Browse Articles

Direct experimental constraints on the spatial extent of a neutrino wavepacket

Investigation on the formation of two dimensional perovskite nanostructures at the water surface through self initiated reaction

Ancient human genomes offer clues about the earliest migrations out of Africa

Numerical simulation on the pressure distribution of hydraulic jet perforation tunnel in natural gas hydrate reservoirs

Transylvanian diaries reveal centuries-old climate extremes

Analyzing patient perspectives with large language models: a cross-sectional study of sentiment and thematic classification on exception from informed consent

Abstract Large language models (LLMs) can improve text analysis efficiency in healthcare. This study explores the application of LLMs to analyze patient perspectives within the exception from informed consent (EFIC) process, which waives consent in emergency research. Our objective is to assess whether LLMs can analyze patient perspectives in EFIC interviews with performance comparable to human reviewers. We analyzed 102 EFIC community interviews from 9 sites, each with 46 questions, as part of the Pediatric Dose Optimization for Seizures in Emergency Medical Services study. We evaluated 5 LLMs, including GPT-4, to assess sentiment polarity on a 5-point scale and classify responses into predefined thematic classes. Three human reviewers conducted parallel analyses, with agreement measured by Cohen’s Kappa and classification accuracy. Polarity scores between LLM and human reviewers showed substantial agreement (Cohen’s kappa: 0.69, 95% CI 0.61–0.76), with major discrepancies in only 4.7% of responses. LLM achieved high thematic classification accuracy (0.868, 95% CI 0.853–0.881), comparable to inter-rater agreement among human reviewers (0.867, 95% CI 0.836–0.901). LLMs enabled large-scale visual analysis, comparing response statistics across sites, questions, and classes. LLMs efficiently analyzed patient perspectives in EFIC interviews, showing substantial sentiment assessment and thematic classification performance. However, occasional underperformance suggests LLMs should complement, not replace, human judgment. Future work should evaluate LLM integration in EFIC to enhance efficiency, reduce subjectivity, and support accurate patient perspective analysis.

Retraction Note: A mechanism for the suppression of homologous recombination in G1 cells

Relationship between vascular aging and left ventricular geometry in patients with obstructive sleep apnea hypopnea syndrome-related hypertension

Decoupling and peak prediction of industrial land carbon emissions in East China for developing countries’ prosperous regions

Optimization of ultrasound-assisted deep eutectic solvents extraction of rutin from Ilex asprella using response surface methodology

Application of deep learning for fruit defect recognition in Psidium guajava L

Beyond boundaries a hybrid cellular potts and particle swarm optimization model for energy and latency optimization in edge computing

Abstract The need to compute data in real-time and manage resources in environments with distributed computing has given edge computing significant importance. However, one of the most critical tasks regarding resources has been to schedule and optimize them in accordance with energy consumption and delay time. These challenges has been addressed in this paper with the introduction of a new integrated method that assumes the Cellular Potts Model and Particle Swarm Optimization. The Cellular Potts Model is used to capture local interaction and dependencies of resources, while PSO acts as a global optimizer for scheduling reducing latency and energy consumption. Based on these considerations, the primary research goal of this work is to mitigate the QoS requirements like energy consumption and end-to-end delay using CPM—spatial modeling complemented by PSO - the global optimization. Based on experimental analysis, the authors of the paper argue that the newly proposed Hybrid model consumes less energy and has less processing time than Round-Robin, Random Offloading, and Threshold-Based techniques. In addition, the approach achieves higher scalability and can perform a large of tasks and edge nodes with a high QoS while working in a resource-limited environment. This paper contributes to presenting the integration procedure of the CPM’s local optimization with the PSO’s global search, which offers high-performance and real-time solutions for resource scheduling in the edge computing environment. The results presented in the paper show that the proposed hybrid CPM-PSO model can offer greater potential as a tool for energy-constrained and time-sensitive applications within the future development of edge computing.

Multimorbidity, medications, and their association with falls, physical activity, and cognitive functions in older adults: multicenter study in Sri Lanka

External Li supply reshapes Li deficiency and lifetime limit of batteries

Remote sensing estimation of aboveground biomass of different forest types in Xinjiang based on machine learning

Aqueous-based recycling of perovskite photovoltaics

Abstract Cumulative silicon photovoltaic (PV) waste highlights the importance of considering waste recycling before the commercialization of emerging PV technologies1,2. Perovskite PVs are a promising next-generation technology3, in which recycling their end-of-life waste can reduce the toxic waste and retain resources4,5. Here we report a low-cost, green-solvent-based holistic recycling strategy to restore all valuable components from perovskite PV waste. We develop an efficient aqueous-based perovskite recycling approach that can also rejuvenate degraded perovskites. We further extend the scope of recycling to charge-transport layers, substrates, cover glasses and metal electrodes. After repeated degradation–recycling processes, the recycled devices show similar efficiency and stability compared with the fresh devices. Our holistic recycling strategy reduces by 96.6% resource depletion and by 68.8% human toxicity (cancer effects) impacts associated with perovskite PVs compared with the landfill treatment. With recycling, the levelized cost of electricity also decreases for both utility-scale and residential systems. This study highlights unique opportunities of perovskite PVs for holistic recycling and paves the way for a sustainable perovskite solar economy.

Design of a tunable multichannel terahertz absorber in one-dimensional photonic crystals incorporating a Dirac semimetal-dielectric defect layer

Understanding key population drivers of the spotted wing Drosophila in cultivated and natural areas in the Andes

The structure of bad cholesterol comes into focus

Design of parallel cascade controller for nonlinear continuous stirred tank reactor

Abstract This work presents an approach to control the temperature of a nonlinear continuous stirred tank reactor (NCSTR) through parallel cascade control structure (PCCS). For the first time, PCCS is used to control the temperature of NCSTR by (1) modelling the dynamic behavior of CSTR with a recirculating jacket heat transfer system into a third order unstable transfer function and (2) using the model matching technique to synthesize the controller parameters. The controller of the secondary loop of PCCS is designed to achieve enhanced regulatory performance whereas, the primary loop controller is designed for better setpoint tracking. The closed loop performance of the proposed method is evaluated by carrying out simulation on the differential equation of the NCSTR and comparing it with other structures such as series cascade control structure (CCS) and parallel control structure (PCS). The response shows that the proposed method provides satisfactory performance in nominal, perturbed and noisy conditions.