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Abstract Fimbristylis complanata is an aquatic halophytic sedge that thrives in salt-affected land, marshes, and water channels. Two ecotypes (HR-Rasool headworks ECe 19.45; SH- Sahianwala 47.49 dS m−1) of F. complanata were collected from two salt-affected wetlands of Punjab, Pakistan. Five rhizomes of each ecotype were grown in plastic pots in the Botanical garden research area and treated with three intensities of salt [0 mM (control), 200 mM (moderate), 400 mM (high) NaCl for three durations (0, 15 and 30 days). The pots were arranged using a completely randomized block design (CRD) with three replications. After each duration, sampling was done. The HR ecotype optimally performed better under moderate salt incubation and moderate to higher salt exposure. This ecotype had improved growth traits, including shoot fresh weight (SFW), shoot dry weight (SDW), leaf area (LA), root length (RL), leaf mass fraction (LMF), relative growth rate (RGR), and unit leaf area (ULA) at higher NaCl (400 mM) in comparison with control NaCl (0 mM). This improvement in growth occurs due to the accumulation of photosynthetic pigments, better photosynthesis, and water use efficiency (A/E). The leaf microstructure increased in HR ecotype as midrib (MrT), leaf blade (LTh), bulliform cells (BTh), and cortical cells (CcT) thicknesses to prevent water loss under salinity, increase aerenchymatous area (ArA) for efficient gas movements at moderate salt levels and less exposure time concerning absolute control (0 mM NaCl). The SH ecotype affirmed more tolerance to salt by securing higher biomass (SFW, SDW), increased growth traits (LA, RL, LMF, ULA), photosynthetic pigments (Chl a, b, Car), and maximum photosynthetic performance at high salt regimes and prolonged duration in comparison to control (0 mM NaCl). Additionally, increased MrT, LTh, BTh, ECA, abaxial and adaxial stomatal area, and density, broadened metaxylem and phloem area, large aerenchyma, more cortical cell thickness under moderate to high salt regimes under moderate to high salt levels and time. Overall, changes in morpho-physiological traits and leaf microstructures in both ecotypes are linked to salt tolerance under temporal salt regimes. Our findings suggest that both ecotypes of F. complanata can potentially rehabilitate the salt-affected wetlands.

The optical and thermal properties of nanoparticles have attracted much interest due to their extinction effectiveness in industries like electronics, medical devices, photonics, materials sciences, drug delivery, hyperthermia, etc. In this work, the optical response of eight different geometrical nanostructures, namely Solid-CuNS, Au–Ag-coated-Solid-CuNS, Hollow-CuNS, Au–Ag-coated-Hollow-CuNS, Solid-CuNR, Au–Ag-coated-Solid-CuNR, Hollow-CuNR, and Au–Ag-coated-Hollow-CuNR, were studied for hyperthermia. Finite element analysis was performed in COMSOL Multiphysics to simulate the optical and thermal responses of the nanostructures. The size range of nanostructures was selected differently based on the hyperthermia condition to attain a temperature between 42 °C and 46 °C. An appropriate temperature distribution of 43.8 °C was achieved in the 500 nm liver domain using an Au–Ag-coated-Solid-CuNS with Dsphere = 20 nm. The minimum thermal response was 42.1 °C for both Solid-CuNS and Solid-CuNR when Dsphere was 35 nm and Lcylinder was 35 nm. The highest electric field distribution of 11.2 V/m in the surrounding area was seen for Au–Ag-coated-Solid-CuNR, with an extinction resonance peak at 475 nm. On the other hand, the Au–Ag-coated-Solid-CuNS showed the least electric field distribution of 2.22 V/m with a corresponding resonance peak of 480 nm when Dsphere was 15 nm.

Wheat yellow rust disease poses a significant threat to global wheat yield and grain quality. Early detection of this disease will help to minimize the loss caused by its effects. Existing models work well on images taken in a controlled environment, whereas a uniform background is placed behind the leaf, but these models fail to produce good results in natural settings. Previous research also involves manual interventions in the pipeline to achieve good classification results such as cropping the images, using uniform backgrounds, etc. These systems are not practical to use in natural environments where there will be a lot of background noise to the image and manual cropping becomes an extra step for the farmer. Moreover, the unavailability of the dataset in which images of leaves are taken in a natural setting became another challenge. In this research, a dataset is curated and leaves are annotated for object detection, object segmentation further the leaves are classified into 3 classes ie healthy, resistant, and susceptible. A novel unsupervised image rotation algorithm is proposed that takes input from YOLOv8 to align the leave in such a way that maximum background can be removed by a rectangular bounding box . Then the comparison between multiple state-of-the-art segmentation models ie. UNET, Segment-Anything (SAM), Segnet, LinkNet, PSPNet, FPN, Deep-Labv3+ (Xception), and DeepLabv3+ (Mo-bileNet) has shown that UNET has outperformed all the other segmentation models with an IOU score of 0.9563. Lastly for classification, the performance of multiple convolution neural networks ie. VGG16, Resnet 101(v2), Xception, Mo-bileNetV2, and Transformer-based models ie. Swin trans-former and MobileVit have been compared. Swin transformer has outperformed the state-of-the-art CNN models with an accuracy of 95.8%. This paper proposes a complete robust pipeline that can be deployed in natural environment and does not need any manual intervention to produce good results. This research shows that good localization of leaves and removal of unwanted background noise at the earliest stage of the pipeline will assist the segmentation model to effectively segment the leaf from the background which will enable classification models to achieve high classification accuracy, even when dealing with very small datasets.

This study focuses on revealing closed-form solutions for traveling waves in the nonlinear dynamical model of dust-acoustic waves in multispecies of dusty plasma. Plasma consists of cold dust grains, along with isothermal electrons and positive ions for balance. Fluid equations simplify through reductive perturbation, yielding the modified Korteweg–de Vries equation for analyzing weak dust-acoustic solitons. Applying this method to a specific scenario involving negatively charged dust grains and protons with all electrons bound to the dust grains shows support for rarefactive supersonic solitons, contrasting with typical compressive ion-acoustic solitons. Introducing streaming reveals a subtle soliton amplitude decrease. Using the Generalized exp(-S(φ)) expansion method effectively derives various soliton solutions, including kink, anti-kink, singular, hyperbolic functions, and diverse solitary waves. Furthermore, the equation’s dynamic behaviors are explored through equilibrium point bifurcations. Employing a Galilean transformation, the model is transformed into a planar dynamical system, and qualitative inspection is performed. Phase portraits depict bifurcation outcomes. Our methodology is significant for tackling the novel problem and applying previously untested approaches in this context, resulting in the development of multiple exact and novel optical soliton solutions. This highlights the efficiency and broad application of our new strategy to addressing nonlinear challenges in astrophysics, laboratories, space, and technology.

Linguistic complex intuitionistic fuzzy aggregation operators are a novel idea for the description of intuitionistic fuzzy information, where linguistic complex concepts are used to describe membership and non-membership. This can convey the hazy information more clearly, and some findings on linguistic complex intuitionistic fuzzy aggregation operators have been attained. Nonetheless, several linguistic complex intuitionistic fuzzy aggregation operators in the literature are founded on conventional operational principles, which have certain limitations when used to multi-attribute group decision-making (MAGDM). In this study, we presented some improved operating rules based on linguistic complex intuitionistic fuzzy variables (LCIFVs) and changed their features to address these issues. Next, we created a few aggregation operators, such as the enhanced linguistic complex intuitionistic fuzzy weighted average (LCIFWA) and the linguistic complex intuitionistic fuzzy ordered weighted averaging (LCIFOWA) operator, to fuse the decision information represented by LCIFVs. We also demonstrated that they had a few favorable qualities. We introduced many novel approaches to address the MAGDM issues in the context of the linguistic complex intuitionistic fuzzy environment, based on the LCIFOWA and linguistic complex intuitionistic fuzzy ordered weighted geometric (LCIFOWG) operators. In short, we employed few real-world scenarios to demonstrate the viability and soundness of the suggested techniques through comparison with other approaches. This unique technique has been applied to plastic waste management selection, and the results are more accurate than the previously used materials and methods.

Abstract This research addresses the critical need to optimize the Energy Efficiency (EE) for Ultra-Dense HetNets amid the ever-increasing demands for high-speed data networks. The rapid increase in high-speed devices highlights the urgent necessity for a transformative change in upcoming 5G cellular networks. According to Ericsson's 2022 report, mobile data traffic volume is expected to double by 2027, with mobile video traffic anticipated to rise by nearly 30% each year. In response to these challenges, researchers have identified essential technologies that facilitate 5G networks, including massive Multiple-input-Multiple-output (m-MIMO) systems and HetNets. Although both promise enhanced coverage and throughput, HetNets emerges as a cost-effective solution, surpassing m-MIMO in implementation cost and coverage. However, achieving maximum EE in HetNets necessitates careful consideration of various constraints, including delay, coverage probability, and Signal-to-Interference-plus-Noise Ratio (SINR) thresholds. This research marks a significant milestone in adopting the Distributed Dynamic Opportunistic Sleep Strategy (D-DOSS) approach. The D-DOSS method organizes small clusters throughout the network and evaluates key Quality of Service (QoS) parameters including Energy Utilization Efficiency (EUE), Coverage Probability, Data Throughput, and Success Probability using Monte Carlo simulations. This research analyzes Distributed Sleep (DS) and Centralized Schemes (CS) concerning given QoS parameters. While DS methodologies often exhibit performance trade-offs compared to CS, they provide significant advantages in terms of ease of implementation and management. CS, though representing the most commonly used method in ultra-dense HetNets involves high computational costs that complicate its management. By integrating the D-DOSS and addressing various constraints, this research not only advances HetNet technologies but also makes a significant contribution to optimizing EE while preserving network performance and QoS. The innovative D-DOSS approach offers a promising solution to the challenges of energy efficiency in wireless communication networks and paves the way for future advancements in HetNet deployments. The results and analysis show that D-DOSS effectively addresses the limitations of DS and outperforms existing CS techniques.

Abstract Salt stress is becoming a major issue for the world’s environment and agriculture economy. Different iron [Fe] sources can give an environmentally friendly alternative for salt-affected soil remediation. In this study the effects of Iron sulfate on Luffa cylindrica (Sponge gourd) cultivated in normal and saline water irrigated soil were examined. When FeSO4 (0.01, 0.025, 0.05, 0.1 ppm) were applied to salt affected soil, the length, fresh and dry biomass of sponge gourd plant roots and shoots inclined by an average of 33, 28, 11, 21, 18 and 22%, respectively. In plants irrigated with saline water, leaf count was raised successively (23–115%) with increasing concentration of FeSO4 (0.025-0.1 ppm) compared to stress only plants. The use of FeSO4 boosted sponge gourd growth characteristics in both normal and salt-affected soils compared to respective controls. The application of Iron sulfate under salt stress boosted photosynthetic indices such as chlorophyll a (22%), chlorophyll b (34%), carotenoids (16%), and total chlorophyll levels (22%). Iron sulfate application also exhibited incline in primary (total free amino acids, 50%; total soluble proteins, 46%) and secondary (total phenolics, 9%; flavonoid content, 51%) metabolites in salt-affected soils. Oxidative enzymatic activities such as catalase (CAT), peroxidase (POD), polyphenol oxidase (PPO) and DPPH scavenging activity (36%) were also increased by foliar spray of FeSO4 in control and salt stressed L. cylindrica plants. FeSO4 had a considerable impact on the growth and development of Luffa cylindrica in normal and salt-affected soils. It is concluded that FeSO4 application can effectively remediate salt affected soil and improve the production of crop plants.

Abstract The major challenge for the construction industry is to design and produce sustainable construction materials that are efficient in their performance and can be affordable for construction projects. The objective of this study is to determine the viability of incorporating waste products such as sugarcane bagasse ash (SCBA) to produce concrete with polypropylene (PP) fibers. SCBA is an industry waste that has certain pozzolanic properties, however, it shows limited mechanical properties when used as a cement replacement. To improve the mechanical and durability performance, metakaolin (15%) and PP fiber (0.5%, 1%, and 1.5%) were incorporated in the SCBA blend as a ternary additive. Some of the important characteristics explored include compressive strength, tensile strength, density, water absorption, acid resistance, and sorptivity. The study reveals that the incorporation of 15% metakaolin in the composite enhanced the compressive strength by 7%, 8.2%, and 9.1% for PP fiber additions of 0.5%, 1%, 1.5%, while the acid resistance enhanced by 4%, 6% and 8% relative to the control mix for the same value of pp fiber. Furthermore, cost evaluation confirms that the overall costs of concrete with 15% metakaolin and 5% SCBA were 12.2% less than the control concrete, thus making this option economically feasible. This research proves that the inclusion of SCBA, metakaolin, and PP fibers into the concrete mixture brings a sustainable approach that enhances mechanical properties and durability while assisting sugar manufacturing plants in the proper disposal of wastes.

Abstract This study investigates the projected impact of air pollution on mortality and Disability-Adjusted Life Years (DALYs) across SAARC countries. Utilizing Time Series and Machine Learning methodologies such as Autoregressive Integrated Moving Average, Exponential Smoothing, and Neural Network, the research aims to accurately forecast the mortality and DALYs attributed to air pollution from 2020 to 2030. Statistical analyses reveal a consistent upward trend in deaths and DALYs during the forecasting period, primarily driven by Ambient Particulate Matter Pollution (APM) and Ambient Ozone Pollution (AOP). Comparing the predictive accuracy of the models, Neural Network outperformed other methods, as indicated by Root Mean Square Error (RMSE) values. Specifically, the study finds that deaths and DALYs due to Ambient Particulate Matter pollution are least prevalent in the Maldives, while India and Pakistan exhibit the highest rates, and deaths and DALYs due to Ambient Ozone pollution are lowest in the Maldives and highest in Bangladesh and Pakistan. Moreover, deaths and DALYs attributed to Household Air Pollution (HAP) are lowest in Pakistan and highest in Nepal. These findings underscore the urgent need for air pollution control measures and informed policymaking in SAARC countries to mitigate the escalating health burden associated with air pollution.

Abstract Purple blotch disease is a major fungal disease of Allium cepa L. plants which is caused by the fungus Alternaria porri. The best conditions for the growth of Alternaria porri are temperatures between 22 °C and 25 °C and relatively high humidity. The Hydrotime, Thermal Time, and Hydrothermal Time models were used to measure different parameters of seed germination; therefore, we used them to measure the interactive effects of temperature and water potential on the germination conidia of Alternaria porri. The laboratory experiments were carried out at five constant temperatures, between 5 and 30 °C, and five different water potentials between 0 MPa and − 6 MPa. The germination of Alternaria porri conidia was highest at 25 °C and 0 MPa and lowest at 5 °C and − 6 MPa. The percentage of conidia germination decreased rapidly after 25 oC. Conidia germination was also affected by different water potentials, decreasing at lower water potential. Models based on HTT showed a reasonable fit to the germination and growth rate datasets. The best fitting model for conidia germination (R2 = 0.98491) was based on variable base and maximum temperature as a function of water potential. Based on the TT, HT, and models, the highest and lowest values for θT1 were observed at -6.0 MPa at 30 °C, and 0 MPa at 5 °C and the highest and lowest θT2 values were recorded at -6.0 MPa at 5 °C and 0 MPa at 30 °C while the lowest and highest θH values were recorded at -6.0 MPa at 5 °C and 0 MPa at 25 °C, respectively, for the HTT model, the predicted θHTT average value is 16.32 (MPa°Ch-1). Based on the statistical analysis, the cardinal hydrothermal time constant (θHTT) accurately explains the interactive effect of T and Ψ on the germination of Alternaria porri conidia under different environmental conditions.

Abstract The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Abstract Cream cheese analogue is a soft cheese with a slightly sweet and pleasantly tangy taste. It is prepared using the substitutes of dairy fats and proteins partially or completely. Cream cheese analogue ingredients are purchased carefully keeping in mind the required specifications. For the above-mentioned research, different samples of cream cheese (T0, T1, T2 and T3) were manufactured with standardized fat and protein and the moisture was maintained at 50–55% during these trials. All ingredients were added in a large container for mixing and cooking treatment for 1–2 min at 75 °C and homogenized for 5–6 min with the help of a hand mixture until completely homogenous sample was obtained. Samples were then transferred in to another container and allowed them to attain temperature between 20 and 25 °C. After that, samples were incubated with the addition of mesophilic culture until desired pH (4.6–4.7) was achieved. Later samples were heated at 50–55 °C for 2–3 min and then placed in a muslin cloth and left overnight for whey drainage. Physicochemical, microbial analysis were performed while sensory evaluation was performed using different parameters and scales. Sample prepared using 75% dairy cream and 25% cocoa butter substitute fat (T1) showed good results. T1 had better consistency which was considered desirable characteristics of cream cheese. This research proved the cost effective as cream cheese prepared with plant-based fat which made its production possible at industrial level. Cream cheese which was made from dairy fat (75%) and cocoa butter substitute fat (25%) had more springiness, smoothness, gumminess, more pleasant taste, color, flavor and aroma than other samples. This dairy product can be used in future in Pakistan.

Papaya (Carica papaya) fruit has a short shelf life and suffers significant postharvest losses due to climacteric, instant ripening and perishable nature. Pre-storage hot water treatment (HWT) has significant potential to delay decay incidence in horticultural commodities. The present study was designed to evaluate effectiveness of HWT on ‘Red lady’ papaya fruit for minimizing the fruit decay and maintaining the quality during storage. Physiologically mature unripe ‘Red lady’ papaya fruit harvested from a commercial papaya orchard. Selected fruits were treated at three different hot water temperatures (50 °C, 52 °C and 54 °C) along with a control (room temp) for 10 minutes thereafter stored at 12 °C and 85–90% RH for four weeks. Different sensory evaluation, fruit quality scales and enzymatic activities were observed at weekly intervals. Fruit stored after 54 °C HWT showed lower weight loss (4.63%), decay incidence (5.1%) and higher fruit firmness (12.92 N) relative to control. Fruit treated at 52 °C HWT improved the fruit acidity, but total soluble solids decreased than control. The results revealed that ascorbic acid (33.33 mg/100 g), total phenolic contents (35 mg/g FW) total antioxidants (47.50% DPPH inhibition), and antioxidative enzyme activities were improved by 52 °C HWT. It is concluded that pre storage papaya fruit treatment (HWT @ 52 °C) preserves the quality of fruit throughout storage on the other hand at 54 °C HWT helps in decreased fruit decaying. It can be summarized that papaya fruit can be treated with hot water in the range of 52–54 °C to support the fruit shelf life and suppress fruit decay along with maintaining fruit quality.Practical applications: Papaya (Carica papaya L.) is a climacteric fruit which is highly susceptible to fungal attacks after harvest. Due to its perishable nature, it becomes decayed and huge amount of highly nutritious and medicinally important fruit is wasted. Recently people are more concerned about food safety which limits the use of fungicides for controlling the fruit decay in papaya. However, heat treatment has tremendous potential to control microbial decay, and keep food commodities safe from hazardous chemicals. In this study, the hot water treatment has successfully suppressed the fruit decay and maintained fruit quality of papaya in the course of entire period of cold storage. These findings indicated that use of hot water treatment is a practical approach for increasing the shelf life of papaya fruit.

Inequalities are essential in solving mathematical problems in many different areas of mathematics. Among these, problems involving coefficient combinations that occurred in the Taylor–Maclaurin series of the inverse of complex-valued analytic functions are the challenging ones to solve. In the current article, our aim is to study certain coefficient-related problems that construct from coefficients of the inverse of specific analytic functions. These problems include the Zalcman and Fekete–Szegö inequalities, as well as sharp estimates of the second and third-order Hankel determinants with inverse function coefficients. Also, one of the obtained results gives an improvement of the problem that has been recently published in the journal "AIMS Mathematics".

Abstract Gladiolus, a widely cultivated cut flower known for its aesthetically pleasing multicoloured spikes, has earned significant commercial popularity. A comprehensive understanding of the rhizosphere bacterial community associated with gladiolus is imperative for revealing its potential benefits. Molecular characterization is considered an effective method to gain insights into the structural and functional aspects of microbial populations. The soil characteristics and bacterial communities in the rhizosphere are typically influenced by quorum sensing (QS) and quorum quenching (QQ) mechanisms. This study aims to explore the niceties and diversity of rhizospheric bacterial populations linked with gladiolus corms, with a specific focus on understanding the dynamics of QS and QQ mechanisms in their complex interactions. The isolation of bacterial strains was achieved through the serial dilution method on nutrient agar (NA) media. The identification of the isolates was accomplished by amplifying 16 S rRNA gene sequences via polymerase chain reaction (PCR) via the use of universal primers. Sequence analysis was conducted via BLAST on the National Center for Biotechnology Information (NCBI) database. The characteristics of the isolated bacteria were elucidated via biosensors. This study identified three QS strains and five QQ strains. A consortium of quenchers was formulated utilizing five strains that demonstrated efficacy in mitigating the impact of disease on gladiolus and fostering growth. Among the three treatments—Scale, Descale, and Descale and Cut Half (DSC)—the DSC treatment emerged as the most effective. This treatment exhibited a broader range of variation in biological parameters over time, aligning with prevailing trends in the local market.

The multiband, multifunctional chiral metasurface with asymmetric transmission exhibits significant potential for diverse applications in modern communication systems, ranging from enhanced signal modulation and polarization control to advanced beam steering and compact antenna design. This research presents a versatile and advanced chiral metasurface operating at multiple bands with diverse functionalities, including asymmetric transmission. The proposed metasurface effectively transforms an incoming Linearly Polarized (LP) wave into a Circularly Polarized (CP) wave. Additionally, it functions as a 90° polarization rotator for the incident LP wave. The design starts with an element of a 2 × 2 supercell comprising a Square Split Ring Resonator (SSRR) and an I-shaped resonator. The right diagonal elements of a supercell undergo scaling down, giving rise to a rotational asymmetry. Chirality is introduced into the design, and cross polarization conversion is enhanced by rotating all four elements by 90° relative to each other. On the back side of the substrate, each element undergoes a 90° rotation compared to its counterpart on the front side, realizing the asymmetric transmission feature. The incorporation of multiband and multifunctional features within a single supercell equips the subject chiral metasurface to be utilized in various engineering applications.

Abstract Road corridor vegetation plays a vital role in maintaining ecosystem stability and providing essential ecological services, particularly in semi-arid regions where environmental conditions are challenging. In this study, we investigated the functional traits of native and non-native plant species along the N5 highway corridor in the semi-arid region of Punjab, Pakistan. The methodology involved extensive field surveys and systematic sampling of herbaceous vegetation, followed by detailed measurements of functional traits diversity. We classified 38 plant species into native and non-native categories and analyzed their distribution, life forms, leaf spectra, and flowering phenology. Our results revealed distinct patterns in the functional traits of native and non-native species, with non-native species exhibiting larger plant heights, leaf sizes, and leaf surface areas compared to native species. Additionally, native species displayed greater root and stem biomass, indicative of adaptations to nutrient-poor soils and water-limited environments. The findings suggest that non-native species possess traits associated with rapid growth and resource acquisition, enabling them to outcompete native vegetation and establish dominance in roadside ecosystems. These results provide valuable insights for understanding the ecological implications of non-native species and designing effective management strategies to mitigate their impacts on native biodiversity and ecosystem resilience in semi-arid regions.

Abstract Linguistic term fuzzy sets provide an intuitive way to express preferences, enhancing understanding and communication among decision-makers. In this article, we introduce the novel concept of p,q-quasirung orthopair fuzzy linguistic sets (p,q-QOFLSs), which merge the principles of p,q-quasirung orthopair fuzzy sets (p,q-QOFSs) with linguistic fuzzy sets. This new framework offers a more robust approach to handle uncertain and imprecise information in decision-making processes, characterized by linguistic membership and non-membership degrees. We establish several fundamental operational laws, alongside score and accuracy functions, to facilitate the comparison of p,q-quasirung orthopair fuzzy linguistic numbers. Leveraging these operational laws, we propose a series of weighted averaging and geometric operators under p,q-QOFLSs. Furthermore, we formulate a multi-attribute decision-making methodology using these operators. The significance of the proposed method lies in its ability to model complex decision-making scenarios with enhanced precision. A numerical example validates the practicality and adaptability of the methodology, supported by sensitivity analyses and comparative evaluations, highlighting the innovation and efficiency of the approach.

Abstract In the fields of mathematics, chemistry, and the physical sciences, graph theory plays a substantial role. Using modern mathematical techniques, quantitative structure-property relationship (QSPR) modeling predicts the physical, synthetic, and natural properties of substances based only on their chemical composition. For a chemical graph, the temperature of a vertex is a local property introduced by Fajtlowicz (1988). A temperature-based graphical descriptor is structured based on temperatures of vertices. Involving a non-zero real parameter $$\beta$$ β , the general F-temperature index $$T_{\beta }$$ T β is a temperature index having strong efficacy. In this paper, we employ discrete optimization and regression analysis to find optimal value(s) of $$\beta$$ β for which the prediction potential of $$T_{\beta }$$ T β and the total $$\pi$$ π -electron energy $$E_{\pi }$$ E π of polycyclic hydrocarbons is the strongest. This, in turn, answers an open problem proposed by Hayat & Liu (2024). Applications of the optimal values for $$T_{\beta }$$ T β are presented a two-parametric family of carbon nanocones in predicting their $$E_{\pi }$$ E π with significantly higher accuracy.

Abstract The threat of antibiotic resistance is escalating, diminishing the effectiveness of numerous antibiotics due to the rapid development of resistant bacteria. In response, the use of green-synthesized nanoparticle, alone or combined with antimicrobial agents, appears promising. This study explores the effectiveness of zinc oxide nanoparticles (ZnONPs) synthesized using Loranthus cordifolius leaf extracts and subsequently coated with anethole. The fabrication of these nanoparticles was confirmed via UV-Vis, FTIR and TEM analyses, ensuring the nanoparticles were produced as intended. Utilizing a nanoprecipitation process that excludes evaporation and drying, a high drug loading capacity of 16.59% was accomplished. The encapsulation efficiency for anethole was recorded at 88.23 ± 4.98%. Antibacterial efficacy was assessed by com paring the green-synthesized ZnONPs (average size: 14.47 nm), anethole-loaded ZnONPs (average size: 14,75 nm), and commercially sourced ZnONPs. The ZnONPs with anethole demonstrated superior inhibition against all tested bacterial strains, including Gram-negative species like Pseudomonas aeruginosa and Escherichia coli, and Gram-positive species like Bacillus subtilis and Staphylococcus aureus, outperforming the commercially available ZnONPs. Additionally, anethole-coated ZnONPs showed the greatest inhibition of Gyr-B activity (IC50 = 0.78 ± 0.2 M), better than both green-synthesized and commercially available ZnONPs. These findings emphasize the enhanced antimicrobial properties of ZnONPs, particularly when combined with green synthesis and anethole loading, highlighting their potential in various biomedical applications.

Abstract The root architecture, more seminal roots, and Deeper roots help the plants to uptake the resources from the deeper soil layer to ensure better growth. The Gibberellic acid-sensitive (GA-sensitive) Rht genes are well known for increasing drought tolerance in wheat. Much work has been performed on the effect of these genes on the plant agronomic traits and little work has been done on the effect of Rht genes on seminal roots and root architecture. This study was designed to evaluate 200 wheat genotypes under normal and osmotic stress. The genotypes were sown in the solution culture and laid under CRD factorial arrangement with three replications and two factors i.e., genotypes and treatments viz. normal and osmotic stress (20% PEG-6000) applied one week after germination. The data was recorded for the root traits. Results demonstrated that out of 200 genotypes, the GA-sensitive Rht13 gene was amplified in 21 genotypes with a fragment length of 1089 bp. In comparison, the GA-insensitive Rht1 gene was amplified in 24 genotypes with a band size of 228 bp. From 200 wheat genotypes, 122 genotypes produced 5 seminal roots, 4 genotypes 4 seminal roots, and 74 genotypes 3 seminal roots. The genotypes G-3 (EBW11TALL#1/WESTONIA-Rht5//QUAIU#1), G-6 (EBW01TALL#1/SILVERSTAR-Rht13B//ROLF07) and G-8 (EBW01TALL#1/SILVERSTAR-Rht13B//NAVJ07) produced 5 seminal roots and have longer coleoptile (> 4.0 cm), root (> 11.0 cm) and shoot (> 17 cm) under normal and osmotic stress. Furthermore, Ujala 16, Galaxy-13, and Fareed-06 produced 3 seminal roots and have short coleoptile (

Background and Aim: Avian influenza is a global threat to avian species, particularly in developing countries. Recombinant vaccines, including virus-like particles (VLPs), are promising strategies for preventing the spread of the disease. VLPs produced through the self-assembly of viral structural proteins without genomic material mimic native virions and are promising platforms for new vaccines. VLPs have been shown to elicit protective antibodies and are effective and safe vaccines against influenza. This study aimed to optimize the protocol for the production and characterization of H9N2 VLPs and their evaluation as a vaccine in broiler birds. Materials and Methods: Low-pathogenic influenza virus (LPAI) H9N2 was isolated and characterized through whole-genome sequencing, and a VLP-based vaccine for LPAI H9N2 was prepared using a baculovirus expression system. Codon-optimized hemagglutinin (HA), neuraminidase (NA), and M1 were successfully cloned in pFastbac1 and expressed in SF9 cells. Proteins were characterized using sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE), western blotting, and electron microscopy after purification. Semi-purified proteins were tested as a vaccine in broiler chickens challenged with LPAI H9N2. Results: Recombinant Bacmid DNA from positive clones was extracted and confirmed using a polymerase chain reaction. The transfection showed cytopathic effects, and the proteins were confirmed through western blotting and SDS-PAGE, which showed the sizes of HA = 62–64 KD, NA = 52 KD, and M1 = 25 KD. The shape and morphology were confirmed through transmission electron microscopy which revealed 100–150 nm size particles. As a result, the semi-purified VLPs (HA assay: 256) were tested as a vaccine for specific-pathogen free broiler birds; administered through subcutaneous and intranasal routes. The birds were challenged on the 28th day after vaccination with the H9N2 strain, and the birds showed significant cross-reactivity with the H9N2 strain. Conclusion: The semi-purified VLP-based vaccine induced a significant immune response in vivo. This vaccine formulation has the potential to control avian influenza outbreaks in Pakistan’s poultry industry.

Abstract Valvular Heart Disease (VHD) is a globally significant cause of mortality, particularly among aging populations. Despite advancements in percutaneous and surgical interventions, there are still uncertainties that remain regarding the risk factors that significantly contribute to this condition within the domain of cardiovascular disease. This study investigates these uncertainties and the role of machine learning in categorizing VHD based on cardiovascular risk factors. It follows a two-part investigation comprising feature extraction and classification phases. Feature extraction is initially performed using a wrapping approach and refined further with binary logistic regression. The second phase employs five classifiers: Artificial Neural Network (ANN), XGBoost, Random Forest (RF), Naïve Bayes, and Support Vector Machine (SVM), along with advanced methods such as SVM combined with Principal Component Analysis (PCA) and a majority-voting ensemble method (MV5). Data on VHD cases were collected from DHQ Hospital Faisalabad using simple random sampling. Various statistical measures, such as the ROC curve, F-measure, sensitivity, specificity, accuracy, MCC, and Kappa are applied to assess the results. The findings reveal that the combination of SVM with PCA achieves the highest overall performance while the MV5 ensemble method also demonstrates high accuracy and balance in sensitivity and specificity. The variation in VHD prevalence linked to specific risk factors highlights the importance of a comprehensive approach to reduce this disease’s burden. The Exceptional performance of SVM + PCA and MV5 highlights their significance in diagnosing VHD and advancing knowledge in biomedicine.

Abstract The performance of various geogrid earth-retaining walls integrated with a non-cohesion granular backfill for reducing earth pressure has been investigated through small-scale shaking table tests and full-scale 3D finite element analysis. This purpose involved a series of physical modeling tests involving different earth-retaining walls (0.83 cm, height 7.5 cm, thickness, and length 1 m) and arrangements of full-scale 3D finite element analysis (5 m, height, 0.3 m, thickness, and length 6 m). This research investigates and designs hollow prefabricated concrete panels, gravity-type stone masonry, and reinforcement concrete (GRE) walls. It also displays comparative studies such as the top displacement of the wall, deflection of the wall, lateral pressure of the wall, settlement of the backfill, and vertical settlement of the foundation across the height of the (GRE) walls. The understanding and findings based on shaking table experiments and FE simulations have been used to develop a critical model for estimating earthquake-induced displacement (GRE) walls. The validity of the proposed FE simulation model has also been examined in the shaking table experiment and the FE simulation results. Based on the findings, the hollow prefabricated concrete panels were the most practical alternative due to their lower deflection and displacement. The observation also found that the hollow prefabricated (GER) wall is the most viable option, as the backfill surface settlement and lateral pressure decreased with the inclusion of geogrid reinforcement.

Abstract For a sustainable ecology, slaughterhouse sludge must be managed effectively in preview of the parasitic or coliforms’ spill over to the community. In order to determine the effectiveness of a customized biological decomposer solution in lowering the parasitic eggs and coliform bacteria, three composting units (Unit 1, Unit 2, and Unit 3) were treated with its different amounts. Over a period of 60 days, pH, temperature, humidity, number of the parasitic eggs per gram (EPG) of faecal material, viability of eggs, and coliform counts were evaluated. By the fifth day of the composting process, pH had significantly (P 45 °C), which persisted for 20 days for Unit 3 and 15 days for Units 1 and 2. Humidity levels initially increased significantly (P 0.05) 12.5% decline in EPG in Unit 2 and Unit 3 was recorded. After that, a significant (P

Abstract Selective and sensitive sarcosine detection is crucial due to its recent endorsement as a prostate cancer (PCa) biomarker in clinical diagnosis. The reduced graphene oxide-cobalt nickel tungsten boron oxides (CoNiWBO/rGO) nanocomposite is developed as a non-enzymatic electrochemical sensor for sarcosine detection in PCa patients’ serum. CoNiWBO/rGO is synthesized by the chemical reduction method via a one-pot reduction method followed by calcination at 500 °C under a nitrogen environment for 2 h and characterized by UV-Vis, XRD, TGA, and SEM. CoNiWBO/rGO is then deposited on a glassy carbon electrode, and sarcosine sensing parameters are optimized, including concentration and pH. This non-enzymatic sensor is employed to directly determine sarcosine in serum samples. Differential pulse voltammetry (DPV) and linear sweep voltammetry (LSV) are employed to monitor the electrochemical behavior where sarcosine binding leads to oxidation. Chronoamperometric studies show the stability of the developed sensor. The results demonstrate a wide linear range from 0.1 to 50 µM and low limits of detection, i.e., 0.04 µM and 0.07 µM using DPV and LSV respectivel. Moreover, the calculated recovery of sarcosine in human serum of prostate cancer patients is 78–96%. The developed electrochemical sensor for sarcosine detection can have potential applications in clinical diagnosis.

Abstract The interplay of exchange rates among nations significantly influences both international and domestic trade, underscoring the pivotal role of the foreign exchange market (Forex) in a country's economic landscape. Forex fluctuations have a significant impact on the everyday lives of both government agencies and the public population, directly influencing a country's prosperity or misfortune. This work proposes an advanced fuzzy time series model that incorporates domain universe sub-partitioning, parameter adjustment optimization methodologies, and interval forecasting methods. We utilized this model to examine annual exchange rate patterns between the Pakistani rupee (PKR) and the US dollar (US$), comparing its forecast accuracy to that of other models. Our proposed methodology outperformed existing methodologies in terms of forecasting precision, providing stakeholders with valuable insights for making informed, data-driven business decisions that benefit both individual firms and the country overall.

Abstract Background Pressure faced by dental students from academic activities, clinical skills training, and patient care may lead to high stress and potential burnout, negatively impacting their well-being and patient safety. Aim The study aimed to explore the burnout level of dental students at Qassim University, Saudi Arabia and to identify the factors that are associated with the level of burnout. Methods The descriptive cross-sectional study was carried out at Dental College, Qassim University, Saudi Arabia, from January to February 2024. The study was conducted to evaluate burnout levels among first to final-year undergraduate dental students enrolled at Qassim University using a pre-validated questionnaire; Burnout Clinical Subtype Questionnaire, via Google Forms. Analysis was performed with IBM SPSS-24, utilizing descriptive statistics and non-parametric tests, Mann-Whitney-U and Kruskal-Wallis. A multiple linear regression analysis was conducted to predict Burnout from demographic and academic factors. Results 151 participants responded to the survey, with 49.7% male and 50.3% female. Results showed that burnout scores increased significantly (p

Abstract Despite the rapid advancements in optimization algorithms for economical reinforced concrete (RC) structures, their application to RC slabs has been significantly limited. The primary constraint is slab depth, governed only by deflection control criteria, which impedes further depth reduction—a key factor in cost optimization. The deflection check relies solely on one design parameter—span length, which is often fixed. This constraint limits the effectiveness of even advanced algorithms, which have consistently failed to deliver substantial cost savings in slab design. To address this issue, this study presents a detailed parametric investigation of three key cost-reducing factors in RC one-way slabs: steel strength, concrete strength, and reinforcing bar sizes, in accordance with the ACI 318–19 code. By bypassing algorithmic constraints, the study formulates optimal design strategies through extensive manual design iterations, providing novel trend-based solutions for cost-effective slab design. The findings indicate that for lower live loads, grade 280 steel offers a 20–30% cost reduction compared to higher-grade steel, despite the latter's strength advantage. However, for higher loads, grade 420 or 500 is recommended. Additionally, optimal bar sizes and concrete strengths are recommended for varying load conditions, offering practical design strategies. This approach provides designers with actionable insights to achieve cost-efficient designs without relying on traditional optimization algorithms.

Abstract Background There are reported studies of Hepatitis C and chronic kidney disease association. However, how this liver virus infection affects the general population’s susceptibility to the onset of the kidney disease is still unknown. Methods To determine if a positive anti-HCV serologic status is linked to a greater incidence of chronic kidney disease in the general adult population, a systematic evaluation of the published medical literature since 2015 was conducted. A summary estimate of the relative risk of chronic kidney disease with HCV was produced using a random-effects model. Moreover, stratified analysis and meta-regression were performed. Results Twelve studies (n = 605858 patients) were filtered and included. Meta-analyses were conducted according to the outcome. Pooling results of longitudinal studies (n = 06 studies, n = 347120 unique patients) demonstrated an association between positive anti-HCV serologic status and increased incidence of CKD. The summary estimate for adjusted hazard ratio was 1.21 with (95% confidence interval 1.13; 1.29, P = 0.001), and between studies heterogeneity was noted (P value by Q test

Abstract Antimicrobial resistance (AMR) poses a significant global health threat, primarily stemming from its misuse and overuse in both veterinary and public healthcare systems. The consequences of AMR are severe, leading to more severe infections, increased health protection costs, prolonged hospital stays, unresponsive treatments, and elevated fatality rates. The impact of AMR is direct and far-reaching, particularly affecting the Sustainable Development Goals (SDGs), underscoring the urgency for concerted global actions to achieve these objectives. Disproportionately affecting underprivileged populations, AMR compounds their vulnerabilities, pushing them further into poverty. Moreover, AMR has ramifications for food production, jeopardizing sustainable agriculture and diminishing the livelihoods of farmers. The emergence of antibiotic-resistant bacteria in underprivileged areas heightens the risk of complications and mortality. Climate change further contributes to AMR, as evidenced by increased instances of foodborne salmonellosis and the development of antibiotic resistance, resulting in substantial healthcare costs. Effectively addressing AMR demands collaboration among governments, entrepreneurs, and the public sector to establish institutions and policies across all regulatory levels. Expanding SDG 17, which focuses on partnerships for sustainable development, would facilitate global antimicrobial stewardship initiatives, technology transfer, surveillance systems, and investment in vaccine and drug research. The World Bank's SDG database, tracking progress towards sustainable development, reveals a concerning picture with only a 15% success rate till 2023 and 48% showing deviation, underscoring a global gap exacerbated by the COVID-19 pandemic. Tackling AMR's global impact necessitates international cooperation, robust monitoring, and evaluation methods. The five priorities outlined guide SDG implementation, while impoverished countries must address specific challenges in their implementation efforts. Addressing AMR and its impact on the SDGs is a multifaceted challenge that demands comprehensive and collaborative solutions on a global scale.

Abstract Background It is well-known that serum uric acid (SUA) can increase the risk of hypertension, diabetes, obesity and dyslipidemia. However, its independent association with the risk of cardiovascular diseases (CVD) is controversial particularly in different populations. Hence, this study was aimed to assess an independent association of SUA with CVD risk in a Punjabi Pakistani cohort. Methods This is a retrospective observational study in which 502 human subjects having CVD, hypertension and/or diabetes were grouped based on SUA levels as normouricemia (n = 266) and hyperuricemia (n = 236). Role of SUA was assessed in increasing the risk of CVD independent of other key confounding factors (i.e. age, gender, dyslipidemia, hypertension, diabetes, dietary and life-style habits). All clinical and biochemical data were analyzed in SPSS (ver. 20). Results Subjects aged 55 ± 13 years were of both genders (males: 52%). SUA levels were significantly different among clinical subtypes of CVD [i.e. acute coronary syndrome (ACS), myocardial infarction (MI) and heart failure (HF)]. Spearman correlation showed a significantly positive association between CVD and SUA (rho = 0.149, p

Abstract Chitosan (CTS) is recognized for enhancing a plant’s resilience to various environmental stresses, such as salinity and drought. Moreover, salicylic acid (SA) is acknowledged as a growth regulator involved in addressing metal toxicity. However, the effectiveness of both compounds in mitigating Cr-induced stress has remained relatively unexplored, especially in the case of Aconitum napellus, a medicinally and floricultural important plant. Therefore, the primary objective of this study was to investigate the potential of CTS and SA in alleviating chromium (Cr)-induced stress in A. napellus. To address these research questions, we conducted a controlled experiment using potted plants to evaluate the individual and combined impacts of CTS and SA on plants exposed to Cr stress. Foliar application of CTS (0.4 g/L) or SA (0.25 mmol/L) led to significant improvements in the growth, chlorophyll content, fluorescence, and photosynthetic traits of A. napellus plants under Cr stress. The most notable effects were observed with the combined application of CTS and SA, resulting in increases in various morphological parameters, such as shoot length (2.89% and 7.02%) and root length (27.75% and 3.36%) under the Cr 1 and Cr 2 treatments, respectively. Additionally, several physiological parameters, such as chlorophyll a (762.5% and 145.56%), chlorophyll b (762.5% and 145.56%), carotenoid (17.03% and 28.57%), and anthocyanin (112.01% and 47.96%) contents, were notably improved under the Cr 1 and Cr 2 treatments, respectively. Moreover, the combined treatment of CTS and SA improved the fluorescence parameters while decreasing the levels of enzymatic antioxidants such as catalase (27.59% and 43.79%, respectively). The application also notably increased osmoprotectant parameters, such as the total protein content (54.11% and 20.07%) and the total soluble sugar content (78.17% and 49.82%) in the leaves of A. napellus in the Cr 1 and 2 treatments, respectively. In summary, these results strongly suggest that the simultaneous use of exogenous CTS and SA is an effective strategy for alleviating the detrimental effects of Cr stress on A. napellus. This integrated approach opens promising avenues for further exploration and potential implementation within agricultural production systems.

Abstract Solid supported catalysts have several synthetic applications. Herein, finely ground eggshells were used as a solid support for the preparation of transition metal (Ni, Zn, Cu, Sn and Co) based catalysts to synthesize 2,4-dinitrophenylhydrazone (3) and dihydropyrimidinones (7 and 8). The effect of catalyst load, time and temperature on product yield was studied. Box Behnken Model was employed, and three predictors named catalyst amount (A), reaction time (B), and reaction temperature (C) were used to find the correlation of the predictors with the yield. Second order polynomial equation was used to estimate the effects of these factors. According to the statistical model, about 12% increase in yield was observed as a result of one-unit increase in reaction time while all other terms were kept constant. The values of S (18.1616) and R2 (71.2%) indicate that the statistical model gave an adequate fit to data. Quadratic model for the response surface was used for the analysis of variance (ANOVA) results, the larger F-values, and smaller p-values indicated that the predictors are in good agreement. The linear model terms of predictors were found to be significantly effective for yield (P

Abstract This work uses rational curve fitting models to investigate the complex link between topological indices and entropy metrics in calcium hydroxide $$(Ca(OH)_{2})$$ ( C a ( O H ) 2 ) networks. Entropy measurements shed light on the complexity and disorder of these networks, whereas topological indices are essential instruments for comprehending the structural characteristics of chemical substances. Our goal is to find new patterns and connections by merging these two fields, improving materials science’s prediction capacity. We calculate many topological indices, such as the Randic, Balaban, and Zagreb indices, and examine the relationship between them and entropy measurements obtained from $$(Ca(OH)_{2})$$ ( C a ( O H ) 2 ) structural arrangement.

Abstract Automated segmentation of biomedical image has been recognized as an important step in computer-aided diagnosis systems for detection of abnormalities. Despite its importance, the segmentation process remains an open challenge due to variations in color, texture, shape diversity and boundaries. Semantic segmentation often requires deeper neural networks to achieve higher accuracy, making the segmentation model more complex and slower. Due to the need to process a large number of biomedical images, more efficient and cheaper image processing techniques for accurate segmentation are needed. In this article, we present a modified deep semantic segmentation model that utilizes the backbone of EfficientNet-B3 along with UNet for reliable segmentation. We trained our model on Non-melanoma skin cancer segmentation for histopathology dataset to divide the image in 12 different classes for segmentation. Our method outperforms the existing literature with an increase in average class accuracy from 79 to 83%. Our approach also shows an increase in overall accuracy from 85 to 94%.

Abstract Background Tomato (Solanum lycopersicum L.) production is severely threatened by bacterial wilt, caused by the phytopathogenic bacterium Ralstonia solanacearum. Recently, nano-enabled strategies have shown tremendous potential in crop disease management. Objectives This study investigates the efficacy of biogenic nanoformulations (BNFs), comprising biogenic silica nanoparticles (SiNPs) and melatonin (MT), in controlling bacterial wilt in tomato. Methods SiNPs were synthesized using Zizania latifolia leaves extract. Further, MT containing BNFs were synthesized through the one-pot approach. Nanomaterials were characterized using standard characterization techniques. Greenhouse disease assays were conducted to assess the impact of SiNPs and BNFs on tomato plant immunity and resistance to bacterial wilt. Results The SiNPs and BNFs exhibited a spherical morphology, with particle sizes ranging from 13.02 nm to 22.33 nm for the SiNPs and 17.63 nm to 21.79 nm for the BNFs, indicating a relatively uniform size distribution and consistent shape across both materials. Greenhouse experiments revealed that soil application of BNFs outperformed SiNPs, significantly enhancing plant immunity and reducing bacterial wilt incidence by 78.29% in tomato plants by maintaining oxidative stress homeostasis via increasing the activities of antioxidant enzymes such as superoxide dismutase (31.81%), peroxidase (32.9%), catalase (32.65%), and ascorbate peroxidase (47.37%) compared to untreated infected plants. Additionally, BNFs induced disease resistance by enhancing the production of salicylic acid and activating defense-related genes (e.g., SlPAL1, SlICS1, SlNPR1, SlEDS, SlPD4, and SlSARD1) involved in phytohormones signaling in infected tomato plants. High-throughput 16 S rRNA sequencing revealed that BNFs promoted growth of beneficial rhizosphere bacteria (Gemmatimonadaceae, Ramlibacter, Microscillaceae, Anaerolineaceae, Chloroplast and Phormidium) in both healthy and diseased plants, while suppressing R. solanacearum abundance in infected plants. Conclusion Overall, these findings suggest that BNFs offer a more promising and sustainable approach for managing bacterial wilt disease in tomato plants. Graphical Abstract

Abstract Background Cotton leaf curl virus disease (CLCuD) is one of the major concerns for cotton growers. The traditional approach to managing CLCuD involves the control of the vector (whitefly) population through the use of pesticides. This study compares the efficacy of zinc oxide, iron oxide, copper and silver nanoparticles with conventional pesticides. Nanoparticles dose was optimized by evaluating their phytotoxic threshold in our previous study. In this study, optimized doses of nanoparticles such as zinc oxide (100 ppm), iron oxide (50 ppm), copper (50 ppm) and silver nanoparticles (25 ppm) were applied in a field trial of cotton against cotton leaf curl virus disease (CLCuD). Morphological parameters (height of stem, monopodial branches, sympodial branches, staple length, boll weight and number of bolls), yield parameters (seed cotton yield and ginning outturn), chlorophyll content (chlorophyll a, chlorophyll b, carotenoids and total chlorophyll), biochemical parameters (superoxide dismutase (SOD), peroxidase (POD), ascorbate peroxidase (APX), catalase (CAT), hydrogen peroxide (H2O2) and electrolyte leakage) and disease parameters (reduction infection, disease severity and disease incidence) were determined in this study. Results The incidence of cotton leaf curl virus was confirmed by triple antibody sandwich–enzyme-linked immunosorbent assay (TAS-ELISA). The pesticide Imidacloprid significantly reduced the infection by 79.3%. However, in comparison to pesticide, application of nanoparticles also reduced the infection. ZnO NPs reduced the infection by 42.33%, FeO NPs by 41%, Cu NPs by 34.7%, and Ag NPs by 44.8%. Moreover, these nanoparticles also improved the plant growth parameters as compared to control treatment. ZnO NPs enhanced morphological, yield parameters, and chlorophyll content by 36%, 22%, and 29%, respectively. FeO NPs showed improvements by 38%, 21%, and 29%; Cu NPs 39%, 25%, and 29%; and Ag NPs 31%, 19%, and 18%, respectively. Conclusion Although treatment pesticide showed the least disease incidence compared to nanoparticles, nanoparticles are eco-friendly and safe as compared to pesticides. Farmers can apply these nanoparticles at their optimal thresholds through foliar application as an alternative to traditional pesticides. It is concluded that nanocomposites and hybrid modes may be used for managing CLCuD efficiently in the future. Graphical abstract

Abstract In the rice-based system of mid-latitudes, mineral nitrogen (N) fertilizer serves as the largest source of the N cycle due to an insufficient supply of N from organic sources causing higher N losses due to varying soil and environmental factors. However, aiming to improve soil organic matter (OM) and nutrients availability using the best environmentally, socially, and economically sustainable cultural and agronomic management practices are necessary. This study aimed to enhance nitrogen use efficiency (NUE) and grain yield in rice-based systems of mid-latitudes by partially replacing inorganic N fertilizer with organic inputs. A randomized complete block design (RCBD) was employed to evaluate the effects of sole mineral N fertilizer (urea) and its combinations with organic sources—farmyard manure (FYM) and poultry compost—on different elite green super rice (GSR) genotypes and were named as NUYT-1, NUYT-2, NUYT-3, NUYT-4, NUYT-5, and NUYT-6. The study was conducted during the 2022 and 2023 rice growing seasons at the Rice Research Program, Crop Sciences Institute (CSI), National Agricultural Research Centre (NARC), Islamabad, one of the mid-latitudes of Pakistan. The key objective was to determine the most effective N management strategy for optimizing plant growth, N content in soil and plants, and overall crop productivity. The results revealed that the combined application of poultry compost and mineral urea significantly enhanced soil and leaf N content (1.36 g kg− 1 and 3.06 mg cm− 2, respectively) and plant morphophysiological traits compared to sole urea application. Maximum shoot dry weight (SDW) and root dry weight (RDW) were observed in compost-applied treatment with the values of 77.62 g hill− 1 and 8.36 g hill− 1, respectively. The two-year mean data indicated that applying 150 kg N ha⁻1, with half provided by organic sources (10 tons ha⁻1 FYM or poultry compost) and the remainder by mineral urea, resulted in the highest N uptake, utilization, and plant productivity. Thus, integrated management of organic carbon sources and inorganic fertilizers may sustain the productivity of rice-based systems more eco-efficiently. Further research is recommended to explore root and shoot morphophysiological, molecular, and biochemical responses under varying N regimes, aiming to develop N-efficient rice varieties through advanced breeding programs.

Abstract Cadmium (Cd) is an unessential and pervasive contaminant in agricultural soil, eventually affecting the food and instigating health issues. The implication of nanocomposites in agriculture attained significant attention to drive food security. Nanocomposites possess exceptional characteristics to stun the challenges of chemical fertilizers that can enhance plant yield and better nutrient bioavailability. Similarly, biochar has the ability to immobilize Cd in soil by reducing mobility and bioavailability. Rice husk biochar is produced at high temperature pyrolysis under anoxic conditions and a stable carbon-rich material is formed. To strive against this issue, rice plants were subjected to Cd (15, 20 mg kg− 1) stress and treated with alone/combined Ca + Mg (25 mg L− 1) nanocomposite and rice husk biochar. In our study, growth and yield traits showed the nurturing influence of Ca + Mg nanocomposite and biochar to improve rice defence mechanism by reducing Cd stress. Growth parameters root length 28%, shoot length 34%, root fresh weight 19%, shoot fresh weight 16%, root dry weight 9%, shoot dry weight 8%, number of tillers 32%, number of grains 20%, and spike length 17% were improved with combined application of Ca + Mg and biochar, with Cd (20 mg kg− 1), rivalled to alone biochar. Combined Ca + Mg and biochar application increased the SPAD 23%, total chlorophyll 26%, a 19%, b 18%, and carotenoids 15%, with Cd (20 mg kg− 1), rivalled to alone biochar. MDA 15%, H2O2 13%, and EL 10% were significantly regulated in shoots with combined Ca + Mg and biochar application with Cd (20 mg kg− 1) compared to alone biochar. POD 22%, SOD 17%, APX 18%, and CAT 9% were increased in shoots with combined Ca + Mg and biochar application with Cd (20 mg kg− 1) compared to alone biochar. Cd uptake in roots 13%, shoots 14%, and grains 21% were minimized under Cd (20 mg kg− 1) with combined Ca + Mg and B. pumilus application, compared to alone biochar. Subsequently, combined Ca + Mg and biochar application is a sustainable solution to boost crop production under Cd stress.

Abstract Interval-valued picture fuzzy (IVPF) set is an extension of the picture fuzzy set theory used to represent uncertainty and vagueness in the processes of decision-making. This study focuses on exploring the interrelationships among multiple IVPFSs and criteria partitions. We investigate the IVPF partitioned Maclaurin symmetric mean operator and the weighted IVPF partitioned Maclaurin symmetric mean operator and discuss their respective properties. Subsequently, we identify certain special cases of these operators based on IVPF sets. Furthermore, we deploy a multi-criteria decision-making procedure utilizing the suggested IVPF partition operators. Through a numerical example, we demonstrate the practicality and validity of the presented approach. Finally, a thorough comparison with existing approaches is conducted to elucidate the superiority of the proposed method.

A bench-scale study of river water treatment using composite polyethersulfone (PES) membrane was carried out. Polyethylene glycol-silver nanoparticles (PEG-AgNPs) additive was designed to support the simultaneous filtration and disinfection performance of PES membranes via in situ incorporation. Significant improvements in the PES membranes after modification with PEG-AgNPs were observed. The experimental results showed that the PEG-AgNPs increased the PES membrane water flux from 2.87 to 172.84 L/m2·h. The anti-fouling test of the PES membrane toward humic acid molecules after the addition of PES/PEG-AgNPs increased the reversible fouling and decreased the irreversible fouling. In terms of filtration performance, the PES/PEG-AgNP membranes showed high filtration performance, with a water disinfection ability of 99.99 %. Moreover, the leach of silver particles from the PES membrane forced by ultrasonication was

Objective This study aims to evaluate the immunomodulatory effects of coconut oil extract (COE) in broilers experimentally infected with velogenic Newcastle disease virus (vNDV). Methods A total of 150 broiler birds (day-old) were equally divided into five study groups i.e., negative control, positive control, COE-1, COE-2, and COE-3. On day 10, broilers of groups COE-1, COE-2, and COE-3 were supplemented with 1, 2, and 3 mL of COE respectively per liter of drinking water for 15 days. On day 13, 0.1 mL/bird (10−5.25 ELD50) of vNDV was inoculated in broilers of positive control, COE-1, COE-2, and COE-3 groups intramuscularly. During this study, growth performance, morbidity, and mortality rates of each study group were recorded. The antibody titer against NDV was determined on days 7, 14, 21, 28, and 35. The levels of immunoglobulins (IgY and IgM) were also determined on the 7th, 14th, and 21st days post-sheep red blood cells (SRBCs) inoculation. On day 33, avian tuberculin was injected between the 1st and 2nd toes of the left side (intradermally) to measure lymphoproliferative responses. On day 35, the phagocytic activity in the blood was assessed through a carbon clearance assay by injecting carbon black ink into the right-wing vein. The visceral organs having gross lesions were also collected for histopathology. Results The COE significantly improved the growth performance, and lowered the morbidity and mortality rates of broilers. There was a significant rise in antibody titers against NDV and levels of IgY and IgM antibodies against SRBC in COE-supplemented broilers. The lymphoproliferative response and phagocytic activity were also enhanced. Among COE-supplemented groups, the broilers of the COE-3 group showed a significant increase in growth performance and boosted immune defense. Conclusion Coconut oil extract has the potential to boost the growth performance and immune status of broilers. It can be used effectively as a feed additive and alternative to antibiotics to prevent the spread of infectious poultry pathogens.

Topological indices play a crucial role as molecular descriptors in QSAR/QSPR research. Graph entropy measurements are of great importance in various fields like chemistry, discrete mathematics, and biology. Information-theoretic values derived from topological indices, influenced by Shannon’s entropy, are used to analyze the structural characteristics of chemical graphs and complex networks. We discuss the analysis of graph entropies obtained from a new information function. It is equivalent to both the total number of edges and the various degrees of vertices. The information function is also employed to compute the entropies of the system and build a connection between connectivity indices and degrees. This study examines the chemical graphs of Carboxylate-terminated Zinc Phthalocyanine (CtZP) and Nanostar (NS), using the function to connect degree-based topological indices like First Zegrab index, Second Zagreb index, Hyper Zagreb index, Forgotten index, First Redefined Zagreb index, The Second Redefined Zagreb index, The Third Redefined Zagreb index, and Somber index, to calculate the entropies of these structures.

Currently, the leatherback turtle population in Indonesia tends to decline. One of the factors that cause turtles' existence is the turtle eggs that fail to hatch because bacteria contaminate them, 80% of the reasons turtle eggs fail to hatch are due to infection by microorganisms found on the eggshell. Turtle eggs have a soft structure and there are pores that function for gas exchange and water absorption. Microorganisms in sand can infect turtle eggs through the pores and cause hatching failure. This study aims to isolate and identify pathogenic bacteria found in leatherback turtle eggshell (Dermochelys coriacea) from Lampuuk Beach, Aceh Besar, based on the 16S rRNA gene analysis. Eight eggshell samples from leatherback turtle eggs that failed and hatched were cultured on Nutrient Agar (NA) medium. Then, the samples were inoculated on a blood agar medium for haemolysis test. Molecular identification of the 16S rRNA gene was also carried out to determine bacterial species. A total of five bacterial colonies from leatherback turtle eggshells were successfully isolated consisting of two Gram-negative bacteria (Penyu Belimbing Aceh (PBA) = PBA-1 and PBA-2 and three Gram-positive bacteria (PBA-3, PBA-4, and PBA-5). Based on the haemolysis test, the five bacterial isolates were unable to haemolyze blood. Bacterial DNA of PBA-1 and PBA-2 were successfully isolated and amplified using polymerase chain reaction (PCR) with a DNA target of ~1500 bp. Analysis using BLASTN and phylogenetic tree construction showed that PBA-1 isolate had 98.64% similarity with Acinetobacter baumannii, while PBA-2 isolate had 97.82% similarity with Enterobacter kobei. Both bacteria are members of the Enterobacteriaceae family. It can be concluded that, there were two pathogenic bacteria can potentially be opportunistic pathogens found in leatherback turtle eggshells that failed or succeeded in hatching, namely A. baumannii and E. Kobei

Dissatisfaction among upper limb prosthetic users is high, reaching over 70%, and 52% of upper limb amputees abandon their prosthetic devices due to limitations such as limited functionality, poor design/aesthetic, and improper fit. The conventional procedure of making prosthetics is time-consuming and expensive. This study was conducted to provide an alternative solution for the several issues of current prosthetic devices. 3D printing technology offers a promising alternative, providing greater accuracy, reduced labor time, and improved fit and comfort. This research explores the application of 3D printing for creating custom silicone prosthetic fingers, using Fused Deposition Modelling (FDM) for the mold. A high-resolution 3D scanner was employed to capture the precise anatomy of the patient’s hand, and CAD software was used to design molds that satisfied the patient preference and were reusable. The resulting prosthetics demonstrated good fit and patient satisfaction, though exact color matching remains a challenge, but still, it did demonstrate that it is possible to fabricate colored prosthetics. Performance tests, such as the Jebsen–Taylor Hand Function Test, indicated that while initial performance with the prosthetic was slightly lower, patient satisfaction and potential for improved functionality over time were high. This study underscores the potential of 3D printing to enhance the customization, cost-effectiveness, and overall quality of prosthetic devices, contributing to Sustainable Development Goals related to health and industry innovation.

The primary emphasis of the present study is to introduce some novel characterizations of the interval-valued $ (\mathcal{I}.\mathcal{V}) $ right symmetric quantum derivative and antiderivative operators relying on generalized Hukuhara difference. To continue the study, we start with the concept of symmetric differentiability in the interval-valued sense and explore some important properties. Furthermore, through the utilization of the $ (\mathcal{I}.\mathcal{V}) $ symmetric derivative operator, we develop the right-sided $ (\mathcal{I}.\mathcal{V}) $ integral operator and explore its key properties. Also, we establish various $ (\mathcal{I}.\mathcal{V}) $ trapezium-like inequalities by considering the newly proposed operators and support line. Later on, we deliver another proof of the trapezium inequality through an analytical approach. Also, we present the numerical and visual analysis for the verification of our results.