Search Results (7,666)

This study presents an in silico proof of concept exploring whether deep learning models can perform conditional mitochondrial DNA (mtDNA) sequence prediction across species boundaries. A CNN–BiLSTM model was trained under a leave-one-species-out (LOSO) scheme on complete mitochondrial genomes from 21 vertebrate species, primarily archosaurs. Model behavior was evaluated through multiple complementary tests. Under context-conditioned settings, the model performed next-nucleotide prediction using overlapping 200 bp windows to assemble contiguous 2000 bp fragments for held-out species; the resulting high token-level accuracy (>99%) under teacher forcing is reported as a diagnostic of conditional modeling capacity. To assess leakage-free performance, a two-flank masked-span imputation task was conducted as the primary evaluation, requiring free-running reconstruction of 500 bp interior spans using only distal flanking context; in this setting, the model consistently outperformed nearest-neighbor and demonstrated competitive performance relative to flank-copy baselines. Additional robustness analyses examined sensitivity to window placement, genomic region (coding versus D-loop), and random initialization. Biological plausibility was further assessed by comparing predicted fragments to reconstructed ancestral sequences and against composition-matched null models, where observed identities significantly exceeded null expectations. Using the National Center for Biotechnology Information (NCBI) BLAST web interface, BLASTn species identification was performed solely as a biological plausibility check, recovering the correct species as the top hit in all cases. Although limited by dataset size and the absence of ancient DNA damage modeling, these results demonstrate the feasibility of conditional mtDNA sequence prediction as an initial step toward more advanced generative and evolutionary modeling frameworks. Full article

In response to the “carbon peak and carbon neutrality” strategy, industrial energy conservation has become increasingly important. Interior Permanent Magnet Synchronous Motors (IPMSMs) exhibit significant potential for efficient flux-weakening control due to their asymmetric rotor reluctance. However, conventional control strategies often cause instability during transitions across speed zones. This paper proposes a novel adaptive fuzzy-based smooth transition strategy to address this issue. First, a composite control framework integrating Maximum Torque per Ampere (MTPA) and leading-angle control is established to enhance flux-weakening capability. Then, within this framework, adaptive fuzzy controllers are designed for different weakening zones, incorporating a Lyapunov-based parameter adaptation mechanism for real-time compensation. Simulation results demonstrate that the proposed strategy achieves smooth switching across the entire speed range of IPMSMs. Quantitatively, it reduces speed overshoot by 5–15%, suppresses torque ripple by over 10%, and virtually eliminates switching current pikes compared to conventional methods, thereby significantly improving system dynamic performance and operational reliability. Full article

Drug-resistant bacteria cause millions of global infections each year, and the development of alternative antimicrobial drugs has become a serious undertaking. Currently, peptides with antimicrobial activity represent potential candidates for new antibiotic discovery as they are less likely to cause drug resistance in bacteria. In this study, bifunctional peptides with potent trypsin-inhibitory activity and antimicrobial activity were obtained by rational computation-based structural modifications to a novel Bowman–Birk-type inhibitor (BBI) peptide. The analogues not only displayed potent bacterial killing ability against two drug-resistant bacteria strains of E. coli but also an excellent safety profile, as assessed by low haemolytic activity and low anti-proliferation activity on HaCaT cells. Throughout the molecular dynamics simulations, the peptides exhibited stable adsorption onto the mixed POPE/POPG membrane; most amino acid residues of the AMPs remained bound to the membrane surface, with a few amino acid residues partially penetrating the membrane interior. This showed that the electrostatic interactions were the dominant driving force mediating the peptide–membrane associations. In addition, the tested peptides displayed a degree of stability in the presence of salt ions, serum, and trypsin. These modified peptides thus possess potential as clinical antibacterial agents, and the strategies used in structural modification may also provide a different path to developing new antimicrobial peptides. Full article

This work explores late-time gravitational collapse using timelike thin-shell methods in classical general relativity. A junction surface separates a regular de Sitter interior from a Schwarzschild or Schwarzschild–de Sitter exterior in a post-transient regime with fixed exterior mass M (ADM for ${\mathrm{\Lambda }}_{+}=0$), modelling a vacuum–energy core surrounded by an asymptotically classical spacetime. The configuration admits a natural thermodynamic interpretation based on a geometric area functional $S_{\mathrm{shell}}\propto R^2$ and Tolman redshift, both derived from classical junction conditions and used as an entropy-like coarse-grained quantity rather than a fundamental statistical entropy. Key results include (i) identification of a deceleration mechanism at the balance radius $R_{\mathrm{thr}}={(3M/{\mathrm{\Lambda }}_{-})}^{1/3}$ for linear surface equations of state $p=w\sigma$; (ii) classification of the allowable radial domain $V\left(R\right)\le 0$ for outward evolution; (iii) bounded curvature invariants throughout the shell-supported spacetime region; and (iv) a mass-scaled frequency bound $f_c{R}_S\le \xi /\left(3\sqrt{3}\pi \right)$ for persistent near-shell spectral modes. All predictions follow from standard Israel junction techniques and provide concrete observational tests. The framework offers an analytically tractable example of regular thin-shell collapse dynamics within classical general relativity, with implications for alternative compact object scenarios. Full article

Melamine-faced particleboards are widely used in interior applications; however, their performance is often limited by the near-surface structure, film adhesion, and edge damage that can be generated during machining and service impacts. Here, model particleboards were produced with 0%, 10%, 20%, and 40% bark content in the face layers and laminated with two melamine films (light and dark décor). Density profiles, mechanical properties (MOR, MOE, internal bond, IB), and laminate adhesion (pull-off) were determined. Edge integrity was evaluated under edge milling, quantified by cumulative tear-out length (ΣL) and the normalized damage index L_i (mm/m) together with tear-out depth, and under edge impact using a 0.5 kg mass dropped from 0.20 m (damage length and indentation depth). All boards were characterized by a typical U-shaped density profile, while increasing bark share reduced surface-layer density differentiation. MOR and MOE decreased significantly only at 40% bark, whereas IB (0.54–0.74 N/mm²) remained unchanged. Bark content significantly affected adhesion (32.76% contribution), whereas film type was not a significant factor. Milling damage depended on laminate: for the dark laminate, bark-containing boards showed much higher L_i (54.82–60.13 mm/m) than the reference (12.26 mm/m); for the light laminate, the lowest L_i occurred at 10% bark (21.24 mm/m). Tear-out depth varied narrowly (≈0.69–1.02 mm), while impact damage length ranged from 6.96 to 8.58 mm. Full article

Background: Incarcerated men experience disproportionately high levels of health inequities shaped by social determinants, including poverty, violence, family adversity, trauma, and limited access to healthcare. These long-standing disadvantages, added to the adverse conditions experienced in prisons, may be associated with elevated rates of suicidality in this population. This study examined the prevalence of suicidal ideation and lifetime suicide attempts among men deprived of liberty in Southern Brazil and investigated the role of cumulative adversities and current protective factors in these outcomes. Methods: A cross-sectional study was conducted with 496 incarcerated men. Participants completed a sociodemographic and background questionnaire assessing lifetime adversity (e.g., hunger, homelessness, sexual abuse, domestic violence, family substance dependence) and current protective factors in prison (e.g., family visits, education, leisure, physical activity, religion, positive self-perception). Cumulative adversity and protective factors were operationalized as composite indices. Logistic regression models tested whether cumulative adversities and protective factors were independently associated with suicidal ideation and suicide attempts. Results: Lifetime prevalence was 9.6% for suicidal ideation and 10.8% for suicide attempts. Cumulative adversities were associated with higher odds of both suicidal ideation (OR = 1.43; 95% CI = 1.11–1.84; p = 0.006) and suicide attempts (OR = 1.94; 95% CI = 1.50–2.52; p < 0.001). Protective factors were associated with lower likelihood of suicidal ideation (OR = 0.74; 95% CI = 0.58–0.96; p = 0.020) but were not significantly associated with suicide attempts. No significant interaction effects were observed, indicating that protective factors did not moderate the impact of adversity. Conclusions: Suicidal tendencies among incarcerated men were associated with cumulative structural and psychosocial adversities. Protective factors in prison were associated with lower odds of ideation but not attempts. These associations may inform person-centered and equity-oriented approaches and are consistent with the relevance of social determinants to mental health, although causal inferences are not supported by this project. Full article

While battery electric vehicles (BEVs) are pivotal for transport decarbonization, existing life cycle assessments (LCAs) often confound vehicle design effects with inter-brand manufacturing variations. In this study, a comparative cradle-to-grave LCA was conducted for three distinct BEV segments—a sedan, an SUV, and an MPV, produced by a single manufacturer on a shared platform. Leveraging detailed bills of materials, plant-level energy data, and region-specific emission factors for a functional unit of 150,000 km, we quantify greenhouse gas emissions across the full life cycle. Results show the total emissions scale with vehicle size from 25 to 31 t CO₂-eq. However, the MPV exhibits the highest functional carbon efficiency, with the lowest emissions per unit of interior volume. Material production and operational electricity use dominate the emission profile, with end-of-life metal recycling providing a 15–20% mitigation credit. Scenario modeling reveals that grid decarbonization can slash life cycle emissions by around 30%, while advanced battery recycling offers a further 15–18% reduction. These findings highlight that the climate benefits of BEVs are closely linked to progress in power system decarbonization, and provide references for future optimization of low-carbon vehicle production and reuse. Full article

Understanding how strain is transferred across the interior of tectonic plates is fundamental to quantifying lithospheric deformation. The Central Anatolia Transition Zone (CATZ), situated between the North and East Anatolian fault systems, provides a unique natural laboratory for investigating how continental deformation evolves from localized faulting to distributed shear. In this study, we integrate InSAR analysis with Global Navigation Satellite System (GNSS) velocity data, and stress tensor inversion with supporting gravity and seismic datasets to characterize the geometry, kinematics, and geodynamic significance of the CATZ. The combined geodetic and geophysical observations reveal that the CATZ is a persistent, left-lateral deformation corridor (i.e., elongated zone of Earth’s crust that accommodates movement where the landmass on the opposite side of a fault system moves to the left relative to an observer) accommodating ~4 mm/yr of shear between the oppositely moving eastern and western sectors of the Anatolian Plate. Spatial coherence among LiCSAR-derived shear patterns, GNSS velocity gradients, and regional stress-field rotations defines the CATZ as a crustal- to lithospheric-scale transition zone linking the strike-slip domains of central Anatolia with the subduction zones of the Hellenic and Cyprus arcs. Stress inversion analyses delineate four subzones with systematic kinematic transitions: compressional regimes in the north, extensional fields in the central domain, and complex compressional–transtensional deformation toward the south. The CATZ coincides with zones of variable Moho depth, crustal thickness, and inferred lithospheric tearing within the retreating African slab, indicating a deep-seated origin. Its S-shaped curvature and long-term evolution since the late Miocene reflect progressive coupling between upper-crustal faulting and deeper lithospheric reorganization. Recognition of the CATZ as a lithospheric-scale transition zone, rather than a discrete active fault, refines the current understanding of Anatolia’s kinematic framework. This study demonstrates the capability of integrated satellite geodesy and stress modeling to resolve diffuse intra-plate deformation, offering a transferable approach for delineating similar transition zones in other continental regions. Full article

Understanding the spatiotemporal evolution of forest biomass energy potential is essential for supporting low-carbon land-use planning and regional energy transitions. China, characterized by pronounced spatial heterogeneity in forest resources and ecological conditions, provides an ideal case for examining how biophysical endowments and management factors shape biomass energy potential. This study constructs a province-level panel dataset for China covering the period from 1998 to 2018 and investigates long-term spatial patterns, regional disparities, and driving mechanisms using spatial visualization, Dagum Gini decomposition, and fixed-effects estimation. The results reveal a gradual spatial reorganization of forest biomass energy potential, with the national center of gravity shifting westward and northwestward, alongside a moderate dispersion of high-potential clusters from coastal areas toward the interior. Interregional transvariation is identified as the dominant source of regional inequality, indicating persistent structural differences among major regions. To explore future dynamics, a random forest model is employed to project provincial forest biomass energy potential from 2018 to 2028. The projections suggest moderate overall growth, smoother distributional structures, and a partial reduction in extreme provincial disparities. Central, southwestern, and northwestern provinces are expected to emerge as important contributors to future growth, reflecting ecological restoration efforts, expanding plantation forests, and improved forest management. The findings highlight a continued upward trend in national forest biomass energy potential, accompanied by a spatial shift toward inland regions and evolving regional disparities. This study provides empirical evidence to support region-specific development strategies, optimized spatial allocation of forest biomass resources, and integrated policies linking ecological sustainability with renewable energy development. Full article

Precise electromagnetic torque estimation for permanent magnet synchronous motors (PMSMs) is crucial for enhancing the dynamic performance and energy efficiency of electric vehicles. To address the dynamic variations in dq-axis inductance caused by magnetic cross-coupling and saturation effects during motor operation—which lead to significant torque estimation errors in traditional fixed-parameter models under variable torque and speed conditions—this paper proposes a dynamic torque estimation method that integrates online dq-axis inductance identification based on a variable-step adaptive linear neural network (ADALINE) with an extended flux observer. The online identified inductance values are embedded into the extended flux observer in real time, forming a closed-loop torque estimation system with adaptive parameter updating. Experimental results demonstrate that, under complex operating conditions with varying torque and speed, the proposed method maintains electromagnetic torque estimation errors within ±3%, with a convergence time of less than 20 ms, while achieving inductance identification accuracy also within ±3%. These results significantly outperform conventional methods that do not incorporate inductance identification. This study provides a highly adaptive and engineering-practical solution for high-precision torque control of interior permanent magnet synchronous motors (IPMSMs) in automotive applications. Full article

This study investigates the influence of varying cold stratification durations (0–165 days) on the germination performance and early seedling development of Rudbeckia fulgida. Seeds were divided into 11 groups at 15-day intervals, using a total of 1320 seeds. For each stratification duration, an equivalent number of seeds stored at room temperature served as non-stratified controls. Results demonstrated a clear and significant increase in germination percentage with longer stratification periods (Kruskal–Wallis, H = 57.03, p < 0.001), with the highest germination observed at 135 and 165 days (96.7%). In contrast, seeds kept at room temperature exhibited low and inconsistent germination. Strong positive correlations were detected between stratification duration and both germination percentage (r = 0.914) and post-stratification seed weight (r = 0.419). Furthermore, a Response Surface Methodology (RSM) model was developed to predict germination behavior, achieving an exceptionally high 99% predictive accuracy. The RSM analysis confirmed that cold stratification duration is the dominant factor shaping germination responses in Rudbeckia fulgida Aiton. Overall, the study demonstrates that cold stratification is essential for breaking seed dormancy in R. fulgida, substantially improving propagation efficiency and offering valuable insights for nursery production, landscape practices, and restoration ecology. Full article

In this paper, we study the behavior of the m-th$(m\ge 0)$ derivatives of general algebraic polynomials in weighted Bergman spaces defined in regions of the complex plane G bounded by piecewise smooth curves $L=\partial G$ with $\lambda \pi$$(0<\lambda \le 2)$ exterior angles relative to G. Upper bounds are found for the growth of the m-th derivatives of the polynomials not only inside the unbounded region but also on the closures of this region with both exterior non-zero angles $\lambda \pi$$(0<\lambda <2)$ and interior zero angles (i.e., exterior angles $2\pi$). The influence of the boundary angles $\lambda \pi$$(0<\lambda \le 2)$ of the region G and the “growth rate” of the weight function on the behavior of the moduli of polynomials and their derivatives in regions of the complex plane that are “symmetric” with respect to L (bounded and unbounded) is found. Full article

Research shows that neurodegenerative processes do not develop from a single “broken” biochemistry process; rather, they develop when a complex multi-physics environment gradually loses its ability to stabilize the neuron via a collective action between the protein, ion, field and fluid dynamics of the neuron. The use of new technologies such as quantum-informed molecular simulation (QIMS), dielectric nanoscale mapping, fluid dynamics of the cell, and imaging of perivascular flow are allowing researchers to understand how the collective interactions among proteins, membranes and their electrical properties, along with fluid dynamics within the cell, form a highly interconnected dynamic system. These systems require fine control over the energetic, mechanical and electrical interactions that maintain their coherence. When there is even a small change in the protein conformations, the electric properties of the membrane, or the viscosity of the cell’s interior, it can cause changes in the high dimensional space in which the system operates to lose some of its stabilizing curvature and become prone to instability well before structural pathologies become apparent. AI has allowed researchers to create digital twin models using combined physical data from multiple scales and to predict the trajectory of the neural system toward instability by identifying signs of early deformation. Preliminary studies suggest that deviations in the ergodicity of metabolic–mechanical systems, contraction of dissipative bandwidth, and fragmentation of attractor basins could be indicators of vulnerability. This study will attempt to combine all of the current research into a cohesive view of the role of progressive loss of multi-physics coherence in neurodegenerative disease. Through integration of protein energetics, electrodynamic drift, and hydrodynamic irregularities, as well as predictive modeling utilizing AI, the authors will provide mechanistic insights and discuss potential approaches to early detection, targeted stabilization, and precision-guided interventions based on neurophysics. Full article

Background: Rainfall-induced landslides are a widespread and destructive geological hazard that resist precise prediction. They pose serious threats to human lives and property, ecological stability, and socioeconomic development. Methods: To address the challenges in mitigating rainfall-induced landslides in high-altitude mountainous regions, this study proposes a digital twin framework that couples multiple physical fields and is based on the spherical discrete element method. Results: Two-dimensional simulations identify a trapezoidal stress distribution with inward-increasing stress. The stress increases uniformly from 0 kPa at the surface to 210 kPa in the interior. The crest stress remains constant at 1.8 kPa under gravity, whereas the toe stress rises from 6.5 to 14.8 kPa with the slope gradient. While the stress pattern persists post-failure, specific magnitudes alter significantly. This study pioneers a three-dimensional close-packed spherical discrete element method, achieving enhanced computational efficiency and stability through streamlined contact mechanics. Conclusions: The proposed framework utilizes point-contact mechanics to simplify friction modeling, enhancing computational efficiency and numerical stability. By integrating stress, rainfall, and seepage fields, we establish a coupled hydro-mechanical model that enables real-time digital twin mapping of landslide evolution through dynamic parameter adjustments. Full article

The textile industry is one of the largest consumers of water worldwide and generates highly complex and pollutant-rich textile wastewater (TWW). Due to its high load of recalcitrant organic compounds, dyes, salts, and heavy metals, TWW represents a major environmental concern and a challenge for conventional treatment processes. Among advanced alternatives, electrooxidation (EO) and membrane technologies have shown great potential for the efficient removal of dyes, organic matter, and salts. This review provides a critical overview of the application of EO and membrane processes for TWW treatment, highlighting their mechanisms, advantages, limitations, and performance in real industrial scenarios. Special attention is given to the integration of EO and membrane processes as combined or hybrid systems, which have demonstrated synergistic effects in pollutant degradation, fouling reduction, and water recovery. Challenges such as energy consumption, durability of electrode and membrane materials, fouling, and concentrate management are also addressed. Finally, future perspectives are proposed, emphasizing the need to optimize hybrid configurations and ensure cost-effectiveness, scalability, and environmental sustainability, thereby contributing to the development of circular water management strategies in the textile sector. Full article