Search Results (3,074)

This paper presents a semi-quantum secret sharing (SQSS) protocol based on three-particle W states, designed for efficient and secure secret sharing in quantum-resource-constrained scenarios. In the protocol, a fully quantum-capable sender encodes binary secrets using W, while receivers with limited quantum capabilities reconstruct the secret through collaborative Z basis measurements and classical communication, ensuring no single participant can obtain the complete information independently. The protocol employs a four-state decoy photon technique ($\left\{\right|0⟩,|1⟩,|+⟩,|-⟩\}$) and position randomization, combined with photon number splitting (PNS) and wavelength filtering (WF) technologies, to resist intercept–resend, entanglement–measurement, and double controlled-NOT(CNOT) attacks. Theoretical analysis shows that the detection probability of intercept–resend attacks increases exponentially with the number of decoy photons (approaching 1). For entanglement–measurement attacks, any illegal operation by an attacker introduces detectable quantum state disturbances. Double CNOT attacks are rendered ineffective by the untraceability of particle positions and mixed-basis strategies. Leveraging the robust entanglement of W states, the protocol proves that the mutual information between secret bits and single-participant measurement results is strictly zero, ensuring lossless reconstruction only through authorized collaboration. Full article

The integration of ultracapacitors into the propulsion systems and implicitly into the hybrid energy storage systems (HESSs) of electric vehicles offers significant prospects for increasing performance, improving efficiency and extending the lifetime of battery systems. However, the realization of these benefits critically depends on the implementation of sophisticated control algorithms. From fundamental rule-based systems to advanced predictive and intelligent control strategies, the evolution and integration of these algorithms are driven by the need to efficiently manage the power flow, optimize energy utilization and ensure the long-term reliability of hybrid energy storage systems. This study briefly presents (in the form of a mini review) the research in this field and the development directions and application of state-of-the-art control algorithms, also highlighting the needs, challenges and future development directions. Based on the analysis made, it is found that from the point of view of performance vs. ease of implementation and computational resource requirements, fuzzy algorithms are the most suitable for HESS control in the case of common applications. However, when the performance requirements of HESSs relate to special and high-tech applications, HESS control will be achieved by using convolutional neural networks. As electric vehicles continue to evolve, the development of more intelligent, adaptive and robust control algorithms will be essential for achieving the full potential of integrating ultracapacitors into electric mobility. Full article

This paper presents a systematic approach for rapid assessment of the performance and robustness of linear control systems through geometric analysis in the complex plane. By combining indirect performance indices within a defined zone of desired performance in the complex s-plane, a connection is established with direct performance indices, forming a foundation for the synthesis of control algorithms that ensure root placement within this zone. Analytical relationships between the complex variables s and z are derived, thereby defining an equivalent zone of desired performance for discrete-time systems in the complex z-plane. Methods for verifying digital algorithms with respect to the desired performance zone in the z-plane are presented, along with a visual assessment of robustness through radii describing robust stability and robust performance, representing performance margins under parameter variations. Through parametric modeling of controlled processes and their projections in the complex s- and z-domains, the influence of the discretization method and sampling period, as forms of a priori uncertainty, is analyzed. This paper offers original derivations for MISO systems, facilitating the analysis, explanation, and understanding of the dynamic behavior of real-world controlled processes in both the continuous and discrete-time domains, and is aimed at integration into expert systems supporting control strategy selection. The practical applicability of the proposed methodology is related to discrete control systems in energy, electric drives, and industrial automation, where parametric uncertainty and choice of method and period of discretization significantly affect both robustness and control performance. Full article

Background/Objectives: The optimal duration of antibiotic therapy for bacterial prosthetic joint infections (PJI) remains a topic of considerable debate. Current recommendations are often based on limited evidence and expert consensus. Emerging data suggest that shorter antibiotic courses may be as effective as prolonged treatments in select cases. Shortening the duration of therapy offers several advantages, including a reduced risk of bacterial resistance, fewer adverse events, and cost savings. However, this approach must be carefully balanced with the individual patient’s risk of treatment failure. This narrative review aims to synthesize current evidence regarding the duration of antibiotic therapy in PJIs, according to surgical strategies—DAIR (debridement, antibiotics, and implant retention), one-stage exchange, two-stage exchange, and resection without reimplantation—and to identify parameters that may guide individualized and potentially shortened regimens. Methods: We conducted a comprehensive search of PubMed, Embase, and Cochrane Library databases through January 2025, including observational studies, randomized controlled trials, and international guidelines. Reference lists of key articles were also screened. Results: Studies on DAIR suggest that longer regimens (e.g., 8–12 weeks) are necessary, especially in staphylococcal infections, as confirmed by the DATIPO trial, which showed higher failure rates with 6 weeks compared to 12 weeks. Evidence on one-stage exchange is limited but increasingly suggests that 6 weeks may be sufficient in selected patients; however, no dedicated trial has confirmed this. In two-stage exchange, small retrospective series report successful outcomes with short antibiotic therapy combined with local antibiotics, but randomized trials show trends favoring longer regimens. For patients treated with permanent resection arthroplasty, arthrodesis, or amputation, antibiotic durations are highly variable, with few robust data. Across all strategies, most studies are limited by methodological weaknesses, including small sample sizes, retrospective design, lack of microbiological stratification, and heterogeneous outcome definitions. Conclusions: Despite growing interest in shortening antibiotic durations in PJIs, high-quality evidence remains limited. Until additional randomized trials are available—particularly in one- and two-stage exchange settings—12 weeks remains the safest reference duration for most patients, especially those with retained hardware. Future studies should incorporate stratification by infection type, causative organism, and host factors to define tailored and evidence-based antibiotic strategies. Full article

This study introduces a novel control strategy aimed at achieving projective synchronization in incommensurate fractional-order chaotic systems (IFOCS). The approach integrates the mathematical framework of fractional calculus with the recursive structure of the backstepping control technique. A key feature of the proposed method is the systematic use of the Mittag–Leffler function to verify stability at every step of the control design. By carefully constructing the error dynamics and proving their asymptotic convergence, the method guarantees the overall stability of the coupled system. In particular, stabilization of the error signals around the origin ensures perfect projective synchronization between the master and slave systems, even when these systems exhibit fundamentally different fractional-order chaotic behaviors. To illustrate the applicability of the method, the proposed fractional order backstepping control (FOBC) is implemented for the synchronization of two representative systems: the fractional-order Van der Pol oscillator and the fractional-order Rayleigh oscillator. These examples were deliberately chosen due to their structural differences, highlighting the robustness and versatility of the proposed approach. Extensive simulations are carried out under diverse initial conditions, confirming that the synchronization errors converge rapidly and remain stable in the presence of parameter variations and external disturbances. The results clearly demonstrate that the proposed FOBC strategy not only ensures precise synchronization but also provides resilience against uncertainties that typically challenge nonlinear chaotic systems. Overall, the work validates the effectiveness of FOBC as a powerful tool for managing complex dynamical behaviors in chaotic systems, opening the way for broader applications in engineering and science. Full article

With the rapid growth of electricity demand in industrial parks and the increasing penetration of renewable energy, vehicle-to-grid (V2G) technology has become an important enabler for mitigating grid stress while improving charging economy. This paper proposes a multi-objective rolling linear-programming-model-based predictive control (LP-MPC) method for coordinated electric vehicle (EV) scheduling in industrial park microgrids. The model explicitly considers transformer capacity limits, EV state-of-charge (SOC) dynamics, bidirectional charging/discharging constraints, and photovoltaic (PV) generation uncertainty. By solving a linear programming problem in a receding horizon framework, the approach simultaneously achieves load peak shaving, valley filling, and EV revenue maximization with real-time feasibility. A simulation study involving 300 EVs, 100 kW PV, and a 1000 kW transformer over 24 h with 5-min intervals demonstrates that the proposed LP-MPC outperforms greedy and heuristic load-leveling strategies in peak load reduction, load variance minimization, and charging cost savings while meeting all SOC terminal requirements. These results validate the effectiveness, robustness, and economic benefits of the proposed method for V2G-enabled industrial park microgrids. Full article

In recent years, academic performance prediction has evolved as a research field thanks to its development and exploration in the educational context. Early student performance prediction is crucial for enhancing educational outcomes and implementing timely interventions. Conventional approaches frequently struggle on behalf of the complexity of student profiles as a consequence of single activation functions, which prevent them from effectively learning intricate patterns. In addition, these models could experience obstacles such as the vanishing gradient problem and computational complexity. Therefore, this research study designed an Activation Ensemble Deep Neural Network (AcEnDNN) model to gain control of the previously mentioned challenges. The main contribution is the creation of a credible student performance prediction model that comprises extensive data preprocessing, feature extraction, and an Activation Ensemble DNN. By utilizing various methods of activation functions, such as ReLU, tanh, sigmoid, and swish, the ensembled activation functions are able to learn the complex structure of student data, which leads to more accurate performance prediction. The AcEn-DNN model is trained and evaluated based on the publicly available Student-mat.csv dataset, Student-por.csv dataset, and a real-time dataset. The experimental results revealed that the AcEn-DNN model achieved lower error rates, with an MAE of 1.28, MAPE of 2.36, MSE of 4.55, and RMSE of 2.13 based on a training percentage of 90%, confirming its robustness in modeling nonlinear relationships within student data. The proposed model also gained the minimum error values MAE of 1.28, MAPE of 2.97, MSE of 4.77, and RMSE of 2.18, based on a K-fold value of 10, utilizing the Student-mat.csv dataset. These findings highlight the model’s potential in early identification of at-risk students, enabling educators to develop targeted learning strategies. This research contributes to educational data mining by advancing predictive modeling techniques that evaluate student performance. Full article

In this paper, a novel robust optimal control strategy is proposed for permanent magnet synchronous motors (PMSMs), simultaneously addressing two critical challenges in speed regulation: flux linkage degradation during long-term operation and abrupt load torque variations. The robust optimal control strategy is implemented through a combination of feedforward control and feedback control. A novel Variable-Gain Proportional Disturbance Observer (VGPDO) is proposed to simultaneously estimate time-varying flux linkage and torque disturbances in PMSM systems. The estimated disturbances are then compensated via a feedforward control loop, significantly improving the system’s robustness against parameter variations and external load changes. An optimal controller based on an actor-critic neural network provides feedback for optimal control performance. The uniform ultimate boundedness (UUB) of the proposed strategy is proved through Lyapunov stability analysis, and comprehensive simulation studies demonstrate the efficacy of both the proposed VGPDO and the proposed robust optimal control strategy. Full article

The adoption of Interior Permanent Magnet Synchronous Motor (IPMSM) in Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV) drives the need for innovative approaches to improve control performance and power conversion efficiency. This paper aims at evaluating advanced Model Predictive Control (MPC) strategies for IPMSM drives in a methodic comparison with the most widespread Field Oriented Control (FOC). Different extensions of direct Finite Control Set MPC (FCS-MPC) and indirect Continuous Control Set MPC (CCS-MPC) MPCs are considered and evaluated in terms of reference tracking performance, robustness, power efficiency, and complexity based on Matlab, Simulink™ simulations. Results confirm the inherent better control quality of MPCs over FOC in general and allow us to further identify some possible directions for improvement. Moreover, indirect MPCs perform better, but complexity may prevent them from supporting real-time implementation in some cases. On the other hand, direct MPCs are less complex and reduce inverter losses but at the cost of increased Total Harmonic Distortion (THD) and decreased robustness to parameters deviations. These results also highlight various trade-offs between different predictive control strategies and their feasibility for high-performance automotive applications. Full article

With the increasing age and traffic load of highway networks in China, preventive maintenance has become a critical strategy for extending pavement service life and improving infrastructure sustainability. However, the lack of standardized post-evaluation systems has hindered the scientific assessment of maintenance effectiveness. This study proposes a systematic post-evaluation framework for highway preventive maintenance projects based on the Hazard Analysis and Critical Control Points (HACCP)-Inspired methodology (Applying Principles of Hazard Analysis and CCP Identification). Adopting a full life-cycle perspective, the framework identifies critical control points (CCPs) across pre-, mid-, and post-implementation phases, targeting six key dimensions: ecological and environmental hazards, resource utilization hazard, engineering safety risks, engineering quality risks, socioeconomic benefit hazards, and social living environment hazards. A multi-level evaluation indicator system is constructed using hierarchical clustering and weighted through the Analytic Hierarchy Process (AHP). The framework is applied to a preventive maintenance project on the Jinghuan Expressway in Tianjin, China, demonstrating strong practical applicability. The final evaluation score of 84.1 out of 100 confirms the technical adequacy of the project while revealing areas for improvement in clean energy adoption and substructure monitoring. This framework provides a robust basis for standardizing post-evaluation practices and promoting sustainable highway maintenance management. Full article

Recombinant human bone morphogenetic protein-2 (rhBMP-2) is widely used to promote bone regeneration. However, conventional surface-attached delivery on absorbable collagen sponges causes a rapid burst release, excessive inflammation, and suboptimal healing. To overcome these limitations, we developed a thermally controlled Poly(DL-lactide-co-glycolide) (P_DLLGA) encapsulation system, designed to stabilize rhBMP-2 and enable controlled release. rhBMP-2 was incorporated in P_DLLGA pellets using the hot-melt extrusion of a lyophilized mixture containing poloxamer 407 and hydroxypropyl-β-cyclodextrin, and terminal sterilization (X-ray irradiation). The released rhBMP-2 maintained its molecular integrity after sterilization and remained stable for up to 732 days in storage, as confirmed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and capillary electrophoresis (CE). Further, high-affinity binding between released rhBMP-2 and BMPR-IA was confirmed by bio-layer interferometry (BLI), and the released protein induced a robust in vitro ALP response, confirming preserved osteogenic activity. Our encapsulation approach for rhBMP-2 using P_DLLGA, including the combination product with β-TCP (LDGraft; Locate Bio, Nottingham, UK), provides a stable and bioactive rhBMP-2 delivery strategy with inherent dose-shielding properties, supporting safe, controlled, and effective bone regeneration therapies. Full article

The accurate monitoring of crop water status is critical for optimizing irrigation strategies in winter wheat. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) technology offers superior spatial resolution, temporal flexibility, and controllable data acquisition, making it an ideal choice for the small-scale monitoring of crop water status. During 2023–2025, field experiments were conducted to predict crop water status using UAV images in the North China Plain (NCP). Thirteen vegetation indices were calculated and their correlations with observed crop water content (CWC) and equivalent water thickness (EWT) were analyzed. Four machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT in the 2024–2025 growing season of winter wheat. The results show that the ratio vegetation index (RVI, NIR/R) exhibited the strongest correlation with CWC (R = 0.97) during critical growth stages. Among the ML models, CatBoost demonstrated superior performance, achieving R² values of 0.992 (CWC) and 0.962 (EWT) in training datasets, with corresponding RMSE values of 0.012% and 0.1907 g cm⁻², respectively. The model maintained robust performance in testing (R² = 0.893 for CWC, and R² = 0.961 for EWT), outperforming conventional approaches like RF and DT. High-resolution (5 cm) inversion maps successfully identified spatial variability in crop water status across experimental plots. The CatBoost-RVI framework proved particularly effective during the booting and flowering stages, providing reliable references for precision irrigation management in the NCP. Full article

Private fifth generation (5G) networks have emerged as a cornerstone for ultra-reliable, low-latency connectivity across mission-critical domains such as industrial automation, healthcare, and smart cities. Compared to conventional technologies like 4G or Wi-Fi, they provide clear advantages, including enhanced service continuity, higher reliability, and customizable security controls. However, these benefits come with new security challenges, particularly regarding the confidentiality, integrity, and availability of data and services. This article presents a review of security vulnerabilities in private 5G networks. The review pursues four objectives: (i) to identify and categorize key vulnerabilities, (ii) to analyze threats that undermine core security principles, (iii) to evaluate mitigation strategies proposed in the literature, and (iv) to outline gaps that demand further investigation. The findings offer a structured perspective on the evolving threat landscape of private 5G networks, highlighting both well-documented risks and emerging concerns. By mapping vulnerabilities to mitigation approaches and identifying areas where current solutions fall short, this study provides critical insights for researchers, practitioners, and policymakers. Ultimately, the review underscores the urgent need for robust and adaptive security frameworks to ensure the resilience of private 5G deployments in increasingly complex and high-stakes environments. Full article

Exhibiting significant potential for sustainable packaging due to their renewability and biodegradability, soy protein isolate (SPI) films are nevertheless critically hampered by inherent brittleness, poor water resistance, and a lack of bioactivity. Herein, we demonstrate a hierarchical multi-network strategy that transforms SPI into a high-performance, functional biocomposite. A robust covalent backbone was forged via Schiff base cross-linking between SPI and dialdehyde cellulose nanocrystals (DACNCs) derived from agricultural biomass, while proanthocyanidins (PAs) were strategically incorporated to create a secondary, pervasive hydrogen-bonding network. This hierarchical architecture effectively overcomes the typical trade-offs between mechanical strength and functionality seen in singly modified biopolymers, unlocking a suite of remarkable performance enhancements. The optimized film exhibited a 491% increase in tensile strength (to 15.54 MPa) and elevated thermal stability to 330 °C. Critically, the film was endowed with potent functionalities, including complete UV-blocking, high antioxidant capacity (93.2% ABTS scavenging), and strong, broad-spectrum antimicrobial activity. The film’s practical efficacy was validated in a preservation test, where the coating extended blueberry shelf life by inhibiting fungal spoilage and reducing weight loss by nearly 30% relative to uncoated controls after 15 days of storage. This work provides a powerful framework for developing advanced biocomposites with tailored properties for active food packaging and beyond. Full article

Flavonoids, a ubiquitous class of plant secondary metabolites, are increasingly pivotal as chemical markers for ensuring the quality, safety, and efficacy of herbal medicines (HMs). Their broad distribution, biological activities, and detectability make them ideal for this role. This comprehensive review critically examines current trends and analytical perspectives regarding flavonoids in HM quality control. We first explore advanced quality control strategies that move beyond single-compound quantification, including chemical fingerprinting, metabolomics, network pharmacology, and the innovative concept of Q-markers. The review then provides an in-depth analysis of the analytical techniques central to flavonoid analysis, from the routine use of HPTLC and HPLC-UV to advanced hyphenated systems like UHPLC-QTOF-MS, highlighting their applications in authentication, standardization, and adulteration detection. Furthermore, we emphasize the growing importance of modern data analysis workflows, particularly the integration of chemometrics and molecular networking, for interpreting complex datasets and identifying robust, bioactivity-relevant markers. By synthesizing recent research (2017–2024), this work underscores a paradigm shift towards holistic, multi-marker approaches and data-driven methodologies. It concludes that the synergistic application of advanced analytical techniques with sophisticated data modeling is essential for the future of HM quality control, ensuring reliable and standardized herbal products for global consumers. Full article