Search Results (958)

Brucellosis is a common zoonotic infection in India, caused by a facultative intracellular bacterium, Gram-negative coccobacillus, and frequently presents with nonspecific symptoms. This study aimed to assess serum cytokine levels (IL-6, IL-10, IFN-γ, and IL-2) by ELISA and to correlate them with inflammatory markers (ESR and CRP) in patients with suspected brucellosis presenting with rheumatoid arthritis-like manifestations and polyarthralgia. This study included 111 patients, comprising 72 brucellosis-positive arthritis patients and 39 brucellosis-negative arthritis patients as controls. In this study, we investigated the variation in levels of four cytokines (IL-2, IL-6, IL-10 and IFN-γ) among brucellosis-positive and brucellosis-negative arthritis patients and their relationship with clinical parameters. The mean serum levels of IL-2 and IL-10 were numerically higher in brucellosis-positive arthritis patients compared to controls; however, these differences were not statistically significant (p > 0.05). Similarly, no statistically significant differences were observed for IL-6 and IFN-γ between the groups. Inflammatory markers such as ESR and CRP were elevated in brucellosis-positive patients, although these differences did not reach statistical significance. These findings indicate variability in cytokine and inflammatory marker levels between groups. The observed associations with raw dairy consumption, contact with unvaccinated livestock, and direct animal exposure highlight the need for early diagnosis, effective livestock immunisation programs, and strengthened One Health-based public health interventions to reduce disease burden. Full article

Accurate detection of cardboard boxes is essential to mobile manipulators to perform pick-and-place operations in warehouses. Conventional object detection methods like YOLOv11 struggle in low-texture and occluded environments. This paper presents YOLO-REFB, a novel object detection framework for real-time cardboard box detection in robotic manipulation using a dual-arm mobile robot (DAMR) operating in indoor warehouse environments. The proposed approach enhances the network by integrating the Rectangular Edge Fusion Block (REFB) into the YOLOv11 architecture; it focuses on learning the geometric and structural features of cardboard boxes. Enhanced edge information extraction and feature fusion improve training stability and localization accuracy. A custom dataset of 3501 annotated images, collected under varied conditions, was utilized. The images were randomly assigned to training and validation sets while keeping an 80:20 ratio. They were manually annotated and trained using Roboflow software, ensuring precise alignment of bounding boxes with cardboard box edges for accurate comparison with existing YOLO models. The model outperformed existing YOLO variants (YOLOv8n and YOLOv5n) in terms of precision (89.29%), recall (83.95%), and F1-score (86.54%). YOLO-REFB achieved improved localization metrics, including mean Average Precision (mAP)@0.5 (91.68%) and mAP@0.5:0.95 (68.61%). The inclusion of REFB was essential to performance gains, enabling effective detection of objects in challenging environments. Future developments may include 3D pose estimation and multi-object grasp planning for advanced robotic manipulation. Full article

Hyperspectral imaging (HSI) provides rich spectral information and serves as a non-destructive technique for forensic stain analysis. Conventional approaches often exhibit degraded performance due to the high dimensionality and spectral redundancy inherent in hyperspectral data. To address this challenge, a hyperspectral dataset comprising nine beverage stains—papaya, coffee, pomegranate, orange, tea, wine, whisky, rum, and brandy—is developed. Building on this dataset, an ensemble framework that combines an optimized autoencoder (AE), channel-attention (CA)-enhanced one-dimensional convolutional neural networks (1D CNNs), and a Limited Memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS-B)-based weighted fusion strategy is proposed. The autoencoder learns compact latent representations from the 204-band hyperspectral vectors, reducing redundancy while preserving discriminative spectral features. CA emphasizes informative spectral bands and improves stain separability. Multiple 1D CNN models are trained using different latent dimensionalities, and their class probability outputs are fused through an optimized L-BFGS-B weighting scheme, where higher-performing models contribute more strongly to the final decision. Experimental results demonstrate classification accuracies of 96.54%, 97.19%, and 97.86% for the AE32 CA, AE64 CA, and AE128 CA models, respectively, with the optimized ensemble achieving an accuracy of 98.28%. Additionally, the time-dependent evolution of beverage stain reflectance is systematically analyzed using overlapped, normalized reflectance signatures acquired at time intervals of 0 min, 1 h, 2 h, 3 h, 4 h, and 5 h. The results confirm that AE-based latent compression, CA, and L-BFGS-B optimized ensemble fusion enhance hyperspectral beverage stain classification, providing an effective and extensible framework for forensic trace evidence analysis. Full article

The genus Trichinella comprises zoonotic nematodes infecting a wide range of carnivorous and omnivorous animals, including humans. Infection occurs through the consumption of raw or undercooked meat containing viable Trichinella larvae. Among the species within this genus, Trichinella spiralis is considered one of the most epidemiologically important due to its high reproductive capacity and its frequent association with infections in domestic animals and humans. In this study, muscle samples from 18 invasive American minks (Neovison vison) were examined for Trichinella larvae using the magnetic stirrer method. Species identification was performed via multiplex polymerase chain reaction (PCR), while the internal transcribed spacer 1 (ITS1) region was amplified to evaluate the intraspecific genetic variability. Trichinella larvae were detected in one of the 18 (5.6%) animals investigated, and all isolates were identified as T. spiralis. Ten ITS1 sequences obtained from individual larvae were 100% identical. Network and principal coordinate analyses revealed that the sequences clustered by geographic origin rather than host species and were more related to isolates from domestic pigs than to wildlife animals. These findings provide the first evidence of T. spiralis in American minks in Baltic and Scandinavian countries and contribute to a better understanding of the epidemiology of trichinellosis in the region. Full article

The proliferation of renewable energy resources has brought numerous challenges to conventional power systems, as grid integration is predominantly achieved through inverter-interfaced technologies such as photovoltaic (PV) plants and Type-IV wind turbines. Unlike synchronous generators (SGs), inverter-based resources (IBRs) exhibit fundamentally different fault behavior by limiting fault current magnitudes, typically within 1.0–1.2 per unit. Furthermore, the phase angle and sequence composition of the injected fault current are largely dictated by the inverter control strategy rather than by the network impedance. Consequently, distance protection schemes developed under assumptions of system homogeneity, a fixed source-to-impedance ratio (SIR), high fault current contribution, and large inertia may exhibit unreliable performance in inverter-dominated power networks. In this work, the influence of IBRs on key distance protection elements, such as starting elements, fault classification techniques, and impedance calculation with or without inter-feed, is reviewed and evaluated using simulations in PSCAD 5.0 software. Further, reduced grid inertia introduces operational limitations in power swing blocking (PSB) schemes, which are discussed in this paper. This work presents an overview of IBR fault responses and critically summarizes prior work on distance protection in IBR-dominated grids, highlighting key challenges, probable solutions, and the current research status to enhance understanding for further research. Full article

Recent advances in machine design, automation, and industrial digitalization have transformed Coordinate Measuring Machines (CMMs) from standalone inspection devices into fully integrated elements of automated manufacturing systems. In the automotive sector, CMMs increasingly operate in workshop, near-line, and in-line environments, interacting with production equipment and contributing directly to process control and zero-defect manufacturing strategies. This paper presents a structured methodology for the industrial deployment of automated CMMs in automotive mechanical manufacturing. The proposed approach is illustrated through an industrial use case involving the dimensional inspection of mechanically machined components under real production conditions. The methodology addresses machine design selection, sensor configuration, environmental constraints, and multi-axis architectures, as well as validation and acceptance procedures based on the ISO 10360 series. Particular attention is given to the integration of CMMs within automated manufacturing systems, including robustness against thermal variations, vibrations, and contamination, and the use of metrological data for feedback to machining processes. Rather than introducing new metrological principles, the proposed approach focuses on the structured integration of established engineering practices into a coherent lifecycle-based deployment framework. Based on industrial experience, the proposed methodology is illustrated through an industrial case study to support the reliable of automated dimensional inspection, reduce measurement-related risks, and support the integration of CMMs as active components of modern automated manufacturing systems. Full article

The present narrative review reports the main preclinical and clinical results obtained by using supplementation of saffron or its pure components in neurodegeneration, with special emphasis on age-related macular degeneration. Beyond that, this article will address shared pathways between neurodegenerative diseases of the eye and the brain. It will be shown that saffron treatment might counteract oxidative damage in the retina and brain, as well as inflammation and inflammatory mediators that induce neuronal degeneration and death. The ways of action are multiple, and saffron chemical components appear to act in a synergistic manner, inducing tissue resilience. These effects critically depend upon the saffron chemical composition and structure. A well-defined ratio among molecules is linked to a patented batch known as Repron^® and offers the maximum protection against neurodegeneration. Full article

Rain-induced shallow landslides persistently disrupt Nepal’s mountain roads, frequently leading to fatalities, transport disruptions, and economic losses. This study develops physically validated, site-specific rainfall thresholds for the landslide-prone Kanti National Roadway (H37) by integrating empirical intensity–duration (I-D) analysis, antecedent rainfall metrics, and satellite-derived soil moisture data. Using 35 years of rainfall records (1990–2024) and 59 field-verified landslides (2017–2024), we derived a localized I-D threshold: I = 19.37 × D^−0.6215 (I: rainfall intensity in mm/h; D: duration in hours), effective for durations of 48–308 h, encompassing short intense storms and prolonged moderate rainfall. The Cumulative Antecedent Rainfall (CAR) method associated most failures with 3-day totals, while the Antecedent Precipitation Index (API) showed superior performance, with a 10-day threshold of 77 mm capturing all events. For physical validation, NASA’s SMAP Level-4 root-zone (0–100 cm) soil moisture data revealed a 1-day lag in response to rainfall; after adjustment, trends matched API saturation predictions and identified an inverse rainfall–moisture pattern before the 11 August 2019 landslide, indicating a potential instability precursor. This integration enhances predictive accuracy, bolsters mechanistic understanding of landslide hazards, and offers a scalable, cost-effective early-warning framework for data-scarce mountain regions, aiding climate-resilient infrastructure in regions with intensifying rainfall extremes. Full article

Integrated information theory (IIT) starts from the existence of consciousness and characterizes its essential properties: every experience is intrinsic, specific, unitary, definite, and structured. IIT then formulates existence and its essential properties operationally in terms of cause–effect power of a substrate of units. Here, we address IIT’s operational requirements for existence by considering that, to have cause–effect power, to have it intrinsically, and to have it specifically, substrate units in their actual state must both (i) ensure the intrinsic availability of a repertoire of cause–effect states, and (ii) increase the probability of a specific cause–effect state. We showed previously that requirement (ii) can be assessed by the intrinsic difference of a state’s probability from maximal differentiation. Here, we show that requirement (i) can be assessed by the intrinsic difference from maximal specification. These points and their consequences for integrated information are illustrated using simple systems of micro units. When applied to macro units and systems of macro units such as neural systems, a tradeoff between differentiation and specification is a necessary condition for intrinsic existence—and therefore, according to IIT, for consciousness. Full article

Could there be quantum superpositions of conscious states, as suggested by the Wigner’s friend thought experiment? Mathematical theories of consciousness, notably integrated information theory (IIT), make this question more precise by associating physical systems with both quantitative amounts of consciousness and structural characterizations of conscious states. Motivated by a recent proposal that ties wave-function collapse to integrated information, we construct a simple quantum circuit that would, on that proposal, place a minimal system—a feedback dyad—into a superposition of states that differ in their associated conscious states. This “Schrödinger’s dyad” provides a controlled setting for evaluating a central desideratum of consciousness-based collapse models: that collapse rates depend on how different the experiences in the superposition are. We prove a structural constraint on collapse dynamics of a standard (Lindblad) type: if collapse is governed by too few collapse operators, collapse rates cannot in general be made to depend solely on qualitative differences between conscious states. Avoiding this limitation requires introducing many commuting operators, leading to a rapid proliferation of collapse terms even for very simple systems. This proliferation bears directly on claims that IIT-based collapse theories may be especially experimentally tractable, since the required dynamics becomes highly complex. More generally, the difficulty is not specific to IIT: any Wigner-style collapse theory that distinguishes experiences using rich internal organization (such as neural connectivity in addition to neural state) will face a comparable explosion in dynamical complexity. Full article

The use of virtual environments to collect the experience required by deep reinforcement learning models is accelerating the deployment of these algorithms in industrial environments. However, once the experience-gathering problem is solved, it is necessary to address how to efficiently transfer the knowledge from the virtual scenario to reality. This paper focuses on examining Progressive Neural Networks (PNNs) as a promising transfer learning technique. The analyses carried out range from studying the capabilities and limits of the layers responsible for learning the state representation from a pixel space, which could arguably be the convolutional blocks, to the forgetting agents suffer when learning a new task. Introducing controlled visual changes in the environment scene can lead to a performance degradation of 50.3% in the worst-case scenario. These visual discrepancies significantly impact the agent’s learning time and accuracy when using a PNN architecture. Regarding the PNN forgetting assessment, partial forgetting occurs in two of the three environments analyzed, those where the agent masters its new task. This could be due to a balance between the relevance of the new features learned and the ones inherited from the teacher agent. Full article

Multi-agent and distributed dynamical systems can exhibit coordinated behavior that is difficult to summarize in terms of independent parts. Integrated Information Theory (IIT) provides one influential notion of system-level irreducibility, but exact computation of causal $\mathrm{\Phi }$ remains intractable except in very small systems. In this work, we introduce ${\mathrm{\Phi }}_{\mathrm{spectral}}$, a scalable observer-relative statistic defined on pairwise mutual information networks extracted from multivariate time-series data. A normalized graph Laplacian and its Fiedler vector identify a bipartition of the mutual information graph, and ${\mathrm{\Phi }}_{\mathrm{spectral}}$ reports the normalized weight of informational coupling crossing that cut. The measure is inspired by IIT’s concern with irreducibility but is not equivalent to intrinsic causal $\mathrm{\Phi }$: it is pairwise, undirected, and functional/statistical rather than intervention-based. We evaluate it on four exploratory simulation regimes: random oscillators, a transitional Kuramoto-like synchronization regime, a perfectly synchronized regime, and a combinatorial threshold-linear network (CTLN). Across these cases, ${\mathrm{\Phi }}_{\mathrm{spectral}}$ is most useful as a measure of observer-relative integration under second-order dependencies, separating redundancy-dominated from transiently differentiated regimes. The current results should be read as a proof-of-concept rather than as a formal validation against exact IIT. We discuss relations to weak IIT, Integrated World Modeling Theory (IWMT), and the perturbational complexity index (PCI), and we outline the stationary benchmarking and small-system validation needed for stronger causal claims. Full article

Power system blackouts remain a major concern for modern electricity networks, as they often result from cascading failures that lead to substantial load shedding and widespread service disruptions. This paper presents a dynamic resilience assessment of hybrid AC/DC power systems and investigates the effectiveness of voltage-source-converter-based high-voltage direct current (VSC-HVDC) technology in enhancing system resilience under outage contingencies. The study contributes by integrating protection devices and their settings into the analysis and by providing a quantitative evaluation of the system response to N-2 and N-3 contingencies using PSS^®E simulations. The demand not served index is used as a measure of resilience, and its cumulative distribution functions are computed to compare the performance of AC and DC interconnections. The results underscore the importance of VSC-HVDC links in mitigating cascading failures, highlighting their potential as a resilience-enhancing component in modern power grids. Full article

This paper presents a zone-based method for the interim verification and spatial metrological characterization of a 2D vision measurement system. The approach relies on a system calibrated along a single axis and employs a stable yet non-calibrated artifact, demonstrating that spatial performance assessment can be achieved without the need for fully calibrated artifacts distributed across the entire field of view. To enable this process, a custom-designed reference standard was developed, providing a straightforward, robust, and cost-effective solution for performing interim verification tasks. The proposed method provides a structured framework for evaluating both precision and spatial consistency across the measurement surface, even in the absence of fully calibrated standards distributed across the surface. The method is applicable to a wide range of vision-based measurement systems, including those supporting industrial Optical Character Recognition (OCR), while maintaining alignment with established metrological principles. When combined with complementary optical performance tests, the approach supports robust and repeatable interim verification strategies in advanced manufacturing metrology. Full article