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The potential of machine learning mechanisms played a key role in improving the intrusion detection task. However, other factors such as quality of data, overfitting, imbalanced problems, etc. may greatly affect the performance of an intelligent intrusion detection system (IDS). To tackle these issues, this paper proposes a novel machine learning-based IDS called i-2NIDS. The novelty of this approach lies in the application of the nested cross-validation method, which necessitates using two loops: the outer loop is for hyper-parameter selection that costs least error during the run of a small amount of training set and the inner loop for the error estimation in the test set. The experiments showed significant improvements within NSL-KDD dataset with a test accuracy rate of 99.97%, 99.79%, 99.72%, 99.96%, and 99.98% in detecting normal activities, DDoS/DoS, Probing, R2L and U2R attacks, respectively. The obtained results approve the efficiency and superiority of the approach over other recent existing experiments.

Background The interaction between intestinal myofibroblasts (iMFBs) and extracellular matrix (ECM) changes contributes to Crohn’s disease (CD) fibrosis, but the exact mechanisms are not yet clear. Our group recently discovered a selection of matrix-derived peptides (matrikines) that specifically appear in the intestinal tissue of patients with CD fibrostenosis.1 Here, we evaluate the in vitro effect of 19 matrikines (with various bioactivity likelihood) on primary human intestinal myofibroblasts (IMFBs), key cells in CD fibrosis. Methods Primary human iMFBS (500K/mL, Passage 4, Novabiosis) were cultured in serum-free medium (SFM) (DMEM high glucose, 1% sodium pyruvate, 1% non-essential aminoacids, 1% Antibiotics-Antimycotics and 3mg Gentamicin), complete medium (SFM with 10% fetal bovine serum), PDGF-bb 10ng/mL or treated with each of the matrikines at 10, 50, 100 μg/mL for 24hours. The effect of each matrikine on cell proliferation, gene expression and migration was assessed by BrdU, qPCR and Transwell assays, respectively. RNA sequencing data was analysed using the GSEA software. Results The effect on iMFBs proliferation and viability was tested with each of the 19 matrikines at 3 different doses (Table 1). Among 9 most likely-bioactive matrikines (M1-M9) (Figure 1), M1, M4, M7, M8 and M9 induced the highest effect in cell proliferation and were selected for further experiments. Gene expression assays showed upregulation of ACTA2 with M1 or M7 and upregulation of COL1A1 with M1, M7 or M8, indicating activation of the iMFBs (Figure 2). Transwell assays also showed that M1, M4 and M8 induce iMFB cell migration. A total of 1948, 1024, 460, 570, and 2749 differentially expressed genes (DEGs) were identified in iMBFs treated with M1, M4, M7, M8 and M9 vs no treatment, showing enrichment in pathways related to “smooth muscle contraction”, “fibroblast growth factor”, “cell migration”, “collagen formation”, “elastin formation” and “ECM organization”; most of which were also enriched by complete medium and/or PDGFbb that served as positive controls. However, pathways related to “proinflammatory and profibrotic mediators” or “vascular permeability” were only enriched in iMFBs treated with M1, M4, M8 and M9 or M1 and M9, respectively (Figure 3). Conclusion A selection of newly discovered CD-specific matrikines induce activation of iMFBs as well as inflammatory response and endothelial changes, suggesting an important role and therefore their potential value as biomarkers in the progression of intestinal fibrostenosis.

Background: Mural cells in ascending aortic aneurysms undergo phenotypic changes that promote extracellular matrix destruction and structural weakening. To explore this biology, we analyzed the transcriptional features of thoracic aortic tissue. Methods: Single-nuclear RNA sequencing was performed on 13 samples from human donors, 6 with thoracic aortic aneurysm, and 7 without aneurysm. Individual transcriptomes were then clustered based on transcriptional profiles. Clusters were used for between-disease differential gene expression analyses, subcluster analysis, and analyzed for intersection with genetic aortic trait data. Results: We sequenced 71 689 nuclei from human thoracic aortas and identified 14 clusters, aligning with 11 cell types, predominantly vascular smooth muscle cells (VSMCs) consistent with aortic histology. With unbiased methodology, we found 7 vascular smooth muscle cell and 6 fibroblast subclusters. Differentially expressed genes analysis revealed a vascular smooth muscle cell group accounting for the majority of differential gene expression. Fibroblast populations in aneurysm exhibit distinct behavior with almost complete disappearance of quiescent fibroblasts. Differentially expressed genes were used to prioritize genes at aortic diameter and distensibility genome-wide association study loci highlighting the genes JUN, LTBP4 (latent transforming growth factor beta-binding protein 1), and IL34 (interleukin 34) in fibroblasts, ENTPD1, PDLIM5 (PDZ and LIM domain 5), ACTN4 (alpha-actinin-4), and GLRX in vascular smooth muscle cells, as well as LRP1 in macrophage populations. Conclusions: Using nuclear RNA sequencing, we describe the cellular diversity of healthy and aneurysmal human ascending aorta. Sporadic aortic aneurysm is characterized by differential gene expression within known cellular classes rather than by the appearance of novel cellular forms. Single-nuclear RNA sequencing of aortic tissue can be used to prioritize genes at aortic trait loci.

To compare the outcomes of the conventional syringe and the outcomes of the vibraject-assisted injection (VAI) in terms of the pain of the needle insertion during various intraoral injections of local anesthesia in children aged 6-9 years.A total number of 75 children aged 6-9 years were selected from patients visiting the pediatric dental clinic at Damascus University. The children were assigned into three equal groups (25 children each) according to the type of intraoral injection needed for the treatment: Groups [Group I: received upper buccal infiltrations (UBI), Group II: received posterior palatal infiltrations (PPI), and Group III: received inferior alveolar nerve block (IANB)]. This study was conducted considering the split-mouth design. Each child was subjected to both anesthetic injections: the conventional and the vibration-assisted in two separate dental visits 2 weeks apart. At each clinic visit, subjective and objective pain levels were assessed using the visual analog scale (VAS) and Face, Leg, Activity, Cry, Consolability (FLACC) scale.Children who received local anesthesia using the Vibraject method had lower VAS and FLACC scores than those who received local anesthesia using the conventional method.Vibraject was more effective in reducing the pain with local anesthetic injection compared to the conventional injection technique in clinical dental procedures for children.In a pediatric dental clinic, pain management is considered a pillar that influences actions. Using the VAI may achieve the ease, cooperation, and compliance during the dental care session.

Last mile supply system takes great importance in the designed supply chain management, especially in the big urban areas, where various goods delivery locations should be tackled. Transportation routes and vehicles play a critical share in the optimization of the energy spent in this system because it is considered a complicated case due to its high solutions possibilities. Also, part of these transport processes is considered reverse logistics, where the goods take the way back, starting from the customer. Using a metaheuristic optimization is usually a good way to increase operations efficiency and save time and energy, next to raising sustainability. Within this paper, the last mile supply system within urban areas focusing on the goods' delivery and collection tasks is presented. The model design is described, mathematical optimization modelling is detailed, and a case study to investigate the impact of using diesel and electric trucks on energy efficiency is solved. After an introduction and theoretical background that includes a brief literature review, the designed system and used methodology are described. The designed system incorporates cloud computing, real routes of vehicles, analysis of collected data, energy consumption optimization, and time windows. Also, a mathematical model is developed with the aim of optimizing the total energy consumption. Real thirty locations in Budapest in the VII district are described and used as a case study for finding the solutions of the optimized taken routes and energy consumption by the genetic algorithm for both diesel and electric trucks. In the end, the results are analyzed and compared against a random solution to clarify the presented optimization's effectiveness.

Clinicians face significant problems in the diagnosis of zoonotic coenurosis. The current study aimed to develop an improved dot-Enzyme-linked-immunosorbent assay (dot-ELISA) for the diagnosis of zoonotic coenurosis using sheep Coenurus cerebralis scolices purified antigen (CcS-Ag) and to compare the obtained results with those of indirect ELISA and Enzyme-linked immunoelectrotransfer blot technique (EITB). Sera were collected from humans and sheep infected or suspected of infection with Coenurus cerebralis, control cases, and cases infected with other parasites. The CcS-Ag was proved to be the most specific antigen. This antigen was fractionated, and its specific polypeptides against anti-C. cerebralis antibodies (ACc-Ab) were identified using EITB. Fractions at the molecular weight (MW) of 48 and 58 kDa were proved as the only specific ones, eluted from the gel and concentrated, then dotted on the NC sheet as pooled antigen before its evaluation in the diagnosis of infection using dot-ELISA. Dot-ELISA demonstrated absolute 100% sensitivity and 100% specificity as recorded by EITB, compared to both fractions on a nitrocellulose (NC) sheet using surgically proved infected human or sheep sera as a gold standard. Diagnosis by ELISA using crude CcS-Ag revealed similar sensitivity but lower specificity (75%). The diagnostic accuracy of dot-ELISA was proved by comparing its results with postmortem data obtained post slaughtering of 20 suspected sheep and patients investigated by computed tomography (CT) and magnetic resonance imaging (MRI). In conclusion, the selection of specific fractions after EITB to be used in dot-ELISA improved the diagnostic value of the test as a diagnostic tool gathering the benefits of ELISA and EITB.

Image segmentation is a fundamental and important step in many computer vision applications. One of the most widely used image segmentation techniques is clustering. It is a process of segmenting the intensities of a non-homogeneous image into homogeneous regions based on their similarity property. However, clustering methods require a prior initialization of random clustering centers and often converge to the local optimum, thanks to the choices of the initial centers, which is a major drawback. Therefore, to overcome this problem, we used the improved version of the sine-cosine algorithm to optimize the traditional clustering techniques to improve the image segmentation results. The proposed method provides better exploration of the search space compared to the original SCA algorithm which only focuses on the best solution to generate a new solution. The proposed ISCA algorithm is able to speed up the convergence and avoid falling into local optima by introducing two mechanisms that take into account the first is the given random position of the search space and the second is the position of the best solution found so far to balance the exploration and exploitation. The performance of the proposed approach was evaluated by comparing several clustering algorithms based on metaheuristics such as the original SCA, genetic algorithms (GA) and particle swarm optimization (PSO). Our evaluation results were analyzed based on the best fitness values of several metrics used in this paper, which demonstrates the high performance of the proposed approach that gives satisfactory results compared to other comparison methods.

Optimal dosing of opioid agonist therapy (OAT) is essential for treatment success. However, initiation and maintenance of OAT in hospital settings can be challenging given differing levels of opioid tolerance, withdrawal, and intoxication among patients.The objective of this study was to characterize the prevalence and factors associated with in-hospital patient perceived suboptimal OAT dosing among people who use illicit drugs (PWUD) in Vancouver, Canada.Data were derived from three prospective cohorts of PWUD in Vancouver, Canada. Bivariable and multivariable logistic regression models were used to examine factors associated with patient perceived suboptimal in-hospital OAT dose.273 study participants were prescribed OAT while in hospital: 83 (30.4%) participants perceived their OAT dose to be suboptimal. In a multivariable model, factors positively associated with a perceived suboptimal OAT dose included: homelessness (adjusted odds ratio [AOR] = 2.85; 95% CI: 1.53-5.28), daily stimulant use (AOR = 2.03; 95% CI: 1.14-3.63) and illicit drug use while in hospital (AOR = 2.33; 95% CI: 1.31-4.16).Almost one third of participants perceived receiving a suboptimal OAT dose while in hospital. These observed correlations indicate that a patient's perception of suboptimal OAT dosing in hospital may be more prevalent for patients who are homeless, report polysubstance use with stimulants and opioids and who obtain illicit drugs while hospitalized. While cautious prescribing of

This study focuses on numerical solution to the Brinkman equation with mixed Dirichlet–Neumann boundary conditions utilizing isogeometric analysis (IGA) based on non-uniform rational B-splines (NURBS) within the Galerkin method framework. The authors suggest using different choices of compatible NURBS spaces, which may be considered a generalization of traditional finite element spaces for velocity and pressure approximation. In order to investigate the numerical properties of the suggested elements, two numerical experiments based on a square and a quarter of an annulus are discussed. The preliminary results for the Stokes problem are presented in References.

In this paper, we approach two nonlinear differential equations applied in fluid mechanics by finite element methods (FEM). Our objective is to approach the solution to these problems; the first one is the “p-Laplacian” problem and the second one is the “Quasi-Newtonian Stokes” problem with a general boundary condition. To study and analyze our solutions, we introduce the a posteriori error indicator; this technique allows us to control the error, and each is shown the equivalent between the true and the a posterior errors estimators. The performance of the finite element method by this type of general boundary condition is presented via different numerical simulations.

Figure Legends for Figures S1-S8.

Figure Legends for Figures S1-S8.

Supplementary Figures 1-8.

Waste production is an indispensable human process that happens daily in all communities. With the population increase and the industry developments, the waste amounts are growing, and their treating processes are taking a bigger share of the transportation and handling tasks in the city logistics. These waste collection, transportation, and treatment are described as waste management has been investigated and developed especially with the various application, solutions, and developments in the logistics, transportation, and industrial areas. Also, with the higher attention to the environmental impact in the different areas, the green aspect of waste management takes more importance particularly in city logistics where congestion occurs regularly. Within this work, waste management is analyzed in Europe generally and Hungary specifically. Eurostat database is used for that purpose next to previous research work tackling this topic. Also, a comparison between the waste management operations in Hungary and Slovakia is discussed to show the difference of these operations’ developments between the two countries between 2014 and 2020.

The bioconjugation of proteins to small molecules serves as an invaluable tool for probing biological mechanisms and creating biomaterials. The most powerful bioconjugation stratagems are those which have rapid kinetics, are site-selective, can be conducted in aqueous solvent under mild conditions and do not require the use of additional potentially toxic catalysts. Herein we present spontaneous coupling via aldol ligation of proteins (SCALP) a catalyst-free “green” site-selective protein ligation between α-oxo aldehyde functionalised proteins and enolisable aldehyde probes at neutral pH, and demonstrate the utility of this system in the targeting of prostate cancer cells with functionalised nanobodies.

Novel designs of porous and semi-porous hip stems attempt to alleviate complications such as aseptic loosening, stress shielding, and eventual implant failure. Various designs of hip stems are modeled to simulate biomechanical performance using finite element analysis; however, these models are computationally expensive. Therefore, the machine learning approach is incorporated with simulated data to predict the new biomechanical performance of new designs of hip stems. Six types of algorithms based on machine learning were employed to validate the simulated results of finite element analysis. Afterwards, new designs of semi-porous stems with outer dense layers of 2.5 and 3 mm and porosities of 10–80% were used to predict the stiffness of the stems, stresses in outer dense layers, stresses in porous sections, and factor of safety under physiological loads using machine learning algorithms. It was determined that decision tree regression is the top-performing machine learning algorithm as per the used simulation data in terms of the validation mean absolute percentage error which equals 19.62%. It was also found that ridge regression produces the most consistent test set trend as compared with the original simulated finite element analysis results despite relying on a relatively small data set. These predicted results employing trained algorithms provided the understanding that changing the design parameters of semi-porous stems affects the biomechanical performance without carrying out finite element analysis.

Smart factories are equipped with Industry 4.0 technologies including smart sensors, digital twin, big data, and embedded software solutions. The application of these technologies contributes to better decision-making, and this real-time decision-making can improve the efficiency of both manufacturing and related logistics processes. In this article, the transformation of conventional milk-run-based in-plant supply solutions into a cyber–physical milk-run supply is described, where the application of Industry 4.0 technologies makes it possible to make real-time decisions regarding scheduling, routing, and resource planning. After a literature review, this paper introduces the structure of Industry 4.0 technologies supported by milk-run-based in-plant supply. A mathematical model of milk-run processes is described including both scheduling and routing problems of in-plant supply. This mathematical model makes it possible to analyze the impact of Industry 4.0 technologies on the efficiency, performance, and flexibility of in-plant supply logistics. The scenarios’ analysis validates the mathematical model and shows that significant performance improvement and energy savings can be achieved using Industry 4.0 technologies. This performance improvement can lead to a more cost-efficient and sustainable in-plant supply solution, where not only logistics aspects but also energy efficiency and emissions can be taken into consideration.

SMEs constitute the backbone of the Syrian economy and have suffered manifold challenges due to the continuous Syrian war. COVID-19 added further pressures on Syrian SMEs and forced them to take certain adaptation strategies to survive. This paper aims to investigate the main challenges that face Syrian SMEs during the pandemic and illustrate how they respond to adversities that emerged from governmental intervention to control the spread of the virus. It also discusses the measures initiated by the government to support SMEs during the pandemic. Through interviewing persons from the Syrian SMEs’ ecosystem, we find that high interest rates on SMEs’ loans decline on demand as well as high inflation represent the main challenges. SMEs respond to these challenges by marketing products online, stock procurement, and strengthening connections with stakeholders. We recommend the Syrian authorities reduce lending rates and increase loan sizes available to SMEs to help them overcome the pandemic adversities. Innovative sources of funding, such as venture capital and equity partnerships, could reduce the funding costs of SMEs. Moreover, SMEs will immensely benefit from training in digital tools to enhance their expansion and survival opportunities. Furthermore, bazaars should be organized during the year to give SMEs the opportunity to gain continuous access to markets. In addition, incubation services should be revised, particularly to SMEs with great potential to grow, to create the suitable environment for them to scale and flourish.

Supplemental material, sj-docx-1-isp-10.1177_00207640231152202 for Urbanization and mental health: A perspective from Riyadh City, Kingdom of Saudi Arabia by Yasmin A Altwaijri, Abdullah S Al-Subaie, AbdulHameed Al-Habeeb, Sandro Galea, Marya Akkad, Mohammad Talal Naseem and Lisa Bilal in International Journal of Social Psychiatry

The evolution of an Internet of Things-enabled Smart Grid affords better automation, communication, monitoring, and control of electricity consumption. It is now essential to supply and transmit the data required, to achieve better sensing, more accurate control, wider information communication and sharing, and more rational decision-making. However, the rapid growth in connected entities, accompanied by the increased demand for electricity, has resulted in several challenges to be addressed. One of these is securing the energy information exchange proactively, before an incident occurs. It is argued that Smart Grid systems were designed without any regard for security, which is considered a serious omission, especially for data security, energy information exchange, and the privacy of both the consumers and utility companies. This research is motivated by the gap identified in the requirements and controls for maintaining cybersecurity in the bi-directional data flow within the IoT-enabled Smart Grid. The Threat Modelling identified 9 internet-based threats. This research develops and confirms a security model which includes 45 relevant security controls and 7 security requirements on 7 access points. For future work, the confirmed model will be verified and validated using formal modelling methods.

Supplemental material, sj-docx-1-isp-10.1177_00207640231152202 for Urbanization and mental health: A perspective from Riyadh City, Kingdom of Saudi Arabia by Yasmin A Altwaijri, Abdullah S Al-Subaie, AbdulHameed Al-Habeeb, Sandro Galea, Marya Akkad, Mohammad Talal Naseem and Lisa Bilal in International Journal of Social Psychiatry

This study was conducted to see the impact of macroeconomic factors, Gross domestic product, interest rate, inflation rate, money supply and the exchange rate on stock price change percentage of the ESG listed companies according to the approach of Arbitrage Pricing Theory (APT). The data used in this research is secondary data from the central bank of Egypt and the Egyptian Stock Exchange. the total sample of this research are 20 out of 30 companies listed in The Egyptian Stock Exchange (EGX) for the period from 2016 to 2020. The analysis technique used descriptive statistics, correlation matrix and multi regression analysis. The results showed that macroeconomic factors especially interest rate and inflation have a significant negative impact on stock price changes of EGX30 ESG (Environment, Social and Governance) companies with a lower effect than traditional companies where their regression analysis showed percentage exceeding ours (16%). (Rageb, 2018) (Awd, 2015) Our main recommendation that all the companies within Egypt have to work hard to transform towards the ESG practices as from the results we have investigated it has low level of risks.

The present paper examines whether financial development (FD) promotes the deployment of RETs, using Panel data fixed effect model for 93 developed and developing countries from 1978 to 2014. Our empirical findings show the importance of the banking sector in the development of RE in comparison to other financial institution. In the opposite, each and overall component of stock market development haven't impact on RE expansion. Composite index includes six variables of financial development positively impact RE expansion.

Industry 4.0 technologies’ adoption became a reality in manufacturing and other industrial companies. The effects of this adoption on several areas including the Circular Economy are interesting in the research field. Deep research and investigation of various Industry 4.0 technologies’ relationships with the Circular Economy are presented in this article. The investigation is based on collected data from 798 companies in five countries, Lithuania, Slovakia, Austria, Croatia, and Slovenia as part of the European Manufacturing Survey project in 2018. After filtering the data, groups’ comparison is used to form potential prospective relationships in connection with the presented literature. A logistics regression test is used by SPSS software to validate the hypotheses and potential relations. Based on the achieved results, it seems that both Industry 4.0 and non-Industry 4.0 technologies can have significant relations with Circular Economy technologies, so they can be potentially influenced or enhanced by both. Similarly, an investigation of the relations between the development of products with improved environmental impact and the use of Industry 4.0 technologies showed no clear dominance of Industry 4.0 technologies over non-Industry 4.0. Finally, there are two of the twelve investigated technologies that have a significant relationship (potential impact) on both the Circular Economy technologies and product development with improved environmental impact.