In this paper, a general Stewart Platform Simulation tool (STEWSIM) is developed for robot designers and researchers. The STEWSIM uses an efficient connection matrix algorithm that has been developed for kinematic design of all possible types of general Stewart platforms (GSPs), such as 3×3, 3×4, 3×5 … 6×5 and 6×6 with an arbitrary number of legs varying between three and six. This new interactive simulation software based on the MATLAB Graphical User Interface (GUI) provides a suite of analyses, such as inverse and forward kinematics, Jacobian matrix, dexterity, workspace, singularity analysis and trajectory planning, using different kinds of roll-pitch-yaw or Euler angle sets. The STEWSIM also provides the visualization of the GSPs, graphical representation of the workspace, and graphic animation of trajectories specified by inverse kinematics in three-dimensional space. Furthermore, it provides an environment to compare the kinematic performances of the designed GSPs with arbitrary configuration. As a kinematic design, analysis and simulation software, the STEWSIM provides researchers with the ability to change the parameters interactively for designing their manipulators with optimum specifications, such as leg lengths and attachment points. The usage of this simulation tool may result in new types of feasible robotic manipulators that can be used for practical applications in industry.

Adjusting the insulin dose in patients with diabetes is crucial for maintaining optimal blood glucose levels. Currently, the insulin dose adjustment of patients registered at the Diabetes Clinic of Kocaeli University Hospital is followed manually using clinical forms, which is time-consuming and often fails to adequately account for individual variations. In this study, a rule-based insulin dose adjustment algorithm was developed based on clinical guidelines. The algorithm analyzes blood glucose levels measured at specific times over the past three days to determine the necessary insulin dose adjustments. The algorithm is implemented as part of the measurement module in a mobile application, the so-called AIDCARE application, which provides an artificial intelligence-enabled self-management tool for diabetic patients.

Background and Objective: Multidrug and carbapenem resistant gram-negative bacilli bloodstream infections cause high mortality in intensive care units (ICUs). Predicting mortality can improve treatment and support end-of-life decisions. This study aimed to develop a machine learning model to predict mortality in ICU patients with these infections. Methods: This retrospective cohort study was conducted at a tertiary care medical center between 2017 and 2023. Adult ICU patients with bloodstream infections caused by multidrug and carbapenem resistant Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii were included. Demographic, clinical, and laboratory data were collected. Mann-Whitney U and Chi-square tests were used to compare the groups. Multivariable analysis with binary logistic regression was used to identify mortality risk factors. Ten machine learning classifiers were evaluated using stratified 5-fold cross-validation, and model predictions were interpreted with SHapley Additive exPlanations (SHAP). Results: 197 patients were included, with a 15-day mortality rate of 48%. The Light Gradient Boosting Machine (LightGBM) classifier showed the best performance, with an AUROC of 0.94, AUPRC of 0.952, accuracy of 0.868, precision of 0.906, recall of 0.822, F1 score of 0.855, Matthews Correlation Coefficient (MCC) of 0.744, and Brier score of 0.131. SHAP analysis revealed coagulopathy, rapid access to antibiotics, septic shock, SOFA score, platelet count, C-reactive protein (CRP) level, and time-related parameters as the most important predictive features. Conclusion: The LightGBM model showed promising results in predicting mortality in ICU patients. This model may support early intervention and assist in complex end-of-life decisions. This study was registered at ClinicalTrials.gov( https://clinicaltrials.gov/ct2/show/NCT06167083 )

In photovoltaic (FV) systems, data collection units are used to monitor the system's stability and performance. Monitoring and analyzing instantaneous changes of the electrical signals collected from the panel helps detection of the errors easily. This is accomplished by transferring data collected from panels by cables. There are some technical and cost limitations of this method. Moreover, considering the high temperature, humidity, rain, snow and harsh environmental conditions, wireless data transfer provides a big advantage over the wired transfer. In this study, an online software system has been developed for sending the data such as voltage, current, radiation, temperature, and humidity obtained from an installed 1000W PV system via the Bluetooth. A website has been designed for monitoring the data online. Software compatible with both Bluetooth 2.1 and 4.0 standards, which runs on Raspberry Pi, collects the electrical measurement data of PV panels that was placed at various locations. The web interface provides monitoring all data received by the server instantaneously and the stored data can be analyzed easily.

Accurate estimation of meal composition from food images can support safer and more reliable insulin bolus decision-making for individuals with Type 1 diabetes. Existing food recognition and nutrition estimation systems are often designed for general dietary logging and do not directly integrate food analysis with personalized insulin therapy parameters. This study presents an image-based nutrition estimation and insulin decision-support module developed within the AI-assisted Diabetes Care (AIDCARE) platform. The proposed system uses a convolutional neural network (CNN) to classify food items from a single meal image and retrieves reference nutritional values from a food composition database. A separate multimodal large language model (MLLM)-based estimation component is then used to estimate portion size, allowing carbohydrate and nutrient values to be scaled according to the observed serving. A curated food image dataset containing 40 food categories was used to evaluate three CNN architectures: ResNet50, Inception V3, and EfficientNet-B0. EfficientNet-B0 achieved the best classification performance, with 94.91% validation accuracy, 95.55% precision, 94.87% recall, and 94.90% F1-score. The portion-estimation component achieved an MAE of 12.27 g and an RMSE of 15.11 g. The estimated carbohydrate value is combined with user-specific clinical parameters, including the insulin-to-carbohydrate ratio and insulin sensitivity factor, to generate advisory bolus guidance. To support safety, the system requires user confirmation or correction of the recognized food category and estimated portion before insulin guidance is displayed. The proposed system is intended for advisory decision support only and is not designed to replace clinical judgment or autonomous insulin delivery systems.