In our previous work, we demonstrated the potential of the amphiphilic cyclic peptide [W4R4] as a promising antimicrobial agent. The extensive data accumulated on the antimicrobial activity of this peptide and its homologs presented an opportunity to unravel the molecular mechanisms governing their action and establish crucial structure-activity relationships (SAR). To achieve this, we employed a comprehensive approach involving peptide synthesis, in vitro biological testing, and molecular modeling to systematically analyze the SAR of a series of cyclic peptides derived from the [W4R4] scaffold. Minimal modifications were introduced to the original peptide by incorporating either one aromatic hydrophobic residue (W) or one polar positively charged residue (R) while maintaining the sequential W/R arrangement. Additionally, we introduced an "intermediate" substitution by incorporating 2,5-diketopiperazine (DKP), possessing both aromatic and polar properties, into the macrocycle [W4R4] between the W and R residues. Notably, this study marked the first synthesis and evaluation of the [W4R4(DKP)] peptide. Structural, dynamic, hydrophobic, and membrane-binding properties of four cyclic peptides ([W4R4], [W5R4], [W4R5], [W4R4(DKP)]) were explored using molecular dynamics simulations within a DOPC/DOPG lipid bilayer that mimics the bacterial membrane. The results obtained revealed distinct SAR profiles for all peptides, enabling the establishment of correlations between their antimicrobial activity and structural/dynamic parameters. These parameters included the degree of conformational plasticity, the depth of immersion into the bi-layer, and the population of the membrane binding mode characterized by the embedding of the hydrophobic peptide pattern into the acyl chain region of lipids. In particular, the peptide [W4R5] exhibited an optimal "activity/binding to the bacterial membrane" pattern. This work highlights the significance of our multidisciplinary approach in efficiently deciphering finely regulated SAR profiles for a series of homologous peptides with diverse activities. Furthermore, this research provides a solid foundation for the rational design of novel antimicrobial peptides.

Despite being extremely potent against malignancies, cisplatin (CIS) has limited practical applicability due to its adverse effects such as testicular damage. Consequently, it becomes necessary to reduce its toxicity. In this study, cilostazol, a selective phosphodiesterase-3 inhibitor that is frequently used in the treatment of intermittent claudication, was examined for its ability to abrogate CIS-induces testicular toxicity and its ameliorative effect was compared with two phosphodiesterase inhibitors, tadalafil and pentoxifylline. The study also focused on the possible mechanisms involved in the proposed protective effect. Ten groups of rats were included; Group (1): control, Groups (2-4): 5 mg/kg tadalafil or 75 mg/kg pentoxifylline or 20 mg/kg cilostazol respectively), Group (5):7 mg/kg CIS, Group (6&7): CIS + 5 mg/kg tadalafil or 75 mg/kg pentoxifylline, Group (8-10) CIS+ 5, 10 or 20 mg/kg cilostazol respectively. CIS treated rats showed a significant decrease in testicular function, serum testosterone and reduced glutathione levels, and significant elevation in malondialdehyde, total nitrite levels, tumor necrosis factor-alpha and nuclear factor-kappa β alongside caspase-3. These outcomes were confirmed by marked testicular architecture deterioration. Contrary, Cilostazol in a dose-dependent manner showed potential protection against testicular toxicity, reversion of the disrupted testicular function and improvement of the histological alterations through rebalancing of oxidative stress, inflammatory and apoptotic biomarkers. In addition, cilostazol exerted more pronounced protective effect in comparison to tadalafil and pentoxifylline. In conclusion, administrations of cilostazol might ameliorate cisplatin-induced testicular impairment through alteration of oxidative stress, inflammation, and apoptotic pathway, offering a promising treatment for cisplatin -intoxication in the testes.

We report on the occurrence of natural Magnesium Alumina Silicate Hydrate (M-(A)-S-H) cement at Feragen and Leka in eastern and western Trøndelag Norway, respectively. Both occurrences are located in the sub-arctic climate zone and form in glacial till and moraine material deposited on ultramafic rock during the Weichselian glaciation. Weathering of serpentinized peridotite dissolve brucite and result in a relative high pH alkaline fluid that subsequently reacts with the felsic minerals of the till (quartz, plagioclase, K-feldspar) to form a cement consisting of an amorphous material or a mixture of nanocrystalline Mg-rich phyllosilicates, including illite. The presence of plagioclase in the till results in the enrichment of alumina in the cement, i.e., forms M-A-S-H instead of the M-S-H cement. Dissolution of quartz results in numerous etch-pits and negative quartz crystals filled with M-A-S-H cement. Where the quartz dissolution is faster that the cement precipitation, a honeycomb-like texture is formed. Compositionally the cemented till (tillite) contains more MgO and has a higher loss of ignition than the till, suggesting that the cement is formed by a MgO fluid that previously reacted with the peridotite. The M-(A)-S-H cemented till represent a new type of duricrust, coined magsilcrete. The study of natural Mg-cement provides information on peridotites as a Mg-source for Mg-cement and as a feedstock for CO2 sequestration.

Data-Flow and Higher-Order Mutation are white-box testing techniques. To our knowledge, no work has been proposed to compare data flow and higher-order mutation. This paper compares all def-uses data-flow and second-order mutation criteria. This compassion investigates the subsumption relation between these two criteria and evaluates the effectiveness of test data developed for each. To compare the two criteria, a set of test data satisfying each criterion is generated, which is used to explore whether one criterion subsumes the other criterion and assess the effectiveness of the test set that was developed for one methodology in terms of the other. The results showed that the mean mutation coverage ratio of the all du-pairs adequate test cover is 80.9%, and the mean data flow coverage ratio of the 2nd-order mutant adequate test cover is 98.7%. Consequently, 2nd-order mutation “ProbSubsumes” the all du-pairs data flow. The failure detection efficiency of the mutation (98%) is significantly better than the failure detection efficiency of data flow (86%). Consequently, 2nd-order mutation testing is “ProbBetter” than all du-pairs data flow testing. In contrast, the size of the test suite of 2nd-order mutation is more significant than the size of the test suite of all du-pairs.

Contamination of the environment with nano- and microplastic particles exerts a threatened impact on the aquatic ecosystems and sustainable catfish aquaculture. The presence of nanoplastics has been found to have a detrimental impact on both aquatic and terrestrial ecosystems. The present study examines the effect of polystyrene nanoplastics on DNA, erythrocytes, oxidative status and renal of catfish in addition to the potential protective effects of Chlorella vulgaris bioremeditation and selenium to hinder this effect. Six equal groups of fish were used as follows: Group 1: served as a control group and received water free from PS NPs; Group 2: was exposed to PS NPs at a concentration of 5mg/L; Group 3: was exposed to PS NPs (5mg/L) + selenium (1mg/kg diet); Group 4: was exposed to PS NPs (5mg/L) + C. vulgaris (25g/kg diet); Group 5: was supplemented with C. vulgaris (25g/kg diet); and Group 6: was supplemented with selenium (1mg/kg diet). The results indicated that PS NPs induced oxidative stress by significantly elevating MDA activities and slightly reducing antioxidant biomarkers, resulting in DNA damage, increased frequency of micronuclei, erythrocyte alterations, and numerous histopathological alterations in kidney tissue. Selenium and C. vulgaris significantly ameliorated the oxidative/antioxidant status, reduced DNA damage, micronucleus frequency, erythrocyte alterations, and morphology of kidney tissue. Nevertheless, further research is needed to evaluate in detail the mechanism behind the toxicity with nano-microplatics in aquatic system.

Discovery of microRNAs (miRNAs) twenty years ago, has advocated a new era of “Molecular Genetics”. About 2000 miRNAs are present, that regulate one third of the genome. MiRNAs dysregulated expression may contribute to several diseases including tumor growth. Their presence in body fluids, reflecting levels alteration in various cancers, merit circulating miRNAs as the “next generation biomarkers” for early stages tumor diagnosis and/or prognosis. Herein, we performed a comprehensive literature search focusing on the origin, biosynthesis and role of miRNAs and summarized the foremost studies centering on miRs value as non-invasive biomarkers in different non-communicable diseases, including various cancer types. Moreover, during chemotherapy many miRNAs were linked to multidrug resistance, via modulating numerous biological processes and/or pathways that will be highlighted as well.

This article presents a novel analysis of Indus Seal M-282 [1] excavated from Mohenjo-Daro, a key Indus Valley Civilization site. Utilizing the proposed Sindhu Prakrit alphabet, the study investigates the relationship between the seal's iconography and the inscribed text. By examining the potential for a close correlation between these elements, the analysis aims to decipher the meaning embedded within the seal's motifs, potentially offering new insights into Indus Valley communication and symbolism.

This study examines the aggregate consumption function of Saudi Arabia from 2000 to 2022, focusing on identifying key determinants of household consumption and evaluating the impacts of disposable income, household wealth, government expenditure, interest rates, and oil revenues. The research uses advanced econometric methods, including the Autoregressive Distributed Lag (ARDL) model and Johansen cointegration test, to analyze the relationships among these variables. The findings reveal that disposable income, household wealth, and government expenditure significantly and positively influence consumption, whereas interest rates show a negative correlation. Oil revenues also play a critical role, reflecting the country's economic reliance on oil. The study highlights the necessity for economic diversification to reduce the impact of oil price volatility on household income and consumption stability. The results offer crucial insights for policymakers, emphasizing the need for strategies that enhance household income and wealth, maintain robust public sector spending, and effectively manage interest rates. These findings also support the importance of consistent and predictable income sources for sustaining consumption. Additionally, this study suggests directions for future research, including developing sophisticated forecasting models to predict consumption trends and exploring other influencing factors such as demographic shifts and technological progress.

Diabetes mellitus is a global health issue that necessitates the development of novel therapeutic approaches. Today numerous experimental methods have been used and set up to evaluate novel drugs with antidiabetic effects. In addition, several research studies have been done to induce diabetes under in vitro and in vivo conditions, but only a small number of these efforts are currently in a usable stage. This review aims to provide useful guidance for researchers who seek to evaluate the antidiabetic potential of drugs, agents, or new medications using the standard methods published by authorities and academics worldwide organizations and research centers. In this review, we collected 132 relevant articles from prominent databases such as PubMed, ScienceDirect, Scopus and others to summarize in vitro and in vivo experimental validation of antidiabetic treatment from 1943 to present.

Bacterial infection poses a serious threat to human life due to its rapidly growing resistance to antibacterial drugs, which is a significant public health issue. This study was focused on the design and synthesis of a new series of 25 analogues bearing 5-cyano-6-oxo-4-substituted phenyl-1,6-dihydropyrimidine scaffold hybridized with different substituted benzene-sulfonamides through thioacetamide linker M1-25 via the treatment of the key intermediates 2-mercapto-6-oxo-4-phenyl-1,6-dihydropyrimidine-5-carbonitriles 1a-c with different 2-chloro-N-substituted benzene-sulfonamide derivatives 4a-e overnight at room temperature. The spectroscopic data of all the new compounds (IR, 1H-NMR, 13C-NMR, and Mass) was studied in detail to confirm their molecular structures. The antimicrobial activity of the new molecules was studied against various Gram-positive, Gram-negative, and fungal strains. All the tested compounds showed promising broad spectrum antimicrobial efficacy, especially against K. pneumoniae and P. aeruginosa, with ZOI values ranging from 15 to 30 mm. Furthermore, the most promising compounds M6, M19,M 20, and M25 were subjected to minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays. In addition, the anti-virulence activity of the compounds was also examined using multiple biofilm assays. The new compounds revealed promising suppressing activity of microbial biofilm formation in the examined K. pneumoniae and P. aeruginosa microbial isolates. Additional in silico ADMET studies were conducted to represent their oral bioavailability, drug-likeness characteristics, and human toxicity risks. The obtained data suggest the newly prepared pyrimidine-benzene-sulfonamide derivatives may serve as model compounds amenable for further optimization and development of new antimicrobial and antisepsis candidates.

This study delves into the intricate interplay of economic growth components, specifically focusing on Consumption and investment in Saudi Arabia from 2000 to 2022. Employing vector error correction models and cointegration techniques, we analyze the short- and long-term dynamics within the relationship of Consumption, investment, and economic growth. Granger causality analysis is also used to discern these pivotal variables' causal connections. Our empirical analysis reveals a persistent long-term cointegration relationship among the variables, underscoring the enduring nature of their interdependency. Furthermore, our findings highlight Consumption and investment's statistically significant positive impact on economic growth. Notably, the short-term analysis unveils a stable model characterized by an annual adjustment to equilibrium of 100%.Moreover, the Granger causality study demonstrates unidirectional causal linkages between Consumption, investment, and economic growth. These findings hold substantial implications for policy formulation in Saudi Arabia. Policymakers must grasp the ramifications of burgeoning prosperity and evolving private consumption patterns on future environmental outcomes. Achieving sustainable long-term results necessitates equal emphasis on bolstering private Consumption and fostering other facets of economic growth.

Abstract: As a low-input crop, Miscanthus offers numerous advantages that, in addition to agricultural applications, permits its exploitation for energy, fuel, and material production. Depending on the Miscanthus genotype, season, and harvest time as well as plant component (leaf versus stem), correlations between structure and properties of the corresponding isolated lignins differ. Here, a comparative study is presented between lignins isolated from M. x giganteus, M. sinensis, M. robustus and M. nagara using a catalyst-free organosolv pulping process. The lignins from different plant constituents are also compared regarding their similarities and differences regarding monolignol ratio and important linkages. Results showed that the plant genotype has the weakest influence on monolignol content and interunit linkages. In contrast, structural differences are more significant among lignins of different harvest time and/or season. Analyses were performed using fast and simple methods such as nuclear magnetic resonance (NMR) spectroscopy. Data was assigned to four different linkages (A: b-O-4 linkage, B: phenylcoumaran, C: resinol, D: b-unsaturated ester). In conclusion, A content is particularly high in leaf-derived lignins at just under 70 % and significantly lower in stem and mixture lignins at around 60 % and almost 65 %. The second most common linkage pattern is D in all isolated lignins, the proportion of which is also strongly dependent on the crop portion. Both stem and mixture lignins, have a relatively high share of approximately 20 % or more (maximum is M. sinensis Sin2 with over 30 %). In the leaf-derived lignins, the proportions are significantly lower on average. Stem samples should be chosen if the highest possible lignin content is desired, specifically from the M. x giganteus genotype, which revealed lignin contents up to 27 %. Due to the better frost resistance and higher stem stability, M. nagara offers some advantages compared to M. x giganteus. Miscanthus crops are shown to be very attractive lignocellulose feedstock (LCF) for second generation biorefineries and lignin generation in Europe.

Abstract: Background: Adolescents frequently self-report pain, according to epidemiological research. The knee is one of the sites where the pain is most common. One of the main factors contributing to the number of years people spend disabled and having substantial personal, societal, and economic burdens globally is musculoskeletal disorders. Insidious, slowly building onset that neither the adolescent nor the parent is aware of [5]. Patellofemoral Pain (PFP) is a clinical knee pain commonly affecting adolescents. In the present study, the authors aimed to estimate the frequency of knee pain in Saudi adolescents. Methods: This cross-sectional survey carried out from June to November 2022 included 676 adolescents, ages 10 to 18. The data are presented as frequencies and percentages for categorical variables. Analysis of variance (ANOVA) was used to compare means between groups, while the chi-square test was used for the comparison of categorical variables. Statistical significance was set at p < 0.05. Results: Adolescents were invited to take part in the study; 57.5% were female and 42.5% were male, and 68.8% were aged between 15 and 18. The prevalence of knee pain was considerably high among females (26%), while it was 19.2% among males. Moreover, age was a significant predictor for knee pain, and BMI was as well. We also found a significant difference between BMI classification and stiffness of the knee (p = 0.008). There is a difference between adolescents who do physical activities in leisure and difficulty bending (p = 0.03). Conclusion: Our study noted a high rate of knee pain among adolescents, so we need to raise awareness about risk factors. Adolescent knee pain can be prevented with conservative methods and some minor lifestyle/activity modifications.

Background and Aim. Since virus-related hepatocellular carcinoma (HCC) pathogenesis involves liver inflammation, therefore, post-hepatitis C virus (HCV) infection would be a cause for liver cirrhosis that would progress to HCC. Cytotoxic T cells (Tc) are known to be involved in post-HCV complications and HCC pathogenesis. The inhibitory checkpoint Leukocyte-Associated Immunoglobulin-like Receptor-1 (LAIR-1) is expressed on Tc. Therefore, we aimed to determine whether the Tc expression level of LAIR-1 is associated with HCC progression post-HCV and moreover, to evaluate LAIR-1 expression as a non-invasive biomarker for HCC progression in the context of liver cirrhosis post-HCV genotype 4 (G4) in Egyptian patients’ peripheral venous blood liquid biopsy. We studied LAIR-1 expression on Tc related to the progression of liver cirrhosis in a case-controlled study enrolled 64 patients with post-HCV G4-HCC and 37 patients with post-HCV G4-liver cirrhosis. Methods: LAIR-1 expression was analyzed by flow cytometry. Results: LAIR-1 expression on Tc and the percentage of Tc positive for LAIR-1 (LAIR-1+Tc %) were significantly higher in the post-HCV G4-HCC group compared to the post-HCV G4-liver cirrhosis

Objective: Rheumatoid Arthritis (RA) is a chronic inflammatory disease affecting the synovium and articular cartilage that initiates joint damage. RA associates with a change in many inflammatory biomarkers. The present study aims to measure the levels of inflammatory adipocytokines (chemerin and visfatin) and their ratio in addition to some related biomarkers in RA patients group in comparison to healthy control group and find out their efficacy in diagnosis of RA.Methods: The study included 60 RA patients and 30 healthy control group. Serum visfatin and chemerin were measured by using ELISA technique, while other biomarkers were determined spectrophotometrically. Multivariate general linear model analysis and Receiver operating curve was used to study the opportunity of using chemerin and visfatin as diagnostic tools for RA.Results: The results indicated that there was a significant increase (p<0.05) in all lipid profile components, except HDLc that decreased, in RA patients in comparison with healthy control group; while significant decrease (p<0.001) in high density lipoprotein-cholesterol of RA patients in comparison with control group. Serum chemerin, visfatin, C-reactive protein, and uric acid levels were significantly higher (p<0.05) in RA patients than those of control group. The results showed a relatively good sensitivity and specificity of chemerin (sensitivity=88.1, specificity=75.9) at a concentration=187.88ng in diagnosis of RA.Conclusions: chemerin is able to diagnose RA efficiently but this diagnoses, due to relatively small specificity, may interfere with other inflammatory disorders. However, with adjuvant with other diagnostic parameters, chemerin may represent a useful addition in diagnosis of RA.

Two experiments were conducted to develop a Nano green manganese and assess its effect on grapevines. The first experiment was microbiological, where several incorporation strategies were utilized to enrich the yeast with manganese, as follows: (1) adding manganese to the liquid medium (growth phase), (2) adding after 24 h of incubation (non-growth phase). As the results, growth phase reduced the possibility of medium contamination. Manganese concentration in the yeast cells was increased as that of manganese sulphate in the medium. Manganese incorporation in yeast cells was 99.93% higher than that of the medium at 0.0195 g/l of manganese. Although the concentration of manganese in the medium raised the OD of yeast cell biomass, manganese sulphate had no passive influence on it. The second experiment was horticultural, done on Flame Seedless grapevines, since both active fresh and frozen yeast enriched with manganese resulted from the first experiment were sprayed twice a year at 10 or 20 cm3/l comparing with manganese sulphate, and manganese chelate. The results demonstrated that yeast extracts in both forms increased the yield and improved fruit properties. The most effective treatment on vegetative growth, yield and fruit properties was the frozen yeast enriched with manganese at 20 cm3/l.

Carbapenems-resistant enterobacteria are considered multidrug-resistant, making the control of their nosocomial infections a major challenge in healthcare settings. It is important to know the incubation site and source of the spread of these strains, especially in neonatal intensive care units (NICU). Colonization of the colon of preterm neonates per se by multidrug-resistant bacterial strains could be the source of dissemination of these multidrug-resistant strains and their site of incubation. In this study, colonization of the colon of preterm neonates with enterobacteria and their multidrug-resistant strains was investigated. The presence of enterobacteria in the stool of preterm neonates was investigated on EMB (eosin-methylene blue) agar, followed by morphological and biochemical characterization and phylogenetic analysis of the 16S rRNA-encoding gene sequences. Antibiotic susceptibility of all isolated strains was tested using VITEK 2 technology. All cases examined at two weeks of age and older showed bacterial colonization of the colon. Klebsiella pneumoniae colonized the colon in 56.2% of the preterm neonates and Klebsiella quasipneumoniae was found in 6.3%, corresponding to 62.5% in total. The colonizing Klebsiella pneumoniae was multidrug-resistant in 2 of 9 cases. One strain of Klebsiella pneumoniae was resistant to carbapenems and another showed intermediate resistance to these antibiotics. These Klebsiella pneumoniae strains showed resistance to most of the other antibiotics tested. Some cases contained more than one genus, with Escherichia coli found in 50% of cases in preterm neonates. In contrast to Klebsiella pneumoniae, none of the E. coli strains colonizing the colon of preterm neonates in this study showed resistance to carbapenems. The results of this study suggest that Klebsiella pneumoniae and its carbapenems multidrug-resistant strains are well marked in preterm neonates colonizing their colon, which is the incubation site and source of such multidrug-resistant bacterial strains in NICU. This study highlights the importance of careful management of preterm neonates in the NICU and possible contamination with multidrug-resistant Klebsiella pneumoniae incubated in their colon. Further studies would be of interest to avoid the possibility of fecal-oral transmission and infection by these multidrug-resistant bacteria and their treatment in the colon, where their incubation site is located.

This research assesses the classification of Arabic coffee into three primary variations (light, medium, and dark) using simulated data based on actual measurements of color information, antioxidant laboratory tests, and chemical composition tests. Two types of simulated data were generated, with the standard deviation of the measures varied. Multiple classifiers were used for classification. The findings demonstrate the use of color information in accurately distinguishing Arabic coffee types. The different CIE color values, as well as the excellent connection between color information and coffee classes, lead to flawless classification results. However, depending primarily on antioxidant information results in lower classification performance, with variability amongst classifiers due to increasing data complexity. Chemical composition information, on the other hand, possesses outstanding discriminatory strength, allowing faultless classification on its own. Certain characteristics, such as crude protein and crude fiber, show strong correlations and are critical in the classification of coffee types. Based on the findings, it is recommended to create a mobile application that uses image recognition to analyze coffee color while providing chemical composition information. End users, such as consumers, will be able to make informed judgments regarding their coffee preferences owing to this application.

In recent years, Neural networks are increasingly deployed in various fields to learn complex patterns and make accurate predictions. However, designing an effective neural network model is a challenging task that requires careful consideration of various factors, including architecture, optimization method, and regularization technique. This paper aims to comprehensively overview the state-of-the-art artificial neural network (ANN) generation and highlight key challenges and opportunities in machine learning applications. It provides a critical analysis of current neural network model design methodologies, focusing on the strengths and weaknesses of different approaches. Also, it explores the use of different learning approaches, including convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN) in image recognition, natural language processing, and time series analysis. Besides, it discusses the benefits of choosing the ideal values for the different components of ANN, such as the number of Input/output layers, hidden layers number, activation function type, epochs number, and model type selection, which help improve the model performance and generalization. Furthermore, it identifies some common pitfalls and limitations of existing design methodologies, such as overfitting, lack of interpretability, and computational complexity. Finally, it proposes some directions for future research, such as developing more efficient and interpretable neural network architectures, improving the scalability of training algorithms, and exploring the potential of new paradigms, such as Spiking Neural Networks, quantum neural networks, and neuromorphic computing.

Type 2 diabetes mellitus (T2DM) is an endocrine illness associate with various changes in the immune system and adaptor protein levels. Cytokine dependent hematopoietic cell linker (CLNK) is an adapter protein that regulates immune receptor signaling and acts as a regulator of the receptor signaling of T-cells and natural killer T-cell. The role of CLNK in T2DM is not studied previously. In the present study, serum CLNK level was measured and correlated with some sociodemographic and insulin resistance (IR) parameters. This is achieved by performing measurement of CLNK and insulin parameters (glucose, insulin, and HbA1c in addition to the calculation of the functions of IR (HOMA2IR), insulin sensitivity (HOMA%S), and beta-cell function (HOMA%B)) in 60 T2DM patients and 30 controls. The results indicated a significant increase (p=0.025) in serum CLNK in patients group in comparison with the controls. Multivariate generalized linear model (GLM) analysis revealed no significant effect of age, BMI, and sex on the CLNK level. The results of tests for between-subjects showed that the CLNK affects diagnosis significantly (F=7.445, p=0.008, partial η² =0.081) and its effect is approximately the same as the effect of insulin (F=8.107, p=0.006, partial η² =0.087). The correlation study showed a highly significant positive correlation between CLNK and the duration of disease (rho=0.420, p<0.001). It can be concluded that the increase CLNK in T2DM revealing the role of the adaptor proteins level in the nature of disease. Elevation of CLNK level may be used as a predictor for diabetes complications, which needs more investigations.

: Clay covers a substantial portion of the Earth. Such soil covers Iraq’s central and southern regions, where the depth of the clay may reach 150 m. All enhancement techniques and methods for transporting large loads cease to function or become prohibitively expensive in such soils. In this case, thermal treatment is the most effective method. In this study, a novel heating system was created and produced utilizing gas as the heat source via boreholes that simulate reality. The borehole heating cases were planted inside soft clay soil to enhance the soil. Different parameters were investigated, including the spacing between boreholes (3, 4, and 5 times the outer diameter of the borehole), the heating depth (1, 1.5, 2, and 2.5 times the width of the model footing), the duration of heating (2, 4, 6, 8, and 10 hrs), and the pattern (square, circular, triangular). The results showed the strength and behavior of the soil treated with heated boreholes at varying spacings, depths, patterns, and heating periods. The best results were found for a spacing, depth, and heating time of three times the outer diameter of the borehole, two times the width of the footing model, and eight hours, respectively. The effect of the heated borehole casing pattern was small when the heated borehole casing was used. The experiments conducted on heated soil showed that the undrained shear strength (Cu) increased from 14 to 360 kPa and then dropped to 140 kPa (as an average with depth). In contrast, the average angle of internal friction (Ø) rose from 0 to 52 degrees and decreased to 16 degrees (as an average with depth) from the center of the heating model to the furthest point affected by heating. In addition, the EDS pattern showed that components like silicon, aluminum, and iron dropped at 300 °C and rose at 400 °C in the treated soils. At 200 °C, the calcium content rose, and then dropped dramatically at 400 °C. The carbon percentage increased at 300 °C and decreased at 400 °C. The elements’ proportions showed little change or remained stable at temperatures between 400 °C and 600 °C.

Wheat is one of the global strategic crops and ranks third in terms of cereals production. Wheat crop is exposed to many fungal infections during the cultivation stages, some of which have the ability to secrete numbers of toxic secondary metabolites that threaten the quality of grains, consumer health, producer economics and global trade exchange. Fifty-four random samples were collected from wheat which originated from different countries. The samples included 14 of soft wheat to study the extent of their contamination with Deoxynivalenol (DON) and T-2 toxin by auto-ELISA technology and r-biopharm Microtiter plate. All samples were contaminated with DON toxin except one sample, and the contamination average ranged between 40.7 and 1018.8 µg kg-1. The highest contamination rates were in Lithuanian and the lowest in Indian wheat. Meanwhile the highest average level of T-2 toxin contamination was in Lithuanian wheat grains with a rate of 377.4µg kg-1 and the lowest in Polish wheat with an average of 115.3µg kg-1. GC-MS/MS and multiple reaction monitoring mode (MRM), was used to detect 15 triazole derivatives in collected samples, which may be used to combat fungal diseases on wheat during the growing season. Only 9 derivatives were found; Simeconazole, Penconazole, Hexaconazole, Cyproconazole, Diniconazole, Tebuconazole, Metconazole, Fenbuconazole and Difenoconazole. These derivatives varied according to the origin of wheat samples as well as their its concentration whereas, another 6 derivatives were not detected in all samples. A direct inverse relationship was found between the DON concentration in the samples and the residues of Simeconazole, Penconazole, Diniconazole, Tebuconazole, Metconazole, Fenbuconazole and Difenoconazole and the T-2 toxin showed the same relationship except for Tebuconazole. The safe and rational use of some triazole derivatives may be a new approach and a promising strategy not only to reduce plant diseases and their problems, but also to get rid of some mycotoxins as contaminates.

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents is growing enormously in recent years through instant messaging, social networking posts, blogs, online portals, and other digital platforms. Unfortunately, the misapplication of technologies has boosted with this rapid growth of online content which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier `tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

Abstract: This article explores the intricate relationship between environmental degradation, specifically air pollution, and economic growth in the Sultanate of Oman spanning the period from 1990 to 2022. We employ cointegration and vector error correction models to uncover both short- and long-term dynamics in the association between air pollution and economic growth. Fur-thermore, Granger causality analysis is utilized to investigate the causal links between these crucial variables. This data encompasses factors related to environmental quality and various control variables. The empirical results unveil a sustained long-term cointegration connection between the variables. Additionally, our findings highlight a statistically significant positive impact of economic growth and energy consumption on CO2 emissions. Furthermore, the short-term analysis reveals an annual adjustment of approximately 14.1% in N2O emissions dis-equilibrium. The Granger causality study indicates unidirectional causal relationships involving N2O emissions, economic growth, and CO2 emissions. The implications of these findings for Oman's policy landscape are substantial. To effectively reduce greenhouse gas emissions, it is imperative for Oman to establish robust climate change policies. Additionally, the government can play a pivotal role in encouraging and endorsing the use of renewable energy sources, such as green hydrogen, as a promising alternative to traditional oil and gas resources.

Background and Objectives: Cardiovascular disease is one of the leading causes of morbidity and mortality among the diabetic population. Given the high prevalence of diabetes mellitus (DM) in Saudi Arabia and the high prevalence of heart failure in the diabetic population, this study assesses the echocardiographic changes in Saudi patients with type 2 DM (T2DM) compared with healthy controls. Materials and Methods: In this retrospective case control study, 80 patients with diabetes (45 males, age: 58.78±10.2 years) were compared with 80 controls (45 males, age: 58.6±10 years) who underwent an echocardiographic study in the King Saud University Medical City, Riyadh, Saudi Arabia. Results: There were no significant differences between patients with diabetes and controls in terms of aortic root diameter, left atrium diameter, posterior wall, interventricular wall thickness, left ventricular diameters and ejection fraction. However, diastolic dysfunction was statistically significantly higher in the diabetic group compared with the control group (P &lt;0.05). Conclusions: This is the first case control study in Saudi Arabia that assesses echocardiographic parameters in T2DM patients. DM is an independent risk factor for diastolic dysfunction regardless of its association with hypertension and dyslipidemia.

This study presents an innovative approach named Optimal Adaptive Continuous Barrier Function Terminal Sliding Mode Control (OACBF-TSMC) approach with a novel switching manifold, to control mathematical model of nonlinear systems under model uncertainties and external disturbances. The proposed OACBF-TSMC approach combines the advantages of adaptive control, continuous barrier functions, and terminal sliding mode control to achieve robust stability of the mathematical model of nonlinear systems, and by using a genetic algorithm that tries to optimize the controller parameters as much as possible so that the proposed controller is the most to have efficiency and performance. The proposed method is designed to address the challenges of systems and provide superior performance in terms of stabilization accuracy, disturbance rejection, and robustness against uncertainties. However, the critical challenge facing the proposed controller is the complete elimination of the chattering phenomenon, which is the most important problem of ordinary sliding mode controllers. The simulation results show that the system trajectories in the proposed approach have converged to the origin with minimal overshoot and under-shoot in a finite time; In addition, the chattering problem in the control inputs has been well eliminated, which makes the proposed method suitable for various systems, including chaotic systems, energy sources, and distributed production systems. Furthermore, the OACBF-TSMC approach has the potential to enhance the stability and reliability of control systems, contributing to the integration of the system states.

The fidelity of the decadal experiment in Coupled Model Intercomparison Project Phase-5 (CMIP5) has been examined, over different climate variables for different temporal and spatial scales, in many previous studies. However, most of the studies were for the temperature and temperature-based climate indices. A quite limited study was conducted on precipitation of decadal experiment and no attention was paid to a catchment level. This study evaluates the performances of eight GCMs (MIROC4h, EC-EARTH, MRI-CGCM3, MPI-ESM-MR, MPI-ESM-LR, MIROC5, CMCC-CM, and CanCM4) for the monthly hindcast precipitation of decadal experiment over the Brisbane River catchment in Queensland, Australia. First, the GCMs datasets were spatially interpolated onto a spatial resolution of 0.050×0.050 (5 km× 5 km) matching with the grids of observed data and then were cut for the catchment. Next, model outputs are evaluated for temporal skills, dry and wet periods, and total precipitation (over time and space) based on the observed values. Skill test results reveal that model performances varied over the initialization years and showed comparatively higher scores from the initialization year 1990 and onward. Models with finer spatial resolutions show comparatively better performances as opposed to the models of coarse spatial resolutions where MIROC4h outperformed followed by EC-EARTH and MRI-CGCM3. Comparing the skills, models are divided into three categories (Category-I: MIROC4h, EC-EARTH, and MRI-CGCM3; Category-II: MPI-ESM-LR and MPI-ESM-MR; and Category-III: MIROC5, CanCM4, and CMCC-CM). Three multimodel ensembles’ mean (MMEMs) are formed using the arithmetic mean of Category-I (MMEM1), Category-I and II (MMEM2), and all eight models (MMEM3). The performances of MMEMs are also assessed using the same skill tests and MMEM2 performed best which suggests evaluating the models before the formation of MMEM.

The severe climate changes due to global warming pushed the world to strive to reduce carbon emissions. This study aims to minimize the carbon emissions for a proposed smart energy hub. This paper prioritizes the minimization of carbon emissions, taking into consideration the minimization of operation running costs, and the maximization of profit. In this paper two optimization scenarios were studied, to compare the results. In the first scenario, the minimization of carbon emissions was achieved, in the second scenario, the minimization of running costs and maximization of profit from the hub assets were studied. The proposed model was designed in MATLAB. Then the results were verified by CPLEX and validated by RTDS. The multi-objective model was presented to obtain the optimal operation. The mitigation of data uncertainty was achieved by applying the Kalman filter. In this work, a novel method was proposed for the estimation of the quarter-hour resolution data from the hourly one via the Kalman filter rather than applying the classic polynomial interpolation methods.

Background: Obesity is a global health crisis with a rapidly increasing prevalence among university students. Medical students are a unique population that may be at a higher risk of obesity due to the demanding nature of their studies and the associated stress. Objective: This study aimed to determine the prevalence of overweight, obesity, and the associated risk factors among medical students at Jazan University in Saudi Arabia. Methods: A cross-sectional study was conducted among 228 medical students from their 2nd to 6th academic years at the Faculty of Medicine, Jazan University. A self-administered questionnaire was used to collect data regarding sociodemographic characteristics, physical activity, dietary habits, comorbidities, medication use, family history, and lifestyle factors. Anthropometric measurements including height, weight, and waist circumference were recorded. Chi-square test and binary logistic regression were used to identify the risk factors associated with obesity. Results: The prevalence of overweight and obesity among the participants was 13.3% and 15% respectively. Hence, the combined prevalence of overweight and obesity is 28.3%. The mean weight was 63.39 ± 18.93 kilograms, and the mean height was 163.48 ± 9.78 centimeters. On the other hand, 17.3% of participants had underweight whereas 54.4% had normal BMI. Most of the participants (61%) did not engage in regular exercise. A high proportion consumed fruits (82.9%) and vegetables (58.8%) 3 or fewer days per week, and 84.2% consumed 3 or fewer meals per day. Fast food consumption more than 3 days per week was reported by 42.1% of participants. Obesity was not significantly associated with sociodemographic factors, physical activity, dietary habits, comorbidities, medication use, or family histories. However, those with a monthly family income of 15,000-24,999 riyals had significantly lower odds of obesity than those in the lowest income group (OR 0.230, p = 0.045). Conclusion: The prevalence of overweight and obesity among medical students at Jazan University is high. Although no significant associations were found between obesity and most risk factors, this study highlights the need for interventions that promote healthy lifestyles among medical students. Further research is needed to identify effective strategies for preventing and managing obesity in this population.

Diabetes mellitus (DM) is one of the major risk factors for COVID-19 complications as it is one of the chronic immune-compromising conditions especially if patients have uncontrolled diabetes, poor HbA1c &/or irregular blood glucose levels. Diabetic patient’s mortality rates with COVID-19 are higher than cardiovascular or cancer patients. Recently Bacillus Calmette–Guérin (BCG) has shown successful results in reversing diabetes in both rats and clinical trials based on different mechanisms from aerobic glycolysis to Beta cells regeneration. BCG is a multi-face vaccine that has been used extensively in protection from TB and leprosy and has been repositioned for treatment of bladder cancer, diabetes & multiple sclerosis. Recently, the COVID-19 epidemiological study confirmed that universal BCG vaccination reduced morbidity and mortality in certain geographical areas. Countries without universal policies of BCG vaccination (Italy, Nederland, USA) have been more severely affected compared to countries with universal and long-standing BCG policies that have shown low numbers of reported COVID-19 cases. Some countries have started clinical trials that included a single dose BCG vaccine as prophylaxis from COVID-19 or an attempt to minimize its side effects. This proposed research aims to use BCG vaccine as a double-edged weapon countering both COVID-19 & diabetes, not only as protection but also as therapeutic vaccination. The work includes a case study of regenerated pancreatic beta cells based on improved C-peptide & PCPRI laboratory findings after BCG vaccination for a 9 years’ patient. The patient was re-vaccinated based on a negative tuberculin test & no scar at the site of injection of the 1st BCG vaccination at birth. Furthermore, the authors in the present article described a prospective BCG multi-dose clinical study in full details that they will apply in case of acceptance of their submitted grant & the ethical committee approval. The aim of the clinical study is to check if double dose BCG (4 weeks apart) will show a significant difference in the protection of health care professionals in Egypt. The authors suggest and invite the scientific community to take into consideration the concept of direct BCG re-vaccination (after 4 weeks) because of the reported gene expressions & exaggerated innate immunity consequently. As the diabetic MODY-5 patient (mutation of HNF1B, Val2Leu) was on low dose Riomet® while eliminating insulin gradually, a simple analytical method for metformin assay was recommended to ensure its concentration before use as it is not approved yet by the Egyptian QC labs.

Cancer ranks second among the causes of mortality worldwide,following cardiovascular diseases.Brain cancer, in particular, has lowest survival rate of any kind of cancer. Brain tumours vary in their morphology, texture, and location, which determine their classification. Accurate diagnosis of the tumour category enables physicians to select optimal treatment strategies and potentially prolong patients’ lives. Researchers who also implemented deep learning models for diagnosing diseases in recent years largely focused on deep neural network optimization to enhance neural network performance. This implicates implementing models with the best performance and incorporating various network architectures by configuring their hyperparameters. This paper presents a novel hybrid approach method for improved brain tumour classification by combining CNN and EfficientNetV2B3 as feature extraction, followed by (KNN) for classification. To evaluate the recommended method’s efficacy, two widely known benchmark MRI datasets were utilized in the experiments. The initial dataset consisted of 3064 MRI images depicting meningiomas,pituitary,and gliomas tumours.Images from two classes of normal brain and brain tumour were included in the second dataset, which was obtained from Kaggle.In order to enhance performance even more, the study concatenates the CNN and EfficientNetV2B3 flattened outputs before feeding them into the KNN classifier. The proposed framework run on two different dataset and demonstrates outstanding performance with an accuracy of 99.51% and 99.8% on each dataset.

The World Health Organization estimated that around 66 thousand HBV are caused by needlestick injuries annually. Healthcare students should be aware of HBV transmission route and preventive measures. This study assessed the knowledge, attitudes, and practices toward HBV among Jordanian healthcare students and its associated factor. A cross-national study conducted from March to August. The participants were asked to complete the questionnaire. It had four sections: participants' sociodemographic, knowledge, attitudes, and practices about HBV. 2322 participants were enrolled, 67.9% were females, 26.4% were medical student, 35.9% were in the 3rd year. 40% of the participants held a high level of knowledge and attitude. 63.9% of participants had good practices toward HBV. Medical students in the last year of study, encountered HBV patients , had better knowledge. Male students from medicine college, encountered HBV patients and had extra HBV courses showed better attitude. High practice level was associated with being dentistry student, at 5th year, encountered HBV patient and extras HBV courses. This study demonstrated insufficient knowledge and attitudes toward HBV, the practices level toward HBV among healthcare students was promising. Subsequently, public health efforts should modify the knowledge and attitude gaps to reinforce awareness and minimizing risks of the infection.

In this short communication, a concept has been presented to model geographical data to predict future temperature of Tabuk, region. Machine learning has been applied to the weather station data to develop a prediction model. The preliminary results are promising and encouraging and are envisaging to further this research towards the determination of unknown temperature rise in the region. This is important to mention here, that the problem has been formulated as a Regression problem, NOT as a classification problem. Hence, applying Convolutional neural networks is not possible, due to the non-existence of classes or converting the temperature values to classes does not make any sense. Hence, this is defined as a regression problem which achieved encouraging desirable results.

In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel Electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer number of sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: Up, Down, Left, and Right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration has been implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based Brain-Computer Interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50% and 92.62% for precision, recall, and F1-score, respectively.

Initially introduced almost thirty years ago for the express purpose of providing electronic warfare systems the capabilities to detect, characterize, and identify radar emitters, Specific Emitter Identification (SEI) has recently received a lot of attention within the research community as a physical layer technique for securing Internet of Things (IoT) deployments. This attention is due in large part to SEI’s demonstrated success in passively and uniquely identifying wireless emitters using traditional machine learning and the success of Deep Learning (DL) within the natural language processing and computer vision areas. SEI exploits distinct and unintentional features present within an emitter’s transmitted signals. The existence of these distinctive and unintentional features is attributed to slight manufacturing and assembly variations that exist within and between the components, sub-systems, and systems that comprise an emitter’s Radio Frequency (RF) front end. Although sufficient to facilitate SEI, these features do not hinder normal operations such as detection, channel estimation, timing, and demodulation. However, despite the plethora of SEI publications, it has remained largely a focus of academic endeavors that primarily focus on proof-of-concept demonstration and little to no use in operational networks for various reasons. The focus of this survey is a review of SEI publications from the perspective of its use as a practical, effective, and usable IoT security mechanism, thus we use IoT requirements and constraints (e.g., wireless standard, nature of their deployment) as a lens through which each reviewed paper is analyzed. Previous surveys have not taken such an approach and have only used IoT as motivation, a setting, or a context. In this survey, we consider operating conditions, SEI threats, SEI at scale, publicly available data sets, and SEI considerations that are dictated by the fact that it is to be employed by IoT devices or IoT infrastructure.

The current study aimed to: i) Monitor levels of PM₁₀, at Shebika, Haram, Masfala, Azizia, Awali and Mina in Makkah city, KSA during the period of 01 Shawwal 1436H – 27 Rabi Al-Awwal 1437H, by using LVS instruments; and; 2) assess health risk (non-cancer and cancer risks) on humans (children and adult) exposed to PM₁₀ in ambient air of Makkah city.The results showed that: the high PM₁₀ levels were found in Haram site, while the lower levels were found in Awali site. These levels were lower than that set for PM₁₀ by PME (Daily limit of 340 µg/m³). Vehicles emissions and constructions sources may be the main source of PM₁₀ levels in Makkah city. The human health risk assessments showed that: the daily exposure doses of PM₁₀ were ranked in the order: D_ing > D_dermal > D_inh for children and adult in Makkah city. Ingestion of PM₁₀ particles was the main exposure pathway for both children and adults. The HIs and cancer risk values were within the safe level, indicating that (non-carcinogenic and carcinogenic) risks for humans exposed to PM₁₀ in Makkah city were negligible.

Cathepsins (Caths) are lysosomal proteases that participate in various physiological and pathological processes. Accumulating evidence suggests that cathepsins play a multifaceted role in radiotherapy response. Firstly, cathepsins contribute to the remodelling of the tumor microenvironment by degrading extracellular matrix components, promoting angiogenesis, and facilitating tumor invasion and metastasis. Their proteolytic activity influences the tumor's response to radiation by affecting oxygenation, nutrient availability, and immune cell infiltration within the tumor microenvironment. Furthermore, cathepsins have been implicated in DNA damage repair processes, including non-homologous end joining and homologous recombination. Cathepsin-mediated DNA repair mechanisms can promote radioresistance in cancer cells, limiting the efficacy of radiotherapy. Additionally, cathepsins have been associated with the activation of prosurvival signalling pathways, such as PI3K/Akt and NF-κB, which can confer resistance to radiation-induced cell death. Radiotherapy plays a crucial role in the treatment of various malignancies. However, the effectiveness of radiotherapy can be limited by intrinsic or acquired resistance mechanisms in cancer cells. In recent years, there has been growing interest in understanding the role of cathepsins, a family of proteases, in modulating the response of tumor cells to radiotherapy. In this study, we shed light on the regulation and expression of cathepsin B in colon carcinoma cell line (Caco-2) before and after exposure to radiation were investigated. Radiation-induced upregulation of cathepsin B in dose-independent manner. Proteolytic inhibition of cathepsin B by cathepsin B specific inhibitor CA074 has increased the cytotoxic effect of ionizing irradiation in caco-2 cells. Similar results were also obtained after cathepsin B knockout by CRISPR CAS9. These results showed that cathepsin B could contribute to ionizing radiation resistance and abolishing of cathepsin B either by inhibition its proteolytic activity or expression has increased in caco-2 cells susceptibility to ionizing irradiation.

There is a rising global concern for the ongoing outbreak of SARS-CoV-2 due to its high transmission rate and unavailability of treatment. Through the binding of its spike glycoprotein with angiotensin type 2 (ACE2), SARS-CoV-2 can efficiently get in the cells of patients and start its pandemic cycle. Herein, the biological diversity of SARS-CoV-2 infection was assessed in Babylon province of Iraq by investigating the possible genetic variations of the spike glycoprotein. A specific coding region of 795 bp within the viral spike (S) gene was amplified from 19 patients who suffered from obvious symptoms of SARS-CoV-2 infection. Sequencing results identified fifteen novel nucleic acid variations with a variety of distributions within the investigated samples. The electropherograms of all the identified variations showed obvious co-infections with at least two different viral strains per sample. Within these co-infections, the majority of samples exhibited three nonsense single nucleotide polymorphism (SNP)s, p.301Cdel, p.380Ydel, and p.436del, which yielded three truncated SARS-CoV-2 spike glycoproteins of 301, 380, and 436 amino acids length, respectively. The network and phylogenetic analyses indicated that for all viral infections were derived from multi-ancestral origins. Results inferred from the specific clade-based tree entailed that some viral strains were derived from European G-clade sequences. In conclusion, our data demonstrated the absence of any single strain infection among all investigated viral samples in the studied area, which may entail a higher risk of SARS-CoV-2 in this country. Through the identified high frequency of truncated spike proteins, we suggest that defective SARS-CoV-2 may depend on helper strains having intact spikes in its infection. Alternatively, another putative ACE2-independent route of viral infection way also suggested. To the best of our knowledge, this is the first report to describe the co-infection of multiple strains of SARS-CoV-2 in patients with COVID-19.

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on mobile data science and intelligent apps in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

This paper investigates the application of blended learning in undergraduate translation classes to enhance the effective incorporation of technology in teaching. Blended learning is the combination of face-to-face instruction with online learning, which provides students with a flexible and diverse learning environment for developing technological proficiency. Using a case study methodology, this research examines the translation undergraduate program at the University of Jeddah, which implemented blended learning as its primary instructional framework starting in 2021. The study aims to explore the advantages and challenges associated with the adoption of blended learning in translation classes while offering practical suggestions that could help in its implementation. The findings show several advantages of integrating blended learning into undergraduate translation classes. Firstly, the students could benefit from a flexible learning environment that accommodates their individual learning preferences and schedules. Secondly, It also helps to promote active engagement and interaction which nurture collaborative learning opportunities among students. Thirdly and lastly, blended learning provides a platform for students to develop their technological skills, which become crucial in today's digital age. Despite this, the study also points out significant challenges that need to be addressed. These important challenges include the required planning and preparation involved in creating effective online components, ensuring equitable access to technology and the internet, and adapting teaching and learning approaches to optimise the blended learning experience.To sum up, this paper contributes to the understanding of blended learning in the context of undergraduate translation classes.

While microcredits are, by the development practitioners, repeatedly hailed as a useful measure of development policy, this empirical research in Bangladesh arrives at different results: the governing and disciplinary measures are necessary to trigger the best possible repayment success. Based on interviews with the microfinance beneficiaries, loan officers, and microfinance experts in Bangladesh, I chart some of the most frequently used governing and disciplinary techniques including fictitious proximity of loan officers, their fields of visibility and surveillance processes. The “success” of credit delivery is a neoliberal stance that ensures the ceaseless debt repayment process using the intentional and planned conduct, capitalizing social capital and credit honour reputation, surveillance over business enterprises, use of equivocal language and different forms of deception. The development strategy through microfinance in Bangladesh is a development “dispositif” with a changing shift in its declared social missions.

Artificial intelligence is dramatically transforming medical imaging. We assessed the levels of artificial intelligence use among radiology trainees and explored their perceived impact of artifi-cial intelligence on the radiology workflow and radiology profession, in correlation with the perceived ease of use and behavioral intention to use artificial intelligence. This cross-sectional study enrolled radiology trainees from Saudi Arabia, and an online 5-part-structured question-naire was disseminated via online networks to trainee in July 2021. We included 98 participants (51 male; age 27.59±2.02 years). Level of use was low; few used it in routine practice (7%). The impact of artificial intelligence on the radiology workflow was positively perceived in all radi-ology workflow steps (3.64–3.97 out of 5). A positive impact on the radiology profession was more frequently perceived for technical and performance aspects (81%–85%) compared with prestige and legal aspects (64%–71%). Perceived ease of use and behavioral intention to use arti-ficial intelligence was associated with the current professional activity, level of use artificial in-telligence use, and perceived impact on the profession as well as on radiology workflow (p<0.05). Levels of artificial intelligence use in radiology are very low. The perceived positive impact of ar-tificial intelligence on radiology workflow and profession is correlated with an increase in be-havioral intention to use artificial intelligence. Thus, increasing awareness about the favorable impact can improve the behavioral use.

This study explores the impact of financial inclusions on financial resilience. In doing so, we utilize the World Bank’s data on global financial inclusions. Our study confirms that the respondents with financial accounts are more resilient than those without accounts. The chances of being financially resilient are around 1.4 times higher for the account holders than their counterparts. We find a significant relationship between gender and financial resilience; males are 1.4 times more resilient than females when other covariates are considered in the regression model.

Titania is one of the most comprehensively studied nanostructures due to its widespread applications in production of catalytic, gas- sensing and corrosion- resistance materials [1]. M-polynomial of nanotubes has been vastly investigated as it produces many degree-based topological indices which are numerical parameters capturing structural and chemical properties. These indices are used in the development of quantitative structure-activity relationships (QSARs) in which the biological activity and other properties of molecules are correlated with their structure like boiling point, stability, strain energy etc of chemical compounds. In this paper, we determine M-polynomials of single-walled titanium (SW TiO2) nanotubes and recover important topological degree based indices of them to theoretically judge these nanotubes. We also use Maple to plot surfaces associated to different types of single-walled titanium (SW TiO2) nanotubes.

The production of nanoparticles enhances the bioactivity of biological molecules for drug delivery to infection sites. This study explains how silver nanoparticles (AgNPs) coating enhanced the hypoglycemic effects of vanillic acid in male diabetic rats with diabetes brought on by streptozotocin. (STZ). Four groups of 24 rats were created for the investigation and divided into them. The first group rats (G1; untreated) were given a basal diet, whereas the other three groups all developed diabetes through STZ injection. Diabetic rats who were not getting therapy were included in the second group (G2; STZ-positive), whereas the other diabetic rats were divided into the third group (G3, vanillic acid-treated) and the fourth group (G4; AgNPs-coated vanillic acid treated). The treatment lasted four weeks. In G2, the induction of diabetes led to an increase in serum glucose, glycated proteins, renal indices, interleukin-6 (IL-6), K+, immunoglobulins, and lipid peroxidation, while decreased Ca++, Na+, and other antioxidants in the kidney tissue homogenate. In addition, aberrant signs were present in the pancreas and kidneys of diabetic rats. The renal and pancreatic tissues were effectively enhanced by vanillic acid or vanillic acid-coated AgNPs, bringing them very close to their pre-diabetic conditions. AgNPs-coated vanillic acid offered a stronger defense against STZ-induced diabetes and lessened the effects of hyperglycemia compared to ordinary vanillic acid. Additionally, using vanillic acid coated with silver nanoparticles greatly reduced inflammation when compared to using vanillic acid alone. This outcome suggests increased antioxidant activity.

RAxML-NG is a new phylogentic inference tool that replaces the widely-used RAxML and ExaMLtree inference codes. Compared to its predecessors, RAxML-NG offers improvements in accur-acy, flexibility, speed, scalability, and user-friendliness. In this chapter, we provide practicalrecommendations for the most common use cases of RAxML-NG: tree inference, branch supportestimation via non-parametric bootstrapping, and parameter optimization on a fixed tree topo-logy. We also describe best practices for achieving optimal performance with RAxML-NG, inparticular, with respect to parallel tree inferences on computer clusters and supercomputers. AsRAxML-NG is continuously updated, the most up-to-date version of the tutorial described inthis chapter is available online at: https://cme.h-its.org/exelixis/raxml-ng/tutorial .

Many spectroscopic diagnostics are routinely used as a technique to infer the plasma parameters from line emission spectra but their accuracy depends on the numerical model or code used for the fitting process. However, the validation of a line shape code requires some steps : comparison of the line shape code with other similar codes for some academic (simple) cases and then more complex ones, comparison of the fitting parameters obtained from the best fit of the experimental spectra with those obtained with other diagnostic techniques and/or comparison of the fitting parameters obtained by different codes to fit the same experimental data. Here we compare the profiles of the hydrogen Balmer β line in a helium plasma computed by six codes for a selected set of plasma parameters and we report on the plasma parameters inferred by each of them from the fitting to a number of experimental spectra measured in a helium corona discharge where the pressure was in the range 1- 5 bar.

Choking and foreign body aspiration are significant causes of mortality and morbidity in young youngsters. This study aimed to investigate the effects of education programs on school students ' knowledge, attitude, and practice towards a choking person. This quasi-experimental study with a pretest-posttest control group design included a total of 282 school students. The students were divided into an intervention group (n = 141) and a control group (n = 141). After both groups completed the pretest, the intervention group received an education program on first aid for a choking person. Then, both groups were subjected to a posttest at the end of the program. The descriptive and inferential statistics analyzed the data. There were no significant differences between the two groups within the study parameters at the pretest (P = > 0.05). However, at posttest, a significant difference was observed in the knowledge (p = 0.001), attitude (p = 0.024), and practice (p = 0.001) of the intervention group compared to the control group. The intervention group showed significant improvement in the students’ knowledge, attitudes, and practices regarding first aid for a choking person. We suggest a continuous training program to improve the competency of first aid for a choking person.

Background: The Internet has become an essential component of college students' lives, serving as a tool for academic pursuits, daily activities, and social interactions with individuals and the global community. Productive internet use is associated with student success, while pathological internet use can negatively impact psychosocial performance. This study aimed to assess the impact of internet addiction on the cognitive function, mood, and physical activity of college students. Study design: The study was based on a correlational observational study design. Settings: The study’s participants were taken using a convenience sampling method from the Manav Rachna International Institute of Research and Studies campus, Faridabad, Haryana, having a student strength of more than two thousand. Methods: With a response rate of 69.93%, one hundred male and female college students who could understand the self-administered questionnaires and obtained the internet addiction test (IAT) scores equal to or above twenty participated (aged 18-25 years) in this study. The participants were assessed for the study’s outcomes, such as the internet addiction (viz. average, moderate, and excessive) cognition, mood, and physical activity level, using an internet addiction test questionnaire (IATQ), cognitive failure questionnaire (CFQ), the profile of mood state questionnaire (POMS), and international physical activity questionnaire (IPAQ), respectively. A categorical analysis with mean scores was carried out for all the outcomes. The correlation between internet dependency and cognitive abilities, emotional state, and levels of physical activity among the participants was ascertained using Pearson's correlation coefficient. The threshold of significance was set to 95%, meaning any p-value < 0.05 was considered significant. Results: The study’s outcome variable- internet addiction, revealed a moderate positive correlation with cognition (r=0.793; p=0.001) and mood (r=0.703; p=012), while a moderate negative correlation with physical activity (r=-0.681; p=003). Conclusion: The study concluded that internet addiction is positively linked to cognition and mood disruption; however, it negatively affects physical activities among college students. An awareness program should be executed on “Internet addiction and its effects on college students’ Cognition, Mood, and Physical Activity” and advocating Internet use in a limited manner for getting aroused, feeling better, and avoiding physical inactivity.

Sexually transmitted infections (STIs) such as Gonorrhea is associated with serious morbidity and mortality rates in the world considering the multiple virulence factors possessed. The disease is manifested as salpingitis, pelvic inflammatory disease (PID), and bacteremia and is controlled by macrophages, dendritic cells, neutrophils, T cells, epithelial cells and cytokines. Dihydroliponamide acetyltransferase, a component of the mitochondrial pyruvate complex can be used as immunogenic target. Recent changes in the strain allowed the bacteria to acquire resistance against antibiotics.Vaccination remains an alternative to prevention against the disease. This study predicts an effective epitope-based vaccine against dihydrolipoamide acetyltransferase of Neisseria Gonorrhea using immunoinformatics approaches. Sequences retrieved from NCBI were passed on several prediction tests to analyze for possible B-cell, T-cell MHC class I epitopes and class II. Two epitopes showed high binding affinity for B-cells, while thirteen epitopes showed high binding affinity for MHCI and forty-five for MHCII. A population coverage of 100% for combined MHC I and II dictates the huge number of individuals who will benefit from formulating the vaccine. We recommend invivo and invitro studies to prove our prediction results.

A novel hybrid cobalt phosphite, (H2DAB)[Co(H2PO3)4]·2H2O, has been synthesized by using slow evaporation method, in the presence of cobalt nitrate, phosphorous acid and 1,4- dia-minobutane (DAB= 1,4- diaminobutane) as a structure-directing agent. Single crystal X-ray diffraction analysis showed that the compound crystallizes in the P-1(n.2) triclinic space group, with the following unit cell parameters (Å, °) a = 5.4814 (3), b = 7.5515 (4), c = 10.8548 (6), α = 88.001 (4), β = 88.707 (5), γ = 85.126 (5), and V= 447.33 (4) Å3. The crystal structure was built up from cor-ner-sharing [CoO6] octahedra, forming chains parallel to [001], which are interconnected by H2PO3 pseudo-tetrahedral units. The deprotonated cations, residing between the parallel chains, interacted with the inorganic moiety via hydrogen bonds leading thus to the formation of the 3D crystal structure. The Fourier transform infrared spectrum showed characteristic bands corresponding to the phosphite group and the organic amine. The thermal behavior of the compound consisted mainly of the loss of its organic moiety and the water molecules. The biological tests exhibited significant activity against Candida albicans and Escherichia coli strains in all used concentrations, while less inhibitory activity was pronounced against Staphylococcus epidermidis and Saccharomyces cerevisiae, and in the case of multi-cellular organisms, no activity against the nematode model Steinernema feltiae was detected.

The efficiency success status of organization built environment assets management needs robust and comprehensive maintenance processes which depends on the efficiency of the contract information documents for the maintenance management within the assets life cycle and achieve positive return of the investments. Therefore, this paper highlights on the appropriate interactive approach to structuring information flow scope of the asset facilities management contract for construction projects based on GIS and BIM integration processes, sustainability standards, and As-built contractual documents of the project to support the organization owner and stakeholder to improve the asset management processes. Using expert interviews with comprehensive understand for the information type in several facility management contracts conducted in common Organization types based on reviewing available international facility management standards manual and contract of procedures for different services types, and required information flow inside international facility management firms. The study classify and build significant integrated information and data flow model contract drafting with measures and indicators of performance efficiency as guideline for monitoring, technically evaluating, and financially procedures for performance efficiency and quality of services provided in the facilities management contract for King Faisal University campus projects as a case study.

Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.

Internet of Things (IoT) opens new horizons by enabling automated procedures without human interaction using IP connectivity. IoT deals with devices, called things which are represented as any item from our daily life that is enhanced with computing or communication facilities. Among various mobile communications, Zigbee communication is broadly used in controlling or monitoring applications due to its low data rate and low power consumption. Securing IoT systems have been the main concern for the research community. In this paper, different security-threats of Zigbee networks in IoT platform have been addressed to predict the potential security threats of Zigbee protocol and a Security Improvement Framework (SIF) has been designed for intelligent monitoring in an office environment. Our proposed SIF can predict and protect various potential malicious attacks in the Zigbee network and respond accordingly through a notification to the system administrator. This framework (SIF) is designed to make automated decisions immediately based on real-time data which are defined by the system administrator. Finally, the designed SIF has been implemented in an office security system as a case study for real-time monitoring. This office security system is evaluated based on the capacity of detecting potential security attacks. The evaluation results show that the proposed SIF is capable of detecting and protecting several potential security attacks efficiently enabling more secure way of intelligent monitoring.

Ionic liquid MIE-NH2 displays a new role of development of modification of glycoproteins of lactoferrin through a reductive amination mechanism to synthesize versatile pharmaceuticals. This work introduces a new strategy of modification of Lactoferrin by using methylimidazolium N-ethylamine, this ionic liquid MIE-NH2 linked to N-glycans in Lactoferrin derivatives. Using UPLC/ESI-QTOF and MALDI-TOF mass spectrometry to perform and detect the modifying of ionic liquid-linked glycoproteins. Relevantly, modifying the lactoferrin by MIE-H2 as a small molecule of ion liquid lactoferrin (IL-Lf), which could be a potential antiviral drug and it is achieved by inhibiting various targets. The probability of the lactoferrin modified as a small IL-Lf-molecule to inhibit any of these targets was investigated to find out its potency as a SARS-CoV-2 inhibitor. Molecular docking disclosed the activity of modifying glycoproteins - small IL-Lf-molecules containing amino groups and interaction with targeted Mpro, RdRp, TMPRSS2, and PLpro. Clinically, this study shows the a volubility to provide small IL-Lf-molecules as significantly important drugs that target main protease (Mpro), RNA dependent RNA polymerase (RdRp), transmembrane protease serine 2 (TMPRSS2), and Papain-like protease (PLpro).

In this paper, two types of the general fractional derivatives of distributed order as well as a corresponding fractional integral of distributed type are defined and their basic properties are investigated. The general fractional derivatives of distributed order are constructed for a special class of the one-parametric Sonin kernels with a power law singularities at the origin. The conventional fractional derivatives of distributed order based on the Riemann-Liouville and the Caputo fractional derivatives are particular cases of the general fractional derivatives of distributed order introduced in this paper.

For bounded unit interval, we propose a new Kumaraswamy generalized (G) family of distributions from a new generator which could be an alternate to the Kumaraswamy-G family proposed earlier by Cordeiro and de-Castro in 2011. This new generator can also be used to develop alternate G-classes such as beta-G, McDonald-G, Topp-Leone-G, Marshall-Olkin-G and Transmuted-G for bounded unit interval. Some mathematical properties of this new family are obtained and maximum likelihood method is used for estimating the family parameters. We investigate the properties of one special model called a new Kumaraswamy-Weibull (NKwW) distribution. Parameter estimation is dealt and maximum likelihood estimators are assessed through simulation study. Two real life data sets are analyzed to illustrate the importance and flexibility of this distribution. In fact, this model outperforms some generalized Weibull models such as the Kumaraswamy-Weibull, McDonald-Weibull, beta-Weibull, exponentiated-generalized Weibull, gamma-Weibull, odd log-logistic-Weibull, Marshall-Olkin-Weibull, transmuted-Weibull, exponentiated-Weibull and Weibull distributions when applied to these data sets. The bivariate extension of the family is proposed and the estimation of parameters is given. The usefulness of the bivariate NKwW model is illustrated empirically by means of a real-life data set.

The work reports a facile synthesis of high thermally-stable nanocrystalline anatase TiO2 nanoparticles (NPs) doped with different atomic concentrations (0.5, 1.0, 3.0, and 6.0%) of Gd3+ and Nd3+ ions by a template-free and one-step solvothermal process, using titanium(IV) butoxide as titanium precursor and dimethyl sulfoxide (DMSO) as solvent. The structure and morphology of the Gd3+, Nd3+, and 0.5%Gd3+-0.5%Nd3+/doped TiO2 NPs have been characterized by using various analytical techniques. The Gd3+/ and Nd3+/TiO2 molar ratios were found to have a pronounced impact on the crystalline structure, size, and morphology of TiO2 NPs. X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) studies revealed the proper substitution of Ti4+ by Gd3+ and Nd3+ ions in the TiO2 host lattice. The as-prepared Gdx/TiO2, Ndx/TiO2, and Gd1.0/Ndx/TiO2 bimetallic NPs, x = 0.5, 1.0, 3.0, and 6%, have been investigated as electrocatalysts for hydrogen evolution reaction (HER) in 1.0 M KOH solution using a variety of electrochemical techniques. At any doping percentage, the Gd1.0/Ndx/TiO2 bimetallic NPs showed higher HER catalytic performance than their corresponding counterparts i.e. Gdx/TiO2 and Ndx/TiO2. Upon increasing the Nd content from 0.5 to 6.0%, the HER catalytic performance of the Gd1.0/Ndx/TiO2 bimetallic NPs was generally enhanced. Among the studied materials, the bimetallic Gd1.0/Nd6.0/TiO2 NPs emerged as the most promising catalyst with an onset potential of -22 mV vs. RHE, a Tafel slope of 109 mV dec-1, and an exchange current density of 0.72 mA cm-2. Such HER electrochemical kinetic parameters are close to those recorded by the commercial Pt/C (onset potential: -15 mV, Tafel slope: 106 mV dec-1, and exchange current density: 0.80 mA cm-2), and also comparable with those measured by the most active electrocatalysts reported in the literature. The synergistic interaction of Gd and Nd is thought to be the major cause of the bimetallic catalyst's activity.

In this paper, we present a framework that automatically labels Latent Dirichlet Allocation (LDA) generated topics using sentiment and aspect terms from COVID-19 tweets to help the end-users by minimizing the cognitive overhead of identifying key topics labels. Social media platforms especially Twitter are considered as one of the most influential sources of information for providing public opinion related to a critical situation like the COVID-19 pandemic. LDA is a popular topic modelling algorithm that extracts hidden themes of documents without assigning a specific label. Thus automatic labelling of LDA-generated topics from COVID-19 tweets is a great challenge instead of following the manual labelling approach to get an overview of wider public opinion. To overcome this problem, in this paper, we propose a framework named \texttt{SATLabel} that effectively identifies significant topic labels using \textit{top unigrams features of sentiment terms and aspect terms clusters from LDA generated topics} of COVID-19 related tweets to uncover various issues related to the COVID-19 pandemic. The experimental results show that our methodology is more effective, simpler, and traces better topic labels compare to the manual topic labelling approach.

Extracellular vesicles (EV) released by tumor cells are a major aspect of the resistance-associated secretory phenotype (RASP), by which immune evasion can be established. Heat shock proteins (HSPs) are an evolutionarily conserved family of molecular chaperones, which stabilize proteins, minimize protein misfolding and aggregation within the cell, besides facilitating protein translocation, refolding and degradation. (i) Releases of extracellular HSPs (ex-HSP) and EV-associated HSPs (EV-HSP) are essential in RASP, by which molecular cotransfer of HSPs with oncogenic factors into recipient cells can promote cancer progression and resistance against stress such as hypoxia, radiation, chemicals, and immune system. (ii) RASP of tumor cells can eject anticancer drugs, molecularly targeted therapeutics, and immune checkpoint inhibitors with EVs. (iii) Cytotoxic lipids can be also released from tumor cells as RASP. Nevertheless, ex-HSP and EV-HSP can play immunostimulatory and immunosuppressive roles by binding to receptors such as LRP1/CD91/A2MR, scavenger receptors, and toll-like receptors expressed on recipient cells. Liquid biopsy of HSPs in body fluids may be useful in diagnosis, prognosis, and treatment in cancer. Regarding HSP90-targeted therapeutics, we summarize the pros, cons, and problem solutions in this review. Although production of HSPs are canonically induced by heat shock factor 1 (HSF1) and hypoxia-inducible factor 1 (HIF-1), recent studies discovered that production of HSPs is also regulated by matrix metalloproteinase 3 (MMP3) and heterochromatin protein 1 (HP1) and production of cochaperone CDC37 is reciprocally regulated by myeloid zinc finger 1 (MZF1) and SCAN-D1.

The fitting and modeling of skewed, complex, symmetric, and asymmetric datasets is an exciting research topic in many fields of applied sciences, notably lifetime, medical, and financial sciences. The Heavy tailed Nadarajah Haghighi model is introduced by compounding the heavy tailed family and Nadarajah Haghighi distribution in this paper. The so obtained model has three parameters accounting for the scale and shape of the distribution. The proposed distribution's fundamental characteristics such as probability density, cumulative distribution, hazard rate, and survival functions are provided, and several key statistical properties are established, and several entropy information measures are proposed. The estimation of model parameter is performed via the ML procedure. Further, different simulation experiments are conducted to demonstrate the performance of proposed estimator’s using some measure, like the average estimate, the average bias, and associated mean square error. Finally, we are applied our proposed model to analyze three different real datasets. In our illustration, we are compared the practicality of the recommended model with several well-known competing models.

This paper examines the determinants of CO2 emissions in Oman from 1990 to 2022, focusing on the impacts of energy consumption, economic growth, urbanization, financial development, and foreign direct investment. Utilizing stepwise regression, robust least squares, Fully Modified Least Squares (FMOLS), Canonical Cointegrating Regression (CCR), and various cointegration tests including the Johansen cointegration test, Engle-Granger cointegration test, and Vector Error Correction (VEC) model, the study reveals several key findings. Energy use per capita (ENGY) and real GDP per capita (RGDPC) are consistently significant positive predictors of CO2 emissions. Urbanization (URB) also significantly contributes to higher emissions. Conversely, Financial Development Index (FDX) and foreign direct investment (FDI) do not show significant effects on CO2 levels. The high R-squared values across models indicate that these variables explain a substantial portion of the variation in emissions. Cointegration tests confirm long-term equilibrium relationships among the variables, with the Johansen test identifying two cointegrating equations and the Engle-Granger test showing significant tau-statistics for FDX, ENGY, and URB. The VEC model further highlights the short-term dynamics and adjustment mechanisms. These findings underscore the importance of energy policy, economic development, and urban planning in Oman's efforts towards sustainable development and decarbonization.

The fast fashion industry has a significant impact on the environment at every stage of the product life cycle. There is a lack of research in the literature on the impact of fast fashion on the environment through logistics and transport. In this area, the greatest environmental impact is caused by air emissions, which is very important from the point of view of climate change. Therefore, this article deals with the analysis of the carbon footprint resulting from the fast fashion logistics process. The identification and assessment of fast fashion companies' sustainability efforts in the area of transport-related emissions and sustainable transformation strategies was conducted using a case study method for selected brands (H&M Group; Inditex, Shein). The study uses secondary data available in non-financial reports from 2023. After analyzing the data and content in non-financial reports on transport-related emissions, sev-eral important conclusions emerge. As a dominant practice, companies emphasize their reliance on external frameworks and standards, in particular the Global Reporting Initiative and the Task Force on Climate-related Financial Disclosures. Companies prioritize reducing emissions from significant factors, such as production processes. The data disclosed in the reports (scope 1, 2, 3) are not clearly formulated, but they can be used to calculate the level of CO2 emission reduction/increase. The ar-ticle contains recommendations for companies to improve their reporting by including additional indicators expressing the efficiency of transport-related emissions.

To address the issue of reduced orbit estimation accuracy resulting from discrepancies between in-orbit measured geomagnetic field values and IGRF model values in autonomous orbit estimation of microsatellites based on geomagnetic field measurements, this study introduces an adaptive cubature H-infinity filtering algorithm. Firstly, it presents the low-altitude orbital dynamics model and geomagnetic field measurement model for microsatellites. Subsequently, within the adaptive cubature H-infinity filtering algorithm, a spherical simplex rule and a second-order Gaussian-Legendre quadrature criterion are employed to compute the spherical surface integral and radial integral. Additionally, a spherical simplex-radial quadrature rule is proposed to enhance the approximation accuracy of nonlinear Gaussian weighted integrals. Furthermore, by integrating extended H-infinity filtering based on game theory with the cubature quadrature rule, an inverse proportional relationship between constraint level and filtering innovation is established. This allows for adaptive adjustment of the constraint level to enhance robustness against model errors. Finally, through semi-physical simulation experiments, it is demonstrated that compared with traditional algorithms, the proposed algorithm improves autonomous orbit estimation position accuracy of microsatellites by 7.49%, while also enhancing velocity accuracy by 3.1%, thus validating its effectiveness.

The high flammability and lack of charrability of polystyrene (PS) pose significant limitations on its broader applications. Therefore, there is an urgent need for a straightforward and efficient method for the synthesis of flame-retardant PS, which still poses a considerable challenge. In this study, we present an efficient approach to enhance the flame-retardant properties of PS through direct phosphorylating aromatic C(sp2)-H bonds using commercially available and inexpensive manganese catalyst. A range of phosphonates served as reactive substrates to enable a tunable degree of polymer functionalization. The corresponding phosphorylated PS specimens were determined by means of 1H NMR, 31P NMR, FTIR spectroscopy and GPC. Microscale combustion calorimetry (MCC) tests indicate that this protocol indeed enhances the flame-retardant performance of PS. Moreover, other advantages associated with the incorporation of the phosphonate group have been observed, including improved thermal resistance and wettability. This cost-effective strategy of Mn-catalyzed C-H can also be utilized to directly obtain phosphonate modification of waste foamed PS and styrene acrylonitrile copolymer, providing a new method for the purpose of recycling and upgrading PS plastics.

Artificial Intelligence (AI) play an important role in many fields, including medical education, practice, and research. Many medical educators started using ChatGPT at the end of 2022 for many purposes. The aim of this article was to explore the potential uses, benefits and risks of using ChatGPT in integrated pharmacotherapy of infectious disease module education. This study was an experimental study with content analysis of the potential applications of the ChatGPT model for integrated pharmacotherapy of infectious diseases module education was performed. The findings of this study shows that there are many potential uses, benefits and risks associated with the use ChatGPT. In conclusion, medical and health sciences educators can use ChatGPT as a guide in many aspects related to the integrated pharmacotherapy of infectious disease curriculum development, syllabus design, lecture notes preparation and exam preparation with caution.

Cancer is a condition in which the body's cells proliferate unchecked. Skin cancer is one of the deadliest diseases that impacts the skin on many levels. There are several different types of the disease, including melanoma, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevus. Due to its increased prevalence, skin cancer, in particular melanoma, is be-coming a serious health problem. Early identification of skin lesions is crucial for successful treatment. Due to their resemblance, many skin lesions are misclassified, which is a severe issue. Researchers seek computer-aided diagnostic tools for early malignant tumor detection. First, a new model based on the combination of "you only look once" (YOLOv5) and "ResNet50" is proposed for melanoma detection with its degree using humans against a machine with 10,000 training images (HAM10000). Second, feature maps integrate gradient change, which allows rapid inference, boosts precision, and reduces the number of hyperparameters in the model, making it smaller. Finally, to get the desired outcomes, the current YOLOv5 model is changed by adding new classes for dermatoscopic images of typical lesions with pigmented skin. The pro-posed approach improves melanoma detection with a real-time speed of 0.4 ms non-maximum suppression (NMS) per image. The performance metrics average is 99.0%, 98.6%, 98.8%, 99.5, 98.3%, and 98.7% for precision, recall, dice similarity coefficient (DSC), accuracy, mean average precision (MAP) from 0.0 to 0.5, and MAP from 0.5 to 0.95, respectively. Compared to cur-rent melanoma detection approaches, the provided approach is more efficient using deep fea-tures.

Non-Vitamin K Oral Anticoagulants (NOACs) have revolutionized anticoagulant therapy, offering improved safety and efficacy over traditional agents like warfarin. This review comprehensively examines the dual roles of NOACs—Apixaban, Rivaroxaban, Edoxaban, and Dabigatran—not only as anticoagulants but also as modulators of inflammation via Protease-Activated Receptor (PAR) signaling. We highlight the unique pharmacotherapeutic properties of each NOAC, supported by key clinical trials demonstrating their effectiveness in preventing thromboembolic events. Beyond their established anticoagulant roles, emerging research suggests that NOACs influence inflammation through PAR signaling pathways, implicating factors such as FXa and Thrombin in the modulation of inflammatory responses. This review synthesizes current evidence on the anti-inflammatory potential of NOACs, exploring their impact on inflammatory markers and conditions like atherosclerosis and diabetes. By delineating the mechanisms by which NOACs mediate anti-inflammatory effects, this work aims to expand their therapeutic utility, offering new perspectives for managing inflammatory diseases. Our findings underscore the broader clinical implications of NOACs, advocating for their consideration in therapeutic strategies aimed at addressing inflammation-related pathologies. This comprehensive synthesis not only enhances understanding of NOACs' multifaceted roles but also paves the way for future research and clinical applications in inflammation and cardiovascular health

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

The persistent security challenges in Industry 4.0 due to resource-constrained IoT devices necessitate innovative solutions. Addressing this, a study introduces the ASCON algorithm for lightweight Authenticated Encryption with Associated Data, enhancing confidentiality, integrity, and authenticity within IoT limitations. By integrating Digital Twins, the framework emphasizes the need for robust security in Industry 4.0, with ASCON ensuring secure data transmission, bolstering system resilience against cyber threats. Practical validation using MQTT protocol confirms ASCON’s efficacy over AES-GCM, highlighting its potential for enhanced security in Industry 4.0. Future research should focus on optimizing ASCON for microprocessors and developing secure remote access tailored to resource-constrained devices, ensuring adaptability in the digital era

The preparation of transition metal salt ammine complexes having oxidizing anions is a great challenge due to the reaction between the ammonia and oxidizing anions during the synthesis of these materials. However, they have an important role in the development of new oxidants in organic chemistry and especially as precursors in the preparation of mixed metal oxides and alloys in controlled temperature thermal decomposition reactions. Therefore, the synthetic procedures to prepare the complexes of transition metal permanganate, pertechnetate, and perrhenate (VIIB group tetraoxometallates) salts with ammonia as ligand or co-ligand have been comprehensively reviewed. The available data about the structure and spectroscopic properties of these compounds, including the hydrogen bonds that act as redox reaction centers under their thermal decomposition are given and evaluated. Their thermal decomposition processes and products have been summarized. The available pieces of information about their role in organic oxidation reactions like the oxidation of benzyl alcohols, and regeneration of oxo-compounds from oximes and phenylhydrazones including the kinetics of these processes have also been collected.

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (on the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection

The medicinal herb Centaurium erythraea is well-known for its natural ability to treat digestive issues like dyspepsia and liver insufficiency by promoting the secretions of the liver and esophagus. This study aimed to analyze the effectiveness of Centaurium erythraea extracts in treating diabetes. Decoction and Soxhlet procedures were used to examine the extracts, with water serving as the solvent. The extracts were tested for antioxidant capacity, antimicrobial effects, and inhibitory effects on alpha-amylase activity. In comparison to the aqueous extract, the decocted extract had higher levels of polyphenols, total tannins, and anthocyanins, as well as more effective antioxidant capacities. Due to its reduction of alpha-amylase activity and enteric absorption of glucose, Centaurium erythraea has also shown considerable anti-hyperglycemic action.

This study reports the synthesis and characterization of novel per-fluorinated organic polymers with azo- and azomethine-based linkers using nucleophilic aromatic substitution. The polymers reveal variations in the fluorine content via the incorporation of decafluorobiphenyl and hexafluorobenzene linkers, which enhanced their hydrophobic nature. The rich fluorine polymers were slightly soluble in THF and have shown molecular weights between 4886 to 11948 g/mol. All polymers exhibit thermal stability in the range of 350-500°C, with varying thermal stability depending on the fluorine and nitrogen content. Conjugation of the polymers was confirmed through changes in the UV-Vis spectra, with a hypsochromic shift observed in all cases, more pronounced in azo-based fluorinated chains due to H-bonding on the nitrogen sites, chain conformations, and planarity. The optical band gap (Eg) of the polymers was determined from the UV-Vis data, with the Eg values of the azo-based fluorinated polymers being 1 eV higher than those of their corresponding linkers. The cross-linking formation was characterized by porosity measurements, with the azo-based polymer exhibiting the highest surface area of 770 m²/g with a pore volume of 0.35 cm³/g, while the open-chain azomethine-based polymer exhibited the lowest surface area of 285 m²/g with a pore volume of 0.0872 cm³/g. Porous structures with varied hydrophobicities were investigated as adsorbents for separating water-benzene and water-phenol mixtures and selectively binding methane/carbon dioxide gases from the air. The most hydrophobic polymers containing the decafluorbiphenyl linker were suitable for benzene separation, while the best methane uptake values were 6.14 and 3.46 mg/g for DAB-Z-1O and DAB-A-1O, respectively. DAB-Z-1h, with the highest surface area and rich in nitrogen sites, exhibited the highest CO₂ uptake at 298 K (17.25).

This paper presents an advanced microstructural analysis of the AlSiMg, Ti64 and N700 powders used for additive manufacturing. The internal microstructure of the regular and irregular powder grains were characterized down to atomic resolution by using scanning electron microscopy and high resolution scanning transmission electron microscopy.The accretionary forms on top of the irregular AlSiMg powder grains exhibit a slightly coarse microstructure with a network of eutectic Si consisting of nano-crystallites, suggestinga slower cooling than the grain itself that contain a predominately amorphous Si network. A nm thin amorphous C layer on the surface of some Ti64 plasma atomized powder grains promoted the attachment of satellites and growth of envelopes. In case of gas atomized N700 powder grains, we identified thin oxide and carbon amorphous layers as well as metal segregations at the interface between the grain body and the accretionary forms.

The study of localized surface plasmons (LSPs) in nanoscale structures is an essential step towards identifying optimal plasmonic modes that can facilitate robust optomechanical coupling and deepen our understanding of light-matter interactions at the nanoscale. This paper investigates numerically, using the finite element method, LSPs modes in a design comprising two coupled nano-ridges deposited on a gold layer, with an interposing polymer spacer layer. Such a structure, usually referred to as particles-on-mirror, shows exquisite optical properties at the nanoscale. We first examine the LSPs modes of a single nano-ridge through the analysis of its scattering cross-section in the visible and infrared range. To enhance the plasmonic response, a thin polymer layer is placed at the middle of the ridge, which introduces additional LSPs modes confined within the former. Then, we extend the analysis to the dimer configuration, which exhibits more complex and enhanced plasmonic behavior compared to a single nano-ridge. In particular, the dimer configuration yields LSPs resonances with a quality factor enhancement of approximately threefold relative to single nano-ridge. Furthermore, the presence of the polymer layer within the ridges significantly improves plasmon field localization and quality factor. These findings underscore the potential of nano-ridge-based structures in advancing optomechanical coupling and plasmon-phonon interaction studies, offering valuable insights for the development of high-performance acousto-plasmonic devices. In particular, the proposed device could help significantly improve the design of nano-acousto-optic modulators operating in the visible or in the near infrared, that require enhanced light-photon coupling rate.

.Various synthetic nematicides are currently used effectively in the control of plant parasitic nematodes. However, these nematicides have adverse effects on humans and the environment. Neem oil is eco-friendly alternatives to synthetic nematicides. The effectiveness of repeated neem oil application in suppressing the root-knot nematodes infestation in figs seedlings was therefore examined in this study, both in its natural form (10 ml/L) and in its nano-emulsion form (5 and 7 ml/L). The recorded reduction parameters of M. incognita were J2/250g soil, galls and egg masses / 5g roots of infected fig seedlings were measured at that time, and the effects of these natural additives were compared with the Fosthiazate (Krenkel 75%EC). The impact of such soil amendments on some typical plant parameters, the total microbial count of soil, and the overall enzymatic activity of the fig rhizosphere were also included. Obtained results revealed that the soil population density of root knot nematode suppressed by 80.3% , reduction of nematode egg masses (60.4%) due to the application of nano-emulsion of neem oil (at 7ml/L). Present work recommend the use of nano-emulsion of neem oil as one of the effective and eco-friendly means to reduce the root- knot nematode on fig .

Background: Respiratory syncytial virus[ RSV] is a lethal overwhelming defectiveness that influences people all over the world. The infection gets down in the lower respiratory tract and does not disperse to the remainder of the body. The objective of the study: Bioinformatics was used to create an mRNA vaccine against the Respiratory syncytial virus( RSV). Methodology: Lipid nanoparticle vaccines containing the Respiratory syncytial virus's surface fusion protein's mRNA were created for the current screening experimental investigation. Lipid nanoparticles[ LNP] with a particle size of around 90 nanometres that were created using the hot micro-emulsion process comprise the current vaccine delivery system. In stages 1 and 2 of clinical trials, the immunogenicity of the current mRNA RSV vaccine was evaluated. Results: The current vaccine demonstrated 81% immunogenicity in animal testing, but only 69% in stages 1 and 2 of clinical trials. Its side effects were manageable. The results persisted for a while. In the current trial, the vaccination proved effective as a preventative measure against RSV infection. The current immunization lacks antibody-dependent enhancement, which causes non-protective antibodies to develop. These non-neutralizing antibodies worsen infection by enhancing viral entrance and replication in the host cells, activating cytokines and the complement cascade through the production of immunological complexes, or both. However, the current LPN-RSV mRNA vaccine of the surface fusion protein resulted in the formation of potent neutralizing antibodies with persistent protection, especially for newborns and elderly individuals who get the ideal dose regimen. Conclusion: The invention of the LNP-mRNA RSV vaccine, which might prevent RSV-caused bronchiolitis and fatal pneumonia, made the current study promising

Metabolic dysfunction-associated fatty liver disease (MAFLD) has now affected nearly one third of global population and became the number one cause of chronic liver disease in the world because of the obesity pandemic. Chronic hepatitis resulting from hepatitis B virus (HBV) and hepatitis C virus (HCV) also remain significant challenges to liver health even in the 21st century. The coexistence of MAFLD and chronic viral hepatitis can markedly alter the disease course of individual diseases and can complicate the management of each of these disorders. Thorough understanding of the pathobiological interlink and interactions between MAFLD and these two chronic viral hepatitis infections are crucial for appropriate management of patients. We update these interlinks in this comprehensive clinical review.

In this paper, we first define the concepts of r-fuzzy soft α-open (semi-open and δ-open) sets on fuzzy soft topological spaces based on the article Aygunoglu et al. (Hacet. J. Math. Stat., 43 (2014), 193-208), and the relations of these sets with each other are established. In addition, we introduce the concepts of fuzzy soft δ-closure (δ-interior) operators, and study some properties of them. Also, the concept of r-fuzzy soft δ-connected sets is introduced and studied with help of fuzzy soft δ-closure operators. Thereafter, we define the concepts of fuzzy soft α-continuous (β-continuous, semi-continuous, pre-continuous and δ-continuous) functions, which are weaker forms of fuzzy soft continuity, and some properties of these functions along with their mutual relationships are discussed. Moreover, a decomposition of fuzzy soft α-continuity and a decomposition of fuzzy soft semi-continuity is obtained. Finally, as a weaker form of a fuzzy soft continuity, the concepts of fuzzy soft almost (weakly) continuous functions are defined, and some properties are specified. Additionally, we explore the notion of continuity in a very general setting called, fuzzy soft (L, M, N, O)-continuous functions and a historical justification is introduced.

Cadmium telluride (CdTe), a metallic dichalcogenide material, has been utilized as an absorber layer for thin film-based solar cells with appropriate configurations, and the SCAPS-1D structures program has been used to evaluate the results. In both known and developing thin film photovoltaic systems, a CdS thin film buffer layer has been frequently employed as a traditional n-type heterojunction partner. In this study, numerical simulation was used to find a suitable non-toxic material for the buffer layer instead of CdS, among various types of buffer layers (ZnSe, ZnO, ZnS, and In2S3), and carrier concentrations for the absorber layer (NA) and buffer layer (ND) were varied to determine the optimal simulation parameters. carrier concentrations (NA from 2 x 1012 cm-3 to 2 x 1017 cm-3 and ND from 1 x 1016 cm-3 to 1 x 1022 ??−3) have been differed. The results showed that the CdS as buffer layer based CdTe absorber layer solar cell has the highest efficiency (?%) of 17.43%. Furthermore, high conversion efficiencies of 17.42% and 16.27% have been found for ZnSe and ZnO based buffer layers, respectively. As a result, ZnO and ZnSe are potential candidates for replacing the CdS buffer layer in thin-film solar cells. Here, the absorber (CdTe) and buffer (ZnSe) layers were chosen to improve the efficiency by finding the optimal density of the carrier concentration (acceptor and donor). The simulation findings above provide helpful recommendations for fabricating high-efficiency metal oxide-based solar cells in the lab.

This work presents updated reconstructions of watershed runoff to San Francisco Estuary from tree-ring data to AD 903, coupled with models relating runoff to freshwater flow to the estuary and salinity intrusion. We characterize pre-development freshwater flow and salinity conditions in the estuary over the past millennium and compare this characterization with contemporary conditions to better understand the magnitude and seasonality of changes over this time. This work shows that the instrumented flow record spans the range of runoff patterns over the past millennium (averaged over five, ten, twenty and one hundred years), and thus serves as a reasonable basis for planning-level evaluations of historical hydrologic conditions in the estuary. Over annual timescales we show that, although median freshwater flow to the estuary has not changed significantly, it has been more variable over the past century compared to pre-development flow conditions. We further show that the contemporary period is generally associated with greater spring salinity intrusion and lesser summer-fall salinity intrusion relative to the pre-development period. Thus, salinity intrusion in summer and fall months was a common occurrence under pre-development conditions and has been moderated in the contemporary period due to the operations of upstream reservoirs, which were designed to hold winter and spring runoff for release in summer and fall. This work also confirms a dramatic decadal-scale hydrologic shift in the watershed from very wet to very dry conditions during the late 19th and early 20th centuries; while not unprecedented, these shifts have been seen only a few times in the past millennium. This shift resulted in an increase in salinity intrusion in the first three decades of the 20th century, as documented through early records. Population growth and extensive watershed modification during this period exacerbated this underlying hydrologic shift. Putting this shift in the context of other anthropogenic drivers is important in understanding the historical response of the estuary and in setting salinity targets for estuarine restoration. By characterizing the long-term behavior of San Francisco Estuary, this work supports decision-making in the State of California related to flow and salinity management for restoration of the estuarine ecosystem.

Background: As the only place in a store where all customers must pass through and wait, the checkout lane may be particularly influential over consumer purchases. Because most foods and beverages sold at checkout are unhealthy (e.g., candy/sweets, sugar-sweetened beverages, and salty snacks), policymakers and advocates have expressed growing interest in healthy checkout policies. To understand the extent to which such policies could improve nutrition equity, we as-sessed the prevalence and sociodemographic correlates of purchasing items from checkout. Methods: We assessed self-reported checkout purchasing and sociodemographic characteristics in a national convenience sample of adults (n=10,348) completing an online survey in 2021. Re-sults: Over one-third (36%) of participants reported purchasing foods or drinks from checkout during their last grocery shopping trip. Purchasing items from checkout was more common among men; adults <55 years of age; low-income consumers; Hispanic, non-Hispanic American Indian and Alaska Native, and non-Hispanic Black consumers; those with at least a bachelor’s degree; parents; and consumers diagnosed with type 2 diabetes or pre-diabetes (p-values<0.05). Conclusions: Purchasing foods or beverages from store checkouts is common and more prevalent among low-income and racial and ethnic minority groups. These results suggest that healthy checkout policies have the potential to improve nutrition equity.

Gastric perforations and leaks are significant complications that can arise from various gastrointestinal disorders and surgical interventions. Over the past decade, endoscopic techniques have emerged as effective method for managing these conditions. Furthermore, as endoscopic resection techniques have grown in popularity, the risk of acute and delayed perforations has also grown. This review aims to provide an in-depth analysis of the endoscopic management strategies employed in the management of gastric perforations and leaks. We will discuss the etiology, diagnostic modalities, and various endoscopic techniques utilized, and emerging trends in endoscopic management. Our attempt in writing this review paper is to educate and guide clinicians in making informed decisions when faced with gastric perforations and leaks.

Colorectal cancer prognosis get worse with advancement of disease into metastatic stage. There is a pertinent need to develop prognostic biomarkers that can be used for personalized and precision medicine. Alternative splicing provides an insight into understanding of changes at isoform expression level which may not be evident at gene level. In this direction, we utilized our prior knowledge about significant alternatively spliced genes and chose ADAM12 and MUC4 for further characterization in a metastatic cell line model. These genes were found to be good prognostic indicators in The Cancer Genome Atlas database. We studied the gene organization and designed primers to specifically amplify a group of isoforms. Differential expression of these group of isoforms was observed in normal, primary and metastatic colorectal cancer cell lines. We further validated the results using sanger sequencing. Isoform expression was found to respond to the 5-fluorouracil treatment. RNAseq analysis of the cell lines further validated the differential expression of gene isoforms. Successful detection of ADAM12 and MUC4 in cell lysates varied according to the antibody used which may reflect differential expression of isoforms. This comprehensive study underscores the importance of studying alternatively spliced isoforms and their probable used as prognostic or predictive biomarkers.

Based on DFT (density functional theory) using B3LYP/6-31G method, theoretical investigations applied to demonstrate the structural, electronic properties and stability of (N,Nˊ-(1,4-phenylene)bis(1,8-naphthalimide)) is more stable than the compound (N-(4-amino phenyl)1,8-naphthalimide) by (-1.2762 eV or -29.4299 Kcal.mol-1) depending on the values of HOMO, synthesis reaction of imide is spontaneous and endothermic at temperature 275˚C according to the values of ∆rS, ∆rG and ∆rH.

Evolutionary psychology theories propose that contact with green, natural environments may benefit physical health, but little comparable evidence exists for brown, natural environments, such as the desert. In this study, we examined the association between “brownness” and “greenness” with fasting glucose among young residents of El Paso, Texas. We defined brownness as the surface not covered by vegetation or impervious land within Euclidian buffers around participants’ homes. Fasting glucose along with demographic and behavioral data was obtained from the Nurse Engagement and Wellness Study (N = 517). We found that residential proximity to brownness was not associated with fasting glucose when modeled independently. In contrast, we found that residential greenness was associated with decreased levels of fasting glucose, despite the relatively low levels of greenness within the predominantly desert environment of El Paso. A difference between the top and bottom greenness exposure quartiles within a 250 m buffer was associated with a 3.5 mg/dL decrease in fasting glucose levels (95% confidence interval: -6.2, -0.8). Our results suggest that within the understudied context of the desert, green vegetation may be health-promoting to a degree that is similar to other, non-desert locations in the world that have higher baselines levels of green.

The composition simplex (N, P, K, Ca, and Mg) of the leaf is the main scores used by different approaches like the Diagnosis and Recommendation Integrated System and Compositional Nutrient Diagnosis to study the nutrient interactions and balance in plant leaves. However, the application and validation of these concepts to grain composition remains unexplored. Contrary to foliar analysis's early intervention for nutrient deficiency detection and correction, applying this approach to seeds assesses diverse cultivars' potential, enabling anticipation of their adaptation to climate conditions and informed selection for future crops. In the present study, a collected database of more than 924 scores including the grain yield (kg ha-1) and the nutrient composition (mg kg-1) of different corn varieties is used to develop a novel nutrient-based diagnostic approach to identify reliable markers of nutrient imbalance. A 'nutrient signature' model is proposed based on the impact of the environ-mental conditions on the nutrient indices and composition (N, P, K, Ca, and Mg) of the corn grains. The yield threshold used to differentiate between low and high-yielding subpopulations is established at 12.000 kg ha-1and the global nutrient imbalance index (GNII) of 2.2 is determined using the chi-square distribution function and vali-dated by the Cate-Nelson partitioning method, which correlated yield data distribution with the GNII. Therefore, the nutrient compositions were classified into highly balanced (GNII ≤ 1.6), balanced (1.6 < GNII ≤ 2.2), and imbalanced (GNII > 2.2). In addition, we found that the Xgboost model's predictive accuracy for GNII is signif-icantly affected by soil pH, organic matter, and rainfall. These results pave the way for adapted agricultural practices by providing insights into the nutrient dynamics of corn grains under varying environmental conditions.

Epithelioid hemangioendothelioma (EHE) is an extremely rare vascular sarcoma with variable aggressive clinical behavior. In this retrospective study we aimed to investigate prognostic factors based on clinicopathologic findings in a molecular/immunohistochemical confirmed nationwide multicenter cohort of 57 EHE. Patients had unifocal disease (n=29), multifocal disease (n=5), lymph node metastasis (n=8) and/or distant metastasis (n=15) at the time of diagnosis. The overall survival rate was 69.6% at 1 year and 50.5% at 5 years. Survival did not correlate with sex, age or histo-pathological parameters. No survival differences were observed between multifocal and metastatic disease suggesting that multifocality represents early metastases and treatment options are limited in comparison to unifocal disease. In unifocal tumors, survival could be predicted using the risk stratification model of Shibayama et al. dividing the cases into low (n=4), intermediate (n=15) and high (n=3) risk-groups. Larger tumor size (>3cm) and higher mitotic count (>1/10HPF) was asso-ciated with progressive unifocal disease course. Lymph node metastases at time of diagnosis oc-curred in 14.0% of the cases and was mainly associated with tumor localization in the head and neck area proposing lymph node dissection. In conclusion, our results demonstrate the aggressive behavior of EHE, emphasize the prognostic value of a previous described risk stratification model and may provide new insights regarding tumor focality, therapeutic strategies and prognosis.

Technological advancements and innovations have profoundly changed the lives of people giving rise to smart environments, cities, and societies. As homes are the building block of cities and societies, smart homes are critical to establishing smart living and are expected to play a key role in enabling smart cities and societies. The current academic literature and commercial advancements on smart homes have mainly focused on developing and providing smart functions for homes to provide security management and facilitate the residents in their various activities such as ambiance management. Homes are much more than physical structures, buildings, appliances, operational machines, and systems. Homes are composed of families and are inherently complex phenomena underlined by humans and their relationships with each other, subject to individual, intragroup, intergroup, and intercommunity goals. There is a clear need to understand, define, consolidate existing research, and actualize the overarching roles of smart homes, the roles of smart homes that would serve the needs of future smart cities and societies. This paper introduces our data-driven parameter discovery methodology and uses it to provide, for the first time, an extensive, rather fairly comprehensive, analysis of the families and homes landscape seen through the eyes of academics and the public using over a hundred thousand research papers and nearly a million tweets. We develop a methodology using deep learning, natural language processing (NLP), and big data analytics methods and apply it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The 66 discovered parameters and the knowledge space comprising 100s of dimensions are explained by reviewing and referencing over 300 articles from the academic literature and tweets. The knowledge and parameters discovered in this paper can be used to develop a holistic understanding of matters related to families and homes facilitating the development of better, community-specific, policies, technologies, solutions, and industries for families and homes, leading to strengthening families and homes, and in turn, empowering sustainable societies across the globe.

Background/Objectives: Meconopsis has long been used in traditional Tibetan medicine to treat various diseases. However, blue-flowered Meconopsis (M. betonicifolia) is becoming increasingly scarce due to overharvesting. As a potential alternative, yellow-flowered Meconopsis (M. integrifolia) shows promise but requires comprehensive characterization. This study aimed to evaluate and compare the anti-inflammatory potential of yellow- and blue-flowered Meconopsis species. Methods: LC-MS was used to analyze the chemical profiles of yellow- and blue-flowered Meconopsis. Putative targets of shared constituents were subjected to GO and disease enrichment analysis. The LPS-induced RAW264.7 macrophage model was employed to assess anti-inflammatory effects. Metabolomics was applied to gain mechanistic insights. Results: LC-MS revealed over 70% chemical similarity between species. Enrichment analysis associated targets with inflammation-related pathways. In macrophage assays, both species demonstrated dose-dependent anti-oxidative and anti-inflammatory activities, with yellow Meconopsis exhibiting superior efficacy. Metabolomics showed modulation of key inflammatory metabolic pathways. Conclusions: This integrative study validated yellow-flowered Meconopsis as a credible alternative to its blue-flowered counterpart for anti-inflammatory applications. Metabolic profiling provided initial clues regarding their multi-targeted modes of action, highlighting clinical relevance and potential for sustainable utilization and biodiversity conservation.

Synthesis of new series of 1-phenyl-3-(4-(pyridin-3-yl) phenyl) urea derivatives and its in vitro antiproliferative activities against NCI-60 human cancer cell lines of nine different cancer types are described. Fourteen compounds 5a-n have been synthesized with three different hydrogen bondable moieties (4-hydroxylmethylpiperidinyl and trimethoxyphenyloxy and 4-hydroxyethylpiperazine) attached to the core structure 1-phenyl-3-(4-(pyridin-3-yl) phenyl) urea. Different substituents with different π and σ values were added on the terminal phenyl group. Compounds with 4-hydroxymethylpiperidine moiety showed higher mean percentage inhibition values over the 60-cell line panel at 10-µM concentration. They showed broad-spectrum antiproliferative activity over many cell lines of different cancer types. For instance, compound 5a elicited some lethal rather than inhibition effects on SK-MEL-5 melanoma cell line, 786-0, A498, RXF 393 renal cancer cell lines, and MDA-MB-468 breast cancer cell line by 146.1, 108.7, 136.2, 134.8, 116.6 % at 10 µM, respectively. Compounds 5a-e exhibited superior antiproliferative activity than Paclitaxel and Gefitinib against the most sensitive cell lines. Two compounds, 5a and 5d showed promising mean growth inhibitions and thus were further tested at five-dose testing mode to determine their IC₅₀ values. The data revealed that 5a and 5d urea compounds are the most active derivatives with significant efficacies and superior potencies than Paclitaxel in 21 different cancer cell lines, belonging particularly to renal cancer and melanoma cell lines. Moreover, 5a and 5d had superior potencies than Gefitinib in 38 and 34 cancer cell lines, respectively; belonging particularly to colon cancer, breast cancer and melanoma cell lines.

In this paper, inspired by recent articles of G. Anatriello and G. Vincenzi (see \cite{Ana}), and G. Cerda-Morales (see \cite{Cer1,Cer2}), we will introduce the third-order $\overline{h}$-Jacobsthal and third-order $\overline{h}$-Jacobsthal--Lucas sequences and their associated quaternions. The new results that we have obtained extend most of those obtained in \cite{Cer2}.

Recently, flares have been considered as a major source of air pollution from the petroleum refining industry. The United Nations have instigated an international effort related to the management of flare emissions to reduce the global warming impact related to flaring. Eliminating or removing the need for gas flares is difficult because these devices are generally used as safety devices to allow combustion of flammable gases in a controlled fashion which supports safe operation. However, reducing flaring is generally possible using well designed efficiently operated flare equipment. In general, flare performance can be enhanced following the API-521 methodology and using assist- media including air and steam to achieve smokeless operation. This present work will discuss flare emissions in petroleum refining industry, and a method to manage flare emissions. Moreover, this work will discuss flare Combustion Efficiency (CE) and Distraction and Removal Efficiency (DRE) in terms of efficient flare operation. This work uses actual operating flare data, published previously, will be used in this work together with the CFD Code C3d. This code, developed at the USDOE Sandia National Laboratory is based on a standard LES methodology to conduct transient flare analysis is used to simulate flare operation to estimate flame shape and emissions produced. In this work, a new air-assisted flare tip design which uses the Coanda effect to improve flare operation was analysed. This new flare design reduces the emission rate that demonstrates the design effectiveness. The analysis considers a flare 1m high and (6") diameter in the centre of a 4m*4m*4m domain. Boundary conditions considering no cross wind and an ambient temperature of 300K. The initial condition is a hydrostatic pressure profile across the computational domain. In the air assist simulation, stoichiometric ratio will be a variable and therefore more than one case was considered.

Software engineering is a comprehensive process that requires developers and team members to collaborate across multiple tasks. In software testing, bug triaging is a tedious and time-consuming process. Assigning bugs to the appropriate developers can save time and maintain their motivation. However, without knowledge about a bug's class, triaging is difficult. Motivated by this challenge, this paper focuses on the problem of assigning the suitable developer to new bug by analyzing the history of developers’ profiles and analyzing history of bugs for all developers using machine learning-based recommender systems. Explainable AI (XAI) is AI that humans can understand. It contrasts with "black box" AI, which even its designers can't explain. By providing appropriate explanations for results, users can better comprehend the underlying insight behind the outcomes, boosting the recommender system's effectiveness, transparency, and confidence. In this paper, we propose two explainable models for recommendation. The first one is an explainable recommender model for personalized developers generated from bug history to know what the preferred type of bug is for each developer. The second model is an explainable recommender model based on bugs to generate the best developer for each bug from bug history.

Although carbon (C) efflux from soils treated with organic wastes was widely covered in temperate and cold regions, still such data is not sufficient from arid and semi-arid regions. Saudi Arabia produces more than 335,000 tons/year of cow manure (CM), this CM either left as raw manure or being composted. The application of high C/N amendments is expected to increase soil organic carbon and reduce CO₂ fluxes. A 90-day incubation experiment was conducted to study CO₂ efflux, organic C microbial biomass C, available NH₄+and NO₃-when added to agricultural soil. Six manure types were added: cow manure, cow manure compost, cow manure biochar, cow manure stripped ammonia at pH 12 with a temperature of 95oC, cow manure stripped ammonia at pH 9 with a temperature of 95oC and control. The application of CM resulted in a considerable increase in soil available nitrogen, CO₂ efflux compared to other treatments. Cow manure biochar showed the lowest CO₂ efflux. Cumulative CO₂ effluxes of cow manure effluents were lower than CM this possibly due to the relatively high C:N ratio of manure effluent. The content of, P, Fe, Cu, Zn and Mn were decreased as incubation time increased. microbial biomass C of cow manure stripped ammonia at pH 12 with a temperature of 95oC were increased at 7 and 60 days illustrating to temperature effect on the decomposing of manure materials.

Eyes are quintessential complex traits and our understanding of their evolution guides models of trait evolution in general. A long-standing account of eye evolution argues natural selection favors morphological variations that allow increased functionality for sensing light (Darwin 1859; v. Salvini-Plawen and Mayr 1977; Nilsson and Pelger 1994; Nilsson 2013). While certainly true in part, this focus on visual performance does not entirely explain why diffuse photosensitivity persists even after eyes evolve, or why eyes evolved many times, each time using similar building blocks. Here we briefly review a vast literature indicating most genetic components of eyes historically responded to stress caused directly by light, including UV damage of DNA, oxidative stress, and production of aldehydes. We propose light-induced stress had a direct and prominent role in the evolution of eyes by bringing together genes to repair and prevent damage from light-stress, both before and during the evolution of eyes themselves. Stress-repair and stress-prevention genes were perhaps originally deployed as plastic responses to light and/or as beneficial mutations genetically driving expression where light was prominent. These stress-response genes sense, shield, and refract light but only as reactions to ongoing light stress. Once under regulatory-genetic control, they could be expressed before light stress appeared, evolve as a module, and be influenced by natural selection to increase functionality for sensing light, ultimately leading to complex eyes and behaviors. Recognizing the potentially prominent role of stress in eye evolution invites discussions of plasticity and assimilation and provides a hypothesis for why similar genes are repeatedly used in convergent eyes. Broadening the drivers of eye evolution encourages consideration of multi-faceted mechanisms of plasticity/assimilation and mutation/selection for complex novelties and innovations in general.

Is the amount of “greenness” within a 250-meter, 500-meter, 1000-meter or a 2000-meter buffer surrounding a person’s home a good predictor of their physical health? The evidence is inconclusive. We reviewed Web of Science articles that used geographic information systems buffer analyses to identify trends between physical health, greenness, and distance within which greenness is measured. Our inclusion criteria were: (1) use of buffers to estimate residential greenness; (2) statistical analyses that calculated significance of the greenness-physical health relationship; and (3) peer-reviewed articles published in English between 2007 and 2017. To capture multiple findings from a single article, we selected our unit of inquiry as the analysis, not the article. Our final sample included 260 analyses in 47 articles. All aspects of the review were in accordance with PRISMA guidelines. Analyses were independently judged as more, less, or least likely to be biased based on the inclusion of objective health measures and income/education controls. We found evidence that larger buffer sizes, up to 2,000m, better predicted physical health than smaller ones. We recommend that future analyses use nested rather than overlapping buffers to evaluate to what extent greenness not immediately around a person’s home (i.e., within 1,000-2,000m) predicts physical health.

This study measures key indicators of perceived precarity and decent working conditions and their relative effects on life satisfaction of workers in small and medium-sized enterprises. Using a random sample survey, we interviewed 103 workers in four districts of Sylhet division in Bangladesh. Utilizing Smart PLS application for structural equation modeling, we iterated the samples 1000 times which resulted in a new sample size standing at 499. We used a Likert-type five-point scaling technique, with appropriate items related to the latent constructs of the model, for path analysis. We found perceived precariousness has the highest causal relations with a path coefficient of 0.706 with social dialogue (an indicator of decent working conditions) followed by 0.539 with working hour issue, 0.345 with life satisfaction and 0.301 with the unacceptable work issue. The importance-performance map analysis confirms that perceived precarity is the most critical construct for worker’s life satisfaction. Thus, in a ceteris paribus situation, an increase of one point in the performance on the perceived precarity is expected to increase in the performance on the life satisfaction by a total effect level of 0.428. Therefore, we recommend further attention should be paid by the policymakers to the issue of consciousness of the precarity at the workplace for a better life satisfaction of workers.

Objectives: The mechanism of action of drugs that depress the central nervous system (CNS) was unknown until molecular pharmacology discovered each drug's exact role. The benefit of knowing the mechanism of action is to design a new drug that could have the same efficacy as the prototype drug but with fewer side effects. Some of the available CNS depressant drugs that were abused or illegally used could be modified to make them used medically. Methods: We reviewed various published articles in PubMed and Google Scholar that focused on CNS depressants' molecular pharmacology.Results: It was clear that at specific plasma concentrations of ethyl alcohol showed almost same mode of action of propofol by targeting the extrasynaptic GABA-A receptors, which causes tonic GABAergic inhibition. Besides, the High affinity of some benzodiazepines (e.g., midazolam) to α1 subunit of GABA-A receptor causes sedative, hypnotic, amnesic, and some antiepileptic effects; however, some other benzodiazepines (e.g., diazepam) have high affinity to α3 subunits, which causes anxiolytic, muscle relaxant, and strong antiepileptic effects. The CB1 receptor partial agonism effect of tetrahydrocannabinol has a sedative advantage over full agonism due to desensitization of CB1 receptors. Conclusion: From the molecular pharmacology prospect, it is possible to design new drugs with more specific CNS depressants effect and fewer side effects by targeting particular receptors with a precise reaction.