Search Results (5,321)

This paper deals with J. A. Wheeler’s proposal that each piece of reality owes its existence to observation—an approach to physics, which implies that all physical entities at their bottom are informational in character. Focusing on the double-slit experiment with photons, which is the key evidence for the wave–particle dualism of photons, this paper follows Wheeler’s observational approach and interprets this experiment as a question posed to nature. Considering how the enquiry regarding the wave–particle duality of photons is answered by nature, it is shown that experimental questions are being answered by nature in the form of spatiotemporal patterns of elementary observations (EOs) which are binary pieces of information, produced by the dissipation of energy. Working through this line of thought, Wheeler’s statements of “binary information gain”, “observer participance” and the “impossibility of continuum idealizations of physical laws” are elucidated and connections to the Landauer Principle are made. Full article

The southeastern Tibet region is characterized by rugged terrain and relative isolation, which has significantly constrained the development of agriculture. However, due to the extremely limited archaeological and historical records available, its important role in the history of agricultural development in Tibet has been overlooked. This study focuses on the Linzhi and Changdu regions of southeastern Tibet, integrating limited archival, historical, and documentary data. By reconstructing historical settlement patterns and population data, this study estimates the arable land area during the Tubo, Yuan, Ming, and Qing dynasties. Using a grid-based model, it reconstructs the distribution patterns of arable land during these periods, aiming to provide a reference for the development of agriculture in Tibet. The research findings indicate the following: (1) During historical periods, settlements in southeastern Tibet were primarily distributed in flat, resource-rich alluvial plains at medium to high altitudes. Settlement types exhibited spatial differentiation: Post stations were primarily situated along major transportation routes that connected river valleys, as well as at high mountain passes. Temples tended to occupy moderately steep slopes, while manors were concentrated in low-lying valleys. (2) During the Tubo, Yuan, Ming, and Qing periods, the total arable land area and cultivation rate in southeastern Tibet were generally low, with total arable land areas of 28,085 hm², 29,449 hm², 25,319 hm², and 24,371 hm², respectively, and cultivation rates of 0.12%, 0.13%, 0.11%, and 0.11%, respectively. (3) Farmland was predominantly distributed along the Yarlung Zangbo, Jinsha, Lancang, and Nu Rivers and their broader tributary valleys. Natural constraints resulted in a highly fragmented farmland distribution. Full article

Molecular networking (MN) is a novel mass spectrometry data analysis method that has advanced significantly in recent years and has rapidly emerged as a popular technique. By visualizing the connections between structurally similar compounds in mass spectra, MN greatly enhances the efficiency with which harmful substances and bioactive ingredients in cosmetics are screened. In this review, we summarize the principles and main categories of MN technology and systematically synthesize its progress in cosmetic testing applications based on 83 recent studies (2020 to 2025). These applications include screening banned additives, analyzing complex matrix components, and identifying efficacy-related ingredients. We highlight MN’s successful application in detecting prohibited substances, such as synthetic dyes and adulterants, with limits of detection (LOD) as low as 0.1–1 ng/g, even in complex matrices, such as emulsions and colored products. MN-guided isolation has enabled the structural elucidation of over 40 known and novel compounds in the analysis of natural ingredients. We also discuss current challenges, such as limitations in instrument sensitivity, matrix effects, and the lack of cosmetic-specific component databases. Additionally, we outline future prospects for expanding MN’s application scope in cosmetic testing and developing it toward computer-aided intelligence. This review aims to provide valuable references for promoting innovation in cosmetic testing methods and strengthening quality control in the industry. Full article

Natural disasters can disrupt communication services, leading to severe consequences in emergencies. Maintaining connectivity and communication quality during crises is crucial for coordinating rescues, providing critical information, and ensuring reliable and secure service. This study proposes FedResilience, a Federated Learning (FL) system for classifying Long-Term Evolution (LTE) network coverage in both normal operation and natural disaster scenarios. A three-tier architecture is implemented: (i) edge nodes, (ii) a central aggregation server, and (iii) a batch processing interface. Five FL aggregation methods (FedAvg, FedProx, FedAdam, FedYogi, and FedAdagrad) were evaluated under normal conditions and disaster simulations. The results show that FedAdam outperforms the other methods under normal conditions, achieving an F1 score of 0.7271 and a Global System Adherence ($S{A}_{\mathrm{global}}$) of 91.51%. In disaster scenarios, FedProx was superior, with an F1 score of 0.7946 and $S{A}_{\mathrm{global}}$ of 61.73%. The innovation in this study is the introduction of the System Adherence (SA) metric to evaluate the predictive fidelity of the model. The system demonstrated robustness against Non-Independent and Identically Distributed (non-IID) data distributions and the ability to handle significant class imbalances. FedResilience serves as a tool for companies to implement automated corrective actions, contributing to the predictive maintenance of LTE networks through FL while preserving data privacy. Full article

β-Hydroxy-β-methylbutyrate (HMB), a natural metabolite derived from the essential amino acid leucine, is primarily recognised for its anabolic and anti-catabolic effects on skeletal muscle tissue. Recent studies indicate that HMB may also play a role in influencing the structural organisation of extracellular matrix (ECM) components, particularly collagen, which is crucial for maintaining the mechanical integrity of connective tissues. In this investigation, bovine type I collagen was polymerised in the presence of two concentrations of HMB (0.025 M and 0.25 M) to explore its potential function as a molecular modulator of fibrillogenesis. The morphology of the resulting collagen fibres and their molecular architecture were examined using atomic force microscopy (AFM) and Fourier-transform infrared (FTIR) spectroscopy. The findings demonstrated that lower levels of HMB facilitated the formation of more regular and well-organised fibrillar structures, exhibiting increased D-band periodicity and enhanced stabilisation of the native collagen triple helix, as indicated by Amide I and III band profiles. Conversely, higher concentrations of HMB led to significant disruption of fibril morphology and alterations in secondary structure, suggesting that HMB interferes with the self-assembly of collagen monomers. These structural changes are consistent with a non-covalent influence on interchain interactions and fibril organisation, to which hydrogen bonding and short-range electrostatics may contribute. Collectively, the results highlight the potential of HMB as a small-molecule regulator for soft-tissue matrix engineering, extending its consideration beyond metabolic supplementation towards controllable, materials-oriented modulation of ECM structure. Full article

This research is based on the acoustic package design of new energy vehicles, investigating the application of the hybrid Finite Element–Statistical Energy Analysis (FE-SEA) model in predicting the high-frequency dynamic response of automotive structures, with a focus on the modeling and correction methods for hybrid point connections. New energy vehicles face unique acoustic challenges due to the special nature of their power systems and operating conditions, such as high-frequency noise from electric motors and electronic devices, wind noise, and road noise at low speeds, which directly affect the vehicle’s ride comfort. Therefore, optimizing the acoustic package design of new energy vehicles to reduce in-cabin noise and improve acoustic quality is an important issue in automotive engineering. In this context, this study proposes an improved point connection correction factor by optimizing the division range of the decision circle. The factor corrects the dynamic stiffness of point connections based on wave characteristics, aiming to improve the analysis accuracy of the hybrid FE-SEA model and enhance its ability to model boundary effects. Simulation results show that the proposed method can effectively improve the model’s analysis accuracy, reduce the degrees of freedom in analysis, and increase efficiency, providing important theoretical support and reference for the acoustic package design and NVH performance optimization of new energy vehicles. Full article

The habitat landscape pattern of Mrs Hume’s pheasant in Jinzhongshan, northwestern Guangxi, was studied using field survey data and the LANDSAT satellite images by the ArcGIS 10.8 and Fragstats 3.3 software. The results showed that the Jinzhongshan region covers 38,716.6 hm², including 1708 patches and 11 landscape types. Although the area of farmland and village only occupies 10%, their number and density have led Jinzhongshan habitats to fragment. The degree of connection of suitable habitat was found to be relatively low, and seven landscape indices were below 0.5, which implied that the extent of habitat fragmentation in Jinzhongshan for Mrs Hume’s Pheasant is very high. The fragmentation index of Jinzhongshan Nature Reserve is 0.9887, landscape connectivity is 1.861, and AWS index is 425.3024. The broad-leaved forest, considered a matrix in the Jinzhongshan area, was the dominant landscape type controlling structure, function, and dynamic changes. The total suitable habitat of Mrs Hume’s pheasant (Syrmaticus humiae) was determined to be 29,552.3 hm², accounting for 76.3% of the total study area; the suitable habitat of Mrs Hume’s pheasant in Jinzhongshan Nature Reserve was determined to be 16,990.1 hm², accounting for 81.14% of the protected area. It is absolutely necessary and urgent to strengthen the management and protection of Mrs Hume’s pheasant’s habitat to control its fragmentation. Therefore, we have provided some useful advice, such as habitat restoration and corridor reconstruction, which are beneficial to the conservation of Mrs Hume’s pheasant in this sensitive region. Full article

This article proposes an improved Visible Light Communication (VLC) solution that, besides the indoor lighting and data transfer, offers an energy-efficient alternative for modern workspaces. Unlike Light-Fidelity (LiFi), designed for high-speed data communication, VLC primarily targets applications where fast data rates are not essential. The developed prototype ensures reliable communication under variable lighting conditions, addressing low-speed requirements such as test bench monitoring, occupancy detection, remote commands, logging or access control. Although the tested data rate was limited to 100 kb/s with a Bit Error Rate (BER) below 10⁻⁷, the key innovation is the light dimming dynamic adaptation. Therefore, the system self-adjusts the LED duty cycle between 10% and 90%, based on natural or artificial ambient light, to maintain a minimum illuminance of 300 lx at the workspace level. Additionally, this work includes a scalability analysis through simulations conducted in an office scenario with up to six users. The results show that the system can adjust the lighting level and maintain the connectivity according to users’ presence, significantly reducing energy consumption without compromising visual comfort or communication performance. With this light intensity regulation algorithm, the proposed solution demonstrates real potential for implementation in smart indoor environments focused on sustainability and connectivity. Full article

Cave systems are a kind of natural laboratory for interdisciplinary research on karstogenesis in the context of global changes. In this study, we investigate the concentration of ²²²Rn at 65 points in 37 representative caves of Bulgarian karst through continuous monitoring with passive and active detectors with a duration of 1 to 13 years. The concentration changes strongly both in the long term and seasonally, with values from 0.1 to 13 kBq m⁻³. These variations are analyzed from different perspectives (location and morphological features of the cave system, cave climate, ventilation regime, etc.). The seasonal change in the direction and intensity of ventilation is a leading factor determining the gas composition of the cave atmosphere during the year. Parallel measurements of ²²²Rn and CO₂ concentrations in the cave air show that both gases have a similar seasonal fluctuation. Cases of coincidences of an anomalous increase in the concentration of ²²²Rn with manifestations of seismic activity and micro-displacements along tectonic cracks in the caves have also been registered. The dependencies between the ²²²Rn concentration in the caves and in the soil above them are also discussed, as well as the possible connections between global trends in climate change and trends in ²²²Rn emissions. Special attention is paid to the risks of radiation exposure in show caves. A calculation procedure has been developed to achieve the realistic assessment of the effective dose of cave guides. It is based on information about the annual course of the ²²²Rn concentration in the respective cave and the time schedule of the guides’ stay in it. The calculation showed that the effective dose may exceed the permitted limits, and it is thus necessary to control it. Full article

This paper presents an experimental evaluation of the connectivity reliability of a LoRaWAN (Long Range Wide Area Network), deployed in a rural area of Mozambique, focusing on the influence of distance and relative altitude between end nodes and the gateway. The absence of telecommunications and power infrastructure in the study region provided a realistic and challenging scenario to assess LoRaWAN’s feasibility as a low-cost, low-power solution for remote sensing in disconnected environments. Field trials were conducted using an Arduino-based node (with 2 dBi antenna) powered by a 2200 mAh power bank, with no GPS or cellular support. Data were collected at four georeferenced points along a 1 km path, capturing Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Packet Delivery Rate (PDR). Results confirmed that both distance and terrain elevation strongly affect performance, with significantly degraded metrics when the end nodes were located at lower altitudes relative to the gateway. Despite operational constraints, such as the need for manual firmware resets and lack of real-time monitoring, the network consistently achieved PDR above 89% and remained operational autonomously for over 24 h. The study highlights the effectiveness of installing gateways on natural elevations to improve coverage and demonstrates that even with basic hardware, LoRaWAN (Low Power Wide Area Network), is a viable and scalable option for rural connectivity. These findings offer valuable empirical evidence to promote national digital inclusion policies and future LPWAN deployments. Full article

Thermo-catalytic decomposition (TCD) presents a promising pathway for producing hydrogen from natural gas without emitting CO₂. This process represents a form of fossil fuel decarbonization where the byproduct, rather than being a greenhouse gas, is a solid carbon material with potential for commercial value. This study examines the dynamic behavior of TCD, showing that carbon formed during the reaction first enhances and later dominates methane decomposition. Three types of carbon materials were employed as starting catalysts. Methane decomposition was continuously monitored using on-line Fourier transform infrared (FT-IR) spectroscopy. The concentration and nature of surface-active sites were determined using a two-step approach: oxygen chemisorption followed by elemental analysis through X-ray photoelectron spectroscopy (XPS). Changes in the morphology and nanostructure of the carbon catalysts, both before and after TCD, were examined using high-resolution transmission electron microscopy (HRTEM). Thermogravimetric analysis (TGA) was used to study the reactivity of the TCD deposits in relation to the initial catalysts. Partial oxidation altered the structural and surface chemistry of the initial carbon catalysts, resulting in activation energies of 69.7–136.7 kJ/mol for methane. The presence of C₂ and C₃ species doubled methane decomposition (12% → 24%). TCD carbon displayed higher reactivity than the nascent catalysts and sustained long-term activity. Full article

Urban Waterlogging Susceptibility Assessment (UWSA) is vital for resilient urban planning and disaster preparedness. Conventional methods depend heavily on Historical Waterlogging Records (HWR), which are limited by their reliance on extreme rainfall events and prone to human omissions, resulting in spatial bias and incomplete coverage. While hydrodynamic models can simulate waterlogging scenarios, their large-scale application is restricted by the lack of accessible underground drainage data. Recently released flood control plans and risk maps provide valuable physics-informed priors (PI-Priors) that can supplement HWR for susceptibility modeling. This study introduces a dual-source integration framework that fuses HWR with PI-Priors to improve UWSA performance. PI-Priors rasters were vectorized to delineate two-dimensional waterlogging zones, and based on the Three-Way Decision (TWD) theory, a Multi-dimensional Connection Cloud Model (MCCM) with CRITIC-TOPSIS was employed to build an index system incorporating membership degree, credibility, and impact scores. High-quality samples were extracted and combined with HWR to create an enhanced dataset. A Maximum Entropy (MaxEnt) model was then applied with 20 variables spanning natural conditions, social capital, infrastructure, and built environment. The results demonstrate that this framework increases sample adequacy, reduces spatial bias, and substantially improves the accuracy and generalizability of UWSA under extreme rainfall. Full article

Climate adaptation convenings have emerged in the last decade to share knowledge and accelerate learning in the field. Convenings provide a wealth of information for understanding what issues are being researched and addressed, for evaluating what practices and key components of convenings are considered particularly valuable to practitioners, and for understanding where there are gaps in our knowledge or trends in learning that should be supported. We analyzed survey and attendance data from online and in-person climate convenings in the U.S. to assess perceived outcomes and future intentions, as well as explored thematic changes in sessions at in-person conferences. We performed descriptive analyses on survey and attendance data and conducted thematic analysis of sessions at in-person conferences. Both online and in-person formats achieved high levels of learning and satisfaction reported by respondents, but with higher connectivity and relationship building at in-person events. Topics addressed in forums showed small but meaningful shifts, as some areas of interest increased (e.g., social justice, nature-based solutions) while others decreased (e.g., decision-making tools, infrastructure) or showed variable responses. These trends and feedback provide a foundation for continuing to grow effective practices to support climate adaptation practitioners with the knowledge and opportunities for connection needed to advance the adaptation field. Full article

The pursuit of a comprehensive understanding of human consciousness, which includes the subjective experience of perception, is a long-standing endeavor. A multitude of disciplines have sought to elucidate and define consciousness, with a particular emphasis on its etiology. What is the cause of consciousness? One particularly eye-opening idea is that humans attempt to identify the source of consciousness by leveraging their own consciousness, as if something is attempting to elucidate itself. Strikingly, the results of brain-imaging experiments indicate that the brain processes a considerable amount of information outside conscious awareness of the organism in question. Perhaps, the vast majority of decision making, thinking, and planning processes originate from non-conscious brain processes. Nevertheless, consciousness is a fascinating phenomenon, and its intrinsic nature is both intriguing and challenging to ascertain. In the end, it is not necessarily given that consciousness, in particular the phenomenon of perception as the subjective experience it is, is a tangible function or process in the first place. This is why it must be acknowledged that this theoretical paper is not in a position to offer a definitive solution. However, it does present an interesting new concept that may at least assist future research and potential investigations in achieving a greater degree of elucidation. The concept is founded upon a physical (mathematical) phenomenon known as stochastic resonance. Without delving into the specifics, it is relatively straightforward to grasp one of its implications, which is employed here to introduce a novel direction regarding the potential for non-conscious information within the human brain to become conscious through the introduction of noise. It is noteworthy that this phenomenon can be visualized through a relatively simple approach that is provided in the frame of this paper. It is demonstrated that a completely white image is transformed into an image depicting clearly recognizable content by the introduction of noise. Similarly, information in the human brain that is processed below the threshold of consciousness could become conscious within a neural network by the introduction of noise. Thereby, the noise (neurophysiological energy) could originate from one or more of the well-known activating neural networks, with their nuclei being located in the brainstem and their axons connecting to various cortical regions. Even though stochastic resonance has already been introduced to neuroscience, the innovative nature of this paper is a formal introduction of this concept within the framework of consciousness, including higher-order perception phenomena. As such, it may assist in exploring novel avenues in the search for the origins of consciousness and perception in particular. Full article

Anomaly detection aims to identify abnormal instances that significantly deviate from normal samples. With the natural connectivity between instances in the real world, graph neural networks have become increasingly important in solving anomaly detection problems. However, existing research mainly focuses on static graphs, while there is less research on mining anomaly patterns in dynamic graphs, which has important application value. This paper proposes a Transformer-based semi-supervised anomaly detection framework for dynamic graphs. The framework adopts the Transformer architecture as the core encoder, which can effectively capture long-range dependencies and complex temporal patterns between nodes in dynamic graphs. By introducing time-aware attention mechanisms, the model can adaptively focus on important information at different time steps, thereby better understanding the evolution process of graph structures. The multi-head attention mechanism of Transformer enables the model to simultaneously learn structural and temporal features of nodes, while positional encoding helps the model understand periodic patterns in time series. Comprehensive experiments on three real datasets show that TSAD significantly outperforms existing methods in anomaly detection accuracy, particularly demonstrating excellent performance in label-scarce scenarios. Full article