3D printed lightweight honeycomb vent structures with subsequent coating of silver nanowires for efficient electromagnetic interference (EMI) shielding.

In light of the rapid advancements within the electronic industry, the urgent need for the development and implementation of advanced electromagnetic interference (EMI) shielding materials has become paramount. Herein a novel approach is presented for developing of lightweight honeycomb structures using 3D printing technology, combined with subsequent conductive spray coating, containing Silver Nanowires (AgNWs), to achieve effective EMI shielding as well as air vent functionality for thermal cooling. Using polyol method, AgNWs were synthesized having high aspect ratio and crystallinity for to be used as conductive coating on 3D printed structures. The EMI shielding results in X-band demonstrated that the developed structures exhibit promising EMI shielding properties, up to 35 dB attenuation with 2 mm honeycomb cell size, making them suitable for applications requiring EMI protection along with air venting. More importantly in all samples major contribution of the shielding efficiency comes from the absorption of the EM waves (up to 75 %) inside the structures which is helpful to reduce reflected EM noise. Effort was to effectively addresses the inherent limitations of conventional processing technology, by using additive manufacturing and material science to create structures for EMI shielding applications, bridging the gap between existing materials and desired components.

Weapon Violence Dataset 2.0: A synthetic dataset for violence detection.

In the current era, satisfying the appetite of data hungry models is becoming an increasingly challenging task. This challenge is particularly magnified in research areas characterised by sensitivity, where the quest for genuine data proves to be elusive. The study of violence serves as a poignant example, entailing ethical considerations and compounded by the scarcity of authentic, real-world data that is predominantly accessible only to law enforcement agencies. Existing datasets in this field often resort to using content from movies or open-source video platforms like YouTube, further emphasising the scarcity of authentic data. To address this, our dataset aims to pioneer a new approach by creating the first synthetic virtual dataset for violence detection, named the Weapon Violence Dataset (WVD). The dataset is generated by creating virtual violence scenarios inside the photo-realistic video game namely: Grand Theft Auto-V (GTA-V). This dataset includes carefully selected video clips of person-to-person fights captured from a frontal view, featuring various weapons-both hot and cold across different times of the day. Specifically, WVD contains three categories: Hot violence and Cold violence (representing the violence category) as well as No violence (constituting the control class). The dataset is designed and created in a way that will enable the research community to train deep models on such synthetic data with the ability to increase the data corpus if the needs arise. The dataset is publicly available on Kaggle and comprises normal RGB and optic flow videos.

Effect of different preservatives on the physicochemical characteristics and shelf stability of Rasmalai: A comparative study.

Rasmalai is a very popular, delicious, and nutritious indigenous sweet dish in Indo-Pakistani civilization. It has a very short shelf life, i.e., up to 3 days. The study was designed to assess the effect of preservatives (potassium sorbate and calcium propionate) on the shelf stability of Rasmalai. Moreover, proximate composition and sensory evaluation of prepared Rasmalai were also carried out in the present study. In general, potassium sorbate and calcium propionate significantly increased the shelf life of Rasmalai. But treatment (R5) containing a combination of both potassium sorbate and calcium propionate (500 ppm each) improved its shelf life by up to 12 days by keeping good sensorial characteristics. The maximum total plate counts as well as yeast and molds were observed in control Rasmalai (without any preservatives) whereas minimum counts were found in R5 treatment containing a combination of both potassium sorbate and calcium propionate (500 ppm each). In conclusion, all the preservatives used in the present study were effective in enhancing the shelf life of Rasmalai but R5 treatment containing a combination of both potassium sorbate and calcium propionate (500 ppm each) was the most effective in enhancing shelf life without deleterious effect on sensorial characteristics.

Comparative mineral and biochemical characterization of Citrus reticulata fruits and leaves to citrus canker pathogens, Xanthomonas axonopodis.

Pakistan's economy greatly benefits from citrus production since these fruits are sold and consumed all over the world. Although citrus fruits are easy to cultivate, they are susceptible to diseases caused by bacteria, viruses, and fungi. These challenges, as well as difficulties in obtaining the proper nutrients, might negatively impact fruit yields and quality. Citrus canker is another complicated problem caused by the germ Xanthomonas axonopodis. This germ affects many types of citrus fruits all over the world. This study looked closely at how citrus canker affects the leaves and the quality of the fruit in places like Sargodha, Bhalwal, Kotmomin, and Silanwali, which are big areas for growing citrus in the Sargodha district. What we found was that plants without the disease had more chlorophyll in their leaves compared to the sick plants. Also, the healthy plants had better amounts of important minerals like calcium, magnesium, potassium, and phosphorus in their fruits. But the fruits with the disease had too much sodium, and the iron levels were a bit different. The fruits with the disease also didn't have as much of something that protects them called antioxidants, which made them more likely to get sick. This study helps us understand how citrus canker affects plants and fruit, so we can think of ways to deal with it.

Exploring the Quality of Life (QOL) of medical students in Karachi, Pakistan.

BACKGROUND: The pursuit of medical and dental education is challenging and can affect the overall quality of life of medical students. Assessing the quality of life of medical students is the first step in the preparation of efficient future health care professionals. This study used the World Health Organization Quality of Life Brief Version (WHOQOL-BREF) to evaluate the quality of life of medical and dental students in Karachi, Pakistan. OBJECTIVES: The study objectives include: assessing the QoL of medical and dental students and their general health satisfaction and self-satisfaction. MATERIALS AND METHODS: This cross-sectional study was conducted among 344 medical and dental students from different medical and dental schools in Karachi, Pakistan. The World Health Organization Quality of Life Brief Version (WHOQOL-BREF) questionnaire was used to assess QOL, which included 26 items covering four domains: physical, psychological, social, and environmental. All scores for the domains ranged from 4 to 20. Scoring was done according to the WHOQOL-BREF procedure manual. The questionnaire was disseminated to medical students using Google Forms. SPSS software was used to analyze the data. Cronbach's alpha and the Kaiser-Meyer-Olkin (KMO) test were used to evaluate the reliability and sampling adequacy of the data for factor analysis. Descriptive statistics were computed for each variable and QoL domain, including frequencies, percentages, averages, and standard deviations. Domain scores were compared using a t-test and one-way ANOVA, with p-values less than 0.05, indicating statistical significance. RESULTS: Among the 344 medical students, 56.7% (n = 195) were female and 43.3% (n = 149) were male. The WHOQOL-BREF demonstrated excellent reliability, with a Cronbach's alpha of 0.918. Most medical students rated their overall QOL (62.2%) and health satisfaction (46.8%) as good, and were able to get around well (71.3%). No significant sex differences were found across the various QOL domains. Marital status significantly affected QOL scores (p < 0.005). Single students had significantly higher QOL scores than married/separated/divorced students did. Overall, the environmental domain had the highest mean score (26.81 ± 6.17), while social relationships had the lowest mean score (9.68 ± 2.93). CONCLUSION: The findings of this study provide valuable insights into the QoL of medical and dental students. Most participants reported moderate satisfaction with their physical health and lower satisfaction with the psychological, social, and environmental components of QoL. Marital status was found to significantly impact the QoL as compared to single students with greater QoL. These findings can help form targeted interventions to enhance medical students' quality of life and prepare efficient future healthcare professionals.

Computational analysis of antiviral drugs using topological descriptors.

Many health challenges are attributed to viral infections, which represent significant concerns in public health. Among these infections, diseases such as herpes simplex virus (HSV), cytomegalovirus (CMV), and varicella-zoster virus (VZV) infections have garnered attention due to their prevalence and impact on human health. There are specific antiviral medications available for the treatment of these viral infections. Drugs like Cidofovir, Valacyclovir, and Acyclovir are commonly prescribed. These antiviral drugs are known for their efficacy against herpesviruses and related viral infections, leveraging their ability to inhibit viral DNA polymerase. A molecular descriptor is a numerical value that correlates with specific physicochemical properties of a molecular graph. This article explores the calculation of distance-based topological descriptors, including the Trinajstic, Mostar, Szeged, and PI descriptors for the aforementioned antiviral drugs. These descriptors provide insights into these drugs' structural and physicochemical characteristics, aiding in understanding their mechanism of action and the development of new therapeutic agents.

Multicultural personality traits of Chinese university students and their effects on the psychological adjustment in the aftermath of COVID-19 in Shanghai: a scale validation.

Objective: This study aims to explore Chinese university students' multicultural personalities and examine how they predict the psychological adjustment of students in Shanghai. In addition, the validation of Multicultural Personality Questionnaire Short Form (MPQ-SF) scale developed to assess the multicultural personality traits of individuals is also aimed in Chinese context. Data were collected after the psychological stresses from restrictions imposed by COVID-19 in China that influenced life adjustments for nearly three years. Method: A total of 1,099 university students participated in this multi-stage study. First, the Chinese version of MPQ-SF (MPQ-SF-C) was developed and validated. The impact of MPQ-SF-C dimensions was then tested through path analysis to establish the effects of Chinese university students' multicultural personality traits on their psychological adjustment using the Schwartz Outcome Scale (SOS-10). Results: The MPQ-SF-C yielded a five-factor solution which accounted for 60.14% of the common variance. The findings indicated that cultural empathy (ß = 0.23, p < 0.05), certainty seeking (ß = 0.13, p < 0.05), open-mindedness (ß = 0.48, p < 0.05), and emotional stability (ß = 0.24, p < 0.05) had significant influences on adjustment. Only flexibility was found to have a statistically insignificant impact on adjustment at this time in this context. MPQ-SF-C and SOS-10 scales represented very good psychometric properties in terms of their reliability and validity. Conclusion: The MPQ-SF-C shows good psychometric properties and appropriateness for evaluating multicultural personalities in Chinese contexts. The multicultural personality characteristics of university students using this scale well predicted their psychological adjustment.

Quality and antioxidant potential of goat's milk paneer prepared from different citrus juices and its whey.

The experiments reported in this research paper aimed to evaluate the physico-chemical and sensory characteristics, microbial quality and antioxidant potential of goat's milk paneer during storage (0-12 d, 4 ± 1°C). The juices from five different citrus fruits were used as coagulant (treatments) to make goat's milk paneer. The pH of all paneer samples decreased during storage whereas titratable acidity increased. Ash (%) fat (%) and protein (%) of paneer increased slightly during storage, whereas sensory perception decreased. The juices from all the citrus fruit varieties showed high contents of total phenolics and total flavonoids which ultimately influenced ferric reducing antioxidant power, total antioxidant capacity and radical scavenging activities. As the contents of different juices were also retained in the paneer matrix, so paneer coagulated with citrus juices also showed encouraging results in terms of total phenolic and flavonoid contents, ferric reducing antioxidant power and radical scavenging activities. Amongst all the paneers, the most promising was that coagulated by kinnow juice. In addition, the whey obtained from paneer coagulated by citrus juices also showed appreciable quantities of total phenolic and flavonoid contents, thereby beneficially influencing ferric reducing antioxidant power andradical scavenging activities. It is concluded that citrus juices improve the sensorial quality and antioxidant potential of goat's milk paneer and its whey.

Topological Insights into Nanostar Dendrimers by Computing the Augmented Zagreb Index.

BACKGROUND: The field of nanobiotechnology uses precise nanofabrication techniques to advance our understanding and control of biological systems. Due to their remarkable properties, dendrimers, which are hyperbranched macromolecular structures with distinct and well-defined architectures, have emerged as pivotal entities within this field. They are gaining increasing attention for their potential to catalyze a paradigm shift in medical therapeutics, biotechnological applications, and advanced material sciences. OBJECTIVE: This paper focuses on a novel analytical expression and determines the precise value of the augmented Zagreb index, a topological descriptor, for eight classes of nanostar dendrimers. METHODS: The Zagreb index is a topological invariant to predict molecular behaviour and reactivity. In this paper, we have explored its application in characterizing the branching of nanostar dendrimers through computational modelling and mathematical rigor. RESULTS: Our research has measured the augmented Zagreb index for nanostar dendrimers, which fall into eight distinct classes. The results better explain the relationship between the dendrimers' topology and chemical properties. This correlation has implications for their structural stability and reactivity, potentially leading to new applications. CONCLUSION: Developing the augmented Zagreb index for nanostar dendrimers is a significant breakthrough in nanobiotechnology. Based on the correlation between the calculated topological index and the corresponding molecular attributes, our analytical approach has opened up new possibilities for designing and synthesizing dendrimers tailored to specific functions in medical and material science applications. This precise topological quantification could significantly enhance the utility and functionalization of dendrimers in cutting-edge nanotechnological applications.

Analyzing the dynamical sensitivity and soliton solutions of time-fractional Schrödinger model with Beta derivative.

In physical domains, Beta derivatives are necessary to comprehend wave propagation across various nonlinear models. In this research work, the modified Sardar sub-equation approach is employed to find the soliton solutions of (1+1)-dimensional time-fractional coupled nonlinear Schrödinger model with Beta fractional derivative. These models are fundamental in real-world applications such as control systems, processing of signals, and fiber optic networks. By using this strategy, we are able to obtain various unique optical solutions, including combo, dark, bright, periodic, singular, and rational wave solutions. In addition, We address the sensitivity analysis of the proposed model to investigate the truth that it is extremely sensitive. These studies are novel and have not been performed before in relation to the nonlinear dynamic features of these solutions. We show these behaviors in 2-D, contour 3-D structures across the associated physical characteristics. Our results demonstrate that the proposed approach offers useful results for producing solutions of nonlinear fractional models in application of mathematics and wave propagation in fiber optics.

Co-exposure to tire wear particles and nickel inhibits mung bean yield by reducing nutrient uptake.

Soil and terrestrial contamination with microplastics and nanoplastics has been discussed extensively, while tire wear particles (TWPs) have been largely overlooked. We investigated the root-surface interactions and growth response of mung bean (Vigna radiata L.) plants exposed to tire wear particles (TWPs) (0.05, 0.1, and 0.25% w/w) and nickel sulfate (50 and 100 mg kg-1 NiSO4) alone and in co-exposure scenarios for the full life cycle (105 days) under soil conditions. The results show that TWPs adhered to the root surface and reduced the water and nutrient uptake by the plant, particularly at higher concentrations of TWPs (0.25% w/w), without any observed organic contaminant accumulation in the root tissue. TWPs alone at 0.01, 0.1, and 0.25% (w/w) decreased mung bean yield by 11, 28, and 52%, respectively. Co-exposure to TWPs at 0.01, 0.1 and 0.25% w/w with 100 mg kg-1 NiSO4 decreased yield by 73, 79 and 88%, respectively. However, co-exposure to TWPs at 0.01 and 0.1% w/w with 50 mg kg-1 NiSO4 enhanced the yield by 32% and 7%, respectively. These changes in yield and nutritional aspects appear to be linked to Ni's regulatory influence on mineral homeostasis. Moreover, exposure to NiSO4 at 100 mg kg-1 increased Ni uptake in the root, shoot, and grain by 9, 26, and 20-fold, respectively as compared to the unamended control; this corresponded to increased antioxidant enzyme activity (10-127%) as compared to the control. TWPs caused blockages, significantly reducing plant yield and altering nutrient dynamics, highlighting emerging risks to plant health.

Genome-wide association studies identifies genetic loci related to fatty acid and branched-chain amino acid metabolism and histone modifications under varying nitrogen treatments in safflower (Carthamus tinctorius).

Effective identification and usage of genetic variation are prerequisites for developing nutrient-efficient cultivars. A collection of 94 safflower (Carthamus tinctorius ) genotypes (G) was investigated for important morphological and photosynthetic traits at four nitrogen (N) treatments. We found significant variation for all the studied traits except chlorophyll b (chl b ) among safflower genotypes, nitrogen treatments and G×N interaction. The examined traits showed a 2.82-50.00% increase in response to N application. Biological yield (BY) reflected a significantly positive correlation with fresh shoot weight (FSW), root length (RL), fresh root weight (FRW) and number of leaves (NOL), while a significantly positive correlation was also observed among carotenoids (C), chlorophyll a (chl a ), chl b and total chlorophyll content (CT) under all treatments. Superior genotypes with respect to plant height (PH), FSW, NOL, RL, FRW and BY were clustered into Group 3, while genotypes with better mean performance regarding chl a , chl b C and CT were clustered into Group 2 as observed in principal component analysis. The identified eight best-performing genotypes could be useful to develop improved nitrogen efficient cultivars. Genome-wide association analysis resulted in 32 marker-trait associations (MTAs) under four treatments. Markers namely DArT-45481731 , DArT-17812864 , DArT-15670279 and DArT-45482737 were found consistent. Protein-protein interaction networks of loci associated with MTAs were related to fatty acid and branched-chain amino acid metabolism and histone modifications.

High performance electrochemical CO2 reduction over Pd decorated cobalt containing nitrogen doped carbon.

Efficient electrocatalytic CO2 reduction reaction (eCO2RR) to various products, such as carbon monoxide (CO), is crucial for mitigating greenhouse gas emissions and enabling renewable energy storage. In this article, we introduce Pd nanoparticles which are deposited over in-house synthesized nitrogen doped tubular carbon (NC) whose ends are blocked with cobalt oxide (CoOx). This composite material is denoted as Pd@CoOx/NC. Among the series of synthesized electrocatalysts, the optimum ratio (Pd@CoOx/NC1) within this category exhibits exceptional performance, manifesting an 81% faradaic efficiency (FE) for CO generation which was quantitatively measured using a gas chromatograph. This remarkable efficiency can be attributed to several scientific factors. Firstly, the presence of Pd nanoparticles provides active sites for CO2 reduction. Secondly, the NC offer enhanced electrical conductivity and facilitate charge transfer during the reaction. Thirdly, the CoOx capping at the ends of the NC serves to stabilize the catalyst, favoring the formation of CO. The remarkable selectivity of the catalyst is further confirmed by the qualitative CO detection method using PdCl2 strips. Pd@CoOx/NC1 exhibits a high current density of 55 mA cm-2 and a low overpotential of 251 mV, outperforming Pd decorated multiwalled carbon nanotubes (Pd@MWCNTs) which shows a higher overpotential of 481 mV. Pd@CoOx/NC1 shows long-term stability at different potentials and rapid reaction kinetics. These findings highlight Pd@CoOx/NC1 as promising CO2 reduction catalysts, with implications for sustainable energy conversion techniques.

Analytical solution of fuzzy heat problem in two-dimensional case under Caputo-type fractional derivative.

This work aims to investigate the analytical solution of a two-dimensional fuzzy fractional-ordered heat equation that includes an external diffusion source factor. We develop the Sawi homotopy perturbation transform scheme (SHPTS) by merging the Sawi transform and the homotopy perturbation scheme. The fractional derivatives are examined in Caputo sense. The novelty and innovation of this study originate from the fact that this technique has never been tested for two-dimensional fuzzy fractional ordered heat problems. We presented two distinguished examples to validate our scheme, and the solutions are in fuzzy form. We also exhibit contour and surface plots for the lower and upper bound solutions of two-dimensional fuzzy fractional-ordered heat problems. The results show that this approach works quite well for resolving fuzzy fractional situations.

The dynamical perspective of soliton solutions, bifurcation, chaotic and sensitivity analysis to the (3+1)-dimensional Boussinesq model.

In this study, we examine multiple perspectives on soliton solutions to the (3+1)-dimensional Boussinesq model by applying the unified Riccati equation expansion (UREE) approach. The Boussinesq model examines wave propagation in shallow water, which is derived from the fluid dynamics of a dynamical system. The UREE approach allows us to derive a range of distinct solutions, such as single, periodic, dark, and rational wave solutions. Furthermore, we present the bifurcation, chaotic, and sensitivity analysis of the proposed model. We use planar dynamical system theory to analyze the structure and characteristics of the system's phase portraits. The current study depends on a dynamic structure that has novel and unexplored results for this model. In addition, we display the behaviors of associated physical models in 3-dimensional, density, and 2-dimensional graphical structures. Our findings demonstrate that the UREE technique is a valuable mathematical tool in engineering and applied mathematics for studying wave propagation in nonlinear evolution equations.

Applicability of SCoT markers in unraveling genetic variation and population structure among sugar beet (Beta vulgaris L.) germplasm.

BACKGROUND: Sugar beet (Beta vulgaris L.) holds significant importance as a crop globally cultivated for sugar production. The genetic diversity present in sugar beet accessions plays a crucial role in crop improvement programs. METHODS AND RESULTS: During the present study, we collected 96 sugar beet accessions from different regions and extracted DNA from their leaves. Genomic DNA was amplified using SCoT primers, and the resulting fragments were separated by gel electrophoresis. The data were analyzed using various genetic diversity indices, and constructed a population STRUCTURE, applied the unweighted pair-group method with arithmetic mean (UPGMA), and conducted Principle Coordinate Analysis (PCoA). The results revealed a high level of genetic diversity among the sugar beet accessions, with 265 bands produced by the 10 SCoT primers used. The percentage of polymorphic bands was 97.60%, indicating substantial genetic variation. The study uncovered significant genetic variation, leading to higher values for overall gene diversity (0.21), genetic distance (0.517), number of effective alleles (1.36), Shannon's information index (0.33), and polymorphism information contents (0.239). The analysis of molecular variance suggested a considerable amount of genetic variation, with 89% existing within the population. Using STRUCTURE and UPGMA analysis, the sugar beet germplasm was divided into two major populations. Structure analysis partitioned the germplasm based on the origin and domestication history of sugar beet, resulting in neighboring countries clustering together. CONCLUSION: The utilization of SCoT markers unveiled a noteworthy degree of genetic variation within the sugar beet germplasm in this study. These findings can be used in future breeding programs with the objective of enhancing both sugar beet yield and quality.

The fractional analysis of thermo-elasticity coupled systems with non-linear and singular nature.

It is mentioned that understanding linear and non-linear thermo-elasticity systems is important for understanding temperature, elasticity, stresses, and thermal conductivity. One of the most crucial aspects of the current research is the solution to these systems. The fractional form of several thermo-elastic systems is explored, and elegant solutions are provided. The solutions of fractional and integer thermo-elastic systems are further discussed using tables and diagrams. The closed contact between the LRPSM and exact solutions is displayed in the graphs. Plotting fractional problem solutions demonstrates their convergence towards integer-order problem solutions for suitable modeling. The tables confirm that greater precision is rapidly attained as the terms of the derived series solution increase. The faster convergence and stability of the suggested method support its modification for other fractional non-linear complex systems in nature.

Predicting current and future habitat of Indian pangolin (Manis crassicaudata) under climate change.

Climate change is among the greatest drivers of biodiversity loss, threatening up to 15-30% of described species by the end of the twenty-first century. We estimated the current suitable habitat and forecasted future distribution ranges of Indian pangolin (Manis crassicaudata) under climate change scenarios. We collected occurrence records of Indian pangolin using burrow counts, remote camera records and previously published literature in Pakistan during 2021-2023. We downloaded bioclimatic data for current (1970-2000) and future (2041-2060, 2061-2080, 2081-2100) climate scenarios from the WorldClim database using the Hadley Global Environment Model (HadGEM3-GC31-LL). We used MaxEnt software to predict current and future distributions of Indian pangolin, then computed the amount of habitat lost, gained, and unchanged across periods. We obtained 560 Indian pangolin occurrences overall, 175 during the study, and 385 from our literature search. Model accuracy was very good (AUC = 0.885, TSS = 0.695), and jackknife tests of variable importance showed that the contribution of annual mean temperature (bio1) was greatest (33.4%), followed by the mean temperature of the coldest quarter (bio-12, 29.3%), temperature seasonality (bio 4, 25.9%), and precipitation seasonality (bio 15, 11.5%). The maxent model predicted that during the current time period (1970-2000) highly suitable habitat for Indian pangolin was (7270 km2, 2.2%), followed by moderately suitable (12,418 km2, 3.7%), less suitable (49,846 km2, 14.8%), and unsuitable habitat (268,355 km2, 79.4%). Highly suitable habitat decreased in the western part of the study area under most SSPs and in the central parts it declined under all SSPs and in future time periods. The predicted loss in the suitable habitat of the Indian pangolin was greatest (26.97%) under SSP 585 followed by SSP 126 (23.67%) during the time 2061-2080. The gain in suitable habitat of Indian pangolin was less than that of losses on average which ranged between 1.91 and 13.11% under all SSPs during all time periods. While the stable habitat of the Indian pangolin ranged between 64.60 and 83.85% under all SSPs during all time periods. Our study provides the current and future habitat ranges of Indian pangolin in the face of a changing climate. The findings of our study could be helpful for policymakers to set up conservation strategies for Indian pangolin in Pakistan.

Vulnerability detection in Java source code using a quantum convolutional neural network with self-attentive pooling, deep sequence, and graph-based hybrid feature extraction.

Software vulnerabilities pose a significant threat to system security, necessitating effective automatic detection methods. Current techniques face challenges such as dependency issues, language bias, and coarse detection granularity. This study presents a novel deep learning-based vulnerability detection system for Java code. Leveraging hybrid feature extraction through graph and sequence-based techniques enhances semantic and syntactic understanding. The system utilizes control flow graphs (CFG), abstract syntax trees (AST), program dependencies (PD), and greedy longest-match first vectorization for graph representation. A hybrid neural network (GCN-RFEMLP) and the pre-trained CodeBERT model extract features, feeding them into a quantum convolutional neural network with self-attentive pooling. The system addresses issues like long-term information dependency and coarse detection granularity, employing intermediate code representation and inter-procedural slice code. To mitigate language bias, a benchmark software assurance reference dataset is employed. Evaluations demonstrate the system's superiority, achieving 99.2% accuracy in detecting vulnerabilities, outperforming benchmark methods. The proposed approach comprehensively addresses vulnerabilities, including improper input validation, missing authorizations, buffer overflow, cross-site scripting, and SQL injection attacks listed by common weakness enumeration (CWE).

Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.).

Industrial hemp (Cannabis sativa L.) is a highly recalcitrant plant under in vitro conditions that can be overcome by employing external stimuli. Hemp seeds were primed with 2.0-3.0% hydrogen peroxide (H2O2) followed by culture under different Light Emitting Diodes (LEDs) sources. Priming seeds with 2.0% yielded relatively high germination rate, growth, and other biochemical and enzymatic activities. The LED lights exerted a variable impact on Cannabis germination and enzymatic activities. Similarly, variable responses were observed for H2O2 × Blue-LEDs combination. The results were also analyzed by multiple regression analysis, followed by an investigation of the impact of both factors by Pareto chart and normal plots. The results were optimized by contour and surface plots for all parameters. Response surface optimizer optimized 2.0% H2O2 × 918 LUX LEDs for maximum scores of all output parameters. The results were predicted by employing Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Moreover, the validity of these models was assessed by using six different performance metrics. MLP performed better than RF and XGBoost models, considering all six-performance metrics. Despite the differences in scores, the performance indicators for all examined models were quite close to each other. It can easily be concluded that all three models are capable of predicting and validating data for cannabis seeds primed with H2O2 and grown under different LED lights.