Search Results (3,422)

This study numerically investigates pavement damage caused by explosions in buried leaking natural gas pipelines using a coupled Lagrangian–Eulerian (CLE) framework in LS-DYNA. The gas phase is described by a Jones–Wilkins–Lee-based equation of state, while soil and pavement are modeled using a pressure-dependent soil model and the Riedel–Hiermaier–Thoma concrete model with strain-based erosion, respectively. The approach is validated against benchmark underground explosion tests in sand and blast tests on reinforced concrete slabs, demonstrating accurate prediction of pressure histories, ejecta evolution, and crater or damage patterns. Parametric analyses are then conducted for different leaked gas masses and pipeline burial depths to quantify shock transmission, soil heave, pavement deflection, and damage evolution. The results indicate that the dynamic response of the pavement structure is most pronounced directly above the detonation point and intensifies significantly with increasing total leaked gas mass. For a total leaked gas mass of 36 kg, the maximum vertical deflection, the peak kinetic energy, and the peak pressure at the bottom interface at this location reach 148.46 mm, 14.64 kJ, and 10.82 MPa, respectively. Moreover, a deflection-based index is introduced to classify pavement response into slight (<20 mm), moderate (20–40 mm), severe (40–80 mm), and collapse (>80 mm) states, and empirical curves are derived to predict damage level from leakage mass and burial depth. Finally, the effectiveness of carbon fiber reinforced polymer (CFRP) strengthening schemes is assessed, showing that top and bottom surface reinforcement with a total CFRP thickness of 2.67 mm could reduce vertical deflection by up to 37.93% and significantly mitigates longitudinal cracking. The results provide a rational basis for safety assessment and blast resistant design of pavement structures above buried gas pipelines. Full article

This paper proposes a novel seven-level switched-capacitor multilevel inverter featuring a shared front-end DC-link structure that achieves triple voltage gain with reduced component count. A distinctive feature of this design is its inherent capacitor voltage self-balancing capability, thereby eliminating the need for complex control algorithms typically associated with multilevel converters. Moreover, the topology demonstrates particularly significant advantages in three-phase implementations, where a single DC source, front-end switching devices, and capacitors can be shared across all phases—thus substantially reducing component count and system complexity compared to conventional designs. Additionally, this paper proposes an improved carrier-based modulation strategy for this topology requiring only a single triangular carrier, along with a systematic method for determining optimal capacitance values. Through detailed comparative assessment against state-of-the-art switched-capacitor seven-level inverters, the superior performance characteristics of the proposed topology are clearly demonstrated. Finally, simulation results under various operating conditions are presented and subsequently validated through experimental testing on a laboratory prototype, confirming the practical viability of the proposed solution. Full article

In the immediate aftermath of the Big Bang, the universe existed in an extremely hot, dense state in which particle interactions occurred not in vacuum but within a thermal medium. Under such conditions, the standard framework of quantum field theory (QFT) requires a finite-temperature extension, wherein propagators—and hence the fundamental structure of the theory—are modified to reflect thermal background effects. These thermal modifications are central to understanding the nature of electroweak symmetry breaking (EWSB) as a high-temperature phase transition, potentially leading to qualitatively different vacuum structures for the Higgs field as the universe cooled. Finite-temperature corrections naturally regulate ultraviolet divergences in propagators, hinting at a possible route toward ultraviolet completion. However, these same thermal effects exacerbate infrared pathologies and can lead to imaginary contributions to the effective potential, particularly when analyzing metastable or multi-vacuum configurations. Additional theoretical challenges, such as gauge dependence and renormalization scale ambiguity, further obscure the precise characterization of the electroweak phase transition—even in minimal extensions of the Standard Model (SM). This review presents the theoretical foundations of finite-temperature QFT with an emphasis on how different field species respond to thermal effects, identifying the bosonic sector as the primary source of key theoretical subtleties. We focus particularly on the scalar extension of the SM, which offers a compelling framework for realizing first-order electroweak phase transitions, electroweak baryogenesis, and accommodating dark matter candidates depending on the underlying ${\mathbb{Z}}_2$ symmetry structure. Full article

Financial markets evolve through recurring phases of stability, turbulence, and structural transformation. Standard Hidden Markov Models (HMMs) assume fixed transition probabilities, which limits their ability to capture such higher-order changes in market behavior. This study introduces an Adaptive Hierarchical Hidden Markov Model (AH-HMM), where regime transitions depend on an unobserved meta-regime that reflects the broader macro-financial environment. Each meta-regime defines its own transition matrix across market states such as bull, bear, and turbulent phases. In this way, the model adapts dynamically to structural changes arising from crises, policy shifts, or variations in investor sentiment. Using weekly data for major equity indices, aggregated from daily prices, together with macro-uncertainty indicators, we show that the AH-HMM identifies key turning points including the Global Financial Crisis, the COVID-19 shock, and the post-2022 tightening cycle. In our empirical application, where we approximate the latent structural layer by low- and high-uncertainty environments defined from the VIX, the adaptive model attains a higher in-sample likelihood and delivers competitive out-of-sample forecasts and Value-at-Risk coverage relative to conventional HMMs and time-varying transition alternatives. Overall, the results highlight a mechanism of structural learning within market regimes and offer tools for risk management and policy analysis under uncertainty. Full article

Indium-doped SnTe (Sn_1−xIn_xTe) provides a model platform for exploring the emergence of superconductivity within a topological crystalline insulator. Here, we present a systematic investigation of the structural, transport, and thermodynamic properties of high-quality single crystals with 0.0 ≤ x ≤ 0.5. All compositions up to x = 0.4 form a single-phase cubic structure, enabling a controlled study of the superconducting state. Electrical resistivity and specific heat measurements reveal a bulk, fully gapped s-wave superconducting phase whose transition temperature increases monotonically with In concentration, reaching Tc ≈ 4.7 K at x = 0.5. Analysis of the electronic specific heat and McMillan formalism shows that the electron–phonon coupling constant λ_el-ph systematically increases with doping, while the Debye temperature systematically decreases, resulting in the lattice softening. This behavior, together with the observed evolution of the normal-state resistivity exponent from Fermi-liquid (n ≈ 2.04) toward non-Fermi-liquid values (n ≈ 1.72), demonstrates a clear crossover from weak to strong interaction with increasing In content. These results establish Sn_1−xIn_xTe as a tunable superconducting system in which coupling strength can be continuously controlled, offering a promising platform for future studies on the interplay between phonon-mediated superconductivity and crystalline topological band structure. Full article

Four new porous homochiral metal–organic frameworks (MOFs), [M₂(camph)₂(bpa)]∙Solv (M = Co(II), Ni(II), Cu(II) and Zn(II)), based on (+)-camphoric acid (H₂camph) and 1,2-bis(4-pyridyl)ethane (bpa) were synthesized and characterized. The crystal structures of [Ni₂(camph)₂(bpa)] and [Zn₂(camph)₂(bpa)] were established by single-crystal X-ray diffraction analysis. Powder X-ray data prove the phase purity and isostructural nature of all four compounds. The thermal stability of [M₂(camph)₂(bpa)] was found to depend on the electronic configuration, as well as on the redox properties of the metal cation, and varied from 225 °C (M = Zn²⁺) to 375 °C (M = Ni²⁺). The reversible, solvent-induced sponge-like dynamics of the coordination frameworks was thoroughly investigated. Changes in the positions of reflexes, related to the length of the flexible bpa linker, were observed by powder XRD, pointing to transitions between an open-framework phase and a squeezed, non-porous phase in a crystal-to-crystal manner, while the integrity and connectivity of the coordination network were maintained. Size-selective adsorption from a benzene–cyclohexane 1:1 mixture on [Zn₂(camph)₂(bpa)] was studied by ¹H NMR analysis. The benzene-favorable composition of guest molecules (C₆H₆:C₆H₁₂ = 5:1) occluded within the host crystalline sponge revealed a preferable adsorption affinity towards smaller benzene compared with larger cyclohexane. High framework stability in various solvents, as well as successful molecular separation in the liquid state, validates the potential utilization of chiral porous metal(II) camphorate MOFs in important stereoselective applications. Full article

Major depressive disorder (MDD) is increasingly recognized as a disorder of impaired neuroplasticity and large-scale network dysfunction rather than a simple monoaminergic deficit. Converging evidence indicates that chronic stress and depression erode synaptic connectivity, reduce glial support, and destabilize functional interactions among the default mode, salience, and executive networks. Conventional antidepressants indirectly restore circuit function over weeks, but the advent of rapid-acting glutamatergic agents has opened a new path for targeting these abnormalities directly. In this narrative review, we synthesize molecular, cellular, and connectomic findings to outline a conceptual Connectomic Glutamate Framework of Depression. We first examine how NMDAR blockade and subsequent AMPAR facilitation activate mTORC1 and BDNF signaling, driving synaptogenesis and dendritic spine formation. We then highlight the role of astrocytes and microglia in shaping the “quad-partite synapse” and sustaining network integrity. Neuroimaging studies demonstrate that glutamatergic modulators remodel dysfunctional networks: dampening DMN hyperconnectivity, enhancing fronto-limbic coupling, and normalizing salience-driven switching. Integrating these domains, we propose a hypothesis-generating, two-phase model in which glutamatergic agents destabilize maladaptive attractor states and then reintegrate circuits through structural remodeling. This framework bridges molecules, cells, and networks, offering mechanistic insight into the rapid efficacy of glutamatergic antidepressants and highlighting priorities for clinical translation. Full article

Background: An anterior cruciate ligament (ACL) rupture is one of the most psychologically demanding injuries in professional sport. This study aimed to describe a structured psychological intervention conducted during the rehabilitation process following an ACL rupture in a professional female futsal player. Methods: A single-case longitudinal design was implemented with three phases (pre-test, intervention, post-test) across a 12-month rehabilitation period. Psychological assessment was conducted at four key points: initial evaluation, rehabilitation follow-up, medical discharge, and three- and six-month follow-ups. The battery included perfectionism (FMPS), anxiety (STAI), depression (BDI-II), mental health indicators (DASS-21, GHQ-12), sleep quality (PSQI), pain perception and catastrophizing (VAS, PCS), mood states (POMS), psychological readiness for return to play (PRIA-RS), and perceived intervention effectiveness. The program consisted of 15 individual sessions plus a follow-up, combining cognitive–behavioral therapy principles, mindfulness-based techniques (relaxation, body scan, visualization), cognitive restructuring, sleep hygiene, goal setting, problem-solving, and emotional expression strategies. Results: Progressive and sustained improvements were observed in mood states and pain catastrophizing, along with enhanced sleep quality, psychological readiness, and reintegration into competition. Improved overall mental health indicators were also observed, supporting adherence to rehabilitation and return-to-play confidence. Conclusions: This case highlights the relevance of structured psychological intervention as an integral component of injury rehabilitation in professional athletes with ACL rupture, supporting its inclusion in multidisciplinary care and future research to optimize recovery and prevent maladaptive outcomes. Full article

AlCoCrFeNi high-entropy alloys (HEAs) are promising materials due to their exceptional mechanical properties and thermal stability. This study employs first-principles calculations based on density functional theory (DFT) to investigate the phase stability and electronic properties of AlCoCrFeNi HEA. The atomic size difference (δ) was determined to be 5.44%, while the mixing enthalpy (ΔH_mix) was found to be −14.24 kJ/mol, and the valence electron concentration (VEC) was measured at 7.2, indicating a dual-phase structure consisting of the BCC and B2 phases. The formation energies indicated that the BCC phase exhibits the highest stability under typical conditions. The elastic properties were assessed, revealing Young’s modulus of 250 GPa, a shear modulus of 100 GPa, and a bulk modulus of 169 GPa, which suggest high stiffness. The alloy demonstrated a Poisson’s ratio of 0.25 and a G/B ratio of 0.59, indicating relatively brittle behavior. Microhardness simulations predicted a value of 604 HV0.2, which closely aligns with experimental measurements of 602 HV0.2 at 1300 W laser power, 532 HV0.2 at 1450 W, and 544 HV0.2 at 1600 W. The electronic structure analysis revealed metallic behavior, with the d-orbitals of Co, Fe, and Ni contributing significantly to the electronic states near the Fermi level. These findings offer valuable insights into the phase behavior and mechanical properties of AlCoCrFeNi HEA, which are crucial for the design of high-performance materials suitable for extreme engineering applications. Full article

Public procurement in many European Union member states remains strongly price-oriented, often at the expense of delivery performance, innovation, and effective risk management. This study examines how the Best Value Approach (BVA) operates within a post-transition, legality-focused administrative environment through a document-based embedded case study of a major public construction contract in the Czech Republic. By analysing artefacts from the Selection, Clarification, and Execution phases, the study traces how BVA’s core governance mechanisms—expert signalling, vendor-led risk ownership, and information-centric oversight—functioned under locally constraining conditions. The findings show that BVA improved capability sorting, surfaced risks earlier, and enhanced transparency through structured reporting instruments such as Weekly Risk Reports (WRRs), Directors’ Reports (DRs), and Key Performance Indicators (KPI)s. However, the performance effects were partial. Three boundary conditions attenuated BVA’s mechanisms: a 40% price weighting that constrained qualitative differentiation, the omission of a formal Value-Added (VA) pathway for supplier-initiated optimisation, and the absence of continuous expert facilitation to support methodological fidelity. A documented execution-phase cost variance of approximately five percent further indicates residual volatility where key BVA complements are incomplete. The study integrates Principal–Agent theory, New Public Governance, and institutional isomorphism to explain why BVA’s governance architecture activated only in attenuated form and identifies the institutional conditions that moderate its effectiveness. While limited to a single revelatory case, the findings support analytical generalisation to similarly price-dominant, audit-driven procurement regimes in post-transition EU member states and offer practical guidance for evaluation design, innovation pathways, and facilitation models. Full article

Cognitive deterioration and the transition to neurodegenerative disease does not develop through simple, linear regression; it develops as rapid and global transitions from one state to another within the neural network. Developing understanding and control over these events is among the largest tasks facing contemporary neuroscience. This paper will discuss a conceptual reframing of cognitive decline as a transitional phase of the functional state of complex neural networks resulting from the intertwining of molecular degradation, vascular dysfunction and systemic disarray. The paper will integrate the latest findings that have demonstrated how the disruptive changes in glymphatic clearance mechanisms, aquaporin-4 polarity, venous output, and neuroimmune signaling increasingly correlate with the neurophysiologic homeostasis landscape, ultimately leading to the destabilization of the network attraction sites of memory, consciousness, and cognitive resilience. Furthermore, the destabilizing processes are exacerbated by epigenetic silencing; neurovascular decoupling; remodeling of the extracellular matrix; and metabolic collapse that result in accelerating the trajectory of neural circuits towards the pathological tipping point of various neurodegenerative diseases including Alzheimer’s disease; Parkinson’s disease; traumatic brain injury; and intracranial hypertension. New paradigms in systems neuroscience (connectomics; network neuroscience; and critical transition theory) provide an intellectual toolkit to describe and predict these state changes at the systems level. With artificial intelligence and machine learning combined with single cell multi-omics; radiogenomic profiling; and digital twin modeling, the predictive biomarkers and early warnings of impending collapse of the system are beginning to emerge. In terms of therapeutic intervention, the possibility of reprogramming the circuitry of the brain into stable attractor states using precision neurointervention (CRISPR-based neural circuit reprogramming; RNA guided modulation of transcription; lineage switching of glia to neurons; and adaptive neuromodulation) represents an opportunity to prevent further progression of neurodegenerative disease. The paper will address the ethical and regulatory implications of this revolutionary technology, e.g., algorithmic transparency; genomic and other structural safety; and equity of access to advanced neurointervention. We do not intend to present a list of the many vertices through which the mechanisms listed above instigate, exacerbate, or maintain the neurodegenerative disease state. Instead, we aim to present a unified model where the phenomena of molecular pathology; circuit behavior; and computational intelligence converge in describing cognitive decline as a translatable change of state, rather than an irreversible succumbing to degeneration. Thus, we provide a framework for precision neurointervention, regenerative brain medicine, and adaptive intervention, to modulate the trajectory of neurodegeneration. Full article

Achieving high-precision 3D reconstruction of highly reflective objects remains a major challenge in structured light measurement due to local overexposure and fringe degradation caused by specular reflections, which destabilize phase retrieval and reduce reconstruction accuracy. To address this problem, we propose an enhanced structured-light reconstruction network, FaNIC-Net, enabling robust feature extraction and fringe restoration under strong reflective interference. FaNIC-Net comprises two complementary modules: a Frequency-aware Multi-scale Convolution (FaMC) module that embeds DFT/IDFT operations into a multi-scale convolution pipeline to enhance critical frequency components and preserve fringe periodicity, and a Nearest-Neighbor Interpolation Convolution (NIC) module that decouples resolution enhancement from convolution, effectively mitigating checkerboard artifacts and improving high-frequency texture continuity. Experiments on real and synthetic datasets demonstrate that FaNIC-Net outperforms state-of-the-art methods in terms of MAE and SSIM, achieving superior depth recovery particularly in severely reflective regions, thus providing a robust and generalizable solution for fringe degradation on high-reflective surfaces. Full article

This work provides the first systematic survey of the two–photon properties of 97 natural 9,10–anthraquinones from plants and fungi. A comprehensive computational dataset of two–photon absorption properties calculated using RI–CC2/aug–cc–pVDZ is presented. Single degenerate photon energies are required for two–photon excitation span 575.8–1007.9 nm across the five lowest singlet states, with all S₀→S₁ transitions falling within the biological therapeutic window. Remarkably, S₃ state exhibits systematically enhanced TPA efficiency, with 60% of compounds surpassing 1 GM and achieving a mean cross–section of 29.9 GM–substantially higher than S₁ (mean: 7.5 GM) or S₅ (mean: 12.2 GM). Three compounds demonstrate exceptional performance: cynodontin (73.6 GM, S₂), dermocybin (68.7 GM, S₄), and morindone (50.7 GM, S₃). Natural transition orbital analysis reveals that these excitations possess high configurational purity (82.5–94.2% single–excitation character) and diagnostics validating the single–reference treatment. The observed spatial separation between hole and particle NTOs, combined with extreme transition dipole anisotropy along the molecular long axis, indicates dipolar charge–transfer enhancement. Comprehensive structure–property analysis establishes that strategic peri–hydroxylation (1,5 or 1,8), alkoxylation, and multi–site donor substitution maximise TPA cross–sections through enhanced charge–transfer character and longitudinal polarisability. Comparison with aqueous–phase calculations for three compounds reveals non–systematic solvent–induced redistributions of TPA activity across excited states, indicating that gas–phase outcomes serve primarily as internal benchmarks and intrinsic descriptors of structure–property relationships rather than quantitative predictors of photoactivity. Full article

Tables, as an efficient form of structured data representation, are widely applied across domains. However, traditional manual processing methods are inadequate in the big data era, and existing table recognition models, such as SLANet, still face performance limitations. To address these issues, this paper proposes an improved SLANet framework. First, the original H-Swish activation is replaced with the Mish function to enhance feature representation. Second, an end-of-sequence (EOS) termination mechanism is introduced to reduce computational redundancy during inference. Third, a three-phase training strategy is designed to achieve progressive performance improvements. Experimental evaluation on the PubTabNet benchmark demonstrates that the improved SLANet achieves 77.25% accuracy with an average inference time of 774 ms, outperforming the baseline and most mainstream algorithms while retaining lightweight efficiency. The proposed algorithm achieves a TEDS score of 96.67%, significantly surpassing SLANet-based and other state-of-the-art methods. The code will be released upon acceptance. Full article

Thermal limitations restrict the performance of low-cost, vertically stacked embedded systems. This paper presents a self-referencing digital twin framework for thermal and task management in a multi-device ESP32-S3 stack. The system combines a Mixture-of-Experts (MoE) model for task allocation with a neural network for short-term temperature prediction. Acting as a lightweight digital replica of the physical stack, the digital twin continuously monitors device states, forecasts thermal behavior 30 s into the future, and adapts workload distribution accordingly. The MoE model evaluates each device individually and asynchronously, estimating the portion of workload it should receive based on current state features including SoC temperature, CPU frequency, stack position, and recent task history. A separate neural network predicts future temperatures using real-time data from local and neighboring devices, enabling proactive thermal-aware scheduling. Training data for both models is collected through controlled experiments involving fixed-frequency operation and structured frequency switching with idle phases. All predictions and control actions are driven by in-built sensor feedback from the ESP32-S3 microcontrollers. The resulting digital twin supports distributed task scheduling based on temperature and works well in simple, low-cost edge systems with heat constraints. In one-hour experiments on a 6 ESP32-S3 stack, the proposed scheduling method completes up to 572 computation rounds at a $50{}^{\circ }$C temperature limit, compared with 493 and 542 rounds under logistic regression based control and 534 rounds at fixed 240 MHz operation, while keeping peak temperature at $51{}^{\circ }$C. Full article