Search Results (2,404)

While Western theology typically locates omniscience in a personal Creator-God, Indian philosophy presents a notable spectrum. This article traces the dialectical arc of omniscience (sarvajñāna) across major Indian philosophical traditions, arguing that what appears as an epistemological question—“who knows everything?”—is ultimately an ontological puzzle about the nature of consciousness itself. Moving from the Vedic oscillation between cosmic personhood (Puruṣa Sūkta) and primordial uncertainty (Nāsadīya Sūkta), through the Upaniṣadic internalization of omniscience as Self-knowledge (ātmajñatā), the article examines how Nyāya-Yoga affirms divine omniscience as a logical and soteriological necessity, how Mīmāṃsā displaces it onto an impersonal authorless text, and how Jainism and Buddhism reappropriate it as a perfected human achievement. The final section demonstrates that both Sāṃkhya’s isolation (kaivalya) and Advaita Vedānta’s non-dual realization ultimately transcend encyclopedic omniscience, revealing that authentic liberation requires not the possession of maximal information but a transformation from representational object-knowledge to non-objectifying awareness. Together, these trajectories constitute Indian philosophy’s most enduring contribution to the global philosophy of religion: the recognition that the “All” cannot be an object of knowledge, because it is the very condition for any knowledge whatever. Full article

The risks associated with hazardous materials (HazMat) transportation exhibit typical characteristics of chain-like distribution, spatiotemporal regularity, and individual heterogeneity. A personalized trip-chain feature spectra recognition framework for HazMat vehicles is proposed to enhance the capability to assess and analyze individual risks using vehicle positioning data. The proposed framework addresses the challenges of deriving personalized risk feature maps arising from missing real-time trajectory data, complex sub-trip-chain segmentation, and the extraction of personalized risk feature representations. An improved conditional Wasserstein Generative Adversarial Network (WGAN) model is initially developed to impute trajectories with missing positional data, and it can robustly reconstruct trajectories with large-scale missing segments by integrating a multi-head self-attention mechanism and a gradient penalty. A two-layer clustering algorithm, K-Means-multiplE-THreshOlds-adaptive-DBSCAN (KMETHOD), which combines an adaptive mechanism with threshold rules, is subsequently designed to identify the dwell time and related spatial attributes of dwell points along vehicle trips. A BERT-based model is incorporated to filter Points of Interest (POIs) around dwell points, which enables the extraction of their detailed location semantics and trip characteristics and thus supports trip chain identification and segmentation. A threshold-activated multilayer trajectory feature-map method (TAFEM) is constructed to generate feature maps for each trip chain. The Liquefied Natural Gas (LNG) transportation trajectory data from Guangdong Province is selected to evaluate the effectiveness of the proposed methods. The experimental results demonstrate that the proposed framework can effectively identify trip chains and generate their corresponding feature maps. The trajectory imputation model achieved the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Dynamic Time Warping (DTW) of 2.34–3.33, 6.05–7.74, and 0.74–1.21, respectively, across different missing-rate scenarios, outperforming other benchmark models. The identification accuracy of dwell-point duration and location reaches 98.35%. The BERT-based method achieves a maximum accuracy of 92.83% in origin–destination (OD) point recognition, effectively capturing comprehensive trip-chain information. TAFEM accurately characterizes the spatiotemporal distribution and potential causal factors of personalized HazMat transportation safety risks, providing a reliable foundation for risk identification and safety management strategies. Full article

This paper defends the thesis that LLM hallucinations are best explained as a truth representation problem: Current models lack an internal representation of propositions as truth-bearers, so truth and falsity cannot constrain generation in the way factual discourse requires. It begins by surveying leading explanations—computational limits on self-verification, deficiencies in training data as truth sources, and architectural factors—and argues that they converge on the same underlying representational deficit. Next, it reconstructs the philosophical background of current LLM design, showing how optimization for fluent continuation aligns with coherence-style evaluation and with broadly structuralist, relational semantics, before turning to David Chalmers’s recent attempt to secure propositional interpretability by drawing on Davidson/Lewis-style radical interpretation and by locating propositional content in “middle-layer” structures; it argues that this approach downplays the ubiquity of hallucination and inherits instability from post-training edits. Finally, the paper offers a positive proposal: Atomic propositions should be represented in the basic vector layer, reviving a logical atomist program as a principled route to reducing hallucination. Full article

Background: The UCP1 A-3826G polymorphism, located in the gene’s regulatory region, is associated with obesity and altered fat metabolism. Because UCP1 plays a central role in thermogenesis, variation in its expression may influence metabolic efficiency and dietary fat preference. Methods: We examined associations between the A-3826G polymorphism and food preferences in healthy young Japanese adults (50 males, 48 females). Preferences for high-fat and basic-taste foods were assessed using a self-administered questionnaire, with sweet foods classified as low- or high-fat. Genotypes (AA, AG, GG) were analyzed using a two-way mixed-design ANOVA to evaluate genotype × fat level interactions. Results: Preference scores for basic tastes did not differ significantly among genotypes in either sex (except for sour taste in males). In males, no significant genotype × fat level interaction was observed, although AA carriers preferred high-fat to low-fat sweet foods (p < 0.05). In females, a significant genotype × fat level interaction was detected (p < 0.01), with AG carriers showing lower preference for high-fat sweet foods. Conclusions: These findings indicate that the UCP1 A-3826G polymorphism may modulate preference for high-fat sweet foods in a sex-dependent manner, suggesting a link between thermogenic genetic variation and dietary fat preference relevant to obesity prevention. Full article

We present PIRATE (Precision Imaging Real-time Autonomous Tracker and Explorer), a fully autonomous unmanned surface vehicle designed to enable self-operating data collection and persistent tracking of mobile underwater targets through the tight integration of acoustic localization, onboard visual perception, and closed-loop navigation. PIRATE employs a single mobile acoustic receiver to estimate target position using time-difference-of-arrival (TDoA) measurements acquired at different times and locations through planned autonomous motion and uses these estimates to drive adaptive vehicle behavior and activate fine-grained visual sensing in real time. This architecture enables sustained target-driven operation, in which navigation, acoustic monitoring, and visual processing are dynamically coordinated based on mission context and localization uncertainty. The system integrates real-time AI-based visual detection and tracking with automatic mission control, allowing visual perception to operate opportunistically within an acoustically guided tracking loop rather than as a standalone sensing modality. Field experiments in a shallow-water environment demonstrate reliable autonomous navigation, single-receiver acoustic localization with meter-scale accuracy, and stable onboard visual inference under sustained operation. By enabling coupled acoustic tracking and onboard visual perception in a fully autonomous surface platform free of external infrastructure, PIRATE provides a practical foundation for fine-scale behavioral observation, adaptive marine monitoring, and long-duration studies of mobile underwater organisms. We demonstrate this advantage with two possible applications. Full article

Weight stigma often appears in research as individual prejudice and its interpersonal harms, yet women’s accounts show that devaluation also persists through routine, socially organized evaluation. This article examines weight stigma through visibility by treating looking as a patterned interpretive practice with moral and relational consequences. We conducted three in-person focus groups with women in Chile who self-identified as fat (N = 20) in Santiago, Coquimbo, and Valdivia between April and September 2024 and analyzed the data using reflexive thematic analysis. Participants described visibility as a shifting landscape of evaluative looks that travel across everyday domains while retaining recognizable moral logics. We develop a typology of four gazes: an expulsive/invisibilizing gaze that denies fit and belonging; a disciplinary gaze that frames correction as care and produces self-surveillance; a derisive gaze that punishes through contempt; and a brave gaze that offers conditional recognition by praising ordinary presence as exceptional. Women located these gazes in ordinary interactions and in infrastructures that stabilize evaluation, including public seating norms, retail sizing routines, clinical measurement, mirrors, and photographic and digital practices. These findings suggest that reducing weight stigma requires changing not only attitudes but also the scripts and material arrangements that organize visibility and make evaluation routine. Full article

Environmental perception is a cornerstone for autonomous underwater vehicles (AUVs) to achieve robust self-localization and scene understanding, which are pivotal for the intelligent management of marine ranching. However, underwater image degradation and weak-textured scenes significantly hinder reliable self-localization and fine-grained environmental perception. To address the perceptual asymmetry arising from these challenges, this paper proposes a robust visual–inertial simultaneous localization and mapping (SLAM) and biomass assessment scheme for marine ranching. Specifically, we first propose a robust tightly coupled underwater visual–inertial localization scheme, which leverages a multi-sensor fusion strategy to solve the image degradation problem of localization in complex underwater environments. Furthermore, we propose a novel underwater scene perception method, which enables the simultaneous visual reconstruction of aquaculture species and the quantitative mapping of their spatial distribution in marine ranching. Finally, we develop a low-cost, agile, and portable multisensor-integrated system that consolidates autonomous localization and aquaculture biomass assessment modules, with its performance validated through extensive real-world underwater experiments. The experimental results demonstrate that the proposed methods can effectively overcome the interference of complex underwater environments and provide high-precision perception support for both AUV state estimation and aquaculture asset management. Full article

In response to the growing need for accurate forecasting of electricity generation from PV installations, which is crucial both for enhancing self-consumption and for balancing the power grid, this study presents a comparative analysis of selected machine learning models. The research focuses on the XGBoost algorithm and LSTM neural networks, applied to predict PV energy production based on meteorological data and historical generation records from four medium-sized PV installations (30–50 kWp) located in Poland. Meteorological data were retrieved from open sources and combined with actual production measurements to build the training dataset. This paper discusses the challenges posed by these data at the given latitude, as well as issues related to processing data from newly launched installations. The performance of both approaches was evaluated in short- and medium-term forecasting, with particular attention to prediction accuracy, robustness to noisy data, and the ability to capture nonlinear relationships. Full article

Challenges caused by climate change increase concern for achieving global sustainability. Although citizen awareness is increasing, ensuring sustainability in key sectors like construction is necessary. Achieving sustainability requires essential actions that, however, could have a negative impact on economic performance. Studies on renewable energy installations tend to prioritize performance or sustainability, rather than facing the strategic challenge to find the balance between both. The present work fits this framework through managing renewable energy operations in a construction materials factory of Grupo Puma, located in Spain. The objective of the proposed methodology is to identify key performance indicators (KPIs) for the FV installation and to simulate energy flows using a validated model within a digital simulation environment. This study proposes a trinomial of KPIs—self-consumption, solar utilization, and avoided CO₂ emissions—as more stable indicators than conventional metrics. The Pareto front analysis shows that self-consumption can be increased by up to 20% with only an approximate 10% reduction in solar utilization. This finding offers a clear strategic recommendation: prioritizing higher self-consumption is a viable industrial strategy to enhance Grupo PUMA’s sustainability performance while maintaining acceptable economic efficiency. Full article

The digital nomad economy—the ecosystem in which professional skills are traded through online platforms independent of geographic co-location—dynamically recombines skills into project-based portfolios with absent firm-level hierarchy. Yet it remains shaped by platform taxonomies, interfaces, and ranking/recommendation incentives. This study examines the emergent structure within this setting using the Semantic-Structural Systems Analysis (S²SA) framework, which integrates LLM-assisted skill extraction, transformer-based semantic embeddings, and multi-layer network analysis. We analyze a dual-source dataset comprising approximately 50,000 public Upwork profiles from a top-rated/high-earning segment (January–March 2023) and 2.0 million Reddit posts and comments (2018–2023) from remote-work and digital-nomad communities. The resulting skill network exhibits a pronounced core–periphery organization and modular “skill ecotopes” corresponding to coherent functional specializations. In predictive models of skill-level effective hourly rates, semantic brokerage and semantic diversity function as robust predictors of higher rates, significantly outperforming popularity-only baselines. Longitudinal discourse analyses surrounding the COVID-19 pandemic and the generative AI shock reveal rapid attentional shifts followed by the emergence and recombination of new skill clusters. We interpret these results as evidence consistent with constrained self-organization in platform-mediated labor markets. To support replication, prompts, parameters, and robustness checks are fully reported. Full article

Recently, numerous countries have experienced devastating wildfires, leading to significant destruction and loss of life. These catastrophic events highlight the shortcomings in current building regulations and testing methods. There is a pressing need for a more profound understanding of the characteristics and behaviour of large outdoor fires to address these inadequacies effectively. Wildfires can spread to structures located at the wildland–urban interface, leading to further fire propagation from one building to another. In this study, the Fire Dynamics Simulator (FDS) model was validated using experimental data from the National Institute of Standards and Technology (NIST). The experiment consisted of a target wall and a small wooden shed containing six wooden cribs as fuel, with a separation distance of 3 m. Both FDS and the experiment proved that 3 m is the safe separation distance. Different shed materials, such as steel, were used, which reduced the total heat release rate by 40% and the flame height by 20%. The effects of wind speed and direction were investigated using two wooden sheds in FDS to observe fire spread between them. The safe separation distance was 3 m for both wind speeds (2 and 5 m/s) in all directions, where the critical temperature was not reached to cause self-ignition of the second shed, except in the north direction (inward) at a speed of 5 m/s. When the separation distance increased to 3.5 m, the average heat flux at the other shed reduced to 3.18 kW/m², which did not cause self-ignition. Therefore, the safe separation distance between two structures for a wind speed of 5 m/s should be 3.5 m to mitigate the spread of fire based on the shed dimensions and the fire source load. Full article

Clouds are a key factor affecting solar radiation, and their dynamic variations directly cause uncertainty and fluctuations in photovoltaic (PV) power output. To improve PV power prediction accuracy, this paper proposes an enhanced short-term photovoltaic power forecasting approach based on a hybrid neural network architecture using features extracted from satellite cloud images. First, a dual-layer image fusion method is developed for satellite cloud images from different wavelengths and spectral bands, effectively improving fusion accuracy. Second, texture descriptors derived from the Gray-Level Co-occurrence Matrix and multiscale information obtained via the wavelet transform are employed for feature extraction from fused images. Combined with a residual network (ResNet), an optical flow method, as well as an LSTM-based temporal modeling module, multidimensional features of the predicted cloud images are obtained. An improved Bayesian optimization (IBO) algorithm is then employed to derive the optimal fused features, thereby improving the matching between cloud image features and PV power. Third, an enhanced hybrid architecture integrating a convolutional neural network and long short-term memory units with a multi-head self-attention mechanism is developed. Numerical weather prediction (NWP) meteorological features are incorporated, and a tilted irradiance model is introduced to calculate the solar irradiance received by PV modules for use in near-term photovoltaic power forecasting. Finally, measurements collected at a photovoltaic power plant located in Hebei Province are used to validate the proposed method. The results show that, relative to the SA-CNN-MSA-LSTM and BO-CNN-LSTM models, the developed approach lowers the RMSE to an extent of 22.56% and 4.32%, while decreasing the MAE by 24.84% and 5.91%, respectively. Overall, the proposed model accurately captures the characteristics of predicted cloud images and effectively improves PV power prediction accuracy. Full article

Although underwater gliders are highly energy-efficient platforms capable of long-duration and large-scale ocean observation, their lack of self-propulsion requires external assistance for recovery upon mission completion. In harsh and dynamic marine environments, reliably detecting the glider and accurately estimating its three-dimensional position are critical to ensuring the recovery operations are safe and efficient. This paper proposes a perception framework based on deep learning to detect underwater glider hulls and estimate their three-dimensional relative positions using camera–sonar multi-sensor fusion. This approach integrates a hierarchical convolutional neural network (CNN) vision encoder and a transformer-based architecture to estimate the glider’s spatial location and heading direction simultaneously. The hierarchical CNN encoder extracts multi-level, semantically rich visual features, thereby improving robustness to visual degradation and environmental disturbances common in underwater settings. Additionally, the transformer incorporates a variable mixture-of-experts (vMoE) mechanism that adaptively allocates expert networks across layers, enhancing representational capacity while maintaining computational efficiency. The resulting pose estimates enable precise, collision-free ROV navigation for automated recovery and onboard sensor inspection tasks. Experimental results, including ablation studies, validate the effectiveness of the proposed components and demonstrate their contributions to accurate glider hull detection and three-dimensional localization. Overall, the proposed framework provides a scalable, reliable perception solution that allows for the safe, autonomous recovery of underwater gliders with an ROV in realistic ocean environments. Full article

The aim of this study was to examine the factors associated with COVID-19-related fear in older adults from Kazakhstan, and to explore its associations with sociodemographic characteristics, health status and multiple domains of quality of life in a regional context. A total of 445 individuals aged 60 and above from both urban and rural locations in Kazakhstan participated in this cross-sectional study. To assess the quality of life among older people we used the OPQoL (Older People’s Quality of Life) Scale. Further variables were evaluated: sociodemographic (age, gender, education level, marital status, and place of residence); health-related (self-reported overall health, hypertension, diabetes, cerebrovascular disease, cardiovascular disease, and chronic obstructive pulmonary disease (COPD); and COVID-19-related fear variable. Female gender (OR = 2.344; p = 0.001), present hypertension (OR = 2.106; p = 0.008), the specialized secondary educational level (OR = 2.321; p = 0.012) and at the border of significance university educational level (OR = 1.832; p = 0.051) were variables significantly associated with the COVID-19-related fear in older adults. For individuals with reported COVID-19-related fear, advanced age was significantly negatively associated with leisure and activities domain (B = −0.747; p = 0.020) of OPQoL; better self-reported overall health was significantly positively associated with life overall domain (B = 0.691; p < 0.001), health domain (B = 1.320; p < 0.001), psychological and emotional well-being domain (B = 0.395; p = 0.001), home and neighborhood domain (B = 0.249; p = 0.036), independence, control over life and freedom domain (B = 1.082; p < 0.001), financial circumstances domain (B = 1.132; p < 0.001), and leisure and activities domain (B = 0.556; p = 0.026) of OPQoL; present hypertension was significantly negatively associated with health domain (B = −0.888; p = 0.004) of OPQoL; present cardiovascular disease was significantly negatively associated with life overall domain (B = −0.588; p = 0.027), health domain (B = −0.967; p = 0.009), and independence, control over life and freedom domain (B = −0.542; p = 0.039) of OPQoL; being single was significantly negatively associated with life overall domain (B = −0.481; p = 0.033), social relations domain (B = −0.671; p = 0.014) and financial circumstances domain (B = −0.694; p = 0.036) of OPQoL; and urban place of residency was significantly positively associated with health domain (B = 0.735; p = 0.011) and psychological and emotional well-being domain (B = 0.483; p = 0.010) of OPQoL. Our findings pointed that numerous variables were associated with the COVID-19-related fear and quality of life domains regarding COVID-19-related fear in older adults from Kazakhstan during pandemics. Full article

Most existing studies on the cost competitiveness of self-consumption PV systems fail to jointly consider key technical, economic, and user-specific factors—such as the share of PV electricity self-consumed, energy exported or imported from the grid, and time-of-use electricity pricing—all of which significantly influence investment viability. To address these gaps, this study introduces a novel method based on a new model to calculate the unit cost of electricity consumption from the user’s perspective (C_EC, in €·kWh⁻¹). The array DC power rating is then optimally sized—assuming ideal orientation and tilt—to minimize C_EC. A self-consumption PV system is considered cost-competitive when the annualized minimized C_EC is lower than the applicable regulated electricity tariff. Colombia is selected as a case study to demonstrate the novel method due to the limited deployment and analysis of self-consumption PV systems in the country. The method is applied across residential, commercial, and industrial sectors in various locations. The resulting annualized minimized C_EC values (0.35–8.85 c€/kWh) are consistently below the corresponding regulated tariffs, demonstrating the economic viability of properly sized PV systems. The method’s adaptability to international tariff frameworks makes it a valuable tool for global application and a useful resource for policymakers and stakeholders. Full article