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In this article, we adapt the edge-graceful graph labeling definition into block designs and define a block design V , B with V = v and B = b as block-graceful if there exists a bijection f : B ⟶ 1,2 , … , b such that the induced mapping f + : V ⟶ Z v given by f + x = ∑ x ∈ A A ∈ B f A mod v is a bijection. A quick observation shows that every v , b , r , k , λ − BIBD that is generated from a cyclic difference family is block-graceful when v , r = 1 . As immediate consequences of this observation, we can obtain block-graceful Steiner triple system of order v for all v ≡ 1 mod 6 and block-graceful projective geometries, i.e., q d + 1 − 1 / q − 1 , q d − 1 / q − 1 , q d − 1 − 1 / q − 1 − BIBDs. In the article, we give a necessary condition and prove some basic results on the existence of block-graceful v , k , λ − BIBDs. We consider the case v ≡ 3 mod 6 for Steiner triple systems and give a recursive construction for obtaining block-graceful triple systems from those of smaller order which allows us to get infinite families of block-graceful Steiner triple systems of order v for v ≡ 3 mod 6 . We also consider affine geometries and prove that for every integer d ≥ 2 and q ≥ 3 , where q is an odd prime power or q = 4 , there exists a block-graceful q d , q , 1 − BIBD. We make a list of small parameters such that the existence problem of block-graceful labelings is completely solved for all pairwise nonisomorphic BIBDs with these parameters. We complete the article with some open problems and conjectures.

Over the years, there have been many studies on the performance improvement of asphalt mixtures using different materials and additives, especially those of the recycled variety, because of the environmental benefits they bring to the design. This study investigated the properties of asphalt mixtures made with different amounts of calcium lignosulfonate (CLS), which is a waste product, and used the Balanced Mix Design (BMD) approach to determine the appropriate mix design for CLS-containing mixtures. In this process, the results of the dynamic creep test were used as a measure of rutting resistance, the results of the indirect tensile test were used as a measure of moisture susceptibility, and the results of the Illinois Flexibility Index Test (IFIT) and Indirect Tensile Cracking Test (IDEAL-CT) were used independently as a measure of cracking resistance. On the other hand, since previous studies have suggested that CLS has antiaging properties, the specimens were made and tested in two groups: those with long-term aging and without long-term aging. So the objective was to determine the amount of CLS that should ideally be used as a bitumen additive in aged and unaged asphalt mixtures. The results showed that using 15% or 20% CLS will always yield acceptable outcomes in terms of BMD criteria for both aged and unaged conditions. However, a CLS content of 10% may also be acceptable for both aged and unaged conditions if IDEAL-CT results are used as the measure of cracking resistance.

Objective. To analyze the influencing factors of tumor volume, body immunity, and poor prognosis after 125I particle therapy for differentiated thyroid cancer. Methods. A total of 104 patients with differentiated TC who were treated with 125I particles during January 2020 to January 2021 was picked. These subjects were graded as low-dose group (80Gy-110Gy) and high-dose group (110Gy-140Gy) according to the minimum dose received by 90% of the target volume (D90) after surgery. The tumor volume before and after treatment was compared, and fasting venous blood was collected before and after treatment. The content of thyroglobulin (Tg) was detected by electrochemiluminescence immunoassay. The levels of absolute lymphocyte count (ALC), lymphocytes, neutrophils, and monocytes were detected on automatic blood cell analyzer. The lymphocyte to monocyte ratio (LMR), neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ration (PLR) were calculated. The changes in the condition of patients were closely observed, and the occurrence of adverse reactions in the two groups were compared. The risk factors influencing the efficacy of 125I particle therapy for differentiated TC were analyzed through multivariate logistic regression analysis. Results. The total effective rate of patients in the low- and high-dose groups was 78.85% and 82.69%, respectively ( P > 0.05 ). Compared with the pretreatment period, the tumor volume and Tg level in both groups were much lower ( P < 0.05 ), and the differences in tumor volume and Tg level had no statistically significant difference between the two groups before and after treatment ( P > 0.05 ). At 1 week of the treatment, the total incidence of adverse reactions such as nausea, radiation gastritis, radiation parotitis, and neck discomfort was obviously higher in the high-dose group than in the low-dose group ( P < 0.05 ). At 1 month of treatment, the incidence of adverse reactions such as nausea was markedly higher in the high-dose group than in the low-dose group ( P < 0.05 ). After treatment, serum NLR and PLR contents were memorably elevated and LMR level was sharply decreased in both groups, and serum NLR and PLR contents were higher and LMR content was lower in the high-dose group than in the low-dose group ( P < 0.05 ). Multivariate logistic regression analysis showed that the pathological type of follicular adenocarcinoma, tumor size ≥ 2 cm, clinical stage of III~IV, distant metastasis, and high TSH level before 125I particle treatment were all risk factors related to the efficacy of 125I particle treatment of TC ( P < 0.05 ). Conclusion. The efficacy of low-dose and high-dose 125I particles in the treatment of differentiated thyroid cancer is comparable, among which low-dose 125I particles have fewer adverse effects and have less impact on the immunity of the body, which is well tolerated by patients and can be widely used in clinical practice. In addition, the pathological type of follicular adenocarcinoma, tumor size ≥ 2 cm, clinical stage III~IV, distant metastasis, and high TSH level before 125I particle treatment are all risk factors that affect the poor effect of 125I particles on thyroid cancer treatment, and early monitoring of the above index changes can help evaluate the prognosis.

In recent years, there have been many studies on regional geological hazard risk assessment. However, most previous research focused on natural geological disasters such as landslides and debris flows. There were few special regional risk studies aimed at highway slopes influenced deeply by artificial factors. According to the characteristics of highway slope, based on the evaluation of regional natural influencing factors, the artificial influencing factors and geological engineering information of an individual slope are introduced in this paper. The information evaluation model, combined with the analytic hierarchy process (AHP) and ArcGIS spatial analysis, was used to evaluate the risk of disaster in the study area. Then, the risk assessment results and risk zoning map along the highway were obtained. By comparing and analyzing the data of the management unit, the research results were in good agreement with the actual slope. The results of this paper can effectively guide the maintenance and management of highway slopes and provide a reference for site selection of subsequent highway reconstruction and expansion projects. In general, this study can improve the level of geological risk management in highway engineering.

A two-dimensional numerical model incorporating solid mechanics, electrochemistry, mass diffusion, and ion migration processes is developed to investigate the load effect on the crevice corrosion. The model is a transient model of crevice corrosion occurring in cracks of 304 stainless steel in a dilute NaCl solution, and the interaction between stress and electrochemical corrosion was considered. By solving the multiphysical coupling model in COMSOL, the effect of applied load on electrochemical corrosion in the crack tip region was calculated, and the local corrosion current density in the crack tip region with stress concentration within the crack was also calculated by using the Tafel relationship. The distribution of Fe2+ ion, Na+ ion, CL− ion, and H and O2 substance concentrations within the crack cavity is predicted by the equation analysis of substance transport. The results show that metal oxidation is more clearly affected by plastic deformation, the rate of hydrogen evolution reaction increases with stress enhancement, and the oxygen absorption reaction is not affected by stress strain. The distribution of iron ions, hydrogen, and oxygen within the crack is affected by the electrochemical reaction rate, and the distribution of iron ions, sodium ions, and chloride ions is affected by the electrolyte potential.

Biodiesel is a promising alternative to petrodiesel, but its production from acid oils retains several technical challenges. Therefore, this study developed a new approach involving 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU)-catalyzed one-pot reaction (simultaneous esterification-transesterification) to produce biodiesel with enhanced reaction efficiency. The reaction was performed under water removal conditions and optimized using response surface methodology to maximize the biodiesel yield. The optimal conditions for the one-pot reaction had a reaction time of 2.73 h, temperature of 143°C, methanol:oil molar ratio of 24.3 : 1, and DBU loading of 30.1%. Under the optimal conditions, maximum total biodiesel of 97.1% was obtained. DBU could be repeatedly used for at least 5 cycles to yield over 91% biodiesel. This study suggests that DBU efficiently catalyzed esterification and transesterification simultaneously and the DBU-catalyzed one-pot reaction is an efficient method for biodiesel production from high-free-fatty acid oils.

The world is having a vast collection of text with abandon of knowledge. However, it is a difficult and time-taking process to manually read and recognize the text written in numerous regional scripts. The task becomes more critical with Gurmukhi script due to complex structure of characters motivated from the challenges in designing an error-free and accurate classification model of Gurmukhi characters. In this paper, the author has customized the convolutional neural network model to classify handwritten Gurmukhi words. Furthermore, dataset has been prepared with 24000 handwritten Gurmukhi word images with 12 classes representing the month’s names. The dataset has been collected from 500 users of heterogeneous profession and age group. The dataset has been simulated using the proposed CNN model as well as various pretrained models named as ResNet 50, VGG19, and VGG16 at 100 epochs and 40 batch sizes. The proposed CNN model has obtained the best accuracy value of 0.9973, whereas the ResNet50 model has obtained the accuracy of 0.4015, VGG19 has obtained the accuracy of 0.7758, and the VGG16 model has obtained value accuracy of 0.8056. With the current accuracy rate, noncomplex architectural pattern, and prowess gained through learning using different writing styles, the proposed CNN model will be of great benefit to the researchers working in this area to use it in other ImageNet-based classification problems.

This article highlights the importance of learning models which prevent the resilient attack of robotic technology with a subset of trajectories. Many complement models are introduced in the field of path planning robots without any knowledge of representation procedures, so robotic data are subject to different attacks from several users. During such attacks, the data will be misplaced and commands specified to robots will be disorganized so a new training data set has to be incorporated which is a difficult task. Therefore, to prevent probability of data failure time-dependent binary probability prototypes are introduced with low training data. Furthermore, a regularized boosting procedure (RBP) has been applied with different weights to switch multiple robots with discrete knowledge representation. Then a high space block is incorporated for maximizing coverage areas during loss functions and this is implicit as an innovative technique as compared with existing procedures. To validate the effectiveness of proposed learning techniques in robots, four scenarios are considered which include accuracy and success rate of detection. Subsequently, the outcomes prove that the robotic path with learning models are highly effective for an average percentile of 86% as compared to conventional techniques.

This paper proposes a prediction method based on chaos theory and an improved empirical-modal-decomposition particle-swarm-optimization long short-term-memory (EMD-PSO-LSTM)-combined optimization process for passenger flow data with high nonlinearity and dynamic space-time dependence, using EMD to process the original passenger flow data and generate several eigenmodal functions (IMFs) and residuals with different characteristic scales. Based on the chaos theory, each component of the PSO algorithm was improved by introducing an inertia factor to facilitate the adjustment of its search capability to improve optimization. Each subsequence of the phase-space reconstruction was built into an improved PSO-LSTM prediction model, and the output of each prediction model was summed to determine the final output. Experimental studies were performed using data from the North Railway Station of Chengdu Rail Transit, and the results showed that the proposed model can generate better prediction results. The proposed model obtained root mean square error (RMSE) and mean absolute error (MAE) of 16.0908 and 11.3704, respectively. Compared with the LSTM, the improved PSO-LSTM, the improved EMD-PSO-LSTM, and the model proposed in this paper improved the RMSE values by 25.53%, 29.97%, and 58.76%, respectively, and the MAE values by 30.41%, 40.13%, and 63.08%, respectively, of the prediction results.

This paper presents a novel approach, based on the whale optimization algorithm (WOA), for channel estimation in wireless communication systems. The proposed method provides a means to accurately estimate the wireless channel, while not requiring the statistical characteristics of the channel. This method uses the WOA to search for the best channel statistical characteristics toward the ultimate goal of having the smallest bit error rate (BER). The proposed approach is aimed at enhancing the efficiency of pilot-based OFDM systems under frequency-selective fading channels used in the performance testing of 5G New Radio gNodeB. In terms of BER and mean square error (MSE), the performance of the proposed WOA-based channel estimation algorithm is evaluated and compared with the conventional least square (LS) and minimum mean square error (MMSE) algorithms. The simulation results demonstrate that the proposed algorithm provides highly competitive performance over the MMSE algorithm and significantly outperforms the LS algorithm in a variety of system configurations. Since the requirement on prior channel statistics information makes the MMSE algorithm impractical or extremely complex, the proposed WOA-based channel estimation algorithm should be a suitable and promising candidate for dealing with channel estimation problems. The simulation framework is implemented in MATLAB and available upon request.

The development of technology has brought some alternative means in education. The enabling role of technology has been widely recognized in second language teaching and learning. Second Life (SL) is an online virtual world and is popular with digital natives. Some language educators apply this application to foreign-language teaching in university courses because of its benefits to students’ immersion and focus, imagination, and creativity in language learning. Language learners can immerse themselves in the target language environment to imitate real-world tasks, which promotes the provision of reality-oriented tasks and optimizes task-based language teaching tenets such as learning by doing, authenticity, negotiation of meaning, and exposure to abundant and authentic input. Based on the definition of tasks, the role of SL in foreign-language teaching, and previous pedagogic models and studies on designing tasks with SL, this study proposed two types of SL tasks for Chinese as a foreign-language learner in university settings, along with the rationale behind them. Additionally, challenges in designing tasks in SL were identified. Overall, this study offered valuable insights and guidance for the application of SL in teaching Chinese to speakers of other languages in UK universities.

Hepatocellular carcinoma (HCC) is a malignancy with one of the worst prognoses. Long noncoding RNAs (lncRNAs) may be important in cancer development and may serve as new biomarkers for the diagnosis and treatment of various tumors, according to mounting research. The purpose of this study was to investigate the expression of INKA2-AS1 and clinical importance in HCC patients. The TCGA database was used to obtain the human tumor samples, while the TCGA and GTEx databases were used to gather the human normal samples. We screened differentially expressed genes (DEGs) between HCC and nontumor tissues. Investigations were made into the statistical significance and clinical significance of INKA2-AS1 expression. A single-sample gene set enrichment analysis (ssGSEA) was used to examine potential relationships between immune cell infiltration and INKA2-AS1 expression. In this investigation, we found that HCC specimens had considerably greater levels of INKA2-AS1 expression than nontumor specimens. When utilizing the TCGA datasets and the GTEx database, high INKA2-AS1 expression showed an AUC value for HCC of 0.817 (95% confidence interval: 0.779 to 0.855). Pan-cancer assays revealed that numerous tumor types had dysregulated levels of INKA2-AS1. Gender, histologic grade, and pathologic stage were all substantially correlated with high INKA2-AS1 expression. A survival study indicated that HCC patients with high INKA2-AS1 expression have shorter OS, DSS, and PFI than those with low INKA2-AS1 expression. Multivariate analysis indicated that INKA2-AS1 expression was an independent prognostic factor for OS of patients with HCC. According to immune analysis, the expression of INKA2-AS1 was favorably correlated with T helper cells, Th2 cells, macrophages, TFH, and NK CD56bright cells and negatively correlated with Th17 cells, pDC, cytotoxic cells, DC, Treg, Tgd, and Tcm. The results of this study collectively suggest that INKA2-AS1 has the potential to be a novel biomarker for predicting the prognosis of HCC patients as well as a significant immune response regulator in HCC.

This manuscript aims to highlight the existence and uniqueness results for the following Schrödinger problem in the extended Colombeau algebra of generalized functions. 1 / ı ∂ / ∂ t u t , x − △ u t , x + v x u t , x = 0 , t ∈ R + , x ∈ R n , v x = δ x , u 0 , x = δ x , where δ is the Dirac distribution. The proofs of our main results are based on the Gronwall inequality and regularization method. We conclude our article by establishing the association concept of solutions.

Objective. There is a bidirectional interaction between circulating testosterone and blood glucose levels. We aim to investigate the testosterone levels in men with early-onset type 2 diabetes (T2DM). Methods. A total of 153 drug naive men with T2DM were enrolled in the study. Early- ( n = 63 ) and late-onset ( n = 90 ) T2DM was classified according to age 40 years old. Clinical characteristics and plasma for biochemical criterions were collected. Gonadal hormones were measured using chemiluminescent immunometric assay. The concentrations of 3β- and 17β-HSD were determined using ELISA. Results. Compared with men with late-onset T2DM, those with early-onset T2DM had lower serum total testosterone (TT), sex hormone-binding globulin (SHBG), and FSH, but higher dehydroepiandrosterone sulfate (DHEA-S) level ( p < 0.05 ). The mediating effect analysis showed that the decreased TT levels in patients with early-onset T2DM were associated with the higher HbA1c, BMI, and triglyceride in these patients (both p < 0.05 ). The early-onset of T2DM directly correlated with increased DHEA-S (both p < 0.01 ). The 3β-HSD concentration in the early-onset T2DM group was lower than that in the late-onset T2DM group ( 11.07 ± 3.05 vs. 12.40 ± 2.72 pg/mL, p = 0.048 ) and was positively correlated with fasting C-peptide, while negatively correlated with HbA1c and fasting glucagon ( p all < 0.05). Conclusions. Patients with early-onset T2DM showed inhibition of conversion from DHEA to testosterone, which may attribute to the low level of 3β-HSD and high blood glucose in these patients.

A novel method is presented for solving MADM under a sine trigonometric Pythagorean neutrosophic normal interval-valued set (ST-PyNSNIVS). An identifying feature of ST-PyNSNIVS is that it is a combination of PyNSIVS, PyNSS, and IVNSS. This article proposes a novel concept of ST-PyNSNIVWA, ST-PyNSNIVWG, ST-GPyNSNIVWA, and ST-GPyNSNIVWG. In addition, we acquired a flowchart and an algorithm that interact with MADM and are called ST-PyNSNIVWA, ST-PyNSNIVWG, ST-GPyNSNIVWA, and ST-GPyNSNIVWG, respectively. In addition to Euclidean and Hamming distances, we addressed new types of two distances in the suggested models, which are future expansions of real-life instances. The sine trigonometric aggregation operations were examined using the PyNSNIV set technique. They are more straightforward and practical, and you can arrive at the best option quickly. Consequently, the conclusions of the defined models are more accurate and closely correlated with Σ . Our analysis shows that the investigated models are valid and useful by comparing them to some of the current models. As a final result of the study, some intriguing and enthralling findings are presented.

In this paper, we extend the results obtained by Ezearn on annihilated points for his higher-order nonexpansive mappings to the context of general higher-order nonexpansive mappings. Precisely in his thesis, Ezearn introduced the concept of annihilated points, which extends the notion of fixed points, and it is only meaningful in the context of higher-order nonexpansive mappings and gave some mild conditions when the annihilated points could exist in strictly convex Banach spaces. In the last direction, we also extend Ezearn’s result on the approximate fixed point sequence for higher-order nonexpansive mappings to general higher-order nonexpansive mappings.

The discrete differential evolution (DDE) algorithm is an evolutionary algorithm (EA) that has effectively solved challenging optimization problems. However, like many other EAs, it still faces problems such as premature convergence and stagnation during the iterative process. To address these concerns in the DDE algorithm, this work aims to achieve the following objectives: (i) investigate the causes of premature convergence and stagnation in the DDE algorithm; (ii) propose techniques to prevent premature convergence and stagnation in DDE, including a quantitative measurement of premature convergence based on the level of mismatching between the population solutions and then divide the population into individual groups based on the level of mismatching between the population solutions and the best solution; and applying the roulette wheel selection (RWS) approach to determine whether a higher degree of nonmatching is more suitable for choosing a population of separate groups to be able to produce a new solution with more options to prevent the occurrence of premature convergence; (iii) evaluate the effectiveness of the proposed techniques through employing the DDE algorithm to solve the quadratic assignment problem (QAP) as a standard to evaluate our results and their effect on avoiding premature convergence and stagnation issues, which led to the enhancement of the algorithm’s accuracy. Our comparative study based on the statistical analysis shows that the DDE algorithm that uses the proposed techniques is more efficient than the traditional DDE algorithm and the state-of-the-art methods.

Risk assessment of water disaster in karst tunnels is a nonlinear, complex, and uncertain system problem. Based on the reliability measurement method and extension cloud theory, the reliability measurement weighting and improved extension cloud model are established for the risk assessment of water disaster in karst tunnels. Firstly, on the basis of fully considering the relevance of evaluation indicators, a dynamic weighting theory for reliability measurement of indicators based on Jousselme distance is proposed. Secondly, according to the two different entropy algorithms of the extension cloud model, an improved extension cloud model is proposed based on the entropy algorithm fusion of game theory. The comprehensive cloud correlation degree obtained by the improved extension cloud model is combined with the indicator reliability measure weight. Then, the dangerous state of the water disaster in karst tunnels is judged, and the visualization of the water disaster level judgment is realized. And the model is tested with sample data from six typical karst tunnels. Finally, the model is applied to the Yangpeng Karst Tunnel on the Beijing-Zhuhai expressway. The results show that the water disaster level of the tunnel is I, and the evaluation results are consistent with the actual situation. Compared with the matter element extension model and the extension cloud model, the calculation results are more accurate. The corresponding treatment measures have been taken, and the good effect of water disaster treatment has been achieved. The practical value of the model is further proved, and a new reference method is provided for the prediction and prevention of water disaster in karst tunnels.

In this article, a new version of the generalized F-expansion method is proposed enabling to obtain the exact solutions of the Biswas-Arshed equation and Boussinesq equation defined by Atangana’s beta-derivative. First, the new version generalized F-expansion method is introduced, and then, the exact solutions of the nonlinear fractional differential equations expressed with Atangana’s beta-derivative are given. When the results are examined, it is seen that single, combined, and mixed Jacobi elliptic function solutions are obtained. From the point of view, it is understood that the new version generalized F-expansion method can give significant results in finding the exact solutions of equations containing beta-derivatives.

This study focuses on the psychological characteristics and empirically tests of the factors influencing distracted driving behaviours. This information is used as a reference for an intervention on dangerous driving behaviours. First, a distracted driving scale is constructed based on the theory of planned behaviour (TPB). The questionnaires are distributed in Chongqing, China, and 321 completed questionnaires are obtained. Data are analyzed using mean-variance analysis, one-way ANOVA, T-test, and multivariate test by SPSS 26.0 to determine the significance of distracted behaviours and demographic variables. We use a structural equation model to determine the path coefficients of each latent variable. Finally, we select the drivers with high tendency of distraction from the results of the questionnaires, conduct a four-stage rational emotional behaviour therapy (REBT) experiment, and use a repeated measures ANOVA analysis to test the validity and persistence of the intervention method. Results show that subjective norm is the most influential psychological factor. There are significant differences between the experimental group (2.38, SD = 0.41) and the control group (2.89, SD = 0.40) in the scores of distractions. This indicates that the distracted behaviour intervention achieves adequate validity and consistency. Educational research on distracted driving behaviour can help identify and correct drivers with high distraction tendency.

Secondary hydrocarbon migration is an important aspect of oil-gas accumulation research. While previous studies have relied on geological and fluid geochemical characteristics to predict migration direction, these results are often limited by the number of samples. In recent years, basin simulation has emerged as a valuable tool in hydrocarbon migration research due to its extensive algorithms and adaptable modeling capabilities. It has obvious technical advantages especially for resource evaluation in areas with a lower exploration degree and scarce data. The deep oil and gas in the Cambrian petroleum system in the Tarim Basin is a deep hydrocarbon challenging area in China. There has been no breakthrough in exploration due to the large burial depth ( generally > 6000 m ) and the long accumulation and transformation processes. Therefore, predicting the secondary migration of Cambrian oil and gas has become the key to solving this problem. In this study, first, the thermal evolution of the Lower Cambrian source rocks in the Tarim Basin was recovered, and the three thermal evolution models were developed. The secondary migration process of the Cambrian petroleum system was restored using the geologic model of the source rocks and paleotectonic evolution and the latest fluid potential information. By comparing the simulation results, four secondary migration models of the ultradeep oil-gas migration and accumulation were developed: multisource, multiphase, multidirectional accumulation; multisource, multiphase, single-directional accumulation; single-source, multiphase, multidirectional accumulation; and single-source, multiphase, single-directional accumulation. The fluid potential simulation results indicate that the Cambrian oil and gas have salient inheritance characteristics. The dominant migration channels in the uplift and slope are beneficial to oil-gas migration and accumulation, and the Katake uplift and the west Bachu uplift have multisource charging accumulation. The east Bachu uplift and the Tabei uplift are oil-gas accumulation zones that are beneficial to the lower petroleum system due to the continuous charging of a single petroleum system. This fluid potential simulation provides a new solution for studying the secondary migration of deep oil and gas. It provides an important reference for studying hydrocarbon accumulation in deep and ultradeep areas.

Considering the shortcomings of the existing network key node identification methods based on multiattribute fusion, which have single evaluation methods and low decision accuracy, combined with the advantages of the high accuracy of TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) algorithm and the applicability of grey relational analysis method for incomplete information evaluation, the concept of relative closeness is proposed, and nodes are ranked in importance based on the relative closeness; a key node identification method algorithm based on improved multiattribute fusion is designed. First, the identification problem of key nodes is transformed into multiattribute decision-making method, and the decision matrix is obtained. Second, the weighting matrix is obtained by weighting them in both subjective and objective dimensions, the relative closeness is calculated for the weighting matrix. Finally, sort the network nodes by relative closeness, and network performance simulation experiments are designed using various combinations of evaluation methods and key node identification methods. The simulation results show that this method is more adaptable and improves the identification accuracy of the network key nodes.

Colon adenocarcinoma (COAD) is one of the tumors with the highest mortality rates. It is of the utmost significance to make an accurate prognostic assessment and to tailor one’s treatment to the specific needs of the patient. Multiple lines of evidence point to the possibility that genetic variables and clinicopathological traits are connected to the onset and development of cancer. In the past, a number of studies have revealed that gamma-aminobutyric acid type A receptor subunit delta (GABRD) plays a role in the advancement of a number of different cancers. However, its function in COAD was rarely reported. In this study, we analyzed TCGA datasets and identified 29 survival-related differentially expressed genes (DEGs) in COAD patients. In particular, GABRD expression was noticeably elevated in COAD specimens. There was a correlation between high GABRD expression and an advanced clinical stage. According to the results of the survival tests, patients whose GABRD expression was high had a lower overall survival time and progression-free survival time than those whose GABRD expression was low. GABRD expression was found to be an independent predictive predictor for overall survival, as determined by multivariate COX regression analysis. Additionally, the predictive nomogram model can accurately predict the fate of individuals with COAD. In addition, we observed that GABRD expressions were positively associated with the expression of T cells regulatory (Tregs), macrophages M0, while negatively associated with the expression of T cells CD8, T cells follicular helper, macrophages M1, dendritic cells activated, eosinophils, and T cells CD4 memory activated. The IC50 of BI-2536, bleomycin, embelin, FR-180204, GW843682X, LY317615, NSC-207895, rTRAIL, and VX-11e was higher in the GABRD high-expression group. In conclusion, we have shown evidence that GABRD is a novel biomarker that is connected with immune cell infiltration in COAD and may be utilized to predict the prognosis of COAD patients.

Pancreatic cancer (PC) is a malignant tumor of the digestive system that has a bad prognosis. N6-methyladenosine (m6A) is involved in a wide variety of biological activities due to the fact that it is the most common form of mRNA modification in mammals. Numerous research has accumulated evidence suggesting that a malfunction in the regulation of m6A RNA modification is associated with various illnesses, including cancers. However, its implications in PC remain poorly characterized. The methylation data, level 3 RNA sequencing data, and clinical information of PC patients were all retrieved from the TCGA datasets. Genes associated with m6A RNA methylation were compiled from the existing body of research and made available for download from the m6Avar database. The LASSO Cox regression method was used to construct a 4-gene methylation signature, which was then used to classify all PC patients included in the TCGA dataset into either a low- or high-risk group. In this study, based on the set criteria of cor > 0.4 and p value < 0.05. A total of 3507 gene methylation were identified to be regulated by m6A regulators. Based on the univariate Cox regression analysis and identified 3507 gene methylation, 858 gene methylation was significantly associated with the patient’s prognosis. The multivariate Cox regression analysis identified four gene methylation (PCSK6, HSP90AA1, TPM3, and TTLL6) to construct a prognosis model. Survival assays indicated that the patients in the high-risk group tend to have a worse prognosis. ROC curves showed that our prognosis signature had a good prediction ability on patient survival. Immune assays suggested a different immune infiltration pattern in patients with high- and low-risk scores. Moreover, we found that two immune-related genes, CTLA4 and TIGIT, were downregulated in high-risk patients. We generated a unique methylation signature that is related to m6A regulators and is capable of accurately predicting the prognosis for patients with PC. The findings might prove useful for therapeutic customization and the process of making medical decisions.

Real-time medical image classification is a complex problem in the world. Using IoT technology in medical applications assures that the healthcare sectors improve the quality of treatment while lowering costs via automation and resource optimization. Deep learning is critical in categorizing medical images, which is accomplished by artificial intelligence. Deep learning algorithms allow radiologists and orthopaedic surgeons to make their life easier by providing them with quicker and more accurate findings in real time. Despite this, the classic deep learning technique has hit its performance limits. For these reasons, in this research, we examine alternative enhancement strategies to raise the performance of deep neural networks to provide an optimal solution known as Enhance-Net. It is possible to classify the experiment into six distinct stages. Champion-Net was chosen as a deep learning model from a pool of benchmark deep learning models (EfficientNet: B0, MobileNet, ResNet-18, and VGG-19). This stage helps choose the optimal model. In the second step, Champion-Net was tested with various resolutions. This stage helps conclude dataset resolution and improves Champion-Net performance. The next stage extracts green channel data. In the fourth step, Champion-Net combines with image enhancement algorithms CLAHE, HEF, and UM. This phase serves to improve Enhance-performance. The next stage compares the Enhance-Net findings to the lightness order error (LoE). In Enhance-Net models, the current study combines image enhancement and green channel with Champion-Net. In the final step, radiologists and orthopaedic surgeons use the trained model for real-time medical image prediction. The study effort uses the musculoskeletal radiograph-bone classification (MURA-BC) dataset. Classification accuracy of Enhance-Net was determined for the train and test datasets. These models obtained 98.02 percent, 94.79 percent, and 94.61 percent accuracy, respectively. The 96.74% accuracy was achieved during real-time testing with the unseen dataset.

A two-year field experiment was carried out to scrutinize the integrated use of NPSZnB blended fertilizer and farmyard manure (FYM) on maize growth and yield components at Koga and Bachima villages in Mecha district and Geray and Jiga villages in Jabi Tehran district. Factorial combinations have five levels of NPSZnB (100, 150, 200, 250, and 300 kg·ha−1), and four rates of FYM (0, 12, 16, and 20 t·ha−1), plus one blanket recommendation (200 kg·ha−1 DAP and 150 kg·ha−1 Urea). A randomized complete block design with three replications was used to set up the studies. Except for days to silking and leaf area index at both locations, and days to physiological maturity, and ear length at Mecha, main and interaction effects on all parameters were detected at both locations. The only main effect of NPSZnB was detected on the harvest index at Jabi Tehnan. Generally, the results achieved from the interaction effect were better and greater than those obtained from the blanket recommendation although this was not the case for the main effects. Grain yields at Jabi Tehnan and Mecha were 5618.5 and 5421 kg·ha−1, respectively, as a result of the unified use of 250 kg·ha−1 NPSZnB and 20 t·ha−1 FYM and shortened days to 50% tasselling by 3.46 days and delayed 90% physiological maturity by 2.95 days than the blanket recommended fertilizer at Jabi Tehnan. As the main effect, compared to the blanket recommendation, 250 kg·ha−1 at Jabi Tehnan and 300 kg·ha−1 NPSZnB at Mecha reduced the days to 50% silking by 0.225 and 0.292 days, respectively. Contrasted with the conventional recommendation, the application of 300 kg·ha−1 NPSZnB minimizes days to 90% physiological maturity by 0.89 at Mecha, and the rise in FYM level from 0 to 20 t·ha−1 caused 0.832 and 0.279 extra days to reach 50% silking at Jabi Tehnan and Mecha, respectively. The yield had a very strong correlation that is positive with growth and yield components. Economic study: the use of 200 kg·ha−1 NPSZnB with 20 t·ha−1 FYM resulted in the maximum net benefit of 36221.06 and 35431.04 ETB ha−1 at Jabi Tehnan and Mecha, respectively. Thus, 250 kg·ha−1 NPSZnB with 20 kg·ha−1 FYM and 200 kg·ha−1 NPSZnB with no FYM application were the most acceptable rates at both locations with a low cost of production to small-scale farmers. For resource-rich farmers, 200 kg·ha−1 NPSZnB with 20 t·ha−1 FYM was also favorable, with the best net benefit at both locations.

Purpose. To explore the therapeutic efficiency of acupuncture and the related molecular mechanism of neural plasticity in depression. Methods. Chronic unpredictable mild stress- (CUMS-) induced rats were established for the depression animal model. There were a total of four rat groups, including the control group, the CUMS group, the CUMS+acupuncture group, and the CUMS+fluoxetine group. The acupuncture group and the fluoxetine group were given a 3-week treatment after the modeling intervention. The researcher performed the open-field, elevated plus maze, and sucrose preference tests to evaluate depressive behaviors. The number of nerve cells, dendrites’ length, and the prefrontal cortex’s spine density were detected using Golgi staining. The prefrontal cortex expression, such as BDNF, PSD95, SYN, and PKMZ protein, was detected using the western blot and RT-PCR. Results. Acupuncture could alleviate depressive-like behaviors and promote the recovery of the neural plasticity functions in the prefrontal cortex, showing the increasing cell numbers, prolonging the length of the dendrites, and enhancing the spine density. The neural plasticity-related proteins in the prefrontal cortex, including BDNF, PSD95, SYN, and PKMZ, were all downregulated in the CUMS-induced group; however, these effects could be partly reversed after being treated by acupuncture and fluoxetine ( P < 0.05 ). Conclusion. Acupuncture can ameliorate depressive-like behaviors by promoting the recovery of neural plasticity functions and neural plasticity-related protein upregulation in the prefrontal cortex of CUMS-induced depressed rats. Our study provides new insights into the antidepressant approach, and further studies are warranted to elucidate the mechanisms of acupuncture involved in depression treatment.

This study aims to develop a hybridized deep learning model for generating semantically meaningful image captions in Amharic Language. Image captioning is a task that combines both computer vision and natural language processing (NLP) domains. However, existing studies in the English language primarily focus on visual features to generate captions, resulting in a gap between visual and textual features and inadequate semantic representation. To address this challenge, this study proposes a hybridized attention-based deep neural network (DNN) model. The model consists of an Inception-v3 convolutional neural network (CNN) encoder to extract image features, a visual attention mechanism to capture significant features, and a bidirectional gated recurrent unit (Bi-GRU) with attention decoder to generate the image captions. The model was trained on the Flickr8k and BNATURE datasets with English captions, which were translated into Amharic Language with the help of Google Translator and Amharic Language experts. The evaluation of the model showed improvement in its performance, with a 1G-BLEU score of 60.6, a 2G-BLEU score of 50.1, a 3G-BLEU score of 43.7, and a 4G-BLEU score of 38.8. Generally, this study highlights the effectiveness of the hybrid approach in generating Amharic Language image captions with better semantic meaning.

Damage in bolts, which are used as connecting fasteners in steel structures, affects structural safety. Sophisticated machine vision methods have been formulated for the detection of loose bolts, but their accuracy remains an area for improvement. In this paper, a method based on a stacked hourglass network is proposed for automatically extracting the key points of a bolt and for obtaining the bolt loosening angle by comparing the rotations of the key points before and after the bolt is damaged. A data set containing 100 images of key bolt loosening points was collected, and rotation was performed as data augmentation to yield 1800 images. Moreover, a method was designed for automatically annotating the augmented image data set. In this study, 70%, 10%, and 20% of the annotated image data set were used for training, validation, and testing, respectively. Subsequently, a neural network model based on a stacked hourglass network was established to train the annotated image data set. The detection results were evaluated in terms of normalized errors (NEs), percentage of correct key points (PCK), detection speed, and training time. In testing, the proposed method accurately and efficiently identified the bolt loosening angle, with a PCK value as high as 99.3%. The accuracy of the proposed method was also highly robust to different shooting distances, viewing angles, and illumination levels.

Background. Trimeresurus stejnegeri (T.s) accounts for most snakebites in southern China, which always leads to coagulation dysfunction. Coagulopathy due to venom is widely considered to be a characteristic phenomenon of the DIC-like syndrome. It is vitally important for first-line clinicians to improve this condition as soon as possible. However, clinical factors associated with coagulation function in Trimeresurus stejnegeri has not been well characterized yet. Materials. Patients bitten by vipers were admitted to the Emergency Department of a hospital in Shenzhen, southern China, from 2021 to 2022 and were retrospectively reviewed. Patient clinical characteristics and laboratory data were compared in the eligible patients bitten by T.s by their prothrombin time (PT), fibrinogen level (FIB), and platelet count on 2-3 days after bitten. Results. 105 patients were included in this study. The mean values of PT, FIB, and PLT are as follows: 12.8 ± 0.79 s, 2.25 ± 0.47 g/L, and 196.2 ± 57.1 × 109/L. Uric acid (UA) (367.9 ± 103.85), blood glucose (6.53 + 1.64) show negative trend of correlation, while CRP (2.12 + 4.17) shows positive trend of association with coagulation function. The smoke and systolic blood pressure may exert negative effects on PT and PLT, respectively. Logistic regression analysis indicated that uric acid (UA) shows significant connection with PT (OR = 1.15 and P value P value = 0.026), and PLT (OR = 0.79 and P value = 0.007). CRP is also shown to be associated with FIB (OR = 1.33 and P value = 0.043).Conclusion: Uric acid (UA) shows a significant association with PT, FIB, and PLT. CRP is related to FIB. Blood glucose shows a negative trend of correlation with PT. We do recommend physician should low the level of UA in some degree on the basis of injection of an antivenom serum.

The analysis of load characteristics is the basis and premise of load actively participating in power grid regulation. This paper proposes a multi-factor load classification method considering the load of clean energy power generation, the rapidity of load classification, and various subjective and objective factors that may affect the behavior of load consumption. First, it describes the characteristic index of load consumption behavior and analyzes the subjective and objective factors that affect the power grid consumption behavior. The effect of clean energy generation on load side is considered. Based on the load characteristics, the K-means algorithm is used for main clustering. Then, the confidence level of the uncertainty of the actual load adjustable capacity is analyzed by quantifying the load adjustable potential index and the fuzzy C-means clustering method was used for secondary clustering of the adjustable capacity. Finally, DBI and SC indexes are used to evaluate the clustering results, standard values of evaluation indexes are set, and unqualified clustering results are recalculated and corrected. 31 industrial users in a province are selected as research objects, and the load data of the past 365 days are collected to verify the effectiveness and practicability of the proposed method. The classification results show that the classification accuracy is still good when the noise is 30%, and the maximum deviation between the clustering results and the actual load regulation potential is 12%. It can meet the actual engineering error standard.

Ovarian metastasis of gastric cancer indicates that the disease has reached the late stage and the opportunity for radical surgery is restricted. However, the clinical characteristics and prognosis of patients with gastric cancer ovarian metastasis (GCOM) remain to be illustrated. Here, we retrieved the information of 780 GCOM cases from the Surveillance, Epidemiology, and End Results (SEERs) database and analyzed their clinicopathological characteristics as well as their survival. According to our data, most GCOM patients showed poor pathological differentiation, advanced T and N stages. The prognostic factors include patients’ age, tumor size, surgical resection, and chemotherapy treatment. Of note, the marriage status was also identified as an independent prognostic factor. Besides the identification of prognostic factors, we established nomograms to help predict the overall survival and cancer-specific survival of GCOM, respectively.

In the process of coal seam mining, there are often hard thick key layers in the overlying strata. Due to the high strength and good integrity of the hard thick key layer, after the hard thick key layer is broken, the overlying strata will collapse and lose stability in a large area, which is very easy to induce dynamic disasters such as rock burst, mine earthquake, coal wall caving, and roof slab caving. Aiming at the hard and thick key layer overlying the working face, the dynamic response of the mine under the strong mine earthquake induced by the breaking of the main key layer of high-level magmatic rock is numerically simulated and analyzed by using FLAC2D numerical simulation software, and the variation laws of the stress field, displacement field, and velocity field of the coal seam roadway under different boundary conditions and different focal heights are studied. The research shows that the roof of solid coal roadway is prone to vibration in a small range, and the displacement increases and decreases with the disturbance. The displacement of the floor and two sides of the solid coal roadway and the top floor and two sides of the roadway along the goaf continues to increase in the initial stage of the disturbance, and the displacement will remain stable with the continuation of the disturbance. The displacement of both sides and roof and floor of gob roadway can reach stability in the later stage of disturbance, and with the increase of the number of adjacent goaf, the longer it takes for the displacement of surrounding rock to reach stability. When the focal height is lower than 90 m, the variation of surrounding rock response increases sharply with the decrease of focal height. When a strong earthquake occurs in the low rock stratum, the impact damage of roadway surrounding rock is almost inevitable. The influence degree of strong earthquake on the stability of roadway surrounding rock is arranged as follows: gob-side roadway (mined out on one side) > solid coal roadway (mined out on both sides) > solid coal roadway (mined out on one side). The evolution process also shows that the working face boundary conditions have an important influence on the energy propagation of mine earthquake. With the increase of the number of adjacent goafs, the faster the energy attenuation rate of mine earthquake propagation is. The research results have important reference significance for the safe mining of working face under similar geological conditions.

This paper provides an in-depth study and analysis of information security protocols for industrial Ethernet using wireless sensor networks. The optimal number of cluster heads for nonuniform subclustering is derived based on the sensor energy consumption model, and then, the EEUC contention radius formula is optimized to select candidate cluster heads with random values and energy as weights. A multihop approach based on the shortest offset is also proposed for intercluster information transmission. Experimental results show that the EEUC-based improved cluster routing protocol proposed in this paper balances the node energy consumption and extends the network lifetime. In response to the problem that the coarsened hop value and average hop distance of the DV-Hop localization algorithm cannot reflect the network topology, the improved DV-Hop algorithm based on multicommunication radius and hop distance correction is proposed. Simulation experiments show that the improved algorithm based on multiple communication radius and hop count correction can significantly reduce the localization error and improve the accuracy of the algorithm. Aiming at the shortcomings of MRP with excessive risk concentration and transmission medium limitation, this paper proposes a fast self-healing mechanism of industrial Ethernet with a multiexpert strategy. The PCP-AP common platform architecture for openSAFETY sites is designed on the base sleeve of the implementation of the industrial Ethernet protocol Ethernet POWERLINK; the main communication part of POWERLINK is implemented through an FPGA hardware solution, and the openSAFETY site is implemented using AM335X high-performance processor to implement openSAFETY security application functions. Finally, the article conducts field tests on the wireless signal information transmission, WSN data transmission, network connection, and power supply system in the system and compares and analyzes the data collected by the system with the monitoring data of the national control site. The data obtained by the system has real reliability. The communication module used is inexpensive, lightweight, and easy to operate. It can realize the collection of multiple pollution sources, and compared with traditional monitoring equipment, it avoids the difficulties of complicated wiring, difficult positioning of pollution sources, and restricted monitoring areas and largely reduces the investment in human and material resources.

Purpose. Evaluate the effect of different mini-implant numbers on overdenture retention and evaluate attachment wear following one year of simulated placement/removal. Material and Methods. Nine models simulating atrophic mandibles held 27 mini dental implants in three groups of 2, 3, and 4 mini-implants. A total of 1080 simulated placement/removal cycles were carried out, and a digital force gauge was used to measure the overdenture dislodgment force. The means of the retention forces were analyzed using SPSS with one-way ANOVA and post hoc ( p < 0.05 ). The inner diameter of attachment inserts was evaluated using a light microscope before and after testing. A paired t -test was used to compare the mean of inner ring diameters ( p < 0.05 ). Results. The retention was significantly reduced regardless of the mini dental implant number, but the number affected overdenture retention. The placement of 4 mini dental implants provided higher retention and less reduction in retentiveness. However, no significant difference was found when 3 mini dental implants were compared to 2 mini dental implants ( p = 0.21 ). Microscopic examination showed abrasion wear in all inserts following testing. However, the inserts of the 4 mini dental implants showed less wear than those used for 2 or 3 mini dental implants with p ≤ 0.001 and p ≤ 0.001 , respectively. Conclusion. Mini dental implant overdenture retention force and attachment wear could improve by increasing the mini dental implants to 4. However, there was no difference in retention force or attachment wear when 2 or 3 mini dental implant overdentures were compared.

Background. Patients with moderate or severe COVID-19 infection suffer from varying levels of fatigue; however, there is a lack of understanding regarding the effect of inflammation on fatigue; and whether these relationships differ according to the severity of the infection. Aim. To assess the relationships between selected inflammatory biomarkers and fatigue levels among hospitalized Jordanian patients with moderate or severe COVID-19 infection. Methods. A quantitative cross-sectional design was used. A total of 352 participants were recruited for the study. Data regarding fatigue type and level were collected using the Chalder fatigue scale. Laboratory test results regarding several selected inflammatory biomarkers (e.g., ESR, CRP, IL-6, D-dimer, and others) were collected from patient records. The severity of the COVID-19 infection was determined using the criteria of the Ministry of Health in Jordan based on the results of O2% (oxygen saturation). Results. The mean scores of the total fatigue level significantly differed between the two levels of the severity of COVID-19 infection (moderate and severe levels) (t = −3.0, p < 0.05 ). Similar findings were observed with physiological fatigue (t = −3.50, p < 0.05 ), and no significant difference was observed in psychological fatigue. Out of the selected inflammatory markers, only neutrophil and lymphocyte count had a significant influence on total fatigue level. Conclusion. The level and type of fatigue was affected by the severity of the disease. However, the disease process itself represented by the levels of the inflammatory markers showed little influence on fatigue. The implications such as continuous screening of fatigue, and monitoring of the levels of the inflammatory markers are important to assist in diagnosing and managing COVID-19 patients. Furthermore, the relationship between the inflammatory process and fatigue is complex and requires further exploration.

In this note, a general class of contractive inequality, namely, admissible hybrid H − α − ϕ contraction, is proposed in metric space endowed with a graph, and new criteria for which the mapping is a Picard operator are examined. The significance of this type of contraction is connected with the possibility that its inequality can be particularized in more than one way, depending on the provided constants. Relevant examples are designed to support the assumptions of our obtained notions and to show how they are different from the known ones. A corollary which reduces our obtained result to a recently published result in the literature is pointed out and discussed.

The mining of key nodes is an important topic in complex network research, which can help identify influencers. The study is necessary for blocking the spread of epidemics, controlling public opinion, and managing transportation. The techniques thus far suggested have a lot of drawbacks; they either depend on the regional distribution of nodes or the global character of the network. The gravity formula based on node information is a good mathematical model that can represent the magnitude of attraction between nodes. However, the gravity model requires less node information and has limitations. In this study, we propose a gravity model based on Shannon entropy to effectively address the aforementioned issues. The spreading probability method is employed to enhance the model’s functionality and applicability. Through testing, it has been determined that the suggested model is a good alternative to the gravity model for selecting influential nodes.

Background. Sexually transmitted blood-borne infections (STBBIs) contribute to negative outcomes of pregnancy. Hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), and syphilis infections in pregnancy contribute significantly to maternal and child morbidities and mortalities. This study assessed the prevalence, knowledge, and risk factors of STBBIs (HBV, HCV, HIV, and syphilis) among pregnant women attending antenatal clinics in Jirapa. Methods. A cross-sectional study design involving 246 pregnant women was employed for the study. A structured questionnaire was used to solicit information about the knowledge, prevalence, and risk factors of STBBIs. Results. The overall prevalence of STBBIs was 11.4%; HBV prevalence was 9.8% and 0.8% each for HCV, HIV, and syphilis. About 66% of mothers were aware of mother-to-child transmission of infections during pregnancy. Knowledge of transmission of HIV (93.9%), hepatitis (67.1%), and syphilis (53.7%) in pregnancy was relatively high. Knowledge of risk factors for HIV, hepatitis, and syphilis was 97.6%, 74.4%, and 76.0%, respectively. More than 98% of respondents knew about the prevention of HIV, hepatitis, and syphilis. Significant risk factors associated with and predictive of STBBIs were female genital mutilation (FGM) and gravidity. Conclusion. The occurrence of STBBIs among pregnant women was strongly associated with FGM and gravidity. Public health education should be directed at stopping the practice of FGM and improving reproductive health in the study area.

Acoustic metamaterials have been widely concerned by researchers because of their excellent sound absorption properties. Traditional uncertainty analysis methods for response analysis of acoustic metamaterials with evidence variables have limitations. The excessive time spent on repetitive extreme analysis of focal elements severely impedes the practical application of evidence theory. To reduce the computational costs of uncertainty quantification for acoustic metamaterials under the evidence theory, a reduced space optimization-based evidence theory method (RSO-ETM) is proposed. In RSO-ETM, a simplified surrogate model is first constructed by a modified adaptive arbitrary orthogonal polynomial (MAAOP) expansion method, and then, the monotonicity of the response surface is examined to reduce the space. Subsequently, the Newton iteration technique and a transformation boundary method are used to obtain the extreme points in the reduced space, through which the boundary of the response over each focal element can be readily obtained. By using RSO-ETM, the optimization for each focal element can be avoided, and correspondingly the computational costs are reduced. Two mathematical examples and acoustic problems are employed to demonstrate the practicality of the methods.

Our work was devoted to studying the effect of different concentrations of Mentha spicata aqueous extract on the green synthesis of silver nanoparticles (AgNPs) in order to obtain the most effective of these concentrations for bacteria inhibitory activity. Different concentrations of the aqueous M. spicata extract (0.25, 0.50, 0.75, and 1.00 mM) were used as biological solvent to synthesize AgNPs by means of the reduction method. The crystal structure and morphology of the NPs were characterized UV–vis spectra, X-ray diffraction (XRD), and scanning electron microscopy (SEM). The inhibition effect of AgNPs on Escherichia coli was studied to determine the minimum inhibitory concentration (MIC). The dark yellow color of the M. spicata extract aqueous solution indicates the successful synthesis of the AgNPs. UV spectra of the NPs show a gradual increase in absorption with increasing concentration of aqueous M. spicata extract solution from 0.25 to 1.00 mM, accompanied by a shift in the wavelength from 455 to 479 nm along with a change in the nanoparticle size from 31 to 9 nm. The tests also showed a high activity of the particles against bacteria (E. coli) ranging between 15.6 and 62.5 µg/ml. From the AgNPs, it was confirmed that aqueous M. spicata extract is an effective biosolvent for the synthesis of different sizes of AgNPs according to the solvent concentration. The AgNPs also proved effectual for the killing of bacteria.

The nanostructures synthesized using the green chemistry method have recently attracted the attention of scientists due to their significance in many scientific domains. This work provides an overview of the biosynthesis of zinc oxide (ZnO) nanosheets (NSs) using Phyllanthus emblica plant (PEP) extract. X-ray diffraction analysis (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and Fourier transform infrared (FTIR) were used to analyze the synthesized ZnO-NSs. Evaluation of the antibacterial activity of biosynthesized ZnO-NSs was performed. ZnO-NSs exhibit effective antibacterial activity against Gram-positive (S. pyogenes and S. aureus) and Gram-negative (S. typhi and E. coli) bacterial strains. S. typhi is the most sensitive microbe towards ZnO-NSs and formed a 21 mm zone of inhibition (ZOI). ZnO-NSs are also tested as a photocatalyst in the degradation of methyl orange (MO) and rhodamine B (RB). The degradation rate of MO was 90%, and RB was 96% after being exposed to UV light for 120 min. The as-synthesized ZnO-NSs exhibited selective dye degradation and showed relatively better photocatalytic activity for positively charged (cationic) dyes. This work could lead to the fabrication of high-yield photocatalysts, which have the potential to degrade textile dyes from aqueous solution.

People of all ages enjoy playing games, making online gaming a part of the modern lifestyle. Online games as digital products can reach gamers without being hindered by various limitations such as time and location. With a large selection of freemium and premium games in the virtual marketplace, gamers have the opportunity to switch between various games from different genres and make purchases of virtual goods. Therefore, the challenge that developers have to overcome is whether gamers will have the intention to play, pay, and recruit players (3Ps). To manage future behavior intentions, game developers need to ensure that gamers and the games they play have a solid engagement. Data collection comes from the results of distributing questionnaires to Twitter users with the self-reporting method. The amount of data processed was 370 respondents. The model was assessed using the partial least square of structural equation modeling. The results of this study show that video game engagement plays a crucial role as a mediator between gamer experience and the intention to continue playing, purchase game items, and recruit new players. Enjoyment is the strongest predictor of gamer experience, followed by arousal, social interaction, escapism, and challenge.

Estrogen deficiency causes bone loss via diverse pathological cellular events. The involvement of the vasculature in bone formation has been widely studied, and type H vasculature has been found to be closely related to bone healing. Ovariectomy- (OVX-) induced estrogen deficiency reduces type H vessel density and promotes reduction of bone density. Analysis of early events after OVX showed that estrogen deficiency preferentially induces oxidative stress, which might provoke endothelial dysfunction and reduce angiogenic factors systemically and locally. The instability of the vascular potential is expected to promote bone loss under estrogen deficiency. Substance P (SP) is an endogenous neuropeptide that controls inflammation and prevents cell death under pathological conditions. SP can elevate nitric oxide production in endothelial cells and inhibit endothelial dysfunction. This study is aimed at investigating the preventive effects of systemically injected SP on OVX-induced vascular loss and osteoporosis onset. SP was systemically administered to OVX rats twice a week for 4 weeks, immediately after OVX induction. OVX conditions could decrease antioxidant enzyme activity, type H vessels, and angiogenic growth factors in the bone marrow, followed by inflammation and bone loss. However, pretreatment with SP could block type H vessel loss, accompanied by the enrichment of nitric oxide and sustained angiogenic factors. SP-mediated early vascular protection inhibits bone density reduction. Altogether, this study suggests that early administration of SP can block osteoporosis development by modulating oxidative stress and protecting the bone vasculature and angiogenic paracrine potential at the initial stage of estrogen deficiency.

The structural connectivity from the primary olfactory cortex to the main secondary olfactory areas was previously reported as relatively increased in the medial orbitofrontal cortex in a cohort of 27 recently SARS-CoV-2-infected (COV+) subjects, of which 23/27 had clinically confirmed olfactory loss, compared to 18 control (COV-) normosmic subjects, who were not previously infected. To complement this finding, here we report the outcome of an identical high angular resolution diffusion MRI analysis on follow-up data sets collected in 18/27 COV+ subjects (10 males, mean age ± SD : 38.7 ± 8.1 years) and 10/18 COV- subjects (5 males, mean age ± SD : 33.1 ± 3.6 years) from the previous samples who repeated both the olfactory functional assessment and the MRI examination after ~1 year. By comparing the newly derived subgroups, we observed that the increase in the structural connectivity index of the medial orbitofrontal cortex was not significant at follow-up, despite 10/18 COV+ subjects were still found hyposmic after ~1 year from SARS-CoV-2 infection. We concluded that the relative hyperconnectivity of the olfactory cortex to the medial orbitofrontal cortex could be, at least in some cases, an acute or reversible phenomenon linked to the recent SARS-CoV-2 infection with associated olfactory loss.

Background. The poor prognosis of the highly malignant tumor osteosarcoma stems from its drug resistance and therefore exploring its resistance mechanisms will help us identify more effective treatment options. However, the effects of miR-125b-5p on drug resistance in osteosarcoma cells are still unclear. Methods. To study the effects of miR-125b-5p on drug resistance in osteosarcoma cells. Osteosarcoma-resistant miR-125b-5p was obtained from the databases GeneCards and g:Profiler. CCK8, western blot, and transwell were applied for the detection of the miR-125b-5p effects on proliferation, migration, invasion, apoptosis, and drug resistance in osteosarcoma. Bioinformatics is aimed at demonstrating the targeting factor miR-125b-5p, performing protein interaction enrichment analysis by Metascape, and finally validating by binding sites. Results. Upregulation of miR-125b-5p restrains proliferation, migration, and invasion of osteosarcoma and promotes apoptosis. In addition, miR-125b-5p can restore drug sensitivity in drug-resistant osteosarcoma. miR-125-5p restrains the signal transducer and inhibits the transcription 3 (STAT3) expression activator via targeting its 3 ′ -UTR. STAT3 affects drug-resistant osteosarcoma to regulate the ABC transporter. Conclusion. miR-125b-5p/STAT3 axis mediates the drug resistance of osteosarcoma by acting on ABC transporter.

Liang-Ge (LG) decoction could ameliorate coagulation dysfunction in septic model rats. However, the mechanism of LG in treating sepsis still needs to be clarified. Our current study established a septic rat model to evaluate the effect of LG on coagulation dysfunction in septic rats first. Second, we investigated the effect of LG on NET formation in septic rats. Finally, NETs and PAD4 inhibitors were further used to clarify if LG could improve the mechanism of sepsis coagulation dysfunction by inhibiting NET formation. Our findings indicated that treatment with LG improved the survival rate, reduced inflammatory factor levels, enhanced hepatic and renal function, and reduced pathological changes in rats with sepsis. LG could also alleviate coagulation dysfunction in septic model rats. Besides, LG treatment reduced NETs formation and decreased PAD4 expression in neutrophiles. In addition, LG treatment showed a similar result in comparison to the treatment with either NET inhibitors or PAD4 inhibitors alone. In conclusion, this study confirmed that LG has therapeutic effects on septic rats. Furthermore, the improvement of coagulation dysfunction in septic rats by LG was achieved through inhibiting PAD4-mediated NET formation.

Shelf life of bottled natural fruit juice (BNFJ) provides relevant information on quality and authenticity for consumer protection. However, existing techniques for monitoring the shelf life of BNFJ are destructive and time-consuming. We report on using laser-induced autofluorescence (LIAF) spectroscopic technique in combination with multivariate analysis for shelf life monitoring of BNFJ. The LIAF spectra data were acquired for nine (9) continuous days on three batches of BNFJ samples purchased from a certified retailer. Deconvolution of the LIAF spectra revealed underlying peaks representing constituents of the BNFJ. Principal component analysis (PCA) was able to monitor the trend in the changes of the BNFJ as it aged. Partial least square regression (PLSR) predicted the exact day from the production of the BNFJ accurately at 96.6% and 98.8% in the training and testing sets, respectively. We, therefore, propose the LIAF combined with multivariate analysis as a potential tool for nondestructive, rapid, and relatively inexpensive monitoring of the shelf life of BNFJ.

In this article we study the existence and stability of bounded solutions for semilinear abstract dynamic equations on time scales in Banach spaces. In order to do so, we use the definition of the Riemann delta-integral to prove a result about closed operator in Banach spaces and then we just use the representation of bounded solutions as an improper delta-integral from minus infinite to t . We prove the existence, uniqueness, and exponential stability of such bounded solutions. As particular cases, we study the existence of periodic and almost periodic solutions as well. Finally, we present some equations on time scales where our results can be applied.

Bacteria develop resistance against antimicrobial drugs, and new remediations are constantly being introduced in the market. Silver and its compounds have strong resistance against different bacteria. The vegetable waste extract-synthesized silver nanoparticles (VWE-AgNPs) have distinct properties and potential applications because of their unique size and morphology. The fundamental purpose of this study was to develop an environment-friendly method for the synthesis of VWE-AgNPs to avoid the use of hazardous chemicals that cause danger to the environment as well as recycling vegetable waste material. The VWE-AgNPs were synthesized by mixing 1 mM AgNO3 solution and VWE at room temperature. The VWE-AgNPs were characterized by UV-visible spectroscopy, FTIR, SEM, and EDX. The synthesized particles showed good antibacterial properties against poultry bacteria Salmonella gallinarum and Salmonella enteritidis (growth reduction of 31 mm and 18 mm at 80 mg/mL AgNPs, respectively). The results demonstrated that VWE-AgNPs inhibited the growth of tested bacterial strains. The fabricated VWE-AgNPs also had the potential to act as a green photocatalyst for degradation of 87.7% of methylene blue (MB) and 90.76% of methyl orange (MO) nearly at 3.25 h and 1 h sunlight exposure time, respectively. The highest antifungal activity, which was determined to be 36.5 mm and 31.8 mm against Alternata sp. and C. albican, was discovered to be in VWE-AgNPs.