Bond incident degree–based indices (BID indices) are degree–based topological indices determined by the degree contributions of the end vertices of the bonds connecting the vertices. In this paper, we study the structure of connected graphs that attain the maximum value of bond incident degree–based indices when the order and size of the graphs are fixed. Using these structural insights, we propose a unified method to determine upper bounds for various BID indices among connected graphs. We also provide upper bounds for all classes of bond incident degree–based indices for graphs with fixed order and fixed cyclomatic number c, where 0≤c≤3. Furthermore, we characterize the graphs that attain these extremal values.
Dust emissions from Iraq are a major environmental issue affecting Kuwait and other countries in the Arabian Gulf. This study develops a regional framework for identifying potential dust-emission zones in Iraq using indicators of drought, vegetation cover, surface water, and water-transition dynamics. Terra MODIS products MOD13Q1 V6 and MOD44W V6 at a 250 m spatial resolution from 2000 to 2015 were used to assess vegetation coverage and surface water dynamics across 14 hydrologically classified zones in Iraq, considering five land utilization classes. Suspended dust was the most frequently recorded dust event type. It showed the strongest associations with Iraqi land surface variables (R2 = 0.966), followed by dust storm (R2 = 0.417), while rising dust showed the weakest relationship (R2 = 0.156), suggesting that local meteorological conditions predominantly control it. Based on interactions among vegetation, surface water, water transition classes, and dust events, five potential dust-prone zones were identified and ranked by environmental sensitivity: Zone 10-S, Zone 10-M, Zone 9, Zone 12, and Zone 4. Zone 10-S in southern Iraq exhibited the highest dust emission potential. Vegetation coverage showed strong positive correlations with the Mesopotamian marshes, surface water extent, rainfall, and Hammar Lake, while displaying negative correlations with suspended dust activity. The findings suggest that ephemeral and seasonal water surface, combined with upstream water extraction and poor water management, may contribute to vegetation degradation and increased dust emissions. The study highlights the importance of conducting finer-scale investigations within identified hotspot regions to support vegetation restoration and regional dust mitigation strategies.
For q∈(0,1) and a fraction ξ>0, this research investigates the quantum difference sequence spaces ℓ∞(∇q(ξ)), c(∇q(ξ)), and c0(∇q(ξ)). Various inclusion properties among these spaces are examined, which emerge from the unique characteristics of the ∇q(ξ) operator. The Schauder bases are constructed for each space, and their α-, β-, and γ-duals are explicitly determined. Furthermore, a thorough characterization of matrix operators mapping between these new spaces and classical sequence spaces is provided. The paper concludes with determination of spectrum of ∇q(ξ) in the space c0 of null sequences.
The internet of things (IoT) plays a vital role in drug-drug interaction (DDI) monitoring. Unlike traditional approaches lacking security, the proposed framework ensures secure DDI storage using a quantum random number generated key-based Hill cipher algorithm (QRNG-HCA) and a backward feature eliminated maxout activated deep neural network (BFE-MA-DNN) for reliable and secure classification. The proposed framework aims to achieve secure communication for healthcare data, reliable side-channel attack detection, computationally efficient encryption and data processing, and accurate drug-drug interaction prediction to support dependable IoT-enabled healthcare monitoring. Patients equipped with sensor units are modeled as network nodes and grouped into clusters using squared chamfer distance-based fuzzy C-means (SCD-FCM). Cluster heads (CHs) are selected through the TriGen distributed mud ring algorithm (TD-MRA). Each CH employs a BFE-MA-DNN classifier to identify trusted and compromised nodes based on radiation and node features. Data from trusted nodes are collected, pre-processed, and transmitted to the decision controller with encrypted information. The controller classifies DDI outcomes using ResNet-50, then secures the results with QRNG-HCA encryption before storing them on the blockchain. The proposed framework is designed for deployment in hospital IoT environments and remote patient monitoring applications, where continuous drug interaction surveillance, secure medical data transmission, and timely clinical decision support are essential. Its lightweight architecture enables secure and reliable healthcare monitoring across distributed IoT-enabled medical infrastructures. To access and monitor the results, users register with a unique ID and keystroke for authentication, access monitoring results, adjust medication dosages, while a Python-based implementation outperforms existing models experimentally.
This paper introduces a new subclass of normalized analytic univalent functions whose geometry is governed by symmetric-point starlikeness of Sakaguchi type. The class is generated by a special function-type conformal mapping that yields a characteristic four-leaf-shaped image domain, providing a concrete geometric model within geometric function theory. By employing a suitable subordination scheme associated with this mapping, coefficient estimates for the initial Taylor–Maclaurin coefficients and corresponding Fekete–Szegö type inequalities are established. Detailed planar and three-dimensional geometric visualizations of the image domains induced by the four-leaf structure are also presented to illustrate the non-emptiness of the proposed class and clarify its geometric behavior. The combined analytical and geometric treatment demonstrates the effectiveness of the four-leaf mapping as a structural tool within this framework.
This paper introduces a novel, unified family of generalized fractional integral operators characterized by a kernel function based on the E-function. The suggested operator creates a unified mathematical framework by encapsulating numerous well-known fractional integral operators as specific examples, emphasizing its generality. For the analytical analysis of fractional-order models, The Mellin transform of this novel operator derived , yielding a closed-form expression in Gamma functions. To demonstrate its practical application, the operator is used to create a generalized fractional diffusion equation. The fundamental solution of the diffusion model is obtained in closed analytical form through the application of the Mellin and Laplace transform techniques. The findings show that this unique operator not only broadens the theoretical scope of fractional calculus, but also acts as a versatile and powerful tool for accurately simulating complex anomalous diffusion events in mathematical physics.
The Bell regression model (BRM) is a single-parameter count regression model built on the Bell distribution, offering an alternative to Poisson and negative binomial regression when data exhibit over-dispersion. Like other generalized linear models, BRM relies on maximum likelihood estimation (MLE) for parameter estimation. MLE performs well under standard conditions but collapses under multicollinearity, inflating standard errors, destabilizing coefficient estimates, reversing coefficient signs, and rendering t-statistics unreliable. To more effectively handle the multicollinearity issue, this paper introduces a new two-parameter ridge-Liu estimator for BRM that addresses multicollinearity directly. The proposed estimator shrinks the MLE by combining the ridge parameter k and the Liu parameter d into a single biasing matrix, allowing simultaneous control of both bias and variance. We derive its mean squared error (MSE), establish necessary and sufficient conditions for its superiority over MLE, the ridge estimator, and the Liu estimator in the MSE sense, and determine the optimal parameter values analytically. A Monte Carlo simulation across varying levels of multicollinearity, sample sizes, and dispersion confirms the theoretical results: the proposed estimator achieves lower MSE across all scenarios examined. A real dataset on the mussels dataset corroborates the simulation findings. The estimator is a practical and theoretically grounded tool for count regression under multicollinearity.
Gadolinium oxide–doped lithium borate glasses with the composition x%B2O3–y%Li2O–(100−x−y)%Gd2O3 were fabricated using the melt-quenching and annealing techniques. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses confirmed the amorphous nature of all samples, while Fourier transform infrared (FTIR) spectra revealed characteristic vibrational modes of the borate network. The physical properties exhibited increases in density, molar volume, Gd3+ ion concentration, and Gd–O bond field strength with increasing Gd2O3 content. The observed decrease in oxygen packing density suggests a more loosely bound glass network, whereas the polaron radius and mean inter-ionic spacing between Gd3+ ions also increased. Optical absorption spectra showed no sharp absorption edge, further confirming the amorphous nature of the glass system. With increasing Gd2O3 concentration, a decrease in the optical band gap and Urbach energy was observed, accompanied by an increase in the refractive index. Temperature-dependent electrical conductivity measurements showed an increase in conductivity with increasing temperature due to thermally activated ionic conduction. However, at a fixed temperature, the conductivity decreased with increasing Gd2O3 content, while the activation energy for conduction increased with higher Gd2O3 incorporation.
The L-network is one among many interconnection network topologies that have the node distribution in an L-shape structure with wrap-around links. This paper derives a multi-dimensional topology of this network by using the Cartesian product between the lower dimensions of L-network. In this paper, we explore different topology metrics of multi-dimensional L-network including the number of nodes, diameter, and average distance. In addition, a network simulation is performed for the multi-dimensional L-network where a comparison is carried out to outline the main differences from other compared topologies. Furthermore, the derived multi-dimensional L-network is compared with existing interconnection networks such as BCC(a) and multi-dimensional torus. The simulation results show that the proposed multi-dimensional L-network outperforms the existing compared interconnection networks. The proposed routing and broadcasting algorithms have also been introduced in this paper that portrays different network and message transfer operations.
Antimicrobial resistance (AMR) has emerged as a critical global health concern, limiting the efficacy of conventional antibiotics and demanding novel therapeutic strategies. Nanomaterials, due to their unique physicochemical and surface properties, have demonstrated significant potential in combating AMR-associated infections, including those caused by multidrug-resistant (MDR) pathogens. This review evaluates the antimicrobial performance of various nanomaterials, including metal and metal oxide nanoparticles (Ag, ZnO, TiO2), carbon-based nanomaterials, polymeric systems, lipid-based carriers, and quantum dots. Experimental studies report that Ag and ZnO nanoparticles can achieve up to 95% bacterial reduction at concentrations below 50 μg/mL, while nanocomposite systems (i.e., multi-component or matrix-supported materials) enhance antibiotic efficacy by 3–5-fold. The mechanisms responsible for these effects such as reactive oxygen species (ROS) generation, membrane disruption, inhibition of biofilm formation (>80%), and genetic modulation are discussed in detail (with emphasis on antibacterial models and endpoints). The review also examines the emergence of microbial resistance to nanomaterials, highlighting adaptive responses including efflux pump activation and biofilm-associated resistance. Challenges hindering clinical translation, particularly cytotoxicity, biocompatibility, and scalability, are critically analyzed. Finally, recent advances in the development of smart and stimuli-responsive nanomaterials, and their integration with artificial intelligence and vaccine technology, are presented as promising future directions. This review provides a scientific overview of current progress, challenges, and perspectives in applying nanomaterials to mitigate AMR and facilitate their transition from laboratory research to clinical implementation.
Agroecosystems receive significant inputs of potentially toxic metals from traffic activities, yet current understanding of their movement through the water-fodder-livestock pathway is limited. The focus of this study was the identification, characterization, and calculation of risk for Pb, Cd, Cu, Zn, Fe, Mn, Co, and Mo found in irrigation water, Pennisetum glaucum, and cow milk from both roadside and non-roadside agricultural areas. Quantitative analysis of these metals was performed on samples collected via acid digestion of each respective environment and subsequent determination of concentrations via atomic absorption spectrophotometry, with rigorous quality control procedures in place. The results of the analysis consistently showed higher metal concentrations in roadside fodder relative to their respective controls, but metals in wastewater are still below international safety standards. In milk, metal concentrations remained low, with Pb (0.0003–0.0016 mg/L), Cd (0.001–0.0024 mg/L), Zn (0.24–0.29 mg/L), Fe (0.0267–0.0406 mg/L), and Cu (0.0005–0.001 mg/L) all below established safety limits despite significant site-related differences. Copper emerged as the most critical element, with health risk index (HRI) values exceeding unity at specific roadside locations, indicating a localized health concern. Thus, despite generally low metal transfer to milk, the elevated Cu risk identified in roadside fodders underscores the need for targeted risk assessment to safeguard livestock-derived food products.
Although silver is widely used as an antimicrobial agent, its primary mechanism of antimicrobial action remains unclear. Therefore, this study aimed to investigate the rapid multitarget metabolic inhibition induced by silver ions in Saccharomyces cerevisiae. Silver nitrate was added to yeast suspensions, and changes in glycolysis- and dehydrogenase-dependent reduced nicotinamide adenine dinucleotide phosphate [NAD(P)H] fluorescence intensity, as well as inhibition of glucose-induced proton release, were measured. Yeast viability was subsequently evaluated by liquid culture. The addition of 100 μM silver nitrate to yeast suspensions caused an irreversible decrease in NAD(P)H fluorescence and completely inhibited proton release and subsequent growth. In contrast, silver nitrate at concentrations of 10 μM or lower did not cause an irreversible decrease in NAD(P)H fluorescence and did not completely inhibit growth. Silver ions rapidly inhibit energy metabolism involving NAD(P)H production and proton pumping, thereby disrupting intracellular redox potential, membrane potential, and pH homeostasis, ultimately leading to irreversible cytotoxicity.
Staphylococcus aureus is a bacterial pathogen that is resistant to multiple antibiotics, necessitating the development of alternative treatment approaches. Apigenin (APGN) has demonstrated antibacterial efficacy. This study aimed to evaluate the antibacterial efficacy of APGN against S. aureus compared with antibiotics using in silico modeling and in vitro validation. Five important S. aureus targets, sortase A/B, penicillin-binding protein (PBP1b), the PASTA domain, and exotoxin (3URY), were explored via molecular docking and dynamics simulations. Interaction energy analysis and Molecular Mechanics/Poisson–Boltzmann Surface Area (MM-PBSA) were also calculated. Swiss Absorption, Distribution, Metabolism, and Excretion (SwissADME), Absorption, Distribution, Metabolism, Excretion, and Toxicity Laboratory, version 3.0 (ADMETLab 3.0), and System for Toxicity Prediction/System to Predict Acute Toxicity (STOP-Tox) were used to compare the pharmacokinetics and toxicity patterns of APGN and cefotaxime (CFTX). Zone diameter of inhibition (ZDI) was used to test the in vitro antibacterial efficacy against three clinical S. aureus strains (LMEM-2501, LMEM-2502, and LMEM-2503). APGN was administered alone and in combination with CFTX/amikacin (AMKC) antibiotics. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were further determined, along with a 72-h biofilm inhibition study. APGN exhibited combinatorial synergy with conventional antibiotics, with ZDI values ranging from 7 mm to 25 mm. The MICs of APGN were determined to be 100 μg/ml, 175 μg/ml, and 150 μg/ml for the S. aureus target strains, respectively. Similarly, the MBCs of APGN were 125 μg/ml for LMEM-2501 and 175 μg/ml for LMEM-2502 and LMEM-2503. APGN demonstrated statistically significant, concentration-dependent antibiofilm efficacy of up to 80.77% against S. aureus. Although the current study demonstrates the antibacterial and biofilm-inhibitory efficacy of APGN against S. aureus, in vivo validation and clinical trials are warranted before APGN can be used as a prescription drug.
Predictive and prescriptive analytics are increasingly combined to support accurate forecasting and optimal decision-making in complex logistics systems. However, existing approaches often fail to effectively integrate machine learning predictions with simulation-based optimization under uncertain and data-driven environments. In the current study, novel framework is applied to collaborative freight transportation to ensure that managers gain insights into the uncertain future of collaboration between carriers in terms of profitability and sustainability. The framework consists of a predictive module that improve the attention-based artificial neural network (attention-ANN) and extreme gradient boosting (XGBoost) through a new function for combining and outputting a single, reliable, and robust prediction. The prescriptive part is based on a what-if simulation, which involves the integration of uncertainty based on price prediction and carbon footprint in the demand allocation optimization problem. Results indicate that the combined predictions have fewer errors. Furthermore, integrating prediction results into what-if simulation workflow revealed improved performance indicators for adopting a collaborative strategy. The study also presents essential findings and managerial insights and suggests improvements for future research.
Ribosome-inactivating proteins (RIPs) are plant proteins that are most known for their rRNA N-glycosidase activity among other biological functions. In the context of the present study, a RIP/RIP-type protein was purified in Agave sisalana leaves, and characterized with the help of biochemical, functional, and preliminary in silico techniques. The protein of about 27 kDa was isolated after protein extraction through ion exchange and size exclusion chromatography. Based on the functional analysis, it was found to have rRNA N-glycosidase activity and DNase-like activity, which validates its designation of RIP or RIP-like proteins. The purified protein had antibacterial effects on Staphylococcus aureus, Bacillus cereus, and Escherichia coli in a concentration dependent manner. Cytotoxic examination utilizing the MTT assay demonstrated lower viability against human cancer cell lines (MCF-7, MDA-MB-231, HepG2, and A549), while the non-cancerous HEK-293 cell line had a slightly higher vitality. Gene expression study in MCF-7 cells revealed upregulation in the expression of apoptosis-associated genes, such as BAX, p53, and Caspase-9 and downregulation of Bcl-2 expression. Nano LC-MS/MS de novo peptide sequencing did not provide a complete homology to known proteins, suggesting the protein that was isolated had not been previously described. Secondary and tertiary structure predictions proposed a mixed composition of alpha-helical and random coil, and low structural coverage. Combined, these findings provide experimental evidence that Agave sisalana holds a bioactive RIP-like protein.
Doxorubicin (DOX) induces hepatotoxicity through oxidative stress, inflammation, and ferroptosis driven by GPX4/SLC7A11 downregulation and dysregulated iron homeostasis (TfR1/ferritin/FPN1). This study evaluated 5,4′-dihydroxy-6,8-dimethoxy-7-O-rhamnosylflavone (DDR), a flavonoid with reported antioxidant/anti-inflammatory activity, against DOX-induced liver injury in mice (n = 6/group). DOX (2.5 mg/kg IP weekly ×6 weeks) elevated serum enzymes (ALT/AST/ALP/bilirubin), malondialdehyde (MDA), cytokines (IL-6/TNF-α/IL-1β), while depleting glutathione (GSH)/antioxidant enzymes and downregulating GPX4/SLC7A11 (all p < 0.01). 5,4′-dihydroxy-6,8-dimethoxy-7-O-rhamnosylflavone (DDR) co-administration (25/50 mg/kg oral, twice weekly) dose-dependently restored these parameters, upregulating GPX4/SLC7A11 (2.1-3.4-fold), normalizing iron-handling proteins, and improving hepatic architecture that is absent with DDR alone. These findings demonstrate that DDR mechanistically counteracts DOX hepatotoxicity by simultaneously attenuating oxidative stress/inflammation and inhibiting ferroptosis through GPX4/SLC7A11-mediated lipid peroxidation suppression and iron homeostasis restoration, positioning DDR as a promising chemotherapy adjuvant.
The important function of tropical watersheds in absorbing carbon from the atmosphere is highly dynamic in space and time due to climate parameters and human interference. The unique patterns of climate parameters and human activities on land, such as land conversion and conservation in tropical areas, drive the dynamics of ecosystem productivity processes. The objective of this study is to analyze the spatiotemporal changes in carbon sequestration within a watershed area subject to significant climate parameters and human interference on land. Land-use conversion was modeled using the Land Change Modeler based on land-use maps from 2015 to 2024, which were retrieved using maximum likelihood classification. The Carnegie-Ames-Stanford Approach (CASA) method was used to model the net primary productivity (NPP) using Sentinel 2A imagery and climate data. The results showed that NPP in the Keduang Watershed exhibits a temporal pattern over one year due to variations in precipitation and solar radiation. NPP has higher values during the wet season (December-May) compared to the dry season (June-October). NPP reaches its lowest value at the peak of the dry season in July. However, annual NPP is relatively constant compared with other climate zones because climatic conditions in tropical areas are consistently suitable for optimal photosynthesis. NPP also varies over multi-year periods due to human activities on land. A decline in NPP occurs when vegetated land is converted to other land uses, while an increase in NPP occurs in reforestation areas and agricultural areas where land conservation (Tumpangsari cropping pattern) has been applied.
Industrial activities, particularly those in the sugar industry, generate large volumes of wastewater that threaten crop production and soil health. Maize, a major cereal crop, is highly sensitive to such stress conditions. This study aimed to determine the influence of sugar mill effluents (SME) at five concentrations (10, 25, 50, 75, and 100%) on the growth of maize and physiological, nutritional, and antioxidant parameters in the presence of plant-derived smoke (PDS) solution (1:500). It was found that a low concentration of SME (10%) had a significant positive effect on seed germination, root and shoot fresh weight, plant height, root number, chlorophyll a/b, carotenoids, and crude fiber content, but a high concentration (25-100) had a significant negative effect on these characteristics. Oxidative stress was also strongly induced by high SME concentrations because they induced antioxidant enzymes, such as superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX). Notably, PDS co-application mitigated the adverse impacts of increased concentrations of SMEs by enhancing growth, photosynthetic pigments and regulating antioxidant enzymes activities. Further, physiochemical analysis of Chashma sugar mill effluent (SME) revealed that there were major deviations of WHO standards and high temperature, conductivity, turbidity, organic load, and heavy metals (Fe, Zn, Cd, Cu, Cr, Pb). Exposure of SME decreased the germination, growth, photosynthetic pigments, antioxidant activities and nutritional content of maize seeds in a concentration-dependent fashion. Plant-derived smoke (PDS, 1:500) reduced these effects, and improved germination, biomass, plant height, root number, pigments, antioxidants, and nutritional quality, fresh weight of roots under 100% SME, which improved by an average of 41%. PDS is an effective reliever of the phytotoxicity of the SMEs in maize. These results indicate that SME exerts a dual influence on maize, showing beneficial effects at lower concentrations and inhibitory effects at higher concentrations, while PDS application partially alleviated SME-induced stress and improved maize tolerance under controlled conditions.
Tomato (Solanum lycopersicum L.) is an economically important crop in Pakistan, but its yield is often reduced due to infections caused by the root-knot nematode Meloidogyne incognita. Given the environmental and health risks associated with chemical nematicides, there is an increasing need to explore sustainable biological alternatives. This study aimed to isolate and evaluate a nematicidal bacterial strain from the rhizosphere of Bergenia ciliata. Soil samples were cultured on bacterial growth medium, and isolated strains were screened for nematicidal activity using Caenorhabditis elegans as a model organism. Among bacterial isolates, strain NA12 showed approximately 54% nematode mortality on nematode growth medium. In food choice and lawn-leaving assays, nematodes actively avoided bacterial strain NA12, indicating the production of repellent or toxic metabolites. Furthermore, the impact of growth medium on bacterial virulence was also determined; the virulence of NA12 was influenced by the growth medium, with higher activity observed on PGS medium. The bacterial strain was identified as Bacillus cereus through 16S rRNA gene sequencing. For in vivo testing, NA12 was applied to tomato plants via soil drenching, followed by inoculation with Meloidogyne incognita. Treated tomato plants showed significantly reduced root galling and improved growth parameters compared to untreated controls. Importantly, Bacillus cereus NA12 was non-pathogenic to tomato and slightly promoted plant growth, as shown by increased biomass and flower production. These findings suggest that Bacillus cereus NA12 has potential as a biocontrol agent against Meloidogyne incognita. Further research is recommended to elucidate its mode of action and potential for field application.
The development of natural polymer-based transdermal patches with enhanced mechanical stability, therapeutic functionality, and economic feasibility remains a significant challenge in pharmaceutical applications. This study introduces an innovative transdermal patch incorporating pectin and Aloe vera (AV) as a carrier matrix for Citrus aurantifolia-derived limonene for addressing the critical need for mechanically strong and multifunctional delivery platforms. Transdermal patches were fabricated with varied pectin concentrations (7–11 g) and AV extract (0–10% w/w). The optimized formulation (11 g pectin, 10% AV) demonstrated superior physicochemical properties, including enhanced density and controlled thickness. Notably, the incorporation of AV significantly augmented the patch's therapeutic potential through expanded antimicrobial inhibition zones against Staphylococcus aureus, Escherichia coli, and Candida albicans with strong antioxidant activity (IC50) of 24.870 ± 0.162 μg/mL. Scaled-up economic analysis revealed favorable industrial viability. These findings establish a foundation for developing commercially viable, multifunctional transdermal patches with enhanced structural integrity, though further in vivo studies are warranted to validate clinical efficacy and safety.
