Knee osteoarthritis presents a significant health challenge for many adults globally. At present, there are no pharmacological treatments that can cure this medical condition. The primary method for managing the progress of knee osteoarthritis is through early identification. Currently, X-ray imaging serves as a key modality for predicting the onset of osteoarthritis. Nevertheless, the traditional manual interpretation of X-rays is susceptible to inaccuracies, largely due to the varying levels of expertise among radiologists. In this paper, we propose a multimodal model based on pre-trained vision and language models for the identification of the knee osteoarthritis severity Kellgren-Lawrence (KL) grading. Using Vision transformer and Pre-training of deep bidirectional transformers for language understanding (BERT) for images and texts embeddings extraction helps Transformer encoders extracts more distinctive hidden-states that facilitates the learning process of the neural network classifier. The multimodal model was trained and tested on the OAI dataset, and the results showed remarkable performance compared to the related works. Experimentally, the evaluation of the model on the test set comprising X-ray images demonstrated an overall accuracy of 82.85%, alongside a precision of 84.54% and a recall of 82.89%.

It is difficult to find optimal solutions to multi-objective optimization problems, because they involve balancing conflicting objectives under complicated constraints. Metaheuristics are widely applied for solving problems, since they are easy to apply and may produce promising near-optimum solutions. However, achieving an optimal balance between exploration and exploitation still remains a key challenge, especially in high-dimensional and constrained design spaces. This research work presents MOGWO2Arc, a new multi-objective optimizer utilizing a dual-archive strategy to promote solution diversity and convergence. Eight benchmark truss structure optimization problems were used to test the suggested algorithm with the objectives of minimizing weight and compliance subject to safe stress levels. Other constraints, such as dimensional constraints, standard safety codes, and unique cross-sectional domains, were also considered. The hypervolume, generational distance, inverted generational distance, and spacing were used as the key parameters to compare performance with eight state-of-the-art optimization algorithms. Based on the outcome of the Friedman rank test, MOGWO2Arc outperformed the competing approaches, especially when utilized to solve large-scale structural optimization problems, producing higher-quality solutions at significantly lower computational costs. MOGWO2Arc also provides a powerful and efficient way to solve complex multi-objective structure optimization problems by promoting exploration and diversifying Pareto-optimal solutions across decision and objective spaces.

Background: Large language models (LLMs) have seen a significant boost recently in the field of natural language processing (NLP) due to their capabilities in analyzing words. These autoregressive models prove robust in classification tasks where texts need to be analyzed and classified. Objectives: In this paper, we explore the power of base LLMs such as Generative Pre-trained Transformer 2 (GPT-2), Bidirectional Encoder Representations from Transformers (BERT), Distill-BERT, and TinyBERT in diagnosing acute inflammations of the urinary bladder and nephritis of the renal pelvis. Materials and Methods: the LLMs were trained and tested using supervised fine-tuning (SFT) on a dataset of 120 examples that include symptoms that may indicate the occurrence of these two conditions. Results: By employing a supervised fine-tuning method and carefully crafted prompts to present the data, we demonstrate the feasibility of using minimal training data to achieve a reasonable diagnostic, with overall testing accuracies of 100%, 100%, 94%, and 79%, for GPT-2, BERT, Distill-BERT, and TinyBERT, respectively.

Wireless sensor networks (WSNs) are fundamental to the Internet of Things (IoT) and are widely used in environmental, industrial, and healthcare applications. However, their operational lifetime is constrained by the limited energy resources of sensor nodes. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol reduces energy consumption through clustering but suffers from random cluster head (CH) selection, leading to uneven energy usage and reduced stability. This study introduces a hybrid optimization approach, LEACH-CSA, which integrates the Crow Search Algorithm (CSA) with LEACH to enhance CH selection and positioning. The proposed method employs CSA’s intelligent search behavior to minimize intra-cluster distances and balance energy consumption across nodes. MATLAB simulations with 100 sensor nodes in a 100 × 100 m² area demonstrate that LEACH-CSA significantly reduces energy consumption and extends network lifetime compared with LEACH and its variants. Furthermore, CSA parameters were optimized using a progressive randomized tuning strategy with 1000, 2000, and 4000 candidate configurations. A comparative evaluation against LEACH-based GA, PSO, GWO, and WOA demonstrated that LEACH-CSA consistently improved the FND metric under different node density and area-scaling scenarios.

X-means and k-means are clustering algorithms proposed as a solution for prolonging wireless sensor networks (WSN) lifetime. In general, X-means overcomes k-means limitations such as predetermined number of clusters. The main concept of X-means is to create a network with basic clusters called parents and then generate (j) number of children clusters by parents splitting. X-means did not provide any criteria for splitting parent’s clusters, nor does it provide a method to determine the acceptable number of children. This article proposes fitness function X-means (FFX-means) as an enhancement of X-means; FFX-means has a new method that determines if the parent clusters are worth splitting or not based on predefined network criteria, and later on it determines the number of children. Furthermore, FFX-means proposes a new cluster-heads selection method, where the cluster-head is selected based on the remaining energy of the node and the intra-cluster distance. The simulation results show that FFX-means extend network lifetime by 11.5% over X-means and 75.34% over k-means. Furthermore, the results show that FFX-means balance the node’s energy consumption, and nearly all nodes depleted their energy within an acceptable range of simulation rounds.