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A hybrid pythagorean fuzzy decision-making model for evaluating corporate environmental behavior through social responsibility dimensions
Commercial cannabis product testing: Fidelity to labels and regulations
Background In Colorado, regulations for recreational and medical cannabis sales require tetrahydrocannabinol (THC) concentration is printed on all products. Labeled THC concentrations can vary by +/-15% of what is in the product. Studies show THC concentrations recorded on product labels are not always reflective of the THC concentration in the cannabis product and there is evidence consumers make purchasing decisions based on label claims. Aims Explore the accuracy of cannabis product labels and differences between THC label accuracy and product type. Design Data for this analysis come from a larger observational study of cannabis impairment. N = 74 flower, concentrate, and edible product samples from licensed Colorado dispensaries were collected and independently tested for THC concentration. Setting This study was conducted in Colorado, in the Denver Metro Area. Participants Participants in the study voluntarily enrolled and provided one-gram samples of the cannabis they consumed during the study to be independently tested. The cannabis tested for this analysis was donated on a voluntary basis, not all participants chose to donate. Measurement The main outcomes of interest for this analysis are accuracy of cannabis product labels compared to observed THC content, accuracy in the context of legally allowable variation, and difference between accuracy by product. Findings Overall, label values were higher than observed values in flower and edible products (p < 0.001) but was not significant for concentrates (p = 0.85). Flower products were observed to be significantly lower on labels versus the 15% legally allowable range (p = 0.04). Concentrate and edible products were not significantly different (p = 0.9 and p = 0.5, respectively). Conclusions There is tension between legally allowable THC concentration claims on cannabis product labels and how consumers purchase cannabis. As cannabis policy evolves, standards and regulations that ensure accurate THC concentrations are reported on product labels are urgently needed.
Dust dispersion and prevention in deep concave open-pit coal mines
Heat-actuated valve implementation in a point-of-care, paper-based microfluidic device for infectious disease detection
Paper-based microfluid devices have great potential to perform complex assays like nucleic acid amplification tests (NAATs) in point-of-care (POC) and low resource settings, but have not often succeeded because there is frequently need for affordable and simple solutions to address complex fluidics and sample delivery without risk of contamination. These issues are exacerbated when multiple sample processing procedures must be automated by valve integration in the absence of advanced supportive instrumentation or trained personnel. For NAAT application in remote or home environments, valving within a POC device must facilitate precise fluidic movement and physically separate each chemical processing step. Ideally, the valve should also be affordable, easy to manufacture, robust in varying climate environments, reliable when stored under ambient conditions, affordable, and non-reactive with chemical reagents or samples. Here, we report the design and integration of two adaptable thermally-actuated valves for application in a point-of-care, paper-based rapid analysis device. Both valves are designed to facilitate the fluidic movement and control necessary to enable reverse-transcriptase loop-mediated isothermal amplification (RT-LAMP) reactions in a paper-based device. Each valve could be used independently or alongside the other valve to control fluidic movement at specific locations along the paper fluidic pathway. The valves differ in their material composition, fabrication, and mechanisms for fluidic control, but both are affordable, durable, and simple to use. We demonstrated that each valve could be used to enable RT-LAMP reactions in a paper-based device for detecting either RNA or DNA biomarkers for respiratory infectious diseases (COVID and Flu A) from human nasal swabs.
AI-enabled privacy-preserving cardiac diagnostics via electrocardiograms
Correction: Population pharmacokinetics model of pyrazinamide to optimize tuberculosis treatment: An interethnic cohort study of diabetes mellitus effect on drug exposure
Short-term exposure to ambient air pollution and emergency ambulance dispatches for precipitous labor: a time-series study in Guangzhou, China
Real-time P$$_2$$O$$_5$$ quantification in industrial phosphoric acid using laser-induced breakdown spectroscopy: a paradigm shift in process analytical chemistry
Early detection of oral squamous cell carcinoma using five tumor-associated autoantibodies and a Naive Bayes-based machine learning model
RAGE signaling inhibition prevents the malignant phenotypes of pancreatic ductal adenocarcinoma cells
Spatio-temporal profiling of coastal hazard / vulnerability assessment along the Gujarat coast, India, over a period of 35 years
Image type reveals evolutionarily shaped perceptual and conceptual mechanisms of pareidolia
Abstract Pareidolia—the perception of illusory patterns such as faces or objects in ambiguous stimuli—has been extensively studied, but less is known about how different visual environments shape this phenomenon. This study investigated pareidolia experienced in unmodified natural images (NI) and white noise (WN) images in 81 healthy participants under open-ended instructions. Across both conditions, animals were the most commonly perceived pareidolia, consistent with evolutionary pressures favouring rapid detection of biologically relevant stimuli. Importantly, the semantic categories of pareidolia differed by image type: NI evoked significantly more “Natural World” pareidolia, while WN images primarily elicited “Human-Created” pareidolia. This dissociation suggests that NI stimuli engage bottom-up perceptual organization processes whereas WN images depend more heavily on top-down semantic knowledge and mental imagery. Together, these findings indicate that stimulus type modulates the relative contribution of perceptual and conceptual processes in the resolution of visual ambiguity. Beyond their methodological implications for pareidolia research, these results carry broader relevance for understanding creative perception, hallucinations, and cognitive biases that may have been shaped by evolutionary pressures. They also underscore the need for careful and deliberate selection of visual stimuli when employing open-ended tasks in future studies.
Isolation of an Americium Complex Containing a Radical Ligand
FNDC1 is closely related to poor prognosis and immune cell infiltration in gastric cancer
Machine learning long-term electricity demand forecasting system for strategic energy investments
Abstract The Energy Institute’s 2025 report revealed that the global electricity consumption is rising by 4% annually, with regional variances driven by complex economic, social and climatic factors. Given the critical role of long-term consumption forecasting in informing energy investment decisions and ensuring the resilience of national power infrastructure against future demand fluctuation risks, the development of demand prediction systems that are both accurate and robust is imperative. Current univariate—assuming stable demand patterns, and multivariate models—constrained by narrow feature sets, possess a limited ability to capture cross-domain interactions among economic, social and climatic factors. To address these gaps, this study introduces a forecasting system that encompasses three-phases: i ) Data Acquisition and Structuring; ii ) Predictive Modeling and Optimization; and iii ) Model Evaluation and Analysis. The developed system integrates multi-domain predictors, including electricity generation, as well as economic, social and climatic features, while also leveraging deep learning and SHAP-based interpretability to ensure accurate and robust predictions and provide insights into feature contributions. Deployed on Egypt using data from 2000 to 2023, the system results indicate that population and GDP per capita are the primary drivers of electricity demands, with generation capacity and external debt exerting secondary influences. The system model achieved a mean R²=0.83 across multiple random weight initializations and a root mean square error of 4.1% of the mean testing value, demonstrating its reliability for long-term scenario-based planning, strategic investment, and energy security policy formulation.