Search Results (1,012)

Geographical authentication of medicinal plant materials remains challenging because multispectral variables are often highly collinear and sample grouping can complicate reliable model validation. Existing correlation-based feature-selection strategies also require careful adaptation to multiclass problems to avoid artificial ordering of class labels and information leakage during model development. Therefore, this study aimed to develop a compact and leakage-controlled multispectral learning framework for geographical-origin discrimination. This study analyzed 800 physical Pinellia ternata samples from Gansu Xihe, Sichuan Neijiang, Sichuan Chengdu, and Chongqing Dianjiang (200 samples per origin). Each physical sample was represented by 31 mean grayscale intensities calculated from Otsu-segmented multispectral regions of interest. A Pearson-correlation-guided deep multilayer perceptron (PCG-DeepMLP) was constructed by estimating one-vs-rest band relevance and inter-band redundancy only within the training data. The key methodological innovation is a unified multiclass-aware, relevance–redundancy spectral-learning framework in which class-specific one-vs-rest Pearson relevance is coupled with inter-band redundancy control and embedded within leakage-controlled grouped model development. By learning the spectral subset exclusively from each training partition before nonlinear classification, the framework produces compact and complementary multispectral signatures while preserving multiclass structure and strict independence of held-out groups. Model and feature-selection settings were chosen by three-fold grouped cross-validation within each training partition. PCG-DeepMLP retained 9–21 bands and achieved the highest mean accuracy (0.9812 ± 0.0135), macro-F1 (0.9812 ± 0.0135), Matthews correlation coefficient (MCC; 0.9752 ± 0.0179), and macro-AUC (0.9994 ± 0.0006) among seven models. Its macro-F1 was higher than that of 1D-CNN, 1D-ResNet, full-band MLP, PLS-DA, and random forest after Holm correction. Performance was estimated through a strict nested group-wise internal validation scheme, with every outer test fold remaining isolated from feature selection, preprocessing, and model optimization. These findings demonstrate that multiclass-aware relevance–redundancy learning can retain complementary Pinellia ternata origin-discriminative information in a compact and stable spectral representation, enabling accurate geographical-origin authentication while providing a principled basis for reduced-channel acquisition and future independent multi-batch validation. Full article

This paper proposes a novel mixed numerical scheme for approximating linear Fredholm integral equation systems (LFISs) using a combination of Bernoulli polynomials (BPs) and enhanced block-pulse functions $\left(\mathcal{E}\mathcal{B}\mathcal{P}Fs\right)$. This suggested representation makes use of both the local support nature and computation efficiency of the $\left(\mathcal{E}\mathcal{B}\mathcal{P}Fs\right)$ as well as the high-order approximating nature of BPs. Using the operational matrices, the system of coupled integrals can be transformed into a finite-dimensional algebraic system (AS) of expansion coefficients. A theoretical analysis is established to investigate the solvability, convergence, stability, and approximation error of the resulting scheme. In addition, the effects of polynomial degree and partition refinement on the numerical accuracy are examined. Several test problems are considered, and the obtained results demonstrate that the proposed $\mathfrak{B}\mathcal{E}\mathcal{B}\mathcal{P}\mathcal{F}$ approach provides highly accurate approximations while requiring relatively small basis dimensions. Comparisons with previously reported numerical techniques further illustrate their computational effectiveness and accuracy. Full article

Non-intrusive load monitoring (NILM) aims to estimate appliance-level power consumption from aggregate household measurements and provides an effective solution for fine-grained energy management. However, multi-appliance NILM remains challenging because the measured aggregate power generally contains unobserved household loads, jointly predicted appliance powers may violate basic physical relationships, and heterogeneous appliance activation patterns can lead to imbalanced optimization. To address these issues, this paper proposes a Residual-Aware Physical Consistency Network (RAPC-Net) for multi-appliance NILM. RAPC-Net jointly estimates multiple target appliances and the remaining household consumption through a shared temporal encoder, a multi-appliance prediction branch, and a residual-load prediction branch. Two watt-space physical consistency constraints are incorporated into the learning objective. The aggregate reconstruction constraint encourages the target-appliance predictions and the predicted residual load to jointly reconstruct the measured aggregate power, while the overestimation constraint suppresses physically infeasible predictions in which the sum of the predicted target-appliance powers exceeds the aggregate measurement. In addition, an automatic appliance-aware weighting strategy is developed based on appliance activation statistics to balance the contributions of different appliances and emphasize active samples without manually assigning appliance-specific loss coefficients. Experiments on the REFIT dataset, including an expanded ablation study and two house-independent comparisons, are conducted to evaluate the contributions of residual-load modeling, physical consistency constraints, and appliance-aware weighting. In the primary house-independent comparison, RAPC-Net achieves a Target-sum MAE of $85.97\pm 15.45$ W, which is close to the best competing result of $83.59\pm 3.71$ W, while reducing the Over-ratio to $0.025\pm 0.020\%$ and the violation energy to $9.10\pm 5.47$ Wh. Under an additional household partition, RAPC-Net maintains a Target-sum MAE of $127.29\pm 7.05$ W and achieves an Over-ratio of $0.019\pm 0.011\%$. These results indicate that RAPC-Net improves the physical feasibility of joint multi-appliance predictions while maintaining comparable target-sum estimation accuracy. Full article

Rapid surrogate prediction of ducted propeller performance is challenging when only a limited number of independent geometries are available and operating points belonging to the same geometry are strongly correlated. This study proposes a sensitivity-informed physics-regularized backpropagation neural network (SIPR-BP) for simultaneous prediction of the thrust coefficient K_T and the scaled torque coefficient 10K_Q. A CFD database comprising 20 Ka4-70-derived parameterized geometries, each evaluated at five advance ratios, provides 100 observations and 20 complete performance curves. The framework combines three main strategies. First, two-component multi-output partial least-squares (PLS) curve surrogates are reconstructed exclusively from the training geometries of each outer fold to generate leakage-controlled conditional Sobol gate priors. Second, the operating coordinate J is separated from geometric gating and represented by five ordered curve nodes, which guarantee non-increasing K_T and 10K_Q responses over the investigated interval. Third, training-only physics-consistency reliability weighting and a three-member ensemble improve robustness to locally irregular CFD responses and initialization variability. Under a ten-round geometry-grouped holdout protocol, SIPR-BP achieves a geometry-balanced MAPE of 2.65%, RMSE of 0.0112, MAE of 0.00911, and pooled R² of 0.951. When evaluated under the same outer partitions, a two-component PLS baseline yields a MAPE of 4.38%. Across the evaluated PLS, Extra Trees, GPR, and SVR baselines, SIPR-BP reduces geometry-balanced MAPE by approximately 39.6–79.2%. The results indicate that the proposed framework improves unseen-geometry prediction while preserving the prescribed response-curve structure. Full article

Background/Objectives: The application of organic soil amendments constitutes an important source of antibiotic residues in agricultural soils. However, studies have mainly focused on a few parent tetracyclines, whereas information on their transformation products (TPs) in organic amendments remains scarce. Therefore, this study assessed the occurrence and environmental risk of six tetracyclines and seven TPs in organic amendments. Methods: Processed livestock manures applied in the European Union (horse, poultry, and bovine manure), as well as fresh and treated sewage sludge, were analyzed using matrix solid-phase dispersion (MSPD) combined with online SPE-LC-MS/MS. Environmental risk was assessed using risk quotients (RQs) based on predicted environmental concentrations in soil (PEC_soil) calculated from the maximum measured concentrations and predicted no-effect concentrations in soil (PNEC_soil) obtained either directly from terrestrial ecotoxicity data or derived from aquatic ecotoxicity data using the equilibrium partitioning method and soil-water distribution coefficients. Results: Tetracyclines and their TPs were widely detected in both sample types, although concentrations varied according to matrix type and treatment. Overall, sludge showed higher detection frequencies and concentrations than manure. Doxycycline and tetracycline were the predominant parent compounds, reaching concentrations up to 2838 ng g⁻¹ dry weight (dw) in manure and 2892 ng g⁻¹ dw in sludge. Epimerized TPs were frequently detected and sometimes exceeded the concentrations of their parent compounds, especially epitetracycline and epioxytetracycline. Among the sludge types and treatment conditions investigated, anaerobically digested sludge showed the highest tetracycline concentrations, whereas the composted sludge sample presented the lowest concentrations. Individual RQs indicated insignificant to low ecotoxicological risk, whereas cumulative RQ (ΣRQ) values, used as conservative estimates of co-exposure to all evaluated tetracyclines and their TPs, fell within the medium-risk category for bovine manure and anaerobically digested sludge, with the composted sludge sample showing the lowest ΣRQ. Conclusions: These findings highlight the importance of including TPs in environmental monitoring and the differences in tetracycline occurrence and environmental risk to soil among the processed manure types and sludge treatment conditions investigated. Full article

Respiratory-sound event classification is challenged by non-stationarity, class imbalance, heterogeneous acquisition, and participant-correlated recordings. This study evaluates whether direct fusion of short-time Fourier transform (STFT), mel-frequency cepstral coefficient (MFCC), and wavelet-packet features improves a temporal one-dimensional convolutional neural network (1D-CNN), and whether temporal convolution offers an advantage over conventional classifiers. A radial basis function support vector machine (RBF-SVM) and random forest were included deliberately to separate the value of the engineered representation from classifier complexity. Experiments used 920 recordings and 6898 annotated cycles from the International Conference on Biomedical and Health Informatics (ICBHI) 2017 Respiratory Sound Database. The predefined 60:40 recording-level benchmark partition was retained; model selection used five-fold participant-grouped cross-validation, and 95% confidence intervals were estimated from 1000 participant-level bootstrap resamples. The complete hybrid 1D-CNN achieved a macro-averaged F1-score of 0.313. MFCC alone yielded 0.320, but the paired difference was not statistically resolved. The RBF-SVM and random forest achieved 0.401 and 0.378, respectively. These findings apply to direct early concatenation with the shared 1D-CNN backbone and do not imply that feature fusion is generally ineffective. The study provides leakage-aware baselines, controlled ablations, clustered uncertainty estimates, and frozen artifacts for reproducible comparison. Full article

Accurate state-of-charge (SOC) prediction on personal mobile devices requires a model that can represent heterogeneous discharge mechanisms, transfer knowledge across users, and adapt from scarce target records while raw telemetry remains privacy isolated. This study proposes a module-decomposition-driven SOC prediction model that separates baseline drain, synchronous state response, and asynchronous multi-scale event memory within one discharge-dynamics formulation. The same three semantic coefficient blocks remain aligned through local source learning and round-wise sample-count-weighted federated aggregation. Target-side personalization then adjusts one locally derived personal discharge-rate scalar while preserving the shared temporal response, and forward SOC propagation produces threshold-based time-to-empty (TTE) estimates. A user-held-out evaluation on a public small-sample smartphone-log dataset uses eight target partitions and 447 final evaluation origins. Personalized fine-tuning reduced TTE MAE by 67.07% and RMSE by 71.88%, yielding an $R^2$ of 0.936. The resulting task-specific architecture connects module-structured data processing, federated synthesis, few-shot adaptation, and local TTE inference while raw usage logs remain within user-specific data boundaries. Full article

Accurate mapping of rubber plantations is essential for sustainable land management and forest conservation in tropical regions. However, existing methods face two major challenges: persistent cloud cover limits the effectiveness of optical remote sensing in tropical areas, and regional phenological heterogeneity hinders the transferability of fixed-parameter approaches. This study proposes a Phenology-Adaptive Rubber Plantation Mapping (PARM) framework that integrates Sentinel-1 SAR time-series data with optimally selected spectral indices through a cascading constraint architecture. The framework operates as a structurally coherent system wherein SAR-derived phenological anchors explicitly govern downstream optical analysis across three internally dependent modules. First, three key phenological nodes—leaf-off start (LOS), fastest greening point (FGP), and full canopy point (FCP)—are extracted directly from SAR VH-polarization backscatter time series, enabling cloud-independent extraction of phenological temporal anchors. Second, the Jeffries–Matusita (JM) distance, evaluated within SAR-constrained phenological windows, is employed to identify the optimal vegetation and water indices for each region from six candidate spectral indices. Third, a time-weighted Rubber Plantation Discrimination Index (RPDI) is constructed using the selected indices and locally extracted phenological nodes, thereby amplifying the coupled signals of canopy greenness and moisture dynamics during critical phenological transitions. The framework was validated in Hainan Island and Vietnam, two regions with contrasting phenological regimes, using a spatial-block partitioning protocol (leave-one-subregion-out combined with DBSCAN-based clustering) designed to prevent samples from the same plantation from occurring in both training and test subsets. Within the Dynamic World forest mask, PARM achieved overall accuracies of 92.04% and 91.24%, respectively (93.57% and 91.00% on the fully held-out Qionghai City and Gia Lai province subregions), with Kappa coefficients exceeding 0.81 in both regions, consistently outperforming schemes based on raw spectral bands, individual spectral indices or direct multi-source time-series stacking. Error structure analysis revealed that residual classification failures are primarily associated with landscape fragmentation, stand immaturity, and residual cloud contamination, delineating the generalizability boundaries of the framework. These results demonstrate that tightly coupling SAR-based phenological characterization with adaptive optical index selection through a cascading constraint architecture provides a reliable foundation for rubber plantation mapping in cloud-prone tropical regions. Full article

Distributed acoustic sensing (DAS) converts fiber cables into dense strain-rate sensor arrays, capturing direct, reflected and guided seismic phases with ultra-fine spatial sampling. However, DAS interrogators suffer far stronger random noise than traditional geophones, limiting its microseismic imaging capacity. This work adopts curvelet-transform denoising to suppress noise. Curvelets partition the frequency–wavenumber plane into multiscale directional sectors; coherent wave energy concentrates in limited angular wedges, while stochastic noise disperses evenly across all transform coefficients, enabling noise-signal separation via wedge-wise thresholding. We test four default threshold schemes on synthetic downhole DAS microseismic data. Parameter tuning proves all methods deliver comparable performance, so we compare their out-of-box reliability for shale reservoir monitoring. Three noise-statistic-based strategies perform stably: median-absolute-deviation (MAD), quiet-window and empirical-cumulative-distribution-Function (ECDF percentile) thresholding. By contrast, the default knee-point algorithm from mainstream DAS toolboxes fails, as its preset threshold falls within noise components and barely removes interference. We propose MAD as a robust default for the tested downhole DAS microseismic setting for it estimates thresholds directly from noisy traces without blank reference windows and offers superior operational stability. Applied to field DAS records from a southwest China shale-gas horizontal monitor well, the MAD curvelet workflow greatly enhances microseismic arrivals with negligible spurious events. Benchmarks against standard 2D Daubechies-4 wavelet and adaptive Goldstein FK filtering verify curvelet denoising as a physically interpretable, efficient tool for DAS wavefields. Full article

Generalized Lotka–Volterra (GLV) systems, with roots in ecology, constitute one of the most studied classes of positive ODEs. Recently, a reaction-network perspective for a generalization useful in mathematical epidemiology, called block GLV systems, was offered by Adenane, Avram and Halanay. These authors developed a “siphon calculus” in which the boundary stability of block GLV equations is studied through (i) invariant faces associated with minimal siphons, (ii) transversal Jacobians, (iii) invasibility expressed via R-invasion functions, (iv) relay graphs, (v) exclusion partitions and (vi) Lyapunov functions, without leaving the original state space. Another reaction-network perspective for GLV systems was offered by Rojas La Luz, Yu and Craciun, who developed a global stability theory for positive equilibria by introducing associated polyexponential systems obtained through the logarithmic change of variables $x_i=e^{{\xi }_i}$. In these logarithmic coordinates, compatibility classes become affine subspaces and simple quadratic Lyapunov functions establish global convergence of complex-balanced systems. The purpose of the present paper is to combine and compare these two perspectives. Our block GLV results here start with a general Perron–Volterra relay theorem (Theorem 7) and its explicit verification for the rank-one multi-strain class. The theorem constructs a face-adapted Lyapunov function in which resident blocks enter through Perron-weighted entropy terms and missing blocks through positive left Perron functionals, and reduces global convergence to resident and transversal closing conditions together with compactness. For the rank-one block model, the canonical Perron normalization gives the closing terms explicitly on an arbitrary resident support I: for every missing block $k\notin I$, the corresponding coefficient has the sign of $R_k\left(E_I\right)-1$. Hence, the relay-sink conditions $R_k\left(E_I\right)<1,k\notin I,$ verify the transversal closing hypothesis and yield convergence to the resident invariant set selected by the resident closing condition (Corollary 20); when that set consists of a single equilibrium $E_I$, the convergence is global to $E_I$. For nonlinear scalar GLV systems, Theorem 6 gives an exact characterization of global Volterra admissibility through the Jacobian averaged along the segment joining the positive equilibrium $x^{*}$ to each point x: $a\in \mathcal{W}\left(x^{*}\right)\iff {(x-x^{*})}^T\left[A\overline{J}(x;x^{*})+\overline{J}{(x;x^{*})}^{T}A\right](x-x^{*})\le 0\mathrm{for}\mathrm{every}x\in {\mathbb{R}}_{>0}^n.$ It also identifies the averaged-Jacobian matrix inequality as a sufficient global certificate, shows that uniform diagonal stability of the pointwise Jacobian family is a stronger sufficient condition, and shows, via a nonlinear counterexample, that even strict diagonal stability of $Df\left(x^{*}\right)$ at the equilibrium does not imply global Volterra admissibility. Then, we give a complex-balanced GLV example for which no positive Volterra weight yields a Lyapunov function on the whole positive orthant (Theorem 11). Thus, complex balance does not imply global Volterra admissibility. Interestingly, Volterra decrease is recovered on the compatibility manifold in this example, which leads to the open question whether such compatibility-restricted Volterra functions exist more generally for complex-balanced GLV systems (Problem 2). Finally, we show that all four logical combinations of complex balance and global Volterra admissibility occur among GLV systems, three of them already among affine systems (Theorem 12). Full article

Floating offshore wind turbines (FOWTs) experience coupled aero–hydro–servo-elastic excitations that produce structurally distinct kinematic responses at different measurement heights. While field monitoring campaigns increasingly deploy multi-level inertial sensors, the quantitative spectral partitioning of response energy across measurement levels and its relationship to operational and environmental conditions remain poorly characterised for operational FOWTs. This study presents a systematic multi-level spectral decomposition of operational FOWT structural response using synchronised tower-base and nacelle strapdown inertial measurements acquired at 8 Hz over a six-day campaign (18–23 April 2023) at a semi-submersible FOWT in Chinese coastal waters. Six kinematic channels—three translational acceleration components and three translational velocity components—from each sensor are decomposed into four physically defined frequency bands: drift (0.005–0.05 Hz), wave (0.05–0.30 Hz), structural (0.30–0.50 Hz), and rotor (0.50–0.80 Hz). Three derived scalar metrics—band energy ratio (BER), Wave-to-Structural Dominance Ratio (WSDR), and Structural Amplification Factor (SAF)—are defined, with their complete computation specifications and parameter sensitivity analysis provided to ensure reproducibility. Across 36 ten-minute windows spanning diverse conditions (mean wind 4.2–12.1 m/s, $H_s$ 0.8–3.1 m), results reveal a pronounced and consistent spectral separation: the tower base is strongly wave-dominated (${\mathrm{BER}}_{\mathrm{wave}}$ = 75.9%, coefficient of variation CV = 15.0% across days), whereas the nacelle exhibits substantially elevated structural-band energy (${\mathrm{BER}}_{\mathrm{struct}}$ = 10.3%, 4.72-fold amplification relative to tower base, 95% CI [3.63, 5.81]) and rotor-band energy (11.8%, 3.77-fold amplification). The WSDR at the tower base (mean 200.9, 95% CI [101.4, 300.4]) exceeds that at the nacelle (mean 48.4, 95% CI [13.1, 83.7]) by a factor of 4.1×. One-way analysis of variance (ANOVA) reveals that nacelle structural-band BER is significantly modulated by SCADA operational regime ($F=5.20$, $p=0.024$, ${\eta }^2=0.16$) and by significant wave height ($p=0.031$), while tower-base wave-band BER is primarily driven by $H_s$ ($p=0.018$). Comparison with baseline features—root-mean-square acceleration, spectral peak frequency, and traditional broad-band energy ratio—demonstrates that the band-resolved BER provides finer discrimination between excitation mechanisms than aggregate metrics. Importantly, no structural damage events occurred during the monitoring period; therefore, the reported stability of these features is interpreted as a baseline characterisation under normal operational conditions, which could support future anomaly detection efforts but does not constitute validation of damage detection capability. A comprehensive limitations assessment is provided, covering single-turbine validation, frequency-band sensitivity, regime sample imbalance, and generalisability constraints. Full article

(1) Background: Phishing remains a pervasive and economically damaging cyberthreat. The dominant detection paradigm has moved toward deep neural and transformer-based classifiers, a literature that reports high accuracy and that does not, in general, expose a per-decision justification, whereas interpretability and auditability are increasingly required in regulated environments; no comparison against transformer-scale detectors is made in this paper. This work asks how far a fully interpretable detector can close the accuracy gap to an opaque text classifier while preserving per-decision explanations, and what such a detector returns that accuracy alone does not measure. (2) Methods: HIEF, an interpretable evidence-fusion framework, is presented. Each email is represented by eighteen human-readable signals: fourteen structural and linguistic cues and four lexical aggregates derived from a published sparse log-odds lexicon. The signals are fused by three transparent layers, namely an $L_1$-regularized logistic model, a shallow interaction-rule tree, and a calibrated Dempster–Shafer stage that reports belief, disbelief and ignorance masses together with an order-invariant global conflict coefficient derived in closed form. A logistic meta-learner fitted on out-of-fold component scores integrates the three layers. The evidential layer uses a type-aware calibration in which discrete signals are calibrated on their attainable values and continuous signals by isotonic regression. Evaluation uses 38,908 public emails, 38,512 of them after exact-duplicate removal, with near-duplicate control, group-aware partitioning, ten repeated splits, a source-held-out protocol, a two-class cross-source test set, a component ablation and a human audit of 100 messages annotated independently by two evaluators. (3) Results: Under group-aware partitioning, HIEF attains an $F_1$ of 0.855 and the strongest term frequency–inverse document frequency (TF–IDF) baseline 0.954; a compact character n-gram neural reference model, evaluated over the same ten partitions, attains 0.973. The linear layer alone attains 0.872, so the two fusion layers do not improve accuracy over it, and the paired difference of 0.017 excludes zero. Type-aware calibration raises the evidential layer from 0.771 to 0.780 and more than halves its partition-to-partition standard deviation, but does not make it competitive; the weakness, therefore, lies in the fusion formulation rather than in the binning. What the evidential layer does supply is a decomposable account of decision uncertainty: the ignorance mass separates errors from correct decisions, 0.265 against 0.175. The human audit reaches an inter-annotator Cohen’s kappa of 0.950 over the five categories before adjudication, and shows that the permissive corpus label agrees with human phishing judgment at a Cohen’s kappa between 0.18 and 0.21, against 0.70 to 0.77 for the automatic strict rule; the audited block is annotated by two of the authors and its human positives are confined to the advance-fee family, so the audit is a bounded comparison of label assignments and not an independent annotation study. (4) Conclusions: HIEF is positioned as an uncertainty and explanation framework rather than as an accuracy-improving fusion method, since the measured accuracy cost of the fusion layers is not compensated by an accuracy gain. Quantifying how much of the performance reported on these widely used corpora is attributable to template leakage and to label permissiveness is a contribution independent of the detector itself. Cross-source operation has not been demonstrated: specificity falls to 0.041 on an unseen collection, so all evaluation reported here is proof-of-concept and no operational deployment claim is made. The Spanish-language evaluation rests on a small and entirely positive subset and is reported as preliminary. Full article

Understanding niche differentiation and interspecific coexistence patterns of macrobenthos provides critical references for ecological assessment of bivalve aquaculture. Seasonal field surveys (Autumn 2023–Summer 2024) across 12 sampling sites were conducted in an intensive Meretrix meretrix tidal flat farm in Rudong, Jiangsu. Synchronous water depth, sediment grain size and total organic carbon (TOC), and salinity were measured at each station to characterize habitat gradients; no unfarmed reference tidal flats were included, so all community patterns only reflect within-farm variation. The study area has sustained 10-year continuous bottom culture with baseline juvenile stocking density of 250 ind./m² and twice-yearly manual harvesting. A total of 58 macrobenthic taxa were identified, and 13 dominant species were screened with a dominance index threshold Y > 0.02. All abundance data were fourth-root-transformed before Bray–Curtis community analysis to reduce bias of hyper-abundant taxa. Standardized Shannon niche breadth (0–1 normalized, r = 48 site–season composite units) and permutation-tested (999 replicates) Pianka niche overlap/similarity coefficients were calculated to quantify resource utilization similarity. Niche breadth ranged from 0.018 to 0.971, with farmed M. meretrix showing the maximum value, indicating broader seasonal resource adaptation. High niche overlap (>0.50) was detected between M. meretrix and four wild dominant species (Nephtys californiensis, Moerella iridescens, Lingula anatina, Umbonium thomasi). Notably, niche metrics derived solely from spatial abundance data cannot directly confirm interspecific competition or trophic segregation; the stable coexistence observed in our assemblage is tentatively hypothesized to be driven by vertical sediment partitioning and divergent feeding guilds based on published functional studies, without direct in situ trophic or stratified sediment evidence from this work. Hierarchical UPGMA clustering (0.35 Bray–Curtis distance threshold) and corrected NMDS ordination (48 site–season units, dominant-species fitted vectors) illustrated distinct functional groups. Based on the observed niche convergence among filter-feeding bivalves under local farming conditions, the range of 230–280 ind./m² is presented as an observational reference. Controlled manipulative experiments with gradient stocking densities are needed to test whether this range can balance aquaculture yield and benthic biodiversity. Full article

Background/Objectives: This study presents a gas chromatography–mass spectrometry (GC–MS) method for the derivatization-free quantification of the three major short-chain fatty acids (SCFAs)—acetate, propionate, and butyrate—in gilthead sea bream (Sparus aurata) fecal samples. SCFAs are key metabolites involved in intestinal physiology and are widely recognized as indicators of gut microbiota activity and host health across animal species. In aquaculture research, SCFA profiling can provide valuable insight into diet–microbiota interactions and gut functional status. Methods: The method was developed and optimized using gut-content samples collected from 105 gilthead sea bream at the end of two independent feeding trials, yielding 42 pooled samples (18 from FT1 and 24 from FT2). An extraction protocol based on acidified aqueous extraction with water followed by liquid–liquid extraction using methyl tert-butyl ether was developed (ExA) and subsequently optimized (ExB) to improve extraction consistency and efficiency. SCFA separation and quantification were performed by GC–MS using external calibration. Method validation included evaluation of linearity, matrix effects, and partition coefficients between water and methyl tert-butyl ether. Results: The optimized extraction method (ExB) demonstrated high analytical performance, with linearity coefficients exceeding 0.992 and matrix effects below 17.4%. Conclusions: The proposed method represents a practical and reliable approach for routine SCFA analysis in fish fecal samples. It provides a useful analytical tool for studies investigating diet–microbiota interactions, gut health, and functional responses to nutritional interventions in aquaculture species. Full article

The factorial Mathieu multiplier used in the nearest bi-univalent model is substituted with a gamma-shifted family using deformation parameter $\tau \ge 0$. In this case, one differential operator describes all class operators introduced previously, whereas the function and inverse subordination can be controlled by two different generalized Ma–Minda functions. Explicit bounds for $|a_2|$, $|a_3|$ and the Fekete–Szegő functional follow from identities involving exact second-order coefficients. These are sharpened by using the full Schwarz–Pick estimate $\left|{\omega }_2\right| \le |1-|{\omega }_1{|}^2$. The estimates continue to hold even when $\mathcal{Q}=0$. The positive-real-part, strongly starlike, Janowski, mixed Mathieu, and phase-dependent Noshiro families are included with explicit admissibility criteria, except for the Noshiro family, which has its phase limited to $-\pi <\varphi <\pi$, since ${\Delta }_2$ vanishes at the excluded endpoint. The numerical analysis compares the gamma deformation with the factorial case, partitions parameter space according to the active coefficient estimate, locates $\mathcal{Q}=0$, and shows how unequal targets displace the center of the Fekete–Szegő bound. The auxiliary Schwarz inequalities are sharp, but simultaneous equality within the full bi-univalent class is not established. Full article