Detectors, sources, and channels. by Ural Akincioglu (21772538)

“…In the pre-processing stage, intrinsic mode functions of the signals were obtained using the ICEEMDAN algorithm. …”

Block diagram of the proposed method. by Ural Akincioglu (21772538)

“…In the pre-processing stage, intrinsic mode functions of the signals were obtained using the ICEEMDAN algorithm. …”

Interpolated fNIRS signal. by Ural Akincioglu (21772538)

“…In the pre-processing stage, intrinsic mode functions of the signals were obtained using the ICEEMDAN algorithm. …”

Triple window shifting operation. by Ural Akincioglu (21772538)

“…In the pre-processing stage, intrinsic mode functions of the signals were obtained using the ICEEMDAN algorithm. …”

Experimental flowchart. by Ural Akincioglu (21772538)

“…In the pre-processing stage, intrinsic mode functions of the signals were obtained using the ICEEMDAN algorithm. …”

PAM graphs of the runs in Strategy 1. by Ural Akincioglu (21772538)

“…In the pre-processing stage, intrinsic mode functions of the signals were obtained using the ICEEMDAN algorithm. …”

ENQUIRE’s flowchart. by Luca Musella (19986312)

Table 1_Late-onset first epileptic seizure and cerebral small vessel disease: role of juxtacortical white matter lesions.docx by Adrian Nasca (20601542)

“…Compared to TIA group, LOFES patients gray matter volume was regionally decreased in the right pre- and postcentral gyrus.</p>Significance<p>By using algorithm-based automated lesion segmentation software tools and VBM analysis we could highlight that a juxtacortical weighting of WML distribution and regionally decreased gray matter volume distinguished LOFES from TIA and PC groups in our sample.…”

Table 1_Recognition and treatment of attention deficit-hyperactivity disorder in patients with treatment-resistant burning mouth syndrome: a retrospective case study.docx by Kaori Takahashi (187346)

“…However, the role of ADHD diagnosis and treatment on BMS and associated brain function abnormalities remains unexplored. Therefore, we aimed to investigate the prevalence of ADHD comorbidity and its assessment using ADHD scales and the therapeutic efficacy of an ADHD-focused algorithm, including pre- and post-treatment cerebral blood flow single-photon emission computed tomography (SPECT) results, in patients with treatment-resistant BMS referred from the outpatient clinic of dental psychosomatic specialists at a tertiary care institution for multidisciplinary treatment.…”

Predictive power of ALBI score-based nomogram for 30-day mortality following transcatheter aortic valve implantation by Abdullah Yildirim (14775768)

“…</p> <p>This study exhibits the significance of liver dysfunction with AS patients and suggests incorporating liver function parameters in pre-operative risk assessments for better clinical outcomes in TAVI procedures.…”

Study design and deep-learning model architecture. by Sehoon Park (1466026)

“…Conv, Convolutional layer; SepConv, Separable convolutional layer; MBConv, Mobile inverted bottleneck convolutional layer (numbers after MBConv indicate layer depth); k3/k5, kernel size 3 or 5; GAP, Global average pooling; FC, Fully connected layer; Swish, Swish activation function; DBP, Diastolic blood pressure, SBP, Systolic blood pressure; HR, Heart rate; DL-IVSS, A deep-learning algorithm leveraging time-series intraoperative vital sign signals; preOp ML, A machine learning model with 103 baseline characteristics.…”

Supplementary file 1_RadioMe: an automated home-based radio, music playlist, and diary reminder system: Report on recruitment, music compilation, and listening, and preliminary tes... by Alexander Street (16905678)

“…</p>Aims<p>RadioMe was a project designed for people living at home with dementia to build a system to help them maintain the highest quality of life there for as long as possible, with three functional components: 1. Streaming radio; 2. Providing pre-recorded spoken diary reminders; 3. interrupting the radio with pre-compiled playlists when a wrist-worn heart rate (HR) monitor detects stress. …”

Table 1_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx by XinPei Liu (16560699)

“…LASSO regression identified 25 significant features for the models. Among the six algorithms tested, the radial basis function support vector machine (RBF-SVM) had the highest AUC at 0.771. …”

Table 3_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx by XinPei Liu (16560699)

“…LASSO regression identified 25 significant features for the models. Among the six algorithms tested, the radial basis function support vector machine (RBF-SVM) had the highest AUC at 0.771. …”

Table 2_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx by XinPei Liu (16560699)

“…LASSO regression identified 25 significant features for the models. Among the six algorithms tested, the radial basis function support vector machine (RBF-SVM) had the highest AUC at 0.771. …”

Table 4_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx by XinPei Liu (16560699)

“…LASSO regression identified 25 significant features for the models. Among the six algorithms tested, the radial basis function support vector machine (RBF-SVM) had the highest AUC at 0.771. …”

Optimal Network Membership Estimation under Severe Degree Heterogeneity by Zheng Tracy Ke (8293395)

“…To obtain a rate-optimal method, we modify an existing spectral algorithm, Mixed-SCORE, by adding a pre-PCA normalization step. …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><h2>Project Structure</h2><pre><pre>Perception_based_neighbourhoods/<br>├── raw_data/<br>│ ├── ET_cells_glasgow/ # Glasgow grid cells for analysis<br>│ └── glasgow_open_built/ # Built area boundaries<br>├── svi_module/ # Street View Image processing<br>│ ├── svi_data/<br>│ │ ├── svi_info.csv # Image metadata (output)<br>│ │ └── images/ # Downloaded images (output)<br>│ ├── get_svi_data.py # Download street view images<br>│ └── trueskill_score.py # Generate TrueSkill scores<br>├── perception_module/ # Perception prediction<br>│ ├── output_data/<br>│ │ └── glasgow_perception.nc # Perception scores (demo data)<br>│ ├── trained_models/ # Pre-trained models<br>│ ├── pred.py # Predict perceptions from images<br>│ └── readme.md # Training instructions<br>└── cluster_module/ # Neighbourhood clustering<br> ├── output_data/<br> │ └── clusters.shp # Final neighbourhood boundaries<br> └── cluster_perceptions.py # Clustering algorithm<br></pre></pre><h2>Prerequisites</h2><ul><li>Python 3.8 or higher</li><li>GDAL/OGR libraries (for geospatial processing)</li></ul><h2>Installation</h2><ol><li>Clone this repository:</li></ol><p dir="ltr">Download the zip file</p><pre><pre>cd perception_based_neighbourhoods<br></pre></pre><ol><li>Install required dependencies:</li></ol><pre><pre>pip install -r requirements.txt<br></pre></pre><p dir="ltr">Required libraries include:</p><ul><li>geopandas</li><li>pandas</li><li>numpy</li><li>xarray</li><li>scikit-learn</li><li>matplotlib</li><li>torch (PyTorch)</li><li>efficientnet-pytorch</li></ul><h2>Usage Guide</h2><h3>Step 1: Download Street View Images</h3><p dir="ltr">Download street view images based on the Glasgow grid sampling locations.…”

Data Availability for Barrier Island Response to Energetic Storms: a Global View by Valeria Fanti (14857549)

“…</p><p dir="ltr">- Dune height (m): calculated as the vertical distance between the pre-storm dune toe and the pre-storm dune crest. …”

Table 1_Generating normative data from web-based administration of the Cambridge Neuropsychological Test Automated Battery using a Bayesian framework.docx by Elizabeth Wragg (19710640)

“…Markov Chain Monte Carlo algorithms generated a large synthetic dataset from posterior distributions for each outcome measure, capturing normative distributions of cognition as a function of age, sex and education.…”