipitation characteristics and pavement properties. Results show the southern United States to be a primary hotspot of HpR. About 22% of road sections experiencing HpR exhibit an increasing trend in the annual occurrence of HP events with time, indicating a riskier future ahead. Alarmingly, road sections that either experience higher HpR or increasing trend in annual occurrences of HP events are the ones with sizeable traffic. These results emphasize the need to prioritize HP-aware road design, traffic management, and signage in regions with high or fast-evolving risks.

Around 50 million individuals worldwide suffer from epilepsy, a chronic, non-communicable brain disorder. Several screening methods, including electroencephalography, have been proposed to identify epileptic episodes. EEG data, which are frequently utilised to enhance epilepsy analysis, offer essential information on the electrical processes of the brain. Prior to the emergence of deep learning (DL), feature extraction was accomplished by standard machine learning techniques. As a result, they were only as good as the people who made the features by hand. But with DL, both feature extraction and classification are fully automated. These methods have significantly advanced several fields of medicine, including the diagnosis of epilepsy. In this paper, the works focused on automated epileptic seizure detection using ML and DL techniques are presented as well as their comparative analysis is done. The UCI-Epileptic Seizure Recognition dataset is used for training and validation. Some of the conventional ML and DL algorithms are used with a proposed model which uses long short-term memory (LSTM) to find the best approach. Post that comparative analysis is performed on these algorithms to find the best approach for epileptic seizure detection. As a result, the proposed model LSTM gives a validation accuracy of 97% giving the most appropriate and precise result as compared to other mentioned algorithms used in this study.