Search Results (36,755)

Background: The purpose of this in vitro study was to evaluate and compare the fracture resistance of 3D-printed partial veneers with finish lines at three different locations and conventional full veneers with finish lines at the gingival level. All restorations were digitally designed and 3D printed using a nanoceramic filled resin specifically developed for veneer restorations. Methods: Four maxillary right central incisor typodont teeth were prepared for labial veneers with finish lines at different locations: incisal third (InT), middle portion of the middle third (MmT), lower portion of the middle third (LmT), and conventional veneer with the finish line at the gingival level (CoV). Each preparation was scanned, and 15 casts were 3D printed from each scan. A total of 60 3D-printed veneers were fabricated (n = 15 per group) using a nanoceramic-filled resin designed for veneer restorations. The restorations were cemented to the 3D-printed dies using the manufacturer’s adhesive and resin cement. The specimens were artificially aged with 10,000 thermal cycles between 5 °C and 55 °C, with a dwell time of 30 s, and then loaded to failure using a universal testing machine. Fracture load values were analyzed using one-way ANOVA and the Tukey honestly significant difference post hoc test (α = 0.05). In addition, fracture patterns were evaluated using scanning electron microscopy images for descriptive purposes. Results: The mean fracture resistance of the 3D-printed partial and conventional labial veneers differed significantly depending on restoration design (p < 0.05). Among the partial veneers, the LmT group showed the highest fracture resistance (279.86 N), followed by the MmT group (266.92 N), while the InT group showed the lowest value (179.22 N). The conventional veneer group (CoV) demonstrated higher fracture resistance (404.07 N) than all partial veneer groups. Conclusions: The fracture resistance of 3D-printed partial and conventional labial veneers fabricated with nanoceramic-filled resins differed according to finish line location. Conventional veneers demonstrated higher fracture resistance than all partial veneer designs. The smallest partial veneer, with the margin located in the incisal third, showed lower fracture resistance than the partial veneer designs with finish lines in the middle third. Full article

This scoping review examined the current literature on routines and habits in individuals with attention deficit hyperactivity disorder (ADHD). To our knowledge, research in this area remains limited. Therefore, this review mapped which areas of daily routines are most affected in children and adults with ADHD and explored related assessments and interventions. A comprehensive search was conducted across four databases: PubMed, Scopus, CINAHL, and PsycINFO, using keywords including “attention deficit hyperactivity disorder,” “ADHD,” “routine,” “habit,” and “lifestyle.” The findings identified four main domains in which individuals with ADHD experience difficulties: sleep hygiene, feeding, physical activity, and sedentary behaviors, with sleep hygiene addressed in more than half of the included studies. Study habits were addressed in only one included study. Among the 31 included studies, six involved interventions. The review also found that no validated assessment was specifically designed to measure routines or habits in individuals with ADHD, and that broader measures of routines, habits, or lifestyle were often non-validated or developed for a single project. Overall, the existing studies were concentrated primarily in pediatric populations, with limited research involving adults. These findings highlight important gaps in the literature and underscore the need for more research on routines and habits in adults with ADHD. They also support the development of assessments and interventions that specifically address these areas. Full article

Smartwatches are increasingly used in safety-critical scenarios, yet their optical heart-rate (HR) measurements often contain noise, artifacts, and missing data, undermining clinical trust. This paper presents Edge Beats, a data-curation layer and end-to-end architecture that enables the low-cost, open source PineTime smartwatch to function as a practical HR sensing node for distributed wearable systems. Heart-rate packets are streamed from PineTime to an ESP32 at the edge layer over Bluetooth Low Energy (BLE), then forwarded via an embedded Message Queuing Telemetry Transport (MQTT) broker to an edge server laptop for processing and visualization. A lightweight multi-stage algorithm cleans and smooths the HR stream using physiological boundary checks, a configurable data imputation technique, and exponential moving average (EMA) smoothing, all designed for real-time operation on resource-constrained hardware. We have evaluated the system over long monitoring sessions and compared the processed PineTime output against a commercial Huawei GT Pro 2 smartwatch. The system suppresses extreme spikes and short-term oscillations, yielding a more stable HR trace with qualitative agreement to the reference trends while keeping values in a physiologically plausible range. Network measurements show low latency (almost 3 ms one-way, 15 ms RTT) and stable throughput, and power measurements (100–450 mW for ESP32 and 3–70 mW for PineTime watch) confirm that continuous HR streaming over BLE and MQTT is feasible within the PineTime’s energy budget. These results imply that data stream processing combined with a modest publish–subscribe architecture improves the stability and usability of HR streams obtained from commodity wearable sensors, making PineTime a candidate as a complementary component for mission-critical health and safety systems. Full article

Neural ordinary differential equations (NODEs) have emerged as an effective methodology in artificial neural networks (ANNs) and deep learning for capturing unknown or unmodeled dynamics in compartmental and dynamical mathematical models arising from real-life applications, particularly under limited-data conditions, through learned data-driven corrections. Nevertheless, accurate one-step prediction errors do not necessarily guarantee reliable long-horizon rollouts. In this work, we study residual Neural ODE models of the form $\widehat{f}=f+h_{\theta }$ and derive a priori rollout-error estimates showing that long-time prediction behavior is generated by the incremental stability structure of the learned dynamics. Contracting regimes produce uniformly controlled rollout errors, whereas weakly contractive or expansive regimes can amplify persistent approximation errors over long time horizons. The analysis is illustrated on a flow-reactor chemical reaction network (CRN), where the washout parameter controls rollout reliability on the data-supported region. Numerical experiments further demonstrate that models with comparable empirical one-step prediction losses may exhibit substantially different multi-step behaviors. Rollout-error analysis and projected-gradient-descent (PGD) sensitivity directions additionally reveal that locally expansive regions align with worst-case perturbation amplification. Full article

Acidification and the application of exogenous tannins are well-established oenological practices designed to ensure wine stability and quality, playing a pivotal role to address the grape compositional imbalances associated with climate change. This study investigates precision enology techniques using a 2023 Sangiovese di Romagna, analyzing the interaction between pH modulation (3.2, 3.6, 3.8) and the addition of commercial grape seed tannins with varying medium degrees of polymerization (TanA: 3.1 mdp vs. TanB: 10.8 mdp). Following alcoholic fermentation, a full factorial design was implemented, including control batches (pH adjustment only). After a 40-day mild thermal treatment (T = 25 ± 1 °C) to simulate aging, results indicate that the high-mdp tannin (TanB) dominated color evolution regardless of pH, whereas the low-mdp tannin (TanA) effect was pH-dependent. Notably, a pH of 3.8 resulted in colloidal instability across all samples. The findings highlight the importance of customized protocols to mitigate climate-related challenges in winemaking. Full article

Classical fixed-point theorems for Rakotch, Edelstein, and Bianchini contractions require the contractive condition to hold for the mapping itself at every iteration, which severely limits their applicability to many real-world problems. In this paper, we break this limitation by shifting the contractive requirement to the p-th iterate of the mapping. We introduce three novel classes of p-Rakotch, p-Edelstein, and p-Bianchini contractions and prove that each guarantees the existence of a unique fixed point and global convergence of the Picard sequence from any initial point, under appropriate metric space assumptions (completeness for Rakotch and Bianchini; compactness with continuity for Edelstein). A key feature of our approach is that the original mapping T need not satisfy any contractive condition; only its p-th iterate $T^p$ needs to. This allows us to handle mappings where classical theorems simply do not apply. To validate our theoretical findings, we provide explicit numerical examples for $p=3,4,5$. More importantly, we demonstrate the practical power of our results through six diverse applications: ordinary differential equations with large coefficients; planar discrete dynamical systems; nonlinear Hammerstein integral equations; Caputo fractional differential equations with large linear terms; fractional equations exploiting the smoothing property; and implicit fractional differential equations. In each application, the classical contractive condition fails, yet our p-iterate approach succeeds. When $p=1$, all three theorems reduce to their classical counterparts, confirming that our framework is a natural and faithful generalization. Full article

This study presents an integrated framework for agricultural drought monitoring in data-scarce regions, utilizing the Google Earth Engine (GEE) platform to analyze multisource Earth observation data over the South Wollo highlands, Ethiopia, from 2001 to 2024. The analysis was complemented by Mann–Kendall trend testing, Sen’s slope estimation, and Pettitt change-point detection to identify and quantify long-term trends and abrupt shifts in drought dynamics. The methodology integrates climatic and satellite-derived indicators within a hybrid analytical framework. It incorporates the standardized precipitation evapotranspiration index (SPEI), vegetation condition index (VCI), vegetation health index (VHI), temperature condition index (TCI), and land surface temperature (LST), which are derived from MODIS (NDVI, LST, PET) and CHIRPS precipitation datasets. The analysis focused on the main growing season (June–September) to capture critical crop growth and moisture-sensitive periods for agricultural production in the study area. The findings reveal pronounced interannual variability in drought occurrence and intensity across the study period. Severe agricultural drought conditions were most extensive in 2009 and 2014, with VHIs indicating 15% and 4% of the area under severe and extreme drought in 2009, respectively, and 2.6% and 2% in 2014, respectively. In contrast, 2001, 2005, 2020, and particularly 2024 were characterized by predominantly no-drought to mild-drought conditions, with no-drought coverage increasing from 86.7% (2009) to 98.0% (2024). Vegetation-based indices demonstrate that drought impacts are episodic rather than persistent and strongly controlled by rainfall timing and early-season moisture availability. The LST exhibited marked year-to-year variability (28.8 °C to 33.8 °C), with elevated temperatures coinciding with drought periods and suppressed evaporative cooling. Correlation analysis confirmed a strong positive relationship between the SPEI and VHI (r = 0.77), with moderate correlations for the VCI (r = 0.40) and TCI (r = 0.36), underscoring the sensitivity of integrated vegetation health to the climatic water balance. The study concludes that combining the SPEI with satellite-derived vegetation and thermal indices provides a robust, scalable approach for agricultural drought assessment in regions with limited ground-based observations. The integrated framework effectively captures both moisture deficits and thermal stress components, offering a scientific basis for improving drought early warning systems and climate-resilient agricultural planning in Ethiopia and similar environments. Full article

We investigate dynamic weighted fractional information-theoretic measures for linear stochastic differential equations driven by fractional Brownian motion with Hurst parameter $H\in (1/2,1)$. Motivated by recent constructions of fractional Deng entropy and building upon explicit Gaussian solutions and closed-form fractional moments derived in previous work, we establish fully analytical expressions for the Shannon entropy, Rényi entropy, Tsallis entropy, extropy, and a continuous weighted fractional entropy $\mathbb{E}\left[X_t^p(-log{p}_{X_t}\left(X_t\right))\right]$ for $p\ge 0$, expressed directly in terms of known fractional moments without density estimation. All derived measures share a universal asymptotic scaling law growing as $Hlogt$, establishing a precise quantitative link between long-memory effects and information dynamics. The weighted fractional entropy further reveals remarkable structural properties as a function of the weighting order p, exposing a dual role of long memory on the system’s informational content. As a concrete application, we characterize anomalous diffusion in aging soft materials through an explicit critical time linking maximal uncertainty to the memory exponent H and the macroscopic aging rate. All results are validated through extensive Monte-Carlo simulations, demonstrating excellent agreement with the closed-form expressions across a wide range of Hurst exponents H and weighting orders p. Full article

Background/Objective: Autonomous vehicles (AVs) require control algorithms capable of handling complex and dynamic environments while satisfying multiple conflicting objectives such as safety, comfort, energy efficiency, and trajectory accuracy. Model predictive control (MPC) offers a principled framework for multi-constraint optimization, yet its real-time feasibility remains challenging for nonlinear vehicle dynamics. Methods: This paper presents a feedforward neural network (FNN)-based MPC framework for autonomous vehicle trajectory tracking. The FNN approximates the coupled vehicle dynamics and visual preview error model using an algebraic sum of log-sigmoid functions. Three adaptive FNN parameter sets, namely, the scaling factor, convergence parameter, and time-shifting parameter, are jointly optimized using a hybrid algorithm that combines the global search capability of fractional particle swarm optimization (FPSO) with the local refinement of sequential quadratic programming (SQP). Results: Comprehensive scenario-based simulations are performed to evaluate trajectory tracking dynamics under dry conditions with an adhesion coefficient of 0.8 and a vehicle mass of 1723 kg moving at a speed of 80 km/h. The results are quantitatively compared with a traditional PID controller and a structurally comparable MPC framework from the literature under identical simulation conditions; related DRL- and RL-based methods are discussed qualitatively for contextual orientation only. The stability, reliability, and computational complexity of the proposed framework are examined based on the mean square error, fitness value, and computational budget in GFLOPs for 100 independent runs. Conclusions: The proposed FNN-based MPC framework demonstrates improved tracking accuracy and optimizer reliability in simulation. While the present results indicate promising computational behavior, real-time deployment will require further validation on embedded automotive hardware and under closed-loop real-time constraints. Full article

A computational fluid dynamics (CFD) analysis is conducted to systematically investigate heat transfer enhancement in tubes fitted with grooved twisted tapes and to identify the groove geometry that provides the best thermo-hydraulic performance. Three grooved twisted tape configurations—circular-grooved twisted tapes (CGTT), rectangular-grooved twisted tapes (RGTT), and triangular-grooved twisted tapes (TGTT)—are evaluated and compared with a smooth tube and a conventional twisted tape over a Reynolds number range of 5000–20,000 under isothermal wall conditions. The grooved twisted tapes enhance heat transfer through the combined effects of swirl-induced secondary flows and groove-generated flow disturbances, which intensify turbulent mixing and reduce the thickness of the thermal boundary layer. Compared with the plain tube, the grooved configurations increase the Nusselt number by 1.472–1.98 times while increasing the friction factor by 3.21–3.58 times. Relative to the conventional twisted tape, the grooved designs provide an additional 8.0–12.1% enhancement in heat transfer with only a marginal increase of 0.2–1.5% in friction factor. The thermodynamic analysis indicates that the CGTT configuration exhibits the lowest entropy generation rate and exergy loss throughout the investigated Reynolds number range. In particular, the CGTT achieves a Bejan number of 0.999841 at Re = 5000, demonstrating an excellent balance between heat transfer enhancement and frictional losses. Furthermore, the CGTT attains the highest thermal performance factor (TPF) of 1.294 at Re = 5000 and maintains TPF > 1.0 over the entire Reynolds number range. The overall performance ranking is consistently established as CGTT > TGTT > RGTT based on comprehensive analyses of velocity fields, streamline patterns, turbulent kinetic energy distributions, temperature contours, and thermodynamic characteristics. Although the present study identifies the circular-groove configuration as the optimal design for a twist ratio (y/W) of 3.0, further parametric investigations involving variations in twist ratio, groove dimensions, and groove pitch are required to develop generalized design guidelines. Full article

This study presents an integrated computational framework for quantifying industrial impacts on marine ecosystems through the combined assessment of multiple environmental quality indices. The Aquatic Water Quality Index (AWQI) and four diagnostic pollution indices, namely the Heavy Metal Pollution Index (HPI), Metal Index (MI), Degree of Contamination (C_d), and Pollution Index (PI), were applied across 23 offshore sites in Mesaieed Industrial City, Qatar, to establish a high-resolution baseline for evaluating the effects of industrial effluents and brine discharge. Multivariate statistical analyses, including Principal Component Analysis (PCA) and Cluster Analysis (CA), identified Cr, Pb, Mn, Ni, and Zn as the principal drivers of water quality variability, effectively distinguishing anthropogenic influences from natural background conditions. To enable rapid and automated marine environmental assessment, three machine learning models—Artificial Neural Networks (ANN), Random Forest (RF), and Decision Trees (DT)—were developed and evaluated for predicting the investigated indices. Model performance was assessed through rigorous training–testing validation and the Diebold–Mariano test. The results demonstrated that model selection significantly influences predictive accuracy. Among the evaluated algorithms, RF achieved the highest predictive performance for AWQI (R² = 0.88) and C_d (R² = 0.92), whereas ANN performed best for HPI (R² = 0.89), and DT yielded the most accurate predictions for MI (R² = 0.82). Despite the index-specific strengths of individual models, RF emerged as the most robust and generalizable approach, consistently providing superior performance across heterogeneous environmental datasets. The proposed framework advances marine water quality assessment from conventional descriptive monitoring toward a proactive, data-driven paradigm, offering a scalable and cost-effective decision support tool for environmental management, pollution mitigation, and evidence-based coastal governance in industrialized coastal regions. Full article

This study developed a sustainable and cost-effective method for producing alginate-encapsulated activated carbon hydrogel beads from almond shell waste biomass, aimed at the efficient removal of methylene blue (MB) dye from aqueous solutions. The activated carbons were developed by heating biomass to different temperatures (500, 600, and 700 °C) and then mixing them with a calcium alginate matrix biopolymer to make composite hydrogel beads labeled AC500@Alg, AC600@Alg, and AC700@Alg. Zeta potential measurement, SEM, EDS, and FTIR analyses were carried out to evaluate the structural, morphological, chemical, and surface properties of the beads. Adsorption experiments showed that raising the activation temperature greatly improved porosity, surface carbon content, and adsorption performance. Among the adsorbent beads, AC700@Alg hydrogel beads had the best ability to adsorb MB, with a maximum Langmuir monolayer capacity of 316.46 mg/g. The pH of the solution and the charge on the surface had a great effect on the adsorption process. The best removal was achieved at alkaline pH due to the electrostatic attractions. The pseudo-second-order model best explained the kinetic data, which meant that surface interactions controlled the adsorption process. Thermodynamic analysis verified that MB adsorption was spontaneous and endothermic. Also, AC700@Alg beads were reusable, keeping their removal efficiency at over 80% after four cycles of adsorption and desorption. These results show that alginate-encapsulated activated carbon made from agricultural waste could be a good, eco-friendly, and reusable adsorbent for cleaning up wastewater. Full article

This paper presents the design and implementation of an X-band voltage-controlled oscillator (VCO) fabricated in a standard 180-nm CMOS process. To sustain stable oscillation under a constrained power budget, a gm-boosted topology is employed, integrating vertically stacked cross-coupled transistors with a center-tapped transformer to enhance the equivalent negative conductance. The boosting is achieved through two complementary mechanisms: the center-tapped transformer performs an impedance transformation that repurposes the layout parasitic capacitances into transconductance-enhancing elements, while the stacked cross-coupled pair reuses the DC current and suppresses the source-degeneration of a conventional pair, jointly sustaining a robust start-up margin at a low 0.75 V supply. On-wafer measurement results demonstrate a frequency tuning range from 8.78 GHz to 9.13 GHz as the control voltage is swept from 0 V to 1.8 V, with an average VCO gain K_VCO of 447.5 MHz/V. Under a total DC power consumption of 6.9 mW, the oscillator delivers an output power of 4.54 dBm and exhibits a measured phase noise of −103 dBc/Hz at a 1-MHz offset. Full article

Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical remote-monitoring ecosystem must also convert sensor alerts, clinician-facing summaries, and historical electronic clinical records (ECRs) into ranked evidence that supports care decisions. This study reframes a large-AI clinical retrieval model as the intelligence layer of an edge–cloud IoMT architecture. The proposed framework combines Transformer-Based Sequence (TBS) encoding, BioBERT-driven representation learning, explicit retrieval, and domain-guided re-ranking to connect sensor-originated narratives, patient records, and clinician queries. The empirical evaluation is conducted on Medical Information Mart for Intensive Care III (MIMIC-III) and i2b2, two de-identified clinical text benchmarks that approximate the documentation layer of real-world remote patient monitoring. Compared with strong baselines, including DeepBio, UniT2T, Web4IR, A2A-API, CoLTiD, VLRG, ColBERT, DeepSDH, BiRex, and DL4BTM, the proposed model achieves the best overall performance, reaching F1/Pre/NDCG scores of 0.8399/0.8338/0.5235 on MIMIC-III and 0.8090/0.8100/0.5129 on i2b2. Ablation experiments confirm the importance of exploratory data adaptation, critical feature modeling, critical token learning, cross-disciplinary supervision, and data-driven regularization. Parameter sensitivity analysis shows stable behavior for beta values greater than or equal to 1, with the strongest results at beta = 5. The study concludes that large-AI retrieval can strengthen the clinical interpretation layer required for IoMT-enabled remote monitoring, while future work should validate the approach on live multimodal sensor streams and privacy-preserving deployments. Full article

Farmer Producer Organizations (FPOs) have gained increasing attention as institutional mechanisms for improving the resilience of smallholder farming systems under changing climatic conditions. This study examines the role of FPOs in promoting the adoption of Climate-Smart Agriculture (CSA) practices, improved irrigation strategies, and sustainable land management in the arid region of Pali district, Rajasthan, India. A comparative assessment was conducted between FPO-associated member and non-member farmers to evaluate differences in climate change perception, adoption behaviour, and adaptive capacity. The study employed a mixed-methods research design using primary data collected from 408 farm households through structured interviews, focus group discussions, and key informant consultations. Descriptive statistics, mean comparison tests and regression analysis were used to examine adoption patterns and identify the major factors influencing farmers’ responses to climate risks. The findings indicate that delayed rainfall, rising temperatures, and increasing drought frequency are widely perceived by farmers as major threats to agricultural production. FPO membership was associated with higher levels of climate-risk awareness and greater reported adoption of CSA practices; however, these findings should be interpreted as associations rather than causal effects. Farmers linked with FPOs reported stronger uptake of improved and stress-tolerant crop varieties, crop diversification, mixed farming systems, agroforestry, soil moisture conservation, rainwater harvesting, improved irrigation methods, and integrated pest management practices. Education, farm size, access to extension services, market linkages, and climate information were also found to significantly influence adoption decisions. The study highlights the important contribution of FPOs in reducing transaction costs, improving access to inputs, technical knowledge, credit and markets, and encouraging collective responses to climate stress. Strengthening FPO governance, expanding extension support, and targeting vulnerable farmer groups can substantially enhance climate resilience and support sustainable agricultural transitions in arid regions. The findings demonstrate that farmer organizations can serve as effective intermediary institutions linking household-level adaptation strategies with broader goals of irrigation efficiency, land management, and rural sustainability. Full article