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This study aims to comprehensively explore the intricate dynamics among positive and negative emotions, self-efficacy, and task performance within the unique context of the Covid-19 pandemic in Bahrain, specifically placing emphasis on potential gender-related distinctions within the proposed relationships. The ongoing pandemic accentuates the need to investigate the interplay between emotional states, self-efficacy beliefs, and task performance in academic and teaching domains, especially considering potential gender variations within the framework of a society promoting gender equality. Employing a quantitative survey instrument and rigorous statistical techniques, the study validates its proposed model through indicators such as the coefficient of determination (R²) and predictive relevance (Q²). The diverse sample comprises academic and teaching staff of both genders from Bahrain. Advanced statistical methodologies, including Measurement Invariance (MICOM) and Multigroup Analysis (MGA) facilitated by SmartPLS PLS-SEM, provide deeper insights into gender disparities. Significantly contributing to existing knowledge, this paper elucidates the complex relationships among emotions, self-efficacy, and task performance amid a crisis, with a distinctive focus on meticulously investigating gender differences. The study underscores the consistent positive impact of positive emotions on task performance across genders in Bahrain. Recommendations advocate for prioritizing support for academic and teaching staff during crises, emphasizing the positive impact on academic outcomes. Future research should explore demographic intricacies and potential mediating or moderating factors, deepening the comprehension of these complex dynamics. Highlighting the cascading impact of prioritizing the well-being and morale of academic and teaching staff, the study envisions a positive transformation resonating across various facets of society, extending beyond the confines of academia. Doi: 10.28991/ESJ-2024-SIED1-09 Full Text: PDF

Background:. The quality of reporting in randomized controlled trials (RCTs) is crucial for accurate interpretation and synthesis of evidence. The Consolidated Standards of Reporting Trials (CONSORT) guidelines provide a standardized framework for reporting RCT abstracts. This study aimed to evaluate the adherence of RCT abstracts published in three major plastic surgery journals to the CONSORT tool guideline for reporting abstracts, utilizing Generative Pre-trained Transformer 4 artificial intelligence (GPT-4 AI) technology. Methods:. Abstracts of RCTs published between 2010 and 2023 were collected. The GPT-4 AI model was utilized to assess the abstracts based on the CONSORT criteria. Descriptive statistics were used to report the compliance scores and identify areas where abstracts lacked compliance. Results:. Of the initially identified 500 abstracts, a total of 371 RCT abstracts met the inclusion criteria and were analyzed. The mean CONSORT score was 10.05 (±2.22), with a median score of 10.72. Specific areas where abstracts lacked compliance included trial design (39.6%), participant details (28.8%), intervention descriptions (15.6%), randomization process (25.3%), and the number of participants analyzed (33.4%). Trial registration (18.3%) and funding information (15.1%) were also frequently missing. Conclusions:. Our study’s innovative use of the GPT-4 AI model for analysis demonstrated the potential of AI technology in streamlining and enhancing the evaluation of research compliance. We advocate for heightened awareness and more rigorous application of CONSORT guidelines among authors, reviewers, and journal editors. Emphasizing the role of AI technology in the evaluative process can further improve the reporting quality of future RCTs in plastic surgery, contributing to more reliable and transparent research in the field.

Background:. Patient satisfaction is crucial for evaluating healthcare services, including plastic surgery. This systematic review aims to analyze questionnaires assessing patient satisfaction in plastic surgery, identifying their strengths and weaknesses to improve outcomes and enhance the quality of care. Methods:. A comprehensive literature search was conducted using electronic databases. Studies were included if they were original research articles, written in English, and focused on patient satisfaction questionnaires in plastic surgery. Data extraction and descriptive statistics were used to summarize the data. Results:. A total of 105 studies were included. General/overall satisfaction was the most common topic addressed (99.04%). Cosmetic outcomes were the most frequently assessed category (34.3%). Breast reconstruction was the most common procedure (33.3%). Most studies used a combination of generic and procedure-specific questionnaires (45.71%). The most frequently used measurement tools were BREAST-Q and self-developed questionnaires, each accounting for 28.57% and 27.61%. Conclusions:. This review provides a comprehensive analysis of patient satisfaction questionnaires in plastic surgery, emphasizing the importance of a holistic approach and well-established, validated tools. The findings contribute to improving plastic surgery outcomes and enhancing the quality of care. Future research should refine assessment tools to address patients’ needs and promote patient-centered outcomes in plastic surgery.

Nowadays, Internet technology is developing very quickly, because of which webpages are generated exponentially. Web content categorization is mandatory to explore and search related webpages based on queries of users and becomes a dreary task. Most web content categorization methods ignore the contextual knowledge and semantic features of the web page. Pornographic webpage–filtering system does not deliver perfect extraction of advantageous datasets in unstructured web content. Such mechanisms take no reasoning ability to intellectually filter web content to categorize medical websites in adult content webpages. This study introduces a Type-2 Fuzzy Ontology with Dendritic Neural Network Based Semantic Feature Extraction for Web Content Classification (TFODNN-SFEWCC) method. The presented method mainly focused on the detection of different types of web content and blocking pornographic content. It uses the DNN model for the extraction of useful keywords from web pages and eliminates unwanted ones. In addition, the proposed technique employs type 2 fuzzy ontology for the automated classification of web content into multiple classes. The pigeon swarm optimization algorithm is applied to optimize the performance of the Dendritic Neural Network approach for hyperparameter tuning. The experimental evaluation of the proposed method occurs utilizing a web database, and the outcomes are studied under various aspects. The comprehensive comparison study highlighted the betterment of the proposed technique over other existing approaches.

Oncolytic viruses (OVs) can selectively replicate in tumor cells and remodel the microenvironment of immunologically cold tumors, making them a promising strategy to evoke antitumor immunity. Similarly, agonists of the stimulator of interferon genes (STING)‐interferon (IFN) pathway, the main cellular antiviral system, provide antitumor benefits by inducing the activation of dendritic cells (DC). Considering how the activation of the STING‐IFN pathway could potentially inhibit OV replication, the use of STING agonists alongside OV therapy remains largely unexplored. Here, we explored the antitumor efficacy of combining an HSV‐1‐based OV, C‐REV, with a membrane‐impermeable STING agonist, 2′3′‐GAMP. Our results demonstrated that tumor cells harbor a largely defective STING‐IFN pathway, thereby preventing significant antiviral IFN induction regardless of the permeability of the STING agonist. In vivo, the combination therapy induced more proliferative KLRG1‐high PD1‐low CD8+ T‐cells and activated CD103+ DC in the tumor site and increased tumor‐specific CD44+ CD8+ T‐cells in the lymph node. Overall, the combination therapy of C‐REV with 2′3′‐cGAMP elicited antitumor immune memory responses and significantly enhanced systemic antitumor immunity in both treated and non‐treated distal tumors.

Summary: Background: Facelift procedures are a popular method of facial rejuvenation. The most common technique is superficial muscular aponeurotic system (SMAS) plication, with several variations. However, the optimal approach remains unclear. This review analyzed previous studies to compare SMAS facelift techniques, their outcomes, and complication rates. Methods: A systematic search was conducted using the MEDLINE, Cochrane, Embase, and Google Scholar electronic databases in September 2022. The search included studies published from January 2000 to September 2022 using keywords such as “facelift,” “complications,” and “outcomes.” Results: This review examined 27 selected studies that evaluated 6 SMAS facelift techniques. The studies involved 6086 patients in total, over 85% of who were satisfied with the outcome of their surgery. The complication rates varied depending on the technique used, with the SMAS flap and composite SMAS technique having the highest (5.75%) and lowest (0.05%) complication rates, respectively. The most common complications were temporary facial nerve injury (0.85%) and skin necrosis (0.41%). To date, only one case of permanent facial nerve injury has been reported. Conclusions: On the basis of our findings, SMAS facelift techniques achieve high patient satisfaction rates, with complication rates that vary by technique. The composite SMAS technique showed the lowest complication rates, whereas the SMAS flap showed the highest rate. However, some studies have not reported all complications, making it difficult to determine the best approach. Therefore, future studies are required to identify the most aesthetically pleasing technique with the lowest complication risk.

With the widespread use of Internet, Internet of Things (IoT) devices have exponentially increased. These devices become vulnerable to malware attacks with the enormous amount of data on IoT devices; as a result, malware detection becomes a major problem in IoT devices. A reliable and effective mechanism is essential for malware detection. In recent years, research workers have developed various techniques for the complex detection of malware, but accurate detection continues to be a problem. Ransomware attacks pose major security risks to corporate and personal information and data. The owners of computer-based resources can be influenced by monetary losses, reputational damage, and privacy and verification violations due to successful assaults of ransomware. Therefore, there is a need to swiftly and accurately detect the ransomware. With this motivation, the study designs an Ebola optimization search algorithm for enhanced deep learning-based ransomware detection (EBSAEDL-RD) technique in IoT security. The purpose of the EBSAEDL-RD method is to recognize and classify the ransomware to achieve security in the IoT platform. To accomplish this, the EBSAEDL-RD technique employs min-max normalization to scale the input data into a useful format. Also, the EBSAEDL-RD technique makes use of the EBSA technique to select an optimum set of features. Meanwhile, the classification of ransomware takes place using the bidirectional gated recurrent unit (BiGRU) model. Then, the sparrow search algorithm (SSA) can be applied for optimum hyperparameter selection of the BiGRU model. The wide-ranging experiments of the EBSAEDL-RD approach are performed on benchmark data. The obtained results highlighted that the EBSAEDL-RD algorithm reaches better performance over other models on IoT security.

This study explores herd production characteristics and phenotypic traits of indigenous dairy cattle in the grassland agroecology of Nigeria. The study highlighted the crucial role of agroecology as a modifier of cattle production operations and emphasized the need for further research to understand the genetic basis of variations in the production. Herd production data were collected through focus group meetings using FEAST software, while body measurements and phenotypic traits of lactating and breeding cattle were recorded in the agroecological zone within the grassland. The data obtained were subjected to descriptive statistics, and Moses Test of Extreme Reaction using SPSS v.20.0.0. The results indicate that the agroecology type significantly influenced various herd production characteristics (p < 0.05). Additionally, agroecology had a significant effect on body measurements and phenotypic trait expressions in the cattle, including live weight, body condition score, testis circumference, age at puberty, and age at first calving (p < 0.05). Furthermore, age differences were observed among cows based on the agroecological zones (p = 0.008), while no significant variation was found in the age of breeding bulls across both agroecology. This study concludes that within the Nigeria's grassland, agroecology plays a crucial role as a modifier of herd production characteristics and phenotypic trait expressions in smallholder dairy cattle operations. These cannot be unconnected with genetics, hence, there is a need for understanding the genetic basis of the variations.

Cybersecurity in the Internet of Things (IoT) is the practice of implementing measures to secure networks and connected devices from data breaches, cyber threats, and unauthorized access. It is essential owing to the increasing interconnectivity of devices, ranging from smart home appliances to industrial sensors. The potential attack surface expands, necessitating strong cybersecurity measures to protect sensitive data, ensure privacy, and prevent disruptions to critical services with these increasing number of IoT devices. Artificial intelligence (AI) technologies, particularly deep learning (DL) and machine learning (ML) approaches, hold the potential to mitigate and identify cyberattacks on IoT networks. DL demonstrates promise for effectively preventing and detecting security threats within IoT devices. Despite the importance of Intrusion Detection Systems (IDS) in maintaining confidentiality by detecting suspicious activities, classical IDS solutions might face difficulties in the IoT platform. Therefore, this study presents an Artificial Orca Algorithm with Ensemble Learning cyberattack detection and classification (AOAEL-CDC) methodology in an environment of IoT. The presented AOAEL-CDC technique exploited the feature selection (FS) approach with an ensemble learning approach for cyberattack recognition and identification in the IoT atmosphere. In the developed AOAEL-CDC model, the feature selection takes place using the AOA technique. For the cyberattack detection process, the ensemble learning process is carried out by the use of three models such as bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and extreme learning machine (ELM). Finally, the hyperparameter range of the DL techniques takes place using the marine predator’s algorithm (MPA). To examine the performance analysis of the AOAEL-CDC methodology, a series of simulations take place using a benchmark dataset. An extensive comparative study reported that the BCODL-SDSC technique reaches an effective performance with other models with a maximum accuracy of 99.31%.

Abstract Effective larviciding for malaria control requires detailed studies of larvicide efficacies, aquatic habitat characteristics, and life history traits of target vectors. Mosquitoes with brief larval phases present narrower timeframes for biolarvicidal effects than mosquitoes with extended periods. We evaluated two biolarvicides, VectoBac (Bacillus thuringiensis israelensis (Bti)) and VectoMax (Bti and Bacillus sphaericus) against Anopheles funestus and Anopheles arabiensis in shaded and unshaded habitats; and explored how larval development might influence retreatment intervals. These tests were done in semi-natural habitats using field-collected larvae, with untreated habitats as controls. Additionally, larval development was assessed in semi-natural and natural habitats in rural Tanzania, by sampling daily and recording larval developmental stages. Both biolarvicides reduced larval densities of both species by >98% within 72 h. Efficacy lasted one week in sun-exposed habitats but remained >50% for two weeks in shaded habitats. An. funestus spent up to two weeks before pupating (13.2(10.4–16.0) days in semi-natural; 10.0(6.6–13.5) in natural habitats), while An. arabiensis required slightly over one week (8.2 (5.8–10.6) days in semi-natural; 8.3 (5.0–11.6) in natural habitats). The findings suggest that weekly larviciding, which is essential for An. arabiensis might be more effective for An. funestus whose prolonged aquatic growth allows for repeated exposures. Additionally, the longer residual effect of biolarvicides in shaded habitats indicates they may require less frequent treatments compared to sun-exposed areas.

Abstract Introduction Lebanon is a middle-income country facing substantial fragility features. Its health profile shows a high burden of NCD morbidity and mortality. This paper intends to analyse the political economy of NCD prevention and control in Lebanon. Methods This study adopted a literature-based case study research design using a problem-driven political economy analysis framework. A total of 94 peer-reviewed articles and documents from the grey literature published before June 2019 were retrieved and analysed. Results Lebanon’s political instability and fragile governance negatively affect its capacity to adapt a Health-in-All-Policies approach to NCD prevention and enable the blocking of NCD prevention policies by opposed stakeholders. Recent economic crises limit the fiscal capacity to address health financing issues and resulting health inequities. NCD care provision is twisted by powerful stakeholders towards a hospital-centred model with a powerful private sector. Stakeholders like the MOPH, UN agencies, and NGOs have been pushing towards changing the existing care model towards a primary care model. An incremental reform has been adopted to strengthen a network of primary care centres, support them with health technologies and improve the quality of primary care services. Nevertheless, outpatient services that are covered by other public funds remain specialist-led without much institutional regulation. Conclusion Our study revealed a locked equilibrium in NCD prevention policymaking in Lebanon, but with an incremental progress in service delivery reforms towards a primary care model. Advocacy and close monitoring by policy entrepreneurs (such as civil society) could initiate and sustain the implementation of policy change and care model reforms.

Abstract Background Northwest Syria (NWS) is a conflict area with challenging political, economic, demographic and social dynamics. The region has a high number of internally displaced persons with increasingly disrupted delivery of basic services, including healthcare. Mental health needs have been increasing in the region while the infrastructure and capacity of the health sector has been negatively affected by the conflict. This study aimed to explore the provision of mental health and psychosocial support (MHPSS) services to communities in NWS (including healthcare workers) and to assess the experiences of beneficiaries with MHPSS services. Methods The study followed a mixed-methods research design that included qualitative and participatory methods (44 semi-structured interviews and a group model building workshop with 15 participants) as well as a survey with 462 beneficiaries. Results Findings suggested an improvement of MHPSS services in the region over the last few years due to the creation of a specific Technical Working Group for MHPSS that contributed to assessment of community needs and support of the MHPSS response. The key elements of this response were: (1) training non-specialized health workers to address the shortage in specialized providers; (2) securing funding and coordination of services between different organizations; and (3) addressing gaps in the availability and geographical distribution of other needed resources, such as medicines. While those elements contributed to improving access to services and the quality of services—especially among health workers seeking MHPSS services—findings suggested gaps in the sustainability of services and a need to scale up those interventions in an integrated approach. Conclusion The study findings add to the evidence base on the challenges in scaling up MHPSS interventions and their long-term sustainability concerns. Priority actions should address the intermittent funding of the MHPSS response, incorporate MHPSS outputs and outcomes in the reimbursement of routine services, improve coordination between health partners and non-health actors in order to expand the scope of MHPSS response, and address the inequitable availability of resources in the region.

Research Question: Why the banking sector in some countries experienced more severe panic than the banking sector in other countries? And why some countries recovered faster than others? Motivation: In a response to the global financial crisis, research on the motivation risk-taking or risk-aversion has been increasingly grown to investigate whether culture differences among countries affect the behaviors of individuals inside societies to be risk-taking or risk-aversion? Idea: Test the effect of the four cultural dimensions of Hofstede (2001) i.e. (individualism, uncertainty avoidance, power distance and masculinity) on risk-taking in the banking sector. Data: Our sample consists of 2620 bank-year observations of 262 banks from four countries covering the period from (2011 to 2020) collected from Refinitiv Eikon database. Tools: The statistical techniques used are descriptive analysis, correlation and OLS regression. Findings: We found the effect of national culture on risk-taking is significant for all dimensions. Individualism and masculinity are negatively related to risk-taking and uncertainty avoidance is positively related to risk-taking. For power distance dimension, we found power distance of Hofstede (2001) is significantly and negatively related to risk-taking, while power distance of House et al. (2004) is significantly and positively related to risk-taking. We confirmed our findings with robustness test. Contribution: Our results confirmed the “cushion hypothesis” formulated by Hsee & Weber (1999). We provide evidence on the significant effect of masculinity, long-term orientation and indulgence on bank risk-taking where most previous studies either excluded or found them insignificant. The impact of all cultural dimensions has been confirmed using a small sample of countries.

Background:. As artificial intelligence makes rapid inroads across various fields, its value in medical education is becoming increasingly evident. This study evaluates the performance of the GPT-4.0 large language model in responding to plastic surgery board examination questions and explores its potential as a learning tool. Methods:. We used a selection of 50 questions from 19 different chapters of a widely-used plastic surgery reference. Responses generated by the GPT-4.0 model were assessed based on four parameters: accuracy, clarity, completeness, and conciseness. Correlation analyses were conducted to ascertain the relationship between these parameters and the overall performance of the model. Results:. GPT-4.0 showed a strong performance with high mean scores for accuracy (2.88), clarity (3.00), completeness (2.88), and conciseness (2.92) on a three-point scale. Completeness of the model’s responses was significantly correlated with accuracy (P < 0.0001), whereas no significant correlation was found between accuracy and clarity or conciseness. Performance variability across different chapters indicates potential limitations of the model in dealing with certain complex topics in plastic surgery. Conclusions:. The GPT-4.0 model exhibits considerable potential as an auxiliary tool for preparation for plastic surgery board examinations. Despite a few identified limitations, the generally high scores on key parameters suggest the model’s ability to provide responses that are accurate, clear, complete, and concise. Future research should focus on enhancing the performance of artificial intelligence models in complex medical topics, further improving their applicability in medical education.

This study presents the results of the impact of a short training on vocabulary learning strategies (VLSs). The aim was to raise participants' awareness of a wide range of VLSs and consequently to encourage and motivate them to utilize these strategies in their vocabulary learning. The participants were 29 Saudi male students in their first semester majoring in English as a foreign language in the Department of English and Translation in the College of Languages and Translation at King Saud University, Saudi Arabia. The data collection tool was a questionnaire which consists five main categories of VLSs with 8 sub-strategies under each type with a total of 40 sub-strategies. The same questionnaire was administered twice: before and after training. The results showed an awareness-raising impact as reflected in the increase use of VLSs following the training. The increase was in all five strategy categories with statistically significant differences in three categories; Determination, Memory, and Cognitive strategies. Furthermore, the participants of this study reported that they benefited a lot from the training and they not only increased their exploitation of the strategies in this course but this training led them to utilize these strategies in other courses, such as reading and grammar. Although the training was short, the effect was evident, thus it is assumed that longer period of training will be conducive to better results in terms of the use of VLSs and consequently vocabulary knowledge. It is recommended that such intervention should be implemented in other courses as an initial step in understanding learning strategies in general with the goal of enhancing learners' autonomy in different types of learning strategies.

Intracranial haemorrhage (ICH) detection is a critical task in radiology and neurology, as timely recognition of haemorrhages in the brain can assist in rapid intervention and treatment. Several imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI), are widely utilized to detect and classify ICH. Traditional methods for intracranial haemorrhage detection relied on manual inspection of CT images by radiologists. However, with advancements in machine learning (ML) and deep learning (DL) techniques, automated and computer-aided systems have been developed to assist radiologists in detecting and diagnosing ICH efficiently. DL models, particularly convolutional neural network (CNN), has shown promising results in ICH detection on CT images. With this motivation, this study focuses on the development of a Political Optimizer with Deep Learning based Intracranial Haemorrhage Diagnosis on Healthcare Management (PODL-ICHDHM) technique. The presented PODL-ICHDHM technique majorly concentrates on the recognition and classification of ICH on CT images. In this study, bilateral filtering (BF) is initially applied to pre-process the CT images. For feature extraction purposes, the Faster SqueezeNet approach is utilized in this study. At last, the PO algorithm with denoising autoencoder (DAE) model is utilized for the classification of ICH accurately. The experimental result analysis of the PODL-ICHDHM approach was validated on a benchmark dataset. The outcomes emphasized the improved performance of the PODL-ICHDHM algorithm over other recent approaches with a maximum detection accuracy of 98.43%.