Search Results (18,155)

Background/Objective: Smoking is known to impair fracture healing and worsen surgical outcomes, but its effect on psychological recovery in spine trauma patients remains unclear. The purpose of this study is to assess how smoking affects pain and anxiety in patients with spine fractures treated either conservatively or surgically. Methods: We conducted a retrospective analysis looking at spine fracture patients > 18 years old seen at a single institution between 11/2015 and 9/2019. Patient variables such as age, sex, race, ethnicity, mechanism of injury, fracture location, presence of spinal cord injury, surgical intervention, hospital and ICU LOS, disposition, and EQ-5D-3L at 3 and 12 months were collected and analyzed. Results: Non-operative management was selected for 403 patients, of which 304 never smoked and 99 were smokers. Surgical management was utilized for 126 patients, of which 90 never smoked and 36 were smokers. Studying non-smokers and current smokers, higher levels of extreme pain and anxiety at 3 and 12 months were reported in smokers managed conservatively. Smokers managed surgically reported higher levels of pain and anxiety than non-smokers at 3 months but not at 12 months. No significant differences were seen with regards to changes in pain or anxiety between the 3- and 12-month follow-up. Conclusions: Smoking is independently associated with higher levels of pain and anxiety in conservatively managed spine fracture patients. These findings suggest a need for early intervention and cessation efforts in the trauma setting. Further investigation is warranted to clarify whether underlying psychological or physiological phenomena are impacting patient outcomes. Full article

Endoreversible engine cycles are a cornerstone of finite-time thermodynamics. We show that endoreversible Stirling engines operating with a one-component plasma as a working medium run at maximal power output with the Curzon–Ahlborn efficiency. As a main result, we elucidate that this is actually a consequence of the fact that the caloric equation of state depends only linearly on temperature and only additively on volume. In particular, neither the exact form of the mechanical equation of state nor the full fundamental relation are required. Thus, our findings immediately generalize to a larger class of working plasmas, far beyond simple ideal gases. In addition, we show that for plasmas described by the photonic equation of state, the efficiency is significantly lower. This is in stark contrast to endoreversible Otto cycles, for which photonic engines have an efficiency larger than the Curzon–Ahlborn efficiency. Full article

The digital business environment is rapidly evolving with advancements in information technology (IT), increasing the risk of information security incidents. Grounded in the resource-based view and in contingency theory, this study adopts a different approach from prior research by conceptualizing security management activities not as mere risk control mechanisms, but as strategic innovation drivers that can enhance corporate performance (sales revenue and operating profit). The authors develop a research model with six independent variables, including internal and external security management activities, CISO role configuration (independent or dual-role with CIO), and investment levels in IT and information security. The dependent variables include sales revenue and operating profit, with ISMS or ISO certification as a moderating variable. Using information security (IS) disclosures and financial data from 545 Korean firms that have reported their security management activities to the Ministry of Science and ICT, multiple regression and moderation analyses reveal that high IT investment negatively impacts performance, but this effect is mitigated when formal security systems, like ISMS or ISO, are in place. The results suggest that integrating recognized security frameworks into management strategies can enhance both innovation and financial outcomes, encouraging a proactive approach to security management. Full article

Accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is fundamental to ensuring safe operation. However, due to the complex electrochemical processes during battery operation and the limited availability of training data, accurate estimation of the state of health remains challenging. To address this, this paper proposes a prediction framework based on dual-view voltage signal features and an improved Long Short-Term Memory (LSTM) neural network. By relying solely on readily obtainable voltage signals, the data requirement is greatly reduced; dual-view features, comprising kinetic and aggregated aspects, are extracted based on the underlying reaction mechanisms. To fully leverage the extracted feature information, Scaled Dot-Product Attention (SDPA) is employed to dynamically score all hidden states of the long short-term memory network, adaptively capturing key temporal information. The experimental results based on the NASA PCoE battery dataset indicate that, under various operating conditions, the proposed method achieves an average absolute error below 0.51% and a root mean square error not exceeding 0.58% in state-of-health estimation, demonstrating high predictive accuracy. Full article

In the task of PolSAR image classification, effectively utilizing convolutional neural networks and vision transformer models with limited labeled data poses a critical challenge. This article proposes a novel method for PolSAR image classification that combines channel-wise convolutional local perception, detachable self-attention, and a residual feedforward network. Specifically, the proposed method comprises several key modules. In the channel-wise convolutional local perception module, channel-wise convolution operations enable accurate extraction of local features from different channels of PolSAR images. The local residual connections further enhance these extracted features, providing more discriminative information for subsequent processing. Additionally, the detachable self-attention mechanism plays a pivotal role: it facilitates effective interaction between local and global information, enabling the model to comprehensively perceive features across different scales, thereby improving classification accuracy and robustness. Subsequently, replacing the conventional feedforward network with a residual feedforward network that incorporates residual structures aids the model in better representing local features, further enhances the capability of cross-layer gradient propagation, and effectively alleviates the problem of vanishing gradients during the training of deep networks. In the final classification stage, two fully connected layers with dropout prevent overfitting, while softmax generates predictions. The proposed method was validated on the AIRSAR Flevoland, RADARSAT-2 San Francisco, and RADARSAT-2 Xi’an datasets. The experimental results demonstrate that the proposed method can attain a high level of classification performance even with a limited amount of labeled data, and the model is relatively stable. Furthermore, the proposed method has lower computational costs than comparative methods. Full article

This paper addresses the challenge of fostering cooperation among virtual power plant (VPP) operators in competitive electricity markets, focusing on the application of evolutionary game theory (EGT) and static reward–punishment mechanisms. This investigation resolves four critical questions: the minimum reward–punishment thresholds triggering stable cooperation, the influence of initial market composition on equilibrium selection, the sufficiency of static versus dynamic mechanisms, and the quantitative mapping between regulatory parameters and market outcomes. The study establishes the mathematical conditions under which static reward–punishment mechanisms transform competitive VPP markets into stable cooperative systems, quantifying efficiency improvements of 15–23% and renewable integration gains of 18–31%. Through rigorous evolutionary game-theoretic analysis, we identify critical parameter thresholds that guarantee cooperation emergence, resolving longstanding market coordination failures documented across multiple jurisdictions. Numerical simulations and sensitivity analysis demonstrate that static reward–punishment systems enhance cooperation, optimize resources, and increase renewable energy utilization. Key findings include: (1) Reward–punishment mechanisms effectively promote cooperation and system performance; (2) A critical region exists where cooperation dominates, enhancing market outcomes; and (3) Parameter adjustments significantly impact VPP performance and market behavior. The theoretical contributions of this research address documented market failures observed across operational VPP implementations. Our findings provide quantitative foundations for regulatory frameworks currently under development in seven national energy markets, including the European Union’s proposed Digital Single Market for Energy and Japan’s emerging VPP aggregation standards. The model’s predictions align with successful cooperation rates achieved by established VPP operators, suggesting practical applicability for scaled implementations. Overall, through evolutionary game-theoretic analysis of 156 VPP implementations, we establish precise conditions under which static mechanisms achieve 85%+ cooperation rates. Based on this, future work could explore dynamic adjustments, uncertainty modeling, and technologies like blockchain to further improve VPP resilience. Full article

The advancement of next-generation lubricants is pivotal for enhancing energy efficiency and mitigating environmental impacts across diverse industrial applications. This review systematically examines recent developments in lubricant technologies, with a particular focus on sustainable strategies incorporating bio-based feedstocks, nanostructured additives, and hybrid formulations. These innovations are designed to reduce friction and wear, decrease energy consumption, and prolong the operational lifespan of mechanical systems. A critical assessment of tribological behavior, environmental compatibility, and functional performance is presented. Furthermore, the integration of artificial intelligence (AI) into lubricant formulation and performance prediction is explored, highlighting its potential to accelerate development cycles and enable application-specific optimization through data-driven approaches. The findings emphasize the strategic role of eco-innovative lubricants in supporting low-carbon technologies and facilitating the transition toward sustainable energy systems. Full article

This study employs k-means clustering to analyze local organizations responsible for land improvement in Greece, identifying four distinct groups with consistent geographic patterns but divergent financial and operational characteristics. By integrating unsupervised machine learning with spatial analysis, the research offers a novel perspective on irrigation water pricing and cost recovery. The findings reveal that organizations located on islands, despite high water costs due to limited rainfall and geographic isolation, tend to achieve relatively strong financial performance, indicating the presence of adaptive mechanisms that could inform broader policy strategies. In contrast, organizations managing extensive irrigable land or large volumes of water frequently show poor cost recovery, challenging assumptions about economies of scale and revealing inefficiencies in pricing or governance structures. The spatial coherence of the clusters underscores the importance of geography in shaping institutional outcomes, reaffirming that environmental and locational factors can offer greater explanatory power than algorithmic models alone. This highlights the need for water management policies that move beyond uniform national strategies and instead reflect regional climatic, infrastructural, and economic variability. The study suggests several policy directions, including targeted infrastructure investment, locally calibrated water pricing models, and performance benchmarking based on successful organizational practices. Although grounded in the Greek context, the methodology and insights are transferable to other European and Mediterranean regions facing similar water governance challenges. Recognizing the limitations of the current analysis—including gaps in data consistency and the exclusion of socio-environmental indicators—the study advocates for future research incorporating broader variables and international comparative approaches. Ultimately, it supports a hybrid policy framework that combines data-driven analysis with spatial intelligence to promote sustainability, equity, and financial viability in agricultural water management. Full article

Private array equality comparison (PAEC) aims to evaluate whether two arrays are equal while maintaining the confidentiality of their elements. Current private comparison protocols predominantly focus on determining the relationships of secret integers, lacking exploration of array comparisons. To address this issue, we propose a swap test-based quantum protocol for PAEC, which satisfies both functionality and security requirements using the principles of quantum mechanics. This protocol introduces a semi-honest third party (TP) that acts as a medium for generating Bell states as quantum resources and distributes the first and second qubits of these Bell states to the respective participants. They encode their array elements into the received qubits by performing rotation operations. These encoded qubits are sent to TP to derive the comparison results. To verify the feasibility of the proposed protocol, we construct a quantum circuit and conduct simulations on the IBM quantum platform. Security analysis further indicates that our protocol is resistant to various quantum attacks from outsider eavesdroppers and attempts by curious participants. Full article

High-temperature heat pump systems are essential for industrial processes that usually require high-temperature and high-pressure steam. An efficient design of these systems is critical for minimizing fossil fuel consumption, thereby contributing to a significant reduction in carbon emissions. One of the key components of these systems is the horizontal falling film evaporator, which is commonly employed due to its high thermal efficiency and low refrigerant charge. This study presents a preliminary design of a falling film evaporator to meet the target of the heat duty value of 2.2 MW. The phase-change dynamics inherent to the falling film evaporation process were critically analyzed using ANSYS Fluent (2024 R2). The low-global warming potential refrigerant R1336mzz(Z) was incorporated as a refrigerant on the shell side, while hot water was used in the tubes. The study identified key regions of film flow to maximize vapor production and design optimizations. The discussed performance parameters and operational mechanisms of the evaporator are prevailing features, particularly with the adoption of environmental regulations. Overall, the simulation results offer valuable insights into heat transfer mechanisms and evaporator effectiveness for advancing heat pump technologies in industrial applications. Full article

Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for early warning. This study proposes a real-time monitoring technique for gas content in drilling fluid based on the attenuation principle of Ba-133 γ-rays. By integrating laboratory static/dynamic experiments and Geant4-11.2 Monte Carlo simulations, the influence mechanism of gas–liquid two-phase media on γ-ray transmission characteristics is systematically elucidated. Firstly, through a comparative analysis of radioactive source parameters such as Am-241 and Cs-137, Ba-133 (main peak at 356 keV, half-life of 10.6 years) is identified as the optimal downhole nuclear measurement source based on a comparative analysis of penetration capability, detection efficiency, and regulatory compliance. Compared to alternative sources, Ba-133 provides an optimal energy range for detecting drilling fluid density variations, while also meeting exemption activity limits (1 × 10⁶ Bq) for field deployment. Subsequently, an experimental setup with drilling fluids of varying densities (1.2–1.8 g/cm³) is constructed to quantify the inverse square attenuation relationship between source-to-detector distance and counting rate, and to acquire counting data over the full gas content range (0–100%). The Monte Carlo simulation results exhibit a mean relative error of 5.01% compared to the experimental data, validating the physical correctness of the model. On this basis, a nonlinear inversion model coupling a first-order density term with a cubic gas content term is proposed, achieving a mean absolute percentage error of 2.3% across the full range and R² = 0.999. Geant4-based simulation validation demonstrates that this technique can achieve a measurement accuracy of ±2.5% for gas content within the range of 0–100% (at a 95% confidence interval). The anticipated field accuracy of ±5% is estimated by accounting for additional uncertainties due to temperature effects, vibration, and mud composition variations under downhole conditions, significantly outperforming current surface monitoring methods. This enables the high-frequency, high-precision early detection of kick events during the shut-in period. The present study provides both theoretical and technical support for the engineering application of nuclear measurement techniques in well control safety. Full article

The ergodicity and ageing phenomena in a vibrational motor system driven by a periodic external force are investigated. Within the tailored parameter regime, the amplitude and frequency demonstrate contrasting effects on ergodicity. An increase of amplitude induces a transition from non-ergodic to ergodic behavior, whereas a higher driving frequency leads to a transition from ergodic to non-ergodic dynamics. These transitions are attributed to the enhanced ability of larger amplitudes to overcome potential energy barriers and the improved responsiveness of the system to external variations at lower frequencies. Moreover, pronounced ageing effects are observed at low amplitudes or high frequencies. These findings offer new insights into the intrinsic dynamical mechanisms of vibrational motor systems and provide a theoretical foundation for predicting their long-term operational performance. Full article

To enhance the fire safety protection level of deeply buried metro stations, this study conducted full-scale fire experiments based on Wulichong Station of Guiyang Metro Line 3. It systematically investigated the laws of smoke movement and temperature distribution under the coupled effects of different fire source powers and smoke extraction system states. Through the set up of multiple sets of comparative test conditions, the study focused on analyzing the influence mechanism of the operation (on/off) of the smoke extraction system on smoke spread characteristics and temperature field distribution. The results indicate that under the condition where the smoke extraction system is turned off, the smoke exhibits typical stratified spread characteristics driven by thermal buoyancy, with the temperature rising significantly as the vertical height increases. When the smoke extraction system is activated, the horizontal airflow generated by mechanical smoke extraction significantly alters the flame morphology (with an inclination angle exceeding 45°), effectively extracting and discharging the hot smoke and leading to a more uniform temperature distribution within the space. Full article

In wet flue gas desulfurization and denitrification processes, nitrite accumulation inhibits denitrification efficiency and induces secondary pollution due to its acidic disproportionation. This study developed a Mn-modified ZSM-5 zeolite catalyst, achieving efficient resource conversion of nitrite in nitrogen-containing wastewater through an O₃ + Mn/ZSM-5 catalytic system. Mn/ZSM-5 catalysts with varying SiO₂/Al₂O₃ ratios (prepared by wet impregnation) were characterized by BET, XRD, and XPS. Experimental results demonstrated that Mn/ZSM-5 (SiO₂/Al₂O₃ = 400) exhibited a larger specific surface area, enhanced adsorption capacity, abundant surface Mn³⁺/Mn⁴⁺ species, hydroxyl oxygen species, and chemisorbed oxygen, leading to superior oxidation capability and catalytic activity. Under the optimized conditions of reaction temperature = 40 °C, initial pH = 4, Mn/ZSM-5 dosage = 1 g/L, and O₃ concentration = 100 ppm, the $\mathrm{N}{\mathrm{O}}_2^{-}$ oxidation efficiency reached 94.33%. Repeated tests confirmed that the Mn/ZSM-5 catalyst exhibited excellent stability and wide operational adaptability. The synergistic effect between Mn species and the zeolite support significantly improved ozone utilization efficiency. The O₃ + Mn/ZSM-5 system required less ozone while maintaining high oxidation efficiency, demonstrating better cost-effectiveness. Mechanism studies revealed that the conversion pathway of $\mathrm{N}{\mathrm{O}}_2^{-}$ followed a dual-path catalytic mechanism combining direct ozonation and free radical chain reactions. Practical spray tests confirmed that coupling the Mn/ZSM-5 system with ozone oxidation flue gas denitrification achieved over 95% removal of liquid-phase $\mathrm{N}{\mathrm{O}}_2^{-}$ byproducts without compromising the synergistic removal efficiency of NO_x/SO₂. This study provided an efficient catalytic solution for industrial wastewater treatment and the resource utilization of flue gas denitrification byproducts. Full article

This study presents the design, fabrication, and performance assessment of a novel, small-scale (30–70 W), hybrid ocean energy system that captures energy from wave-induced heave motion using a point-absorber buoy and from ocean currents via a vertical axis water turbine (VAWT). Key innovations include a custom designed and built dual-rotor generator that accepts independent mechanical input from both subsystems without requiring complex mechanical coupling and a bi-directional mechanical motion rectifier with an overdrive. Numerical simulations using ANSYS AQWA (2024R2) and QBLADE(2.0.4) guided the design optimization of the buoy and turbine, respectively. Wave resource assessment for the Khobar coastline, Saudi Arabia, was conducted using both historical data and field measurements. The prototype was designed and built using readily available 3D-printed components, ensuring cost-effective construction. This mechanically simple system was tested in both laboratory and outdoor conditions. Results showed reliable operation and stable power generation under simultaneous wave and current input. The performance is comparable to that of existing hybrid ocean wave–current energy converters that employ more complex flywheel or dual degree-of-freedom systems. This work provides a validated pathway for low-cost, compact, and modular hybrid ocean energy systems suited for remote coastal applications or distributed marine sensing platforms. Full article