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OBJECTIVES:To systematically identify, summarize, and qualitatively synthesize the available clinical evidence on orthodontic traction-based management strategies for impacted maxillary central incisors, with particular emphasis on treatment duration, eruption success, and periodontal outcomes. METHODS:A comprehensive literature search was conducted across six databases (PubMed®, Google Scholar, Embase®, Scopus®, Web of Science, and the CENTRAL). Randomized and non-randomized clinical studies evaluating orthodontic traction-based management strategies for impacted maxillary central incisors were included. Two reviewers independently screened studies, extracted data, and assessed the risk of bias using the Cochrane RoB 2.0 and ROBINS-I tools. The certainty of the evidence was evaluated using the GRADE approach. RESULTS:Six studies involving 264 patients were included in the analysis. The evidence suggests that active orthodontic intervention can achieve favourable eruption outcomes in selected clinical contexts. Treatment duration varied widely, ranging from approximately 10 to over 21 months, and was influenced by impaction height, root morphology, and patient age. Several studies suggested shorter treatment durations in protocols incorporating preoperative space creation and immediate traction. Closed-eruption techniques were frequently associated with favourable gingival outcomes, though labial bone loss around treated teeth was a common finding. The overall certainty of evidence was assessed as low for primary outcomes and very low for secondary outcomes, due to the risk of bias and heterogeneity in the included studies. CONCLUSIONS:Active orthodontic traction-based management strategies have been associated with favourable clinical outcomes in selected cases; however, the certainty of evidence remains low, and results should be interpreted cautiously due to heterogeneity in interventions, including surgical exposure, type of traction, and space creation protocols. Treatment efficiency may be influenced by creating preoperative space, and periodontal health should be a key consideration. The conclusions are based on low-certainty evidence, highlighting the need for more rigorous and long-term studies.
This study introduces a novel hybrid methodology that integrates Particle Swarm Optimization (PSO) and Social Network Analysis (SNA) to enhance conversion prediction in Meta digital advertising campaigns. Real-world data was collected from four international Meta ad accounts promoting coaching programs, structured across three dimensions: demographic targeting (age, gender), platform placement, and daily performance metrics. Following an initial cleaning and balancing process, a baseline Random Forest classifier was trained, achieving an F1-score of 0.81. PSO was then employed to optimize hyperparameters, resulting in an improved F1-score of 0.83. In parallel, we applied SNA techniques to construct behavioral graphs based on feature similarity and centrality, generating new network-based predictors. Finally, we combined both optimized hyperparameters and SNA-derived features into a hybrid PSO-SNA model. The hybrid model demonstrated superior performance, achieving an accuracy of 0.89, precision of 0.90, recall of 0.90, and an F1-score of 0.87, significantly outperforming previous models. These results underscore the effectiveness of integrating topological behavioral insights with traditional optimization techniques. By bridging swarm intelligence and social graph analysis, this hybrid model offers a scalable and explainable approach for advertisers seeking to maximize conversion rates without altering Meta's black-box algorithms.
With the rapid advancement of audio deepfake technologies, detecting such manipulations has become a critical cybersecurity challenge. This study proposes a novel hybrid model that combines Convolutional Neural Networks (CNNs) with Bidirectional Long Short-Term Memory (BiLSTM) networks to detect spoofed audio. The research is based on the Release-in-the-Wild dataset, which simulates real-world acoustic conditions, and employs a preprocessing pipeline involving the extraction of Mel-Frequency Cepstral Coefficients (MFCCs) enhanced with first- and second-order derivatives. The proposed model achieved an accuracy of 99% with an Equal Error Rate (EER) of 0.011, while maintaining remarkable lightness with only 473k trainable parameters. Beyond numerical performance, the model demonstrates strong robustness against acoustic variability, environmental noise, and speaker diversity, highlighting its potential for deployment in uncontrolled real-world scenarios. Its compact design ensures low computational demand, making it practical for integration into online verification systems, intelligent voice assistants, and security monitoring infrastructures. Comparative experiments further confirm that the hybrid CNN–BiLSTM architecture achieves a superior balance between accuracy, efficiency, and generalization compared to recent Transformer-based models. Overall, this work contributes an interpretable and resource-efficient framework for generalized audio deepfake detection. The findings underline that high detection accuracy and lightweight design are not mutually exclusive, and future research will focus on extending the approach to multimodal systems that jointly analyze both audio and visual cues for more reliable deepfake forensics.
“Khans” (caravanserais) are unique socio-economic urban structures and significant commercial focal points that shaped the traditional markets of oriental cities. The urban distribution of these structures in the city indicates the significant role they had for the city’s structure, economy and communities. In addition, the architecture of these khans clearly reflects the level of cultural, social and economic prosperity of these urban centres. The City of Homs is one of the key cities in Syria that, through its history, was recognised as an important trade centre, and thus accommodated a large number and diverse types of khans, that spread across the city. Unfortunately, only few of these structures exist today due to the lack of appreciation for their value as an important heritage component of the city’s history. Therefore, conserving the remaining structures (khans) is considered urgent in order to respond to the current interest in replacing them with modern commercial buildings, especially after the consequences of the Syrian conflict. Thus, this paper develops a historical study of the khans in the City of Homs, documents their urban distribution, and analyses the architectural features of the surviving structures. The study also presents an overview of the architecture of the khans, their relationship with the city's urban development, and investigates the cultural, social and economic characteristics of these structures to inform a set of recommendations for their preservation and revitalisation. The study uses analysis of literature, archives and historic resources as well as empirical architectural/urban analysis informed by interviews with experts and communities to frame the cultural significance and contemporary social values of the khans in the City of Homs and accordingly synthesise appropriate conservation and development strategies for these structures in their contemporary context.
Traumatic brain injury is a major global health concern, with penetrating brain trauma representing an uncommon but highly lethal subset, especially in conflict-affected regions. Outcomes in penetrating brain trauma are strongly influenced by admission Glasgow Coma Scale (GCS), extent of structural injury, projectile characteristics, and imaging findings. Early expert assessment, rapid neuroimaging, and timely surgical intervention are critical for improving survival in selected patients although long‑term recovery often requires multidisciplinary rehabilitation. We report a rare case of penetrating brain trauma in a 14‑year‑old male who achieved complete neurological recovery following prompt and appropriate management. A 14‑year‑old Syrian male presented with a penetrating gunshot injury to the left frontal region, arriving with a GCS of 10, left pupillary non-reactivity, periorbital ecchymosis, cerebrospinal fluid leak, and a depressed comminuted frontal fracture. Computed tomography imaging revealed multiple bone fragments penetrating the left frontal lobe, an anterior skull base fracture, pneumocephalus, contusions, and a bullet lodged in the right maxillary sinus. He underwent urgent left frontal craniectomy with removal of bone fragments, dural repair, and abdominal preservation of the bone flap. Postoperatively, his neurological status improved rapidly to a GCS of 15 within 12 h, with spontaneous resolution of cerebrospinal fluid leakage. The patient remained neurologically intact and was discharged with plans for subsequent neuropsychological follow-up and cranioplasty (refer to Graphical Abstract). This case highlights the importance of rapid assessment and timely intervention in penetrating brain trauma despite its high morbidity and mortality. The patient’s full recovery from an initial GCS of 10 underscores the value of appropriate rehabilitation. Accurate projectile identification and avoidance of management delays are essential, and multidisciplinary collaboration may benefit selected cases.
