Pursuing agricultural intensification to raise productivity has brought challenges such as involvement of high capitals, often in the form of loans, environmental damage, and ecosystem disruption. These challenges increase risks in agricultural practice that require good management and control. This increases the need for real-time, non-destructive monitoring technologies that can improve crop productivity, enhance land use, and facilitate environmentally friendly agriculture. Due to its unique capacity to non-destructively examine plants’ internal biological and structural properties, ultrasound has emerged as a promising non-invasive technique providing insights often unattainable with traditional optical, spectral, or chemical sensors. This review aims to provide an up-to-date state of the art in ultrasound-based monitoring applications within major agricultural areas: soil characterization, seed quality control, plant health, stress monitoring, pests and diseases detection, and fruit ripening assessment. This review explores how contact and non-contact ultrasound measurements are scalable and versatile, bridging the gaps between laboratory and field-deployed systems. Integrating ultrasound monitoring with artificial intelligence and Internet of Things (IOT) frameworks further enhances modality accuracy and can detect stress, diseases, and other physiological changes in crops sooner. Overcoming challenges such as environmental acoustic noise will require further work. Still, recent advances such as improved signal filtering algorithms, new transducer designs, better field sensitivity, and broader collaboration to standardize ultrasound measurement protocols indicate a growing trend toward increased on-field use of ultrasound. Finally, the review also discusses the current limitations and future research directions of how ultrasound-based monitoring can catalyse a new paradigm of sustainable data-driven agriculture that meets food security needs.

The rapid integration of large language models (LLMs) into healthcare offers significant potential for improving diagnosis, treatment planning, and patient engagement. However, it also presents serious ethical challenges that remain incompletely addressed. In this review, we analyzed 27 peer-reviewed studies published between 2017 and 2025 across four major open-access databases using strict eligibility criteria, robust synthesis methods, and established guidelines to explicitly examine the ethical aspects of deploying LLMs in clinical settings. We explore four key aspects, including the main ethical issues arising from the use of LLMs in healthcare, the prevalent model architectures employed in ethical analyses, the healthcare application domains that are most frequently scrutinized, and the publication and bibliographic patterns characterizing this literature. Our synthesis reveals that bias and fairness (n=7, 25.9%) are the most frequently discussed concerns, followed by safety, reliability, transparency, accountability, and privacy, and that the GPT family predominates (n=14, 51.8%) among examined models. While privacy protection and bias mitigation received notable attention in the literature, no existing review has systematically addressed the comprehensive ethical issues surrounding LLMs. Most previous studies focus narrowly on specific clinical subdomains and lack a comprehensive methodology. As a systematic mapping of open-access literature, this synthesis identifies dominant ethical patterns, but it is not exhaustive of all ethical work on LLMs in healthcare. We also synthesize identified challenges, outline future research directions and include a provisional ethical integration framework to guide clinicians, developers, and policymakers in the responsible integration of LLMs into clinical workflows.

Healthcare professionals in low- and middle-income countries often deal with persistent stress at work, driven by heavy workloads, irregular shift schedules, and limited institutional support. This cross-sectional study explored levels of stress, emotional well-being, sleep issues, and shift work-related challenges among healthcare workers in Pakistan. Overall, participants reported moderate levels of stress and sleep disturbances. Interestingly, positive emotions outweighed negative ones. Age stood out as the strongest predictor of stress; those in their mid to late careers experienced significantly higher stress levels. While female participants tended to report more stress than males, the difference wasn’t statistically significant. Marital status and exposure to secondhand smoke were linked to higher stress in univariate analyses, and higher body weight showed a slight association in adjusted models. However, factors like shift type and the number of weekly working hours didn’t significantly predict stress. Notably, perceived stress was a strong independent predictor of negative emotional states, even after accounting for past mental health issues and work-related injuries. Age emerged as the strongest predictor of stress, with mid- and late-career professionals reporting significantly higher levels than younger colleagues. These findings emphasize the need for age-sensitive mental health interventions and stress management strategies in healthcare settings.

Introduction: Secondary students often struggle to visualize complex biological structures, leading to low engagement and shallow understanding. These challenges are greater in resource-limited classrooms lacking laboratory equipment or modern teaching aids. To address this, we developed ScienceAR, a curriculum-aligned AR application that transforms textbook diagrams into interactive 3D models. This study evaluates its effectiveness in secondary school biology in Lahore, Pakistan.

Methods: A quasi-experimental design was used with 60 ninth-grade students randomly assigned to an experimental group (n = 30) receiving AR-enhanced instruction or a control group (n = 30) receiving traditional instruction. The seven-day intervention covered challenging biology topics such as human anatomy. Data included pre- and post-tests, student surveys, teacher observations, and student feedback. Post-test scores were analyzed using t-tests and effect size.

Results: The experimental group significantly outperformed the control group (81.0% vs. 76.1%, t(58) = 2.36, p = 0.022, Cohen's d = 0.61). Surveys showed higher ratings for enjoyment, motivation, confidence, and clarity, all above 4.0. Teachers reported greater attentiveness, questioning, and participation in AR lessons.

Discussion: AR improved test performance, engagement, and attitudes toward biology. ScienceAR demonstrates potential as a low-cost, scalable instructional tool for underserved classrooms. Limitations include the short intervention and single-site design. Future research should explore long-term impacts, cross-subject applications, and teacher training for broader implementation.

Large language models (LLMs) are artificial intelligence (AI) based computational models designed to understand and generate human like text. With billions of training parameters, LLMs excel in identifying intricate language patterns, enabling remarkable performance across a variety of natural language processing (NLP) tasks. After the introduction of transformer architectures, they are impacting the industry with their text generation capabilities. LLMs play an innovative role across various industries by automating NLP tasks. In healthcare, they assist in diagnosing diseases, personalizing treatment plans, and managing patient data. LLMs provide predictive maintenance in automotive industry. LLMs provide recommendation systems, and consumer behavior analyzers. LLMs facilitates researchers and offer personalized learning experiences in education. In finance and banking, LLMs are used for fraud detection, customer service automation, and risk management. LLMs are driving significant advancements across the industries by automating tasks, improving accuracy, and providing deeper insights. Despite these advancements, LLMs face challenges such as ethical concerns, biases in training data, and significant computational resource requirements, which must be addressed to ensure impartial and sustainable deployment. This study provides a comprehensive analysis of LLMs, their evolution, and their diverse applications across industries, offering researchers valuable insights into their transformative potential and the accompanying limitations.