Search Results (1,240)

In this paper, we present an algorithmic modeling approach based on Disjunctive Programming using Boolean logic “OR” to solve the Optimal Phasor Measurement Unit Placement (OPP). We propose a framework for modeling the optimal PMU placement subject to disjunctive constraints. A convex objective function is minimized subject to a bilinear equality constraint with a piecewise linear structure. The polynomial constraint constitutes a union of linear segments conceptually analyzed in the two-dimensional continuous space, separating the infeasible from the feasible region. This work investigates the trajectory of iterates from infeasible initial points to stationary solutions and analyzes the convergence behavior using Interior-Point Method (IPM) and Sequential Quadratic Programming (SQP). Our algorithmic model addresses the progress of infeasible and feasible iterates, step computation using line-search and trust-region mechanisms. Combined with second-order correction (SOC) and filter methods, these mechanisms enable the algorithm to maintain a unit primal step, even when starting from an infeasible initial point. Network observability constraints are transformed from a Conjunctive Normal Form (CNF) into a Disjunctive Normal Form (DNF) via Balas’s theory. This geometry transformation reformulates the feasible set into a union of convex affine pieces, effectively eliminating constraint curvature issues. This affine reformulation ensures that gradient-based algorithms maintain a smooth optimization trajectory along a convex local manifold. This trajectory enables the algorithm to preserve the full Newton step, maintaining a superlinear convergence rate. Its underlying piecewise linear convexity inherently enables the gradient-based algorithm to avoid the Maratos effect. Numerical results on IEEE power systems validate the optimization problem. Our framework uses the IEEE-14 bus system to address the high-degree non-convexities. Monte Carlo simulations further enhance the argument that piecewise convexity enables IPM and SQP to converge to binary local minima. Depending on multiple-run initialization, these methods reach the same objective function value. These local minima can be characterized as non-strict optimum points that are structurally symmetric but exhibit unequal basins of attraction. Hence, this geometry-driven formulation enables gradient-based methods to reliably identify binary-valued optimal solutions for the OPP. Full article

The Standard Model of particle physics has achieved unprecedented success in describing fundamental interactions through the formalism of Quantum Field Theory (QFT), where particles are treated as point-like excitations of underlying fields. In this paper, we introduce an alternative, highly elegant mathematical hypothesis: an effective model that describes particle properties not as fundamental points but as 3D projections of complex topological defects originating in a 4D manifold. By applying the geometry of Kaleidoscopic Iterated Function Systems (KIFS) to a zero-viscosity hydrodynamic framework, we demonstrate a formal isomorphism between standard quantum numbers and 4D fractal attractors. This approach seeks not to replace established phenomenological interactions but rather to offer a complementary geometric lens—a “topological translation”—through which mass generation, spin, and localization can be visualized as emergent properties of higher-dimensional fluid mechanics. Full article

Background/Objectives: Fibromyalgia is a chronic pain condition characterized by widespread musculoskeletal pain, fatigue, sleep disturbance, cognitive dysfunction, and multisensory hypersensitivity. It is increasingly conceptualized as a heterogeneous nociplastic pain condition in which altered nociceptive processing interacts with dysfunctional pain regulation, neuroimmune mechanisms, and variable peripheral contributions. This integrative narrative review aims to synthesize current evidence on the major mechanisms underlying pain in fibromyalgia, with particular emphasis on central sensitization, descending pain modulation, neurochemical dysregulation, small-fiber pathology, neuroimmune processes, and psychobiological modulators. Methods: An integrative narrative review was conducted using iterative, mechanism-oriented searches of the biomedical literature, primarily in PubMed/MEDLINE and complemented by targeted bibliographic searches and reference tracking. Research published up to July 2026 was considered, with emphasis on human mechanistic studies, systematic reviews, meta-analyses, and landmark experimental evidence relevant to the major pathophysiological domains of fibromyalgia. Results: Central sensitization and altered nociceptive gain remain prominent mechanisms of pain amplification in fibromyalgia, but they do not fully account for the clinical phenotype. Evidence also supports impaired and heterogeneous descending pain modulation, neurochemical imbalance, neuroimmune activation, autonomic and stress-system dysregulation, and peripheral contributions, including small-fiber pathology in a substantial subgroup of patients. These mechanisms appear to interact rather than operate independently, while cognitive and emotional factors further modulate symptom severity, persistence, and functional impact. Conclusions: Fibromyalgia is best understood as a heterogeneous nociplastic pain syndrome arising from partially overlapping central, peripheral, neuroimmune, autonomic, and psychobiological mechanisms whose relative contribution varies across patients. Recognizing this mechanistic heterogeneity may improve phenotypic stratification, biomarker development, and the design of more individualized, mechanism-informed therapeutic strategies. Full article

Background: Endometriosis affects approximately 6–10% of women of reproductive age—an estimate that varies substantially with the diagnostic standard applied—and is increasingly recognized as a systemic inflammatory disease. Growing evidence implicates shared immunological pathways between endometriosis and autoimmune thyroid disease, yet large-scale real-world evidence from standardized multi-site data remains limited. We evaluated the association between endometriosis and newly recorded autoimmune thyroid disease using federated Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) data from 12 Korean hospitals. Methods: We conducted a propensity score (PS)-matched cohort study using OMOP-CDM version 5.3 data from 12 Korean tertiary academic medical centers. Women aged 18–60 years with a first recorded endometriosis diagnosis were matched 1:1 to controls without endometriosis on age, calendar year, hypertension, and selected Charlson comorbidity index components. Site-specific Cox proportional hazards models, fitted to patient-level records locally at each institution, estimated hazard ratios (HRs) for newly recorded autoimmune thyroid disease, defined as Hashimoto’s thyroiditis or Graves’ disease. Pooled estimates were derived using DerSimonian–Laird random-effects meta-analysis; leave-one-out meta-analysis, meta-regression on site-specific follow-up ratio, and an E-value were used to assess robustness. Results: After 1:1 PS matching, 71,619 women with endometriosis and 71,619 matched controls were included. Mean follow-up was 2693 days in the endometriosis group and 1677 days in the control group. A directionally positive but statistically inconclusive association was observed between endometriosis and autoimmune thyroid disease (pooled HR 1.22, 95% CI 0.97–1.53; p = 0.090; I² = 38.2%). Seven of twelve sites reported HRs above 1.0, including two sites reaching individual statistical significance: AUMC (HR 1.38, p = 0.030) and KHUH (HR 2.69, p < 0.001). The pooled HR remained above 1.0 in all 12 leave-one-out iterations (range 1.17–1.31); omission of KHUH reduced I² to 6.6%. Site-specific follow-up imbalance was not associated with the site-specific log HR (meta-regression p = 0.887). The E-value for the point estimate was 1.74. Conclusions: In this multi-site OMOP-CDM analysis, endometriosis showed a directionally positive but statistically inconclusive association with newly recorded autoimmune thyroid disease. The confidence interval is compatible with both no association and a clinically meaningful increase in risk, and the findings are hypothesis-generating rather than confirmatory. They do not support any change in the clinical evaluation of women with endometriosis. Further validation is warranted using standardized outcome definitions, thyroid autoantibody measurements, and thyroid function tests. Full article

Background: Spinal cord injury (SCI) is associated with persistent motor impairment, neuropathic pain, and reduced quality of life. While spinal cord stimulation (SCS) is an established therapy for neuropathic pain, motor effects have largely been demonstrated in experimental settings with activity-based training protocols. Whether such effects can be achieved within routine clinical care remains unclear. Case Presentation: We report on motor effects after the clinical implementation of percutaneous SCS using a commercially available system in a patient with chronic, posttraumatic, incomplete thoracic SCI. Following a standard inpatient trial and implantation of two percutaneous leads for neuropathic pain, all subsequent management, programming, and rehabilitation were conducted in an outpatient setting. Task-specific stimulation programs were iteratively developed to support functional activities during routine rehabilitation. Lower-extremity muscle strength was assessed using standardized handheld dynamometry at baseline and at monthly intervals over six months under stimulation-ON and stimulation-OFF conditions. Results: Serial dynamometry showed early stimulation-associated increases in force generation, with higher values under stimulation-ON than stimulation-OFF conditions. Over time, absolute strength values under OFF conditions also increased across individual muscle measurements, while stimulation-associated ON–OFF differences became less pronounced during intermediate follow-up but remained evident at month six. These observations included both acute stimulation-associated differences in force generation and longitudinal improvements in OFF-state motor performance. Stimulation was well tolerated during daily use and successfully integrated into outpatient rehabilitation. Functional outcomes showed concordant improvements in ambulatory performance and patient-reported domains. No stimulation-related adverse effects were observed during follow-up. Conclusions: This case illustrates how clinically indicated percutaneous SCS can be combined with motor-oriented programming within routine outpatient rehabilitation. The observed stimulation-associated motor effects and longitudinal changes in stimulation-OFF strength are limited to this individual patient and cannot establish efficacy or causality. Prospective controlled studies are needed to determine the reproducibility and clinical relevance of these observations in chronic incomplete SCI. Full article

Optical systems in the real world often create image problems, such as Gaussian blur from defocus or atmospheric turbulence, and radial vignetting caused by lens shape. Standard mixed-data fine-tuning helps the task head handle these issues, but it does not actually fix them. We introduce the Iterative Correction of Optical Perturbations ICOP framework, which corrects encoder feature representations before they reach the task head using a physics-aware plug-in module. ICOP does this by modeling blur as an isotropic-Gaussian point-spread function (PSF) and uses gradient-based, self-supervised optimization (Adam) to discover feature-space corrections that steer degraded representations toward their clean-data distribution. It includes a BlurEstimator that builds a degradation descriptor using fixed Laplacian and Sobel operators, and a PromptGenerator that turns this descriptor into modulation parameters for the frozen encoder output. The framework comes in two versions based on the task: an additive correction (ICOP-Add) for image classification, and a Feature-wise Linear Modulation correction (ICOP-FiLM) for object detection. We observe a convergence between clean-task performance and blur-induced degradation across datasets, consistent with greater reliance on high-frequency features in stronger backbones; we treat this as an empirical, cross-dataset observation rather than a demonstrated causal claim (task difficulty, category structure, texture, and object scale also differ across datasets). Independently of this, ICOP-FiLM’s corrective benefit does not scale with degradation severity, revealing a more nuanced relationship between backbone quality and robustness. For classification, ICOP-Add improves distorted-condition accuracy over strong mixed fine-tuning by $+2.4$, $+9.1$, and $+10.7$ percentage points on MNIST, FashionMNIST, and CIFAR-10, respectively (McNemar’s test, $p<0.001$ on all three, 5000 paired predictions per dataset). On three object detection datasets, ICOP-FiLM improves distorted-condition mAP over a mixed-fine-tuning null hypothesis by $+0.026$, $-0.005$, and $+0.003$ mAP, respectively (all values mean over 3 seeds). Against a matched-blur-ratio control that isolates the correction module’s own contribution, ICOP-FiLM wins by a consistent margin on two of the three datasets ($+0.037$ and $+0.024$ mAP, winning in every one of 3/3 seeds on each) and loses on the third ($-0.039$ mAP, losing in 3/3 seeds); it outperforms parameter-efficient (VPT, Adapter) baselines trained on identical data on the same two datasets. This dataset-dependent pattern is discussed in detail in the main text. ICOP-FiLM adds only 82,672 parameters to a 42-million-parameter Real-Time DEtection TRansformer (RT-DETR) detector. All reported results are obtained under synthetic Gaussian blur and radial vignetting applied to clean images from the six benchmark datasets studied. Full article

Under climate change, the demand for high-quality urban ecological security is rising. This study focuses on how rapid urbanization and climate change affect ecological risk, resilience, and land use functions (LUFs) spatial co-variation in the Pearl River Delta. Land use was simulated under Shared Socioeconomic Pathways (SSPs) using system dynamics (SD) and the interaction network–Patch-generating Land Use Simulation (intPLUS) model. Ecological risk was quantified via the landscape ecological risk index (LERI) and habitat degradation index (HDI), while ecological resilience was obtained using an adaptability–resistance–recovery framework, producing a comprehensive ecological risk–resilience index (CERRI). Ecosystem services were assessed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Spearman correlation, geographically weighted regression (GWR), constraint lines, and extreme gradient boosting with SHapley Additive exPlanations (XGBoost-SHAP) revealed LUFs patterns, nonlinear relationships and threshold effects, and driving factors. The results indicate that construction land expands mainly at the expense of cropland (~4049–4157 km²) from 2023 to 2035, while woodland and water remain largely stable. CERRI shows a concentric pattern (2023 domain mean = 0.638), with safer peripheral belts and a more risk-dominated central–southern core; under coupling-weight uncertainty with 2023-fixed common-reference normalization, SSP245 was preferred in all Monte Carlo iterations (best-scenario probability = 1.000). EF–LF, EF–PF and LF–PF retain stable nonlinear forms. Elevation, economic vitality and transport accessibility are the leading drivers, with model-derived breakpoints near low-elevation, high-vitality and moderately accessible transport nodes. This study provides the CERRI framework to support ecological security monitoring and adaptive land-use management, contributing to more sustainable regional development under climate change. Full article

This paper proposes a supervisory soft disagreement framework for adaptive uncertainty regulation in Interval Type-2 (IT2) fuzzy servo control and validates its performance through embedded implementation on an industrial servo platform. The proposed framework introduces a supervisory learning layer that combines supervised classification and unsupervised fuzzy clustering to characterize servo operating conditions using experimentally extracted performance indicators, including rise time, settling time, overshoot, steady-state error, Integral Absolute Error (IAE), control-effort energy, tracking-error standard deviation, and maximum control effort. Operating condition confidence is quantified by measuring the soft disagreement between the posterior class probabilities of a Support Vector Machine (SVM) classifier and the normalized membership degrees of a Fuzzy C-Means (FCM) clustering algorithm using the Bhattacharyya coefficient. The resulting disagreement index adaptively regulates the Footprint of Uncertainty (FOU) of the antecedent membership functions in IT2 fuzzy controller. A closed-form Uncertainty Avoider Defuzzification (UAD) strategy enables computationally efficient uncertainty-aware type reduction for real-time embedded implementation without iterative procedures. The framework was trained using experimental data collected from a Delta ASDA-B2 400 W industrial servo drive under diverse operating conditions. The complete controller was implemented on a Raspberry Pi and experimentally compared with conventional Proportional–Integral–Derivative (PID), Type-1, and fixed-FOU IT2 fuzzy controllers. Experimental results show that the proposed controller achieved an average IAE of 1.08, representing improvements of 55.6% and 27.5% over the PID and fixed-FOU IT2 controllers, respectively. Overshoot was reduced to 2.2% and settling time to 0.24 s, while the supervisory computation required only 4.55 ms, confirming real-time feasibility. The scientific significance of this work lies in introducing a new disagreement-driven supervisory paradigm that links probabilistic machine learning confidence with adaptive fuzzy uncertainty regulation. By establishing a principled connection among supervised learning, unsupervised learning, and Interval Type-2 fuzzy control, the proposed framework provides a general foundation for confidence-aware adaptive uncertainty management in intelligent control systems operating under uncertain and time-varying conditions. Full article

This paper investigates the damped and periodical oscillations of a specific system that depends on four physical parameters. Exact parametric solutions are established based on a smooth function. The system is explicitly integrated without admitting prime integrals. The influence of the physical parameters is examined semi-analytically through the Multistage Optimal Parametric Iteration Method (MOPIM). A key advantage of this method is that it used only one iteration, owing to an appropriate choice of auxiliary functions for convergence control. There is accuracy between MOPIM solutions and corresponding numerical results, highlighted qualitatively through figures, quantitatively through tables, and by statistical tests of the residuals. The damped or periodical behaviors of the system’s solutions lead to their application on electronic circuits or other technological application fields. Full article

This paper presents methods and results on optimizing the functionality of pressurized sewerage systems, specifically wastewater pumping stations. A holistic approach is introduced to address major issues in wastewater transport such as sedimentation in suction chambers and fiber-induced issues in wastewater pumps, as well as their effects and detection. A conceptual suction chamber is scaled using hydraulic similarity and is experimentally investigated regarding its susceptibility to sedimentation. Optimization of inlets and manifolds, pumps, and sloped walls contribute to minimizing sedimentation. In terms of wastewater pumps, a semi-open two-channel wastewater impeller is optimized for its efficiency via response surface optimization. The subsequent optimization for functionality showcases the nexus of the two characteristics by means of cut-back and thickened leading edges, which significantly reduce the susceptibility to clogging. Another important consideration in wastewater pump design is the back shroud cavity. Through iterative experiments with different housing recess configurations, the back shroud cavity is optimized to minimize fiber entry and protect the mechanical seal. Lastly, the demonstration of the effects of clogging in the form of transient instationarities in the torque of a wastewater pump underline the importance of optimizing wastewater pumps for their functionality and low susceptibility to clogging. Full article

In this paper, we establish several retarded integral inequalities of Gronwall–Bihari–Pachpatte type for nonnegative functions involving a delayed argument $\alpha \left(t\right)\le t$. The proposed results cover both linear and nonlinear settings and provide explicit upper bounds through an auxiliary integral function and its inverse. In particular, we consider inequalities containing delayed nonlinear terms, kernels depending on both t and s, weighted contributions, and iterated integrals. The considered structures are motivated, in part, by energy estimates arising in nonlinear delay differential systems, where the nonlinear control function is naturally determined by the growth of the nonlinearities. The obtained inequalities extend several classical Gronwall-, Bihari-, and Pachpatte-type estimates by retaining the effect of the variable time delay in the resulting bounds. As applications, suitable a priori estimates are derived for generalized delayed Liénard and Rayleigh systems and are used to establish the global existence of their solutions. The developed framework therefore provides useful tools for the qualitative analysis of nonlinear differential and integral equations with retarded arguments. Full article

Industrial IoT sensor nodes face a triple burden—sampling, compression, and security—under severe resource constraints; yet, the question of which signals can actually benefit from compressed sensing (CS) remains largely implicit in the literature. SCCS answers this question by unifying compression, chaotic obfuscation, and authentication within a single CS measurement and deriving an empirical deployability rule consisting of the PCA energy concentration ratio $\rho$. When $\rho$ exceeds 80%, signals reconstruct at high fidelity; when $\rho$ falls below 50%, they are intrinsically incompressible; and in the intermediate 50–80% band, reconstruction is uncertain and may fail outright rather than degrading gracefully (as shown on CWRU). This empirical deployability rule is supported by evaluation on three real datasets: high-fidelity reconstruction is confirmed on CBM ($\rho =99.9\%$), while CWRU ($\rho =65.9\%$) and CCPP ($\rho =15.4\%$) establish the applicability boundaries and validate the $\rho$-based screening criterion. The enabling system integrates a block-circulant chaotic measurement matrix (BCCM, from a two-dimensional sine-logistic iteration mapping (2D-SLIM) map) that compresses and obfuscates in one operation (online measurement seed 0.84 KB, down from a 512 KB dense matrix; the full reference implementation requires 185 KB Flash, including a 160 KB decoder dictionary); an offline principal component analysis (PCA) dictionary that lifts reconstruction signal-to-noise ratio (SNR) from 5.36 to 33.52 dB at CR = 4 (+28.16 dB over the fixed-basis configuration; Wilcoxon $p<0.001$, 30 independent trials); and a dual-layer authentication scheme combining always-on hash-based message authentication code (HMAC) with adaptive reconstruction-based implicit authentication (RBIA), the latter providing zero-overhead tamper pre-screening that reuses the decoder’s reconstruction residual and automatically falls back to HMAC-only under channel noise. Security boundaries are explicitly disclosed: the chaotic measurement resists known-plaintext attacks but is vulnerable to chosen-plaintext recovery (N plaintexts recover the linear matrix), and 1.13 bits of amplitude side-channel leakage exist. The SCCS framework demonstrates that the three functions need not be separate serial stages, provided the target signals satisfy the $\rho$ screening rule. Full article

In this paper, an adaptive estimation algorithm for heterogeneous group targets considering uncertain multiplicative and additive noises is proposed. Firstly, a state-space model for heterogeneous group targets with composite multiplicative and additive noise is established. Since the coupling effect of multiplicative noise renders the marginal likelihood analytically intractable and induces heavy-tailed characteristics, a tailored hierarchical Gaussian–Gamma model is introduced for robust approximation. Second, a joint posterior probability density function incorporating the target kinematic state, extended morphology, and noise parameters is constructed. Within the variational Bayesian framework, approximate posterior distributions of these variables are derived, and fixed-point iteration is employed to compute the system state and noise statistics. Simulation results demonstrate that, under environments corrupted by unknown and time-varying multiplicative and additive noises, the proposed algorithm adaptively estimates a unified measurement noise covariance, achieving superior estimation performance compared to the random matrix model and the VB-EOT-SN method. Full article

Collagen functional fragments retain selected activities of parent collagens while allowing greater flexibility in sequence design and precision fermentation. Although recent reviews have covered recombinant collagen production technologies, expression platforms, purification strategies, quality control, and biomedical applications, fragment selection, host–process matching, production, and characterization have received less integrated attention. This review focuses primarily on collagen-derived functional fragments, while collagen-mimetic peptides and collagen-like proteins are discussed as related design systems. The biological basis for fragmentation includes receptor-recognition motifs, matrikines and matricryptins, and basement membrane-derived fragments. The review further examines how motif context, Gly-X-Y organization, stabilizing sequence features, protease susceptibility, post-translational modification requirements, and host compatibility influence fragment stability, expression performance, production feasibility, and product integrity. Microbial production using Escherichia coli, Komagataella phaffii, and Saccharomyces cerevisiae is discussed from the perspectives of construct–host matching, secretory or intracellular production, prolyl 4-hydroxylase configuration, fermentation optimization and scale-up, and product characterization. Finally, we discuss AI-assisted, quality-guided design-build-test-learn workflows that integrate computational prediction, curated structural, extracellular-matrix, interaction, and protease resources, two-tier candidate evaluation, and format-appropriate experimental testing to support iterative sequence, host, and process optimization. The development of collagen functional fragments therefore depends on coordinated optimization of biological function, molecular design, microbial host performance, fermentation processes, and product characterization. Full article

High-voltage electric-vehicle (EV) fast charging requires accurate coordination between the off-board charger and the battery management system during voltage and current negotiation. This study evaluates a Hybrid Particle Swarm Optimization-Whale Optimization Algorithm (HPWOA) schedule for training a dual-output feedforward neural network that directly estimates ChargePower_kW and ChargeCurrent_A. Ten protocol-state and battery-condition variables were used as inputs. The 158-dimensional neural-weight vector was optimized using 75 Particle Swarm Optimization (PSO) iterations, followed by 75 Whale Optimization Algorithm (WOA) iterations. Using the supplied 500-record dataset, a reproducible 30-seed sample-level evaluation was conducted with a common 3775 fitness-function-evaluation budget for PSO, the WOA, the SFSA, and the HPWOA. The Stochastic Fractal Search Algorithm (SFSA), therefore, used 30 iterations because it evaluates five diffusion candidates per individual. The reported HPWOA mean ± standard deviation (SD) was RMSE = 4.658 ± 0.986 kW and R² = 0.843 ± 0.071 for power, and RMSE = 12.686 ± 2.687 A and R² = 0.858 ± 0.059 for current. The HPWOA outperformed the WOA and SFSA, but not standalone PSO. A conventional mini-batch Adam-trained dual-output neural network produced RMSE = 1.704 ± 0.121 kW and 4.946 ± 0.273 A, and R² = 0.980 ± 0.003 and 0.979 ± 0.002, respectively. Charger-grouped five-fold validation gave the HPWOA R² = 0.857 ± 0.045 (power) and 0.873 ± 0.048 (current). An analytical P = V × I reconstruction was physically consistent in construction and did not show a statistically significant power–RMSE difference from direct HPWOA outputs. The results, therefore, position the two-phase schedule as a reproducible comparative baseline rather than as a demonstrated replacement for gradient-based training or a physically constrained reconstruction. Full article