Search Results (74)

Accurate crop yield modeling (CYM) is inherently challenging due to the complex, nonlinear, and temporally dynamic interactions of biotic and abiotic factors. Crop traits, which historically capture the cumulative effect of these factors, exhibit functional relationships critical for optimizing productivity. This underscores the necessity of multi-trait-based CYM approaches. Crop growth models enable trait dynamics with reflectance data and spectral indices as proxies for crop health and traits, respectively, to have real-time, spatially explicit monitoring. The Agricultural Production Systems sIMulator was calibrated to simulate multiple traits across the growth season based on geo-tagged wheat field ground information. Reflectance and spectral indices were processed for the geo-tagged fields across temporal observations to enable real-time, spatially explicit monitoring. Based on these parameters, this study addresses a critical gap in existing CYM frameworks by proposing a machine learning-based model that synergized multiple crop traits with reflectance and spectral indices to generate site-specific yield estimates. The performance evaluation revealed that the Long Short-Term Memory (LSTM) model achieved superior accuracy for the integrated parameters (RMSE = 250.68 kg/ha, MAE = 193.76 kg/ha, and R² = 0.84), followed by traits alone. The Random Forest model followed the LSTM model, with an RMSE = 293.56 kg/ha, MAE = 230.68 kg/ha, and R² = 0.78 for integrated parameters, and an RMSE = 291.73 kg/ha, MAE = 223.17 kg/ha, and R² = 0.78 for crop traits. The superior prediction demonstrated the dominant role of multiple crop traits with satellite-derived reflectance metrics to develop robust CYM frameworks capable of capturing intra- and inter-field yield variability. Full article

Nutrient deficiencies, low organic matter content, and a limited soil–water saturation percentage in calcareous soils hinder plant growth and crop production. To address these challenges, sustainable and green-based farming practices have been introduced. This study investigates the synergistic effects of biochar and nitrogen levels as sustainable solutions for improving soil fertility and supporting wheat growth in calcareous soils. A pot experiment assessed the effects of biochar (5-, 10-, and 15-tons ha⁻¹) and nitrogen levels (60, 90, and 120 kg ha⁻¹) on soil physicochemical properties, nutrient availability, and wheat growth. The randomized complete block design included three replicates and a control. The results highlight that the highest biochar rate (15 tons ha⁻¹) combined with the highest nitrogen level (120 kg ha⁻¹) significantly (p ≤ 0.05) improved soil physicochemical properties and nutrient status. Notably, soil pH increased by 2.8%, electrical conductivity by 29.8%, and soil organic matter by 185%, while bulk density decreased by 22.3%. Soil total nitrogen surged by 163.7%, soil–water saturation percentage by 27.2%, plant-available phosphorus by 66.8%, and plant-available potassium by 96.8%. Wheat growth parameters also showed marked improvement, with plant height up 29.7%, spike length by 20.7%, grains per spike by 41.5%, thousand-grain weight by 24.7%, grain yield by 81.3%, and biological yield by 26.5%. There was a strong positive correlation between enhanced soil properties and improved wheat growth, except for soil bulk density, which showed a negative correlation. This underscores the role of biochar in boosting soil fertility and crop productivity. A principal component analysis further validated these findings, suggesting that integrating biochar with appropriate nitrogen fertilization offers a sustainable strategy to enhance soil health, manage nutrient availability, and strengthen crop yields in calcareous soil. Biochar application combined with elevated nitrogen levels significantly enhances soil fertility and wheat productivity in semi-arid regions, offering a sustainable solution for improving calcareous soils. Future studies should explore the long-term impacts and scalability of this approach. Full article

Consistent and accurate data on forest biomass and carbon dynamics are essential for optimizing carbon sequestration, advancing sustainable management, and developing natural climate solutions in various forest ecosystems. This study quantifies the forest biomass in designated forests based on GEDI LiDAR datasets with a unique compartment-level monitoring of unexplored hilly areas of Mansehra. The integration of multisource explanatory variables, employing machine learning models, adds further innovation to the study of reliable above ground biomass (AGB) estimation. Integrating Landsat-9 vegetation indices with ancillary datasets improved forest biomass estimation, with the random forest algorithm yielding the best performance (R² = 0.86, RMSE = 28.03 Mg/ha, and MAE = 19.54 Mg/ha). Validation with field data on a point-to-point basis estimated a mean above-ground biomass (AGB) of 224.61 Mg/ha, closely aligning with the mean ground measurement of 208.13 Mg/ha (R² = 0.71). The overall mean AGB model estimated a forest biomass of 189.42 Mg/ha in the designated moist temperate forests of the study area. A critical deficit in the carbon sequestration potential was analysed, with the estimated AGB in 2022, at 19.94 thousand tons, with a deficit of 0.83 thousand tons to nullify CO₂ emissions (20.77 thousand tons). This study proposes improved AGB estimation reliability and offers insights into the CO₂ sequestration potential, suggesting a policy shift for sustainable decision-making and climate change mitigation policies. Full article

Accurate detection and diagnosis of brain tumors at early stages is significant for effective treatment. While numerous methods have been developed for tumor detection and classification, several rely on traditional techniques, often resulting in suboptimal performance. In contrast, AI-based deep learning techniques have shown promising results, consistently achieving high accuracy across various tumor types while maintaining model interpretability. Inspired by these advancements, this paper introduces an improved variant of EfficientNet for multi-grade brain tumor detection and classification, addressing the gap between performance and explainability. Our approach extends the capabilities of EfficientNet to classify four tumor types: glioma, meningioma, pituitary tumor, and non-tumor. For enhanced explainability, we incorporate gradient-weighted class activation mapping (Grad-CAM) to improve model interpretability. The input MRI images undergo data augmentation before being passed through the feature extraction phase, where the underlying tumor patterns are learned. Our model achieves an average accuracy of 98.6%, surpassing other state-of-the-art methods on standard datasets while maintaining a substantially reduced parameter count. Furthermore, the explainable AI (XAI) analysis demonstrates the model’s ability to focus on relevant tumor regions, enhancing its interpretability. This accurate and interpretable model for brain tumor classification has the potential to significantly aid clinical decision-making in neuro-oncology. Full article

The integration of the Crop Growth Model (CGM), Radiative Transfer Model (RTM), and Machine Learning Algorithm (MLA) for estimating crop traits represents a cutting-edge area of research. This integration requires in-depth study to address RTM limitations, particularly of similar spectral responses from multiple input combinations. This study proposes the integration of CGM and RTM for crop trait retrieval and evaluates the performance of CGM output-based RTM spectra generation for multiple crop traits estimation without biased sampling using machine learning models. Moreover, PROSAIL spectra as training against Harmonized Landsat Sentinel-2 (HLS) as testing was also compared with HLS data only as an alternative. It was found that satellite data (HLS, 80:20) not only consistently performed better, but PROSAIL (train) and HLS (test) also had satisfactory results for multiple crop traits from uniform training samples in spite of differences in simulated and real data. PROSAIL-HLS has an RMSE of 0.67 for leaf area index (LAI), 5.66 µg/cm² for chlorophyll ab (Cab), 0.0003 g/cm² for dry matter content (Cm), and 0.002 g/cm² for leaf water content (Cw) against the HLS only, with an RMSE of 0.40 for LAI, 3.28 µg/cm² for Cab, 0.0002 g/cm² for Cm, and 0.001 g/cm² for Cw. Optimized machine learning models, namely Extreme Gradient Boost (XGBoost) for LAI, Support Vector Machine (SVM) for Cab, and Random Forest (RF) for Cm and Cw, were deployed for temporal mapping of traits to be used for wheat productivity enhancement. Full article

Radiative transfer models (RTMs) provide reliable information about crop yield and traits with high resource efficiency. In this study, we have conducted a systematic literature review (SLR) to fill the gaps in the overall insight of RTM-based crop yield prediction (CYP) and crop traits retrieval. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 76 articles were found to be relevant to crop traits retrieval and 15 for CYP. China had the highest number of RTM applications (33), followed by the USA (13). Crop-wise, cereals, and traits-wise, leaf area index (LAI) and chlorophyll, had a high number of research studies. Among RTMs, the PROSAIL model had the highest number of articles (62), followed by SCOPE (6) with PROSAIL accuracy for CYP (median R² = 0.62) and crop traits (median R² = 0.80). The same was true for crop traits retrieval with LAI (CYP median R² = 0.62 and traits median R² = 0.85), followed by chlorophyll (crop traits median R² = 0.70). Document co-citation analysis also found the relevancy of selected articles within the theme of this SLR. This SLR not only focuses on information about the accuracy and reliability of RTMs but also provides comprehensive insight towards understanding RTM applications for crop yield and traits, further exploring possibilities of new endeavors in agriculture, particularly crop yield modeling. Full article

Inverted distributions, also known as inverse distributions, are essential statistical models for analyzing real-life data in biomedical sciences, engineering, and other fields. In this paper, we use the odd beta prime-G family and the inverted Kumaraswamy distribution to introduce a new inverted distribution called the odd beta prime inverted Kumaraswamy. The new distribution exhibits right-skewed, J-shaped densities and features increasing-constant, concave-convex, and bathtub hazard functions. Some of its statistical properties are explored. The parameters are estimated via the maximum likelihood method. The empirical importance of the new model is proved through its application to COVID-19 mortality data from Italy. Numerical results demonstrate that the proposed model outperforms its competitors. We hope that this proposed distribution can be considered as a viable alternative to some well-established distributions for modeling real-life data across various application areas. Full article

The Burr III distribution is extended in this work as a substitute for the numerous Burr III distributions. A new distribution is developed by applying the log transformation technique to define the transformed log-Burr III distribution. Moments and quantile function are the structural features established in this study. The model parameters are derived using the maximum likelihood technique. The applicability of the new distribution was assessed using real-world data on the transformed total milk production in the first birth of 107 cows of the SINDI race. The results showed that the proposed distribution might be used as the optimal distribution for this dataset. Full article

The intricate interplay between the environment and the economy entails numerous multifaceted factors that require thorough investigation. Civic activism, intergroup cohesion, and gender equality are among the pertinent factors that hold the potential to significantly impact CO₂ emissions in developing economies. However, these variables have not been explored to the extent that their importance warrants, leaving much to be studied and understood about their complex relationships with carbon emissions. Currently, developing nations find themselves more vulnerable and exposed to a plethora of environmental issues. In response to this pressing matter, the focus of this study is to expound upon the impact of various factors on the environment. To achieve this aim, this study utilizes annual data from 46 developing countries, spanning the extensive period from 1990 to 2014. Using the generalized method of moments and empirical Bayes methods, this study’s results emphasize the significant impact that civic activism, gender equality, intergroup cohesion, and financial development can have on increasing CO₂ emissions. However, civic activism reduces CO₂ emissions. These findings highlight the crucial importance of adopting a comprehensive approach that accounts for both economic and social cohesion indicators when tackling environmental challenges. Full article

In this article, we pioneer a new Burr X distribution using the odd beta prime generalized (OBP-G) family of distributions called the OBP-Burr X (OBPBX) distribution. The density function of this model is symmetric, left-skewed, right-skewed, and reversed-J, while the hazard function is monotonically increasing, decreasing, bathtub, and N-shaped, making it suitable for modeling skewed data and failure rates. Various statistical properties of the new model are obtained, such as moments, moment-generating function, entropies, quantile function, and limit behavior. The maximum-likelihood-estimation procedure is utilized to determine the parameters of the model. A Monte Carlo simulation study is implemented to ascertain the efficiency of maximum-likelihood estimators. The findings demonstrate the empirical application and flexibility of the OBPBX distribution, as showcased through its analysis of petroleum rock samples and COVID-19 mortality data, along with its superior performance compared to well-known extended versions of the Burr X distribution. We anticipate that the new distribution will attract a wider readership and provide a vital tool for modeling various phenomena in different domains. Full article

Counterfeit products have become a pervasive problem in the global marketplace, necessitating effective strategies to protect both consumers and brands. This study examines the role of cybersecurity in addressing counterfeiting issues, specifically focusing on a multi-level grayscale watermark-based authentication system. The system comprises a generator responsible for creating a secure 2D code, and an authenticator designed to extract watermark information and verify product authenticity. To authenticate the secure 2D code, we propose various features, including the analysis of the spatial domain, frequency domain, and grayscale watermark distribution. Furthermore, we emphasize the importance of selecting appropriate interpolation methods to enhance counterfeit detection. Our proposed approach demonstrates remarkable performance, achieving precision, recall, and specificities surpassing 84.8%, 83.33%, and 84.5%, respectively, across different datasets. Full article

Electromagnetic interference is considered a serious threat to electrical devices, the environment, and human beings. In this regard, various shielding materials have been developed and investigated. Graphene is a two-dimensional, one-atom-thick nanocarbon nanomaterial. It possesses several remarkable structural and physical features, including transparency, electron conductivity, heat stability, mechanical properties, etc. Consequently, it has been used as an effective reinforcement to enhance electrical conductivity, dielectric properties, permittivity, and electromagnetic interference shielding characteristics. This is an overview of the utilization and efficacy of state-of-the-art graphene-derived nanocomposites for radiation shielding. The polymeric matrices discussed here include conducting polymers, thermoplastic polymers, as well as thermosets, for which the physical and electromagnetic interference shielding characteristics depend upon polymer/graphene interactions and interface formation. Improved graphene dispersion has been observed due to electrostatic, van der Waals, π-π stacking, or covalent interactions in the matrix nanofiller. Accordingly, low percolation thresholds and excellent electrical conductivity have been achieved with nanocomposites, offering enhanced shielding performance. Graphene has been filled in matrices like polyaniline, polythiophene, poly(methyl methacrylate), polyethylene, epoxy, and other polymers for the formation of radiation shielding nanocomposites. This process has been shown to improve the electromagnetic radiation shielding effectiveness. The future of graphene-based nanocomposites in this field relies on the design and facile processing of novel nanocomposites, as well as overcoming the remaining challenges in this field. Full article

Research on polymer nanocomposite nanofibers has seen remarkable growth over the past several years. One of the main driving forces for this progress is the increasing applicability of polymer nanocomposite nanofibers for technological applications. This review basically aims to present the current state of manufacturing polymer/graphene nanofiber nanocomposites, using appropriate techniques. Consequently, various conducting and thermoplastic polymers have been processed with graphene nano-reinforcement to fabricate the nanocomposite nanofibers. Moreover, numerous methods have been adopted for the fabrication of polymer/graphene nanocomposites and nanofibers including interfacial polymerization, phase separation, freeze drying, template synthesis, drawing techniques, etc. For the formation of polymer/graphene nanocomposite nanofibers, electrospinning can be preferable due to various advantages such as the need for simple equipment, control over morphology, and superior properties of the obtained material. The techniques such as solution processing, melt spinning, and spin coating have also been used to manufacture nanofibers. Here, the choice of manufacturing techniques and parameters affects the final nanofiber morphology, texture, and properties. The manufactured nanocomposite nanofibers have been examined for exceptional structural, microstructure, thermal, and other physical properties. Moreover, the properties of polymer/graphene nanofiber rely on the graphene content, dispersion, and matrix–nanofiller interactions. The potential of polymer/graphene nanocomposite nanofibers has been investigated for radiation shielding, supercapacitors, membranes, and the biomedical field. Hence, this review explains the literature-driven significance of incorporating graphene in polymeric nanofibers. Conclusively, most of the studies focused on the electrospinning technique to design polymer/graphene nanofibers. Future research in this field may lead to advanced innovations in the design and technical applications of nanocomposite nanofibers. To the best of our knowledge, research reports are available on this topic; however, the stated literature is not in a compiled and updated form. Therefore, field researchers may encounter challenges in achieving future advancements in the area of graphene-based nanocomposite nanofibers without first consulting the recent literature, such as an assembled review, to gain necessary insights, etc. Consequently, this state-of-the-art review explores the manufacturing, properties, and potential of polymer/graphene nanocomposite nanofibers. Full article

Among conducting polymers, polythiophene has gained an important stance due to its remarkable physical features. Graphene is a unique, two-dimensional, nanocarbon nanomaterial. As in other polymers, graphene has been reinforced in polythiophene to form advanced nanocomposites. This comprehensive review covers the design, essential features, and methodological potential of significant polythiophene and graphene-derived nanocomposites. In this context, various facile approaches, such as in situ processing, the solution method, and analogous simplistic means, have been applied. Consequently, polythiophene/graphene nanocomposites have been investigated for their notable electron conductivity, heat conduction, mechanical robustness, morphological profile, and other outstanding properties. Studies have revealed that graphene dispersion and interactions with the polythiophene matrix are responsible for enhancing the overall characteristics of nanocomposites. Fine graphene nanoparticle dispersal and linking with the matrix have led to several indispensable technical applications of these nanocomposites, such as supercapacitors, solar cells, sensors, and related devices. Further research on graphene nanocomposites with polythiophene may lead to remarkable achievements for advanced engineering and device-related materials. Full article

Polyurethane is a multipurpose polymer with indispensable physical characteristics and technical uses, such as films/coatings, fibers, and foams. The inclusion of nanoparticles in the polyurethane matrix has further enhanced the properties and potential of this important polymer. Research in this field has led to the design and exploration of polyurethane foams and polyurethane nanocomposite foams. This review article reflects vital aspects related to the fabrication, features, and applications of polyurethane nanocomposite foams. High-performance nanocellular polyurethanes have been produced using carbon nanoparticles such as graphene and carbon nanotubes. Enhancing the amounts of nanofillers led to overall improved nanocomposite foam features and performances. Subsequently, polyurethane nanocomposite foams showed exceptional morphology, electrical conductivity, mechanical strength, thermal stability, and other physical properties. Consequently, multifunctional applications of polyurethane nanocomposite foams have been observed in shape memory, electromagnetic interference shielding, and biomedical applications. Full article