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Abstract Background Understanding the connection between parental wellbeing and its impact on childhood depression is crucial in order to develop targeted interventions and support systems that can mitigate potential long-term effects on mental health. This study focuses on examining the properties of an Arabic translation of a questionnaire called Short Mood and Feelings Questionnaire Parent Version (SMFQ-P) as a preliminary step toward validating a culturally relevant screening tool for childhood depression in Lebanon. Methods A total of 502 parents, recruited through a snowball method, took part in the survey with an age of 36.24 years (SD ± 8.29). Among them 74.5% were females 88.8% were married and 72.9% had completed university level education. The children’s mean age was 7.95 ± 1.14 years. The SMFQ-P was evaluated using confirmatory factor analysis (CFA), reliability measures, and correlations with parental distress using the Depression Anxiety Stress Scale-8 (DASS-8). Results Confirmatory factor analysis (CFA) demonstrated an excellent fit for the one-factor model of SMFQ-P scores (RMSEA = 0.059, 90% CI [0.049, 0.070]; SRMR = 0.034; CFI = 0.967; TLI = 0.960), with standardized factor loadings ranging from 0.58 to 0.82. Male parents reported more depression in their child than female parents (p = .016). Parental distress levels (r = .60, p

The Internet of Things (IoTs) revolutionizes the consumer electronics landscape by presenting a degree of personalization and interactivity that was previously unimaginable. Interconnected devices are now familiar with user characteristics, giving custom skills that improve the user's satisfaction. Still, IoT remains to transform the consumer electronics field; security in IoT becomes critical, and it is utilized by cyber attackers to pose risks to public safety, compromise data privacy, gain unauthorized access, and even disrupt operations. Robust security measures are crucial for maintaining trust in the proliferation and adoption of interconnected technologies, mitigating those risks, protecting sensitive data, and certifying the integrity of the IoT ecosystem. An intrusion detection system (IDS) is paramount in IoT security, as it dynamically monitors device behaviours and network traffic to detect and mitigate any possible cyber threats. Using machine learning (ML) methods and anomaly detection algorithms, IDS can rapidly identify abnormal activities, unauthorized access, or malicious behaviours within the IoT ecosystem, thus preserving the integrity of interconnected devices and networks, safeguarding sensitive data, and protecting against cyber-attacks. This work presents an Improved Crayfish Optimization Algorithm with Interval Type-2 Fuzzy Deep Learning (ICOA-IT2FDL) technique for Intrusion Detection on IoT infrastructure. The main intention of the ICOA-IT2FDL technique is to utilize a hyperparameter-tuned improved deep learning (DL) method for intrusion detection, thereby improving safety in the IoT infrastructure. BC technology can be used to accomplish security among consumer electronics. The ICOA-IT2FDL technique employs a linear scaling normalization (LSN) approach for data normalization. In addition, features are selected using an improved crayfish optimization algorithm (ICOA). This is followed by the ICOA-IT2FDL technique, which applies the interval type-2 fuzzy deep belief network (IT2-FDBN) model to identify intrusions. Finally, the bald eagle search (BES) model strategy improves the intrusion recognition rate. A series of investigations is accomplished to ensure the enhanced accomplishment of the ICOA-IT2FDL model. The experimentation results specified that the ICOA-IT2FDL model shows better recognition results compared to recent models.

The Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can save money and time, increase efficiency, and enable greater access to digital experiences. Cybersecurity analysts often refer to this as increasing the attack surface from which hackers can benefit. Implementing the proper security measures is crucial since IoT devices can be vulnerable to cyberattacks and are often built with limited security features. Securing IoT devices involves implementing security measures and best practices to secure them from potential vulnerabilities and threats. Deep learning (DL) models have recently analyzed the network pattern for detecting and responding to possible intrusions, improving cybersecurity with advanced threat detection abilities. Therefore, this study presents a new Hybrid Dung Beetle Optimization-based Dimensionality Reduction with a Deep Learning-based Cybersecurity Solution (HDBODR-DLCS) method on the IoT network. The primary goal of the HDBODR-DLCS technique is to perform dimensionality reduction with a hyperparameter tuning process for enhanced detection results. In the primary stage, the HDBODR-DLCS technique involves Z-score normalization to measure the input dataset. The HDBO model is used for dimensionality reduction, which mainly selects the relevant features and discards the irrelevant features. Besides, intrusions are detected using the attention bidirectional recurrent neural network (ABiRNN) model. Finally, an artificial rabbits optimization (ARO) based hyperparameter tuning process is performed, enhancing the overall classification performance. The empirical analysis of the HDBODR-DLCS method is tested under the benchmark IDS dataset. The simulation outcomes indicated the HDBODR-DLCS method's improved abilities over existing approaches.

Background: Latent TB infection (LTBI) affects one fourth of the global population. Currently, there is an absence of an optimal strategy for distinguishing between active tuberculosis (aTB) and LTBI. While some researchers have explored cytokines other than interferon-gamma (IFN-γ) as biomarkers, results have shown significant variability in their ability to differentiate between these conditions. This meta-analysis aims to evaluate the performance of activation phenotype and chemokine markers in distinguishing between aTB and LTBI. Objectives: To assess the diagnostic accuracy of specific biomarkers (HLA-DR + IFNγ + , CD38 + IFNγ + , MCP-1, and RANTES) in differentiating aTB from LTBI. Design: This study was conducted in accordance with the PRISMA guidelines for systematic reviews and meta-analyses of diagnostic studies. Data sources and methods: We conducted a comprehensive search of PubMed, Scopus, Sciences Direct, and Web of Science for primary studies published in English up to 2023. Studies were included if they reported sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) for the biomarkers in question. We calculated pooled diagnostic sensitivity, specificity, DOR, and AUC, and used the summary receiver operating characteristic curve (SROC) to summarize the diagnostic performance of each biomarker. Results: Sixteen studies involving 1696 participants were included in the analysis. Among them, 925 individuals were diagnosed with aTB, while 771 were classified as having LTBI. The specificity, sensitivity, DOR, and AUC for CD38 + IFNγ + , HLA-DR + IFNγ + , RANTES, and MCP-1 were (0.97 [95% CI: 0.72-1.00], 0.90 [95% CI: 0.75-0.96], 291.863, and 0.9432), (0.90 [95% CI: 0.70-0.97], 0.83 [95% CI: 0.63-0.94], 41.819, and 0.8598), (0.68 [95% CI: 0.55-0.79], 0.72 [95% CI: 0.56-0.84], 5.733, and 0.7979), and (0.63 [95% CI: 0.54-0.72], 0.63 [95% CI: 0.50-0.75], 2.892, and 0.7290) respectively. Conclusion: The findings indicate that CD38 + IFNγ + and HLA-DR + IFNγ + demonstrated the highest diagnostic accuracy. Additional prospective research is necessary to identify the optimal combination of biomarkers to enhance diagnostic accuracy in clinical settings. Registration: This review has been registered on PROSPERO: (CRD42023472091). Available from: https://www.crd.york.ac.uk/prospero/#recordDetails