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To produce enough food, smallholder farmers in developing countries apply fertilizer nitrogen (N) to cereals, sometimes even more than the local recommendations. During the last two decades, hand-held chlorophyll meters and canopy reflectance sensors, which can detect the N needs of the crop based on transmission and reflectance properties of leaves through proximal sensing, have been studied as tools for optimizing crop N status in cereals in developing countries. This review aims to describe the outcome of these studies. Chlorophyll meters are used to manage fertilizer N to maintain a threshold leaf chlorophyll content throughout the cropping season. Despite greater reliability of the sufficiency index approach, the fixed threshold chlorophyll content approach has been investigated more for using chlorophyll meters in rice and wheat. GreenSeeker and Crop Circle crop reflectance sensors take into account both N status and biomass of the crop to estimate additional fertilizer N requirement but only a few studies have been carried out in developing countries to develop N management strategies in rice, wheat and maize. Both chlorophyll meters and canopy reflectance sensors can increase fertilizer N use efficiency by reduction of N rates. Dedicated economic analysis of the proximal sensing strategies for managing fertilizer N in cereals in developing countries is not adequately available.

Scientific evidence suggests that dromedary camels are the intermediary host for the Middle East respiratory syndrome coronavirus (MERS-CoV). However, the actual number of infections in people who have had contact with camels is unknown and most index patients cannot recall any such contact. We aimed to do a nationwide serosurvey in Saudi Arabia to establish the prevalence of MERS-CoV antibodies, both in the general population and in populations of individuals who have maximum exposure to camels. In the cross-sectional serosurvey, we tested human serum samples obtained from healthy individuals older than 15 years who attended primary health-care centres or participated in a national burden-of-disease study in all 13 provinces of Saudi Arabia. Additionally, we tested serum samples from shepherds and abattoir workers with occupational exposure to camels. Samples were screened by recombinant ELISA and MERS-CoV seropositivity was confirmed by recombinant immunofluorescence and plaque reduction neutralisation tests. We used two-tailed Mann Whitney U exact tests, χ2, and Fisher's exact tests to analyse the data. Between Dec 1, 2012, and Dec 1, 2013, we obtained individual serum samples from 10 009 individuals. Anti-MERS-CoV antibodies were confirmed in 15 (0·15%; 95% CI 0·09–0·24) of 10 009 people in six of the 13 provinces. The mean age of seropositive individuals was significantly younger than that of patients with reported, laboratory-confirmed, primary Middle Eastern respiratory syndrome (43·5 years [SD 17·3] vs 53·8 years [17·5]; p=0·008). Men had a higher antibody prevalence than did women (11 [0·25%] of 4341 vs two [0·05%] of 4378; p=0·028) and antibody prevalence was significantly higher in central versus coastal provinces (14 [0·26%] of 5479 vs one [0·02%] of 4529; p=0·003). Compared with the general population, seroprevalence of MERS-CoV antibodies was significantly increased by 15 times in shepherds (two [2·3%] of 87, p=0·0004) and by 23 times in slaughterhouse workers (five [3·6%] of 140; p<0·0001). Seroprevalence of MERS-CoV antibodies was significantly higher in camel-exposed individuals than in the general population. By simple multiplication, a projected 44 951 (95% CI 26 971–71 922) individuals older than 15 years might be seropositive for MERS-CoV in Saudi Arabia. These individuals might be the source of infection for patients with confirmed MERS who had no previous exposure to camels. European Union, German Centre for Infection Research, Federal Ministry of Education and Research, German Research Council, and Ministry of Health of Saudi Arabia.

This study discusses energy management in thermal and electrical microgrids while taking heat pumps, renewable sources, thermal and hydrogen storages into account. The weighted total of the operating cost, grid emissions level, voltage and temperature deviation function, and other factors makes up the objective function of the suggested method. The restrictions include the operation-flexibility model of resources and storages, micro-grid flexibility limits, and optimum power flow equations. Point Estimation Method is used in this work to simulate load, energy price, and renewable phenomenon uncertainty. A fuzzy decision-making methodology is used to arrive at a compromise solution that satisfies network operators’ operational, environmental, and financial goals. The innovations of this paper include energy management of various smart microgrids, simultaneous modeling of several indicators especially flexibility, investigation of optimal performance of resources and storage devices, and modeling of uncertainty considering low computational time and an accurate flexibility model. Numerical findings indicate that the fuzzy decision-making approach has the capability to reach a compromise point in which the objective functions approach their minimum values. The integration of the proposed uncertainty modeling with precise flexibility modeling results in a reduction in computational time when compared to stochastic optimization based on scenarios. For the compromise point and uncertainty modeling with PEM, by efficiently managing resources and thermal and hydrogen storages, scheme is capable of attaining high flexibility conditions. Compared to load flow studies, the approach can enhance the operational, environmental, and economic conditions of smart microgrids by approximately 33–57%, 68%, and 33–68%, respectively, under these circumstances.

The effective thermal management of Lithium-Ion Batteries (LIBs) is essential for ensuring safety, extending cycle life, and maintaining performance in electric vehicle applications. Among various approaches, passive battery thermal management systems (PBTMS) using phase change materials (PCMs) provide a cost-effective and reliable solution compared to conventional active cooling. This study proposes a novel conical cylindrical chamber (CCC) design for PCM encapsulation and evaluates its impact on LIB temperature regulation. A three-dimensional Computational fluid dynamics (CFD) model based on the enthalpy–porosity method was developed to simulate coupled heat transfer and phase change phenomena under dynamic discharge conditions. The effects of chamber geometry (top and bottom radii), different PCM types, and discharge rates (1–3 C) were systematically investigated. Results show that chamber configuration strongly influences PCM melting efficiency and battery thermal response. For example, the optimized CCC geometry reduced peak battery temperature by nearly 30 °C compared to less efficient designs, while poorly configured chambers left up to 38% of the PCM unmelted at end of discharge. The study demonstrates that balancing CCC surface area and PCM volume is critical for maximizing heat absorption, minimizing thermal gradients, and enhancing passive cooling. These findings provide design guidelines for next-generation passive thermal management systems in LIB applications.

In this paper, the influence of surface energy (SE) on the linear and nonlinear frequencies of anodic aluminum micro beams with [100] and [111] crystalline orientations resting on an elastic substrate are analyzed based on the Timoshenko beam (TB) and Euler–Bernoulli (EB) models, spanning Nano- to micro-scale dimensions. Given the high ratio of surface-to-volume the studied micro beams, the proposed model incorporates SE effects. To extract the micro beam frequencies, the governing the Galerkin method with trigonometric and polynomial shape functions corresponding to clamped–clamped, clamped-simply supported, and simply supported boundary conditions are used. By assuming a harmonic temporal response, the natural frequencies are derived. The study examines the influence of crystalline orientation ([100] and [111]), beam length, elastic substrate coefficient, moment of inertia, and shear deformation (via the TB) under various boundary conditions on the nonlinear and linear frequencies of the micro Timoshenko beam models and Euler–Bernoulli. A comparative analysis reveals that the EB yields higher estimates for both linear and nonlinear frequencies compared to the TB, which accounts for rotational inertia and shear deformation effects. Furthermore, the results demonstrate that crystalline orientation significantly impacts the linear and nonlinear frequencies. Specifically, the anodic aluminum microbe am with [111] crystalline orientation exhibits higher linear and nonlinear frequencies due to its greater stiffness compared to the [100] orientation.

The constraints of pure PMMA have made the cross-linkage of polymethyl methacrylate (PMMA) an interesting issue to meet application requirements. Cross-linked PMMA offers several advantages over its uncross-linked counterpart, due to its exceptional electrical insulation, chemical stability, clarity, improved heat resistance, superior mechanical properties, and abrasion resistance, and one of the most used polymer cements in orthopedic surgery is polymethyl methacrylate, or PMMA. PMMA, or poly(methyl methacrylate), is widely utilized in a variety of industries, including optical equipment, aviation, architecture, and health care. However, PMMA's physical characteristics play a major role in its utilization. An increase in molecular weight results in an increase in mechanical qualities, for example, elasticity, Young's modulus, shear modulus, and fracture surface energy. Therefore, it is essential to synthesize high molecular weight PMMA. This review briefly discusses the impact of adding an organometallic component to the polymer's backbone to pendant functionalized poly(methyl methacrylate) PMMA, UV radiation, free radical cross-linking, small-molecule cross-linking, condensation methods, the use of coordination polymerization, atom transferring radical polymerization, and conventional polymerization by free radical to produce high molecular weight PMMA; additionally, the impact of cross-linkers on the characteristics of polymers is discussed. The paper also looks at a number of important uses of cross-linked PMMA. Graphical Abstract

Accurate assessment of intracranial aneurysm rupture risk, particularly in Middle Cerebral Artery (MCA) aneurysms, relies on a detailed understanding of patient-specific hemodynamic behavior. In this study, we present an integrated framework that combines Computational Fluid Dynamics (CFD) with Proper Orthogonal Decomposition (POD) and machine learning (ML) to efficiently model pulsatile blood flow using a Casson non-Newtonian fluid model, without incorporating fluid-structure interaction (FSI). Patient-specific vascular geometries were reconstructed from DICOM imaging data and simulated using ANSYS Fluent to capture key hemodynamic factors, including velocity components, pressure, wall shear stress (WSS), and oscillatory shear index (OSI). POD was applied to reduce the dimensionality of the CFD data while retaining the dominant energetic flow structures. Results showed that fewer than 10 POD modes were sufficient to capture over 99% of the energy for pressure and WSS, while OSI required significantly more modes due to its inherent complexity. Machine learning models were trained on the reduced-order features to predict hemodynamic fields across time snapshots. The hybrid POD-ML approach yielded reasonable predictions for pressure and WSS in both training and test sets, while OSI prediction accuracy decreased in the test region, indicating the need for more advanced modeling strategies. The proposed method significantly reduces computational cost while preserving critical hemodynamic information, making it well-suited for real-time or near-real-time clinical decision support. This work demonstrates the potential of combining data-driven techniques with CFD for efficient, non-invasive risk assessment and treatment planning in cerebral aneurysm management.

Improving oil recovery in sandstone reservoirs with higher concentrations of clay particles (clay-rich) presents a persistent challenge, especially in heavy oil extraction. Although low-salinity water flooding has been investigated for sandstone reservoirs, the synergistic effects of heavy oil molecular composition, cationic surfactants (e.g., cetyltrimethylammonium bromide, CTAB), clay particles, and ion-tuned brines on emulsion stability and oil recovery remain poorly understood. This study investigated the molecular behavior of asphaltene under the synergistic effects of CTAB and low salinity water flooding in clay-rich systems. Advanced experimental techniques, including interfacial tension (IFT) measurements, viscosity analysis, and zeta potential assessment, revealed that sulfate-enriched seawater (SW5d.3SO4) in the presence of clay and CTAB hindered asphaltene migration. However, cation-enriched seawater (SW5d.3Mg) promoted asphaltene migration, increasing IFT by ~ 18 mN/m to 48.23 mN/m and decreasing viscosity by approximately 351.3 cP to 249.5 cP. ATR (Attenuated total reflection)-FTIR (Fourier transform infrared spectroscopy) analysis demonstrated that sulfate-rich brines preferentially mobilized less-polar components, whereas cation-rich brines reduced the polar content of the oil phase. Additionally, (SW5d.3Mg) increased the asphaltene onset point precipitation (AOP) by 11% and reduced asphaltene concentration by ~ 5%, enhancing flow assurance. These findings provide critical insights into emulsion stabilization mechanisms and fluid-rock interactions, offering a sustainable strategy to optimize low-salinity water flooding with CTAB for enhanced heavy oil recovery in sandstone reservoirs.

Soil degradation due to global warming, water scarcity and diminishing natural resources negatively impacts food security. Soil fertility deterioration, particularly phosphorus (P) deficiency, remains a challenge in the arid and semi-arid regions. In this study, field experiments were conducted in different geographical locations to investigate the effects of organic amendments coupled with P fertilization and irrigation on soil physical-chemical properties, and the growth, yield and quality of wheat. Application of P fertilizers combined with organic amendments mitigated soil salinity, increased organic matter content, available water, hydraulic conductivity and available macronutrients, but decreased soil bulk density. Application of organic amendments slightly increased total Cd, Ni and Pb in soil, but Cd and Ni concentration was below allowable limits whilst Pb reached a hazardous level. Soil P fractions were significantly increased with the combined application of mineral P and organic amendments irrespective of salinity and irrigation. Crop growth yield and quality of wheat improved significantly in response to the integrated application of mineral P and organic amendments. In conclusion, the combination of mineral P sources with organic amendments could be successfully used as a cost-effective management practice to enhance soil fertility and crop production in the arid and semi-arid regions stressed with water scarcity and natural resource constraints.

Objective The aim of this study was to develop a hybrid diagnostic framework integrating radiomic and deep features for accurate and reproducible detection and classification of wrist fractures using X-ray images. Materials and Methods A total of 3,537 X-ray images, including 1,871 fracture and 1,666 non-fracture cases, were collected from three healthcare centers. Radiomic features were extracted using the PyRadiomics library, and deep features were derived from the bottleneck layer of an autoencoder. Both feature modalities underwent reliability assessment via Intraclass Correlation Coefficient (ICC) and cosine similarity. Feature selection methods, including ANOVA, Mutual Information (MI), Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE), were applied to optimize the feature set. Classifiers such as XGBoost, CatBoost, Random Forest, and a Voting Classifier were used to evaluate diagnostic performance. The dataset was divided into training (70%) and testing (30%) sets, and metrics such as accuracy, sensitivity, and AUC-ROC were used for evaluation. Results The combined radiomic and deep feature approach consistently outperformed standalone methods. The Voting Classifier paired with MI achieved the highest performance, with a test accuracy of 95%, sensitivity of 94%, and AUC-ROC of 96%. The end-to-end model achieved competitive results with an accuracy of 93% and AUC-ROC of 94%. SHAP analysis and t-SNE visualizations confirmed the interpretability and robustness of the selected features. Conclusions This hybrid framework demonstrates the potential for integrating radiomic and deep features to enhance diagnostic performance for wrist and forearm fractures, providing a reliable and interpretable solution suitable for clinical applications.