Search Results (164)

Decontamination robots are becoming more common in environments where reducing human exposure to hazardous substances is essential, including healthcare settings, laboratories, and industrial cleanrooms. Designing terrain-capable decontamination robots quickly is challenging due to varying operational surfaces and mobility limitations. To tackle this issue, a structured recommendation framework is proposed to automate selecting optimal locomotion types and track configurations, significantly cutting down design time. The proposed system features a two-stage evaluation process: first, it creates an annotated compatibility score matrix by validating locomotion types against a robust dataset based on factors like friction coefficient, roughness, payload capacity, and slope gradient; second, it employs a weighted scoring model to rank wheel/track types based on their appropriateness for the identified environmental conditions. User needs are processed dynamically using a large language model, enabling flexible and scalable management of various deployment scenarios. A prototype decontamination robot was developed following the proposed algorithm’s guidance. This framework speeds up the configuration process and establishes a foundation for more intelligent, terrain-aware robot design workflows that can be applied to industrial, healthcare, and service robotics sectors. Full article

With rising multidrug-resistant yeast pathogens, conventional antifungals are becoming less effective, urging the need for adjuvants that enhance their activity at lower doses. This study evaluated the synergistic activity of antimicrobial peptidomimetics (TM8 and RK758) or colistin sulphate in combination with conventional antifungals against Candida albicans, C. tropicalis, C. parapsilosis, Meyerozyma guilliermondii, Nakaseomyces glabratus, Pichia kudriavzevii and Kluyveromyces marxianus, and Candidozyma auris using the checkerboard microdilution test. RK758 was synergistic with fluconazole in 78% of isolates, with the remaining 22% of isolates still showing partial synergy; it showed synergy with amphotericin B in 56% of isolates, and with caspofungin, 78% of isolates exhibited either synergy or partial synergy. TM8 showed synergy with fluconazole in 44% (with partial synergy in another 44%) of isolates, with amphotericin B in 67% of isolates, and with caspofungin in 44% (with partial synergy in another 44%) of isolates. Colistin with fluconazole or caspofungin exhibited synergy or partial synergy in 56% of the isolates. No antagonism was observed in any of the combinations. Additionally, a time-kill assay further demonstrated synergistic activity between fluconazole and TM8 or RK758. The effects of these peptidomimetics on cell membrane integrity were demonstrated in an ergosterol binding assay, supported by SYTOX Green and cellular leakage assays, both indicating a lytic effect. These results suggest that peptidomimetics can synergise with conventional antifungals, offering a potential strategy for combination therapy against yeast infections. The membrane lytic activity of the peptidomimetics likely plays a role in their synergistic interaction with antifungals, thereby enhancing the antimicrobial activities of both compounds at sub-MIC levels. Full article

Nocturnal monitoring of continuous, cuffless blood pressure (BP) can unleash a whole new world for the prognostication of cardiovascular and other diseases due to its strong predictive capability. Nevertheless, the lack of an accurate and reliable method, primarily due to confounding variables, has prevented its widespread clinical adoption. Herein, we demonstrate how optimized machine learning using the Catch-22 features, when applied to the photoplethysmogram waveform and personalized with direct BP data through transfer learning, can accurately estimate systolic and diastolic BP. After training with a hemodynamically compromised VitalDB “calibration-free” dataset (n = 1293), the systolic and diastolic BP tested on a distinct VitalDB dataset that met AAMI criteria (n = 116) had acceptable error biases of −1.85 mm Hg and 0.11 mm Hg, respectively [within the 5 mm Hg IEC/ANSI/AAMI 80601-2-30, 2018 standard], but standard deviation (SD) errors of 19.55 mm Hg and 11.55 mm Hg, respectively [exceeding the stipulated 8 mm Hg limit]. However, personalization using an initial calibration data segment and subsequent use of transfer learning to fine-tune the pretrained model produced acceptable mean (−1.31 mm Hg and 0.10 mm Hg) and SD (7.91 mm Hg and 4.59 mm Hg) errors for systolic and diastolic BP, respectively. Levene’s test for variance found that the personalization method significantly outperformed (p < 0.05) the calibration-free method, but there was no difference between three machine learning methods. Optimized multimodal Catch-22 features, coupled with personalization, demonstrate great promise in the clinical adoption of continuous, cuffless blood pressure estimation in applications such as nocturnal BP monitoring. Full article

This study introduces the concept of an intuitionistic fuzzy SBCK-subalgebra (SBCK-ideal) and explores the level set of an intuitionistic fuzzy set within the context of Sheffer stroke BCK-algebras. These newly defined concepts are crucial for understanding the interaction between intuitionistic logic and Sheffer stroke BCK-algebras. The paper establishes a connection between subalgebras and level sets in the framework of Sheffer stroke BCK-algebras, demonstrating that the level set of intuitionistic fuzzy SBCK-subalgebras corresponds precisely to their subalgebras, and conversely. Additionally, the study provides novel results regarding the structural properties of Sheffer stroke BCK-algebras under intuitionistic fuzzy logic, specifically focusing on the conditions under which fuzzy sets become SBCK-subalgebras or SBCK-ideals. This work contributes to the theoretical foundations of fuzzy logic in algebraic structures, offering a deeper understanding of the interplay between intuitionistic fuzzy sets and the algebraic operations within Sheffer stroke BCK-algebras. Full article

In this study, we examine bipolar fuzzy SBCK-subalgebras and their corresponding level sets of bipolar fuzzy sets in the setting of Sheffer stroke BCK-algebras. These concepts contribute significantly to the analysis of bipolar logical structures within this algebraic context. We demonstrate a bidirectional relationship between SBCK-subalgebras and their level sets, proving that each level set derived from a bipolar fuzzy SBCK-subalgebra constitutes a subalgebra, and, conversely, each such subalgebra defines an associated level set. This duality emphasizes the structural interplay between bipolar fuzzy logic and the Sheffer stroke operation in BCK-algebras. Full article

In this paper, we explore the application of fuzzy set theory in the context of triangular norms, with a focus on strong Sheffer stroke NMV-algebras. We introduce the concepts of $\mathfrak{T}$-fuzzy subalgebras and $\mathfrak{T}$-fuzzy filters, analyze their properties, and provide several illustrative examples. Our study demonstrates that $\mathfrak{T}$-fuzzy subalgebras and filters generalize classical subalgebras and filters, with level subsets preserving algebraic structures under t-norms. Notably, $\mathfrak{T}$-fuzzy sets exhibit strong closure properties, and homomorphisms between SSNMV-algebras extend naturally to fuzzy settings. Furthermore, we examine the relationships between $\mathfrak{T}$-fuzzy subalgebras (or filters) and their classical counterparts, as well as their corresponding level subsets and homomorphisms. These results contribute to refined uncertainty modeling in logical systems, with potential applications in fuzzy control and AI. Full article

Background/Objectives: As with many viral fusion proteins, the native conformation of SARS-CoV-2 Spike is metastable. Most COVID-19 vaccines utilize a stabilized Spike (Spike-2P) containing two proline substitutions, and subsequently, a further stabilized variant with four additional proline substitutions, Spike-6P, has been developed. In an alternative approach, we introduced two aspartic acid residues (2D) in the HR1 region of Spike at positions that are exposed and buried in the pre- and postfusion states, respectively, to destabilize the postfusion conformation. Methods: The recombinant protein constructs were expressed in a mammalian cell culture and characterized for their yield and antigenicity, and the formulations were then used to immunize hamsters. After two immunizations, the hamsters were challenged with live B.1.351 SARS-CoV-2 virus for an evaluation of the protective efficacy. Results: The introduction of the two aspartic acid mutations resulted in an approximately six-fold increase in expression, comparable to that in Spike-2P. When the 2D mutations were combined with the above four proline mutations (Spike-4P-2D), this led to a further three- to four-fold enhancement of protein expression, similar to that seen in Spike-6P. When formulated with the oil-in-water emulsion adjuvant Sepivac SWE, the 2P, 2D, 6P, and 4P-2D Spike variants all protected female hamsters against heterologous challenge with the B.1.351 SARS-CoV-2 virus and elicited high titers of neutralizing antibodies. Conclusions: We suggest that destabilization of the postfusion conformation through the introduction of charged amino acids at sites that are exposed in the pre- and buried in the postfusion conformation offers a general strategy to enhance the yield and stability of the native, prefusion conformation of viral surface proteins. Full article

Fumigation effectively manages pests, yet manual spraying poses long-term health risks to operators, making autonomous fumigation robots safer and more efficient. Path planning is a crucial aspect of deploying autonomous robots; it primarily focuses on minimizing energy consumption and maximizing operational time. The Payload and Energy-aware Tactical Allocation Loop (PETAL) algorithm integrates a genetic algorithm to search for waypoint permutations, applies a 2-OPT (two-edge exchange) local search to refine those routes, and leverages an energy cost function that reflects payload weight changes during spraying. This combined strategy minimizes travel distance and reduces energy consumption across extended fumigation missions. To evaluate its effectiveness, a comparative study was performed between PETAL and prominent algorithms such as A*, a hybrid Dijkstra with A*, random search, and a greedy distance-first approach, using both randomly generated environments and a real-time map from an actual deployment site. The PETAL algorithm consistently performed better than baseline algorithms in simulations, demonstrating significant savings in energy usage and distance traveled. On a randomly generated map, the PETAL algorithm achieved 6.05% higher energy efficiency and 23.58% shorter travel distance than the baseline path-planning algorithm. It achieved 15.69% and 31.66% in energy efficiency and distance traveled saved on a real-time map, respectively. Such improvements can diminish operator exposure, extend mission durations, and foster safer, more efficient urban pest control. Full article

Generative AI (Gen-AI) revolutionizes design by leveraging machine learning to generate innovative solutions. It analyzes data to identify patterns, creates tailored designs, enhances creativity, and allows designers to explore complex possibilities for diverse industries. This study uses a Gen-AI design generation process to develop an urban landscape fumigation service robot. This study proposes a machine-learned multimodal and feedback-based variational autoencoder (MMF-VAE) model that incorporates a readily available spraying robot dataset and includes design considerations from various research efforts to ensure real-time deployability. The objective is to demonstrate the effectiveness of data-driven and feedback-based approaches in generating design specifications for a fumigation robot with the targeted requirements of autonomous navigation, precision spraying, and an extended runtime. The design generation process comprises three stages: (1) parameter fixation, emphasizing functionality-based and aesthetic-based specifications; (2) design specification generation using the proposed MMF-VAE model with and without a spraying robot dataset; and (3) robot development based on the generated specifications. A comparative analysis evaluated the impact of the dataset-driven design generation. The design generated with the dataset proved more feasible and optimized for real-world deployment with the integration of multimodal inputs and iterative feedback refinement. A real-time prototype was then constructed using the model’s parametric constraints and tested in actual fumigation scenarios to validate operational viability. This study highlights the transformative potential of Gen-AI in robotic design workflows. Full article

Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data. However, the observer-dependent nature of image interpretation and the complex morphology of PCLs can lead to diagnostic uncertainty and variability in patient management strategies. This review critically evaluates current PCL diagnosis and surveillance practices, showing features of the different lesions and highlighting the potential limitations of conventional methods. We then explore the potential of artificial intelligence (AI) to transform PCL management. AI-driven strategies, including deep learning algorithms for automated pancreas and lesion segmentation, and radiomics for analyzing heterogeneity, can improve diagnostic accuracy and risk stratification. These advanced techniques can provide more objective and reproducible assessments, aiding clinicians in decision-making regarding follow-up intervals and surgical interventions. Early results suggest that AI-driven methods can significantly improve patient outcomes by enabling earlier detection of high-risk lesions and reducing unnecessary procedures for benign cysts. Finally, this review emphasizes that AI-driven approaches could potentially reshape the landscape of PCL management, ultimately leading to improved pancreatic cancer prevention. Full article

Endometrial cancer (EC) presents a substantial health challenge, with increasing incidence and mortality rates. Despite advances in diagnosis and treatment, understanding the molecular underpinnings of EC progression remains unknown. In this study, we conducted a comprehensive investigation utilizing The Cancer Genome Atlas (TCGA-UCEC n = 588) data to analyze gene co-expression patterns, elucidate biological process pathways, and identify potential prognostic and diagnostic biomarkers for EC, using weighted gene co-expression network analysis (WGCNA), differential gene expression, survival analysis, and functional analysis, respectively. We determined that the Green module (M5) was significantly correlated with patient survival. Functional analysis of the genes in module M5 indicates involvement in cell cycle regulation, mitotic spindle assembly, and intercellular signaling. TPX2, BUB1, and ESPL1 were among the top differentially expressed genes in the Green module, suggesting their involvement in critical pathways that contribute to disease progression and patient survival outcomes. The biological and clinical assessments of our findings provide an understanding of the molecular landscape of EC and identified several potential prognostic markers for patient risk stratification and treatment selection. Full article

This study addresses the persistent challenge of polysulfide dissolution in lithium–sulfur (Li–S) batteries by introducing magnesium oxide (MgO) nanoparticles as a novel additive. MgO was integrated with sulfur using a scalable process involving solid-state melt diffusion treatment followed by planetary ball milling. XRD measurements confirmed that sulfur (S₈) retains its orthorhombic crystalline structure (space group $F_{ddd}$) following the MgO incorporation, with minimal peak shifts indicating slight lattice distortion, while the increased peak intensity suggests enhanced crystallinity due to MgO acting as a nucleation site. Additionally, Raman spectroscopy demonstrated sulfur’s characteristic vibrational modes consistent with group theory (point group $D_{2}h$) and highlighted multiwalled carbon nanotube (MWCNT′s) D, G, and 2D bands, with a low I_D/I_G ratio (0.47), which indicated low defects and high crystallinity in the prepared cathode. The S–MgO composite cathode exhibited superior electrochemical behavior, with an initial discharge capacity (950 mA h g⁻¹ at 0.1 C), significantly improved compared to pristine sulfur’s. The presence of MgO effectively mitigated the polysulfide shuttle effect by trapping polysulfides, leading to enhanced stability over 400 cycles and the consistent coulombic efficiency of over 99.5%. After 400 cycles, EDS and SEM analyses confirmed the structural integrity of the electrode, with only minor fractures and slight sulfur content loss. Electrochemical impedance spectroscopy further confirmed the enhanced performance. Full article

In recent times, the deployment of advanced structural health monitoring techniques has increased due to the aging infrastructural elements. This paper employed an enhanced You Only Look Once (YOLO) v4-tiny algorithm, based on the Crack Detection Model (CDM), to accurately identify and classify crack types in reinforced concrete (RC) members. YOLOv4-tiny is faster and more efficient than its predecessors, offering real-time detection with reduced computational complexity. Despite its smaller size, it maintains competitive accuracy, making it ideal for applications requiring high-speed processing on resource-limited devices. First, an extensive experimental program was conducted by testing full-scale RC members under different shear span (a) to depth ratios to achieve flexural and shear dominant failure modes. The digital images captured from the failure of RC beams were analyzed using the CDM of the YOLOv4-tiny algorithm. Results reveal the accurate identification of cracks formed along the depth of the beam at different stages of loading. Moreover, the confidence score attained for all the test samples was more than 95%, which indicates the accuracy of the developed model in capturing the types of cracks in the RC beam. The outcomes of the proposed work encourage the use of a developed CDM algorithm in real-time crack detection analysis of critical infrastructural elements. Full article