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.
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. This study reports 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, and the average marginal component effect of each signal on the probability of recommendation is estimated. 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, while eco-certification ranks higher than the published human work places it, management response is ignored, and the expected brand premium reverses in favour of independent properties. List position, a content-free artifact, shifts recommendations causally and is worth about 12 per night pooled across the panel, an average that conceals wide variation, since most models are close to position-neutral while one is an order of magnitude more order-sensitive. Stated reasons track revealed weights imperfectly, omitting the position and volume effects the models demonstrably act on. Theoretically, the study extends signaling theory to a receiver that bears none of the risk the signals were designed to resolve, and shows that machine intermediation reprices reputation signals according to their textual salience rather than their behavioural value. Practically, it supplies a causal priority list for generative engine optimization and a price-equivalent metric that places reputation investments and platform-controlled placement on a single managerial scale. Because the estimates describe the audited model versions at one point in time, they are offered as a baseline against which drift can be measured rather than as a standing property of these systems.
Newcastle disease affects many avian species, predominantly chickens. It is known to cause a substantial economic burden to the poultry industry. The velogenic strain of Newcastle disease virus (NDV) can cause mortality up to 100 %. Real-time PCR and ELISA are currently used to diagnose NDV; however, these techniques have some limitations. To address the limitations, we developed an aptamer-probed paper-based analytical device (ePAD) to detect NDV. The NDV HN protein-specific aptamer was identified following 10 rounds of SELEX. The identified aptamer was investigated for its binding and stability with the HN protein with the help of docking, molecular dynamics simulations, and CD spectroscopy. ZnO nanorods were used to enhance the conductivity of the fabricated ePAD aptasensor. The aptamer was then immobilized on the working electrode to detect NDV. Various concentrations of NDV were used to optimize the fabricated aptasensor. All the electrochemical changes were analysed with the help of cyclic voltammetry and impedance. Our data showed the detection of a very low concentration of NDV using the designed ePAD biosensor. The specificity of the aptasensor was studied using a non-related virus from the samples as a negative control. The developed aptasensor could offer a cost-effective, user-friendly, and specific alternative to detect NDV from clinical samples.
