Abstract: Temperature-dependent electrical resistivity ρ(T) is one of the most common types of experimental data analysed in condensed matter physics. For one group of pure metals, the actinides, experimental ρ(T) curves differ radically from one another to the point that there is no unified theoretical approach to understanding and fitting ρ(T) data in these elements. First-principles calculations result in ρ(T) curves that differ from experimental data, even qualitatively. In an attempt to unravel this long-standing problem, here I propose a simple model that accurately fits the ρ(T) data for eight phases of elemental actinides (from thorium (Th) to curium (Cm)) for which experimental data are publicly available to date. The model is based on the concept of two parallel conduction channels: one is described by the Bloch-Grüneisen equation, which is associated with the classical electron-phonon dissipation mechanism, and the other by the Arrhenius equation, which is associated with the nearest-neighbor hopping (NNH) conductivity. Debye temperatures Θ_D derived from application of the model to ρ(T) data for eight elemental actinide phases agree well with reported values deduced from heat capacity measurements. For neptunium (Np) a maximum Arrhenius activation energy (among all actinides) of was derived. The model was also successfully applied to ρ(T) data measured on d-phase plutonium-based alloys Pu-Ce and Pu-Ce-Ga.

Abstract: Climate change affects the intensity of extreme rainfall, which is an important factor for design flood assessment and dam safety, particularly in the monsoon region of Southeast Asia and the Mekong Basin, where seasonal rainfall variability is high and there are many hydropower dams that are vulnerable to changes in severe rainfall. This study develops a framework for assessing design rainfall and Probable Maximum Precipitation (PMP) under climate change for three dam catchments of different sizes in Thailand and Lao PDR, namely the Ubolrat Dam, the Nam Theun 1, and the Nam Kong 3 Dam. The framework connects the bias correction of GCM data from ten CMIP6 models using CMhyd, extreme-focused model selection with catchment-specific ranking, and the construction of the Top-3 Ensemble median, through to the analysis of the Return Period, DDF, IDF, PMP, and extreme rainfall trends under the SSP24.5 and SSP58.5 scenarios. The results show that the suitable models are catchment-specific, and that each catchment has a different risk profile under SSP58.5. The UB shows a 63.20% increase in the 100-year 1-day rainfall, the Nam Theun 1 shows a 40.64% increase in the 100-year 7-day rainfall, while the Nam Kong 3 has the highest PMP value and the steepest Rx7day trend at 18.03 mm per decade. The results indicate that the assessment of extreme rainfall risk must consider magnitude, variability, and trend together, and the outputs can serve as input data for the assessment of PMF and hydropower dam safety under future climate variability.

Abstract: Digital services coordinate production networks, so restrictions on digitally enabled trade may propagate beyond the regulating economy. We model global manufacturing as a bounded system of country-sector nodes linked by fixed bilateral trade weights. The panel combines OECD Digital Services Trade Restrictiveness Index data with input-output-derived participation for 35 countries, 14 sectors, and 2014-2021 (3,904 observations). Country-sector, sector-year, and country-year fixed effects distinguish domestic regulation from partner exposure and test moderation by pre-sample network position and sector digital intensity. Country-clustered inference is supplemented by 999 restricted wild-cluster bootstrap replications. Average domestic and partner-exposure coefficients are imprecise. In contrast, partner exposure is more negatively associated with participation among top-quartile position nodes (interaction = -0.0947 per 0.1 index point; wild p = 0.007; Holm-adjusted p = 0.028). One exposure standard deviation corresponds to 3.9% of mean participation. The differential is concentrated in forward participation, persists with lagged exposure and multi-year pre-sample position, and remains negative in every leave-one-country-out estimate. It weakens under a within-country position cutoff, while sector digital intensity does not robustly moderate the association. The evidence is associational and supports a network-contingent rather than uniform account of digital trade restrictions.

Abstract: Static stretching is one of the most widely prescribed interventions in sports medicine, rehabilitation, and exercise science for improving joint range of motion (ROM) and flexibility. Despite decades of research, however, the biological mechanisms underlying its effects remain incompletely understood. Traditional explanations have primarily focused on muscle extensibility and passive tissue stiffness, whereas emerging evidence suggests that stretching induces coordinated adaptations across multiple mechanically interconnected biological systems. This integrative narrative review aimed to synthesize current evidence on the biological mechanisms underlying static stretching and to propose a conceptual framework explaining how externally applied mechanical loading is translated into coordinated biological adaptation. Current evidence from biomechanics, fascial biology, mechanotransduction, connective tissue biology, vascular physiology, and neuroscience was critically integrated to develop a unified mechanobiological framework describing the biological responses to static stretching. The available evidence indicates that static stretching should no longer be interpreted solely as a muscle-centered flexibility intervention. Instead, externally applied mechanical loading is transmitted throughout the myofascial continuum, where it is detected by mechanosensitive cellular structures and converted into biochemical signals through mechanotransduction. These signaling pathways regulate extracellular matrix remodeling, vascular adaptation, inflammatory modulation, and neural regulation, thereby generating coordinated biological adaptations. This framework provides a biologically plausible explanation for the heterogeneous findings reported in the stretching literature and the substantial inter-individual variability observed following identical stretching interventions. The proposed mechanobiological framework redefines static stretching as a biologically active mechanical stimulus coordinating adaptive responses across multiple levels of biological organization. By integrating evidence from previously independent biological systems, this conceptual model advances the current understanding of static stretching and provides a theoretical foundation for future mechanobiological research in sports medicine, rehabilitation, and exercise science.

Abstract: Background/Objectives: Cadherin-16 (CDH16, Ksp-cadherin) possesses unique seven extracellular cadherin repeats, and its expression is restricted to normal kidney epithelium. CDH16 is downregulated in renal cell carcinoma (RCC) and is associated with poor prognosis. Therefore, developing mAbs that specifically recognize cell-surface CDH16 is essential for tumor diagnosis and for isolating CDH16-positive renal epithelial cells. Methods: Anti-human CDH16 mAbs (designated as Ca₁₆Mabs) were developed by immunizing mice with CDH16-overexpressed tumor cells, followed by a high-throughput flow cytometry-based screening. Results: Among the 58 established Ca₁₆Mabs, a clone, Ca₁₆Mab-56 (IgG₁, κ), specifically recognized CDH16-overexpressed Chinese hamster ovary-K1 (CHO/CDH16) cells with no detectable cross-reactivity to 21 other CDHs in flow cytometry. Ca₁₆Mab-56 also detected endogenous CDH16 in human RCC cell lines (OS-RC-2 and KMRC-20) and normal kidney epithelial cell lines. The dissociation constant (K_D) values of Ca₁₆Mab-56 for CHO/CDH16 and OS-RC-2 were determined as 6.5 × 10⁻⁹ M and 1.2 × 10⁻⁹ M, respectively. Furthermore, Ca₁₆Mab-56 detected endogenous CDH16 by Western blotting and showed potent staining in normal kidney tubular epithelium and clear membranous staining in renal cell carcinoma in immunohistochemistry. Conclusion: Ca₁₆Mab-56 is a versatile tool for detecting CDH16 and has potential for tumor diagnosis.

Abstract: Background: Staphylococcus arlettae is a coagulase-negative staphylococcal species increasingly recognized as a reservoir of antimicrobial resistance determinants in animal, human, and environmental. Methods: In the present study, a multidrug-resistant S. arlettae isolate (BVC_SA31) isolated from bovine mastitis milk was characterized by whole-genome sequencing and comparative pangenomic analyses. Results: Antimicrobial susceptibility testing revealed resistance to penicillin, oxacillin, clindamycin, and erythromycin. Whole-genome sequencing of isolate generated a high-quality draft genome comprising of 36 contigs, 2,521 coding sequences, 10 rRNA genes and 57 tRNA genes. Species identity was confirmed by average nucleotide identity and tetranucleotide correlation analysis. Isolate was assigned to sequence type ST1203 and harbored antimicrobial resistance genes, including mph(C), blaARL-2, vanT, vanY, sdrM, sepA, and norC. Mobile genetic elements like insertion sequences (49), genomic islands (4), CRISPR loci (2), and plasmid-associated recombination systems (2) were identified. Comparative genomic analysis of 74 S. arlettae genomes revealed an open and highly diverse pangenome comprising 10,153 gene clusters, including 547 core genes, 1,024 soft-core genes, 1,267 shell genes, and 7,315 cloud genes. ANI-based clustering and codon usage analyses grouped the genomes into four distinct clusters, while core proteome phylogeny characterised two major global evolutionary lineages. Diverse repertoire of plasmid replicons, rep7a, rep7b, rep7c, rep10, rep13, rep15, rep19c, rep20, rep21, rep22, rep24, rep39, repUS10, repUS12, and repUS19 were identified. A total 39 antimicrobial resistance genes were identified, with msr(A) (46.62%), blaARL-2 (25.9%), mph(C) (19.24%), blaARL-1 (18.5%), mecA (8.88%), aph(2'')-Ii (7.4%), erm(C) (7.4%), lnu(A) (7.4%), tet(K) (7.4%), and dfrG (6.66%) being the most prevalent, confer resistance to macrolides, β-lactams, aminoglycosides, lincosamides, tetracyclines, and trimethoprim. Only two strains were found to harbour SSCmecA elements. Conclusions: In conclusion two global lineages of S. arlettae were identified and global antimicrobial resistance profiling of S. arlettae, highlighting its potential role as an emerging reservoir of antimicrobial resistance genes were identified.

Abstract: This paper introduces Hill-Enigma-SPN (HESPN), a 128-bit byte-oriented substitution–permutation network research construction combining rotor-scheduled GF(2) byte-matrix diffusion, the AES S-box, a round-dependent inter-byte routing permutation, and an Argon2id profile for password-based key derivation. HESPN is not proposed as a deployment-ready alternative to standardized ciphers; its contribution is architectural. The key-setup admissibility filter guarantees branch number B ≥ 4 for every scheduled matrix orientation, trading weaker per-round intra-byte diffusion for 64 scheduled key-dependent (seed, orientation) pairs across 16 rounds. Across five experimental sessions, avalanche, sampled-differential, random-mask-uniformity, algebraic-degree, and selected NIST SP 800-22 screens approach their observable ideals at 16 rounds. A new whole-cipher boomerang-style returned-difference calibration tested eight (α,δ) jobs over eight keys and 20,000 nondegenerate quartets per key at rounds 4–16, with randomized SPN and Feistel null controls. All 64 broad tests remained significant through 10 rounds and 51/64 at 12 rounds; none was significant at 14 or 16 rounds. At 16 rounds no exact or weight-1 returns occurred in 1.28 million trials, and the lowest Benjamini–Hochberg q-value was 0.092. These bounded screens do not establish resistance to optimized boomerang or differential trails; low-weight iterative trails remain an open threat.

Abstract: With Industry 5.0, human-robot collaboration has moved to the center of attention. This introduces new challenges, where workspaces become highly dynamic leading to safety concerns for robots and especially for humans. This makes it important to have a realistic and accurate digital representation of a production cell and its components in the environments for movement planning and remote supervision. This paper presents a digital twin of an Industry 5.0 smart production cell, synchronizing objects bidirectional between a real and virtual workspace with minimal effort using only a single RGB-D camera. A fine-tuned YOLO-based detector identifies tools and items in the scene, estimates their spatial position, and spawns them in Unity relative to the robot via coordinate transformation. Experiments with different scanning velocities demonstrate a mean planar spawn deviation of 5.82mm (standard deviation 2.24mm) at 0.1m/s at 0.7m height, while maintaining a constant depth bias of −0.54mm. Once spawned, objects can be manipulated freely via drag-and-drop within the simulation. Upon confirmation, a motion-planning module calculates trajectories to execute these changes physically. Across 95 trials and 1805 object placements, the system achieves 100% success within working bounds, successfully executing complex tasks such as repositioning objects and stacking them into pyramid structures. The presented system provides a framework to see, spawn, and synchronize industrial workspaces, enabling rapid setup and safe remote supervision of smart production cells in high dynamic Industry 5.0 environments.

Abstract: Background: Repeated endurance exercise induces transient inflammatory, oxidative and metabolic responses that drive skeletal muscle adaptation. This study investigated whether the consumption of San Carlo 1931® water, a mineral water with a human-like electrolyte profile, modulates these responses in a murine model of repeated swimming exercise. Methods: Male C57BL/6 mice underwent swimming exercise (10 min/day) for 2, 3 or 4 weeks while receiving either San Carlo 1931® water or control water. Grip strength, inflammatory cytokines (IL-6 and TNF), oxidative stress markers (MDA, GPx, and CAT), lactate metabolism (lactate, LDH, and monocarboxylate transporters), citrate synthase activity and the expression of PGC-1α, FNDC5, MuRF1 and myostatin were evaluated. Results: San Carlo 1931® water prevented the early decline in grip strength, attenuated the early exercise-induced inflammatory response, reduced circulating lactate and hepatic LDH activity, enhanced citrate synthase activity and progressively decreased MCT4 expression, indicating improved oxidative metabolism. No significant changes were observed in MCT1 protein expression. These adaptations were accompanied with unchanged PGC-1α expression, early FNDC5 induction and a time-dependent regulation of muscle remodeling markers, with transient progressive MuRF1 upregulation and myostatin induction. Conclusions: The consumption of San Carlo 1931® water modulate inflammatory and metabolic responses to repeated endurance exercise, suggesting a potential role in supporting physiological adaptation to exercise-induced stress.

Abstract: Large language models are increasingly being adopted in clinical education and decision support, yet their reasoning remains largely opaque and susceptible to hallucinations, limiting their reliability in high-stakes medical settings. This paper presents ISA (Informatore Scientifico Artificiale), a neuro-symbolic framework that constrains Gemini 3.5 to perform clinical reasoning within a propositional proof system based on sequent-style tableaux. The approach, which we term Logic-Constrained Prompt Learning, encodes the inference rules of the calculus through system instructions written in and restricts every derivation to a knowledge base extracted from official drug package leaflets. Since the underlying calculus is analytic, every inference is confined to the concepts explicitly contained in the premises, making unsupported reasoning immediately identifiable while producing derivations that are fully inspectable and formally verifiable. To assess the educational value of the approach, we conducted a randomized crossover pilot study involving twenty-six pharmacy students, comparing ISA with unconstrained conversational systems in the analysis of a clinical pharmacology case. Participants consistently rated the constrained system more highly with respect to inferential rigor, transparency, clinical reliability, and perceived safety. These findings suggest that proof-theoretic constraints can substantially improve the explainability of LLM-based clinical reasoning without sacrificing usability. Beyond the present implementation, the framework naturally extends to richer proof systems, including modal, default, and abductive logics, providing a general foundation for trustworthy neuro-symbolic reasoning in medical education and clinical decision support.

Abstract: Background: Undifferentiated chest pain is one of the most common reasons for emergency medical service (EMS) activation, yet its aetiological spectrum remains poorly characterised in the prehospital setting. Furthermore, no clinical prediction model has been specifically developed to identify patients at risk of significant coronary lesions using only information available before hospital arrival. This study aimed to describe the aetiologies of undifferentiated prehospital chest pain and develop a proof-of-concept clinical prediction model. Methods: We conducted a retrospective, single-centre study including 409 consecutive patients managed by the Orléans Mobile Intensive Care Unit (MICU) for undifferentiated chest pain between January and June 2024. Predictors of significant coronary lesions requiring coronary revascularisation were identified using multivariable logistic regression. Model performance was assessed by discrimination and calibration, and internally validated using 1000 bootstrap resamples. Results: Cardiological aetiologies accounted for 19% of cases, including 53 patients (13%) with significant coronary lesions. Four independent predictors were identified: age (OR 6.7–8.9 according to category), male sex (OR 2.2), typical chest pain (OR 6.6), and a positive family history of cardiovascular disease (OR 3.4). These variables were combined to develop the HATS (History, Age, Typical chest pain, Sex) model. The model demonstrated good discrimination (AUC 0.81), excellent calibration (Hosmer–Lemeshow P=0.88), and satisfactory internal validity after bootstrap validation. Conclusions: This study characterises the aetiological spectrum of undifferentiated prehospital chest pain and proposes the HATS model as a proof-of-concept clinical prediction tool. Prospective multicentre external validation is required before routine clinical implementation.

Abstract: Body image (BI) is an important psychosocial aspect of well-being; however, its relationship with quality of life (QoL) among adults with cerebral palsy (CP) remains insufficiently explored. This study aimed to examine the association between BI and QoL across specific QoL domains, and to investigate whether BI and QoL differ according to selected socio-demographic characteristics among adults with CP. A cross-sectional study included 30 adults with CP aged 21-50 years. Participants completed a socio-demographic questionnaire, the Body Image Quality of Life Inventory (BIQLI) and the Short Form Health Survey (SF-36). Given partial departures from normality, the small sample size, and the ordinal nature of the response scales, non-parametric statistical analyses were applied. The results showed that BI was significantly associated with the following specific QoL domains: Mental Health, Vitality and Social Functioning, whereas it was not significantly associated with Physical Functioning, General Health, Bodily Pain, or role limitations due to physical and emotional problems. BI was also weakly associated with overall QoL. Regarding socio-demographic characteristics, no significant differences in BI or QoL were identified according to gender. Significant age-related differences were observed in Physical Functioning and Bodily Pain, with the oldest group (41-50 years) reporting poorer physical functioning and more bodily pain than the younger groups. Being in a romantic relationship was associated with better Mental Health and Social Functioning, while employed participants reported more positive BI, better Mental Health, and greater Vitality. These findings highlight the importance of integrating BI into comprehensive rehabilitation and psychosocial interventions aimed at improving QoL in adults with CP. Future longitudinal mixed-methods studies with larger, more diverse samples are needed to further elucidate the complex relationships between BI and QoL and to inform the development of evidence-based interventions aimed at improving the well-being of adults with CP.

Abstract: Binary sequences (binary codes), where the elements are − 1 or +1, are useful in many fields, including communications, radar, sonar, mathematics, physics, and cryptography. This paper considers binary sequences with low aperiodic autocorrelations and focuses on the small peak sidelobe levels. Two families of binary sequences are considered, namely Rudin–Shapiro and Legendre sequences. Two conjectures regarding Legendre sequences are proposed: (1) The obtained binary sequences with the best-known peak sidelobe levels have merit factor ≈ 5.0, (2) The number of elements that differ between the resulting binary sequences and the initial Legendre sequences follows a linear dependence on the sequence length (n), namely ≈ 0.01n. The Rudin–Shapiro sequences do not exhibit these properties, as worse peak sidelobe level and merit factor values were obtained. The number of elements that differ between the resulting binary sequences and the initial Rudin–Shapiro sequences is also much higher compared to that of the Legendre sequences.

Abstract: Rock cohesion (c ) and angle of internal fricti on (φ ) are core parameters for rock mass stability analysis and engineering design; however, traditional triaxial tests are costly and time-consuming, limiting engineering efficiency.Based on a total of 199 sets of measured data from four rock types (shale, limestone, quartzite, and quartz-mica schist) in the Himalayan region, this study uses P-wave velocity (V_p ), density (ρ ), uniaxial compressive strength (UCS), and tensile strength (TS) as input variables. It employs four models—Support Vector Regression (SVR), Random Forest (RF), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) to predictc andφ . Hyperparameters were tuned using grid search and Bayesian optimization. We compared unified modeling with rock-type-specific modeling, performed interpretability analysis using SHAP, and tested robustness by introducing Gaussian noise. The results show that XGBoost produced the best predictions atc (test setR^2 = 0.9901, RMSE = 0.512 MPa), while the Bayesian-optimized SVR model yielded the best results atφ (R^2 = 0.9776, RMSE = 0.744°). Rock-type-specific modeling improved theR^2 for limestone atφ by 0.3541; the SHAP contribution for UCS and TS exceeded 70%; Random Forest demonstrated the best noise resistance, with a decrease inR^2 of less than 0.04 under 10% noise. In summary, the strategy proposed in this paper allows for the selection of prediction schemes based on data quality and lithological differences, providing a feasible approach for rapidly obtaining rock strength parameters.

Abstract: Introduction. The development and adoption of generative artificial intelligence (GenAI) technologies is rapidly expanding, but its integration into psychological services depends on clinician acceptance. Little empirical work has examined how identity-related threat shapes psychology professionals’ adoption intentions of GenAI. The present research tested an extended technology acceptance model (TAM), with the addition of personal and professional identity threats, to explain psychology professionals’ adoption intentions of GenAI technologies for clinically relevant usage. Methods. 201 UK psychology professionals completed a cross-sectional online survey. Three sequential structural equation models were tested, incorporating perceived usefulness, perceived ease of use, and social influence as core predictors, with threats to professional capabilities and self-threat added as extensions. Two open-ended qualitative questions were analysed using content analysis to provide broader contextual insight into current use and barriers to adoption. Results. All models demonstrated acceptable fit (CFI = .936–.938; RMSEA = .071–.072). Perceived usefulness emerged as the strongest correlate of adoption intention across all models (β =.67-.69). Threats to professional capabilities and self-threat were each independently and reliably negatively associated with adoption intention above and beyond core TAM predictors (β = −.20 and β = −.16 respectively). Social influence exerted a reliable indirect effect on behavioural intention through perceived usefulness across all models, as did perceived ease of use in the two models incorporating identity threat. Content analysis identified ethical concerns, lack of trust, and professional identity threats as prominent barriers to adoption. Discussion. Findings highlight the importance of considering both classic TAM factors and identity-related concerns when developing and implementing GenAI into psychological services.

Abstract: Neurodegenerative diseases are a growing global health burden associated with aging and characterized by progressive neuronal dysfunction, metabolic failure, mitochondrial impairment, oxidative stress, and chronic neuroinflammation. Among the metabolic pathways implicated in these disorders, coenzyme A (CoA)-linked biology has emerged as a potentially important but still underexplored contributor to neuronal resilience and vulnerability. Pantethine, a disulfide derivative of pantetheine and a CoA-related metabolic precursor, has attracted attention because of its reported effects on cellular metabolism, redox balance, and inflammatory signaling. However, its relevance across neurodegenerative diseases remains unevenly defined, with direct support strongest in pantothenate kinase-associated neurodegeneration (PKAN) and more limited evidence in common disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). This narrative review critically examines the mechanistic and translational evidence linking pantethine to neurodegeneration. PKAN represents the most logical disease context for pantethine investigation because impaired CoA biosynthesis is proximal to disease pathogenesis, although pantethine remains investigational and its clinical efficacy has not been established. By contrast, proposed applications in AD and PD remain highly theoretical and hypothesis-generating. Nevertheless, research on pantethine and related CoA-restoring strategies may identify new intervention targets across neurodegenerative diseases and other disorders characterized by impaired cellular bioenergetics, including selected neuropsychiatric disorders. These possibilities require biomarker-informed, disease-specific studies that establish active-species exposure, target engagement, and clinically meaningful effects.

Abstract:

The series of ceramics with a host composition corresponding to popular 45S5 bioglass doped with manganese ions was prepared using the modified sol-gel technique with a citric acid as a catalyst. The same line of ceramics, for the first time in the case of bioactive glasses or ceramics, was obtained in the procedure applying an addition of a plant extract – aloe vera one -as a surfactant to get more porous material. The structure and properties of the glasses were characterized with SEM, XRD, DSC, Raman and IR spectroscopy. All samples, regardless the Mn ions concentration, are bioactive as it was shown by in vitro immersion in artificial plasma – increase of manganese content improves the bioactivity of the material. The addition of aloe vera gel leads to a significant increase in the surface porosity of the samples; its scale is correlated with content of the aloe vera. Studies reveal that Mn exists in tetrahedral positions coordinating oxygen and reducing concentration of NBO. An addition of aloe vera change this situation lowering cross-linking of the ceramic glassy host. Amount of aloe vera in the presence of Mn dopant plays an important role in speeding the growth of phosphate compounds layers. The phosphorous layer growth during immersion in SBF is both quicker and more intense in the case of material prepared with the plant extract. This study reveal, using Raman 3D mapping, that the HpA is not located only at the surface of the samples but also in the volume and that its volume concentration is bigger than the surface one.

Abstract: The first divider in an integer- or fractional-N frequency synthesizer runs at the full oscillator frequency and so constrains both the speed and the power of the loop. Current-mode logic (CML) reaches high speed but draws a constant bias current independent of activity, whereas a fully static-CMOS (all-digital) divider draws only dynamic and leakage current at the cost of much greater timing sensitivity to process, voltage, and temperature (PVT) variation. This paper presents a wide-range all-digital multi-modulus divider in a 22 nm fully depleted silicon-on-insulator (FDSOI) technology, characterized entirely by post-layout and Monte Carlo simulation, together with the methodology that makes the timing-sensitive digital approach robust. The all-digital chain exhibits three PVT failure modes, setup at the slow-hot corner, hold at the fast-cold corner, and internal duty-cycle collapse at the skewed corners; each is predicted analytically and closed by a three-method scheme built on the FDSOI back gate, a per-stage delay trim, and a systematic integral-nonlinearity trim. The divider covers N=8 to 127 at up to 39.5 GHz at the typical corner, with every process corner held at or above 35 GHz by back-gate compensation, the highest speed among the compared dividers and the only one with explicit PVT compensation. At the 33 GHz target it consumes 5.3 mW at N=127 (6.2 GHz/mW) with an activity-scaling supply current, benchmarked against the measured power of published CML dividers. Per-die body-bias and trim raise the Monte Carlo yield from 78% to 99.3%, at 150 fs additive jitter, in a compact 0.005 mm² core.

Abstract:

The increasing demand for sustainable and health-oriented foods has encouraged the valorization of agricultural by-products as sources of bioactive compounds for functional beverages. This study enriched grape juice with Vitis vinifera leaf extract to enhance its phytochemical composition, antioxidant capacity, and aromatic profile. The fortified juice was compared with non-enriched grape juice and a commercial sample. Samples were analyzed at production (t₀) and after 30 days of refrigerated storage (4 °C) for polyphenols, flavonoids, anthocyanins, carotenoids, tocopherols, and antioxidant capacity (ABTS, DPPH, and CUPRAC assays). Volatile organic compounds (VOCs), sensory properties, and microbiological stability were also evaluated. Enriched beverages showed significantly higher concentrations of polyphenols, flavonoids, and anthocyanins. α-Tocopherol, γ-tocopherol, β-carotene, and lutein were detected exclusively in fortified samples. ABTS and CUPRAC assays revealed the highest antioxidant activity in enriched juices. Although storage reduced anthocyanin, carotenoids, and tocopherols, substantial levels persisted after 30 days. HS-SPME/GC-MS identified 63 VOCs, with leaf fortification increasing monoterpenes and C6 alcohols linked to floral, citrusy, herbaceous, and green notes. Sensory analysis indicated greater sourness, astringency, density, and aromatic complexity, while maintaining satisfactory overall quality. All samples remained microbiologically stable. These findings support grapevine leaf extract as a promising sustainable ingredient for functional grape-based beverages.

Abstract: Surface electromyography (sEMG) signals acquired with low-cost sensors tend to exhibit variable degradation that can compromise the reliability of myoelectric control systems outside controlled conditions. This work presents an adaptive processing pipeline for sEMG signals composed of three chained stages: a motor-intent classifier based on dilated temporal convolutional networks, whose function is to determine whether a signal window contains muscle activity associated with a voluntary gesture; a dual-output quality assessor that estimates a continuous score and a binary acceptability label, aimed at deciding whether the signal can be used directly or requires intervention; and a convolutional autoencoder that recovers the morphology of degraded windows before they are used in prosthetic control. The decision policy for reconstruction operates on two independent thresholds and classifies each window into one of three states: signal discard, direct acceptance, or active restoration. The models within the proposed architecture are trained on the public NinaPro DB1, DB3, and DB10 datasets and validated without recalibration on signals recorded from two participants with transtibial amputation over four weekly sessions. The results show that the pipeline correctly handles signal profiles with opposing characteristics. Inference latency remained below 5 ms at the 50th percentile across all scenarios, consistent with real-time operation. An integrated prototype using an Arduino UNO R4 WiFi was used to evaluate the results, and validate the feasibility of the system on physical hardware.