Prolonged Operation Research Articles

Control Strategy of Flywheel Energy Storage System for Improved Model Reference Adaptive System Based on Tent-Sparrow Search Algorithm

This study addresses speed sensor aging and electrical parameter variations caused by prolonged operation and environmental factors in flywheel energy storage systems (FESSs). A model reference adaptive system (MRAS) flywheel speed observer with parameter identification capabilities is proposed to replace traditional speed sensors. The proposed method uses reference and adjustable models to identify the stator resistance and permanent magnet flux (PM Flux) to mitigate the adverse effects of electrical parameter changes on control performance. The Tent chaotic mapping-improved Sparrow Search Algorithm (SSA) optimizes the Proportional-Integral (PI) controller parameters for the dual closed-loop and MRAS speed adaptation laws of the flywheel motor. Moreover, a self-switching parameter identification (SSPI) scheme, which constructs a cost function based on the current, parameter identification, and speed errors, is proposed to prevent inaccuracies in parameter identification. The MRAS observer selects the appropriate PI adaptive mechanism based on the error values, thereby enhancing identification accuracy. Simulink simulations show significant improvements in the rapidity and accuracy of the Tent-SSA optimized MRAS flywheel speed observer, enhancing the stability and robustness of the flywheel rotor. Experimental validation on a constructed FESS platform confirms the feasibility of this method.

Heat Transfer Performance and Operation Scheme of the Deeply Buried Pipe Energy Pile Group

This paper describes a study on the heat transfer properties of the deeply buried pipeline energy pile group, which is an efficient and convenient geothermal development technology. Through in situ experiments and a simulation algorithm, the research investigated the heat transmission characteristics of the deeply buried pipe energy pile group and optimized different intermittent operation schemes. The findings suggest that prolonged operation of the pile cluster intensifies heat buildup within the pile foundation, thereby adversely affecting the system’s overall heat exchange efficiency. Employing an intermittent operating mode can alleviate this heat accumulation phenomenon, thereby promoting sustained heat exchange performance of the piles over time. To evaluate the comprehensive thermal interaction and energy efficiency ratio of the energy pile heat exchange system, various intermittent operation strategies were compared in the study. Among them, the intermittent operational scheme with a ratio of n = 5 was found to be optimal, with the total average heat transfer rate of the pile set only 0.51% lower than that of the continuous operational mode, but the overall energy efficiency ratio improved by 19.6%. The intermittent operational mode proposed in this study can achieve the goal of saving energy and efficiently extracting geothermal resources, providing theoretical guidance for the extraction and utilization of subsurface geothermal power by energy piles.

Concomitant Procedures, Black Race, Male Sex, and General Anesthesia Show Fair Predictive Value for Prolonged Rotator Cuff Repair Operative Time: Analysis of the National Quality Improvement Program Database Using Machine Learning

PurposeThe purpose of this study is to develop machine learning models using the American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database to predict prolonged operative time (POT) for rotator cuff repair (RCR). Furthermore, this study aims to use the trained machine learning (ML) models, cross-referenced with traditional multivariate logistic regression (MLR), to determine the key perioperative variables that may predict POT for RCR. MethodsData were obtained from a large, national database (NSQIP) from 2021. Patients with unilateral RCR procedures were included. Demographic, preoperative, and operative variables were analyzed. A multivariable logistic regression (MLR) model and various other machine learning techniques, including random forest (RF) and artificial neural network (ANN), were compared using area under the curve (AUC), calibration, Brier score, and decision curve analysis. Feature importance was identified from the overall best-performing model. ResultsA total of 6,690 patients met inclusion criteria. The random forest (RF) ML model had the highest AUC upon internal validation (0.706) and the lowest Brier score (0.15), outperforming the other models. The RF model also demonstrated strong performance upon assessment of the calibration curves (Slope = 0.86, Intercept = 0.08) and decision curve analysis. The model identified concomitant procedure, specifically labral repair and biceps tenodesis, as the most important variable for determining POT, followed by age <30 years, Black or African American race, male sex, and general anesthesia. ConclusionsDespite the advanced machine learning models used in this study, the NSQIP dataset was only able to fairly predict POT following RCR. The RF model identified concomitant procedures, specifically labral repair and biceps tenodesis, as the most important variables for determining POT. Additionally, demographic factors such as age <30 years, Black race, and general anesthesia were significant predictors. While male sex was identified as important in the RF model, the MLR model indicated that its predictive value is primarily in conjunction with specific procedures like biceps tenodesis and subacromial decompression.

Predictors of Nonhome Discharge After Cervical Disc Replacement.

Retrospective review of a national database. The aim of this study was to identify the factors that increase the risk of nonhome discharge after CDR. As spine surgeons continue to balance increasing surgical volume, identifying variables associated with patient discharge destination can help expedite postoperative placement and reduce unnecessary length of stay. However, no prior study has identified the variables predictive of nonhome patient discharge after cervical disc replacement (CDR). The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent primary 1-level or 2-level CDR between 2011 and 2020. Multivariable Poisson regression with robust error variance was employed to identify the predictors for nonhome discharge destination following surgery. A total of 7276 patients were included in this study, of which 94 (1.3%) patients were discharged to a nonhome destination. Multivariable regression revealed older age (OR: 1.076, P <0.001), Hispanic ethnicity (OR: 4.222, P =0.001), BMI (OR: 1.062, P =0.001), ASA class ≥3 (OR: 2.562, P =0.002), length of hospital stay (OR: 1.289, P <0.001), and prolonged operation time (OR: 1.007, P <0.001) as predictors of nonhome discharge after CDR. Outpatient surgery setting was found to be protective against nonhome discharge after CDR (OR: 0.243, P <0.001). Age, Hispanic ethnicity, BMI, ASA class, prolonged hospital stay, and prolonged operation time are independent predictors of nonhome discharge after CDR. Outpatient surgery setting is protective against nonhome discharge. These findings can be utilized to preoperatively risk stratify expected discharge destination, anticipate patient discharge needs postoperatively, and expedite discharge in these patients to reduce health care costs associated with prolonged length of hospital stay. IV.

Predictive factors for critical care dependency after posterior spinal fusion for adolescent idiopathic scoliosis

AimsHistorically, patients undergoing surgery for adolescent idiopathic scoliosis (AIS) have been nursed postoperatively in a critical care (CC) setting because of the challenges posed by prone positioning, extensive exposures, prolonged operating times, significant blood loss, major intraoperative fluid shifts, cardiopulmonary complications, and difficulty in postoperative pain management. The primary aim of this paper was to determine whether a scoring system, which uses Cobb angle, forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and number of levels to be fused, is a valid method of predicting the need for postoperative critical care in AIS patients who are to undergo scoliosis correction with posterior spinal fusion (PSF).MethodsWe retrospectively reviewed all AIS patients who had undergone PSF between January 2018 and January 2020 in a specialist tertiary spinal referral centre. All patients were assessed preoperatively in an anaesthetic clinic. Postoperative care was defined as ward-based (WB) or critical care (CC), based on the preoperative FEV1, FVC, major curve Cobb angle, and the planned number of instrumented levels.ResultsOverall, 105 patients were enrolled. Their mean age was 15.5 years (11 to 25) with a mean weight of 55 kg (35 to 103). The mean Cobb angle was 68° (38° to 122°). Of these, 38 patients were preoperatively scored to receive postoperative CC. However, only 19% of the cohort (20/105) actually needed CC-level support. Based on these figures, and an average paediatric intensive care unit stay of one day before stepdown to ward-based care, the potential cost-saving on the first postoperative night for this cohort was over £20,000. There was no statistically significant difference between the Total Pathway Score (TPS), the numerical representation of the four factors being assessed, and the actual level of care received (p = 0.052) or the American Society of Anesthesiologists grade (p = 0.187). Binary logistic regression analysis of the TPS variables showed that the preoperative Cobb angle was the only variable which significantly predicted the need for critical care.ConclusionMost patients undergoing posterior fusion surgery for AIS do not need critical care. Of the readily available preoperative measures, the Cobb angle is the only predictor of the need for higher levels of care, and has a threshold value of 74.5°.Cite this article: Bone Joint J 2024;106-B(7):713–719.

Novel resistance control scheme for mitigating current sharing mismatches in parallel dual active bridge converters for DC fast charging stations

Abstract Dual Active Bridge (DAB) converters have gained popularity in electric vehicle charging stations due to their high efficiency and electrical isolation. As the demand for high-powered devices and large-capacity energy storage systems grows, charging systems that integrate multiple interconnected DAB modules are emerging as a promising solution. However, prolonged operation of these modules at high power levels can cause parameter deviations from the initial DAB circuit, resulting in power variations between modules. To overcome parameter deviations, this study presents an enhanced power control approach based on output resistance adjustment, intending to achieve consistent output capacity for multiple DAB modules. In the proposed enhanced power control method, the output resistance of the DAB module is considered to be controllable, and the current-sharing mismatches among DAB modules are fed back to tune the converter output resistance for mitigating current mismatches between modules. Thanks to the proposed control method, each DAB module can operate autonomously and balance the charging current between modules. When one DAB module is suddenly cut out of the system, the other DAB modules still maintain their stability with fully guaranteed load capacity. To demonstrate the feasibility of the enhanced control approach, the small signal model of the DAB system with three modules is derived together with its frequency-amplitude diagram. Then, the effect of virtual resistance on current balancing is comprehensively tested, and the proper control signal with virtual resistance is added to the DAB voltage control loop. The simulation results have demonstrated the reliability of the proposed control method with the ability to balance the charging current between modules and stabilize the system when a single DAB module fails.

Venous resection increases risk of chyle leak after total pancreatectomy for pancreatic tumors

BackgroundExisting research on chyle leak (CL) after pancreatic surgery is mostly focused on pancreaticoduodenectomy and lacks investigation on total pancreatectomy (TP). This study aimed to explore potential risk factors of CL and develop a predictive model for patients with pancreatic tumor undergoing TP.MethodsThis retrospective study enrolled 90 consecutive patients undergoing TP from January 2015 to December 2023 at Peking Union Medical College Hospital. According to the inclusion criteria, 79 patients were finally included in the following analysis. The LASSO regression and multivariate logistic regression analysis were performed to identify risk factors associated with CL and construct a predictive nomogram. Then, the ROC analysis, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were performed to assess its discrimination, accuracy, and efficacy. Due to the small sample size, we adopted the bootstrap resampling method with 500 repetitions for validation. Lastly, we plotted and analyzed the trend of postoperative drainage volume in CL patients.ResultsWe revealed that venous resection (OR = 4.352, 95%CI 1.404–14.04, P = 0.011) was an independent risk factor for CL after TP. Prolonged operation time (OR = 1.473, 95%CI 1.015–2.237, P = 0.052) was also associated with an increased incidence of CL. We included these two factors in our prediction model. The area under the curve (AUC) was 0.752 (95%CI 0.622–0.874) after bootstrap. The calibration curve, DCA and CIC showed great accuracy and clinical benefit of our nomogram. In patients with CL, the mean drainage volume was significantly higher in venous resection group and grade B CL group.ConclusionVenous resection was an independent risk factor for chyle leak after TP. Patients undergoing vascular resection during TP should be alert for the occurrence of CL after surgery. We then constructed a nomogram consisted of venous resection and operation time to predict the odds of CL in patients undergoing TP.

Multi-Objective Optimization Study on the Coupling Mechanism of Underwater Wireless Power Transfer Systems

Magnetically coupled resonant wireless power transfer (MCR-WPT) technology offers longer effective transmission distances and higher efficiency compared to traditional charging methods, making it better suited to the prolonged and efficient operation of autonomous underwater vehicles. This paper first establishes a traditional mathematical model and then refines it while analyzing the variations in the self-inductance and mutual inductance of underwater coils. To further enhance the system’s performance, a multi-objective optimization of the coupling mechanism is conducted. An orthogonal experiment is employed to determine the effects of various influencing factors on the coils’ self-inductance and mutual inductance. Subsequently, an RBF neural network is used to create a regression prediction model based on the results of the orthogonal experiment. The NSGA-II algorithm is then applied for the multi-objective optimization of the coupling mechanism, resulting in a Pareto front solution set. The optimized efficiency is 93.35%, representing an approximately 6% improvement over the original system, with the power density increasing from 1.267×106 W/m3 before optimization to 4.782×106 W/m3 after optimization. Significant enhancement in system performance is achieved.

Robotic-Assisted Nephrectomy for Living Kidney Donation-Single Center Initial Experience (Case Series) and Review of the Literature.

Background: Robotic-assisted nephrectomy for living kidney-donation (RANLD) has the potential of becoming the leading technique of harvesting kidney, if expertise is available. The aim of this work is to present our initial experience with robotic technique with additional hand-assistance. Materials and Methods: We initiated RANLD at our clinic using the DaVinci System in September 2022, since then harvesting six kidneys, four left and two right; in two cases, multiple arteries existed. The renal vessels were ligated using vascular staplers. All the operations included a hand-assist with the use of Gelport. The mean operation time was 119.2 min (SD 12 min). Results: There were no conversions or donors' post-operative complications. Time of discharge from the hospital was 4.5 days post-operatively. Total hospital length of stay was 7.8 days. All the harvested kidneys were transplanted, five of them with adequate function, three with initially delayed function, and one needed to be removed due to thrombotic complications. Post-operative was pain assessed on the VAS scale and overall pain was assessed according to the NRS scale. At the discharge day, donors' performance status was about 87.5% according to the Karnofsky scale. The donors resumed their normal life activity within 15.7 days and returned to work within 45.2 days. The serum mean creatinine level before the donation was 0.85 mg/dL (SD 0.1 mg/dL), and mean eGFR (MDRD) = 91.8 mL/min/1.73 m2 (SD 16.1 mL/min/1.73 m2). Conclusions: Further development of RANLD could lead to an increase in the number of living kidney donors, particularly in Poland where the number is currently lower than that of deceased donors. Prolonged operation time, longer warm ischemic time, and high equipment costs are significant drawbacks of RANLD.

Task division by multiple console surgeons is beneficial for safe robotic pancreaticoduodenectomy implementation and education.

The optimal approach for the safe implementation and education of robotic pancreaticoduodenectomy (RPD) remains unclear. Prolonged operation time may cause surgeon fatigue and result in perioperative complications. To solve this issue, our department adopted task division by the console surgeon turnover between resection and reconstruction in 2022. This study retrospectively investigated consecutive patients who underwent RPD from November 2009 (initial introduction of RPD) to December 2023. The analysis excluded patients who underwent concomitant resection of other organs. The cases performed by a single console surgeon (single approach) were compared with those performed by two or more console surgeons (multiple approach). This study analyzed 85 consecutive RPD cases, including 51 with the single approach and 34 with the multiple approach. The operation time was significantly shorter (832 vs. 618min, p < 0.001), and the postoperative major complication was less frequent (45% vs. 12%, p = 0.003) in the multiple approach group, although less experienced surgeons performed the multiple approach (number of RPD experiences: 19 cases vs. 5 cases, p < 0.001). The console surgeon turnover between the resection and reconstruction resulted in a safe pancreatojejunostomy performed by the less experienced surgeon (number of pancreatic reconstruction experiences: 6.5 vs. 14 cases, p = 0.010). Surgeons who started RPD with a multiple approach observed a reduction in surgical time and a lower incidence of complications earlier than those who started with a single approach. Task division during the early introduction phase of RPD using the multiple approach demonstrated potential contributions to improved surgical outcomes and enhanced educational benefits.

A Novel Approach for Mobility-Aware Energy-Efficient Clustering-Based Routing Using EANN With GA Algorithm on WSNs

Aim: This research aims to explore and evaluate various strategies for improving energy efficiency within wireless sensor networks (WSNs). Specifically, the study focuses on the critical challenge of extending network lifespan through energy conservation by establishing balanced clusters within the WSN architecture. Background: In wireless sensor networks (WSNs), ensuring prolonged network operation while conserving energy resources is a significant concern. One promising approach to address this challenge is the implementation of equalized clusters, which requires an effective selection of cluster heads (CHs). However, this task presents considerable complexity and demands innovative solutions to overcome. Objective: The primary objective of this study is to develop and assess a novel methodology for selecting precise cluster heads (CHs) within WSNs. This methodology is based on the utilization of Bluetooth low energy (BLE) sensors deployed in a randomly distributed manner across the study area. By employing an enhanced artificial neural network and greedy approach (EANN-GA), the proposed technique seeks to identify CHs with optimal proximity to the cluster center and substantial remaining energy reserves. Methods: The proposed methodology involves the deployment of BLE sensors distributed randomly throughout the study region, which are then organized into clusters. Using the enhanced artificial neural network and greedy approach (EANN-GA), the sensor node nearest to the cluster center with the highest remaining energy is selected as the cluster head (CH). Additionally, a mobile sink (MS) is introduced to harness the power of CHs, and the number of paths utilized by the MS is estimated through a genetic approach. Based on this path information, the MS enters each cluster to initiate the data-gathering process. Results: Performance analysis of the presented methodology demonstrates significant improvements in energy efficiency and the extension of network lifetime. By employing the proposed EANN-GA technique for CH selection and optimizing MS path utilization, the study showcases enhanced operational effectiveness within WSNs. Conclusion: The findings of this research underscore the effectiveness of the proposed methodology in enhancing energy efficiency and prolonging the lifespan of wireless sensor networks. Through the innovative integration of BLE sensors, EANN-GA CH selection, and genetic-based MS path estimation, the study contributes valuable insights toward addressing the critical challenges of energy conservation in WSNs.

An innovative AR-assisted self-adjusting digital twin for speed control of a servomechanism

A machine might undergo changes in its system due to prolonged operation. These changes can significantly impact the performance and behavior of the machine’s controller. The general digital twin solutions typically use compensators to adjust the physical entity instead of updating the internal model parameters of the digital twin. Therefore, if deviations are significant, the physical entity may never fully match its digital twin. To address potential degradation in controller performance, this study introduces an AR (augmented reality)-assisted self-adjusting digital twin (SADT) servomechanism monitoring system. The primary objective of this system is to provide real-time monitoring, maintenance, and adjustments to ensure the optimal operation of the machine’s controller, even in the presence of significant deviations caused by extended operation or environmental changes. In smart manufacturing, servomechanisms are often used in wheeled mobile robots and conveyor mechanisms. Hence, this study selects servomechanisms as the target application. The AR-assisted SADT accomplishes this by comparing the angular velocity error between the servomechanism and its digital twin to identify when significant changes have occurred. Upon detecting such a change, the SADT calculates new internal model parameters, generating a modified digital twin. This allows resynchronization with the changed physical entity, restoration of the virtual-physical mapping, and continuous, accurate monitoring and optimized control. Furthermore, to enhance human–machine interaction, AR technology is used to offer users visual information, enabling intuitive monitoring of the servomechanism’s performance. In summary, the AR-assisted SADT system makes human–machine interaction more intuitive for operators and enhances the adaptability and accuracy of system monitoring.

Urea-assisted preparation of N-CNOx catalysts for enhanced low-temperature catalytic oxidation of toluene

In this study, a variety of CoNi oxide (N-CNOx) catalysts were synthesized by treating MOF-74 with urea, demonstrating excellent performance in catalyzing the oxidation of toluene. Specifically, the produced 1.0 N-CNOx exhibited outstanding catalytic activity in a Plasma co-catalysis system (T90 = 251 ℃), which is 32 ℃ lower than that of CNOx (T90 = 283 ℃), along with strong stability and water resistance during prolonged operation and exposure to water vapor. Experimental results showed that heat treatment with urea enhanced the catalyst’s redox capacity by promoting the formation of high-valent Co and Ni species and increasing the amount of reactive oxygen species on its surface. This paper proposes a novel technique for developing efficient toluene catalytic oxidation catalysts through urea treatment, providing a theoretical foundation for the design of non-precious metal catalysts.

Electrical aging of a piezoelectric MEMS fast steering mirror and its impact on reliability

Reliability is a critical characteristic of aerospace-grade components, particularly fast steering mirror (FSM), which is one of the key devices in Free-Space laser communication terminals. The previous long-term work of FSM exposes noteworthy reliability issues about degradation of this device. This paper first demonstrates the electrical aging effect that occurs in piezoelectric-driven micro electro mechanical system (MEMS) FSM during prolonged operation. This is a significant reliability issue as it causes the decay of mechanical angle of MEMS FSM, ultimately resulting in functional failure. In order to study the electrical aging effect, firstly, a mechanical angle monitoring system is developed for the rapid and accurate identification of electrical aging failure. Secondly, we performed failure analysis by using scanning electron microscope - focused ion beam (FIB - SEM) and conducted thermal analysis experiments to confirm heat accumulation within the MEMS FSM actuator, where the electrical aging effect takes place. Thirdly, by setting up a finite element simulations (FEM) model of the step region in the actuator, the electrical aging effect resulting from heat accumulation caused by high current density under a significant electric field at the lower step is confirmed. Finally, the critical reliability issue of electrical aging, which affects the lifetime of piezoelectric-driven MEMS FSM, is improved by adjusting the layout to eliminate large electric field intensity in local region.

Effect of cooling rate on the precipitation characteristics and growth mechanism of Bi3Ni in liquid lead‑bismuth eutectic

Austenitic stainless steel is a promising candidate structural material for direct contact components with lead‑bismuth eutectic (LBE) in lead-cooled fast reactor (LFR). However, the preferential dissolution of Ni from austenitic steels at low oxygen concentrations will lead to the precipitation of Ni-containing phases in the low-temperature parts of reactors, which has a risk of pipe clogging. In this paper, three samples, comprising one extracted from a corrosion tank after prolonged operation and two prepared, respectively, by quenching in liquid nitrogen and cooling in the furnace, were obtained to investigate the effect of cooling rate on the precipitation characteristics and growth mechanism of Bi3Ni that forms by the reaction of dissolved Ni and Bi in LBE. The results showed that rod-like Bi3Ni was found in all three Ni-containing LBE samples. As the cooling rate increased, the width of Bi3Ni decreased. The Bi3Ni in all three samples displayed a pseudo-hexagonal prismatic shape and had a typical faceted growth character; the cooling rate had no effect on the morphology of them. The crystallographic analysis showed that the growth axis of Bi3Ni was in the 〈010〉 direction, and the prism planes were composed of {001} and {101}. In addition, the precipitation of Bi3Ni inevitably increased the content of Pb7Bi3, which would in turn enhance the volumetric expansion of solid LBE samples. For the growth mechanism, Bi3Ni phases were controlled by the screw dislocation mechanism, with the step growth rate of the dislocation core being greater than that of the outer edges. Given the low cooling rate and low Ni content in the LBE coolant of reactors, the precipitation of Bi3Ni with a rod-like morphology seems to be inevitable.

Nd–IrO2: High-performance electrocatalyst for enhanced overall water splitting in acidic media

Water electrolysis is one of the most promising green hydrogen production technologies. However, using precious metal materials as electrodes for these electrolyzers incurs high costs. To address these challenges and simultaneously retain the catalytic activity of precious metals, we synthesize a low-iridium catalyst, Nd–IrO2. At 10 and -10 mA/cm2, this catalyst exhibits 233 and 36 mV overpotentials. Nd–IrO2 maintains stability over prolonged operation in acidic environments, as evidenced by its consistent overpotential. Advanced calculations using density functional theory (DFT) have demonstrated that incorporating Nd and an ample amount of oxygen vacancies can modulate the d-band center of Ir and reduce the adsorption energy of electrochemical reaction intermediates. In summary, we present the synthesis of a low-iridium catalyst, which provides valuable insights for the design and development of catalysts with improved electrocatalytic performance.

Cost-effective strategy for high-temperature energy storage performance of polyimide nanocomposite films

The performance of most polymer-based film capacitors deteriorates severely at high temperatures, while high Tg polymer capacitors, despite their good performance at high temperatures, but their performance still decays severely after prolonged operation at high temperature. This study involves the deposition of a wide bandgap SiO2 inorganic layer on both surfaces of polyimide (PI) films using electron beam thermal evaporation. The findings indicate a substantial enhancement in the breakdown field strength and energy storage density of the composite films at elevated temperature following the deposition of the SiO2 inorganic layer. Specifically, a 100-nm-thick inorganic layer resulted in a breakdown field strength of 459.65 MV m−1 and an energy storage density of 3.2 J cm−3 at 150 °C. Subsequently, the polyimide film coated with a 100 nm SiO2 inorganic layer was infused with small quantities of SrTiO3 nanoparticles, leading to a breakdown field strength of 504.08 MV m−1 and an energy storage density of 6.75 J cm−3 and maintained an efficiency of 90.9 %. These results surpass the performance of the majority of high-temperature polymer films reported to date, with the inorganic layer deposited via electron beam thermal evaporation matching that of the costly magnetron sputtering method. The study presents a cost-effective method suitable for large-scale industrial production, significantly enhancing the electrical performance of PI at elevated temperatures and offering an economical solution for the commercialization of poly composite-based high-temperature capacitors.

Influence of Intraoperative Fluid Management on Postoperative Outcome and Mortality of Cytoreductive Surgery for Advanced Ovarian Cancer-A Retrospective Observational Study.

Background: The surgical treatment of advanced ovarian cancer is associated with extensive tissue trauma, prolonged operating times and a considerable volume shift. It, therefore, represents a challenge for anaesthesiological management. Aim: The aim of this single-centre, retrospective, observational study was to investigate whether intraoperative extensive volume supply influences postoperative outcomes and long-term survival. Methods: The study included 73 patients with a mean (SD) age of 63 (13) years who underwent extensive tumour-reducing surgery for ovarian cancer between 2012 and 2015. The effect of the intraoperative fluid balance on postoperative complications, such as anastomotic insufficiency or pleural effusions, was investigated using logistic regression. Further, the influence of fluid balance, lactate and creatinine levels on 5-year survival was analysed in a Cox regression model. Associations between anaesthesia time and the intraoperative fluid balance were examined using Spearman's rank correlation coefficients. Results: The mean (SD) postoperative fluid balance in the considered patient cohort was 9.1 (3.4) litres (l) at a mean (SD) anaesthesia time of 529 (106) minutes. Cox regression did not reveal a statistically significant effect of the fluid balance, but it did reveal a statistically significant association between the lactate level 24 h following surgery and the 5-year survival (HR [95%-CI] fluid balance: 0.97 [0.85, 1.11]; HR [95%-CI] lactate: 1.79 [1.24, 2.58]). According to logistic regression, the intraoperative fluid balance was associated with an increased chance of postoperative complications in the considered patient cohort (OR [95%-CI] 1.28 [1.1, 1.54]). Conclusions: We could not detect a negative impact of an increased fluid balance on 5-year survival, but a negative impact on postoperative complications was found in our patient cohort.

Enhancing Dust Control for Cage-Free Hens with Electrostatic Particle Charging Systems at Varying Installation Heights and Operation Durations

The poultry industry is shifting towards more sustainable and ethical practices, including adopting cage-free (CF) housing to enhance hen behavior and welfare. However, ensuring optimal indoor air quality, particularly concerning particulate matter (PM), remains challenging in CF environments. This study explores the effectiveness of electrostatic particle ionization (EPI) technology in mitigating PM in CF hen houses while considering the height at which the technology is placed and the duration of the electric supply. The primary objectives are to analyze the impact of EPI in reducing PM and investigate its power consumption correlation with electric supply duration. The study was conducted in a laying hen facility with four identical rooms housing 720 laying hens. The study utilized a Latin Square Design method in two experiments to assess the impact of EPI height and electric supply durations on PM levels and electricity consumption. Experiment 1 tested four EPI heights: H1 (1.5 m or 5 ft), H2 (1.8 m or 6 ft), H3 (2.1 m or 7 ft), and H4 (2.4 m or 8 ft). Experiment 2 examined four electric supply durations: D1 (control), D2 (8 h), D3 (16 h), and D4 (24 h), through 32 feet corona pipes. Particulate matter levels were measured at three different locations within the rooms for a month, and statistical analysis was conducted using ANOVA with a significance level of ≤0.05. The study found no significant differences in PM concentrations among different EPI heights (p &gt; 0.05). However, the duration of EPI system operation had significant effects on PM1, PM2.5, and PM4 concentrations (p &lt; 0.05). Longer EPI durations resulted in more substantial reductions: D2—17.8% for PM1, 11.0% for PM2.5, 23.1% for PM4, 23.7% for PM10, and 22.7% for TSP; D3—37.6% for PM1, 30.4% for PM2.5, 39.7% for PM4, 40.2% for PM10, and 41.1% for TSP; D4—36.6% for PM1, 24.9% for PM2.5, 38.6% for PM4, 36.3% for PM10, and 37.9% for TSP compared to the D1. These findings highlight the importance of prolonged EPI system operation for enhancing PM reduction in CF hen houses. However, utilizing 16 h EPI systems during daylight may offer a more energy-efficient approach while maintaining effective PM reduction. Further research is needed to optimize PM reduction strategies, considering factors like animal activities, to improve air quality and environmental protection in CF hen houses.

A novel method for settlement imputation and monitoring of earth-rockfill dams subjected to large-scale missing data

Various missing values inevitably exist in the settlement monitoring data of prolonged operation of earth-rockfill dams, which delays or even interrupts the dam structure analytical procedure. This study aims to develop a reliable method for addressing the missing data problem and monitoring earth-rockfill dam settlement behavior. First, an imputation model, support vector regression based on the finite element method and time input (FEMTSVR), is proposed and offers superior performance when capturing complex nonlinear mappings from environmental variables to settlement on small sample data. Herein, an improved particle swarm optimization algorithm (IPSO) is developed, realizing the nonlinear adjustment of weights and parameter reduction during hyperparameter optimization. Then, this study establishes a sequential prediction model based on gate recurrent unit (GRU) networks to monitor dam behavior on the imputed and complete dataset. Eventually, the proposed method is evaluated using real-world earth-rockfill dam monitoring data with the help of statistical indicators, demonstrating its efficiency in monitoring settlement data subjected to large-scale missingness. This study provides a robust database for dam structural health monitoring, while also providing a promising framework for the safety assessment of other civil or hydraulic engineering based on raw monitoring data.