Hamdard University (Urdu: جامعہَ ہمدرد) is a private research university with campuses in Karachi and Islamabad, Pakistan. It was founded in 1991 by the renowned philanthropist Hakim Said of the Hamdard Foundation. Hamdard is one of the first and the oldest private institutions of higher education in Pakistan. In Karachi, Hamdard University is the largest private research university with a campus area of over 350 acres.Hamdard University's central library Bait-ul-Hikmah is one of the largest research libraries in South Asia with a collection of over half a million books, some of them dating back to the 17th century. The university includes eight faculties, nine research institutes, three teaching hospitals and three affiliated engineering institutes. There are more than 15,000 alumni of Hamdard University employed in organisations in Pakistan and worldwide.
Background and Aim Tuberculosis (TB), caused by Mycobacterium tuberculosis , continues to present a major health challenge, particularly in low- and middle-income countries with constrained diagnostic resources. Although chest radiography remains a standard screening method, the shortage of trained radiologists and variability in image interpretation hinder early and reliable detection. This study aimed to develop a lightweight convolutional neural network (CNN), trained from scratch with Swish activation, for accurate and efficient TB detection from chest radiographs. Patients and Methods A heterogeneous dataset was created by aggregating multiple publicly available repositories, including Montgomery, Shenzhen, TB Base, and TBX11K. Images were standardized through grayscale conversion, resizing, and normalization, and subsequently partitioned into stratified training, validation, and test sets. The proposed CNN consisted of multiple convolutional layers with Swish activation and max pooling, followed by dense layers with dropout regularization. Model optimization was performed using the Adam optimizer, and performance was evaluated using accuracy, precision, recall, and F1-score metrics. External validation was conducted using unseen and independent datasets to assess generalizability. Results The model achieved an overall accuracy of 92%, with precision and recall values exceeding 0.90 and an F1-score of 0.92. External validation confirmed the model's generalizability, with inference times averaging 30-40 ms (specifically utilizing the Intel i5 CPU for inference) per image on consumer-grade hardware, demonstrating computational efficiency. Conclusions The findings demonstrate that a lightweight, scratch-trained CNN can provide accurate and computationally efficient TB detection from chest radiographs. The deliberate choice of dataset heterogeneity and lightweight architecture allows strong diagnostic performance without reliance on high-end hardware, making the system suitable for TB screening programs in resource-limited healthcare environments.
The study of soliton theory is crucial for understanding nonlinear phenomena in nonlinear science and engineering. This work analyzes bifurcation analysis and soliton solutions analytically and numerically. The study investigates a beta-fractional Lonngren-wave model to understand nonlinear electrical signal behavior in telegraph lines containing tunnel diodes. Initially, a new technique known as bifurcation analysis is applied to study critical points or phase portraits, where systems transition to novel behaviors, such as stability shifts or the emergence of chaos. Then, the chaotic nature of the dynamical system is analyzed by adding a trigonometric perturbation term. The shockwave, quasi-periodic wave, super-periodic wave, and sensitivity analysis are illustrated through 3D and 2D phase portraits with initial conditions. Finally, the modified F-expansion technique is also employed to analytically obtain soliton solutions of the beta fractional Lonngren-wave model. The solutions are presented using transcendental functions under specific conditions, and their physical interpretation, including bright bell, dark bell shape soliton solutions, bright and dark periodic soliton solutions, periodic lump wave solution, and linked lump wave solutions for several free parameters. It also demonstrates the impact of beta fractional parameters on these solutions. Bifurcation and chaotic nature analysis, and soliton solutions through the modified F-expansion technique are explored in this work for the first time. The findings provide insight into the physical characteristics of waves propagating in a dispersive medium, potentially enhancing our understanding of these phenomena.
Vestibular compensation (VC) represents a remarkable aspect of neuroplasticity, showcasing the brain’s ability to adapt to disruptions in balance and spatial orientation caused by various vestibular disorders. This extended review provides a comprehensive overview of the mechanisms underlying VC, focusing on the distinct challenges posed by unilateral and bilateral vestibular disease. By examining the pathophysiological processes associated with these conditions, we gain critical insights into how the central nervous system employs adaptive strategies to restore functional balance. Additionally, this review underlines the multifaceted nature of VC, which emphasizes the necessity for personalized approaches in treatment, as not all patients will respond similarly to therapeutic interventions. Advancing our understanding of VC enriches the field of neurorehabilitation and holds significant promise for improving the quality of life for patients affected by vestibular disorders. By continuing to explore the intricate mechanisms of compensation and the factors that influence recovery, we can enhance our approaches to diagnosis, treatment, and rehabilitation. This will ultimately lead to better patient outcomes and a deeper comprehension of the brain’s remarkable adaptability in the face of vestibular challenges. The journey toward improved care for individuals with vestibular disorders is ongoing, and it is imperative that we remain committed to research, education, and innovation in this vital area of medical science.
Travelers increasingly ask large language model (LLM) assistants which hotel to book, making these systems gatekeepers of property visibility -- yet what moves their recommendations is undocumented. We conduct a pre-specified algorithm audit using a randomized choice-based conjoint: across personas, prompt templates, and twelve open-weight and proprietary models, assistants choose among five hotels whose guest rating, review volume and recency, management response, chain affiliation, price, eco-certification, and list position are independently randomized. We estimate the average marginal component effect of each signal on the probability of recommendation. Guest rating and price dominate (a top rating raises selection by 31.6 percentage points; a high price lowers it by 30.0), reproducing human valence-and-price primacy but over-weighting eco-certification and ignoring management response. List position -- a content-free artifact -- shifts recommendations causally, worth about \$12 per night. Stated reasons track revealed weights imperfectly. The findings ground generative engine optimization and the accountability of AI infomediaries in causal evidence.
Discovering compact governing equations from experimental observations is one of the defining objectives of quantitative science, yet practical discovery pipelines routinely fail when measurements are noisy, relevant state variables are unobserved, or multiple symbolic structures explain the data equally well within statistical uncertainty. Here we introduce SymLang (Symmetry-constrained Language-guided equation discovery), a unified framework that brings together three previously separate ideas: (i) typed symmetry-constrained grammars that encode dimensional analysis, group-theoretic invariance, and parity constraints as hard production rules, eliminating on average 71.3
