Search Results (1,402)

Epigenetic regulation of gene expression is studied at three physical scales: micro: DNA sequence-level methylation/demethylation; meso: nucleosome occupancy and remodeling; and macro: chromosomal domain silencing by Polycomb complexes, heterochromatin, and topologically associating domain (TAD) boundaries. The challenge to fully understand epigenetic gene regulation patterns is that these scales are not independent. Their influence overlaps and they share a recurring architectural theme across scales of a targeted molecular pattern followed by cooperative, feedback-driven, spatially bounded spreading. We argue here that disruption of this shared architecture at any one scale is independently sufficient to tip a bistable silencing domain into an oncogenic state. This paper discusses how such a cross-scale architectural rule set has concrete implications (yet underexploited) for computational cancer epigenomics, e.g., functional enrichment analyses generally focus on epigenetic features at one scale as an independent line of evidence, ignoring corroborating signals that could be reinforced by underlying hierarchical levels. This paper outlines options for functional enrichment statistics that combine multiple corroborating molecular features within a scale and corroborating evidence across scales into composite confidence scores calibrated against an empirical null that preserves correlations between assays. We propose benchmarking this approach against conventional single-feature enrichment in matched multi-omic cancer datasets as a direct test of the model. Full article

The shoe last is central to footwear design. Modern footwear CAD operates on parametric digital lasts, yet much last geometry—legacy collections and lasts that skilled last makers still sculpt by hand and copy by pantograph turning—exists only as physical models or as point-cloud scans lacking the structured parametric form that footwear CAD requires. This paper presents a complete template-based method for reconstructing a watertight parametric last from a segmented point cloud without intermediate triangulation. The only manual input is three landmark points—for which the system proposes standard positions—and the interactive confirmation of two boundary lines on the digitized last. From these, the method defines four feature points, a median plane, and a four-curve boundary network; all subsequent stages run without user interaction. A curvature-adaptive quadrilateral grid is constructed on the cloud by geodesic tracing and monitor-weighted area-orthogonality relaxation. A periodic Coons tube interpolates the grid and initializes the parameterization for a periodic tensor-product cubic B-spline surface fitted by penalized least squares with cyclic/open difference penalties, exact boundary interpolation, and toe-aware weighting. Cap surfaces close both collar and sole openings, and the model is exported as a watertight B-rep solid. Tests on sixteen industrial lasts using one fixed parameter set produced a mean one-sided deviation of 0.034 mm (RMS 0.058 mm) from the withheld industrial reference meshes in approximately 12 s per last. With synthetic noise at 50 dB SNR, the mean deviation increased by only 0.011 mm. A sampling-density study indicated near-second-order convergence before the control-net reaches an upper limit. The resulting solids import directly into CAD systems and support re-lasting, footwear design, and customization. Full article

This paper proposes an adaptive low-voltage ride-through (LVRT) control framework for grid-forming virtual synchronous generators (VSGs) in weak and ultra-weak grids based on feasible-region and fault ride-through limit-boundary analysis. The proposed method achieves coordinated active–reactive power regulation during fault conditions and enhances the fault ride-through capability and synchronization stability of the system. First, an equivalent voltage-vector decomposition is used to establish the fault-stage operating model of the VSG, based on which the feasible active–reactive power region is characterized under current, line-reactance, and apparent-power constraints. Then, the maximum active power transfer capability, maximum reactive power support capability, and critical voltage-sag boundary are derived by considering both current limitation and power-angle stability. Furthermore, unlike existing feasible-domain-based methods that mainly focus on voltage-command limitation, a unified power-circle–capability-cone constraint model is developed to directly generate feasible active–reactive power references within the original VSG framework. A voltage-dependent adaptive droop coefficient is introduced to dynamically coordinate active and reactive power allocation, thereby enlarging the feasible LVRT region and improving the stability margin. Finally, a distributed consensus mechanism is designed to coordinate active and reactive power references among multiple VSGs within their feasible regions and suppress fault-induced power oscillations. Simulation results verify that the proposed method enhances voltage support, expands the feasible LVRT operating region, suppresses power and power-angle oscillations, and improves transient stability under weak-grid conditions. Full article

Reliable determination of the surge limit of an installed centrifugal compressor cannot rely solely on OEM performance maps, theoretical predictions, or previously configured surge lines. The actual field boundary is influenced by installation-specific characteristics, including piping volumes, recycle-system dynamics, valve response, and instrumentation, all of which affect compressor behavior as surge is approached. At the same time, field surge testing is inherently demanding, because it is a safety-critical procedure that requires careful execution and often depends on the experience of specialist personnel. This paper presents a semi-automated supervisory framework for field surge limit verification intended for use by trained site engineers under operator supervision. The framework guides the testing process through readiness checks, instrumentation verification, recycle-valve assessment, gradual reduction of surge margin, real-time monitoring, surge-event identification, recovery actions, and structured data logging. Because early surge precursors are not consistently visible using standard plant instrumentation, the proposed approach relies on a practical signal-based event-recognition method derived from installed measurements and referenced to a pre-event baseline. The paper also introduces a structured method for tuning staged Recycle Trip^® recovery actions following surge limit verification using a limited number of field tests. The field evaluation included seven industrial compressor records: five independently confirmed surge cases, one shutdown/non-surge transient, and one confirmed no-surge case. Using a record-specific baseline threshold, all five confirmed surge events were recognized, with reported detection delays of 0–0.1 s. The supporting dynamic simulation environment was used for logic development and qualitative assessment of the supervisory and recovery-tuning concepts. Overall, the proposed approach provides a more structured and repeatable basis for supervised field surge limit verification while preserving operator authority. Full article

Visible-light photovoltaic (PV) inspection is affected by periodic grid-line backgrounds, low-contrast cracks, irregular crack topology, and the extreme aspect ratios of slender defects. To address these difficulties, a frequency-domain hybrid deformation and geometry-regression YOLO network, termed FHDG-YOLO, is proposed in this study. The method introduces frequency-domain dynamic decoupled convolution to attenuate periodic background responses, incorporates a high-resolution P2 detection head and efficient multi-scale attention to retain and recalibrate shallow spatial details, embeds DCNv2 to adapt convolutional sampling to irregular defect boundaries, and replaces the original regression loss with MicroShape-IoU for geometry-sensitive localization. Experiments are conducted on a reorganized two-class visible-light PV dataset containing 6493 images, comprising 6262 screened public images and 231 field images collected by the authors. On the 1300-image validation split, FHDG-YOLO obtains a Precision of 0.954, Recall of 0.943, mAP@0.5 of 0.971, and mAP@0.5:0.95 of 0.861. Compared with YOLOv8n, mAP@0.5 and mAP@0.5:0.95 increase by 3.6 and 6.9 percentage points, respectively. On the held-out 649-image test split, the corresponding mAP values are 0.970 and 0.860, compared with 0.931 and 0.785 for YOLOv8n. Under the original four-class Panel Solar validation protocol, FHDG-YOLO obtains mAP@0.5 and mAP@0.5:0.95 values of 0.954 and 0.843, compared with 0.929 and 0.780 for YOLOv8n. Full article

In high-voltage cable sheaths, incipient weak faults, such as jacket-damage grounding and high-resistance core–sheath breakdown, release little energy, rarely trigger protection, and can evolve into permanent faults. This study examines whether the topology-induced joint structure of multichannel sheath currents can support fault detection without fault samples. A Mahalanobis-distance-based method is formulated for the three-phase sheath circulating currents measured at a single cross-bonding box. An induction–leakage analysis relates the healthy joint structure to the bonding topology and shows how weak faults disturb it. A normalized pointwise Mahalanobis distance is combined with a threshold calibrated on separate healthy data and a K-consecutive-sample rule; the method requires no signal decomposition, and its per-sample cost is constant. On a PSCAD model of a 110 kV cross-bonded system, all 52 development fault cases are detected with confirmation delays below 6 ms; an independent sixteen-record healthy test is false-alarm-free after an envelope recalibration; boundary-grade faults under joint non-ideal conditions retain nine-fold margins; and per-line calibration extends the criterion to asymmetric and longer geometries. A twenty-seed Monte Carlo campaign shows zero noisy false alarms at all tested signal-to-noise ratios, the observable fault range being set by the disturbance-to-noise energy ratio of the acquisition chain. Full article

Urban rail transit infrastructure inspection increasingly relies on Light Detection and Ranging (LiDAR) due to its capability for efficient and high-precision 3D data acquisition. However, robust rail tread segmentation in ballastless metro environments remains challenging due to boundary leakage, interference from geometrically similar structures, and the heavy dependence of existing methods on Red-Green-Blue (RGB) imagery, trajectory priors, or template matching. To address these limitations, this study proposes a lightweight rail tread extraction framework for ballastless track LiDAR point clouds based on multi-stage filtering and curvature-guided region growing. First, intensity thresholding and cloth simulation filtering are leveraged to prune tunnel walls, track beds, and other large-scale non-target structures, thereby reducing computational overhead. Subsequently, local normal vectors and curvature features are estimated via Principal Component Analysis (PCA). A curvature-ranked seed selection strategy and a dual-constrained region growing mechanism, integrating normal consistency and curvature thresholds, are then introduced to suppress excessive growth near rail boundaries and enhance regional homogeneity. Experimental results on field data collected from Shanghai Metro Line 10 demonstrate that the proposed method achieves a recall of 92.23%, a precision of 95.32%, and an F1-score of 93.7%, outperforming conventional Euclidean clustering and standard region growing algorithms. Compared with deep learning approaches, the proposed framework requires no large-scale annotated training data and is independent of RGB information or trajectory priors, making it better suited for lightweight engineering deployment in practical urban rail transit maintenance. Full article

Distributed-drive electric vehicles (DDEVs) improve braking stability through independent four-wheel braking torque control, yet their complex multi-brake systems are prone to braking faults that cause loss of longitudinal and yaw stability. This study investigates DDEV braking performance under normal and faulty conditions and proposes a three-module braking stability control strategy consisting of demand torque calculation, fault constraint reconstruction, and fault-tolerant control. The fault constraint reconstruction module establishes braking capacity boundaries based on real-time fault and vehicle state data and constrains four-wheel braking torque via target projection. A composite fault-tolerant control scheme combining active front steering (AFS) and braking torque distribution is developed to suppress stability degradation. The AFS adopts a sliding-mode algorithm for accurate front wheel steering regulation, while a quadratic programming algorithm optimizes four-wheel braking torque allocation. Hardware-in-the-loop simulations are conducted for straight line and double lane change braking under single wheel regenerative, mechanical, and complete braking failure conditions. The results reveal that the proposed strategy limits peak yaw rates to 1.1 deg/s, 2.8 deg/s, and 3.5 deg/s in faulty regenerative, mechanical, and complete straight line braking, respectively, and achieves excellent yaw rate and trajectory tracking during faulty double lane change braking. This work provides an effective solution for DDEV braking stability optimization and fault-tolerant control. Full article

This paper examines cultural institutions as architectural infrastructures that extend beyond physical boundaries to generate events, shape collective spatial experience, and contribute to the production of urban memory. Instead of interpreting the city primarily through permanent architectural forms, this research conceptualizes the urban environment as an archive of events, arguing that urban memory emerges continuously through interactions between architecture, collective action, and temporally situated spatial practices. The study investigates the Youth Tribune (Serbian: Tribina mladih) in Novi Sad as a case study of an institutional space whose architectural configuration and program enabled neo-avant-garde artistic practices to expand into public space. Attention is given to two performative actions–Linija (English: Line) by Tomaž Šalamun and the OHO Group, and Srce-predmet (English: Heart-Object) by Bogdanka Poznanović–analyzed here as spatial sequences that reorganized relationships between architecture, public space, and the collective. Methodologically, the research combines archival analysis with cartographic mapping to reconstruct movement trajectories, spatial transitions, and points of intensity generated by these ephemeral interventions. The findings suggest that cultural institutions can operate as spatial frameworks for event generation, shifting the analytical understanding of architecture from passive enclosure towards an active framework in memory production. By establishing this infrastructure-event framework, the paper advances architectural and urban theory with an analytical tool applicable to broader studies on architecture’s role in collective urban experience. Full article

Short-circuit flow severely degrades cyclone separation, yet its influence zone has not been clearly defined. In this work, the Reynolds stress model (RSM) was adopted to perform numerical simulations for a 300 mm PV-type cyclone separator, and the simulation results were validated against published tangential-velocity experimental data. The radial velocity along the extension lines of the vortex finder’s inner wall was analyzed to define the short-circuit flow influence zone, and a sector-resolved intensity index was proposed to quantify its circumferential non-uniformity. The results show that the lower boundary of the influence zone is a circumferentially varying curved surface rather than a horizontal plane. Among the four sectors, the 180–270° sector exhibits the greatest penetration depth (55 mm) and the highest inward radial velocity (−15.45 m/s). The proposed index indicates that Sector III contributes 38.7% of the total short-circuit flow intensity, and the dimensionless index is 0.127 for the present case. Within the influence zone, the inward radial velocity peaks at Z = 12 mm and approaches zero by Z = 53 mm, while the tangential velocity increases from 4.22 to 57.13 m/s, indicating Rankine vortex development. The short-circuit flow is governed by the coupled effects of inlet jet squeezing, vortex-core displacement, and the interaction between upward and downward flows. The proposed influence-zone definition and intensity index provide a quantitative framework for describing short-circuit flow and may be useful for evaluating cyclone design and short-circuit-flow suppression strategies. Full article

The vocabulary of artificial intelligence has colonized human self-description. Terms historically reserved for conscious agents—intelligence, learning, understanding, creativity—now routinely designate computational processes that bear no phenomenological resemblance to the experiences that they originally named. This conceptual erosion is not merely semantic; it undermines our capacity to articulate what distinguishes human cognition from machine operation at a moment when that distinction matters most. This article develops a dual-path argument for the identification of irreducibly human capacities. A via negativa assembles four independent lines of formal and philosophical reasoning—Gödel’s incompleteness theorems, Turing’s halting problem, Searle’s Chinese Room, and the frame problem—that establish principled limits to what computation can achieve. A via positiva draws on phenomenology to positively characterize the structures of human existence that lie beyond those limits: embodiment, intersubjectivity, temporality, being-in-the-world, and subjective experience. The convergence of these independent paths—each pointing from its own starting point toward the same boundary—grounds a taxonomy of five Unique Human Faculties (creativity, moral judgment, self-determination, empathy, and sentience) that resist mechanization in principle. The article concludes that the burden of proof now lies with those who claim that formal computation can exhaust human understanding, and argues that the simulation–possession distinction provides a principled basis for restoring conceptual clarity to discourse about artificial intelligence. Full article

Non-contact inclination detection based on LiDAR point clouds has become an effective approach for structural condition assessment. However, due to measurement noise, lattice structural characteristics, and discontinuous point distributions, cross-sectional point clouds of lattice steel towers often contain outliers, local missing regions, and irregular boundaries, which may affect the reliability of extracted geometric features. This study establishes a comparative framework to investigate the influence of cross-sectional feature extraction algorithms on LiDAR-based inclination detection of lattice steel towers. The universality and performance of the RANSAC and Marching Square algorithms were systematically evaluated using a 110 kV overhead transmission line operating tower. Terrestrial laser scanning was employed to acquire the point cloud data. The initial registration results were subsequently further optimized through initial point cloud registration and multi-station adjustment. Four cross-sectional slicing schemes were designed, and the two algorithms were independently applied to extract cross-sectional geometric features and calculate centroid coordinates. The tower inclination was then determined by fitting the spatial distribution of centroid points. Experimental results demonstrated that both algorithms successfully extracted cross-sectional features and achieved reliable inclination detection results, with all inclination ratios satisfying the requirement specified in DL/T 741—2019 (Code of Practice for Operation of Overhead Transmission Lines). The RANSAC-based method produced inclination ratios ranging from 8.52‰ to 8.82‰, with a variation range of 0.30‰ and a mean deviation of 0.12‰. In comparison, the Marching Square-based method showed a larger variation range of 1.00‰ and a mean deviation of 0.41‰. The results indicate that RANSAC provides better robustness against point cloud noise, local data gaps, and boundary irregularities due to its inlier–outlier discrimination capability, whereas Marching Square exhibits advantages in preserving continuous contour representations when point cloud distributions are relatively complete. This study provides practical insights into the selection and optimization of cross-sectional feature extraction algorithms for LiDAR-based inclination assessment of lattice steel towers. Full article

Academic laboratories increasingly deploy DC motor-driven equipment, but wireless control faces two challenges: congestion in the 2.4 GHz band and the absence of application-layer confidentiality. This paper presents a 433 MHz LoRa-based wireless joystick control system with AES-128 confidentiality protection, validated using an ABU Robocon robot as a representative testbed. Performance was characterized outdoors across six Spreading Factors (SF7–SF12) under line-of-sight (LoS) and non-line-of-sight (NLoS) propagation (n = 6 per condition), and across three indoor campaigns (n ≈ 100 packets per condition) to quantify the gap between outdoor boundary estimates and indoor deployment. One-way ANOVA confirmed significant SF effects on latency, RSSI, and SNR (F = 10.4–5812, p < 0.001); normality and homogeneity-of-variance assumptions were formally tested, frequently violated for RSSI/SNR, and corroborated by Kruskal–Wallis tests, with large effect sizes (η² = 0.27–0.99). A preliminary low-antenna indoor test showed severe PDR degradation (as low as 14%), plausibly from ground-reflection multipath; elevating both antennas (≈70/60 cm) produced mixed, not uniformly improved, results. Indoor-NLoS testing showed low Spreading Factors failing almost completely beyond 5 m (SF7/SF8 near 0% PDR at 10–15 m) while SF10–SF12 remained robust (89–100%), a more consequential finding than outdoor boundary conditions (100% PDR at 5–15 m) suggest alone. Interference experiments characterized position-dependent and cross-Spreading-Factor resilience, with an explicit literature-grounded caveat rather than an unconditional quasi-orthogonality claim. SF10/SF11 with a 5 dBi antenna remains the best outdoor compromise (147–176 m range, 206–357 ms latency); indoor deployments require SF-specific de-rating and antenna-height practice. AES-128 added no measurable latency overhead. Contributions include a validated retrofit blueprint, a statistically grounded SF selection matrix spanning outdoor and indoor conditions, a practical antenna-height recommendation, and a controlled characterization of indoor interference resilience. The AES-128 ECB implementation provides confidentiality only, not authentication or replay resistance; limitations and a migration roadmap are discussed. Full article

In this study, the removal of vancomycin and rifampicin from aqueous media by three types of granular activated carbon was investigated under a wide range of operational conditions. There is growing concern about antimicrobial resistance to vancomycin, a last-line antibiotic for serious Gram-positive infections, and rifampicin, a key component of first-line combination therapy for tuberculosis. However, there has been little research into adsorption of these antibiotics from aquatic environments. Effect of temperature (5–45 °C), pH (3–11), contact time (up to 336 h) and the adsorbate initial concentration (5–100 µg.mL⁻¹) were evaluated on removal efficiency and uptake of the antibiotics by activated carbon from water. The results showed that all adsorption processes in this study were mainly controlled by physisorption, as they exhibited the enthalpy values between 5.97 kJ.mol⁻¹ to 21.19 kJ.mol⁻¹ (i.e., lower than 40 kJ.mol⁻¹). Temperature had a limited impact on the adsorption process. On the other hand, the contact time, initial adsorbate concentration and solution pH had a substantial effect in removing and retaining the studied antibiotics from water. The attributes of the antibiotics and the pore characteristics of the adsorbents determined the effectiveness of adsorption. Intra-particle diffusion assessments identified that the rate of the studied antibiotics’ removal from water is not controlled only by intra-particle diffusion and follows the sequential transport mechanisms, containing three steps for vancomycin (bulk transfer, passage through the boundary layer and diffusion into micropores) and two steps for rifampicin (passing through the boundary layer and diffusion into mesopores). Full article

In unmanned aerial vehicle inspection of transmission lines, insulators often exhibit arbitrary orientations and elongated shapes and are frequently embedded in complex backgrounds. Horizontal bounding boxes tend to include substantial redundant regions. Meanwhile, existing oriented object detection methods still suffer from angular discontinuities at periodic boundaries, insufficient geometric supervision for rotated bounding boxes, and difficulties in lightweight deployment. To address these issues, this paper proposes a lightweight oriented object detection model, termed R-YOLOv8-PSGH, which integrates dual-frequency phase-shift encoding and Gaussian geometric supervision. Based on a lightweight R-YOLOv8 architecture, a rotated detection head is developed to decouple the predictions of object categories, bounding-box locations, and orientation angles. To improve the periodic continuity of angle representations and strengthen the geometric constraints on rotated bounding boxes, a dual-frequency phase-shift angle encoding strategy and a Gaussian geometric localization loss are designed. Specifically, the complementary relationship between periodic signals with periods of 180°and 90° is exploited to map orientation angles into continuous phase responses, thereby improving the stability of orientation prediction. Moreover, the spatial structure of each rotated bounding box is modeled as a two-dimensional Gaussian distribution, and overlap consistency, center distance, and shape discrepancy are jointly optimized. In this manner, the orientation representation and bounding-box-level geometric supervision are collaboratively enhanced. Experimental results demonstrate that the proposed method improves the detection accuracy and localization stability of rotated objects while maintaining favorable lightweight deployment capability, providing a new solution for lightweight object detection in complex scenarios. Full article