Search Results (112)

Background/Objectives: Local anaesthetic systemic toxicity (LAST) is a rare but potentially fatal complication of dental and oral and maxillofacial surgical local anaesthesia (LA). Three amide agents are commonly used in the UK: lignocaine (lidocaine) 2% with adrenaline 1:80,000; articaine 4% with adrenaline 1:100,000 (2.2 mL cartridges); and bupivacaine 0.5%. Clinically significant discrepancies between guideline sources for maximum recommended dosages (MRDs) persist, and the additive toxicity of combined amide agents remains underappreciated. The objectives are: to provide clear, evidence-appraised MRD guidance for dental practitioners; to explain safe combination dosing using the fractional dose rule with acknowledgement of its pharmacokinetic limitations; and to outline recognition and management of LAST, including intravenous lipid emulsion (ILE) therapy, setting-stratified response, and differential diagnosis. Methods: These include the following: narrative review of MEDLINE (via PubMed), the Cochrane Library, and Embase (inception to May 2026), supplemented by key regulatory documents (British National Formulary (BNF) 91; US Food and Drug Administration (FDA) prescribing information; UK Summaries of Product Characteristics (SmPCs)); major guideline documents (American Society of Regional Anesthesia and Pain Medicine (ASRA) 2018; Association of Anaesthetists 2021; Resuscitation Council UK 2021); systematic reviews; and peer-reviewed literature, ranked by a jurisdiction-specific UK prescribing and regulatory source hierarchy. Results: BNF 91 and the FDA both support a 7 mg/kg (500 mg) MRD for lignocaine with adrenaline; in practice, the adrenaline ceiling limits administration to 6–7 cartridges (2.2 mL) regardless of the guideline followed. The principal reasons for caution when combining amide agents are; additive systemic toxicity, more complex dose calculation, absence of proven clinical benefit for concurrent mixing, unnecessary drug exposure, and incremental hypersensitivity risk—not metabolic pathway differences. The fractional dose rule is a pharmacologically justified safety heuristic with acknowledged pharmacokinetic limitations. ILE is a specific rescue therapy for severe or cardiovascular LAST; airway support and oxygenation remain the primary interventions. Patient-specific factors substantially lower the effective toxic threshold. Conclusions: Safe LA administration in oral surgery requires systematic MRD calculation, application of the fractional dose rule for combined-agent appointments, attention to patient-specific risk factors, setting-appropriate emergency preparedness, and structured differential diagnosis to distinguish LAST from more common dental emergencies. Full article

Background/Objectives: 5α-Reductase inhibitors (5ARIs) are commonly used to treat and prevent androgenic alopecia and benign prostatic hyperplasia. Despite their well-established effectiveness, they are associated with adverse drug reactions (ADRs), highlighting the need for continuous safety assessment. This study aimed to analyze the ADRs associated with finasteride and dutasteride, both as monotherapy and in combination therapy. Methods: A retrospective analysis of ADRs associated with finasteride and dutasteride reported to EudraVigilance between 1 January 2005 and 27 March 2023 was performed. A total of 7777 reports were selected, and various variables were examined, including the temporal evolution of ADR reports, reporter profile, and the age group of the affected population. ADRs were categorized based on their seriousness and outcome, with particular focus on the most common reactions and their alignment with the Summary of Product Characteristics (SmPC). Results: The most affected age group, excluding the “Not Specified” category, was “18–64 years.” Overall, finasteride was the most reported. The majority of reported ADRs were classified as “Serious,” with a predominant outcome of “Persists without recovery,” and a significant proportion of these ADRs were not listed in the respective 5ARI SmPCs. Among the ADRs classified as “Serious,” the most frequently reported seriousness criterion was “Clinically important”. Conclusions: The results emphasize how crucial it is to continuously monitor these drugs in order to prevent and mitigate ADRs, ensure population safety, and promote public health. Additionally, more research is required to determine whether the ADRs not included in the SmPC could be new warning signs. Full article

Background: With the increasing widespread use of GLP-1 RA and dual GIP/GLP-1 RAs in the treatment of obesity, their safety profile remains a concern for healthcare professionals (HPs). Objective: This study aimed to characterize and evaluate safety data from the EudraVigilance (EV) database for semaglutide (SEM), liraglutide (LIR), and tirzepatide (TIR). Methods: A hierarchical pharmacovigilance approach was applied, integrating SOC- and PT-level analyses with SmPC-based evaluation and both frequentist (ROR, 95% CI) and Bayesian (IC025) disproportionality methods. Within each molecule, reporter type–stratified analyses were performed, while across all molecules, disproportionality analyses were conducted separately in HP reports and in the full database to identify reporting patterns and potential safety signals, including those not described in the SmPCs. Results: Some ADRs, listed in the SmPC of only one or two of the three GLP1-RAs were also reported in the EV database for the other agents whose SmPCs do not specify these ADRs including optic ischemic neuropathy (TIR: 0.28% and LIR: 0.17%), alopecia (LIR: 0.81%), headache (TIR: 2.51%), intestinal obstruction (TIR: 1.55%), angioedema (LIR: 0.19%), hypersensitivity (SEM: 0.58% and LIR: 0.73%), etc. Pancreatitis, in particular, showed a significant but low-magnitude signal, being more frequently reported by HPs compared with non-HPs across all three GLP1-RAs. Additionally, statistically significant signals (IC025 > 0) were observed in both the HPs and full datasets. For example, for SEM vs. TIR, signals were identified for optic ischemic neuropathy (0.17; 0.13), gallbladder disorder (0.09; 0.11), and dysesthesia (0.42; 0.43), respectively. For TIR vs. SEM, signals were observed for injection site erythema (0.05; 0.11), injection site pruritus (0.01; 0.11), and injection site reaction (0.02; 0.08). Conclusions: These findings suggest potential safety signals beyond current SmPC information, emphasizing the need for continuous pharmacovigilance and cautious interpretation of reporting biases. Full article

Urban separate sewer systems face significant challenges from rainfall-derived infiltration and inflow (RDII) during the wet season. To achieve the integrated optimization of operational safety, energy consumption, and carbon emissions, this study proposes a dynamic optimal control method. A real-time regulation framework was developed by coupling a Storm Water Management Model (SWMM) hydraulic model with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization algorithm within a Model Predictive Control (MPC) structure. Based on real-time water level risks, the framework adaptively adjusts the priority among three objectives: overflow reduction, pumping station energy consumption, and methane emission potential. Using a real separate sewer network in CZ city as a case study, the method was evaluated under light, moderate, and heavy rainfall scenarios. Results show that, compared with traditional rule-based control (RBC) and fixed-weight static model predictive control (SMPC), the proposed dynamic model predictive control (DMPC) strategy reduces overflow by 37.2% during heavy rain, and achieves 16.5% energy savings and a 15.8% reduction in methane emission potential during light rain. The strategy also balances network storage utilization, mitigates local overload, and demonstrates enhanced robustness to rainfall forecast errors, providing an effective technical solution for safe, energy-efficient, and low-carbon urban drainage operation. Full article

Sixth-generation (6G) networks are expected to provide ubiquitous connectivity, AI-native orchestration, and seamless integration across terrestrial and non-terrestrial infrastructures. However, these capabilities introduce new privacy challenges related to the classification and protection of personal, quasi-personal, and non-personal data in complex data-driven environments. This paper presents a systematic review of 78 peer-reviewed studies published between 2019 and 2025. Following a PRISMA-based methodology, this review analyzes privacy-enhancing technologies (PETs), regulatory compliance frameworks, and architectural patterns for privacy preservation in 6G networks. The findings show that differential privacy (DP) and federated learning (FL) dominate current research, accounting for nearly 52% of the reviewed studies. Blockchain auditing and zero-knowledge proofs (ZKPs) collectively represent approximately 30%, while the remaining mechanisms, including physical-layer security (PLS), trusted execution environments (TEEs), homomorphic encryption (HE), secure multi-party computation (SMPC), and anonymization, account for roughly 18%. These mechanisms exhibit varying levels of privacy strength, utility preservation, latency, and energy cost. At the same time, evolving regulatory frameworks, including GDPR, PDPL, CCPA/CPRA, LGPD, and PIPL, increasingly extend privacy obligations to quasi-personal and aggregated data. Building on these findings, this paper proposes a unified taxonomy that clarifies the boundary between personal and non-personal data. It also provides a cross-layer mapping between PETs and compliance requirements across the Core/SBA, RAN, Edge/MEC, and NTN layers. Finally, this paper presents a forward-looking roadmap for 2025–2030, highlighting hybrid PET pipelines, post-quantum auditability, and AI-driven compliance automation as key directions for privacy-preserving 6G standardization. Full article

Background/Objectives: Gastro-resistant multiparticulate systems are designed to protect drugs in acidic environments and to ensure intestinal release. In practice, the method of administration may need to be modified: pellet-containing capsules opened or tablets halved for patients with swallowing difficulties, yet the type of liquid used for administration is often not specified. This study examined the stability of gastro-resistant coated pellets after exposure to various aqueous media prior to ingestion. Methods: To evaluate administration instructions, 103 Summaries of Product Characteristics of gastro-resistant products were reviewed. Pellets were produced using a bottom-spray fluidized bed process and coated with Eudragit L 30 D-55. Dissolution testing in pH 1.2 medium was performed after pre-soaking the pellets for 5, 15, and 30 min in beverages with various pH and conductivity. Drug release was measured by UV-VIS method, and morphological changes were assessed by image analysis. Marketed gastro-resistant products were also examined visually. Results: SmPC review revealed that the beverage for intake was frequently unspecified. Among the tested beverages differences in pH and conductivity were observed. Alkaline medicinal mineral waters induced increased and time-dependent premature drug release compared to tap and filtered water. Image analysis indicated a reduction in surface area after exposure to alkaline media. Conclusions: Contact with non-specified aqueous media before swallowing may weaken the protective function of gastro-resistant films. More explicit recommendations on suitable administration manipulation and media may improve therapeutic consistency. Full article

Uncertainty in travel and service times poses significant challenges for vehicle routing in logistics systems. This paper proposes a stochastic model predictive control (SMPC) strategy to manage a Vehicle Routing Problem with time windows (VRPTW) under stochastic service times with correlation across customers. The approach combines a dynamic optimization model with single and joint chance constraints and a forecasting tool for updating travel plans as new information becomes available. A deterministic reformulation of the stochastic constraints is developed so that the problem can be solved via mixed-integer programming. The aim of this paper is to demonstrate that the SMPC strategy can maintain a high level of time-window reliability (meeting customer time windows with high probability) at a reasonable cost by re-optimizing routes over a moving horizon. In numerical case studies, the SMPC approach achieves the desired reliability levels while incurring only modest increases in total cost, and it flexibly adjusts the cost–risk tradeoff by switching between single and joint chance constraints. These results illustrate the potential of the proposed method for real-time distribution routing under uncertainty and highlight the novel contribution of integrating chance-constrained optimization with Model Predictive Control in a VRPTW context. Full article

Electromechanical Actuators (EMAs) are critical components in More-Electric Aircraft (MEA) and Reusable Launch Vehicles (RLVs), yet they remain vulnerable to jamming and fatigue failures under high-stress flight maneuvers. Existing Health-Aware Flight Control approaches often treat failure prediction and control allocation as separate processes, leading to suboptimal sortie generation rates. This paper presents a reliability-adaptive control framework that unifies trajectory tracking with online health management. Empowered by a hierarchical mission-to-control architecture, the system employs stochastic Model Predictive Control (SMPC) to actively modulate control surface deflection profiles in real time. A comparative case study on a coupled EMA drivetrain demonstrates that the proposed controller extends useful life by 65% compared to fixed-gain baselines, achieves 23% higher mission performance than reactive PID controllers, and it maintains zero constraint violations throughout the mission by optimally distributing the health budget across mission phases. Full article

This paper presents a framework that integrates blockchain-enabled Federated Learning (FL) with consensus mechanisms to mitigate poisoning attacks in healthcare environments. The framework incorporates blockchain consensus mechanisms, with Proof-of-Work (PoW) used as a baseline and Proof-of-Stake (PoS) adopted as the proposed approach; both are evaluated independently within the same Secure Multiparty Computation (SMPC)-enabled federated learning architecture for privacy preservation. The proposed system is evaluated on the OCTMNIST and TissueMNIST datasets under both centralized and federated settings, including poisoning scenarios with 10% and 50% malicious clients. Results show that consensus-aware aggregation reduces the influence of unreliable client updates and improves the robustness of the global model under poisoning conditions. In addition, the framework prioritizes trustworthy client contributions during aggregation, supporting reliable model sharing in collaborative healthcare learning environments. Unlike prior blockchain-based federated learning defenses that introduce heavy cryptographic overhead, the proposed PoS-based aggregation explicitly balances robustness and computational efficiency, enabling practical deployment under high poisoning ratios. Full article

Shape memory polymer (SMP) has broad applications in various industries, including automotive, aerospace, and medical, as it can maintain a given shape and return to its original form upon exposure to external stimuli such as heat, magnetic fields, or light. However, the intrinsic limitation of epoxy results in the low thermal conductivity of SMP, which reduces the difference in temperature (ΔT) between the glass transition temperature (T_g) and the actuation temperature, thereby negatively affecting the performance of shape recovery. In this study, the thermal stability and curing characteristics of SMP fabricated by blending Bisphenol-A epoxy with two types of amine curing agents were analyzed by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) to establish optimal fabrication conditions. Subsequently, carbon-based fillers, graphite and 60 μm long carbon fibers, were added to fabricate shape memory polymer composites (SMPCs). The curing and mechanical properties of the SMPCs were subsequently evaluated, and the shape recovery characteristics were found to be optimal at a filler content of 3 wt%. The recovery time for the SMPC with graphite was 25 s, representing a 68.75% improvement in shape recovery time from the SMP. Furthermore, the addition of carbon fibers, with improved dispersion, led to the highest increases in tensile strength and impact strength of 24.71% and 59.36%, respectively. Full article

The increasing integration of photovoltaic (PV) systems into modern power grids poses significant operational challenges, including variability in solar generation, fluctuations in demand, degradation of power quality, and reduced reliability under uncertain conditions. Addressing these challenges requires advanced control strategies that can manage uncertainty while coordinating storage, inverter-level actions, and power quality functions. This paper proposes a unified stochastic Model Predictive Control (SMPC) framework for the optimal management of photovoltaic (PV) systems under uncertainty. The approach integrates chance-constrained optimization with Value-at-Risk (VaR) modeling to ensure system reliability under variable solar irradiance and demand profiles. Unlike conventional deterministic MPCs, the proposed method explicitly addresses stochastic disturbances while optimizing energy storage, generation, and power quality. The framework introduces a hierarchical control architecture, where a centralized SMPC coordinates global energy flows, and decentralized inverter agents perform local Maximum Power Point Tracking (MPPT) and harmonic compensation based on the instantaneous power theory. Simulation results demonstrate significant improvements in energy efficiency from 78% to 85%, constraint satisfaction from 85% to 96%, total harmonic distortion reduction by 25%, and resilience (energy supply loss reduced from 15% to 5% under fault conditions), compared to classical deterministic approaches. This comprehensive methodology offers a robust solution for integrating PV systems into modern grids, addressing sustainability and reliability goals under uncertainty. Full article

The increasing electrification of mobility within smart cities has accelerated the need for intelligent energy management strategies that jointly address cost, emissions, and battery health. This study develops a health-aware hybrid reinforcement–predictive energy manager (H-RPEM) designed for photovoltaic–electric vehicle (PV-EV) microgrids. The proposed controller unifies model-based predictive optimisation with adaptive reinforcement learning to achieve both short-term operational efficiency and long-term asset preservation. A comprehensive dataset of solar generation, EV charging behaviour, and stochastic load profiles was employed to train and validate the hybrid control framework under realistic operating conditions. Quantitative results indicate that the proposed H-RPEM controller achieves an 18.7% reduction in total operating cost and a 22.5% decrease in carbon emissions, whilst maintaining the battery state-of-health above 0.95 throughout a 24 h operational cycle. When benchmarked against standard predictive control, the hybrid strategy converges 30–40 episodes faster and delivers a 25% improvement in reward stability, demonstrating enhanced robustness and learning efficiency. The results confirm that H-RPEM achieves robust and balanced performance across economic, environmental, and technical domains, establishing it as a scalable and health-conscious control solution for next-generation smart city microgrids. Full article

The increasing volume of digital medical data offers substantial research opportunities, though its complete utilization is hindered by ongoing privacy and security obstacles. This proof-of-concept study explores and confirms the viability of using Secure Multi-Party Computation (SMPC) to ensure protection and integrity of sensitive patient data, allowing the construction of clinical trial cohorts. Our findings reveal that SMPC facilitates collaborative data analysis on distributed, private datasets with negligible computational costs and optimized data partition sizes. The established architecture incorporates patient information via a blockchain-based decentralized healthcare platform and employs the MPyC library in Python for secure computations on Fast Healthcare Interoperability Resources (FHIR)-format data. The outcomes affirm SMPC’s capacity to maintain patient privacy during cohort formation, with minimal overhead. It illustrates the potential of SMPC-based methodologies to expand access to medical research data. A key contribution of this work is eliminating the need for complex cryptographic key management while maintaining patient privacy, illustrating the potential of SMPC-based methodologies to expand access to medical research data by reducing implementation barriers. Full article

Shape memory polymer composites (SMPCs) are promising materials in aerospace thanks to their light weight and ability to provide an actuation load during shape recovery, the magnitude of which depends on the laminates design. In this work, SMPCs were manufactured by alternating carbon fiber prepregs with a SM interlayer of epoxy resin. The number of composite plies ranged from 2 to 8 and two interlayer thicknesses were selected (100 μm and 200 μm in the lamination stage). Compression molding was performed for consolidation, and the interlayer’s thickness was reduced by edge bleeding. A thermo-mechanical cycle was applied for memorization. The shape fixity and the shape recovery of the vast majority of the SMPCs were above 90%, with the 200 μm/six-ply laminate recording the highest combination of values (94.8% and 95.7%, respectively). A significant effect due to the presence of a thicker interlayer was not evident, underlying the need to determine specific manufacturing procedures. Starting from these results, a lab-scale procedure was implemented to manufacture a smart device by embedding a microheater in the 200 μm/two-ply architecture. The device was memorized into a L-shape (90° bending angle), and a voltage of 24 V allowed it to recover 86.2° in 90 s, with a maximum angular velocity of 1.55 deg/s. Full article

A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach. Full article