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Enhancing automated robotic services efficiency through intelligent dependent robotic computation

This research focuses on enhancing the adaptability and efficacy of service robots in real-time, multi-scenario environments where diverse settings complicate the interpretation of user commands and dynamic environmental fluctuations. It introduces the Commuting Input Valuation Approach (CIVA), which combines transfer learning with a flexible state transfer system to improve robot response rates, minimize unnecessary actions, and enhance learning efficiency across varied environments. The main contributions are (i) a continuous training framework for robots, (ii) a method for sharing information between response and learning stages, (iii) transfer learning approaches suited for both short and long inputs, and (iv) an adaptive reaction rate calculation that accounts for real-time conditions. CIVA was evaluated on 32 interactive tasks using the Daily Interactive Robot Manipulation (DIM) dataset, containing 1,603 dependent and 1,751 independent commands. Performance was assessed relative to baseline robotic models utilizing criteria including task completion duration, input interpreting failure rate, command-to-action efficiency, reaction successful rate, and learning velocity. In a targeted screw-loosening assessment comprising 120 input commands over 70 seconds, CIVA demonstrated a 35% reduction in task completion duration, a 42% decline in interpretation errors, a 28% enhancement in instruction-to-action efficiency, a 45% augmentation in response success rate, and a 50% increase in learning velocity relative to baseline measurements. The findings indicate that CIVA can enhance human-robot interaction and task performance; nevertheless, additional validation is necessary to verify reproducibility and generalization across diverse real-world contexts.

Biomimetic Interface with Dynamic Disulfide Bonds Boosts Durable Photoconversion of Diluted CO ₂

Digital droplet PCR is an accurate and precise method to measure DNA copy number

Correction: The internet of things deployed for occupational health and safety purposes: A qualitative study of opportunities and ethical issues

Bridged-Backbone Strategy Enables Asymmetric Synthesis of Highly Functionalized Oxabicyclo[3.3.1]nonanes with Anti-Osimertinib-Resistant NSCLC Activity

Anti PD-L1 immunotherapy alters macrophage phenotypes via EGR1 and HSP90AB1 supported by integrated methodologies

A hybrid dense convolutional network and fuzzy inference system for pneumonia diagnosis with dynamic symptom tracking

Background Pneumonia is a major cause of mortality among children under five and adults over 65, especially in low-resource settings where access to skilled radiologists is limited. Accurate and early diagnosis is essential, but is often hindered by subjective interpretation and variability in its symptoms. Objectives This study aims to develop a hybrid Artificial Intelligence (AI) based pneumonia diagnosis system that integrates Deep Learning (DL) confidence scores, DenseNet201 with Capsule Network (CapsNet), Mamdani-style fuzzy inference, and a dynamic symptom adjustment mechanism to enhance diagnostic accuracy, transparency, and clinical usability. Methods The system was evaluated using 17,229 labelled chest X-ray images across multiple cross-validation techniques: Stratified, k-fold, Bootstrap, and Monte Carlo methods, each with five dataset iterations or folds. DenseNet was used to extract spatial features, while CapsNet preserved spatial orientation and hierarchical relationships. A DL based confidence score was generated and used as a fuzzy membership input to support classification in borderline cases, where severity scores were nearly tied, and the confidence score guided the final decision. A dynamic adjustment algorithm further refined symptom severity by incorporating recent trends in patient data. Results The DenseNet201 + CapsNet architecture achieved the highest performance in the 5th fold of stratified cross-validation, with a test accuracy of 99.01%. The model also demonstrated strong generalization, with a weighted precision, recall and F1-score of 0.9878, 0.9874, and 0.9876, respectively, across all classes. The paired t-test confirmed that the CapsNet-based approach outperformed traditional fully connected layers, and the fuzzy logic system effectively handled ambiguous cases using DL confidence. The dynamic membership mechanism showed strong adaptability for real-time symptom tracking. Conclusion This hybrid model offers a robust, interpretable, and clinically relevant decision-support tool for pneumonia diagnosis. It bridges high-performance AI with real-world medical decision-making, especially in settings with limited radiological expertise.

An Immunocompatible Conductive Polymer for Long-Term Bioelectronic Implants

In vitro complement activation via nucleocapsid and spike proteins of SARS-CoV-2 in COVID-19 patients

A journey of partnership: Supporting Indigenous science in Western, colonial-grounded academic institutions

Introduction Engagement of Indigenous science (Indigenous research, knowledges, and processes) is increasingly recognized within institutions of higher learning, funding bodies, and publication outlets. Respectful and authentic support for Indigenous science requires transformations of Western, colonial-grounded knowledge and knowledge processes, bodies, and institutions to meaningfully and appropriately include Indigenous ways of knowing, being, and doing. Objective The objective of this study was to identify fundamental changes required to support Indigenous science within Western, colonial-grounded academic institutions focusing on “Identity and Colonial Institutions”. Methods In 2019, a three-day gathering of 18 Indigenous and non-Indigenous researchers and trainees, Elder/knowledge helper/knowledge keepers, and community members was held in Treaty 1 territory and birthplace of the Métis Nation. Through talking circles, participants shared their experiences working with Indigenous communities on projects involving Indigenous knowledges. Results Thematic analysis drew meaning from the talking circles, identifying four main themes: 1) Building Bridges; 2) Institutional Practice; 3) Original Knowledges; and 4) Multifaceted Identity. Focusing on “Identity and Colonial Institutions” stemming from these themes, recommendations for supporting Indigenous science were identified around four central actions: 1) Embedding respectful and authentic support; 2) Acceptance, endorsement, incorporation, and education among the broader research community; 3) Prioritizing and valuing Indigenous research, knowledges, processes, and contributions; and 4) Privileging of multiple worldviews. Conclusions Institutions, funding agencies, journals, and all individuals, organizations, and entities involved in research are encouraged to enact these recommendations and take action to support Indigenous science.

HNO Dimerization as a Chemical Reference Standard for N ₂ O Isotopomer Ratio: Ab Initio Calculations, Formation Kinetics, and Frequency Comb Spectroscopy

Alkali metal substitutional effect on the structural, mechanical, optoelectronic and transport properties of X2LaCuCl6 double perovskites

Hydride Transfer Reactivity of an Open-Shell [Fe ₃ H] ⁻ Cluster

Modeling value integration during decision making under uncertainty with the Florida and Georgia gambling task

Deoxygenative C( <i>sp</i> ³ )–N( <i>sp</i> ³ ) Cross-Coupling Enabled by Nickel Metallaphotoredox Catalysis

Hydrometallurgical extraction of zinc from wastes: optimization, feasibility and practical sustainable approach

Photochemical and Thermal Concerted 1,3-Sigmatropic Rearrangements of a π-Extended Methylenecyclopropane

Unravelling pain in Göttingen Minipigs undergoing experimentally induced closed-chest myocardial infarction: a prospective cohort study

Abstract The pain associated with experimental myocardial infarction in pigs has never been investigated. We aimed at assessing pain and its correlation with myocardial damage. Twenty-four Göttingen minipigs undergoing closed chest myocardial infarction followed by coronary reperfusion under general balanced anaesthesia were included in the trial. Pain was assessed through mechanical and thermal thresholds, sensitivity to Von Frey filaments and behavioural indicators before (Pre MI), the day after (Post MI) and at the study endpoint (Post MI-endpoint). Over time differences in mechanical thresholds (MT) and thermal thresholds (TT) were assessed using one-sample t-test and their correlations with troponin I/cytokines using logistic regression. In four minipigs at Post MI acute pain requiring analgesia was identified. Pain thresholds decreased significantly at Post MI (MT: 51 [35.6; 74] TT: 44.8 [42.7; 48.7]) and Post MI-endpoint (MT: 47.5 [35; 64.3]; TT: 44.3 [43.1; 48.6]) compared to Pre MI (MT: 72 [53.4; 84], TT: 46.3 [43.8; 53.8]). The response to von Frey filaments remained sporadic. Troponin I highly increased at Post MI, but no correlations with pain thresholds were found. Following balanced anaesthesia, acute pain had low incidence and mild to moderate intensity. Somatic hyperalgesia remained until the study endpoint, but its relevance remains to be unravelled.

<i>In Situ</i> Probing Two Radicals in Halogenation Couplings from Photoanodic Single-Atom Catalysis

A new hybrid neural network framework inspired by biological systems for advanced financial forecasting