Search Results (146)

Background: In biosimilar studies, assessing the switchability and interchangeability of biosimilars with their reference products is essential for ensuring reliable clinical evaluation. This study explores optimal trial design strategies incorporating balanced and uniform structures to enhance statistical efficiency in treatment effect under a carryover setting. Methods: Using a linear mixed-effect model for log-transformed responses, we conducted a theoretical variance-based evaluation of all possible two-treatment switching designs in three-period and four-period crossover trials, considering settings with and without carryover effects. A total of 247 distinct three-period designs and 65,519 distinct four-period designs were enumerated and classified according to structural properties, with particular attention to those incorporating a non-switching arm (NSA). Results: SBUwP-NSA (Strongly Balanced Uniform-within-Period designs with a Non-Switching Arm) consistently achieved the minimum variance for treatment effect estimation in both carryover and no-carryover settings. In the absence of carryover effects, UwP-NSA (Uniform-within-Period designs with a Non-Switching Arm) attained equivalent efficiency. In contrast, commonly used dedicated switching designs exhibited substantially lower relative efficiency, achieving as little as 50–55% of the efficiency of the optimal designs, depending on carryover assumptions. Conclusions: This comprehensive theoretical evaluation demonstrates that incorporating strong balance and uniformity properties can yield substantial efficiency gains in switching studies. The results provide quantitative guidance for selecting efficient crossover designs, enabling improved estimation precision while maintaining practical relevance for interchangeability and switching assessments in biosimilar research. Full article

Carrot juice processing generates large amounts of pomace, a fibre-rich by-product with significant valorisation potential. This study explored the feasibility of fermenting carrot by-product with Levilactobacillus brevis AM7 and Leuconostoc pseudomesenteroides DSM20193 to produce exopolysaccharide (EPS)-enriched functional beverages. Beverages were fermented with or without sucrose addition (EPS⁺ and EPS⁻, respectively) and characterized for microbiological, biochemical, rheological, and sensory attributes. Both strains showed robust growth (>8 log cfu/mL) and acidification (final pH below 4.8), comparable to plant-based yoghurt alternatives, with EPS synthesis markedly enhanced in sucrose-supplemented beverages. Leuc. pseudomesenteroides DSM20193 synthesized the highest EPS concentration (16.8 g/100 g dry weight), resulting in a 6-fold viscosity increase compared to EPS⁻ samples, thus improving the adherence to the spoon and preventing syneresis of the beverages. Sensory evaluation revealed that EPS⁺ carrot-based beverages had improved sweetness due to a slight sucrose residue, aroma, and mouthfeel, while maintaining low off-flavours and high colour uniformity. The results highlight carrot by-product as a promising substrate for developing clean-label beverages that are rich in dietary fibres and polyphenols and show antioxidant and potential prebiotic properties through sustainable fermentation processes. Full article

Background: Evidence guiding secondary repair of persistent full-thickness macular holes (FTMHs) remains limited and heterogeneous. Temporal arcuate relaxing retinotomy has been described as a salvage maneuver intended to increase temporal retinal compliance, yet functional safety data are scarce. We report consecutive real-world outcomes of temporal arcuate relaxing retinotomy for persistent FTMHs after failed standard repair(s). Methods: Retrospective consecutive case series of patients with persistent FTMH after ≥1 pars plana vitrectomy (PPV) with internal limiting membrane (ILM) peeling, treated with repeat PPV and temporal arcuate relaxing retinotomy. Outcomes included OCT (Optical Coherence Tomography)-confirmed closure after gas absorption and best-corrected visual acuity (BCVA, logMAR), ellipsoid zone (EZ) status, retinotomy-site morphology on OCT/fundus autofluorescence (FAF), and safety/functional outcomes (systematic scotoma symptom inquiry; Humphrey visual field testing when feasible). Exact binomial 95% confidence intervals (CI) were calculated for proportions. Results: Nine eyes (median age 70 years; range 55–76) underwent temporal arcuate relaxing retinotomy for persistent FTMH. Minimum linear diameter ranged 412–1037 µm (median 613 µm). OCT-confirmed closure was achieved in 7/9 eyes (77.8%; 95% CI 40.0–97.2) at a mean follow-up of 5.9 months (range 2–12). BCVA improved in 8/9 eyes (88.9%; 95% CI 51.8–99.7); mean BCVA improved from 1.26 ± 0.51 logMAR pre-operatively to 0.61 ± 0.18 logMAR at last follow-up (mean change −0.64 logMAR; Wilcoxon signed-rank test p = 0.011). As a sensitivity analysis, the paired t-test yielded p = 0.008. Humphrey visual fields were obtained in 6/9 eyes; one patient reported a new paracentral nasal scotoma, which was subjectively well tolerated. Conclusions: In this small consecutive series, temporal arcuate relaxing retinotomy was associated with a 78% closure rate and mean BCVA improvement in eyes with persistent FTMH after failed standard repair(s), with limited symptomatic scotoma reporting in those assessed. Given the retrospective design, small cohort, and incomplete standardized functional testing, larger comparative studies with uniform functional endpoints (microperimetry, RNFL/GCL metrics, and systematic perimetry) are needed to define patient selection, reproducibility, and relative performance versus contemporary salvage options. Full article

The use of natural bioactive compounds in edible coatings provides a sustainable approach to reducing food spoilage and meeting consumer demand for safer food preservation. In this study, bioactive extracts from Brassica juncea (green mustard, GM) and Raphanus sativus (radish tango, RT) sprouts were encapsulated into zein/chitosan (Z/CH) microparticles (MPs) using a complex coacervation–based encapsulation approach. The encapsulated microparticles (MPs), characterized by FTIR and UV-Vis spectroscopy, demonstrated a high loading efficiency of up to 90% and maintained their antioxidant activity for up to 168 h. TGA and SEM tests confirmed that the edible films produced by incorporating these microparticles (MPs) into polyvinyl alcohol (PVA) and chitosan (CH) matrices had a more uniform microstructure and enhanced heat stability. The Z/CH/RT6:PVA (1:2) and Z/CH/GM6:CH (1:1) formulations of the films showed significant antioxidant and antibacterial action, with up to 22.4% DPPH inhibition and a 1-log decrease in Salmonella enterica CFU, respectively. Overall, the results underscore the promise of sprout-derived microparticles as components for developing active, biodegradable packaging films with improved functional properties. Full article

Valsartan (Val)—a lipophilic non-peptide angiotensin II type 1 receptor antagonist—is highly effective against hypertension and displaying limited solubility in water (3.08 μg/mL), thereby resulting in low oral bioavailability (23%). The limited water solubility of antihypertensive drugs can pose a challenge, particularly for rapid and precise administration. Herein, we synthesize and characterize valsartan-containing silver nanoparticles (Val-AgNPs) using Mangifera indica leaf extracts. The physicochemical, structural, thermal, and pharmacological properties of these nano-conjugates were established through various analytical and structural tools. The spectral shifts in both UV-visible and FTIR analyses indicate a successful interaction between the valsartan molecule and the silver nanoparticles. The resulting nano-conjugates are spherical and within the size range of 30–60 nm as revealed in scanning electron-EDS and atomic force micrographs. The log-normal distribution of valsartan-loaded nanoparticles, with a size range of 30 to 60 nm and a mode of 54 nm, indicates a narrow, monodisperse, and highly uniform particle size distribution. This is a favorable characteristic for drug delivery systems, as it leads to enhanced bioavailability and a consistent performance. Dynamic Light Scattering (DLS) analysis of the Val-AgNPs indicates a polydisperse sample with a tendency toward aggregation, resulting in larger effective sizes in the suspension compared to individual nanoparticles. The accompanying decrease in zeta potential (to −19.5 mV) and conductivity further supports the idea that the surface chemistry and stability of the nanoparticles changed after conjugation. Differential scanning calorimetry (DSC) demonstrated the melting onset of the valsartan component at 113.99 °C. The size-dependent densification of the silver nanoparticles at 286.24 °C correspond to a size range of 40–60 nm, showing a significant melting point depression compared to bulk silver due to nanoscale effects. The shift in Rf for pure valsartan to Val-AgNPs suggests that the interaction with the AgNPs alters the compound’s overall polarity and/or its interaction with the stationary phase, complimented in HPTLC and HPLC analysis. The stability and offloading behavior of Val-AgNPs was observed at pH 6–10 and in 40% and 80% MeOH. In addition, Val-AgNPs did not reveal hemolysis or significant alterations in blood cell indices, confirming the safety of the nano-conjugates for biological application. In conclusion, these findings provide a comprehensive characterization of Val-AgNPs, highlighting their potential for improved drug delivery applications. Full article

The integration of mobile devices into aviation powering electronic flight bags, maintenance logs, and flight planning tools has created a critical and expanding cyber-attack surface. Security for these systems must be not only effective but also transparent, resource-efficient, and certifiable to meet stringent aviation safety standards. This paper presents AVI-SHIELD, a novel framework for developing high-assurance, on-device threat detection. Our methodology, grounded in the MITRE ATT&CK^® framework, models credible aviation-specific threats to generate the AviMal-TinyX dataset. We then design and optimize a set of compact, interpretable detection algorithms through quantization and pruning for deployment on resource-constrained hardware. Evaluation demonstrates that AVI-SHIELD achieves 97.2% detection accuracy on AviMal-TinyX while operating with strict resource efficiency (<1.5 MB model size, <35 ms inference time and <0.1 Joules per inference) on both Android and iOS. The framework provides crucial decision transparency through integrated, on-device analysis of detection results, adding a manageable overhead (~120 ms) only upon detection. Its successful deployment on both Android and iOS demonstrates that AVI-SHIELD can provide a uniform security posture across heterogeneous device fleets, a critical requirement for airline operations. This work provides a foundational approach for deploying certifiable, edge-based security that delivers the mandatory offline protection required for safety critical mobile aviation applications. Full article

Forestry environments—such as logging sites, transport trails, and resource monitoring areas—are characterized by rugged terrain and irregularly distributed obstacles, which pose substantial challenges for AGV route planning. This poses challenges for route planning in automated guided vehicles (AGVs) and forestry machinery. To address these challenges, this study proposes a hybrid path optimization method that integrates an improved A* algorithm with the Dynamic Window Approach (DWA). At the global planning level, the improved A* incorporates a dynamically weighted heuristic function, a steering-penalty term, and Floyd-based path smoothing to enhance path feasibility and continuity. In terms of local planning, the improved DWA algorithm employs adaptive weight adjustment, risk-perception factors, a sub-goal guidance mechanism, and a non-uniform and adaptive sampling strategy, thereby strengthening obstacle avoidance in dynamic environments. Simulation experiments on two-dimensional grid maps demonstrate that this method reduces path lengths by an average of 6.82%, 8.13%, and 21.78% for 20 × 20, 30 × 30, and 100 × 100 maps, respectively; planning time was reduced by an average of 21.02%, 16.65%, and 9.33%; total steering angle was reduced by an average of 100°, 487.5°, and 587.5°. These results indicate that the proposed hybrid algorithm offers practical technical guidance for intelligent forestry operations in complex natural environments, including timber harvesting, biomass transportation, and precision stand management. Full article

Low-frequency acoustic logging transducers are pivotal to far-acoustic imaging logging technology and permeability logging technology. This study presents a monopole acoustic transducer driven by electromagnetic force, exploiting the low-frequency vibration characteristics of a flextensional shell. Finite element simulations were employed to evaluate multiple magnetic circuit configurations under dimensional constraints typical of logging tools. An inner magnet circuit was selected and optimized through parametric analysis. Concurrently, the vibration shell was designed and simulated under borehole conditions, accompanied by the development of a dedicated excitation circuit. The fabricated prototype (64 mm outer diameter, 154 mm height, 100 mm shell height) demonstrated operation frequency at 1300 Hz with a sound pressure level of approximately 150 dB and uniform circumferential radiation, satisfying the requirements of logging applications. Full article

Neuroimaging assumes spatial and temporal uniformity, yet brain activity exhibits a multifractal cascade structure with intermittent bursts and long-range dependencies. We use controlled simulations to test how well standard sampling strategies (random, grid-based, hierarchical; $N=10\text{–}2000$ sensors) recover statistical properties—mean, variability, burstiness, and fractal dimension—from synthetic multifractal brain fields. Estimation errors deviate substantially from the classical $N^{-1/2}$ scaling expected under independent sampling. For higher-order statistics like burstiness, error reduction is remarkably flat in log–log space: orders-of-magnitude increases in sensor density yield virtually no improvement. Grid sampling performs best for fractal dimension at high densities; hierarchical sampling is more stable for burstiness. These results indicate that current neuroimaging fundamentally underestimates brain complexity and variability, with major implications for interpreting both healthy and pathological brain function. Full article

Particle size distribution (PSD), also referred to as grain-size distribution (GSD), is a fundamental characteristic of granular materials, influencing packing density, porosity, permeability, and mechanical behavior across soils, sediments, and industrial powders. Accurate and reproducible representation of PSD is essential for computational modeling, digital twin development (i.e., virtual replicas of physical systems), and machine learning applications in geosciences and engineering. Despite the widespread use of classical distributions (log-normal, Weibull, Gamma), there remains a lack of systematic frameworks for generating synthetic datasets with controlled statistical properties and reproducibility. This paper introduces a unified computational framework for generating virtual PSDs/GSDs with predefined statistical characteristics and a specified number of grain-size fractions. The approach integrates parametric modeling with two histogram-based allocation strategies: the equal-width method, maintaining uniform bin spacing, and the equal-probability method, distributing grains according to quantiles of the target distribution. Both methods ensure statistical representativeness, reproducibility, and scalability across material classes. The framework is demonstrated on representative cases of soils (Weibull), sedimentary and industrial materials (Gamma), and food powders (log-normal), showing its generality and adaptability. The generated datasets can support sensitivity analyses, experimental validation, and integration with discrete element modeling, computational fluid dynamics, or geostatistical simulations. Full article

This paper studies the volatility dynamics of the JSE Top40 Index by estimating a univariate GAS model with time-varying location, scale, and shape parameters (identity score scaling) and comparing its density and point-forecast performance against a stand-alone ARMA(3,2)–EGARCH(1,1) model and a hybrid ARMA(3,2)–EGARCH(1,1)–XGBoost framework. The GAS model is estimated on 3515 daily observations, and several conditional densities are examined. The Student-t GAS model (GAS–STD) obtains the lowest information criteria within the GAS family (AIC = 10,188.142; BIC = 10,243.626) and exhibits statistically significant persistence in location and scale dynamics. Statistical diagnostics provide evidence of correct density calibration (normalised log score = 1.1932; Uniform score = 0.4417), although residual skewness remains (IID-Test skewness $p=0.0134$). Out-of-sample analysis shows that GAS–STD performs strongly in density and risk forecasting, producing accurate 5% VaR and ES paths and passing coverage backtests (Kupiec LRuc $p=0.8414$; DQ $p=0.2281$). However, short-horizon point forecasts are most accurately produced by the Hybrid ARMA(3,2)–EGARCH(1,1)–XGBoost model (RMSE = 0.1386). The full Diebold-Mariano (DM) test confirms that all pairwise differences in predictive accuracy are statistically significant, and the model confidence set (MCS) procedure identifies the Hybrid model as the sole superior model at the 5% significance level, indicating that both ARMA(3,2)–EGARCH(1,1) and GAS–STD are statistically inferior. Simulation experiments illustrate that the tail behaviour of the Student-t distribution is sensitive to the degrees-of-freedom parameter $\nu$. For example, a Student-t distribution with $\nu =5$ exhibits total kurtosis of approximately $7.32$, indicating heavier tails compared to the Gaussian distribution. Overall, GAS–STD is a strong density and risk model for the JSE Top40, while the hybrid framework excels in short-term volatility forecasting. Full article

This paper addresses the challenge of systemic extreme risk in long-service gravity dams under human-controlled operation. It is the first study to construct a Generalized Extreme Value (GEV) distribution model using long-term operational monitoring data. The model, validated by multiple statistical tests and engineering boundary conditions, is then applied within a Response Surface Method-Monte Carlo (RSM-MC) reliability framework. Results indicate that the historical GEV model accurately captures the high-water-level tail characteristics and significantly overcomes the risk underestimation inherent in the uniform distribution model. Compared to the Log-Pearson Type III (Log-P3) design condition model, the GEV model yields a significantly lower probability of failure, e.g., the probability of cracking at the dam heel, the most sensitive failure mode, is reduced by nearly six times. This quantitative difference fully demonstrates GEV’s ability to precisely quantify the effective risk reduction achieved by human control, establishing a more scientific and realistic foundation for risk assessment of long-service gravity dams. Full article

Aperiodic pseudo-random impulse (APPI) trains represent deterministic yet reproducible sequences that mimic the irregularity of natural processes. They allow complete control over inter-spike intervals (ISIs) and pulse widths (PWs). Such signals are increasingly relevant for low-probability-of-intercept (LPI) communications, radar testing, and biomedical applications, where controlled variability mitigates adaptation and enhances stimulation efficiency. This paper presents a modular APPI generator implemented on an Arduino Mega platform, featuring programmable statistical models for ISI (exponential distribution) and PW (uniform distribution), dual-timing mechanisms (baseline loop and Timer/ISR, clear-timer on compare (CTC)), a real-time telemetry and software interface, and a safe output chain with opto-isolation and current limitation. The generator provides both reproducibility and tunable stochastic dynamics. Experimental validation includes jitter analysis, Kolmogorov–Smirnov tests, Q–Q plots, spectral and autocorrelation analysis, and load integration using a constant-current source with compliance margins. The results demonstrate that the Timer/ISR (CTC) implementation achieves significantly reduced jitter compared to the baseline loop, while maintaining the statistical fidelity of ISI and PW distributions, broad spectral characteristics, and fast decorrelation. Experimental verification was extended across a wider parameter space (λ = 0.1–100 Hz, PW = 10 µs–100 ms, 10 repetitions per condition), confirming robustness and repeatability. Experimental validation confirmed accurate Poisson/Uniform ISI generation, sub-millisecond jitter stability in the timer-controlled mode, robustness across λ = 0.1–100 Hz and PW = 10 µs–100 ms, and preliminary compliance with isolation and leakage limits. The accompanying Python GUI provides real-time control, telemetry, and data-logging capabilities. This work establishes a reproducible, low-cost, and open-source framework for APPI generation, with direct applicability in laboratory and field environments. Full article

This review examines the contemporary evidence on digital gamification’s effect on English as a foreign language (EFL) and English as a second language (ESL) classrooms’ outcome. The study focuses on vocabulary and other course-integrated skills in low-input contexts. We synthesise findings from education-wide meta-analyses and recent language-specific studies using a narrative approach organised by four questions on learning performance, classroom dynamics, student perceptions, and teacher practices. Across sources, gamification is associated with minor improvements in assessed performance, particularly in vocabulary and reading. Studies also frequently report gains in motivation and moment-to-moment classroom energy. These benefits are not uniform. Effects depend on element mixes, social format, pacing, and assessment timing, and they can taper with repeated use. Evidence on durability remains limited because immediate post-tests dominate and delayed outcomes are scarce. Most studies rely on perception surveys or platform logs rather than systematic observation. Students typically report enjoyment and usefulness with low to moderate anxiety, while teachers highlight the value of quick feedback and predictable routines alongside practical constraints such as preparation time, connectivity, class size, and tool fit. We propose minimum reporting standards that specify dose, element configuration, social design, assessment windows, reliability, inclusion context, and low-tech fallbacks. Better reporting and longer follow-ups are needed to separate short spikes from durable learning. Full article

Among the various completion strategies used in multi-stage hydraulic fracturing of horizontal wells, the limited entry design has become one of the most common approaches to promote more uniform slurry distribution. This method involves reducing the number of perforations so that higher perforation friction is generated at each entry point. The increased pressure drops force fluid and proppant to be diverted across multiple clusters rather than concentrating at only a few, thereby enhancing stimulation efficiency along the lateral. In this study, Computational Fluid Dynamics (CFD) simulations were performed to investigate how perforation erosion influences acoustic signals measured by Distributed Acoustic Sensing (DAS). Unlike previous studies that assumed perfectly circular perforations, this work uses oval-shaped geometries to better reflect the irregular erosion observed in the field, which provides more realistic modeling. The workflow involved building wellbore and perforation geometries, generating computational meshes, and solving transient turbulent flow using Large Eddy Simulation (LES) coupled with the Ffowcs Williams–Hawkings (FW-H) acoustic model. Acoustic pressure was then estimated at receiver points and converted into sound pressure level for analysis. The results show that, for a given perforation size, changes in flow rate cause log(q) versus sound pressure level to follow a straight line defined by a constant slope and varying intercept. Even when erosion alters the perforation into an oval shape, the intercept increases logarithmically, resulting in reduced sound amplitude, while the slope remains unchanged. Furthermore, when the cross-sectional area and flow rate are equal, oval perforations produce higher sound amplitudes than circular ones, suggesting that perforation geometry has a measurable influence on the DAS signal. This indicates that even when the same amplitude DAS signal is obtained, assuming circular perforations when estimating the fluid distribution leads to an overestimation if the actual perforation shape is oval. These findings highlight the importance of considering irregular erosion patterns when linking DAS responses to fluid distribution during hydraulic fracturing. Full article