Search Results (49,364)

This research focuses on the trajectory tracking control of a Twin Rotor MIMO System (TRMS) with time-varying sinusoidal inputs. Initial design considerations include a backstepping controller integrated with a high-gain observer (HGO) to estimate unmeasured states. While the outcomes of the simulation show good accuracy of tracking, real-time implementation shows instability and performance degradation. This divergence is attributed to the static high gains of the observer that amplify measurement noise and inject inaccurate state estimates into the controller during actual deployment. To overcome this drawback without altering the core control structure, we propose a strategy of online gain tuning based on Interval Type-2 Takagi–Sugeno (TS) fuzzy logic. The proposed mechanism dynamically adjusts the observer gain based on estimation errors to balance the trade-off between convergence speed and noise sensitivity. Experimental evaluations on the physical TRMS confirm that the fuzzy-tuned observer eliminates instability in real-time. Quantitative analysis demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) by 65.6% in the Pitch axis and 92.3% in the Yaw axis compared to the fixed-gain counterpart. Full article

Structure-preserving trajectory tracking control for a six-degree-of-freedom robotic manipulator is developed within the port-Hamiltonian framework. Error Hamiltonian is constructed by incorporating configuration and momentum tracking errors into the system energy. Based on this formulation, a momentum-based tracking controller with feedforward compensation and damping injection is derived without coordinate transformations or matching conditions. A disturbance estimator is further introduced to compensate unknown external torques. Energy-based analysis proves nominal closed-loop stability and uniform ultimate boundedness in the presence of estimation errors. Simulation results on a full rigid-body manipulator demonstrate accurate trajectory tracking under coupled and high-speed joint motions. Full article

The increasing volume and complexity of digital evidence pose significant challenges to its lawful collection and admissibility, particularly in on-site investigative contexts. Selective seizure has emerged as a critical approach for minimizing unnecessary data acquisition while ensuring procedural legality, privacy protection, and investigative efficiency. However, despite its growing importance, systematic evaluation criteria for selective seizure capabilities in digital forensic tools remain underdeveloped. This study proposes a structured evaluation framework for assessing selective seizure functions in Windows-based forensic tools, with a focus on live-response environments. Essential selective seizure functions were identified and organized into three investigative phases—search, selection, and seizure—reflecting practical field procedures. Based on this framework, a dedicated evaluation dataset was constructed, and six representative portable forensic tools were empirically evaluated under a controlled Windows 10 (NTFS) environment simulating active system conditions. The experimental results demonstrate notable differences in tool capabilities across investigative phases. In the search phase, variations were observed in NTFS parsing and Windows artifact analysis, while the selection phase revealed disparities in file filtering, keyword search, encrypted file handling, and preview functions. In the seizure phase, only a subset of tools sufficiently supported evidence collection, integrity verification, and reporting requirements necessary for selective seizure. These findings highlight that no single tool uniformly satisfies all functional requirements, underscoring the need for context-dependent tool selection. The proposed framework and evaluation results provide practical guidance for digital forensic practitioners in selecting appropriate tools for selective seizure in field investigations. Moreover, this study contributes a reproducible methodological foundation for future research on selective seizure evaluation, supporting the development of more precise, proportionate, and legally robust digital evidence collection practices in Windows-based forensic investigations. Full article

Alpha-mangostin (AM), a xanthone from Garcinia mangostana, has shown promising nephroprotective properties, but its mechanisms in acute kidney injury (AKI) remain incompletely defined. In this study, we applied an integrative network pharmacology pipeline combined with molecular docking to clarify AM’s multi-target mechanisms in AKI. We identified 128 predicted AM targets and intersected them with AKI-related genes, yielding 122 shared targets. Protein–protein interaction analysis identified ten hub genes—TNF, AKT1, IL6, SRC, CTNNB1, HSP90AA1, NFKB1, HIF1A, PPARG, and PTGS2—implicating inflammatory, hypoxia, and cell-survival pathways. KEGG enrichment highlighted HIF-1 signaling, PI3K–Akt signaling, chemokine signaling, AGE–RAGE signaling, and pathways related to cellular senescence and oxidative stress, while GO terms emphasized responses to chemical/oxygen-containing compounds, kinase activity, signal transduction, and apoptosis. Molecular docking against the ten hub proteins showed favorable binding energies across multiple targets. The strongest predicted affinities were observed for PTGS2 (−11.13 kcal/mol), TNF (−9.74 kcal/mol), and AKT1 (−9.48 kcal/mol). Docking positioned AM within the COX-2 catalytic pocket, engaging key catalytic and hydrophobic residues similar to known inhibitors. MD simulation interaction analysis confirmed that AM maintained stable contacts with key human PTGS2 residues, characterized by dominant hydrogen bonds and water-bridge interactions with SER353, TYR355, ARG513, and SER530, along with consistent hydrophobic contacts, and persistent interactions sustained throughout the 200 ns trajectory. Collectively, these results suggest that AM modulates interconnected inflammatory, hypoxic, and survival pathways relevant to AKI, acting as a multi-target ligand with notable interaction involving COX-2, TNF, and AKT1. Further experimental validation and formulation strategies to improve bioavailability are recommended for the advancement of AM toward therapeutic evaluation in AKI. Full article

In Time Division Duplex (TDD) Direct-Sequence Code Division Multiple Access (DS/CDMA) architectures, Pre-Rake filtering serves as a powerful transmitter-side strategy to alleviate receiver hardware constraints by leveraging channel reciprocity. Nevertheless, rapid channel fluctuations induced by high Doppler spreads critically undermine this reciprocity assumption. This failure is primarily driven by the unavoidable latency between uplink reception and downlink transmission, leading to severe performance deterioration. To address these challenges and enhance system robustness in modern high-speed scenarios, we propose an improved hybrid transceiver architecture. This scheme integrates multiplexed Pre-Rake processing with a Matched Filter-based Rake receiver and employs a Minimum Mean Square Error (MMSE) equalizer to suppress the severe Inter-Symbol Interference (ISI) and Multi-User Interference (MUI). Furthermore, we conduct a comparative analysis of channel estimation methods tailored for a 10 Mbps high-speed transmission environment.Our investigation reveals that while complex quadratic interpolation is often prioritized in low-data-rate studies, simple averaging is sufficient and even superior in high-speed communications. This is because the shortened slot duration allows simple averaging to effectively track channel variations while avoiding the noise overfitting associated with higher-order interpolation. The simulation results demonstrate that the proposed MMSE-optimized architecture achieves superior Bit Error Rate (BER) performance, providing a practical and computationally efficient solution for next-generation mobile networks. Full article

Background/Objectives: Sodium–glucose cotransporter-2 (SGLT2) inhibitors have demonstrated cardiovascular benefits beyond glycemic control, yet the specific biological pathways potentially linking SGLT2 inhibitor exposure to cardiovascular outcomes after acute myocardial infarction (AMI) remain incompletely characterized. Two biologically plausible pathways, serum uric acid (SUA) reduction and renal functional preservation, have been proposed, but not directly compared in a unified analytical framework. This study aimed to explore whether associations between SGLT2 inhibitor exposure and recurrent post-AMI outcomes may be more strongly linked to SUA reduction and to renal functional changes, using a hypothesis-generating causal mediation analysis. Methods: This retrospective observational cohort study included 142 consecutive patients hospitalized for AMI who underwent percutaneous coronary intervention (PCI) during the index hospitalization, reflecting standard-of-care management for AMI in this tertiary center. Patients were categorized by SGLT2 inhibitor exposure (n = 57) vs. controls (n = 85). Both diabetic (47.2%) and non-diabetic (52.8%) patients were included. The primary endpoint was change in SUA (ΔUA); the secondary endpoint was myocardial infarction (MI) recurrence. Causal mediation analysis with nonparametric bootstrap simulation tested both mechanistic pathways. Results: SGLT2 inhibitor therapy was associated with significant SUA reduction (ΔUA = −0.99 mg/dL vs. +0.56 mg/dL in controls; p < 0.001), consistent across diabetic and non-diabetic subgroups and independent of AMI recurrence. Each 1 mg/dL decrease in SUA was associated with lower odds of recurrent MI in the initial model (β = −0.25; p = 0.041). However, after incorporation of renal functional change, the uric acid-mediated pathway lost significance (ACME p = 0.462), whereas the renal-mediated pathway remained significant (ACME p = 0.038). Serum creatinine change emerged as the strongest independent predictor of MI recurrence (β = 2.22; p = 0.015). Conclusions: The findings are more consistent with a renal-mediated pathway than with an independent uric acid-mediated pathway in explaining the observed associations between SGLT2 inhibitor exposure and recurrent post-AMI outcomes. These hypothesis-generating results from a retrospective design warrant prospective validation. Full article

The snow module of the HYDROTEL (version 2.8.x-078-00-4.1.15.5551) hydrological model was modified to incorporate a multilayer structure composed of ice and air layers within the snowpack, as well as to account for the impact of freezing rain on snow cover. This study examines whether this enhanced physical representation of snow processes improves the accuracy of streamflow simulations. The analysis was conducted across ten watersheds in Quebec, Canada. The multilayer snow model consistently improved low-flow simulations during both calibration and validation periods and enhanced the representation of the falling limb during the calibration period. However, the monolayer snow model performs slightly better during the rising limb of the freshet season for the calibration phase. In addition, the multilayer configuration reduced the bias of the cumulative freshet volumes and annual maximum freshet discharge. Overall, the multilayer snow model achieved comparable performance to the monolayer model for high-flow simulations while outperforming it for low-flow conditions, leading to a more accurate representation of freshet volumes and falling limb dynamics. Full article

Dynamic fee market mechanisms, exemplified by EIP-1559, have been extensively studied for Ethereum’s 12 s block environment but remain uncharacterized at sub-second timescales. We present an agent-based simulation study of an EIP-1559 adaptation for Injective, a Layer 1 blockchain combining native EVM compatibility with CometBFT consensus, operating at 600 ms block times. Across twelve simulation runs (four parameter configurations × three demand scenarios), our analysis yields three findings: (1) temporal smoothing mechanisms (MA-25, 25-block trailing average) produce mixed effects in sub-second environments with up to 47% basefee overshoot during spam attacks and slight smoothing elsewhere, making per-block mechanisms preferable for consistent performance; (2) transitioning from 150M (66.66% target) to 300M (50% target) configuration reduces peak fees by 31% during variable demand; during spam attacks, the 300M configuration peaks 32% higher but recovers faster with block capacity as the primary driver for spam throughput; and (3) per-block mechanisms establish initial spam barriers within 17–32 s versus Ethereum’s 4–6 min, economically justifying lower minimum fees. We provide the first systematic sub-second EIP-1559 analysis and a parameter optimization framework for high-throughput chains. With proper tuning, dynamic fee mechanisms are compatible with high-throughput architectures. Full article

This study presents a pilot methodological investigation of the thermal performance of a Najdi mudbrick dwelling in Ushaiger, Saudi Arabia, using short-term field monitoring and a preliminary digital-twin inspired workflow. Two field campaigns in August and September 2025 measured indoor and outdoor conditions with a portable weather station under severe site constraints, including lack of electrical infrastructure, restricted access, and the use of consumer-grade sensors. The monitored results indicate that the massive earthen walls attenuated part of the outdoor daily temperature swing, but indoor conditions remained very hot: in August, indoor temperatures averaged 38.1 °C, compared with 40.2 °C outdoors, and in September, indoor temperatures averaged 36.3 °C, compared with 36.1 °C outdoors. A simplified IDA ICE model was compared with the monitored indoor temperature over the available windows, and a post-processing affine bias adjustment was tested only as a diagnostic short-window correction rather than as a transferable calibration. Monte Carlo sensitivity analysis was used in an exploratory way. It examined how passive envelope and boundary-related parameters influenced simulated indoor relative humidity, with infiltration emerging as the dominant factor affecting relative humidity dynamics; peak indoor relative humidity increased from about 67% at 0.15 air changes per hour (ACH) to more than 74% at 0.60 ACH, whereas wall thickness had a modest buffering effect. Given the short monitoring duration and field limitations, the study is not presented as a fully validated digital twin but as a feasibility-oriented workflow that combines constrained in situ monitoring with exploratory simulation to support future, longer-term conservation and adaptive reuse research on earthen heritage in hot–arid climates. Full article

Container congestion remains a persistent operational challenge in seaports because berth, yard, and gate processes are tightly coupled, demand is volatile, and control actions often operate under delayed feedback. Reinforcement learning (RL) is increasingly proposed for adaptive terminal decision support, yet the literature still says little about the mechanism through which RL may reduce congestion in practice. This study therefore develops a simulation-based mechanism framework in which RL improves congestion outcomes primarily by increasing Operational Learning Stability (OLStab), defined here as the consistency and governability of learning-enabled operational decisions under variability and disruption. A queueing-based, gate-focused terminal simulator is used as the data-generating process, with gate congestion treated as a reduced-form proxy for broader terminal congestion pressure. The statistical layer is interpreted cautiously as an internal mechanism consistency check within synthetic data rather than as empirical causal identification. Results show that RL is strongly associated with higher OLStab and that OLStab is the dominant pathway linking RL to lower congestion pressure in the simulated environment. Logistics Efficiency (LE) is directionally consistent with congestion reduction in bivariate analysis but adds limited incremental mediation once OLStab is jointly modeled. The theorized moderation by Decision Latency Sensitivity (DLS) is not robustly recovered within the examined latency range. Overall, the study contributes a more bounded explanation of how RL may reduce congestion in a designed gate-focused terminal control environment and highlights learning stability as a practical screening criterion for future digital twin and pilot deployment studies. Full article

CAR-T cell therapy has shown substantial efficacy in haematological malignancies, but its application to solid tumours remains limited by poor effector-cell infiltration, functional exhaustion, antigenic heterogeneity, and an immunosuppressive microenvironment. In this study, we develop a new spatiotemporal mathematical model of CAR-T therapy for solid tumours that integrates these resistance mechanisms within a single reaction–diffusion framework. The model is formulated as a system of partial differential equations describing functional and exhausted CAR-T cells, antigen-positive and antigen-low tumour subpopulations, and chemokine, immunosuppressive, and hypoxic fields. Steady-state analysis and finite-difference simulations showed that therapeutic outcome is governed by the interplay between CAR-T cell infiltration, exhaustion, and antigen escape. The model reproduces partial tumour regression followed by residual tumour persistence, therapy-driven enrichment of antigen-low cells, and reduced efficacy under stronger immunosuppressive and hypoxic conditions. In the combination therapy scenario considered here, repeated simulated CAR-T cell administration together with attenuation of the suppressive microenvironment improves tumour control. The proposed model provides a mechanistic basis for analysing resistance and for future optimisation studies of CAR-T therapy in solid tumours. Full article

The increasing integration of generative artificial intelligence (GenAI) into scientific research raises the question of whether such systems can be evaluated not only through external benchmarks but also through structured analysis of their own meta-evaluative responses. This study introduces a conceptual framework for simulated self-assessment of GenAI models, formalized through a multidimensional self-assessment profile and a metacognitive self-assessment index (MSI). The proposed framework integrates quantitative criteria capturing hallucination propensity, knowledge currency, formal-structure handling, source validity, and terminological precision. To evaluate the reliability of model-generated self-assessments, psychometric instruments traditionally used in human metacognition research—MAI, SRIS, and SDQ—are adapted for large language models. Experimental results across multiple GPT models indicate that, despite the absence of genuine introspective mechanisms, GenAI systems can produce internally consistent and moderately calibrated meta-evaluative responses. These findings suggest that simulated self-assessment, when interpreted within a rigorous methodological framework and combined with external validation, can serve as a complementary quantitative tool for trust analysis and reliability assessment of generative models. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. A total of 84 percent of large businesses use cloud computing services in 2025 to enable remote working and higher flexibility of operation with reduction in the cost of operation. Cloud environments are dynamic and multitenant, often demanding high computational resources for real-time processing. However, the cloud system’s behavior is subjected to various kinds of anomalies in which patterns of data deviate from the normal traffic. The varieties of anomalies that exist are performance anomalies, security anomalies, resource anomalies, and network anomalies. These anomalies disrupt the normal operation of cloud systems by increasing the latency, reducing throughput, frequently violating service level agreements (SLAs), and experiencing the failure of virtual machines. Among all anomalies, virtual machine failures are one of the potential anomalies in which the normal operation of the virtual machine is interrupted, resulting in the degradation of services. Virtual machine failure happens because of resource exhaustion, malware access, packet loss, Distributed Denial of Service attacks, etc. Hence, there is a need to detect the chances of virtual machine failures and prevent it through proactive measures. Traditional machine learning techniques often struggle with high-dimensional data and nonlinear correlations, ending up with poor real-time adaptation. Hence, quantum machine learning is found to be a promising solution which effectively deals with combinatorially complex and high-dimensional data. In this paper, a novel quantum-enhanced support vector machine (QSVM) is designed as an optimized binary classifier which combines the principles of both quantum computing and support vector machine. It encodes the classical data into quantum states. Feature mapping is performed to transform the data into the high-dimensional form of Hilbert space. Quantum kernel evaluation is performed to evaluate similarities. Through effective optimization, optimal hyperplanes are designed to detect the anomalous behavior of virtual machines. This results in the exponential speed-up of operation and prevents the local minima through entanglement and superposition operation. The performance of the proposed QSVM is analyzed using the QuCloudSim 1.0 simulator and further validated using expected value analysis methodology. Full article

Low-level flight or maneuvering defines a flight phase that is particularly common and, in some cases, central to helicopter operations, but brings several safety concerns. At low altitude, helicopters are more susceptible to collisions with objects, while there is also limited time and space in which to perform an emergency landing. A total of 403 helicopter accidents in the low-level flight phase that occurred between 1 January 2009 and 31 December 2022 in the US were analyzed for their most common causes and differentiated based on the type of flight operation to gain insight into low-level flight accidents. It is shown that, for low-level flights, the proportion of fatal accidents in flights conducted under Federal Aviation Regulations Part 91, General Aviation, is 30%, but in flights conducted under Part 137, aerial application or agricultural flights, only 12%. Logistic regression analysis shows that while controlling for other factors, the proportion of fatal accidents was significantly higher in Part 91 operations. Flight experience measured as total flight hours was not a significant factor for estimating fatality. It is recommended that low-level helicopter training includes low-altitude autorotations in simulators to optimize the mitigating effect of this emergency procedure in this flight phase with a specific focus on Part 91 operations. Full article

The rise in aggregation platforms has reshaped the competitive ride-hailing market. Display slots (i.e., platform-determined ranking priority) have become a key tool for influencing order allocation. Their interaction with drivers’ multi-homing behavior poses new challenges for platform sustainability. This paper constructs a two-stage Stackelberg game model with one aggregator and two underlying ride-hailing platforms. Display slots enhance supply-side lock-in, while a waiting time function links passenger utility to demand allocation. Building on theoretical analysis of two-sided market competition and multi-homing effects, we propose two hypotheses: (H1) under specific conditions, competition for display slots may lead to a Prisoner’s Dilemma equilibrium, and (H2) the proportion of multi-homing drivers positively moderates this dilemma, thereby expanding its occurrence range. Numerical simulation results under baseline parameter settings reveal that display slots generate a supply-side amplification effect by locking in multi-homing drivers. In symmetric markets, a prisoner’s dilemma range exists where mutual purchase erodes collective profits; this range expands with the share of multi-homing drivers. Higher driver profit sensitivity raises the threshold required for display slots to be profitable. In asymmetric markets, dominant platforms (strong brands, low costs) gain more from display slots, potentially leading to unilateral purchasing. Social welfare effects of display slot competition depend on a critical threshold of waiting-time sensitivity: social welfare improves above the threshold and declines below it. This study clarifies the boundaries of display slots as supply-side non-price competitive tools, offering quantitative insights for aggregator platform design and regulatory policy. The findings carry managerial implications for platform strategy and policy aimed at sustainable development. Full article