Search Results (162)

Cancer has a threatening impact on human health, and it is one of the primary causes of fatalities worldwide. Different conventional treatments have been employed to treat cancer, but their non-specific nature reduces their therapeutic efficacy. This study employs a C₅N₂-based targeted drug carrier to study the delivery mechanism of anticancer drugs, particularly cisplatin, carmustine, and mechlorethamine, using density functional theory (DFT). The geometries of the drugs, the C₅N₂ substrate, and the drug@C₅N₂ complexes were optimized at the PBE0-D3BJ/def2SVP level of theory. Interaction energy was computed for the complexes which follow the trend, i.e., cisplatin@C₅N₂ (−27.60 kcal mol⁻¹) > carmustine@C₅N₂ (−19.69 kcal mol⁻¹) > mechlorethamine@C₅N₂ (−17.79 kcal mol⁻¹). The non-covalent interaction (NCI) and quantum theory of atoms in molecules (QTAIM) analyses confirmed the presence of van der Waals forces between the carmustine@C₅N₂ and mechlorethamine@C₅N₂ complexes, while weak hydrogen bonding has also been observed between the cisplatin@C₅N₂ complex. Electron localization function (ELF) analysis was performed to analyze the degree of delocalization of electrons within the complexes. The electronic properties of the analytes and the C₅N₂ substrate confirmed the enhanced reactivity of the complexes and illustrated electron density shift between the drugs and the C₅N₂ sheet. Recovery time was determined to assess the biocompatibility and the desorption behavior of the drugs. Moreover, negative solvation energies and increased dipole moments in a solvent phase manifested enhanced solubility and easy circulation of the drugs in biological media. Subsequently, this study illustrates that cisplatin@C₅N₂, carmustine@C₅N₂, and mechlorethamine@C₅N₂ complexes can be utilized as efficient drug delivery systems. Full article

Recently, artificial intelligence (AI) has become a promising solution to the optimization of the energy harvesting and performance of photovoltaic (PV) systems. Traditional maximum power point tracking (MPPT) algorithms have several drawbacks on tracking the global maximum power point (GMPP) under partial shading conditions (PSCs). To track the GMPP, AI enabled methods stand out over other traditional solutions in terms of faster tracking dynamics, lesser oscillation, higher efficiency. However, such AI-based MPPT methods differ significantly in various applications, and thus, a full picture of AI-based MPPT methods is of interest to further optimize the PV energy harvesting. In this paper, various AI-based global maximum power point tracking (GMPPT) techniques are then implemented and critically compared by highlighting the advantages and disadvantages of each technique under dynamic weather conditions. The comparison demonstrates that the hybrid AI techniques are more reliable, which offer higher efficiency and better dynamics to handle PSCs. According to the benchmarking, a modified particle swarm optimization (PSO) GMPPT algorithm is proposed, and the experimental results validate its ability to achieve GMPPT with faster dynamics and higher efficiency. This paper is intended to motivate engineers and researchers by offering valuable insights for the selection and implementation of GMPPT techniques and to explore the AI techniques to enhance the efficiency and reliability of PV systems by providing fresh perspectives on optimal AI-based GMPPT techniques. Full article

This article presents a computational analysis of approximate solutions for the time-fractional nonlinear Kawahara problem (KP) and the modified Kawahara problem (modified KP). This study utilizes the natural homotopy transform scheme (NHTS), which integrates the natural transform (NT) with the homotopy perturbation scheme (HPS). We derive the algebraic expression of nonlinear terms through the implementation of HPS. The fractional derivatives are considered in the Caputo form. Numerical results and visualizations present the practical interest and effectiveness of the fractional derivatives. The accuracy of the approximate results, coupled with their precise outcomes, emphasizes the reliability of the method. These findings demonstrate that NHTS is a robust and effective approach for solving time-fractional problems through series expansions. Full article

Flaxseeds, which have been consumed for thousands of years, have recently gained increasing popularity due to their rich composition, including omega-3 fatty acids, lignans, proteins, and fibers. These components are strongly associated with various health benefits, such as improving cardiovascular health, preventing certain types of cancer, controlling diabetes, promoting gastro-intestinal well-being, and aiding in weight management. This monograph explores the role of flaxseeds in nutrition, as well as their potential risks. Despite their numerous health benefits, flaxseeds also represent concerns due to excessive consumption and possible contamination, particularly from cyanogenic glycosides. Therefore, the levels of these compounds must be controlled, and this monograph also analyzes the available methods to detect and reduce these contaminants, ensuring the safety of flaxseed and flaxseed products consumers. Flaxseed is considered a valuable addition when incorporated into the diet, but it is necessary to continue research and promote technological improvements to maximize their benefits and minimize their risks. Full article

Globally, salt stress is one of the most significant abiotic stresses limiting crop production in dry-land regions. Nowadays, growing crops in dry-land regions under saline irrigation is the main focus. Soil amendment with organic materials has shown the potential to mitigate the adverse effects of salinity on plants. This study aimed to examine the ameliorative impact of soil amendment (manure + sandy, compost + sandy, clay + sandy and sandy soil) on the growth, yield, physiological, and biochemical attributes of Hedysarum scoparium Fisch. et Mey (HS) and Avena sativa L. (OT) under fresh and saline water irrigation in dry-land regions. The results showed that salt stress negatively affected both plant species’ growth, physiological traits, yield, and chloride ions. In response to saline irrigation, plants of both species increased catalase (CAT) and ascorbate peroxidase (APX) activities as part of a self-defense mechanism to minimize damage. Salt stress also significantly raised levels of hydrogen peroxide (H₂O₂), malondialdehyde (MDA), and chloride ions (Cl). However, soil amendment treatments like manure + sandy and compost + sandy soil countered the negative effects of saline irrigation, significantly improving plant growth and yield compared with sandy soil. Thus, organic soil amendment is a promising strategy for sustainable crop production under saline irrigation in dry-land regions. This study provides valuable insights into enhancing agricultural production by fostering resilient halophytes and salt-tolerant plant species in challenging environments. Full article

Seed priming is a simple, inexpensive, and effective pre-sowing technique that enables plants to better tolerate abiotic stresses, including high soil salinity, which is a major limiting factor in the establishment of halophytes for saline agriculture, as germinating seeds and early seedlings of many halophytes are sensitive compared to the mature vegetative stage. This article attempts to provide an overview of the research on the seed priming effects on halophyte seeds and subsequent seedlings/plants. Different physio-chemical and molecular processes, including the induction of priming/stress memory, which enhance salinity tolerance following seed priming, have also been discussed. This review also covers the aspects of reactive oxygen species (ROS), and nitric oxide (NO) signaling(s) that are activated as a result of seed priming. Finally, the limitations and prospects of seed priming to enhance the agronomic potential of halophytes for saline agriculture have been discussed. Full article

Single-atom catalysts (SACs) are presently recognized as cutting-edge heterogeneous catalysts for electrochemical applications because of their nearly 100% utilization of active metal atoms and having well-defined active sites. In this regard, SACs are considered renowned electrocatalysts for electrocatalytic O₂ reduction reaction (ORR), O₂ evolution reaction (OER), H₂ evolution reaction (HER), water splitting, CO₂ reduction reaction (CO₂RR), N₂ reduction reaction (NRR), and NO₃ reduction reaction (NO₃RR). Extensive research has been carried out to strategically design and produce affordable, efficient, and durable SACs for electrocatalysis. Meanwhile, persistent efforts have been conducted to acquire insights into the structural and electronic properties of SACs when stabilized on an adequate matrix for electrocatalytic reactions. We present a thorough and evaluative review that begins with a comprehensive analysis of the various substrates, such as carbon substrate, metal oxide substrate, alloy-based substrate, transition metal dichalcogenides (TMD)-based substrate, MXenes substrate, and MOF substrate, along with their metal-support interaction (MSI), stabilization, and coordination environment (CE), highlighting the notable contribution of support, which influences their electrocatalytic performance. We discuss a variety of synthetic methods, including bottom-up strategies like impregnation, pyrolysis, ion exchange, atomic layer deposition (ALD), and electrochemical deposition, as well as top-down strategies like host-guest, atom trapping, ball milling, chemical vapor deposition (CVD), and abrasion. We also discuss how diverse regulatory strategies, including morphology and vacancy engineering, heteroatom doping, facet engineering, and crystallinity management, affect various electrocatalytic reactions in these supports. Lastly, the pivotal obstacles and opportunities in using SACs for electrocatalytic processes, along with fundamental principles for developing fascinating SACs with outstanding reactivity, selectivity, and stability, have been highlighted. Full article

Accurate characterization of tree stems is critical for assessing commercial forest health, estimating merchantable timber volume, and informing sustainable value management strategies. Conventional ground-based manual measurements, although precise, are labor-intensive and impractical at large scales, while remote sensing approaches using satellite or UAV imagery often lack the spatial resolution needed to capture individual tree attributes in complex forest environments. To address these challenges, this study provides a significant contribution by introducing a large-scale dataset encompassing 40 plots in Western Australia (WA) with varying tree densities, derived from Hovermap LiDAR acquisitions and destructive sampling. The dataset includes parameters such as plot and tree identifiers, DBH, tree height, stem length, section lengths, and detailed diameter measurements (e.g., DiaMin, DiaMax, DiaMean) across various heights, enabling precise ground-truth calibration and validation. Based on this dataset, we present the Forest Stem Extraction and Modeling (FoSEM) framework, a LiDAR-driven methodology that efficiently and reliably models individual tree stems from dense 3D point clouds. FoSEM integrates ground segmentation, height normalization, and K-means clustering at a predefined elevation to isolate stem cores. It then applies circle fitting to capture cross-sectional geometry and employs MLESAC-based cylinder fitting for robust stem delineation. Experimental evaluations conducted across various radiata pine plots of varying complexity demonstrate that FoSEM consistently achieves high accuracy, with a DBH RMSE of 1.19 cm (rRMSE = 4.67%) and a height RMSE of 1.00 m (rRMSE = 4.24%). These results surpass those of existing methods and highlight FoSEM’s adaptability to heterogeneous stand conditions. By providing both a robust method and an extensive dataset, this work advances the state of the art in LiDAR-based forest inventory, enabling more efficient and accurate tree-level assessments in support of sustainable forest management. Full article

Zygophyllum propinquum (Decne.) is a leaf succulent C₄ perennial found in arid saline areas of southern Pakistan and neighboring countries, where it is utilized as herbal medicine. This study investigated how growth, water relations, ion content, chlorophyll fluorescence, and antioxidant system of Z. propinquum change as salinity levels increase (0, 150, 300, 600, and 900 mM NaCl). Salinity increments inhibited total plant fresh weight, whereas dry weight remained constant at moderate salinity and decreased at high salinity. Leaf area, succulence, and relative water content decreased as salinity increased. Similarly, the sap osmotic potential of both roots and shoots declined as NaCl concentrations increased. Except for a transitory increase in roots at 300 mM NaCl, sodium concentrations in roots and shoots increased constitutively to more than five times higher under saline conditions than in non-saline controls. Root potassium increased briefly at 300 mM NaCl but did not respond to NaCl treatments in the leaf. Photosynthetic pigments increased with 300 and 600 mM NaCl compared to non-saline treatments, although carotenoids appeared unaffected by NaCl treatments. Except for very high NaCl concentration (900 mM), salinity showed no significant effect on the maximum efficiency of photosystem II photochemistry (Fv/Fm). Light response curves demonstrated reduced absolute (ETR^*) and maximum electron transport rates (ETR_max) for the 600 and 900 mM NaCl treatments. The alpha (α), which indicates the maximum yield of photosynthesis, decreased with increasing NaCl concentrations, reaching its lowest at 900 mM NaCl. Non-photochemical quenching (NPQ) values were significantly higher under 150 and 300 mM NaCl treatments than under non-saline and higher NaCl treatments. Electrolyte leakage, malondialdehyde (MDA), and hydrogen peroxide (H₂O₂) peaked only at 900 mM NaCl. Superoxide dismutase and glutathione reductase activities and glutathione content in both roots and shoots increased progressively with increasing salinity. Hence, growth reduction under low to moderate (150–600 mM NaCl) salinity appeared to be an induced response, while high (900 mM NaCl) salinity was injurious. Full article

This paper reviews the privacy and security challenges posed by open-source artificial intelligence (AI) models. The increased use of open-source machine learning models, while beneficial for resource efficiency and collaboration, has introduced significant privacy risks and security vulnerabilities. Key threats include model inversion, membership inference, data leakage, and backdoor attacks, which could expose sensitive data or compromise system integrity. Our review highlights that many open-source models are vulnerable to these attacks due to their transparency and accessibility. We also identify that adversarial training, differential privacy (DP), and model sanitization techniques can effectively mitigate some of these risks, though achieving a balance between transparency and security remains a challenge. The findings highlight the need for continuous research and innovation to ensure that open-source AI models remain both secure and privacy-compliant in increasingly critical applications across various industries. Full article

Photovoltaic (PV) energy has been a preferable choice with the rise in global energy demand, as it is a sustainable, efficient, and cost-effective source of energy. Optimizing the power generation is necessary to fully utilize the PV system. Harvesting more power uses cascading of impedance source converters taking input from low-voltage PV arrays which requires multiple maximum power point tracking (MPPT) controllers. To solve this problem, a three-level inverter topology with a proposed PV arrangement, offering higher voltage boosting and a smaller size with a lower cost suitable for low-voltage panels, is designed in this article. The design criteria for parameters are discussed with the help of the small signal analysis. In this paper, three PV arrays are used to harvest maximum energy, which require only one MPPT controller and employ an extended perturb and observe (P&O) algorithm, being faster, highly efficient, and reducing the computational burden of the controller. Moreover, a three maximum power points tracker algorithm, which perturbs one parameter and observes six variables, is designed for the selected converter topology. Finally, the designed 1.1 kVA grid-connected PV system was simulated in MATLAB (R2023a) which shows that the MPPT algorithm offers better dynamics and is highly efficient with a conversion efficiency of 99.2% during uniform irradiance and 97% efficiency during variable irradiance conditions. Full article

Clustering samples based on similarity remains a significant challenge, especially when the goal is to accurately capture the underlying data clusters of complex arbitrary shapes. Existing density-based clustering techniques are known to be best suited for capturing arbitrarily shaped clusters. However, a key limitation of these methods is the difficulty in automatically finding the optimal set of parameters adapted to dataset characteristics, which becomes even more challenging when the data contain inherent noise. In our recent work, we proposed a Differential Evolution-based DENsity CLUstEring (DE-DENCLUE) to optimise DENCLUE parameters. This study evaluates DE-DENCLUE for its robustness in finding accurate clusters in the presence of noise in the data. DE-DENCLUE performance is compared against three other density-based clustering algorithms—DPC based on weighted local density sequence and nearest neighbour assignment (DPCSA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Variable Kernel Density Estimation–based DENCLUE (VDENCLUE)—across several datasets (i.e., synthetic and real). The study has consistently shown superior results for DE-DENCLUE compared to other models for most datasets with different noise levels. Clustering quality metrics such as the Silhouette Index (SI), Davies–Bouldin Index (DBI), Adjusted Rand Index (ARI), and Adjusted Mutual Information (AMI) consistently show superior SI, ARI, and AMI values across most datasets at different noise levels. However, in some cases regarding DBI, the DPCSA performed better. In conclusion, the proposed method offers a reliable and noise-resilient clustering solution for complex datasets. Full article

The fall armyworm, Spodoptera frugiperda, is a major migratory polyphagous insect pest of various crops. The essential nutrient and mineral profile of the host plants determines the feeding fitness of herbivorous insects. As a result, the growth and development of insects is affected. To determine the effect of the nutrient and mineral profile of different host plants (maize, castor bean, cotton, cabbage, okra, and sugarcane) on the growth and development of S. frugiperda, biological parameters like larval weight, pupal weight (male/female), and feeding and growth indices were calculated. The proximate compositions such as crude protein, crude fat, crude fibre, and ash and mineral contents of the tested host plants showed significant differences (p < 0.05). The feeding indices on these host plants also differed significantly (p < 0.05). The maximum relative growth rate (RGR), relative consumption rate (RCR), and consumption index (CI) were recorded in S. frugiperda larvae that fed on maize and castor bean leaves. The crude protein, dry matter, and ash contents in maize and castor bean were significantly higher and positively correlated with the RGR and RCR of S. frugiperda larvae. The larval, male and female pupal weights were the maximum in the larvae feeding on the castor bean host plant. These findings provide novel information based on nutritional ecology to develop sustainable integrated pest management strategies using selective crop rotation. Full article

The soil water retention curve (SWRC) is a vital soil property used to evaluate the soil’s water holding capacity, a critical factor in various applications such as determining soil water availability for plants, soil conservation and management, climate change adaptation, and mitigation of flood risks. Estimating SWRC directly in the field and laboratory is a time-consuming and laborious process and requires numerous instruments and measurements at a specific location. In this context, various estimation approaches have been developed, including pedotransfer functions (PTFs), over the past three decades to estimate soil water retention and its associated properties. Despite the efficiencies, PTFs and semi-physical approach-based models often have several limitations, particularly in the dry range of the SWRC. PTFs-based modeling has become a key research topic due to readily available soil data and cost-effective methods for deriving essential soil parameters, which enable more efficient decision-making in sustainable land-use management. Therefore, advancement and adjustment are necessary for reliable estimations of the SWRC from readily available data. This article reviews the evaluation of the current and past PTFs for estimating the SWRC. This study aims to evaluate PTF techniques and semi-physical approaches based on soil texture, bulk density, porosity, and other related factors. Additionally, it also assesses the performance and limitations of various common semi-physical models proposed and developed by Arya and Paris, Haverkamp and Parlange, the Modified Kovács model by Aubertin et al., Chang and Cheng, Meskini-Vishkaee et al., Vidler et al., and Zhai et al. This assessment will be effective for researchers in this field and provide valuable insight into the importance of new PTFs for modeling SWRC. Full article

Blockchain technology has gained significant attention in recent years for its potential to revolutionize various sectors, including finance, supply chain management, and digital forensics. While blockchain’s decentralization enhances security, it complicates the identification and tracking of illegal activities, making it challenging to link blockchain addresses to real-world identities. Also, although immutability protects against tampering, it introduces challenges for forensic investigations as it prevents the modification or deletion of evidence, even if it is fraudulent. Hence, this paper provides a systematic literature review and examination of state-of-the-art studies in blockchain forensics to offer a comprehensive understanding of the topic. This paper provides a comprehensive investigation of the fundamental principles of blockchain forensics, exploring various techniques and applications for conducting digital forensic investigations in blockchain. Based on the selected search strategy, 46 articles (out of 672) were chosen for closer examination. The contributions of these articles were discussed and summarized, highlighting their strengths and limitations. This paper examines the selected papers to identify diverse digital forensic frameworks and methodologies used in blockchain forensics, as well as how blockchain-based forensic solutions have enhanced forensic investigations. In addition, this paper discusses the common applications of blockchain-based forensic frameworks and examines the associated legal and regulatory challenges encountered in conducting a forensic investigation within blockchain systems. Open issues and future research directions of blockchain forensics were also discussed. This paper provides significant value for researchers, digital forensic practitioners, and investigators by providing a comprehensive and up-to-date review of existing research and identifying key challenges and opportunities related to blockchain forensics. Full article