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The study aimed to identify the reality of the employment of educational electronic games from the point of view of elementary school teachers in the Kingdom of Saudi Arabia. The researchers used the descriptive approach; a questionnaire was used to collect the data needed for the research after verifying its validity and reliability. The study population consisted of 649 elementary school teachers in Al-Khobar. The study results concluded that most teachers agreed on employing educational electronic games to a large extent. The results also revealed that female teachers face obstacles to the employment of educational electronic games to a moderate degree. In addition, there were statistically significant differences in the degree of employing educational electronic games in the educational process according to the experience variable for the least experience; however, it did not affect the obstacles to the employment of educational electronic games in the educational process. Besides, there were statistically significant differences in the degree of employing educational electronic games in the educational process according to the qualification variable and came in favor of masters; nevertheless, it did not affect the responses to the obstacles to employing educational electronic games in the educational process. Finally, no statistically significant differences existed in the degree of the employment of educational electronic games in the educational process and their obstacles attributed to the educational stage. In light of the study results, the researchers recommend holding training courses and workshops to educate elementary school teachers about activating educational electronic games in education. Also, female teachers should be encouraged to use educational electronic games, and the administrative work entrusted to them must be reduced. Besides, the school's infrastructure must be improved by providing quick internet services and adequate computers. Finally, students must be redistributed to the classrooms in proportion to the school's capabilities.

Abstract The current study investigates removing tetracycline from water using batch, column, and tank experiments with statistical modelling using ANN for continuous tests. An artificial neural network (ANN) using the Levenberg-Marquardt back-propagation (LMA) training algorithm is constructed to compare the effectiveness of Tetracycline removal from aqueous solution using the sorption technique with prepared adsorbent. Several characterization analyses XRD, FT-IR, and SEM are employed for prepared Brownmillerite (Ca2Fe2O5)–Na alginate beads. The operating conditions of batch tests involved, contact time (0.1–3 h), initial of tetracycline (Co) of (100–250 mg/L), pH (3–12), agitation speed (50–250) rpm and dosage of adsorbent (0.2–1.2 g/50 mL). The outcomes of experiments have demonstrated that the optimum conditions for the batch test to achieve the maximum adsorbent capacity (qmax =7.845 mg/g) are achieved at pH 7, contact time 1.5 h, adsorbent dose 1.2 g/50 mL, agitation speed of 200 rpm, and initial concentration of TC 100 mg/L. Minimum mean square error (MSE) values of 7.09E-04 for 30 hidden neurons and 0.0029 for 59 hidden neurons in the 1D and 2D systems are accomplished, respectively. The artificial neural network model has exhibited excellent performance with correlation coefficients exceeding 0.980 for the operating variables, demonstrating its accuracy and effectiveness in predicting the experimental outcomes. According to sensitivity analysis, the influential parameter in the column test (1D) is the flow rate (mL/min), with a relative importance of 32.769%. However, in the tank test (2D), time (day) is signified as an influential parameter with a relative importance of 31.207%.

Abstract Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical industry as an environmentally friendly alternative to toxic/harmful organic solvents. They possess gas-like transport characteristics and liquid-like solvation power for solutes. Evaluation of chemotherapeutic drugs’ solubility in supercritical carbon dioxide (SCCO2) has been recently an attractive subject for developing this method in pharmaceutical sector. To reach this purpose, the utilization of accurate models is of great necessity to estimate experimental-based solubility data. In this paper, the authors tried to employ machine learning (ML) approaches to estimate the solubility of Letrozole (LET) drug as chemotherapeutic agent and correlate its values in wide ranges of temperature and pressure. To do this, PAR (Passive Aggressive Regression), RF (Random Forest), and RBF-SVM are the models used (Support Vector Machine with RBF kernel). These models optimized in terms of their hyper-parameters using GA algorithm. The optimized PAR, RF, RBF-SVM models obtained coefficients of determination (R-squared) of 0.8277, 0.9534, and 0.9947. Also, the MSE error rate of the models are 0.1342, 0.0305, and 0.0045, in the same order. The final result of the evaluations shows the optimized RBF-SVM model as the most appropriate model in this research. The model exhibits a maximum prediction error of 0.1289.

This paper presents the process of conducting the structural analysis of AlAinSat-1 CubeSat through a numerical solution using Siemens NX. AlAinSat-1 is a 3U remote-sensing CubeSat carrying two earth observation payloads. The CubeSat is scheduled for launch on SpaceX Falcon 9 rocket. To ensure the success of the mission and its ability to withstand the launch environment, several scenarios should be analyzed. For AlAinSat-1 model the finite element analysis (FEA) method is used, and four types of structural analyses are considered: modal, quasi-static, buckling, and random vibration analyses. The workflow cycle includes idealizing, meshing, assembling, applying connections and boundary conditions, and eventually running the simulation utilizing the Siemens Nastran solver. The simulation results of all analysis types indicate that the model can safely withstand the loads exerted during launch. Also, the numerical results of the Command and Data Handling Subsystem (CDHS) module of AlAinSat-1 are experimentally validated through a vibration test conducted using an LV8 shaker system. The module successfully passed the test based on the test success criteria provided by the launcher.

Abstract Meaningful communication between health service users and providers is essential. However, when stakeholders are unfamiliar with new health services, innovative communication methods are necessary to engage them. The aim of the study was to create, validate, and evaluate a video-vignette to enhance stakeholders’ (physicians, pharmacists, and laypeople) engagement and understanding of an innovative pharmacy-based diabetes screening and prevention program. Also, to assess the video-vignette’s capacity to measure appetite and appeal for such preventive programs. This mixed-methods study consisted of two phases. In phase one, a video-vignette depicting the proposed screening and prevention program was developed and validated following established international guidelines (n = 25). The video-vignette was then evaluated by stakeholders (n = 99). In phase two, the video-vignette’s capacity as a communication tool was tested in focus groups and interviews to explore stakeholders’ perspectives and engagement on the proposed service (n = 22). Quantitative data were analyzed descriptively, while qualitative data underwent thematic analysis. In total, 146 stakeholders participated. The script was well-received, deemed credible, and realistic. Furthermore, the video-vignette received high ratings for its value, content, interest, realism, and visual and audio quality. The focus groups and interviews provided valuable insights into the design and delivery of the new service. The video-vignette compellingly portrayed the novel pharmacy-based diabetes screening and prevention service. It facilitated in-depth discussions among stakeholders and significantly enhanced their understanding and appreciation of such health services. The video-vignette also generated significant interest in pharmacy-based diabetes screening and prevention programs, serving as a powerful tool to promote enrollment in these initiatives.

Background: MRI fusion prostate biopsy has improved the detection of clinically significant prostate cancer (CSC). Continued refinements in predicting the pre-biopsy probability of CSC are essential for optimal patient counseling. We investigated potential factors related to improved cancer detection rates (CDR) of CSC in patients with PI-RADS ≥ 3 lesions. Methods: The pathology of 980 index lesions in 980 patients sampled by transrectal mpMRI-targeted prostate biopsy across four medical centers between 2017–2020 was reviewed. PI-RADS lesion distribution included 291 PI-RADS-5, 374 PI-RADS-4, and 315 PI-RADS-3. We compared CDR of index PI-RADS ≥ 3 lesions based on location (TZ) vs. (PZ), PSA density (PSAD), and history of prior negative conventional transrectal ultrasound-guided biopsy (TRUS). Results: Mean age, PSA, prostate volume, and level of prior negative TRUS biopsy were 66 years (43–90), 7.82 ng/dL (5.6–11.2), 54 cm3 (12–173), and 456/980 (46.5%), respectively. Higher PSAD, no prior history of negative TRUS biopsy, and PZ lesions were associated with higher CDR. Stratified CDR highlighted significant variance across subgroups. CDR for a PI-RADS-5 score, PZ lesion with PSAD ≥ 0.15, and prior negative biopsy was 77%. Conversely, the CDR rate for a PI-RADS-4 score, TZ lesion with PSAD < 0.15, and prior negative biopsy was significantly lower at 14%. Conclusions: For index PI-RADS ≥ 3 lesions, CDR varied significantly based on location, prior history of negative TRUS biopsy, and PSAD. Such considerations are critical when counseling on the merits and potential yield of prostate needle biopsy.

With the fast growth of mobile phone usage, malicious threats against Android mobile devices are enhanced. The Android system utilizes a wide range of sensitive apps like banking apps; thus, it develops the aim of malware that uses the vulnerability of safety measures. Identifying Android malware in smartphones is a vital target for the cyber community to eliminate menacing malware instances. Drawing stimulus from the adaptability and efficacy of biological systems, these methods emulate nature's problem-solving systems for identifying malicious software. By integrating principles, namely, swarm intelligence (SI), neural networks (NN), and genetic algorithms (GA), these bioinspired systems reveal exceptional efficiency in identifying both known and developing Android malware attacks. This bioinspired system provides a capable avenue for robust Android malware detection from an ever-shifting threat landscape. This article designs a Bioinspired Artificial Intelligence-based Android Malware Detection and Classification (BAI-AMDC) technique for Cybersecurity Applications. The BAI-AMDC technique exploits the concept of bioinspired algorithms with a DL approach for the classification and detection of Android malware. In the BAI-AMDC technique, an improved cockroach swarm optimization algorithm-based feature selection (ICSOA-FS) technique can be applied to choose optimum features. The BAI-AMDC technique employs a bidirectional gated recurrent unit (BiGRU) model for Android malware detection. An arithmetic optimization algorithm (AOA) can be utilized to enhance the detection performance of the BAI-AMDC technique. The experimental validation of the BAI-AMDC system can be performed on the CICAndMal2017 database with 10,000 instances. The simulation values highlighted the productive ability of the BAI-AMDC system on the Android malware recognition process.

Background: The Medication-Related Burden Quality of Life (MRB-QoL) is a 31-item valid and reliable patient-reported measure of medicine burden on functioning and well-being in people with long-term conditions (LTC). Objectives: To translate, culturally adapt, and content validate the MRB-QoL into Arabic. Methods: A rigorous approach to cross-cultural adaptation proposed by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) guideline was followed. After 3 forward translations and 2 backward translations, a multidisciplinary expert panel assessed the content validity (CV) of the items through a 2-round e-modified Delphi method followed by two-step cognitive debriefings with patients with LTC using think-aloud and probing techniques. An item-content validity index (I-CVI) score of ≥0.78 was considered acceptable. The original questionnaire developers and other researchers, as members of the review committee, reviewed and approved the Arabic version. Results: Five semantic and 3 cultural translation discrepancies were identified and resolved by rewording the items. The 2 backward translations did not reveal significant problems, and equivalence to the original tool was confirmed following committee review. The Arabic version showed acceptable CV parameters. E-modified Delphi involved 9 experts in round one and 7 in round 2. The I-CVI scores ranged from 0.67 to 1.0, and agreement was reached after 2 rounds. The CVI for the final version of the MRB-QoL was 0.96. Expert panel review showed that the MRB-QoL-Arabic version is relevant (CVI = 0.92), important (CVI = 0.97), clear (CVI = 0.98), and comprehensive in measuring the burden of medicines. Data from 5 cognitive interviews showed that items and concepts included in the Arabic version of the MRB-QoL are relevant to the targeted sample, clear, and easy to understand. Conclusion: The MRB-QoL Arabic version was developed and content validated. However, further evaluation of its other psychometric properties is necessary before it can be utilized in clinical and research settings. Using this tool will enable a more accurate understanding of the effects of treatment burden on patient well-being, thereby guiding care toward minimally disruptive medicine.

Solar energy is critical to the global shift towards sustainable and low-carbon energy solutions. Unlike fossil fuels, solar energy produces no emissions during operation, making it a key driver of environmentally friendly power generation. However, the intermittency of solar power remains a challenge, necessitating efficient energy storage systems to ensure a steady supply. Thermal energy storage systems utilizing phase change materials (PCMs) offer a solution by storing excess solar energy and releasing it when needed. This study focuses on enhancing the charging capacity of the PCM within a novel triplex tube heat exchanger (TTHE). The design incorporates bionic-shaped fins to compensate for thermal conductivity within the PCM. Three artificial neural network (ANN) models were employed to estimate the melting duration for the PCM to attain liquid fractions of 0.5, 0.8, and 1. The expansion angle (θ), initial length (D), and length of fin branches (Z) were systematically varied to investigate their influence on heat absorption. The ANN models exhibited high accuracy, with R2 values of 0.998 for the liquid fraction of 0.5, 0.998 for the liquid fraction of 0.8, and 0.996 for the liquid fraction of 1, demonstrating their precise predictive capability. The results revealed that θ significantly reduced melting time, while Z consistently shortened the duration and eventually became the dominant factor. However, an excessive increase in these geometric parameters led to a counterproductive rise in the total melting time. Through optimization using a genetic algorithm, three optimal designs (design 1 (D1), design 2 (D2), and design 3 (D3)) were proposed. Compared to the fin-less TTHE, these designs reduced the full melting time by 71.58 %, 73.41 %, and 73.54 %, respectively. Faster melting improves the system's ability to store and release thermal energy more quickly, enabling more efficient utilization of solar power within the limited period of sunlight availability (usually 4–6 h a day). These findings underscore the potential of ANN-based approaches in optimizing the efficiency of solar energy storage systems.

This research investigates the thermohydraulic performance and exergy destruction associated with the flow of supercritical carbon dioxide (sCO2) within spirally coiled mini tubes. The study examines the impact of different cross-sectional geometries. The primary objective of the study is to examine the impact of critical parameters, including shape, hydraulic diameter, inlet temperature, mass flux, and operating pressure, on important variables such as friction factor, heat transfer coefficient, and exergy efficiency. The computational simulation employs the RNG k−ε model. The Coupled algorithm was utilized for the determination of velocity and pressure fields, utilizing second-order discretization for domain partitioning and first-order discretization for other terms. The carbon dioxide (CO2) was conceptualized as a compressible gas with complex thermophysical attributes that are contingent upon variations in temperature and pressure. The thermophysical properties of carbon dioxide are evaluated within a defined range of operating conditions (298. 15 K

This article introduces an innovative method for optimizing smart manufacturing system (SMS) by combining digital twin technology (DTT) with teaching–learning-based optimization (TBLO). It creates a simulated model of the physical manufacturing environment, enabling real-time monitoring, simulation and analysis. By leveraging the TLBO algorithm, the system enhances the decision-making process for complex manufacturing tasks, facilitating continuous improvement and adaptation to dynamic production demands. The proposed framework aims to minimize production costs, reduce downtime and improve overall efficiency by optimizing key parameters such as resource allocation, production scheduling and machine performance. Experimental results demonstrate that the DT-TLBO approach can reduce production costs by up to 20%, decrease downtime by 30% and improve overall system efficiency by 25%. This innovative combination of advanced technologies offers a promising solution for modern manufacturing challenges, paving the way for smarter, more responsive production environments.

Background There is a global call for upscaling and optimising the role of community pharmacists (CPs) in the control of non-communicable diseases (NCDs). In the United Arab Emirates (UAE), where NCDs are classified as a public health pandemic, upscaling CPs contributions has become more critical. Several contextual, professional, and educational challenges constrain the role of CPs.Objective To synthesise the perspectives of key stakeholders in the UAE healthcare system and propose a roadmap for advancing the role of CP s in controlling NCDs in the UAE.Methods This research followed a qualitative design using the International Pharmaceutical Federation (FIP) framework for quality assurance of pharmacy profession development. Data were collected using semi-structured interviews with 28 experts and senior leaders, then analysed using the thematic analysis technique with the assistance of NVivo software.Results The analysis yielded three main themes that outlined the prospective roadmap: education, work environment, and policy. Some of the generated subthemes were establishing accredited NCD-specialised programmes, building a national framework for interprofessional education and collaboration, and upscaling the engagement of CPs in public health platforms and initiatives.Conclusion Improving the role of CPs in controlling the NCD pandemic in the UAE requires coherent and well-structured multidisciplinary endeavours from health policymakers, educational institutions, and all groups of healthcare professionals, including the CPs themselves.

Liver cancer ranks as the third most common cause of cancer-related death rate, resulting in an estimated 830,180 deaths globally. Recent, 2023 estimation of the American Cancer Society predicted around 29,380 deaths due to this disease in America alone. However, with early detection and proper drug management the survival rate can be greatly extended. Significantly, the noteworthy observation pertains to the absence of consistent drug responses among patients with identical cancer types at the same stage. Hence the need of the hour is the precision medicine that predict patient specific drug response based on molecular data genome expression. In this regard, we propose drug response system that uses deep learning framework that predicts drug response in new cell lines or patient with the aim of accurate prediction and suggesting précised medicine to that particular cancer patient. The prediction of the framework is on the lines of recommended system’s projection of drugs and cell lines into latent pharmacogenomic space. During our experimentation, we utilized the Genomics of Drug Sensitivity in Cancer dataset and observed that Cancer Drug Response and Patient Specific Recommender System (CDRP-PRS) consistently delivers robust predictions and effective response, even when applied to previously unseen patient-derived cell line datasets. CDRP-PRS inferences in the pharmacogenomic space are helpful to understand the drug mechanism to identify the cellular subtypes and their categorized associations. Further it is aimed is to predict the accuracy of drug responses for patients and suggest precise medications tailored to their specific cancer.

Detection of heart diseases (HD) at an early stage diminishes the mortality rate. However, handling huge data is cumbersome for physicians. Hence, there is a need for a tool regarding the automation and processing of large data to help in making precise decisions. The proposed methodology aims to predict cases of HD or cardiovascular diseases (CD) well in advance based on the features of the patient. In the proposed methodology, the Cleveland Dataset of HD collected from University of California, Irvine (UCI) repository is evaluated. The various phases in the proposed methodology include, insight about the data, analyzing the data, feature engineering and finally building the model. The K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), classifier and convolutional neural network (CNN) are used to predict the HD and are evaluated on the dataset. The metrics considered are classification accuracy (CA), recall (r) and precision (p). The resulted CA, ‘r’ and ‘p’ for KNN-based approach are 66.7%, 91.7% and 88.8%, respectively. The SVM-based approach with ‘linear’ kernel achieved CA, ‘r’ and ‘p’ are 74.2%, 85.0% and 74.0%, respectively. The ANN-based approach resulted in 70.08%, 77.0% and 84.2% of CA, ‘p’ and ‘r’, respectively. Finally, the CNN-based prediction achieved CA, ‘p’ and ‘r’ are 83.61%, 76.0% and 97.0%, respectively. The experimental study concludes that the CNN-based prediction model outperformed the KNN, SVM and ANN-based prediction approaches regarding the prediction accuracy. An optimization algorithm can be incorporated into the model in future.

Background: Chat Generative Pre-Trained Transformer (ChatGPT) has previously been shown to accurately predict colon cancer screening intervals when provided with clinical data and context in the form of guidelines. The National Comprehensive Cancer Network ® (NCCN ® ) guideline on non-muscle invasive bladder cancer (NMIBC) includes criteria for risk stratification into low-, intermediate-, and high-risk groups based on patient and disease characteristics. The aim of this study is to evaluate the ability of ChatGPT to apply the NCCN Guidelines to risk stratify theoretical patient scenarios related to NMIBC. Methods: Thirty-six hypothetical patient scenarios related to NMIBC were created and submitted to GPT-3.5 and GPT-4 at two separate time points. First, both models were prompted to risk stratify patients without any additional context provided. Custom instructions were then provided as textual context using the written versions of the NMIBC NCCN ® Guidelines, followed by repeat risk stratification. Finally, GPT-4 was provided with an image of the NMIBC risk groups table, and the risk stratification was again performed. Results: GPT-3.5 correctly risk stratified 68% (24.5 of 36) of scenarios without context, slightly increasing to 74% (26.5 of 36) with textual context. Using GPT-4, the model had accuracy of 83% (30 of 36) without context, reaching 100% (36 of 36) with textual context ( P = .025). GPT-4 with image context maintained similar accuracy to GPT-4 without context, with accuracy 81% (29 of 36). ChatGPT generally performed poorly when stratifying intermediate risk NMIBC (33%-63%). When risk stratification was incorrect, most responses were overestimations of risk. Conclusions: GPT-4 can accurately risk stratify patients with respect to NMIBC when provided with context containing guidelines. Overestimation of risk is more common than underestimation, and intermediate risk NMIBC is most likely to be incorrectly stratified. With further validation, GPT-4 can become a tool for risk stratification of NMIBC in clinical practice.

This research tested all specifications of synthesized biodiesel according to the American Society for Testing and Materials (ASTM). The spirulina micro-algae synthesized biodiesel is mixed with conventional diesel in different blends named B0, B10, B20, and B30. The number after the letter “B” indicates the ratio of (biodiesel/diesel blend), which will be used instead of net diesel for experimental purposes. Those blend specifications are investigated, and results are tabled. The variable compression ratio, single-cylinder, water-cooled, and compression ignition engine, were utilized to study the effect of algae biodiesel/diesel blends on engine performance and its exhaust emission constituents. No hardware modification was required, and the blends directly injected in the combustion chambers. The experimental investigation showed that B10 with 14.5 CR and 25 % load -as compared with net diesel- enhances almost all performance parameters more than other blends (1.7 % more brake efficiency, 12.7 % less brake specific fuel consumption, 3.45 % less in exhaust temperature, 6.7 % more in A/F ratio, and volumetric efficiency was nearly the same). It also enhances some emission parameters more than other blends (12 % less in unburned hydrocarbons (UHC′s) emissions), but with a slight increase in nitric oxides (NOX) emissions (2.4 % more than net diesel) with nearly the same values of carbon dioxide (CO2) and carbon monoxide (CO) emissions. In general, in comparison with net diesel and with icreasing blend ratio, the following results are observed: the volumetric efficiency results fluctuated with a slight difference. Both brake thermal efficiency, air/fuel ratio, and exhaust temperature were enhanced with a slight increase in brake-specific fuel consumption. For emissions: The carbon dioxide and nitrogen oxides emissions both are reduced significantly, while there was a slight increase in CO and UHC′s levels. Finally, this work recommends using the algae biodiesel as clean and substitution fuel.

The research aims to conduct a standard economic analysis of the most important economic indicators related to sustainable development affecting the GDP of the sample countries (Iraq, Egypt, Jordan), Based on the premise that despite the vigorous pursuit of sustainable development, developing countries remain below the required level through the reduction of many indicators of sustainable development and the challenges facing sample States, The research relied on the descriptive and analytical approach using the Eviews10 programmer using time chains and two periods before forecast (2015-2022) and for forecasting (2023-2030). The data were divided into quarterly views to increase the number of views and to see the impact of some economic indicators of sustainable development on the adopted variable of sample States' GDP, The researcher concluded that sustainable development is not the level required and desired from it between (2015-2022) and that the variables got worse during the prediction that the sample states did not pay this great attention. The researcher recommended paying attention to the economic aspect, which is central to the dimensions and indicators of sustainable development, focusing on the environmental, technological, and social aspects as well, focusing on the establishment of financial funds to support sustainable development programs, and supporting research, media and cultural projects towards the establishment of environmentally friendly and beneficial projects for poor classes.

Abstract This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values "C12 = Laughing for no reason" and "C16 = Notice the sound of the bell" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients.

We propose a model of treatment interference where the response of a unit depends only on its treatment status and the statuses of units within its K-neighborhood. Current methods for detecting interference include carefully designed randomized experiments and conditional randomization tests on a set of focal units. We give guidance on how to choose focal units under this model of interference. We then conduct a simulation study to evaluate the efficacy of existing methods for detecting network interference. We show that this choice of focal units leads to powerful tests of treatment interference that outperform current experimental methods.

Abstract In the current study, we evaluated the in vitro antibacterial efficacy of the roots’ extracts of Jasminum officinale, Rosa damascene and Paeonia officinalis against MRSA (methicillin-resistant Staphylococcus aureus) by well diffusion technique. The root extract of P. officinalis exerted a potent anti-MRSA with MIC 0.4673 µg/ml, while both J. officinale and R. damascene exhibited very weak activity. Therefore, chemical profiling of the crude extract P. officinalis roots assisted by LC-HR-ESI-MS was performed and led to the dereplication of twenty metabolites of different classes, in which terpenes are the most abundant compounds. On a molecular level, network pharmacology was used to determine the targets of active metabolites to bacterial infections, particularly MRSA. Online databases PubChem, UniProt, STRING, and Swiss Target Prediction were used. In addition to using CYTOSCAPE software to display and analyze the findings, ShinyGO and FunRich tools were used to identify the gene enrichment analysis to the set of recognized genes. The results detected the identified metabolites were annotated by 254 targets. ALB, ACHE, TYMS, PRKCD, PLG, MMP9, MMP2, ERN1, EDNRA, BRD4 were found to be associated with MRSA infection. The top KEGG pathway was the vascular smooth muscle contraction pathway according to enrichment FDR. The present study suggested a possible implication of P. officinalis roots as a potent candidate having a powerful antibacterial activity against MRSA.