Search Results (1,625)

Neural scaling laws describe how model performance improves as a power law with size, but existing work has focused almost entirely on models above 100 M parameters. The regime below 20 million parameters, where TinyML and edge AI systems operate, remains largely unexamined. We train 90 models spanning 22 K to 19.8 M parameters across two architecture families (a plain ConvNet and MobileNetV2) on CIFAR-100, varying width while holding depth and training protocol fixed. Both architectures follow approximate power laws, with exponents of $\alpha =0.156$ (ScaleCNN) and $\alpha =0.106$ (MobileNetV2). However, the power law does not hold uniformly: local exponents decay with scale, and MobileNetV2 saturates at 19.8 M parameters (${\alpha }_{\mathrm{local}}=0.006$), hitting a data wall. The structure of errors also changes with scale. The Jaccard overlap between error sets of the smallest and largest ScaleCNN models is only 0.35; compression changes which inputs are misclassified, not merely how many. Small models develop a triage strategy, concentrating capacity on easy classes (Gini of per-class accuracy: 0.26 at 22 K params vs. 0.09 at 4.7 M) while effectively abandoning the hardest ones (bottom-5 class accuracy: 10% vs. 53%). The smallest models achieve the lowest ECE values (0.013 vs. peak 0.110 at mid-size), reversing the typical overconfidence–capacity relationship, though this partly reflects a global-mean matching artifact rather than well-calibrated per-bin confidence. On CIFAR-100, aggregate accuracy alone is therefore a misleading basis for edge deployment decisions; validation must happen at the target model size. All findings in this study are based on CIFAR-100 (32 × 32, 100 classes); their generalizability to other datasets, resolutions, and architectures remains to be verified. Full article

Background/Objectives: The Black Caribbean population of Honduras, also referred to locally as Negro Inglés, constitutes one of the country’s nine recognized indigenous and Afro-descendant peoples. Predominantly settled in the Bay Islands and sections of the Caribbean coast, this community traces its ancestry predominantly to West Africa and has remained culturally and linguistically distinct for more than three centuries. Despite its demographic and historical relevance, no population-specific short tandem repeat (STR) database has been established for this group. Methods: Allele frequencies for 23 autosomal STR loci were characterized in 100 unrelated Black Caribbean individuals from the department of Islas de la Bahía. DNA was extracted from blood on FTA cards and amplified with the PowerPlex Fusion 6C System (Promega Corporation). Statistical parameters were computed using Genepop v4.2, Arlequin v3.5 and GDA v1.0. Results: A total of 241 distinct alleles were detected across all 23 loci (mean 10.48 ± 3.85 alleles/locus). Expected heterozygosity ranged from 0.6541 (D13S317) to 0.9350 (SE33), with a mean of 0.8150 ± 0.0664—values consistent with a population of predominantly West African origin. No locus exhibited a significant departure from Hardy–Weinberg equilibrium after Bonferroni correction (α = 0.0022). The combined power of discrimination exceeded 99.9999% and the combined chance of exclusion surpassed 99.9999%. Conclusions: This first genetic characterization of the Honduran Black Caribbean population delivers an essential, population-specific reference dataset for forensic casework, paternity testing, and population genetics research. The data also deepen the understanding of Afro-descendant genetic diversity in Central America and constitute a critical step towards equitable forensic genetic services for all Honduran ethnic communities. Full article

Exopolysaccharides (EPS) represent an important tool for application in bio- and phytoremediation technologies due to their ability to enhance water and nutrient retention, support microclimate stability, and protect plants from environmental stress. In the present study, xanthan-like EPS produced by Xanthomonas translucens TRK8 was precipitated by ethanol and isopropanol, with the former yielding 9.2 g L⁻¹ compared with 6.7 g L⁻¹ obtained with the latter. The monosaccharide profile of the TRK8-derived EPS indicated a branched structure composed of rhamnose, mannose, glucose, and galactose residues, containing both α- and β-type pyranose units. The rheological properties of the studied EPS were compared with those of commercial xanthan at concentrations of 1–3 wt.%. Fitting the obtained data to the Ostwald–de Waele power-law model revealed that the flow behaviour index (n) values were below 1 (−0.338, −0.499, and −0.647, respectively), indicating shear-thinning behaviour (i.e., pseudoplasticity). The potential of the TRK8-derived EPS as a plant protection agent was validated by coating barley seeds with 2 wt.% EPS, resulting in a 28.6% increase in shoot length and a 64.7% increase in root length relative to the oil-stressed control. Full article

The weak-field, quasi-static regime of gravity is commonly described by the Newton–Poisson equation as an effective response law. We construct this response within a cost-first discrete variational framework. The Recognition Composition Law (RCL) uniquely selects a reciprocal closure cost within the restricted quadratic symmetric composition class; together with the discrete ledger axioms AX1–AX5 (including conservation) and standard DEC refinement, the Newton–Poisson baseline is then recovered in the instantaneous-closure limit. Conditional on Assumption AS1 (scale-free latency) and Assumption AS2 (causal frequency–wavenumber ansatz), allowing finite equilibration introduces fractional memory into the response, yielding a scale-free modification of the source–potential relation characterized by a power-law kernel $w_{\ker }\left(k\right)=1+C{(k_0/k)}^{\alpha }$ in Fourier space. The kernel exponent $\alpha =\frac{1}{2}(1-{\phi }^{-1})\approx 0.191$, where $\phi =(1+\sqrt{5})/2$, is derived from self-similarity of the discrete ledger closure; the amplitude $C={\phi }^{-2}\approx 0.382$ is identified as a hypothesis from a three-channel factorization argument. We evaluate this quasi-static kernel-motivated response against SPARC galaxy rotation curves under a strict global-only protocol (fixed $M/L=1$, no per-galaxy tuning, conservative ${\sigma }_{\mathrm{tot}}$), using a controlled multiplicative surrogate for the full nonlocal disk operator implied by the kernel. In this deliberately over-constrained setting, the surrogate interface achieves $\mathrm{median}({\chi }^2/N)=3.06$ over 147 galaxies (2933 points), outperforming a strict global-only NFW benchmark and remaining less efficient than MOND under identical constraints. The analysis is restricted to the non-relativistic, quasi-static sector and should be read as a falsifier-oriented galactic-regime consistency check of the scaling window, not as a relativistic completion or a claim of Solar System viability without additional UV regularization/screening. Full article

In the sustainability framework, valorization of organic by-products as reservoirs of phytochemicals useful for human health represents a hot topic. Therefore, this study evaluated Norway spruce bark and twigs (NSB, NST), chestnut tree wood (CTW), and pomegranate fruit waste/pomace (PFW) as sources of bioactive compounds by employing green technologies. Microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE), applied individually or sequentially, were optimized by modulating solvent composition, temperature, time, microwave power, and ultrasound amplitude. Hydroalcoholic extraction (50% ethanol) combined with MAE yielded the highest phenolic recovery and antioxidant activity across all matrices. PFW exhibited the highest antioxidant activity assessed through FRAP, ORAC, and DPPH assays. Phytochemical profiling by HPLC-DAD identified stilbenes in spruce extracts, ellagic acid in chestnut wood, and ellagic acid and punicalagins in pomegranate waste as major bioactive constituents. Additionally, NSB and PFW exhibited α-amylase inhibitory activity. Antimicrobial testing demonstrated dose-dependent activity against Gram-positive (Staphylococcus epidermidis and Bacillus subtilis) and Gram-negative (Pseudomonas stutzeri) strains, with PFW exhibiting the strongest inhibition and NSB displaying broad-spectrum effects. Total phenolic content changed moderately after 21 days of storage. These results demonstrate that sustainable extraction enables efficient recovery of bioactive compounds from plant by-products, supporting their further functional, dietary, and medicinal applications. Full article

The proliferation of sensor networks in critical infrastructure, healthcare monitoring, and smart city applications demands robust privacy-preserving mechanisms for data verification. Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) offer a promising cryptographic primitive that enables data integrity verification without revealing sensitive sensor readings. However, the practical feasibility of deploying zk-SNARKs in resource-constrained sensor network environments remains insufficiently characterized. This paper presents a systematic benchmarking study of the Groth16 zk-SNARK protocol across eight representative circuit types spanning six orders of magnitude in computational complexity, from basic arithmetic operations (1 constraint) to ECDSA signature verification (1,510,185 constraints). Using an automated open-source benchmarking framework built on the Circom-snarkjs toolchain, we conducted 160 statistically controlled measurements (20 iterations per circuit) with cold/warm separation, collecting proof generation time, verification time, proof size, memory consumption, and witness generation overhead. Our results demonstrate that Groth16 proofs maintain a constant size of $804.7\pm 1.7$ bytes and near-constant verification time of $0.662\pm 0.032$ s regardless of circuit complexity, with coefficients of variation below 5% across all circuit types. Proof generation time exhibits sub-linear scaling ($\alpha =0.256$, $R^2=0.608$), with statistically significant differences between circuit categories confirmed by one-way ANOVA ($F=355.0$, $p<{10}^{-79}$, ${\eta }^2=0.94$). We identify three operational deployment tiers for sensor network architectures and estimate energy budgets for battery-powered devices. These findings provide actionable guidance for the design of privacy-preserving data verification systems in next-generation sensor networks. Full article

Modern power systems increasingly integrate renewable energy sources (RESs), electric mobility, and dynamic market participation. Dynamic electricity pricing, reflecting real-time market conditions, is increasingly important for prosumers worldwide, enabling flexible and efficient energy management. The growing adoption of electric vehicles (EVs) and bidirectional charging technologies (V2G, V2H) allows EVs to act as mobile battery energy storage systems (mBESSs). This study presents a Python 3.11-based application for simulating and analyzing energy flows in residential systems with photovoltaic (PV) installations, EVs acting as mBESS, and optional stationary battery energy storage systems (BESSs), using real 2024 data on consumption, PV production, and market prices. The energy management system (EMS) employs a rule-based algorithm to optimize energy use and economic benefits, adjusting dispatch between PV systems, the grid, mBESSs, and BESSs based on price coefficients $\alpha$ and $\beta$. Simulation scenarios were developed based on two EV availability patterns: Profile 1, representing users unavailable during standard working hours, and Profile 2, representing users with intermittent availability for brief excursions. The results demonstrate substantial electricity cost reductions: For a Nissan Leaf e+ with Profile 1, annual costs decrease by approximately 20% compared to a system without EVs. With PV generation and Profile 2, costs drop by 57% relative to the baseline, while adding a stationary BESS further reduces costs by nearly 95%. It should be noted that the results were obtained assuming zero energy costs for propulsion. Therefore, the economic benefits reported here represent an upper-bound estimate and would be lower under real-world driving conditions. These findings highlight that coordinated EMS operation with EVs as mBESSs, supported by optional BESSs, can maximize economic performance and provide prosumers with a practical framework for flexible and efficient energy management. Full article

²⁰Na is a well-known $\beta$-delayed $\alpha$ emitter, owing to the large decay energy of ²⁰Na above the α + ¹⁶O threshold in the $A=5\alpha$ daughter nucleus ²⁰Ne. In this work, the decay property of ²⁰Na is investigated in detail via the β-γ β-α and β-γ-α coincidence spectroscopy. As the day-one experiment of the Beijing Rare Isotope Facility (BRIF), the intense ²⁰Na beam was produced using the Isotope Separator On Line (ISOL) technique through the 100 MeV proton bombarding a stack of MgO as a thick target. Specific interest was focused on the exotic decay mode of ²⁰Na; the previously reported low-energy α lines at 713 and 846 keV were confirmed, and several weak β-γ-α decay sequences were clearly identified for the first time, thanks to the strong resolving power of α-γ coincidence spectroscopy. The decay properties of ²⁰Na are compared to the shell model calculation, which agree reasonably well with the allowed β transition strengths and subsequent electro-magnetic transitions with the use of the sd shell-model space with the USDB interaction. Full article

Background: Postoperative neurocognitive disorders (PND) are frequent complications in the elderly surgical patients, with aging recognized as a major risk factor. This study aimed to identify electrophysiological markers and establish an exploratory machine learning framework for PND-related vulnerability prediction using anesthetic electroencephalography (EEG) features in aged mice. Methods: Young and aged mice underwent laparotomy under isoflurane anesthesia with EEG recording. Neurocognitive performance was quantified by 16 standardized behavioral fractions. A semi-supervised K-means algorithm, anchored on young-surgery mice, stratified aged-surgery mice into PND and non-PND clusters. EEG dynamics during anesthesia maintenance and emergence were analyzed, and machine learning models were trained to predict PND from EEG features. Results: At baseline, neurocognitive function was comparable across groups. After anesthesia/surgery, aged mice exhibited selective spatial and contextual memory impairments, with two-thirds classified as PND. During emergence, PND mice displayed elevated δ power and reduced α and β ratios. A Multi-layer Perceptron classifier showed discriminatory performance for PND classification in one evaluation setting (AUC = 0.94). Conclusions: This study identifies emergence-related EEG features associated with postoperative neurocognitive vulnerability in aged mice and provides an exploratory machine learning framework for preclinical risk stratification. These findings support further mechanistic investigation and warrant future validation in human perioperative EEG datasets. Full article

Moderate-to-severe atopic dermatitis (AD) requires safe, long-term management strategies to complement conventional pharmacotherapy. This study evaluated the efficacy and safety of VGH4, a standardized multi-herb traditional Chinese medicine (TCM) formula, as an adjunct to standard care. In a randomized, double-blind, placebo-controlled crossover pilot trial, 19 patients with moderate-to-severe AD (SCOring Atopic Dermatitis Index (SCORAD) ≥ 25) received VGH4 or placebo for 6 weeks, separated by a 2-week washout. Primary outcomes assessed disease severity (SCORAD), while secondary outcomes included quality of life (DLQI/CDLQI) and safety. Eighteen patients completed the study. VGH4 yielded a median within-patient SCORAD reduction 10.2 points greater than placebo (p = 0.054). The primary endpoint did not reach statistical significance at the α = 0.05 level (p = 0.054); nevertheless, the observed magnitude of improvement exceeded the established minimal clinically important differences (MCIDs). The subjective SCORAD component showed a significant between-treatment difference favoring VGH4 (p = 0.015), and a statistically significant improvement in quality of life was also observed in adult patients (p = 0.023). In conclusion, VGH4 was generally well tolerated in this short-term pilot trial, with no serious adverse events, and showed preliminary signals of possible benefits in patient-reported outcomes as an adjunct therapy. These exploratory findings warrant confirmation in larger, adequately powered trials. Full article

The arbitrary-order hidden Markov model ($\alpha$-HMM) is a nontrivial generalization of the standard HMM, designed to model stochastic processes with higher-order dependences among arbitrarily distant random events. The $\alpha$-HMM admits an efficient Viterbi-style optimal decoding algorithm, making it feasible to discover higher-order dependences among data objects in observed sequential data. Because the $\alpha$-HMM exceeds the expressive power of standard HMMs, fixed $k\mathrm{th}$-order HMMs, and stochastic context-free grammars, effective probabilistic parameter estimation approaches are required to translate this theoretical expressiveness of the $\alpha$-HMM into practical utility. This paper introduces a principled methodology for effective estimation of probabilistic parameters of the $\alpha$-HMM from observed data. In large-scale sequential datasets, higher-order dependencies can vary widely across instances, so a single global parameter set may be inadequate. Instead, an amortized parameter inference approach is proposed for the $\alpha$-HMM, in which an input-conditioned parameter estimator is learned from data and used to infer instance-specific parameters for each input instance to the decoding algorithm. Specifically, the neural parameter estimator is trained using a composite learning objective that is partially enabled by the optimal decoding algorithm. The effectiveness of the proposed parameter estimation method is demonstrated through empirical results of the application of the $\alpha$-HMM in biomolecular structure modeling and prediction. Full article

This paper develops a rigorous inferential framework for a class of Gaussian stochastic processes driven by white noise with constant drift, whose temporal evolution is governed by a Caputo fractional derivative of order ${\displaystyle \alpha \in (1/2,1)}$. The model belongs to the family of fractional Volterra processes, where memory is generated by the dynamics themselves rather than by correlated noise. We derive explicit analytical expressions for the mean, variance, and covariance structure of the solution, thereby characterizing in a precise manner how the fractional order ${\displaystyle \alpha }$ governs both variance growth and the strength of temporal dependence. In particular, the process exhibits correlated increments and a power-law variance scaling of order ${\displaystyle t^{2\alpha -1}}$, highlighting the dual role of ${\displaystyle \alpha }$ as a regularity and memory parameter. Building on this structural analysis, we address the statistical problem of estimating the parameter vector ${\displaystyle (\mu ,\sigma ,\alpha )}$ from discrete-time observations. Two complementary procedures are proposed for the estimation of the fractional order: a variance-growth method based on log–log regression of empirical variances, and a wavelet-based estimator exploiting multi-scale scaling properties of the process. For the drift and diffusion parameters ${\displaystyle (\mu ,\sigma )}$, we construct explicit Gaussian pseudo-maximum likelihood estimators derived from the Volterra covariance structure of the increment process. We establish unbiasedness, ${\displaystyle L^2}$-convergence, strong consistency, and asymptotic normality for all estimators. Furthermore, we derive Berry–Esseen type bounds that quantify the rate of convergence toward the Gaussian law, providing sharp distributional approximations in a genuinely fractional and non-Markovian setting. A Monte Carlo study is carried out, using high-resolution Volterra discretizations, large-scale simulation budgets, covariance-structured linear algebra, and multi-scale diagnostic tools. The numerical experiments confirm the theoretical convergence rates, demonstrate the finite-sample reliability of the estimators, and illustrate the sensitivity of the process dynamics to the fractional order ${\displaystyle \alpha }$: smaller values of ${\displaystyle \alpha }$ produce stronger memory effects and higher variability, while values closer to one lead to smoother and more stable trajectories. The proposed methodology unifies statistical inference for long-memory Gaussian processes with fractional differential stochastic dynamics, offering a coherent analytical and computational framework applicable in areas such as quantitative finance, anomalous diffusion in physics, hydrology, and engineering systems with hereditary effects. Full article

Background: Metabolic syndrome (MetS) leads to alterations in cardiac autonomic control that can be detected from electrocardiogram (ECG)-derived markers, particularly when the cardiovascular system is challenged during an oral glucose tolerance test (OGTT). Methods: In this paper, we present an automated framework for MetS identification using RR intervals and heart rate variability (HRV) features extracted from 12-lead ECG recordings acquired during the five OGTT stages in 40 male participants (15 with MetS, 10 controls, and 15 endurance-trained marathon runners). RR intervals were first derived using a multilead Pan-Tompkins approach with fusion-based validation. From these RR series, HRV descriptors were computed from time-domain statistics (RR mean, SDNN, rMSSD, pNN50), spectral indices (VLF, LF, HF, LF/HF), and nonlinear measures (SD1, SD2, SampEn, DFA-${\alpha }_1$). Conventional HRV analysis revealed pronounced physiological differences between groups: MetS subjects exhibited reduced parasympathetic activity, reflected by lower rMSSD and SD1, lower HF power, and higher LF/HF ratios, whereas marathoners showed greater vagal modulation, higher HF power, and increased signal complexity. Healthy controls showed an intermediate autonomic profile. Using RR sequences and HRV descriptors (256 samples per stage), we trained three multimodal classifiers: a CNN-MLP model with a softmax output, a CNN-MLP model with an SVM head, and a CNN + LSTM-MLP + SVM architecture. Results: All models achieved strong discriminative performance, with accuracies ranging from 0.92 to 0.95, F1-macro values from 0.92 to 0.95, and macro-AUC values from 0.96 to 0.97. The CNN-MLP model achieved the best overall performance, whereas the CNN + LSTM-MLP + SVM model showed strong class discrimination, particularly for endurance athletes, while maintaining competitive recall for MetS. Conclusions: These findings support the feasibility of ECG-based autonomic assessment as a complementary non-invasive approach for early metabolic risk detection in clinical and preventive cardiometabolic screening settings. Full article

Multicomponent reactions with isocyanides (IMCRs) enable the one-step assembly of complex molecules and remain a powerful strategy for accessing bioactive scaffolds. Here, we report the first synthesis of an abietane diterpene isocyanide derived from aminoimide methyl maleopimarate 1, a levopimaric acid-maleic anhydride adduct. This isocyanide was further engaged in Passerini, Ugi, and azido-Ugi reactions to provide a series of α-acyloxy- and α-acylaminocarboxamides, as well as tetrazoles, in high yields under optimized conditions. The structures of all products were confirmed by comprehensive physicochemical analysis. In silico ADME, drug-likeness, target prediction, and toxicity studies (SwissADME, ProTox-III) revealed moderate lipophilicity with favorable membrane permeability and solubility, high gastrointestinal absorption, and selective CYP3A4 inhibition with no significant effects on other CYP450 isoforms. The compounds fulfill major drug-likeness criteria, lacking undesirable reactive fragments, with only acceptable deviations in molecular weight and flexibility typical for MCR-derived products. The modifications broaden the spectrum of predicted biological targets while maintaining low overall toxicity and absence of predicted hepato- or carcinogenicity. These results demonstrate that diterpene isocyanide is a valuable building block for chemical libraries of structurally diverse abietane derivatives with peptide-like termini and highlight its potential as a source of cytotoxic, antiviral, and anti-inflammatory candidates. Full article

Background/Objectives: Onion (Allium cepa) peems are an underutilized by-product rich in polyphenols. This study evaluated the physicochemical profile, and bioactive potential (antidiabetic, antimicrobial, antioxidant, and anticoagulant) of Moroccan red onion peels using integrated in vivo, in vitro, and in silico approaches. Methods: Moisture, pH, ash content, and mineral elements were determined, followed by phytochemical screening and three extractions: decoction E0, aqueous Soxhlet E1, and hydroethanolic Soxhlet E2 (70/30; ethanol/water, v/v). The measurement of polyphenols, flavonoids, and tannins was carried out using colorimetric methods, while the molecular profile was studied by high-performance liquid chromatography coupled to ultraviolet detection and electrospray ionization mass spectrometry (HPLC/UV-ESI-MS). Biological activities were determined using 2,2-diphenyl-1-picrylhydrazyl, ferric reducing antioxidant power, and total antioxidant capacity assays (in vitro antioxidant); microdilution (antimicrobial); prothrombin time and activated partial thromboplastin time (anticoagulant); and α-amylase/α-glucosidase enzymatic inhibition and oral glucose tolerance tests on normoglycemic rats. Also, acute toxicity was evaluated, and molecular interactions between these proteins and ligands (docking, molecular dynamics, and MM-PBSA) were analyzed. Results: Physicochemical analyses showed an acidic pH (3.06) and high ash content (15.21%), with the concentration of regulated elements remaining within FAO/WHO limits. The extractive content was between 6.90% E0 and 19.18% E2. The E1 extract had the maximum amount of total polyphenols (178.95 mg GAE/g); on the other hand, E2 was the richest in flavonoids by 121.43 mg QE/g. The HPLC/ESI-MS analysis of E0 revealed 20 compounds, among which flavonoids (84.93%) were predominant, with isorhamnetin (30.26%), followed by quercetin and its glycosylated forms. E1 showed the most potent antioxidant effects (IC₅₀ DPPH, 22.38 µg/mL, as that of ascorbic acid). The antibacterial activity of E0 was especially potent towards Enterobacter cloacae and Pseudomonas aeruginosa (MIC 75 µg/mL). A mild dose-dependent anticoagulant effect was seen. Antidiabetic activity was found to be outstanding: α-amylase (IC₅₀ 62.75 µg/mL) and α-glucosidase (IC₅₀ 8.49 µg/mL, stronger than acarbose) inhibitions were corroborated in vivo by a considerable decrease in the glycemic area under the curve. The molecular docking study in silico demonstrated strong molecular interactions, especially for quercetin 4′-O-glucoside with good binding energies. Conclusions: A. cepa peels from Morocco can be considered a safe plant matrix containing bioactive flavonoids with strong antioxidant and selective antimicrobial activities and promising antidiabetic effects, supported by molecular modeling. Full article