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Silver-decorated nanobeads for high-performance and flexible analysis of Shigella dysenteriae utilizing a paper-based aptasensor
No full textShigella dysenteriae (S. dysenteriae) is a significant pathogen associated with foodborne diseases, yet it is often overlooked, posing a risk of widespread outbreaks. Traditional detection methods rely on complex and lengthy cell culture techniques. In response, we have developed a cost-effective, portable, and versatile paper-based aptasensor that utilizes silver-decorated magnetic nanobeads (Ag/MBs) for S. dysenteriae detection. This aptasensor was designed to enable voltammetric and impedance analyses, offering rapid screening and sensitive detection of S. dysenteriae in food samples. Ag is chemically decorated on carboxyl-functionalized MBs, with aptamer probes attached. The current response from Ag indicated the presence of the pathogen, while bacterial binding reduced the Ag signal due to insulating properties, measured via differential pulse voltammetry (DPV). Concurrently, the formation of immunocomplexes increased charge transfer resistance, facilitating electrochemical impedance spectroscopy (EIS) measurement within a single device and lowering the detection limit. MB-based assays helped concentrate S. dysenteriae, thus achieving broad linearity (DPV = 102–108 CFU/mL, EIS = 101–109 CFU/mL) and high sensitivity (DPV = 90 CFU/mL, EIS = 8.09 CFU/mL) while maintaining specificity. The aptasensor also integrated near-field communication technology for convenient on-site analyses, making it an effective, sensitive, yet portable platform for detecting S. dysenteriae and other microorganisms
Multimodal LLM Guidance for Aligning Text-to-Image Generation
No full textText-to-image diffusion models have achieved great success in both research and applications. Despite their advantages they often struggle in generating objects relationships. Fortunately, large language models have proven their ability to understand the text prompts and visual inputs. However, their full potential in text-to-image generation has yet to be explored. In this study we introduce a new training-free pipeline that leverages several capabilities of Multimodal LLMs on carefully designed steps and achieves enhanced semantic compatibility in text-to-image generation. We showed that our method significantly outperforms existing studies and even competing with cutting-edge giants in Fig. 1. We also introduce a new benchmark dataset containing multiple object relationships from real-life scenes
KENT KİMLİĞİNİ YANSITACAK VE İKLİM DEĞİŞİKLİĞİNE UYUM SAĞLAYABİLECEK ODUNSU BİTKİLER: AMASYA
No full textFundamental Study on Forecasting Surface Ship Maneuvering Characteristics for Autonomous Docking Operations
No full textShip docking simulations have become a valuable tool in maritime operations, offering a wide range of applications from training and risk assessment to the development of autonomous berthing systems for autonomous and semi-autonomous ship operations. Precise and quick control and decision-making are essential for the latter. The primary aim of this study is to create a practical tool and calculation methodology that serves as an efficient alternative to CFD, enabling rapid simulations for ship maneuvering applications, particularly in fundamental docking scenarios. The tested maneuvers are straight-ahead motion, crash-ahead motion, turning and kicking turn at slow speed. The monitored ship’s velocity and position during its move is validated using experimental and numerical data from the literature, ensuring its accuracy and reliability. The results demonstrate that the proposed methodology is capable of making highly accurate maneuvering predictions, thus providing fast and reliable data for learning algorithms that will govern docking operations
Nexus between Energy Generation Sources and Environmental Deterioration under Trade Policy Uncertainty and Geopolitical Risk: Evidence from USA and China by Daily Data and Novel KRLS Approach
No full textInvestigation of Jacobi Function Solutions of the Fractional Benjamin-Ono Equation (SCI-Expanded)
No full textMulti-target deep learning models for syngas yield and exergy estimation in hybrid fixed and fluidized bed biomass-lignite gasifiers
No full textHybrid biomass-lignite co-gasification presents a promising route for sustainable syngas and exergy production. This study explores the predictive modeling of gasification outputs namely CO, CO2, CH4, H2 yields and exergy values using advanced machine learning strategies. A comprehensive dataset comprising elemental compositions and reactor configurations (fixed and fluidized bed) was generated via Aspen Plus simulations. Multi-target deep learning models, including Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Units (GRU), were developed and evaluated through 10-fold cross-validation and hold-out methods. All models demonstrated high predictive performance, with average R2 scores exceeding 0.97 and RMSE values remaining below 0.01 across targets. Bi-LSTM models marginally outperformed others, achieving R2 values up to 0.991. Furthermore, Van Krevelen diagrams were used to visualize the relationship between fuel composition (H/C and O/C ratios) and gasification performance across reactor types and optimization objectives. These visual diagnostics revealed distinct clusters and trends aligned with reactor behavior and compositional characteristics, offering interpretability to the model predictions. The study not only benchmarks deep learning architectures for multi-output regression in thermochemical systems but also demonstrates how visual analytics can bridge the gap between data-driven modeling and process insight
Application of a machine learning-based prediction model for the design of general lighting systems in offices
No full textLighting design is an interdisciplinary practice that aims to create a specific atmosphere through light, while addressing the functional, physiological, and psychological needs of users. The main objective of interior lighting design involves creating an ideal lighting system which fulfills all necessary requirements of a particular space. Designers focus on examining the interactions between various criteria to produce the most suitable lighting solution. Over time, lighting designers develop an intuitive understanding of how to optimize lighting parameters based on accumulated experience. Machine learning is an area of artificial intelligence that enables computers to make predictions about new data through sample data and past experiences. The aim of this study is to develop a prediction model that reflects the knowledge and intuitive expertise of lighting designers over time. The study involves training a Deep Neural Network (DNN) model to recommend luminaire properties and positions for achieving target illuminance levels and uniformity ratios in office spaces. The model accepts spatial parameters, average illuminance and uniformity ratio as input data while it generates luminaire position, quantity, luminous flux and beam angle as output results. A prediction model interface was developed to provide users with access to the system. With the developed user interface, luminaire recommendations suitable for the luminaire technical specifications obtained as output from the prediction model are presented. Training and validation losses were closely aligned, indicating stable learning. Test-set evaluation yielded a mean R² of 0.5531 across luminaire types and 0.8277 for downlights. Validation against DIALux Evo simulations showed an average agreement of approximately 84 % in illuminance level and 88 % in uniformity. These findings suggest that the proposed model, combined with its user interface, has the potential to serve as a useful tool in the lighting design process. The study is expected to contribute to the literature, particularly in filling the gap in luminaire selection, and support the lighting design industry with a user-friendly, data-driven approach
