Antibiotic Prescribing Practices in a Tertiary Care Teaching Hospital: A Retrospective Cross-Sectional Analysis.

Background Antibiotics are among the most frequently prescribed medications in hospitals, yet a significant portion of their use is inappropriate, contributing to the growing global threat of antimicrobial resistance (AMR). In Pakistan, AMR has reached alarming levels with the rise of multi- and extensively drug-resistant bacteria. This study evaluates antibiotic prescribing practices and the use of culture sensitivity (CS) testing to assess the appropriateness of antibiotic therapy.

Unpacking COVID-19 Vaccine Attitudes: Exploring Hesitancy and Acceptance Among Undergraduate Students in Bangladesh.

Background: Vaccine hesitancy is a significant global health concern, and mass vaccination is essential in preventing the spread of COVID-19. Undergraduate students need to be prioritized for vaccination as they continue their academic curriculum physically. However, limited research explores vaccine hesitancy and acceptance among undergraduate students in Bangladesh.

Approximating Functions with Approximate Privacy for Applications in Signal Estimation and Learning.

Large corporations, government entities and institutions such as hospitals and census bureaus routinely collect our personal and sensitive information for providing services. A key technological challenge is designing algorithms for these services that provide useful results, while simultaneously maintaining the privacy of the individuals whose data are being shared. Differential privacy (DP) is a cryptographically motivated and mathematically rigorous approach for addressing this challenge.

Multi-Task Partial Offloading with Relay and Adaptive Bandwidth Allocation for the MEC-Assisted IoT.

The fifth-generation (5G) wireless network is visualized to offer many types of services with low latency requirements in Internet of Things (IoT) networks. However, the computational capabilities of IoT nodes are not enough to process complex tasks in real time. To solve this problem, multi-access edge computing (MEC) has emerged as an effective solution that will allow IoT nodes to completely or partially offload their computational tasks to MEC servers.

A Correlated Noise-assisted Decentralized Differentially Private Estimation Protocol, and its application to fMRI Source Separation.

Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. To leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses that pool the data at one site.

Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization.

In many signal processing and machine learning applications, datasets containing private information are held at different locations, requiring the development of distributed privacy-preserving algorithms. Tensor and matrix factorizations are key components of many processing pipelines. In the distributed setting, differentially private algorithms suffer because they introduce noise to guarantee privacy.