Search Results (1,615)

Manufacturing SMEs in Ecuador operate under macroeconomic volatility and limited financing; improvements in processes and management are key mechanisms for sustaining productivity and competitiveness. In contexts where conventional innovation indicators are unavailable, financial ratios constitute replicable signals that close a measurement gap in emerging economies. This study constructs the Structure-Based Innovation Index (SBII) as the mean of within-sample percentile ranks of capital efficiency (EBIT/Assets) and sales per employee, using financial statements from the SCVS, sectoral indicators from ENESEM, and size classification from REEM. The sample includes 58 formal manufacturing SMEs in Ecuador in 2023, stratified by province and size. Performance is measured through labor productivity and operating profitability (EBIT/Sales). Tercile comparisons reveal clear performance differentiation: the high-SBII group exhibits substantially higher median sales per employee (USD 129,552 vs. USD 40,176 in the low group) and higher operating profitability. Signals are more strongly reflected in productivity than in margins, indicating that operational gains materialize earlier. A robustness check using SBIIalt confirms that gradients are not index artifacts. High-performing SMEs are distinguished by institutionalized operational discipline: asset utilization, throughput stability, and cost control. The SBII is a replicable proxy for structure-based innovation in data-constrained environments. The findings align with SDGs 8, 9, and 12. Full article

Quadrotor UAVs present a significant control challenge due to their underactuated nature; strong coupling effects; nonlinear dynamics; and high sensitivity to unknown effect parameters, external disturbances, and uncertainties. To address this issue, this study proposes a Lyapunov-based backstepping (LYP) controller that ensures robust stability and precise trajectory tracking. The controller employs an inner- and outer-loop architecture for coupled position and attitude control. Its performance is compared with Proportional–Integral–Derivative (PID) and Fractional-Order PID (FOPID) controllers under three scenarios: nominal conditions, external disturbances, and model parameter uncertainties. All controller gains are optimized using Particle Swarm Optimization (PSO). Simulation results, which are evaluated using time-domain metrics and root mean square error (RMSE), demonstrate that the proposed LYP controller achieves superior robustness, faster disturbance rejection, and improved tracking accuracy compared to both PID and FOPID controllers. Full article

Bone tissue plays a vital role in the human body and possesses intrinsic self-repair mechanisms; however, large defects or pathological fractures may exceed its natural healing capacity. Bone tissue engineering provides promising strategies to restore bone integrity through the use of scaffolds, growth factors, and stem cells. While calcium phosphate (CaP)-based ceramics, such as hydroxyapatite (HAp) and tricalcium phosphate (TCP), represent the current benchmark, their limitations, including slow degradation (HAp) and limited osteoinductivity (TCP), have driven the development of alternative biomaterials. In this context, magnesium phosphate (MgP)-based materials have gained increasing attention due to their tunable resorption rate, improved biodegradability, and ability to stimulate osteogenesis and angiogenesis through the release of magnesium (Mg²⁺) ions. This study reports on composite scaffolds based on electrospun poly(ε-caprolactone) (PCL) fibres coated with MgP layers doped with lithium (Li) and zinc (Zn), designed to mimic the nanofibrous architecture of the extracellular matrix. Lithium and zinc were selected due to their known ability to modulate cellular response, with lithium promoting osteogenic activity and zinc contributing to improved cell proliferation and antibacterial potential. The phosphate phases obtained by coprecipitation were deposited onto the PCL fibres using Matrix-Assisted Pulsed Laser Evaporation (MAPLE), enabling controlled surface functionalization. Following thermal treatment, the formation of the crystalline magnesium pyrophosphate (Mg₂P₂O₇) phase was confirmed by chemical and structural characterization. The combination of a slowly degrading PCL matrix, providing sustained structural support, and a bioactive MgP coating, enabling rapid and controlled ion release, results in improved scaffold performance in terms of biocompatibility, biodegradability, and bioactivity. While the slow degradation rate of PCL ensures mechanical stability over an extended period, the surface-deposited MgP phase allows immediate interaction with the biological environment, facilitating faster ion release and enhancing cell–material interactions. These findings highlight the potential of the developed composites as promising candidates for trabecular bone regeneration and as viable alternatives to conventional CaP-based scaffolds in regenerative medicine. Full article

This work investigates the attitude stabilization and synchronization problem for multi-combined spacecraft with time-varying inertia parameters, external disturbances, and input constraints. First, the comprehensive disturbance is reconstructed considering the influence of inertia uncertainties for controller system design. And then, a novel disturbance observer is developed, and a state feedback controller developed through comprehensive disturbance estimation is proposed. The characteristic of uniform ultimate boundedness for the closed-loop attitude system is proved according to Lyapunov stability analysis, producing the sufficient linear matrix inequality (LMI) condition for the disturbance observer and state feedback controller designs. It is worth noting that the observer and controller gain matrices are solved simultaneously. The feasibility of the attitude stabilization control strategy is demonstrated through numerical simulations. Full article

The global shift toward decentralized generation has established standalone energy supply systems as a vital solution for remote regions. However, the integration of intermittent renewable sources and the inherent lack of rotational inertia in power electronic interfaces create significant challenges for frequency stability. This study addresses these issues by introducing an original Two-Tier Fractional-Order PID (TTFOPID) controller designed for robust Load Frequency Control (LFC) in a hybrid system comprising solar, diesel, biodiesel, and battery energy storage (BESS). The research utilizes the Multi-Objective Imperialist Competitive Algorithm (MOICA), enhanced with an attractive and repulsive assimilation phase, to navigate the high-dimensional parameter space. A unique framework is established to simultaneously tune controller gains and high-level system parameters, specifically BESS sizing and droop settings. Results demonstrate that the MOICA-tuned TTFOPID provides superior performance, achieving a 72% improvement in the Integral of Time-Weighted Absolute Error (ITAE) compared to NSGA-II and a 56% improvement in the Integral of the Square of Control (ISC) compared to MOPSO. Furthermore, robustness analysis validates the controller’s stability against significant parametric variations. The study concludes that the integrated TTFOPID-MOICA approach provides a superior pathway for stabilizing autonomous energy supply systems while protecting hardware longevity through optimized control effort. Full article

The Mobile Reliable Opportunistic Routing (MROR) protocol improves data-forwarding reliability in Cognitive Radio Sensor Networks (CRSNs) through mobility-aware virtual contention groups and handover zoning. However, its heuristic decision logic is difficult to optimize under highly dynamic spectrum access and random node mobility. To address this limitation, we present DRL-MROR, a refined routing framework that incorporates deep reinforcement learning (DRL) to enable intelligent and adaptive forwarding decisions. In DRL-MROR, the secondary users (SUs) act as autonomous agents that observe local state information, including primary-user activity, link quality, residual energy, and neighbor-mobility patterns. Each agent learns a forwarding policy through a Deep Q-Network (DQN) optimized for long-term network utility in terms of throughput, delay, and energy efficiency. We formulate routing as a Markov Decision Process (MDP) and use experience replay with prioritized sampling to improve learning stability and convergence. The DQN used at each node is intentionally lightweight, requiring 5514 trainable parameters, about 21.5 kB of weight storage in 32-bit precision, and approximately 5.4k multiply-accumulate operations per inference, which supports practical deployment on edge-capable CRSN nodes. Extensive simulations show that DRL-MROR outperforms the original MROR protocol and representative AI-based routing baselines such as AIRoute under diverse operating conditions. The results indicate gains of up to 38% in throughput, 42% in goodput, a 29% reduction in energy consumed per packet, and an approximately 18% improvement in network lifetime, while maintaining high route stability and fairness. DRL-MROR also reduces control overhead by about 30% and average end-to-end delay by up to 32%, maintaining strong performance even under elevated PU activity and higher node mobility. These results show that augmenting opportunistic routing with lightweight DRL can substantially improve adaptability and efficiency in next-generation IoT-oriented CRSNs. Full article

Aiming at solving the problems of severe chattering, irreconcilable convergence speed, and steady-state accuracy in traditional sliding-mode control (SMC) for the speed regulation system of permanent magnet synchronous motors (PMSMs) in rail transit traction, as well as its poor adaptability to complex disturbances such as frequent acceleration/deceleration and sudden load changes under traction conditions, a sliding-mode control strategy integrating improved piecewise terminal integral sliding-mode control (IPTISMC) with an adaptive smooth exponential reaching law (ASERL) is proposed. Taking the surface-mounted PMSM for rail transit traction as the research object, the d-q axis mathematical model is established, and a terminal integral sliding surface with a piecewise nonlinear function is designed, which resolves the problems of complex solutions and steady-state errors of the traditional sliding surface through a piecewise cooperative mechanism for large and small error stages. The designed ASERL realizes adaptive gain adjustment based on the state variables of the sliding surface and replaces the sign function with the hyperbolic tangent function, thus alleviating the inherent contradiction between convergence and chattering in the fixed-gain reaching law. The global stability and finite-time convergence of the system are rigorously proved based on Lyapunov stability theory. Furthermore, comparative experiments involving no-load operation, acceleration and deceleration, sudden load application and removal, and parameter perturbation are carried out on a DSP experimental platform for SMC-ERL, ISMC-ERL, IPTISMC-ERL and the proposed IPTISMC-ASERL. Experimental results show that the proposed IPTISMC-ASERL strategy can significantly improve the dynamic response and steady-state control accuracy of the PMSM speed regulation system for rail transit traction, effectively suppress chattering to enhance riding comfort, and simultaneously strengthen the system’s anti-disturbance capability and parametric robustness. It can fully meet the engineering control requirements for high precision and high stability of PMSMs in rail transit traction applications. Full article

This study investigated the effects of replacing soybean meal with fermented rapeseed meal (FRSM) in the diets of sika deer (Cervus nippon) during the pre-antler growth period. A single-factor experimental design was employed. A total of 24 male sika deer aged 2–3 years were randomly divided into four groups with six deer per group, including a control group (0% substitution) and three treatment groups fed diets containing 2.8%, 5.6%, and 8.4% fermented rapeseed meal (FRSM), defined as the low (L-FRSM), medium (M-FRSM), and high (H-FRSM) substitution groups, respectively. The feeding trial lasted 63 days, with measurements collected on days 30 and 63. Growth performance, nutrient digestibility, serum biochemical indices, and rectal fecal microbiota were determined. The results showed that the final body weight, total weight gain, and average daily gain L-FRSM were higher in the L-FRSM group than in the control group and other substitution groups (p < 0.05), accompanied by a reduced feed conversion ratio (p < 0.05). In addition, body height and chest circumference were improved in the L-FRSM group. Regarding nutrient digestibility, the apparent digestibility of neutral detergent fiber and dry matter at day 30, as well as calcium digestibility at day 63 were higher in the L-FRSM group compared to the control and higher-substitution groups (p < 0.05). In contrast, crude fat and dry matter digestibility were significantly lower in the H-FRSM group (p < 0.05). No statistical differences were observed among treatments in serum biochemical indices related to energy metabolism, protein metabolism, liver function, lipid metabolism, antioxidant capacity, or humoral immunity (p > 0.05). Similarly, no significant differences were detected in core microbial composition or α-diversity of rectal fecal microbiota among groups (p > 0.05). In conclusion, substituting soybean meal with 2.8% fermented rapeseed meal effectively improves growth performance and nutrient utilization without compromising health status or intestinal microbial stability in sika deer during the pre-antler growth period. The findings provide a scientific basis for optimizing dietary strategies and support the rational application of fermented rapeseed meal in sika deer production. Full article

Minimizing the impacts of coupling, randomness, time variation and uncertain nonlinearity to enhance real-time performance is critical for controlling complex industrial systems. This paper proposes an optimized adaptive filtering method (LAF-MTNF) for time-varying uncertain nonlinear systems with multiple-input multiple-output (MIMO) measurement noise, which integrates the multi-dimensional Taylor network (MTN) with Lyapunov stability theory (LST). Leveraging MTN’s inherent advantages—simple structure, linear parameterization, and low computational complexity—LAF-MTNF achieves efficient real-time filtering while avoiding the exponential computation burden of neural networks. The contributions of this work are threefold: (1) A novel integration of LST and MTN is proposed for MIMO filtering, in which an energy space is constructed with a unique global minimum to eliminate local optimization traps, addressing the stability deficit of traditional MTN filters using LMS/RLS algorithms. (2) Convergence performance is systematically quantified by deriving explicit expressions for the error convergence rate (regulated by a positive constant) and convergence region (a sphere centered at the origin) while modifying adaptive gain to avoid singularity, filling the gap of incomplete performance analysis in existing Lyapunov-based filters. (3) The design is disturbance-independent, relying only on input/output measurements and requiring no prior knowledge of noise statistics, thus enhancing robustness to unknown industrial disturbances. We systematically analyze the Lyapunov stability of LAF-MTNF, and simulations on a complex MIMO system verify that it outperforms existing methods in filtering precision (mean error 0.0227 vs. 0.0674 of RBFNN) and dynamic response speed, while ensuring asymptotic stability and real-time applicability. The proposed LAF-MTNF method achieves significant advantages over traditional adaptive filtering methods in filtering accuracy, convergence speed and anti-cross-coupling capability. This method has broad application prospects in high-precision industrial servo motion control, power system state monitoring and other multi-variable nonlinear industrial scenarios with complex noise environments. Full article

Background: The ketogenic diet has gained substantial popularity in recent years, and an increasing number of caregivers of children with type 1 diabetes are considering it as a nutritional strategy to improve glycemic control. Reported benefits include fewer postprandial glucose fluctuations, lower insulin requirements, and reduced insulin-associated weight gain. However, the use of this diet in children with type 1 diabetes remains highly debated, and scientific evidence regarding its safety and long-term effects in the pediatric population is limited. This narrative review aims to explore the motivations that lead parents to initiate a ketogenic diet in their children with type 1 diabetes and to summarize current knowledge on its potential metabolic and developmental consequences. Methods: A narrative review of the literature was conducted, including original research articles, case reports, and existing reviews addressing the use of ketogenic diets in children with type 1 diabetes. Clinical observations and published accounts of family experiences were also examined to contextualize emerging concerns and motivations. Results: Parents most commonly adopt a ketogenic diet for their children due to the desire for tighter glucose control, concerns about insulin-related weight gain, and the influence of information shared on social media. Some observational data suggest improvements in glycemic stability and reduced insulin requirements under ketogenic dietary regimens, while available evidence also highlights several potential risks, including dyslipidemia, increased susceptibility to hypoglycemia, slowed linear growth, and possible neurocognitive and psychosocial effects. Long-term safety data remain scarce, and current findings are insufficient to establish clear clinical recommendations. Conclusions: Interest in ketogenic diets among families of children with type 1 diabetes is growing, yet existing evidence suggests that the diet may pose significant metabolic and developmental risks in this population. Further well-designed studies are needed to evaluate its safety and efficacy. This review may assist clinicians in counseling families and underscores the need for evidence-based guidelines regarding restrictive dietary patterns in youth with type 1 diabetes. Full article

Background: Hippotherapy, a sensorimotor-rich intervention proposed for children with Autism Spectrum Disorder (ASD), is suggested to influence executive function (EF). However, the underlying electrophysiological mechanisms, particularly changes observed in resting-state Electroencephalography (EEG), remain underexplored. Methods: A total of forty-eight children with ASD, aged 9–12 years, participated in this quasi-experimental, non-randomized pre-test–post-test study. Participants were assigned to either a standardized 12-session hippotherapy program (n = 24) or a waitlist Control group (n = 24). EF was evaluated pre- and post-intervention using validated measures: the Wisconsin Card Sorting Test, Stroop Color–Word Test, Corsi Block-Tapping Task, and Tower of London. Resting-state EEG data (19 channels, 250 Hz) were recorded before and after the intervention and analyzed for spectral power, pairwise Pearson correlation, phase-based functional connectivity using the Phase Lag Index (PLI), and directed effective connectivity using Phase Transfer Entropy (PTE). EEG effects were tested with linear mixed models in MATLAB (fitlme), with the measured values in each ROI as the dependent variable, group and time as fixed effects, and SubjectID included as a random intercept; EF outcomes were analyzed with ANCOVA/MANCOVA, adjusting post-test scores for baseline. The assumptions of homogeneity of slopes, Levene’s test, and the Shapiro–Wilk test were examined, and the Holm–Bonferroni correction together with partial η² effect sizes were reported. Results: Following baseline adjustment, the hippotherapy group showed substantial and statistically significant improvements across all EF measures compared with controls partial η² range = 0.473–0.855; all adjusted p < 0.001; e.g., Stroop Incongruent Reaction Time (F(1,45) = 265.80, p < 0.001, ηp² = 0.855). EEG analyses revealed localized Group × Time interaction effects involving frontal delta power as well as selected alpha-, theta-, and beta-band connectivity measures within frontally anchored networks. In addition to these focal interaction effects, the hippotherapy group exhibited a narrower distribution of pre–post EEG changes across spectral power and connectivity metrics compared with controls, indicating greater temporal consistency in resting-state electrophysiological dynamics across sessions. Because group allocation was non-random (based on scheduling feasibility and parental preference), results should be interpreted as associations rather than causal effects. While the hippotherapy group exhibited significant EF improvements and relative stabilization in EEG spectral and connectivity metrics, particularly in frontal delta/theta/alpha/beta bands, a direct mapping between individual EEG changes and behavioral gains was not observed. Conclusions: A standardized 12-session hippotherapy program was associated with substantial improvements in EF and with relative stabilization of resting-state electrophysiological dynamics in children with ASD. However, the direct mechanistic link between these EEG and behavioral changes warrants further investigation. Larger randomized trials employing active control conditions, task-evoked electrophysiological measures, and extended longitudinal follow-up are needed to confirm efficacy, clarify mechanisms, and establish the durability of effects. Full article

Water invasion has become a critical challenge during the late-stage development of gas reservoirs, particularly under harsh conditions characterized by high temperature, high salinity, and strong reservoir heterogeneity. Chemical water shutoff technologies have thus gained increasing attention as effective solutions for selectively restricting water production while preserving gas deliverability. This review systematically summarizes recent advances in chemical water shutoff for gas reservoirs, focusing on polymer gels, nanocomposite materials, relative permeability modification agents, and emerging functional fluids. The reviewed materials are analyzed in terms of dominant sealing mechanisms, gas–water selectivity, reservoir adaptability, and performance under extreme formation conditions. By critically comparing their advantages, limitations, and field applicability, key challenges related to deep placement, selective sealing, long-term stability, and engineering controllability are identified. To address these limitations, emerging concepts such as zonal synergistic water control and bioinspired gas–water barriers are discussed, integrating wettability regulation, multiscale sealing, and adaptive material responses. These strategies provide a conceptual framework and research direction for the design of next-generation, efficient, and sustainable chemical water shutoff systems in complex gas reservoirs. Full article

Regarding the problems of fifth and seventh order characteristic harmonics existing in the operation of the dual three-phase permanent magnet synchronous motor, repetitive control is often used to improve the steady-state accuracy. However, traditional RC mostly adopts a fixed forward-learning gain and is set through trial-and-error methods, which requires a lot of time. Therefore, this paper proposes an improved repetitive control strategy based on fuzzy dynamic gain scheduling. This strategy precisely extracts the comprehensive distortion characteristic values of the target suppressed harmonics and the warning harmonics online; it designs a fuzzy adaptive adjustment mechanism to actively increase the gain to achieve rapid suppression when the target harmonic is severe, and rapidly reduce the gain to ensure the safety of operation when a low-frequency oscillation trend is detected. Simulation results show that the proposed method effectively reduces the total harmonic distortion of the current while maintaining the stability of the system and improves the harmonic suppression accuracy. Full article

This paper presents a novel hybrid learning-based control scheme for position control of robot manipulators whose structure is based on a closed-kinematic-chain mechanism (CKCM). The developed control scheme integrates two complementary control components: the feedback controller and the learning controller. The feedback controller is designed using linearization about a desired trajectory and a PID control law whose gains are selected by a tuning algorithm to guarantee semi-global stability of the linearized closed-loop feedback system. The learning controller incorporates PID-type iterative learning strategy to generate additional control inputs to compensate for modeling uncertainties and unmodeled dynamics. By updating the control input iteratively from trial to trial, the learning controller progressively improves the overall control performance. The effectiveness of the developed control scheme is demonstrated through computer simulations conducted on a six-degree-of-freedom CKCM robot manipulator. Simulation results are presented and discussed to evaluate the tracking accuracy of the developed approach. Full article

The high-performance control of nonlinear industrial plants in a wide operation range requires intelligent techniques. The aim of the present research is to develop an engineering approach for adaptation of the gains of the well-mastered and widely applied linear PID controller based on an offline-trained backpropagation artificial neural network (BANN) that assesses the plant parameters for the current operation point. The controller’s gains are online-computed from the empirical relationship with the plant parameters. Robust stability and robust performance conditions are derived for the gain-scheduled BANN-PID system. Their fulfilment ensures system feasibility in an industrial environment. The approach is demonstrated for the control of temperature in a laboratory dryer for fruits. The BANN training is based on data derived and validated from experiments using the Takagi–Sugeno–Kang nonlinear plant model. Simulations show that the BANN-PID system outperforms both the gain-scheduled fuzzy logic PID control system, designed in previous research, and the PID real-time control system by reducing overshoot six times and settling time 1.8 times and improving robustness 1.3 times. Full article