Table 1_Changes in self-reported alcohol consumption at high and low consumption in the wake of the COVID-19 pandemic: a test of the polarization hypothesis.docx by Alexander Tran (11038679)

“…We also conducted a multivariate linear regression using mental well-being and sociodemographic variables as predictors of consumption.</p>Results<p>Alcohol consumption decreased from 2015 to 2020, mean = 11.49 cl of pure alcohol (SD = 8.23) vs. mean = 10.71 cl of pure alcohol (SD = 12.12), p <.00001, respectively. …”

Pan-cancer analysis reveals PRRT4 is a potential prognostic factor of AML by Wenqiong Xiang (17738962)

“…After PRRT4 knockdown, the proliferation ability of THP1 cells was significantly enhanced, and the apoptosis ratio was significantly decreased.…”

Table 1_Minimally invasive versus open surgery in uterine serous carcinoma: impact on recurrence and survival in a multicenter cohort.xlsx by Yi Fang (287944)

“…Multivariate analysis confirmed that MIS as an independent predictor of poorer PFS (HR = 2.29, 95% CI: 1.31–4.01, P = 0.004). …”

Image 1_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

Supplementary file 1_Feasibility of repetitive transcranial magnetic stimulation on non-motor symptoms of spinocerebellar ataxia type 3: a secondary analysis of a randomized clinic... by Hua Wu (2707)

“…Correlation analyses revealed no significant predictors of rTMS response based on age at onset, disease duration, number of expanded CAG repeat lengths, or baseline motor symptom severity scores.…”

Image 8_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

Table1_Assessing physical fitness adaptations in collegiate male soccer players through training load parameters: a two-arm randomized study on combined small-sided games and runni... by YanXiu Quan (19645810)

“…Although there were positive trends in variables such as RSA and 30-15IFT, these changes were modest and not statistically significant. The results suggest that while the combined SSGs and HIIT approach shows potential, its impact on physical fitness over a 4-week period is limited, with some variables, like CMJ, even showing decreases.…”

Image 6_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

Image 2_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

Image 7_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

Image 5_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

Image 4_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

Image 3_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.</p>Conclusion<p>The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. …”

Flowchart of the suggested SOA. by Ahmed A. Zaki Diab (12757190)

“…<div><p>Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. …”

Flowchart of the suggested SSA methodology. by Ahmed A. Zaki Diab (12757190)

“…<div><p>Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. …”

Egyptian case study 15-bus MEDN system. by Ahmed A. Zaki Diab (12757190)

“…<div><p>Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. …”

Flowchart of marine predator algorithm. by Ahmed A. Zaki Diab (12757190)

“…<div><p>Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. …”

Flowchart of I-GWO. by Ahmed A. Zaki Diab (12757190)

“…<div><p>Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. …”

Convergence trends for IEEE 33-bus test system. by Ahmed A. Zaki Diab (12757190)

“…<div><p>Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. …”

The updating positions of IGWO. by Ahmed A. Zaki Diab (12757190)

“…<div><p>Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. …”