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Encouraging older and newer mobility alternatives to standard privately owned cars, such as cycling and autonomous vehicles, is necessary to reduce pollution, enhance safety, increase transportation efficiency, and create a more sustainable urban environment. Implementing mobility plans that identify the use of different transport modes in their confidence intervals can lead to the development of smarter and more efficient cities, where all citizens can benefit from safe and environmentally friendly streets. This research aims to provide insights into designing urban streets that seamlessly integrate autonomous vehicles and cyclists, promoting sustainable mobility while ensuring urban transport efficiency. With this aim, the research identifies and prioritizes the factors that are relevant to street design as well as the appropriate strategies to address them. Our methodology combines Multi-Criteria Decision-Making (MCDM) with Game theory to identify and realize the most convenient conditions for this integration. Initially, the basic factors were identified using the value-interval fuzzy Delphi method. Following this, the factors were weighted with the interval-fuzzy Analytic Network Process (ANP), and the cause-and-effect variables were evaluated using the interval-fuzzy Decision-Making Trial and Evaluation Laboratory ANP (DANP). Finally, Game theory was employed to determine the optimal model for addressing these challenges. The results indicate that safety emerged as the most significant factor and two optimal strategies were identified; the integration of green infrastructure and smart technology.

This study explores previous research efforts concerning prediction models related to the textile and polymer industry, especially garment seam strength, emphasizing critical parameters such as stitch density, fabric GSM, thread type, thread count, stitch classes, and seam types. These parameters play a pivotal role in determining the durability and overall quality of denim jeans based on cellulosic polymer. A significant focus is dedicated to the mathematical computational models employed for predicting seam strength in five-pocket denim jeans. Herein, the discussion poses the application of AI for manufacturing industries, especially for textile and clothing sectors, and highlights the importance of using a machine learning prediction model for sewing thread consumption, seam strength analysis, and seam performance analysis. Therefore, the authors suggest the significant importance of the machine learning prediction model, as future trends anticipate advancements in AI-driven methodologies, potentially leading to high-profile predictions and superior manufacturing processes. The authors also describe the limitation of AI and address a comprehensive model of risk outlines of AI in the manufacturing-based industries, especially the garments industry. Put simply, this review serves as a bridge between the realms of AI, mathematics, and textile engineering, providing a clear understanding of how artificial-neural-network-based models will be shaping the future of seam strength prediction in the denim manufacturing landscape. This type of evolution, based on ANN, will support and enhance the accuracy and efficiency of seam strength predictions by allowing models to discern intricate patterns and relationships within vast and diverse datasets.

Background and Methods: A primary survey of a multi-national sample of higher institutional educators has been conducted to investigate the determinants of the adoption of management simulation games. The research model is developed based on the technology acceptance model (TAM) and technology–organisation–environment (TOE). Structural equation modelling has been used to test the research model. The paper focuses on the use of management simulation games among educators in higher educational institutions (HEIs). Its purpose is to determine the factors influencing educators’ use of these games from both individual and institutional perspectives. The TAM captures the individual perspective, while the TOE framework addresses the institutional perspective. The structural equation model confirmed most of the TAM hypotheses. Results: However, the model does not support the hypotheses regarding the relationship between perceived ease of use and attitude toward usage or between perceived ease of use and perceived usefulness. The technological factors within the TOE framework did not significantly impact perceived usefulness, only perceived ease of use. Conclusion: The combined TAM-TOE model has demonstrated valid representativeness. Previous research on the usage of management simulation games has primarily focused on students, neglecting the broader perspective of educators in HEIs in business and economics within both the TAM and TOE frameworks.

The current investigation delved into the utilization of cattle and municipal sanitary inocula for anaerobic digestion of poultry wastes, addressing a crucial and pragmatic challenge in waste management. The emphasis on poultry waste is pertinent due to its well-documented impediments in anaerobic digestion, attributed to heightened levels of ammonia and volatile fatty acids (VFAs). The strategic selection of cattle and municipal sanitary inocula suggests an approach aimed at bolstering the anaerobic digestion process. In this study, we evaluated the use of cattle and municipal sanitary inocula for the anaerobic digestion of various poultry wastes, which is often challenged by high levels of ammonia and volatile fatty acids (VFAs). The substrates tested included belt waste (Poultry A), poultry litter plus feed residues (Poultry B), tray hatchery ©, and stillage. These substrates were processed in two continuous stirred tank reactors (CSTRs), R-1 (with antibiotic monensin) and R-2 (without monensin). Initially, both reactors operated with the same hydraulic retention time (HRT), using a substrate ratio of stillage: belt: tray hatchery (S:B:T) of 70:15:15. On the 41st day, the HRT was adjusted to 20 days, and the substrate ratio was changed to S:A:T 70:40:40. The specific methane yield for R-1 started at 10.768 L g−1 COD, but decreased to 2.65 L g−1 COD by the end of the experiment. For R-2, the specific methane yield varied between 0.45 L g−1 COD and 0.243 L g−1 COD. Microbial composition in the reactors changed over time. In R-1, bacteroides were consistently dominant, while firmicutes were less abundant compared to R-2. Proteobacteria were initially low in abundance, but spirochetes were found in both reactors throughout the experiment. The study concluded that Poultry B substrates, due to their rich nutrient and trace element composition, are suitable for biogas plants. Municipal sanitary inocula also showed promise due to their resilience in high ammonia concentrations. Further research into biofilm interactions is recommended to better understand microbial responses to high ammonia concentrations, which can lead to propionate production in anaerobic digestion (AD).

The rapid growth of e-learning increased the use of digital reviews to influence consumer purchases. In a pioneering approach, we employed AI-powered eye tracking to evaluate the accuracy of predictions in forecasting purchasing patterns. This study examined customer perceptions of negative, positive, and neutral reviews by analysing emotional valence, review content, and perceived credibility. We measured ‘Attention’, ‘Engagement’, ‘Clarity’, ‘Cognitive Demand’, ‘Time Spent’, ‘Percentage Seen’, and ‘Focus’, focusing on differences across review categories to understand their effects on customers and the correlation between these metrics and navigation to other screen areas, indicating purchasing intent. Our goal was to assess the predictive power of online reviews on future buying behaviour. We selected Udemy courses, a platform with over 70 million learners. Predict (version 1.0.), developed by Stanford University, was used with the algorithm on the consumer neuroscience database (n = 180,000) from Tobii eye tracking (Tobii X2-30, Tobii Pro AB, Danderyd, Sweden). We utilised R programming, ANOVA, and t-tests for analysis. The study concludes that AI neuromarketing techniques in digital feedback analysis offer valuable insights for educators to tailor strategies based on review susceptibility, thereby sparking interest in the innovative possibilities of using AI technology in neuromarketing.

The computational study of fixed-point problems in distance spaces is an active and important research area. The purpose of this paper is to construct a new iterative scheme in the setting of Banach space for approximating solutions of fixed-point problems. We first prove the strong convergence of the scheme for a general class of contractions under some appropriate assumptions on the domain and a parameter involved in our scheme. We then study the qualitative aspects of our scheme, such as the stability and order of convergence for the scheme. Some nonlinear problems are then considered and solved numerically by our new iterative scheme. The numerical simulations and graphical visualizations prove the high accuracy and stability of the new fixed-point scheme. Eventually, we solve a 2D nonlinear Volterra Integral Equation (VIE) via the application of our main outcome. Our results improve many related results in fixed-point iteration theory.

Lead-based piezoelectric materials cause many environmental problems, regardless of their exceptional performance. To overcome this issue, a lead-free piezoelectric composite material was developed by incorporating different percentages of carbon fiber (CF) into the ceramic matrix of Bismuth Sodium Titanate (BNT) by employing the microwave sintering technique. The aim of this study was also to evaluate the impact of microwave sintering on the microstructure and the electrical behavior of the carbon-fiber-reinforced Bi0.5Na0.5TiO3 composite (BNT-CF). A uniform distribution of the CF and increased densification of the BNT-CF was achieved, leading to improved piezoelectric properties. X-ray diffraction (XRD) showed the formation of a phase-pure crystalline perovskite structure consisting of CF and BNT. A Field Emission Scanning electron microscope (FESEM) revealed that utilizing microwave sintering at lower temperatures and shorter dwell times results in a superior densification of the BNT-CF. Raman Spectroscopy confirmed the perovskite structure of the BNT-CF and the presence of a Morphotropic Phase Boundary (MPB). An analysis of nanohardness indicated that the hardness of the BNT-CF increases with the increasing amount of CF. It is also revealed that the electrical conductivity of the BNT-CF at a low frequency is significantly influenced by the amount of CF and the temperature. Moreover, an increase in the carbon fiber concentration resulted in a decrease in dielectric properties. Finally, a lead-free piezoelectric BNT-CF showing dense and uniform microstructure was developed by the microwave sintering process. The promising properties of the BNT-CF make it attractive for many industrial applications.

This study aims to improve college magazines, making them more engaging and user-friendly. We combined eye-tracking technology with artificial intelligence to accurately predict consumer behaviours and preferences. Our analysis included three college magazines, both online and in PDF format. We evaluated user experience using neuromarketing eye-tracking AI prediction software, trained on a large consumer neuroscience dataset of eye-tracking recordings from 180,000 participants, using Tobii X2 30 equipment, encompassing over 100 billion data points and 15 consumer contexts. An analysis was conducted with R programming v. 2023.06.0+421 and advanced SPSS statistics v. 27, IBM. (ANOVA, Welch’s Two-Sample t-test, and Pearson’s correlation). Our research demonstrated the potential of modern eye-tracking AI technologies in providing insights into various types of attention, including focus, engagement, cognitive demand, and clarity. The scientific accuracy of our findings, at 97–99%, underscores the reliability and robustness of our research, instilling confidence in the audience. This study also emphasizes the potential for future research to explore automated datasets, enhancing reliability and applicability across various fields and inspiring hope for further advancements in the field.

In the realm of digital transformation, effective leadership and motivation are pivotal for organisations navigating the complexities of today’s systems. This study explores the intersection of intrinsic motivation and mental well-being among mature students—an analogy that sheds light on strategies applicable to organisational contexts. In developed nations like the UK, mental health for mature students is increasingly recognised as a crucial component of their educational journey. Mature students, who typically enrol in higher education after an educational gap and upon turning 21, often face specific challenges that can impact their mental well-being while pursuing academic goals. The primary objective of our study was to assess the relationship between intrinsic motivation and the mental well-being of mature students. The study included 248 full-time undergraduate mature students enrolled in private higher education institutions in the UK. These participants were 21 years and older. The research employed two measurement scales: the four-item Intrinsic Motivation Scale, adapted from Jaramillo, and the Warwick–Edinburgh Mental Well-Being Scale. Data collection utilised online Google Forms with multiple choice self-report formatted questions, and our analysis involved both descriptive and inferential statistics. Our research revealed a significant positive correlation between variables of intrinsic motivation and mental well-being. Confirmatory factor analysis (CFA) results confirmed the construct in the model. The results indicated that intrinsic motivation significantly predicts mental well-being among mature students (Cohen’s effect size value, f2 = 0.54). Recognising and addressing the unique difficulties individuals encounter and providing appropriate support can enhance their well-being and contribute to the overall success of the higher education community.

Eye-tracking technology has emerged as a valuable tool for evaluating cognitive load in online learning environments. This study investigates the potential of AI-driven consumer behaviour prediction eye-tracking technology to improve the learning experience by monitoring students’ attention and delivering real-time feedback. In our study, we analysed two online lecture videos used in higher education from two institutions: Oxford Business College and Utrecht University. We conducted this analysis to assess cognitive demands in PowerPoint presentations, as this directly affects the effectiveness of knowledge dissemination and the learning process. We utilised a neuromarketing-research consumer behaviour eye-tracking AI prediction software called ‘Predict’, which employs an algorithm constructed on the largest neuroscience database (comprising previous studies conducted on live participants n = 180,000 with EEG and eye-tracking data). The analysis for this study was carried out using the programming language R, followed by a series of t-tests for each video and Pearson’s correlation tests to examine the relationship between ocus and cognitive demand. The findings suggest that AI-powered eye-tracking systems have the potential to transform online learning by providing educators with valuable insights into students’ cognitive processes and enabling them to optimise instructional materials for improved learning outcomes.

The Great Reset is a concept proposed by the World Economic Forum to rebuild the global economy sustainably following the COVID-19 pandemic, emphasising stakeholder capitalism, greener practices, and more equitable social contracts. However, most of the literature focuses on the role of business and government actors in the Great Reset. At the same time, research that considers the role of higher education institutions (HEIs) is lacking. However, HEIs have the significant capacity to support various aspects of the Great Reset. In this work, we examine different mechanisms by which HEIs could bring change, such as fostering the growth of workforce skills, promoting entrepreneurship and innovation, participating in community development and others. A survey on a sample of university professors, postdoctoral researchers, and research assistants has been conducted with the goal of evaluating the degree to which HEIs have played a role in influencing economic recovery after COVID-19, leveraging the industry 4.0, enhancing regional development, revitalising global cooperation, formulating sustainable business models, restoring environmental well-being, and restructuring social contracts, skills, and employment opportunities. A two-step cluster analysis has been conducted, indicating that HEIs played different roles in the Great Reset process, being the Leaders, Followers and Laggers. We found a significant difference between the various groups of respondents according to their scientific field, position, and region regarding the perception of the role played by their HEIs in influencing economic recovery after COVID-19. The position of the respondents contributed to their perception of the role that their HEIs participated in the process of the Great Reset, with the assistant professors perceiving the most often that their HEIs are Leaders. Additionally, the scientific field and region of the HEI also impacted their role in the Great Reset, with the HEIs from technology and countries outside of Europe being the Leaders most often. On the other hand, Followers and Laggers were equally from social sciences and other fields and European countries.

Dentin pulp has a complex function as a major unit in maintaining the vitality of teeth. In this sense, the Wnt/β-Catenin pathway has a vital part in tooth development, maintenance, repair, and regeneration by controlling physiological activities such as growth, differentiation, and migration. This pathway consists of a network of proteins, such as Wnt signaling molecules, which interact with receptors of targeted cells and play a role in development and adult tissue homeostasis. The Wnt signals are specific spatiotemporally, suggesting its intricate mechanism in development, regulation, repair, and regeneration by the formation of tertiary dentin. This review provides an overview of the recent advances in the Wnt/β-Catenin signaling pathway in dentin and pulp regeneration, how different proteins, molecules, and ligands influence this pathway, either upregulating or silencing it, and how it may be used in the future for clinical dentistry, in vital pulp therapy as an effective treatment for dental caries, as an alternative approach for root canal therapy, and to provide a path for therapeutic and regenerative dentistry.

In mammals, specificity protein 1 (SP1) was the first Cys2-His2 zinc finger transcription factor to be isolated within the specificity protein and Krüppel-like factor (Sp/KLF) gene family. SP1 regulates gene expression by binding to Guanine–Cytosine (GC)-rich sequences on promoter regions of target genes, affecting various cellular processes. Additionally, the activity of SP1 is markedly influenced by posttranslational modifications, such as phosphorylation, acetylation, glycosylation, and proteolysis. SP1 is implicated in the regulation of apoptosis, cell hypertrophy, inflammation, oxidative stress, lipid metabolism, plaque stabilization, endothelial dysfunction, fibrosis, calcification, and other pathological processes. These processes impact the onset and progression of numerous cardiovascular disorders, including coronary heart disease, ischemia-reperfusion injury, cardiomyopathy, arrhythmia, and vascular disease. SP1 emerges as a potential target for the prevention and therapeutic intervention of cardiac ailments. In this review, we delve into the biological functions, pathophysiological mechanisms, and potential clinical implications of SP1 in cardiac pathology to offer valuable insights into the regulatory functions of SP1 in heart diseases and unveil novel avenues for the prevention and treatment of cardiovascular conditions.

The main purpose of this work is to study the third Hankel determinant for classes of Bernoulli lemniscate-related functions by introducing new subclasses of star-like functions represented by SLλ* and RLλ. In many geometric and physical applications of complex analysis, estimating sharp bounds for problems involving the coefficients of univalent functions is very important because these coefficients describe the fundamental properties of conformal maps. In the present study, we defined sharp bounds for function-coefficient problems belonging to the family of SLλ* and RLλ. Most of the computed bounds are sharp. This study will encourage further research on the sharp bounds of analytical functions related to new image domains.

Bassia indica (Wight) A.J. Scott is an Indian origin plant with documented medicinal and nutritional value, but has not been fully characterized yet. The present study was designed to establish pharmacognostic standards for the proper identification of the B. indica plant and its chemical characterization. The plant was standardized with World Health Organization (WHO) standardization tools and chemically characterized by Fourier transform infrared spectroscopy (FTIR) and gas chromatography-mass spectroscopy (GC-MS) analysis. Antibacterial potential was assessed by the zone of inhibition and minimum inhibitory concentration (MIC), and molecular docking studies were also performed. Pharmacognostic evaluation established the macroscopic and microscopic parameters for the identification of whole plant and its powder. Physicochemical parameters were also set forth while quantitative phytochemical analysis showed that the ethyl acetate fraction had the highest quantity of phenols, flavonoids, and tannins. FTIR analysis showed several functional groups such as phenols, alkanes, and alcohols while 55 phytochemicals were identified in the GC-MS analysis of the crude fraction. The crude extract and other fractions showed marked antibacterial activity, while the ethyl acetate fraction showed the least MIC (1.95–31.25 mg/mL). Phytochemicals identified in the GC-MS showed good molecular docking interactions against the DNA gyrase subunit B of bacteria with binding energies ranging from −4.2 to −9.4 kcal/mol. The current study describes the pharmacognostic characterization and phytochemical profiling of B. indica and provides scientific evidence to support its use in infections.

Health information systems (HISs) are instrumental in improving the efficiency and effectiveness of hospital operations, from managing patient data to enhancing decision-making processes. This study, which holds significant implications for the healthcare industry, aimed to identify the factors that influence users’ intentions to use HISs. The research involved interviews with healthcare professionals licensed to use the HIS of a public hospital. The survey, conducted in 2020, received 113 responses. Statistical methods of descriptive analysis, correlation, and multiple linear regression were used. Two models were examined. The first model investigated the relationship between the dependent variable of the intention to use an HIS and the independent variables of perceived time savings and perceived privacy protection. The second model explored the impact of age and education as control variables in the connection between the intention to use an HIS and time savings and privacy protection. A significant moderate positive correlation was found between the intention to use an HIS and perceived time savings, while a significant weak positive correlation was detected between the intention to use an HIS and education. Regression analysis in the first model revealed a significant connection between the intention to use an HIS and perceived time savings. However, the perceived privacy protection variable did not show a significant relationship with the dependent variable. The second model showed statistical significance in the relationship between the intention to use an HIS and education, but not with the second control variable, age. It can be concluded that education strengthens the positive impact of perceived time savings on the intention to use a health information system (HIS), a finding that has immediate and practical implications for healthcare professionals and researchers in the field of health information systems and healthcare management, underlining the importance of this research in advancing the understanding and adoption of HISs in healthcare settings.

Job satisfaction has been widely studied across several healthcare disciplines and is correlated with important outcomes such as job performance and employee mental health. However, there is limited research on job satisfaction among medical laboratory professionals (MLPs), a key healthcare group that aids in diagnosis, treatment, and patient care. The objective of this study is to examine the demographic and psychosocial factors associated with job satisfaction for MLPs in Ontario, Canada during the COVID-19 pandemic. A survey was administered to medical laboratory technologists (MLTs) and medical laboratory technicians/assistants (MLT/As) in Ontario, Canada. The survey included demographic questions and items from the Copenhagen Psychosocial Questionnaire, third edition. Binary logistic regressions were used to examine the association between job satisfaction and demographic variables and psychosocial work factors. There were 688 MLPs included in the analytic sample (72.12% response rate). Having a higher sense of community at work was correlated with higher job satisfaction in both MLT (OR = 2.22, 95% CI: 1.07–4.77) and MLT/A (OR = 3.85, 95% CI: 1.12–14.06). In addition, having higher stress was correlated with lower job satisfaction in both MLT (OR = 0.32, 95% CI: 0.18–0.57) and MLT/A (OR = 0.26, 95% CI: 0.10–0.66). This study provides preliminary evidence on factors associated with job satisfaction in MLT and MLT/A. The findings can be used to support organizational practices and policies to improve psychosocial work factors.

The COVID-19 pandemic has changed many aspects of work and daily life, with higher education being greatly affected, especially in remote teaching, work, and digital collaboration. Most of these changes are retained in the post-COVID-19 era, e.g., remote work has enabled greater access to educational opportunities and contributed to a more inclusive and diverse workforce. To investigate to what extent these changes impact higher education in the post-pandemic era, we have conducted qualitative research on a sample of 12 professors from Slovenia working in higher education, selected based on their extensive research, professional experience, and significant contributions to the field. The Delphi method was used for this study since its iterative process refines ideas in each round based on feedback from the previous one. Participants were given a five-day window to express their views and share their expertise. The responses to the open-ended questions were examined using qualitative content analysis. Research indicates that pedagogical and organisational characteristics such as the ability to adapt to changes, the capacity for resilience, and the willingness to embrace digital transformation are crucial for preserving long-term changes induced by pandemics.

Reinforced concrete buildings are prone to collapse during seismic events due to the brittle shear failure of non-seismic beam-column joints (BCJ). In this study, two one-third scale reinforced concrete (RC) frames incorporating various non-seismic details were tested under lateral cyclic loading. One of the RC frames was used as control, while the other was strengthened using externally bonded carbon-fiber-reinforced polymer (CFRP) sheets in a L-Shaped configuration with particular attention to anchorage to evade debonding. For the strengthening process, L-shaped CFRP sheets were bonded to the inner face of columns, extended on beams both above and below the joint up to a hinge length. To avert debonding, the L-shaped CFRP sheets were fully wrapped with CFRP sheets around the column, both near the joint and at the end of the sheet. The sheets were also wrapped around the beam, through two slots in the slab that were adjacent to the beam-column interface and at the far end of the sheet. Test results confirmed that the installation of CFRP sheets in an L-shaped configuration altered the brittle-shear failure mechanism of the beam-column joints to a ductile failure by repositioning the hinges away from the columns. Additionally, the proposed anchorage method successfully eradicated the debonding and peel-off of the CFRP sheets. Moreover, strengthening with the CFRP sheets in the L-shaped configuration enhanced the strength and ductility of the RC frame by 45% and 43%, respectively. According to the findings of this study, the application of L-shaped CFRP sheets proved effective in strengthening RC frame structures.

This research investigates the impact of applying neuromarketing techniques to three practical examples of higher education (HE) branding: an official college website page, an official college Facebook page, and recorded online video lectures used for teaching at HE institutions. The study was conducted in three different HE institutions with a representative sample of 720 participants, with n = 529 used for testing the CARE college website, n = 59 for testing the HAZEF Facebook page, and n = 132 for testing the emotional response of students studying online. To assess the participants’ emotional responses, this study utilized automated facial coding through a webcam (15 Hz) and considered mood intensities. Additionally, a sentiment analysis was employed to verify the survey results and determine any discrepancies in the cognitive response. By analyzing gaze activity, movement patterns, and emotional responses, valuable insights were gained into students’ behaviors and preferences. This study recommends incorporating neuromarketing research into HE branding and online teaching to enhance students’ learning experiences. Overall, this study contributes to the understanding of human expectations and behaviors in response to online teaching and provides valuable insights for HE institutions in Europe.

Pharmaceuticals have emerged as a new class of ecological pollutants and have majorly contributed to harmful effects on the environment and human health. The presence of these pharmaceuticals in wastewater treatment plants, ground, and seawater has been reported widely. Organic dyes and other organic contaminants which are being considered as emerging contaminants are now in the race among the top organic pollutants that need effective treatment. Removal of these contaminants via green adsorbents has become an essential requirement towards a green and cleaner environment. Herein, we report the efficacy of the novel greener porous graphene obtained via the near-green synthesis method as an adsorbent material for treating seven organic pollutants: Methyl orange, Methyl red, Rhodamine-B, Ciprofloxacin, Atenolol, Ibuprofen, and Carbamazepine. Batch tests were conducted to investigate the effect of adsorption time and varying adsorbent dosages. The obtained greener porous graphene showed fast kinetics, which was determined to be guided by pseudo second-order kinetics and the maximum pollutant removal efficiency (>80%) was seen at a high adsorbent dosage (2 mL injected from a 5 g/L solution). Furthermore, the nonlinear adsorption modeling confirmed that the greener porous graphene followed the Langmuir model for the dye rhodamine-B sorption and the Freundlich model for all the other six contaminants. This greener porous graphene can be considered an effective adsorbent for the removal of organic pollutants in wastewater.

The present study reports the one-step synthesis of several 3-formyl-4-hydroxycouramin-derived enamines (4a–4i) in good yields (65–94%). The characterization of the synthesized compounds was carried out via advanced analytical and spectroscopic techniques, such as melting point, electron impact mass spectrometry (EI-MS), 1H-NMR, 13C-NMR, elemental analysis, FTIR, and UV-Visible spectroscopy. The reaction conditions were optimized, and the maximum yield was obtained at 3–4 h of reflux of the reactants, using 2-butanol as a solvent. The potato disc tumor assay was used to assess Agrobacterium tumefaciens-induced tumors to evaluate the anti-tumor activities of compounds (4a–4i), using Vinblastine as a standard drug. The compound 4g showed the lowest IC50 value (1.12 ± 0.2), which is even better than standard Vinblastine (IC50 7.5 ± 0.6). For further insight into their drug actions, an in silico docking of the compounds was also carried out against the CDK-8 protein. The binding energy values of compounds were found to agree with the experimental results. The compounds 4g and 4h showed the best affinities toward protein, with a binding energy value of −6.8 kcal/mol.

Wheat stripe rust disease (WRD) is extremely detrimental to wheat crop health, and it severely affects the crop yield, increasing the risk of food insecurity. Manual inspection by trained personnel is carried out to inspect the disease spread and extent of damage to wheat fields. However, this is quite inefficient, time-consuming, and laborious, owing to the large area of wheat plantations. Artificial intelligence (AI) and deep learning (DL) offer efficient and accurate solutions to such real-world problems. By analyzing large amounts of data, AI algorithms can identify patterns that are difficult for humans to detect, enabling early disease detection and prevention. However, deep learning models are data-driven, and scarcity of data related to specific crop diseases is one major hindrance in developing models. To overcome this limitation, in this work, we introduce an annotated real-world semantic segmentation dataset named the NUST Wheat Rust Disease (NWRD) dataset. Multileaf images from wheat fields under various illumination conditions with complex backgrounds were collected, preprocessed, and manually annotated to construct a segmentation dataset specific to wheat stripe rust disease. Classification of WRD into different types and categories is a task that has been solved in the literature; however, semantic segmentation of wheat crops to identify the specific areas of plants and leaves affected by the disease remains a challenge. For this reason, in this work, we target semantic segmentation of WRD to estimate the extent of disease spread in wheat fields. Sections of fields where the disease is prevalent need to be segmented to ensure that the sick plants are quarantined and remedial actions are taken. This will consequently limit the use of harmful fungicides only on the targeted disease area instead of the majority of wheat fields, promoting environmentally friendly and sustainable farming solutions. Owing to the complexity of the proposed NWRD segmentation dataset, in our experiments, promising results were obtained using the UNet semantic segmentation model and the proposed adaptive patching with feedback (APF) technique, which produced a precision of 0.506, recall of 0.624, and F1 score of 0.557 for the rust class.

Radioresistant microorganisms possess inimitable capabilities enabling them to thrive under extreme radiation. However, the existence of radiosensitive microorganisms inhabiting such an inhospitable environment is still a mystery. The current study examines the potential of radioresistant microorganisms to protect radiosensitive microorganisms in harsh environments. Bacillus subtilis strain ASM-1 was isolated from the Thal desert in Pakistan and evaluated for antioxidative and radioprotective potential after being exposed to UV radiation. The strain exhibited 54.91% survivability under UVB radiation (5.424 × 103 J/m2 for 8 min) and 50.94% to mitomycin-C (4 µg/mL). Extracellular fractions collected from ASM-1 extracts showed significant antioxidant potential, and chemical profiling revealed a pool of bioactive compounds, including pyrrolopyrazines, amides, alcoholics, and phenolics. The E-2 fraction showed the maximum antioxidant potential via DPPH assay (75%), and H2O2 scavenging assay (68%). A combination of ASM-1 supernatant with E-2 fraction (50 µL in a ratio of 2:1) provided substantial protection to radiosensitive cell types, Bacillus altitudinis ASM-9 (MT722073) and E. coli (ATCC 10536), under UVB radiation. Docking studies reveal that the compound supported by literature against the target proteins have strong binding affinities which further inferred its medical uses in health care treatment. This is followed by molecular dynamic simulations where it was observed among trajectories that there were no significant changes in major secondary structure elements, despite the presence of naturally flexible loops. This behavior can be interpreted as a strategy to enhance intermolecular conformational stability as the simulation progresses. Thus, our study concludes that Bacillus subtilis ASM-1 protects radiosensitive strains from radiation-induced injuries via biofilm formation and secretion of antioxidative and radioprotective compounds in the environment.

Oilseed crops play a vital role in the economy of Pakistan, yet the production of oilseeds is far less than the demand for them. White mustard (Sinapis alba L.) is an emerging crop, that belongs to the Brassicaceae family. It is considered to be an alternative to all other oilseed crops for dry temperate climates. White mustard develops rapidly, and has a large canopy and deep rooting system; hence, it can draw up nutrients from deeper layers. This study aimed to evaluate the influence of nitrogen on S. alba agro-morphological attributes and enhance nutrient use efficiencies. During the Rabi seasons of 2019-20 and 2020-21, an experiment was conducted at the University Research Farm, Chakwal Road, Rawalpindi. The study included seven treatments, i.e., T1-Control, T2-20, T3-40, T4-60, T5-80, T6-100, and T7-120 kg·ha−1. Each treatment was replicated three times, and the study utilized a randomized complete block design (RCBD). The results revealed that nitrogen at 100 kg·ha−1 was the optimal concentration and significantly increased the agro-morphological parameters, i.e., plant height (47.01%), primary branches (41.36%), secondary branches (45.33%), 1000 seed weight (54.35%), siliques/plant (41.57%), seeds/silique (52.30%) biological yield (68.38%), seed yield (54.90%), harvest index (11%), and oil yield (38.84%), as compared to the control. Moreover, protein contents and oil contents were significantly increased (5.15% and 6%, respectively), as compared to the control, while glucosinolate content was decreased (4.36%). Similarly, maximum agronomic efficiency (AE), apparent recovery efficiency (ARE) (53%), physiological efficiency (PE), and nutrient use efficiency (NUE) were also improved, as compared to the control. Hence, N application at a concentration of 100 kg·ha−1 can be recommended for S. alba under the present cropping system of Pothwar.

The pharmacological effectiveness of indoles, benzoxazepines and benzodiazepines initiated our synthesis of indole fused benoxazepine/benzodiazepine heterocycles, along with enhanced biological usefulness of the fused rings. Activated indoles 5, 6 and 7 were synthesized using modified Bischler indole synthesis rearrangement. Indole 5 was substituted with the trichloroacetyl group at the C7 position, yielding 8, exclusively due to the increased nucleophilic character of C7. When trichloroacylated indole 8 was treated with basified ethanol or excess amminia, indole acid 9 and amide 10 were yielded, respectively. Indole amide 10 was expected to give indole fused benoxazepine/benzodiazepine 11a/11b on treatment with alpha halo ester followed by a coupling agent, but when the reaction was tried, an unexpectedly rearranged novel product, 1,3-bezodiazine 12, was obtained. The synthetic compounds were screened for anticholinesterase and antibacterial potential; results showed all products to be very important candidates for both activities, and their potential can be explored further. In addition, 1,3-bezodiazine 12 was explored by DFT studies, Hirshfeld surface charge analysis and structural insight to obrain a good picture of the structure and reactivity of the products for the design of derivatised drugs from the novel compound.

In this present work, we demonstrated a spectral characterization of copper–iron (Cu-Fe) alloy using optical emission spectroscopy. The Cu-Fe alloy plasma was generated on the target sample surface by directing the laser pulse of Q-switched Nd: YAG of the second harmonic (2ω) with a 532 nm optical wavelength. The optical emission spectrum was acquired using five miniature spectrometers that lie within the wavelength range of 200–720 nm. The emission plasma was characterized by validating the local-thermodynamical equilibrium (LTE) as well as optically thin (OT) plasma condition. In addition, the LTE condition was verified using the McWhirter criterion, and the OT condition was validated by comparing theoretically calculated intensity ratios with experimental ones. Plasma parameters, including electron number density as well as plasma temperature, were estimated. In the first stage, the plasma temperature was estimated using the Boltzmann-plot method and the two-line method. The average calculated value of the plasma temperatures were 8014 ± 800 K and 8044 ± 800 K using the Boltzmann-plot and two-line methods, respectively. In the second stage, electron number density was estimated using the Saha–Boltzmann equation and stark-broadening method (SBM). The average number density calculated from the SBM was 2.73×1016 cm−3 and from the Saha–Boltzmann equation was 3.9×1016 cm−3, showing a good agreement. Finally, the comparative compositional analysis was performed using CF-LIBS, Boltzmann Intercept Method, EDX, and ICP-AES, which showed good agreement with that of the standard composition.

The newly FDA-approved drug, Axitinib, is an effective therapy against RTKs, but it possesses severe adverse effects like hypertension, stomatitis, and dose-dependent toxicity. In order to ameliorate Axitinib’s downsides, the current study is expedited to search for energetically stable and optimized pharmacophore features of 14 curcumin (1,7-bis(4-hydroxy-3-methoxyphenyl)hepta-1,6-diene-3,5-dione) derivatives. The rationale behind the selection of curcumin derivatives is their reported anti-angiogenic and anti-cancer properties. Furthermore, they possessed a low molecular weight and a low toxicity profile. In the current investigation, the pharmacophore model-based drug design, facilitates the filtering of curcumin derivatives as VEGFR2 interfacial inhibitors. Initially, the Axitinib scaffold was used to build a pharmacophore query model against which curcumin derivatives were screened. Then, top hits from pharmacophore virtual screening were subjected to in-depth computational studies such as molecular docking, density functional theory (DFT) studies, molecular dynamics (MD) simulations, and ADMET property prediction. The findings of the current investigation revealed the substantial chemical reactivity of the compounds. Specifically, compounds S8, S11, and S14 produced potential molecular interactions against all four selected protein kinases. Docking scores of −41.48 and −29.88 kJ/mol for compounds S8 against VEGFR1 and VEGFR3, respectively, were excellent. Whereas compounds S11 and S14 demonstrated the highest inhibitory potential against ERBB and VEGFR2, with docking scores of −37.92 and −38.5 kJ/mol against ERBB and −41.2 and −46.5 kJ/mol against VEGFR-2, respectively. The results of the molecular docking studies were further correlated with the molecular dynamics simulation studies. Moreover, HYDE energy was calculated through SeeSAR analysis, and the safety profile of the compounds was predicted through ADME studies.

The work here reflects synthesis, DFT studies, Hirshfeld charge analysis and crystal data exploration of pharmacologically important (R)-2-(2-(1,3-dioxoisoindolin-2-yl)propanamido)benzoic acid methyl ester (5) to understand its properties for further chemical transformations. The methyl anthranilate (2) was produced by the esterification of anthranilic acid in an acidic medium. The phthaloyl-protected alanine (4) was rendered by the fusion of alanine with phthalic anhydride at 150 °C, followed by coupling with (2) furnished isoindole (5). The characterization of products was performed using IR, UV-Vis, NMR and MS. Single-crystal XRD also verified the structure of (5) in which N-H⋯O bonding stabilizes the molecular configuration of (5), resulting in the formation of S(6) hydrogen-bonded loop. The molecules of isoindole (5) are connected in the form of dimers, and the π⋯π stacking interaction between aromatic rings further stabilizes the crystal packing. DFT studies suggest that HOMO is over the substituted aromatic ring, the LUMO is present mainly over the indole side, and nucleophilic and electrophilic corners point out the reactivity of the product (5). In vitro and in silico analysis of (5) shows its potential as an antibacterial agent targeting DNA gyrase and Dihydroorotase from E. coli and tyrosyl-tRNA synthetase and DNA gyrase from Staphylococcus aureus.

This paper presents a comprehensive model predictive control (CMPC) method to control a three-phase four-legged inverter (TP4LI) for PV systems. The proposed TP4LI model aims to predictively model and control switching frequency and higher voltage/current switching to reduce losses. The CMPC model can be operated in four modes, namely standard MPC mode (Mode I), switching frequency reduction (SFR) mode (Mode II), high voltage/current switching loss reduction (SLR) mode (Mode III), and SFR plus SLR mode (Mode IV, a combination of Modes II and III). The proposed CMPC approach controls the TP4LI to (1) successfully track balanced and unbalanced reference currents with balanced or unbalanced loads; (2) reduce switching losses; and (3) keep the generated current total harmonic distortion (THD) within the industry’s recommended limits. The TP4LI model with the CMPC approach was verified and validated in the MATLAB/Simulink for a PV system. The simulation results show good tracking and performance of the TP4LI for balanced and unbalanced reference currents with balanced and unbalanced loads in all four modes of operation.

This study proposes the use of symmetrical ogive-shaped ribs on the walls of microchannel heat sinks (MCHS) to improve their thermal performance with minimal pressure drop. The ribs are arranged in three different configurations: ribs attached to all channel walls (MC-SAWR), ribs attached to side channel walls (MC-SSWR), and ribs attached to the bottom channel wall (MC-SBWR). Numerical investigations are conducted using the laminar conjugate heat transfer model to study the flow and heat transfer characteristics of the MCHS. The augmentation entropy generation number and thermal enhancement factor criterion are used to quantify the overall hydrothermal performance of the MCHS. The results show that the inclusion of symmetrical ogive-shaped ribs improves the Nusselt number of MCHS. The MC-SAWR configuration shows the highest Nusselt number improvement of 13–50% compared to the smooth MCHS over the Reynolds number range of 100–1000. Additionally, the MC-SAWR configuration shows a maximum reduction of 58% in the total entropy generation rate as it has the smallest augmentation entropy generation number value of 0.42. In terms of the thermal enhancement factor criterion, the MC-SSWR configuration shows the highest performance at Reynolds numbers below 400, but the MC-SAWR configuration outperformed the MC-SSWR configuration at Reynolds numbers above 400. Therefore, the MC-SAWR configuration is the best configuration that provides high cooling performance.

The seed extract of Foeniculum vulgare (FV) was used for the preparation of a nanosuspension (NS) with an enhanced bioavailability of phytoconstituents. Subsequently, it was employed as a potent source of polyphenols, such as quercetin and kaempferol, to accelerate the regeneration and recovery of motor and sensory function in injured nerves. The NS was administered through daily gauging as NS1 (0.5 mg/mL) and NS2 (15 mg/mL), at a dose rate of 2 g/kg body weight until the end of the study. The NS-mediated retrieval of motor functions was studied by evaluating muscle grip strength and the sciatic functional index. The recovery of sensory functions was assessed by the hotplate test. Several well-integrated biochemical pathways mediate the recovery of function and the regeneration of nerves under controlled blood glucose and oxidative stress. Consequently, the NS-treated groups were screened for blood glucose, total antioxidant capacity (TAC), and total oxidant status (TOS) compared to the control. The NS administration showed a significant potential to enhance the recuperation of motor and sensory functions. Moreover, the oxidative stress was kept under check as a result of NS treatments to facilitate neuronal generation. Thus, the nanoformulation of FV with polyphenolic contents accelerated the reclamation of motor and sensory function after nerve lesion.

In the present work, the rapid identification of elements and their relative chemical composition in various Peganum harmala seed samples were investigated using a calibration-free laser-induced breakdown spectroscopy technique (CF-LIBS). A pulsed Nd:YAG laser-source with a 5 ns pulse-duration, and 10 Hz pulse repetition rates providing 400 and 200 mJ energy at 1064 and 532 nm wavelength, respectively, was focused on the Peganum harmala seed samples for ablation. A LIBS 2000+ spectrometer within the wavelength range (200 to 720 nm), emission-spectra were recorded. The measured spectra of the Peganum harmala sample gives spectral lines of Carbon (C), Magnesium (Mg), Lithium (Li), Sodium (Na), Calcium (Ca), Silicon (Si), Iron (Fe), Strontium (Sr), Copper (Cu), Potassium (K), and Lead (Pb). A CF-LIBS technique has been employed for the compositional study of the elements exist in the Peganum harmala seed samples. The measured results demonstrate that C, Mg, and Ca are found to be major elements in the Peganum harmala seed samples with compositions of ~36.64%, ~24.09%, and ~19.03%, respectively. Along with the major elements, the elements including Li, Na, Si, K, Fe, and Sr were identified as minor elements with compositions of ~2.87%, ~2.33%, ~3.72%, ~7.17%, ~2.83%, and ~1.14%, respectively. Besides Cu (~8.07 μg/g), and Pb (~1.10 μg/g) elements were observed as trace elements exist in the Peganum harmala seed samples. Furthermore, the electron number density including the plasma excitation-temperature were calculated using the stark-broadening line profile method and the Saha–Boltzmann plot method, respectively. The plasma parameters versus laser-irradiance and the distance from the sample were further investigated. Moreover, a principal component analysis (PCA) method was also utilized to the spectral data obtained by using LIBS to discriminate various seed samples with four classes, namely, α, β, γ, and Δ. Three principal-components (PCs) calculated from eigenvalues of score matrix described 87.6%, 4.6%, and 2.5% of total variance for PC1, PC2, and PC3, respectively. The LIBS spectral data variance covered by the initial 3 PCS was found as ~94.7% of total variance. The PCA results have successfully demonstrated the different classes of the Peganum harmala seed samples based on the different doping compositional ratios of the Zn element. This study confirmed the feasibility and ability of LIBS and PCA for the rapid analysis of Peganum harmala seed samples. Finally, the results achieved using CF-LIBS were incorporated with those obtained from the XRF and EDX analytical techniques.

If Diabetic Retinopathy (DR) patients do not receive quick diagnosis and treatment, they may lose vision. DR, an eye disorder caused by high blood glucose, is becoming more prevalent worldwide. Once early warning signs are detected, the severity of the disease must be validated before choosing the best treatment. In this research, a deep learning network is used to automatically detect and classify DR fundus images depending on severity using AlexNet and Resnet101-based feature extraction. Interconnected layers helps to identify the critical features or characteristics; in addition, Ant Colony systems also help choose the characteristics. Passing these chosen attributes through SVM with multiple kernels yielded the final classification model with promising accuracy. The experiment based on 750 features proves that the proposed approach has achieved an accuracy of 93%.

Quantized conduction achieved in layered materials offers a wide range of applications in electronics. A comprehensive analysis of electronic properties of Sr2ZrO4/TiN- and Sr2ZrO4/TaN-layered heterostructure is carried out using plane wave-based first principles calculations. To understand the origin of quantized conduction, the role of oxygen vacancies (Vos) in 2D layered Ruddleson–Popper perovskite (Sr2ZrO4) is analyzed using density of states, isosurface, and integrated charge density plots. The origin of quantized states formed near the Fermi level is proposed in terms of charge conduction layer formed at the interface. The comprehensive insight of Sr2ZrO4/TiN and Sr2ZrO4/TaN heterostructure interface is provided by shedding light on the charge redistribution from charge density and Bader charge analysis. Meanwhile, work function is calculated for the confirmation of charge conducting behavior of the two layered heterostructures. The interface of these two layered heterostructures revealed the quantized conduction phenomena which cannot be achieved with either layer alone. Stable switching achieved withaTaN electrode being an important task for robust RS and solving sneak path related problem is opening roadmap for 2D layered RRAM devices.

Fluorescent molecules absorb photons of specific wavelengths and emit a longer wavelength photon within nanoseconds. Recently, fluorescent materials have been widely used in the life and material sciences. Fluorescently labelled heterocyclic compounds are useful in bioanalytical applications, including in vivo imaging, high throughput screening, diagnostics, and light-emitting diodes. These compounds have various therapeutic properties, including antifungal, antitumor, antimalarial, anti-inflammatory, and analgesic activities. Different neutral fluorescent markers containing nitrogen heterocycles (quinolones, azafluoranthenes, pyrazoloquinolines, etc.) have several electrochemical, biological, and nonlinear optic applications. Photodynamic therapy (PDT), which destroys tumors and keeps normal tissues safe, works in the presence of molecular oxygen with light and a photosensitizing drugs (dye) to obtain a therapeutic effect. These compounds can potentially be effective templates for producing devices used in biological research. Blending crown compounds with fluorescent residues to create sensors has been frequently investigated. Florescent heterocyclic compounds (crown ether) increase metal solubility in non-aqueous fluids, broadening the application window. Fluorescent supramolecular polymers have widespread use in fluorescent materials, fluorescence probing, data storage, bio-imaging, drug administration, reproduction, biocatalysis, and cancer treatment. The employment of fluorophores, including organic chromophores and crown ethers, which have high selectivity, sensitivity, and stability constants, opens up new avenues for research. Fluorescent organic compounds are gaining importance in the biological world daily because of their diverse functionality with remarkable structural features and positive properties in the fields of medicine, photochemistry, and spectroscopy.

A two-year experiment was carried out at Shenyang Agricultural University’s research field area in China to evaluate the impact of the combined application of straw mulch (0 and 8 t ha−1) and biochar (0, 4, 12, and 36 t ha−1) on the morphological traits and grain development of rainfed maize during 2018 and 2019. The results showed that straw mulch and different biochar application rates significantly impacted the maize growth index. Compared to non-biochar-treated soils, the introduction of straw mulch improved plant height, stem diameter, leaf area index (LAI), leaves, stem, root, and crop growth rate (CGR), and dry weight of rainfed maize crop. The highest plant height, stem diameter, LAI, leaves, stem, root growth rate, CGR, and dry weight of rainfed maize crop were reported when soil was treated with a higher rate of biochar (36 t ha−1). Biochar increased grain filling rate while decreasing grain filling duration in rainfed maize crops. Our results indicate that straw mulch and biochar-based soil management strategies can improve the rainfed maize growth with the environmental benefits of global warming mitigation. However, due to the wide range of biochar properties, the interactions between straw mulch and biochar should be given special consideration in the maize cropping system.

Objective: To study the clinical diagnostic value of neutrophil CD64 index, PCT, and CRP in patients with acute pancreatitis with abdominal infection. Methods: A number of patients with acute pancreatitis (n = 234) participated in the study. According to the infection and health conditions, they were further divided into the non-infection group (n = 122), infection group (n = 78), and sepsis group (n = 34), and 40 healthy subjects were selected in the control group (n = 40). Expression levels of infection indexes, such as CD64 index, PCT, and CRP, were detected and compared. ROC curves were drawn to compare the efficacy of each index in the diagnosis of acute pancreatitis with abdominal infection and sepsis. The study was retrospectively registered under the China Clinical Trial Registry as a trial number ChiCTR2100054308. Results: All indexes were significantly higher in three clinical groups than the healthy control group (p < 0.05). The CD64 index, CD64 positive rate, and PCT in the infected group were significantly higher than those in the uninfected group (ALL p < 0.05). The PCT of patients infected with Gram-negative bacteria was significantly higher than that of Gram-positive bacteria-infected patients (p < 0.05). CD64 index had the best diagnostic efficiency for acute pancreatitis infection, with 82.14% sensitivity, 88.51% specificity, and 0.707 Youden indexes. The CD64 Youden index (0.780) for sepsis diagnosis was the highest, while the AUC of PCT was the highest (0.897). Conclusion: CD64 index combined with PCT has good sensitivity and specificity in diagnosing acute pancreatitis infection and sepsis.

In this paper, we demonstrate a device using a Ni/SiN/BN/p+-Si structure with improved performance in terms of a good ON/OFF ratio, excellent stability, and low power consumption when compared with single-layer Ni/SiN/p+-Si and Ni/BN/p+-Si devices. Its switching mechanism can be explained by trapping and de-trapping via nitride-related vacancies. We also reveal how higher nonlinearity and rectification ratio in a bilayer device is beneficial for enlarging the read margin in a cross-point array structure. In addition, we conduct a theoretical investigation for the interface charge accumulation/depletion in the SiN/BN layers that are responsible for defect creation at the interface and how this accounts for the improved switching characteristics.

Background and Objective: Helicobacter pylori is a human-stomach-dwelling organism that causes many gastric illnesses, including gastritis, ulcer, and gastric cancer. The purpose of the study was to perform differential proteomic analysis on H. pylori isolates from gastritis, ulcer, and gastric cancer patients. Materials and Methods: H. pylori was isolated from antrum and fundus biopsies obtained from patients who visited the Department of Gastroenterology. Using nano-LC-QTOF MS/MS analysis, differentially regulated proteins were identified through proteome profiling of pooled samples of H. pylori isolated from gastritis, ulcer, and gastric cancer patients. Antigenic scores and cellular localization of proteins were determined using additional prediction tools. Results: A total of 14 significantly regulated proteins were identified in H. pylori isolated from patients with either gastritis, ulcer, or gastric cancer. Comparative analysis of groups revealed that in the case of cancer vs. gastritis, six proteins were overexpressed, out of which two proteins, including hydrogenase maturation factor (hypA) and nucleoside diphosphate kinase (ndk) involved in bacterial colonization, were only upregulated in isolates from cancer patients. Similarly, in cancer vs. ulcer, a total of nine proteins were expressed. Sec-independent protein translocase protein (tatB), involved in protein translocation, and pseudaminic acid synthase I (pseI), involved in the synthesis of functional flagella, were upregulated in cancer, while hypA and ndk were downregulated. In ulcer vs. gastritis, eight proteins were expressed. In this group, tatB was overexpressed. A reduction in thioredoxin peroxidase (bacterioferritin co-migratory protein (bcp)) was observed in ulcer vs. gastritis and cancer vs. ulcer. Conclusion: Our study suggested three discrete protein signatures, hypA, tatB, and bcp, with differential expression in gastritis, ulcer, and cancer. Protein expression profiles of H. pylori isolated from patients with these gastric diseases will help to understand the virulence and pathogenesis of H. pylori.

We report a quantitative analysis of various plant-biochar samples (S1, S2 and S3) by utilizing a laser-induced breakdown spectroscopy (LIBS) technique. For LIBS analysis, laser-induced microplasma was generated on the target surface by using a focused beam through a high-power Nd: YAG laser and optical emission spectra were recorded using a charged coupled device (CCD) array spectrometer, with wavelength ranges from 200 nm to 720 nm. The spectroscopical analysis showed the existence of various ingredients, including H, Li, Ca, Na, Al, Zn, Mg, Sr, Si, and Fe, along with a CN molecular emission band due to B2Σ+ − X2Σ+ electronic transition. By assuming conditions of the plasma is optically thin and in LTE, calibration-free laser-induced breakdown spectroscopy (CF-LIBS) was utilized for the compositional analysis of the ingredients present in the three plant-biochar samples. To lower the uncertainties, we used an average composition (%) of the three plant-biochar samples. The quantitative study of the plant-biochar samples was also achieved using the energy dispersive X-ray (EDX) technique, showing good agreement with the CF-LIBS technique. In addition, statistical analysis, such as principal component analysis (PCA), was performed for the clustering and classification of the three plant-biochar samples. The first three PCs explained an overall ~91% of the variation in LIBS spectral data, including PC1 (58.71%), PC2 (20.9%), and PC3 (11.4%). These findings suggest that LIBS is a robust tool for rapid measurement of heavy as well as light elements, such as H, Li, and nutritional metals in plant-biochar samples.

Suaeda fruticosa Forssk. Ex J.F.Gmel is traditionally used for inflammatory and digestive disorders, as a carminative, and for diarrhea. This plant is widely distributed in Asia, Africa, and the Mediterranean region. Aqueous methanolic extract of S. fruticosa (Sf.Cr) was prepared and screened for phytoconstituents through qualitative and GC-MS analysis. Quantification of total phenolic and flavonoid contents was performed, while antioxidant capacity was determined by DPPH, CUPRAC, FRAP, and ABTS assays. The gastroprotective activity was assessed in an ethanol-induced ulcer model. Gastric secretory parameters and macroscopic ulcerated lesions were analyzed and scored for ulcer severity. After scoring, histopathology was performed, and gastric mucus contents were determined. Oral pre-treatment of Sf.Cr demonstrated significant gastroprotection. The gastric ulcer severity score and ulcer index were reduced while the %-inhibition of ulcer was increased dose-dependently. The Sf.Cr significantly elevated the pH of gastric juice, while a decrease in total acidity and gastric juice volume was observed. Histopathology demonstrated less oedema and neutrophil infiltration in gastric mucosa of rats pre-treated with the Sf.Cr in comparison to ethanol-intoxicated animals. Furthermore, the gastric mucus contents were increased as determined by alcian blue binding. Sf.Cr showed marked gastroprotective activity, which can be attributed to antioxidant, antisecretory, and cytoprotective effects.

Background and motivation: Over the last two decades, particularly in the Middle East, Red Palm Weevils (RPW, Rhynchophorus ferruginous) have proved to be the most destructive pest of palm trees across the globe. Problem: The RPW has caused considerable damage to various palm species. The early identification of the RPW is a challenging task for good date production since the identification will prevent palm trees from being affected by the RPW. This is one of the reasons why the use of advanced technology will help in the prevention of the spread of the RPW on palm trees. Many researchers have worked on finding an accurate technique for the identification, localization and classification of the RPW pest. This study aimed to develop a model that can use a deep-learning approach to identify and discriminate between the RPW and other insects living in palm tree habitats using a deep-learning technique. Researchers had not applied deep learning to the classification of red palm weevils previously. Methods: In this study, a region-based convolutional neural network (R-CNN) algorithm was used to detect the location of the RPW in an image by building bounding boxes around the image. A CNN algorithm was applied in order to extract the features to enclose with the bounding boxes—the selection target. In addition, these features were passed through the classification and regression layers to determine the presence of the RPW with a high degree of accuracy and to locate its coordinates. Results: As a result of the developed model, the RPW can be quickly detected with a high accuracy of 100% in infested palm trees at an early stage. In the Al-Qassim region, which has thousands of farms, the model sets the path for deploying an efficient, low-cost RPW detection and classification technology for palm trees.

In this work, we report the results of the compositional analysis of an aluminum gallium arsenide (AlGaAs) sample using the calibration-free laser-induced breakdown spectroscopy (CF-LIBS) technique. The AlGaAs sample was doped with three various concentrations of gallium (Ga), arsenic (As), and aluminum (Al), as reported by the manufacturer, and the CF-LIBS technique was employed to identify the doping concentration. A pulsed Q-switched Nd: YAG laser capable of delivering 200 and 400 mJ energy at 532 and 1064 nm, respectively, was focused on the target sample for ablation, and the resulting emission spectra were captured using a LIBS 2000+ spectrometer covering the spectral range from 200 to 720 nm. The emission spectra of the AlGaAs sample yielded spectral lines of Ga, As, and Al. These lines were further used to calculate the plasma parameters, including electron temperature and electron number density. The Boltzmann plot method was used to calculate the electron temperature, and the average electron temperature was found to be 5744 ± 500 K. Furthermore, the electron number density was calculated from the Stark-broadened line profile method, and the average number density was calculated to be 6.5 × 1017 cm−3. It is further observed that the plasma parameters including electron temperature and electron number density have an increasing trend with laser irradiance and a decreasing trend along the plume length up to 2 mm. Finally, the elemental concentrations in terms of weight percentage using the CF-LIBS method were calculated to be Ga: 94%, Al: 4.77% and As: 1.23% for sample-1; Ga: 95.63%, Al: 1.15% and As: 3.22% for sample-2; and Ga: 97.32%, Al: 0.69% and As: 1.99% for sample-3. The certified concentrations were Ga: 95%, Al: 3% and As: 2% for sample-1; Ga: 96.05%, Al: 1% and As: 2.95% for sample-2; and Ga: 97.32%, Al: 0.69% and As: 1.99% for sample-3. The concentrations measured by CF-LIBS showed good agreement with the certified values reported by the manufacturer. These findings suggest that the CF-LIBS technique opens up an avenue for the industrial application of LIBS, where quantitative/qualitative analysis of the material is highly desirable.

The present work reports the synthesis, characterization, and antimicrobial activities of adipic acid-capped silver nanoparticles (AgNPs@AA) and their utilization for selective detection of Hg2+ ions in an aqueous solution. The AgNPs were synthesized by the reduction of Ag+ ions with NaBH4 followed by capping with adipic acid. Characterization of as-synthesized AgNPs@AA was carried out by different techniques, including UV–Visible spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), X-ray diffraction (XRD), Dynamic Light Scattering (DLS), and zeta potential (ZP). In the UV–Vis absorption spectrum, the characteristic absorption band for AgNPs was observed at 404 nm. The hydrodynamic size of as-synthesized AgNPs was found to be 30 ± 5.0 nm. ZP values (−35.5 ± 2.4 mV) showed that NPs possessed a negative charge due to carboxylate ions and were electrostatically stabilized. The AgNPs show potential antimicrobial activity against clinically isolated pathogens. These AgNPs were found to be selectively interacting with Hg2+ in an aqueous solution at various concentrations. A calibration curve was constructed by plotting concentration as abscissa and absorbance ratio (AControl − AHg/AControl) as ordinate. The linear range and limit of detection (LOD) of Hg2+ were 0.6–1.6 μM and 0.12 μM, respectively. A rapid response time of 4 min was found for the detection of Hg2+ by the nano-probe. The effect of pH and temperature on the detection of Hg2+ was also investigated. The nano-probe was successfully applied for the detection of Hg2+ from tap and river water