The rapid advancement of large language models (LLMs) has profoundly influenced pathology, demonstrating the potential to enhance diagnostic accuracy and support clinical decision-making. As LLMs have shown great success in processing textual data, their integration with visual models has propelled the development of multimodal large language models (MLLMs). This review synthesizes current research on MLLMs in pathology, focusing on their application to both histopathological images and clinical data. We systematically examine key innovations in integrating diverse data modalities within model architectures. Additionally, we present a comprehensive overview of applications in diagnostic support, text analysis, and image processing while critically addressing challenges such as data annotation, model interpretability, and computational efficiency. Furthermore, we explore future advancements, including cost-effective fine-tuning techniques, dynamic resolution strategies, and the creation of large-scale, high-quality datasets. Through a detailed assessment of the current state of research and emerging opportunities, this review offers valuable insights for researchers and clinicians to fully leverage MLLMs, with the goal of advancing precision pathology and improving patient outcomes.

Air pollution, a silent public health crisis, demands smarter solutions. This research explores a novel approach: using machine learning and the Internet of Things (IoT) to predict hyper-local Air Quality Index (AQI). We integrate a Random Forest algorithm with NodeMCU for pinpoint-accurate AQI forecasts. The core lies in a robust machine learning model trained on historical data rich in environmental parameters-air quality indicators like PM2.5 and ozone, coupled with meteorological factors. This empowers the model to predict future AQI levels with exceptional accuracy. Real-time data acquisition is handled by NodeMCU, an IoT platform acting as our environmental guardian. It gathers live data and feeds it to the model, enabling real-time, location-specific AQI predictions. The model's effectiveness is rigorously evaluated, achieving a promising 99% accuracy-a testament to its potential to revolutionize air quality management. By harnessing the power of machine learning and IoT, this research paves the way for a future where proactive air quality management fosters healthier cities and a more sustainable future.

Cataracts, a leading cause of visual impairment and blindness worldwide, necessitate early detection and accurate classification for timely intervention. This study presents a deep learning-based approach to cataract classification, demonstrating remarkable performance. The model achieved a final training accuracy of approximately 90.88% and a final validation accuracy of 91.49%.

Heart failure (HF) is considered a global pandemic because of increasing prevalence, high mortality rate, frequent hospitalization and associated economic burden. This study explores a noninvasive method that may help in managing HF patients by predicting HF readmission. Methods: Seismocardiogram (SCG) signal is the low frequency chest vibration produced by the mechanical activity of heart. SCG signal was acquired from 101 patients with HF including in those readmitted to the hospital during the study period. Features were extracted from SCG signals. Several conventional machine learning (ML) models were developed using selected SCG and heart rate variability features. Furthermore, SCG signals were transformed into images using a time-frequency distribution method. Images were used to train a deep learning model. The models were able to predict the readmission status of the HF patients. Results: ML algorithms achieved higher accuracy than the deep learning model in classifying the readmitted and non-readmitted HF patients. K-nearest neighbor (KNN) achieved the highest classification accuracy (89.4% accuracy, 87.8% sensitivity, and 90.1% specificity). The study results suggest that SCG signal may be useful for readmission prediction of HF patients. Significance: Use of SCG signal may help the management of HF patients and improve their quality of life.

The Cognitive Agents and Interaction Lab (CAIL) at the University of Dhaka has strategically developed a focused High-Performance Computing (HPC) facility, underpinning its niche in artificial intelligence (AI) research, with a special emphasis â\euro” but not limited to â\euro” deep and reinforcement learning within the context of multi-agent systems. This technical report outlines the HPC resources at CAIL, highlighting a number of advanced GPUs that drive our research forward. The facility, supported by multiple grants, exemplifies a model for small-scale, high-impact computational research environments. We detail how these resources empower our team to achieve significant strides in AI, despite the challenges of scale, and set a precedent for innovation in the field.

Background: Prostheses are becoming more advanced and biomimetic with time, providing additional capabilities to their users. However, prosthetic sensation lags far behind its natural limb counterpart, limiting the use of sensory feedback in prosthetic motion planning and execution. Without actionable sensation, prostheses may never meet the functional requirements to match biological performance.Methods: We propose an approach for upper limb prosthetic grasp security feedback, delivered to the wearer through direct nerve stimulation proportional to the likelihood of objects slipping from grasp. This proportional feedback is based on a linear regression of the sensors embedded in a prosthetic hand to predict slip before it occurs. Four participants with transhumeral amputation performed pulling tasks with their prosthetic hand grasping an object at predetermined grip forces, attempting to pull the object with as much force as possible without slip. These trials were performed with two different prediction notification paradigms.Results: At lower grasp forces, where slip was more likely, a strong, single impulse notification of impending slip reduced the incidence of object slip by a median of 32%, but the maximum achieved pull forces did not change. At higher grasp forces, where slip was less likely, the maximum achieved pull forces increased by a median of 19% across participants when provided with a stimulation strength inversely proportional to the grasp security, but slip incidence was unchanged. Conclusions: These results suggest that this approach may be effective in recreating a lost sense of grip stability in the missing limb that can be incorporated into motor planning and ultimately prevent unanticipated object slips.

Planet-scale photo geolocalization involves the intricate task of estimating the geographic location depicted in an image purely based on its visual features. While deep learning models, particularly convolutional neural networks (CNNs), have significantly advanced this field, understanding the reasoning behind their predictions remains challenging. In this paper, we present a novel method that enhances the explainability of CNN-based geolocalization models by combining the information of applying Gradient-weighted Class Activation Mapping (Grad-CAM) to several layers, rather than solely to the final layer as is typically done. This approach provides a more detailed understanding of how different image features contribute to the model’s decisions, offering deeper insights than the traditional approach.

An analytical model for an antenna-embedded wall, also called signal-transmissive wall, is presented in this work. In the signal-transmissive wall, multiple antenna elements are distributed periodically on both wall sides, and connected back-to-back through coaxial cables. Numerical full-wave simulations of the signal-transmissive wall are computationally demanding due to the fine meshes required in the cables while having an electrically large wall size. Therefore the simulations above 8 GHz are not feasible even with a powerful cluster computer of the authors' research site. The analytical model is an attractive alternative to the full-wave simulation of the wall, which combines the individual transmission characteristics of the bare wall, realized gains of antenna elements and cable losses. The analytical model accurately reproduces the full-wave simulated transmission coefficient of the signal-transmissive wall up to 8 GHz for arbitrary polarizations and incident angles of a plane wave. The model therefore allows analyses of the signal-transmissive wall beyond 8 GHz, showing more than 70 dB reduction of the transmission loss at 30 GHz compared to a bare wall.

In the field of dentistry, there is a growing demand for increased precision in diagnostic tools, with a specific focus on advanced imaging techniques such as computed tomography, cone beam computed tomography, magnetic resonance imaging, ultrasound, and traditional intra-oral periapical X-rays. Deep learning has emerged as a pivotal tool in this context, enabling the implementation of automated segmentation techniques crucial for extracting essential diagnostic data. This integration of cutting-edge technology addresses the urgent need for effective management of dental conditions, which, if left undetected, can have a significant impact on human health. The impressive track record of deep learning across various domains, including dentistry, underscores its potential to revolutionize early detection and treatment of oral health issues. Objective: Having demonstrated significant results in diagnosis and prediction, deep convolutional neural networks (CNNs) represent an emerging field of multidisciplinary research. The goals of this study were to provide a concise overview of the state of the art, standardize the current debate, and establish baselines for future research. Method: In this study, a systematic literature review is employed as a methodology to identify and select relevant studies that specifically investigate the deep learning technique for dental imaging analysis. This study elucidates the methodological approach, including the systematic collection of data, statistical analysis, and subsequent dissemination of outcomes. Results: In incorporating 45 studies, we identified selection criteria and research objectives, addressing significant gaps in the existing literature. These studies assist clinicians in examining dental conditions and classifying dental structures, including caries detection and the identification of various tooth types. We evaluated model performance, addressing the identified gaps, using diverse metrics that we strive to list and explain. Conclusion: This work demonstrates how Convolutional Neural Networks (CNNs) can be employed to analyze images, serving as effective tools for detecting dental pathologies. Although this research acknowledged some limitations, CNNs utilized for segmenting and categorizing teeth exhibited their highest level of performance overall.

Cryptography has become an essential tool in information security, preserving data confidentiality, integrity, and availability. However, despite rigorous analysis, cryptographic algorithms may still be susceptible to attack when used on real-world devices. Side-channel attacks (SCAs) are physical attacks that target cryptographic equipment through quantifiable phenomena such as power consumption, operational times, and EM radiation. These attacks are considered to be a significant threat to cryptography since they compromise the integrity of the algorithm by obtaining the internal cryptographic key of a device by seeing its physical implementation. The literature on SCAs has focused on real-world devices, yet with the growing popularity of sophisticated devices like smartphones, fresh approaches to SCAs are necessary. One such approach is electromagnetic side-channel analysis (EM-SCA), which gathers information by listening to electromagnetic (EM) radiation. EM-SCA has been demonstrated to recover sensitive data like encryption keys and has the potential to identify malicious software, retrieve data, and identify program activity. This study aims to evaluate how well EM-SCA compromises encryption under various application scenarios, as well as examine the role of EM-SCA in digital forensics and law enforcement. Regarding this, addressing the susceptibility of encryption algorithms to EM-SCA approaches can provide digital forensic investigators with the tools they desire to overcome the challenges posed by strong encryption, allowing them to continue playing a crucial role in law enforcement and the justice system. Furthermore, this paper seeks to define the current state of EM-SCA in terms of attacking encryption, the encryption algorithms and encrypted devices that are most vulnerable and resistant to EM-SCA, and the most promising EM-SCA on encryption approaches. This study will provide a comprehensive analysis of EM-SCA in the context of law enforcement and digital forensics and point towards potential directions for further research.

In this research, we will build an algorithm (PCO: Particle Collision optimization) to solve continuous optimization problems. We assume that the optimal solution is confined to a limited space, and that a group of particles is scattered in the solution space according to a Brownian motion. These particles may collide with the optimal solution and may collide with each other, or even with the boundaries of the search space. We will test this metaheuristic algorithm on a set of standards benchmarks, and we will compare its results with the results of a group of wellknown algorithms, especially those that rely on the Lévy movement. We will also test its application to an engineering problem.

A document by Sven Ochs. Click on the document to view its contents.

Language models are prone to generating hallucinations, which significantly undermine their reliability and usefulness in critical applications. Introducing a novel approach that combines semantic relevance scoring with K-means clustering, our methodology significantly enhances the model's accuracy and reduces the occurrence of hallucinations. By integrating these techniques, the model can prioritize contextually appropriate synonyms, resulting in more coherent and factually correct outputs. The experimental results demonstrate substantial improvements in accuracy, semantic relevance, and a marked reduction in hallucinations across various tasks. Comprehensive evaluation using diverse metrics demonstrates the robustness and effectiveness of our modifications, highlighting the potential for practical deployment in applications where accuracy and reliability are paramount. This study affirms the viability of combining semantic relevance scoring with clustering techniques to enhance the performance of language models, contributing to the development of more reliable and effective models for a wide range of applications.

Convolutional Neural Networks (CNNs) have opened up new possibilities in a variety of fields, including plant pathology. Through image analysis, this study aims to take advantage of CNNs' capacity to recognize and categorize plant illnesses. Automated disease identification has potential for prompt intervention and crop preservation in modern times, when agricultural output assumes critical relevance. This work elaborates on the application and assessment of a CNN-based model designed for the detection of plant diseases. The technology separates healthy plants from those with problems by taking pictures of plants and putting them through the trained model. The growing demand for effective disease monitoring and management in the agriculture industry highlights the urgency of this research. The suggested model shows promising accuracy and dependability by utilising a large dataset and stringent evaluation metrics. This research highlights the potential of CNNs as a workable tool for upgrading agricultural practices by using a systematic approach. The results of this study shed light on both the strengths and weaknesses of CNNs in the context of automated plant disease diagnosis.

Integrated local multi-energy systems are recognized as a promising option to achieve the ambitious energy and climate goals set by the European Commission for 2030. The nature of integrated systems requires a sound combination of interdisciplinary methodologies and complementary tools. Creating an efficient architecture capable of exploiting synergies between tools is therefore crucial for designing, analysing and operating integrated energy systems. The joint application of different tools needed for analysing a local system can be very demanding and time-consuming due to vastly different data structures and functionalities. To address the need for complementary tools, the aim of this paper is to establish and test an integrated modelling architecture allowing the interaction of tools into a modular toolbox for the optimal planning of integrated local multi-energy systems, and also present key preliminary outcomes.

This study focused on developing a persuasive technology-based mobile intervention system to support users to adopt malaria prevention and control measures.  As a first step towards contributing to research, user-centered research to uncover the factors preventing the adoption of the existing malaria preventive measures and the appropriate persuasive strategies that could be employed to motivate users to adopt the expected malaria prevention and control behaviors was conducted. Findings from this study were mapped into their corresponding persuasive strategies during the research design before being operationalized. The intervention system developed is a mobile phone-based application, code-named Go-Malaria. The intervention system embodies text notifications, and audio and video tutorials made in English, Igbo, and Abakaliki languages to engage users based on their preferences. This research was carried out in Ebonyi State. Three Local Government Areas (LGAs) were selected for the study based on three inclusion criteria: high rate of malaria fatality, proximity to the river, and availability of mobile phone network. The research was conducted in 3 phases viz: Pre-evaluation study to determine the factors militating against malaria prevention, research design and intervention system development and deployment, and evaluation phase to measure the impact of the intervention system using quantitative analysis tool (SPSS). The result of the evaluation study indicated reductions in the rate of malaria cases when compared to the cases recorded during the baseline study, from 96.9% to 68.5%, and ownership of mosquito nets and usage increased from 54% to 85.5% within the intervention group

Cosmology and prime number theory relate through the analysis of metric space in non-decelerative universe model with logarithmic integral function. It has been seen in history that the three components also exhibit relationships among each other, particularly through the zeroes of zeta functions, random matrix theory and energy levels in quantum chaotic systems. How large-scale structure of universe is related to some unknown prime number theories via mathematical tools. With this integration, new vistas are opened for refrigerators stored in mathematics and Cosmology.

This study examines the effects of macroeconomic policies on financial markets using a novel approach that combines Machine Learning (ML) techniques and causal inference. It focuses on the effect of interest rate changes made by the US Federal Reserve System (FRS) on the returns of fixed income and equity funds between January 1986 and December 2021. The analysis makes a distinction between actively and passively managed funds, hypothesizing that the latter are less susceptible to changes in interest rates. The study contrasts gradient boosting and linear regression models using the Double Machine Learning (DML) framework, which supports a variety of statistical learning techniques. Results indicate that gradient boosting is a useful tool for predicting fund returns; for example, a 1% increase in interest rates causes an actively managed fund's return to decrease by-11.97%. This understanding of the relationship between interest rates and fund performance provides opportunities for additional research and insightful, data-driven advice for fund managers and investors.