Search Results (3,146)

This study offers a new approach to probabilistic earthquake hazard assessment (PEHA) in the densely populated regions of Southern Sumatra and West Java, Indonesia. While much attention is given to powerful, offshore megathrust earthquakes, this research focuses on a different yet equally dangerous threat: shallow, moderate-magnitude earthquakes (4.5 ≤ M_w ≤ 6.5) that occur on land. These events, often caused by unmapped faults, pose a significant risk due to their proximity to major cities and infrastructure. To develop a more reliable model, a best-fit earthquake rate model was estimated using declustered shallow earthquake events as a reference. This model enhances existing methods by offering a more precise depiction of where these shallow, damaging earthquakes are likely to occur. We accomplished this by analyzing a comprehensive probability of exceedance (PoE) of earthquakes with magnitudes up to 6.5 and depths up to 50 km that occurred between 1963 and 2022, mapping and modeling both the known active faults and the historical seismic activity in the region, and using advanced statistical methods to create a highly reliable, integrated seismicity rate model. The final product, the Integrated Most Reliable Spatial Seismicity Rate Model (Model_IMRSSR), is proposed as a useful tool for government authorities and urban planners. It can be used to create detailed seismic hazard maps that highlight areas of highest risk, especially those with unmapped faults. By guiding development away from these high-risk zones and identifying specific locations for physical reinforcement, this research provides a framework for sustainable investment. The proactive use of these findings can lead to more resilient communities and a significant reduction in potential damage and loss of life from future earthquakes. Full article

Volume quantification of proglacial lakes is a fundamental prerequisite for reliable hydrodynamic modeling and peak discharge estimation during glacial lake outburst floods (GLOFs). In this study, we integrated in situ bathymetric surveys of 10 proglacial lakes across the Himalaya and Nyainqentanglha ranges with a comprehensive regional dataset to derive optimized empirical models for lake volume and maximum depth. The predictive robustness of these models was rigorously validated using statistical error metrics and independent datasets. Comparative analysis with 14 established formulas demonstrates that our region-specific models yield superior performance in capturing local geomorphological characteristics. Leveraging these refined scaling relationships, we reconstructed the spatiotemporal volume changes in proglacial lakes across the study region from 1990 to 2020. Our analysis reveals significant lake expansion over the past three decades: lake volumes in the Western and Central Himalayas increased by 46.7% and 46.4%, respectively. Notably, the Eastern Himalayas exhibited a volume increase of 51.5%, while the Nyainqentanglha Mountains experienced a substantial expansion of approximately 92.9%. These findings provide critical parametric constraints for satellite-based hydrological monitoring and significantly enhance the reliability of GLOF hazard assessments in the Himalaya and Nyainqentanglha ranges. Full article

This study investigates the dynamic interactions between Bitcoin, global financial indicators, and the Kazakhstan Stock Exchange (KASE) index within a VAR-based econometric framework, addressing a notable gap in the literature on emerging and shallow financial markets. While prior research predominantly focuses on developed economies, evidence suggests that cryptocurrency–stock market linkages are time-varying, crisis-sensitive, and often asymmetric. In this context, the present study examines both short-term causality structures and shock transmission mechanisms among KASE, Bitcoin (BTC), oil prices, the U.S. dollar index (DXY), and the VIX using monthly data for the period 2017M01–2026M04. Empirical findings indicate that, despite the absence of statistically significant Granger causality from individual global variables to KASE, the joint dynamics suggest a non-negligible, albeit indirect, interaction structure. Variance decomposition and impulse-response analyses further reveal that KASE dynamics are predominantly driven by its own shocks, reflecting the relatively segmented and internally driven nature of the market. Diagnostic tests confirm the robustness of the model, with no evidence of serial correlation or heteroskedasticity in residuals. These findings are consistent with the structural characteristics of the Kazakh financial system, including limited market depth, lower investor participation, and high sensitivity to domestic macroeconomic conditions. Unlike developed markets where stronger integration is observed, KASE appears only weakly connected to global financial and cryptocurrency markets. The study contributes to the literature by providing empirical evidence from a frontier market and highlights the importance of considering country-specific structural factors when evaluating financial integration. Policy implications emphasize the need to enhance market depth, transparency, and investor confidence to strengthen the responsiveness of KASE to global financial developments. Full article

The 1991 Gulf War contaminated more than 49 km² of Kuwaiti desert with hydrocarbon spills, a persistent threat to soil resources, infrastructure and the United Nations Sustainable Development Goals embedded in Kuwait Vision 2035. Managing these legacy lands calls for predictive tools that capture spatial variability while remaining computationally tractable and statistically defensible at the small sample sizes typical of post-conflict monitoring. This study develops a multi-resolution physics-informed neural network that combines wavelet-based parameter encoding, scale-dependent regularisation and a progressive upsampling training protocol. The framework is evaluated on nine trial-pit observations at a single depth of 30 cm in the Al-Ahmadi field, where the contaminated pits show a mean internal friction angle of 26.8° compared with 36.0° at co-located control pits sampled at the same time. Generalisation is assessed by leave-one-out cross-validation across the nine locations. The framework attains a friction-angle root-mean-square error of 1.29°. Under the same data and compute budget, ordinary kriging and a standard physics-informed neural network remain statistically competitive. This outcome indicates that the physics residual acts as a mass-conservation-consistent smoothness regulariser rather than a site-calibrated transport predictor. A multi-objective remediation workflow produces a cost-versus-residual-risk Pareto front for a scenario-specific 1–2 km² case, presented as an illustrative decision-support envelope pending external pilot calibration. A projected pathway from these outcomes to six Sustainable Development Goals and two pillars of Kuwait Vision 2035 is also discussed; quantitative attribution at this sample size is beyond scope. The small-sample, single-depth and single-locality limitations that bound the admissible inference are stated explicitly. Full article

The deflection of prestressed (pretensioned) concrete members strengthened with FRP requires a comprehensive evaluation. An extensive parametric study is performed using a rigorous analysis procedure based on a trilinear moment-curvature approach. There are 8100 pretensioned concrete beams analyzed by varying the cross-section dimensions, span length-to-depth ratio, shear span-to-span ratio, concrete compressive strength, prestressing reinforcement ratio, FRP strengthening ratio and FRP material properties. It was determined that the normalized effective moment of inertia at first yielding is statistically correlated with the normalized cracked moment of inertia, with an almost-perfect regression (R² = 0.9886). It was further found that when postulating the inverse of the effective moment of inertia in terms of a parabolic function of the beam maximum moment, the deflections of the cracked beam agree closely with experimental deflections. Boundary conditions for that equation are applied at the cracking and prestress-yielding points. Ultimately, it was realized that the immediate deflection predictions based on the modified beam effective moment of inertia expression proposed yield reliable deflection estimates for cracked prestressed members externally strengthened with FRP, compared with experimental results and other analytical predictions. Full article

Graph machine learning reports steady progress across node, graph, and link prediction, across temporal and hypergraph frontiers, and across the emerging class of graph foundation models. This review asks a prior question: when a method is reported to outperform the alternatives, how far does the evidence support the claim? We organize the answer around four axes of evaluation rigor: statistical rigor (seeds, dispersion, formal significance testing), baseline fairness (budget-parity tuning of trivial and structure-agnostic baselines), data integrity (leakage, duplication, negative sampling, contamination), and claim integrity (whether gains survive fair tuning and discriminative benchmarks). Drawing on a criterion-based corpus of 150 studies, of which 51 were read in full depth, we find a consistent picture. Only two of 25 methodologically central backbone studies apply a formal between-method significance test, and reported gains repeatedly shrink or disappear once a trivial baseline is tuned to parity, a leaked split is repaired, or a pretrained model is evaluated on unseen data. We argue that these failures share one cause: the saturation of benchmarks that can no longer discriminate between methods. The principal output is a minimum reporting standard, a concrete four-axis checklist that authors and reviewers can apply at negligible cost. Full article

Microplastics, plastic particles smaller than 5 mm, are significant pollutants of growing global concern due to their persistence, widespread distribution, and potential ecological and human health risk. Numerous studies have proposed different methods for sampling microplastics in river sediments and water. However, comparative studies evaluating how different sampling methods affect the quantification of microplastic abundance in surface water remain limited. This study focused specifically on surface water, as it represents the primary pathway of microplastic transport, and the yield is most directly influenced by sampling method selection. Therefore, this study aimed to compare the abundance and characteristics of microplastics collected using three sampling methods: plankton nets, buckets, and pumps, which were selected to represent the full spectrum of commonly used riverine surface water collection approaches. Each method collected an equivalent volume of water, 1000 L. Statistical analysis showed no significant differences among the methods in terms of microplastic abundance and characteristics. These results suggest that the selection of a sampling method should be primarily based on river characteristics such as flow velocity, depth, and channel width rather than methodological differences. The findings provide practical guidance for selecting appropriate sampling approaches in low-flow river environments. Full article

Additively manufactured composite architected discs offer a potential route for producing lightweight semi-finished blanks that can subsequently be shaped by conventional forming processes. However, the relationship between infill architecture, feature size, and deep-drawing formability remains poorly understood. This study investigates the deep-drawing response of material-extruded short-fibre-reinforced polymer composite discs by combining experimental tests and finite element simulations. Four infill strategies, namely perforated body, re-entrant, square and triangular, were first compared at drawing depths of 10 and 20 mm. The perforated body and re-entrant geometries were successfully formed at 10 mm, whereas only the perforated body withstood 20 mm without macroscopic failure. A second campaign focused on perforated discs with hole diameters of 2.5, 5, 7.5 and 10 mm. All configurations were drawable at 10 mm, while the 2.5 mm case failed at 20 mm. Statistical analysis confirmed that hole diameter significantly affected both retained cup height and side-hole aspect ratio. At 20 mm, larger holes reduced local ovalization but increased elastic recovery, leading to lower retained cup height. FEM simulations were used as an interpretative first-order model. They supported the experimental trends by comparing deformation modes, tensile/compressive stress redistribution, forming energy and strain localization. The results show that the formability of architected composite blanks is governed not only by material volume or porosity but by the ability of the internal architecture to accommodate deformation through a suitable balance between local stiffness and geometric compliance. These findings provide design-oriented guidelines for the development of additively manufactured architected blanks intended for hybrid additive–forming manufacturing routes. Full article

Older primary schools in Croatia are frequently located in densely built older settlement cores. Micro-locations surrounding school entrances are often not the result of prior urban or traffic planning; instead, they are retroactively managed through infrastructure and signalling interventions. Pupils participate in traffic as pedestrians, cyclists, e-scooter users, or passengers in cars, school buses, or public buses. The proposed integrated research approach includes: an online survey of pupils’ travel behaviour, systematic safety assessments of entrance micro-locations using the iRAP methodology, as well as field measurements and in-depth analysis of vehicle speeds, traffic flow and structure. For classes organised in two shifts, an online survey of parents (for classroom-based education) and pupils (for subject-based education) covered 56% of the pupil population. Because pupils’ travel mode is the factor most susceptible to influence through infrastructure improvements, statistical analysis was conducted using the χ²-test for the purpose of investigating relationships with the other three traffic-relevant determinants: school age group, pupils’ sex, and distance from school. Approximately three-quarters of pupils live less than 2 km from the typical school. If peak vehicle traffic does not coincide with the peak of pupil arrivals and departures during the overlap of two school shifts, part of the traffic on the school-access street may be unrelated to direct school-access activities. Vehicle-type restrictions and one-way traffic operation should be considered as measures to improve pupils’ safety. The proposed groups of measures for improving pupils’ safety include: (i) educational workshops for pupils, parents and teachers; (ii) reconstruction of school entrance micro-locations; (iii) targeted interventions in the traffic environment within a 2 km perimeter. Full article

Gathering spaces within urban parks serve as primary outdoor leisure venues, playing a critical role in facilitating social interaction and restoring the physical and mental well-being of this demographic. This study uses the example of Pipa Mountain Park in Chongqing, China to explore the psychological and physiological perceptual effects of spatial environmental characteristics on young adults in four typical gathering spaces: path platform, elevated point, viewing boundary, and key node. To this end, we employed onsite experimental methods using wearable ergonomic devices to collect participants’ physiological data, including electrophysiological, electroencephalogram (EEG), and eye-tracking data. Visual and auditory psychological perception evaluation data were obtained through on-site questionnaires. Descriptive statistical analysis revealed differential trends in participants’ psychological perceptions and physiological responses across distinct gathering spaces. The elevated point demonstrated the most favorable ratings for the psychological dimension “comfort” (M = 1.63, SD = 2.09). Subsequent principal component analysis elucidated key psychological perception indicators in mountainous settings, while Friedman test, Kruskal–Wallis tests, and random forest modeling quantified the effects of specific spatial environmental indicators on perceptual responses. Results indicated significant differences in psychological perceptions and physiological responses across gathering space typologies (p < 0.05). Influenced by the preferences and behavioral habits of young adults, environmental element complexity significantly enhanced attentional engagement (χ² = 68.428, p < 0.01) and facilitated positive perceptual responses. The synergistic effects of the visual and auditory elements significantly enhance the restorative benefits of space; however, poor accessibility weakens this advantage. This study provides evidence for the in-depth analysis of the intrinsic mechanisms between the spatial environment and multisensory perception in urban mountain parks. Full article

Patterned and multilayer materials used in advanced technologies exhibit complex three-dimensional compositional architectures in which buried interfaces and elemental gradients critically influence performance. However, most non-destructive analytical techniques remain largely surface-sensitive, limiting access to subsurface information in opaque systems. In this work, we present a novel framework for non-destructive three-dimensional elemental characterization based on the integration of artificial neural networks with ion beam analysis techniques, namely, Particle-Induced X-ray Emission (PIXE) and Elastic Backscattering Spectrometry (EBS). The proposed approach enables the reconstruction of depth-resolved 3D elemental distributions by combining complementary spectral information with data-driven analysis. The methodology is demonstrated on a GaSb thermophotovoltaic device featuring multilayer metallic contacts, where the elemental distribution beneath thick gold layers is revealed for the first time. The neural network approach overcomes limitations associated with low counting statistics in pixel-resolved spectra, enhancing sensitivity and enabling reliable classification of compositional features. The fusion of PIXE-derived lateral information with EBS-based depth profiling enables full three-dimensional visualization and quantitative and qualitative mapping of elemental distributions. Beyond the specific case study presented, this approach provides a general and scalable strategy for 3D compositional analysis of complex materials, including systems containing both heavy and light elements. The results highlight the potential of combining advanced data-driven methods with ion beam techniques to expand the capabilities of non-destructive characterization, with broad applicability in energy, electronics, and functional materials. Full article

Background: Flexibility in dosing volume within an autoinjector platform is critical for streamlining product development, enabling dose adjustments without altering concentration. This study presents a framework to evaluate the capability of a spring-actuated autoinjector platform to deliver fill volumes below its original intended design. Research design and methods: This study identifies performance attributes that may be affected by reduced fill volume and introduces a framework to assess them under low-fill conditions. The framework proposes evaluating dose accuracy and injection time using a Zwick machine and analyzing needle dynamics with high-speed imaging. It recommends using SEC, MFI, and HIAC to quantify protein aggregation and subvisible particles for drug product quality assessment. Visual inspection is included to examine syringe integrity. Results: The proposed framework was applied to an existing autoinjector platform comprising three models (AI 1, AI 2, and AI 3) designed for nominal fill volumes of 0.5, 1, and 2 mL, respectively, and evaluated at fill-volume reductions of up to 50%. Despite increased driving-rod acceleration and syringe stress under low-fill conditions, all models maintained consistent performance across the evaluated parameters. Dose accuracy was not significantly different from nominal-fill conditions, while injection time decreased by up to 40% at the lowest tested volumes. Peak penetration depth increased significantly for AI 1 at the lowest fill volume (p < 0.01); however, final penetration depth remained statistically unchanged for all models. Subvisible particles, primarily attributed to silicone oil droplets, decreased under low-fill conditions. No syringe breakage was observed, and Weibull-based failure probability for AI 3 remained below 10⁻⁷ across the tested impact-energy range. Conclusions: The study confirmed that the framework provides a standardized approach to assess low-fill performance and can inform early platform development and design decisions. This is particularly relevant with the advent of large volume autoinjectors, which may be designed not only to deliver larger doses but also to accommodate smaller fill volumes within the same platform. Overall, this approach supports the development of flexible dosing strategies and may help accelerate timelines from dose selection to regulatory submission. Full article

Geospatial monitoring is crucial for landslide research and hazard mitigation. This paper provides a comprehensive overview of contemporary landslide monitoring methods and lays the groundwork for a unified monitoring framework. An in-depth bibliometric analysis and critical review of state-of-the-art approaches developed over the past decade are presented. The study proposes a new classification and systematization of geospatial monitoring methods based on dimensionality (1D, 2D, and 3D) and referencing approach (absolute or relative). The reviewed methods include geodetic techniques, photogrammetry, laser scanning, global satellite navigation systems, UAVs, radar interferometry, and various sensors. The operational characteristics, advantages, and limitations of the existing methods are analyzed with respect to monitoring accuracy, spatial coverage, temporal resolution, and applicability to different deformation conditions. A comparative analysis and systematization of monitoring methods according to landslide velocity classes are presented. This framework links achievable observation accuracy and monitoring frequency to landslide dynamics. Based on the analysis, a refined workflow for geospatial landslide monitoring is proposed. The workflow integrates monitoring design, observation network configuration, data integration, statistical analysis, and forecasting stages. The analysis indicates that effective landslide monitoring requires integrated multi-sensor systems. Future developments are expected to focus on geospatial and non-geospatial data integration, monitoring automation, and next-generation monitoring system design. Full article

Background: Antimicrobial resistance (AMR) is increasingly recognized as a major public health challenge in Zambia. However, limited evidence exists on the factors driving AMR and antimicrobial use behaviours among cattle farmers. This study explored farmers’ knowledge, attitudes, and practices (KAPs) regarding AMR and antimicrobial use (AMU), and explored factors influencing them. Methods: Data were collected from Namwala, Mpongwe, and Chingola districts between January and April 2024. A total of 377 cattle farmers participated in a structured survey, supplemented by ten focus group discussions (FGDs) and seventeen in-depth interviews (IDIs). Qualitative data were analysed thematically to identify recurring patterns, while quantitative data were summarized using descriptive statistics and analysed using bivariate tests and regression models to assess key associations. Results: Overall, a small proportion of farmers demonstrated high levels of knowledge (33.9%), positive attitudes (40.4%), and good practices (19.6%) related to AMU and AMR, with significant differences observed across districts. Major drivers of AMU included poor implementation of biosecurity measures, limited access to veterinary services, high reliance on non-prescribed antimicrobials, and weak enforcement of regulations governing antimicrobial distribution. Conclusions: This study highlights critical gaps in AMR-related knowledge and widespread irresponsible AMU among cattle farmers in Zambia. Strengthening targeted AMU/AMR awareness campaigns, improving veterinary service infrastructure, and enhancing regulatory oversight on antibiotic distribution are urgently needed to protect both animal and public health. These findings can support policymakers in designing evidence-based interventions to curb AMR in the livestock sector. Full article

We introduce the Hamiltonian pseudorandom function (HPRF), a new symmetric cryptographic primitive in which the function family $\left\{F_k\right\}$ is defined by $F_k\left(q\right)=\nabla S_k\left(q\right)$, the gradient of the generating function of a secret Lagrangian submanifold $L_k$ on the symplectic torus ${\mathbb{T}}^{2n}$. The key k specifies a composition of kicked-rotor maps in the strongly chaotic regime, whose classical Lyapunov exponents grow as $log(K/2)$ per kick. The HPRF is best understood as a seeded one-way function with high min-entropy output: $F_k$ is smooth ($C^{\infty }$), so its raw output is not directly usable as a uniform keystream, but it is computationally hard to invert. We construct three symmetric encryption modes—Mode A (key-dependent coordinate frame), Mode C (Lagrangian keystream), and Mode AC (hybrid)—in which the HPRF supplies the hardness and a key derivation function (HKDF) supplies bit-level uniformity. Standard symmetric composition then yields IND-CPA and IND-CCA2 security. Classical security reduces to the Lagrangian identification problem (LIP), shown as equivalent to the Hamiltonian inversion problem of recovering the kick parameters, which we state as an explicit hardness assumption supported by a precision/sample-complexity obstruction from the positive Lyapunov exponents, by the empirical failure of concrete attacks, and (more heuristically) by topological suggestiveness from the Arnold conjecture and Floer theory. We validate a gradient-fitting attack and an algebraic-structure attack and show that both fail. For quantum security, we propose what we believe is the right framing: that the composed Floquet operator ${\widehat{U}}_K^r$ is a candidate pseudorandom unitary (PRU) in the sense of Ji–Liu–Song. We provide three independent pillars of evidence—Wigner–Dyson spectral statistics, Lyapunov-rate scrambling, and conjectural approximate-design behaviour—and reduce the HPRF quantum security to the PRU conjecture for ${\widehat{U}}_K^r$. We then retire the dynamical-localisation argument of previous drafts as inapplicable at cryptographic parameters; the chaotic-pseudorandomness regime that the operator actually inhabits is, we argue, a stronger foundation than the one that localisation would have provided. A deterministic fixed-point arithmetic core ensures cross-platform bit-exact consistency. A reference implementation validates correctness across all modes, and an NIST SP 800-90B analysis of the output min-entropy fixes the parameter sets. As a foundational proposal, the HPRF is intended for settings that seek a symmetric hardness assumption structurally independent of the algebraic problems underlying current cryptography, for example, as a hedge primitive in defence-in-depth designs, or as a basis for further study of geometry- and chaos-based cryptography, rather than as a drop-in replacement for AES or lattice-based schemes at this stage. Full article