Operation Interval Research Articles

Optimizing solar still performance: A response surface methodology approach to evaporation and condensation enhancement

Water crisis is a major problem across the world. It is significant to migrate for effective methodologies to meet future water demand. Solar-based desalination system is environmental friendly and suitable method for potable water yield production from saline/brackish water and also cost effective than other methods. The role of optimization study in maximizing the water yield is paramount for the performance enhancement of solar-based desalination system and its effect was insufficiently addressed. In present study, a new approach of waste scraper and double fan arrangement is introduced inside the conventional solar still (SS). Then, using response surface methodology, Box–Behnken design (BBD)-based optimization is performed to determine optimal solution for the influence of input parameters (operating Time interval, Time duration and voltage of scraper and dual fan) on the output parameter relative humidity (RH) to increase the condensation and evaporation rates of a SS, which helps to enhance the water yield productivity. Experiment is conducted at 9.672° (North) and 77.994° (East) Madurai, Tamil Nadu, India. Validation of experiment results is done using the BBD method. Result reveals that the analysis of variance model with double-fan arrangement inside the still shows better with optimal performance with a coefficient of determination R2 = 97.78%, adequate precision = 46.22, RH = 76.515% than that with scraper arrangement ( R2 = 96.7%, adequate precision = 35.76, RH = 76.327%). The optimized model exhibit promising results with statistically significant P-value less than .0001 for the both methods and its F-value is noticed that 126.96 (for a SS with scraper) and 190.66 (for a SS with double fan).The outcome of this study helps to enhance the condensation rate to produce the more amount of fresh water from hybrid photovoltaic with thermal system.

Bumpless transfer control of switched systems: A dynamic event-triggering switching control approach

This paper studies the bumpless transfer (BT) control issue for switched systems with event-triggered (ET) control input. The aim is to reduce the jumps in control input at both switching and triggering points, while capturing the stability of the switched systems. First, a dynamic ET program is constructed with the triggering condition based on the level of suppression of input signal jumps. This not only reduces input jumps at triggering points, but also prevents Zeno behavior caused by triggering within the operation time interval of subsystems. Second, a sampled state-dependent switching signal is designed to ensure a dwell time for subsystems to prevent Zeno phenomenon induced by switching. Further, a switched ET controller with alleviated jumps is designed. Third, conditions are obtained to simultaneously reach both stabilization and BT of switched systems. Finally, the effectiveness of the theoretical results is demonstrated through a simulation of an engine model.

Low-power adaptive sampling electronic nose system with a Radon transform-based convolutional neural network for optimized gas recognition

The electronic nose (E-nose) systems provide great promise in capturing information about the identity of gaseous chemical analytes as well as their temporal variation. However, the regular and dense sampling events of the E-nose often lead to a substantial amount of redundancy in the temporal structure, which in turn limits their application in efficiency-sensitive scenarios such as advanced robotics and the Internet of Things. Herein, we introduce a novel operating mechanism for E-noses, which dynamically adjusts the time interval of operation to reduce power consumption and information content through an adaptive sampling mechanism. Three strategic schemes with different operating principles are presented to implement this mechanism: the accurate scheme targets the optimized discrimination accuracy, the vague scheme is oriented toward power-sensitive scenarios, and the balanced scheme aims to balance cost and benefit. In order to ensure that the adaptively sampled inhomogeneous data can be compatible with each other, we further propose an algorithm based on the Radon transform that can convert non-uniform time series with timestamp information into the same size tensor. Finally, we designed a convolutional neural network-based classification model for gaseous chemical analytes, providing guidance on scheme and parameter selection with an accuracy of up to 99 % and power savings of up to 96 %. Overall, this work provides a novel solution to optimize temporal redundancy in E-nose systems and a generalized approach to the corresponding deep-learning data processing.

An Adaptive Learning Control for MIMO Nonlinear System with Nonuniform Trial Lengths and Invertible Control Gain Matrix

In the traditional iterative learning control (ILC) method, the operational time interval is conventionally fixed to facilitate a seamless learning process along the iteration axis. However, this condition may frequently be contravened in real-time applications owing to unknown uncertainties and unpredictable factors. In essence, replicating a control system at a consistent time interval proves challenging in practical scenarios. This paper proposes an adaptive iterative learning control (AILC) method for the multi-input–multi-output (MIMO) nonlinear system with nonuniform trial lengths and an invertible control gain matrix. Compared to the existing AILC research that features nonuniform trial lengths, the control gain matrix of the system in this paper is assumed to be invertible. Hence, the general requirement in the conventional AILC method that the control gain matrix of the system is positive-definite (or negative-definite) or even known is relaxed. Moreover, the tracking reference allows it to be iteration-varying. Finally, to prove the convergence of the system, the composite energy function is introduced and to verify the validity of the AILC method, a robot movement imitation with an uncalibrated camera system is used. The simulation results show that the actual output can track the desired reference trajectory well, and the tracking error converges to zero after 30 iterations.

Numerical simulation of non-stationary regime of a submerged combustion setup operation

Relevance. The need to evaporate large quantities of brines at potash industry enterprises. Evaporation of brines in surface evaporators is difficult due to the encrustation of heat exchange surfaces by salt deposits. Therefore, such evaporation is most expedient to be carried out in submerged combustion apparatuses, since they do not contain heat-transmitting surfaces. However, in this type of apparatus, malfunctions may occur due to uncontrolled solid phase deposition. At the moment, the dynamics of the solid phase in submerged combustion devices is poorly studied. This study is part of a scientific program aimed at a comprehensive review of the laws of motion of solid particles in submerged combustion apparatuses. Aim. To study the hydrodynamic processes in the submerged combustion setup in the time interval corresponding to the beginning of its operation; describe the patterns of solid phase motion as a function of time. Object. Laboratory setup of submerged combustion. A simplified model of the thermal mode of operation without the subsequent transition of the liquid phase to steam is analyzed. Methods. The study was conducted by numerical experiment. The hybrid finite volume method was used in simulation in combination with the technology of the finite element method. The multiphase system was considered as two coexisting subsystems: gas–liquid and liquid–solid. Results. The paper considers the final time interval of the setup operation. It is found that during the time under consideration, a stationary mode of solid particle deposition is achieved. The authors have detected liquid flow velocity oscillations, leading to fluctuations in the mass flow rate of solid particles at the bottom of the setup. It was found that the velocity at the tip of the flue gas jet escaping from the burner nozzle, as well as the pressure at the nozzle section, have a similar form of oscillation. The authors substantiated the hypothesis about the determining influence of the instability of the jet movement of flue gases on the oscillatory behavior of the entire hydrodynamic system.

Modelling of JP-8 distributed combustion using a HyChem mechanism under gas turbine conditions

The critical advantages of liquid fuels in aviation over alternative energy carriers imply their dominance in the upcoming decades. The Mixture Temperature Controlled (MTC) combustion concept allows spatially homogeneous, efficient burning (distributed combustion) with low pollutant emission. MTC combustion of jet fuel JP-8 was investigated in an atmospheric burner, and the measured pollutant concentrations in the flue gas were reproduced using the Hybrid Chemistry (HyChem) approach employing Perfectly Stirred Reactor simulations. Using this robust approach comes with losing spatiotemporal characteristics if the mixture homogeneity assumption is globally valid. The effect of residence time and pressure under typical gas turbine operating conditions was investigated. Stable combustion was present above 3 ms residence time at pressures up the 60 bar, which was the upper limit of the investigated regime, while the lower residence time limit of stable combustion was 0.3 ms. The residence time interval of stable operation could be extended by applying flue gas recirculation. NO emission can be reduced by increasing the operating pressure, while the N2O production is dominant only up to 20 bar and in the 10–100 ms residence time range. CO emission vanishes above 10 ms residence time. Ultra-low emission operation requires >20 bar pressure and 10 ms residence time with distributed combustion, requiring larger combustion chambers for future jet engines.

The impact of resident training on robotic operative times: is there a July Effect?

It is unknown whether the July Effect (a theory that medical errors and organizational inefficiencies increase during the influx of new surgical residents) exists in urologic robotic-assisted surgery. The aim of this study was to investigate the impact of urology resident training on robotic operative times at the beginning of the academic year. A retrospective chart review was conducted for urologic robotic surgeries performed at a single institution between 2008 and 2019. Univariate and multivariate mix model analyses were performed to determine the association between operative time and patient age, estimated blood loss, case complexity, robotic surgical system (Si or Xi), and time of the academic year. Differences in surgery time and non-surgery time were assessed with/without resident presence. Operative time intervals were included in the analysis. Resident presence correlated with increased surgery time (38.6 min (p < 0.001)) and decreased non-surgery time (4.6 min (p < 0.001)). Surgery time involving residents decreased by 8.7 min after 4 months into the academic year (July–October), and by an additional 5.1 min after the next 4 months (p = 0.027, < 0.001). When compared across case types stratified by complexity, surgery time for cases with residents significantly varied. Cases without residents did not demonstrate such variability. Resident presence was associated with prolonged surgery time, with the largest effect occurring in the first 4 months and shortening later in the year. However, resident presence was associated with significantly reduced non-surgery time. These results help to understand how new trainees impact operating room times.

Effects of time related operating conditions on the effective drying time and drying rate during interval IR drying of squash (Cucurbita moschata)

Infrared drying is increasingly used in the food industry. Infrared energy can improve drying operations and reduce energy consumption compared to convective drying. Often energy is lost during drying in the form of sensible energy, which increases the temperature of the sample and thus causes irreversible damage. Interval drying was applied to infrared by well-defined intervals. The new drying process IIRAD (Interval Infrared Airflow Drying) was implemented using infrared energy coupled with ambient temperature (18 ± 1 ◦C) airflow (1.41 m s − 1) drying and 0.7 W cm− 2 as IR power. In order to estimate the operating time intervals, the calculation of the energy necessary to evaporate a thin layer of water was estimated according to the initial moisture content. The intervals were then defined with 5s of action time (tON) and a tempering time (tOFF) of 2 min. A second set of time related operating conditions was achieved by modulating tON during the drying process: constant tOFF = 2 min while tON varied from (i) 5 s during the first 240 min (till water content reaches W = 4 g H2O/g db), (ii) tON = 4 s during the next 273 min (till W = 2.6 g H2O/ g db), and finally (iii) tON = 2 s till the end of the process. The three modes, continuous, IIRAD type I and II were compared through the drying kinetics, the effective drying time and the energy consumption. The moisture content rapidly decreased during IIRAD type I and II compared to continuous IR drying. The effective drying time was significantly reduced, which allowed a significant energy gain. The moisture migration from the deep layers to the surface of the samples mainly occurred during the tempering time, which accelerated the water evaporation during the following active times. Calculation and experimental tests showed that the energy required decreased throughout the drying process with water content decrease. Indeed, reducing tON from 5 to 2 s had a positive effect on drying since a higher evaporation rate was noted. Interval Infrared drying appears to be a promising method to intensify drying and save energy. The intervals must be defined according to the sample nature, the operating conditions and the water content of the sample which varies throughout the process.

Transient recovery from heat pipe dryout by power throttling

Heat pipes and vapor chambers are passive heat spreaders driven by capillary pumping of an internal working fluid via a porous wick. The capillary limit is the maximum steady-state heat input at which the fluid pressure drop can be supported by the capillary pressure head generated in the wick. However, heat pipes and vapor chambers often find application in devices where the heat input is highly transient and can exceed the capillary limit for brief time intervals. Operating heat pipes briefly above the capillary limit will not result in a dryout if the operating time interval does not exceed a characteristic time-to-dryout. Operation over a duration that exceeds this time-to-dryout can induce transient dryout and may lead to thermal hysteresis, that is, the original heat pipe thermal resistance may not be recovered even after the heat input is lowered back below the capillary limit. To fully recover the heat pipe performance after a transient dryout event, our recent experiments have shown that the heat input must be lowered (or throttled) significantly below the capillary limit. Due to the highly transient nature of power dissipation from electronic devices, it becomes imperative to characterize the throttling power level and duration required to ensure full recovery of a heat pipe from dryout under transient operations. This work experimentally characterizes recovery of heat pipes from dryout by power throttling under transient conditions, where ‘power throttling’ is the act of reducing the operating power level significantly below the capillary limit to eliminate post-dryout thermal hysteresis. We deduce from the experiments that the power must be throttled for longer than a minimum throttling time interval, defined as the time-to-rewet, in order to eliminate dryout-induced thermal hysteresis. The dependence of this time-to-rewet on the throttling power level is explored, and guidelines are presented on the need for throttling and the choice of throttling power under transient conditions.

Revealing patterns in the influence of variable permeability of well bottomhole zones on the operational modes of underground gas storage facilities

The object of this study is underground gas storage facilities (UGSF). The main problem being solved is to ensure effective management of the operation process of underground gas storage facilities (UGSF) at operational and forecast time intervals. One of the main factors that affect the operating modes of UGSF is significantly non-stationary filtration processes that take place in the bottomhole zones of wells. The complexity of assessing the multifactorial impact on depression/repression around the wells affects both the speed and accuracy of calculating the mode parameters of UGSF operation. Analysis of the results of well studies revealed a significant area of uncertainty in the calculation of the filtration resistance coefficients of their bottomhole zones. A satisfactory accuracy of the result in the expected time was achieved by building a model of integrated consideration of the influence of the parameters of all the bottomhole zones of the wells on the mode of UGSF operation. It turned out that the integrated consideration of the impact on the parameters of the bottomhole zones of the wells neutralized the effect of significant changes in the filtration resistance coefficients of the wells and ensured a sufficient speed of calculation of UGSF operation modes. Simultaneous simulation of ten operating UGSFs under the peak mode of withdrawal the entire available volume of active gas takes no more than six minutes. The speed of simulation of filtration processes in the bottomhole zones of wells ensured finding the best of them according to one or another criterion of operation mode quality. As a result of the research, a model was built and implemented by software, which was tested under real operating conditions and provides optimal planning of UGSF operating modes for given time intervals. Its use is an effective tool for the operational calculation of current modes and technical capacity of UGSF for a given pressure distribution in the system of main gas pipelines. The performance of the constructed mathematical methods has been confirmed by the results of numerical experiments

Time characteristics of microsatellite attitude control at intervals between reac-tion wheels desaturations

The article is devoted to the processes study of changing the microsatellite reaction wheels angular velocities during its stabilization, turn and detumbling. Satellite control is considered under conditions of constant and harmonic external torques, as well as under gravitational, aerodynamic and magnetic disturbing torques. Expressions that describe the change in the reaction wheels angular velocities during satellite stabilization under the action of constant and harmonic torques at a frequency equal to the orbital angular velocity are given. The influence of the magnetic disturbing torque caused by the constant component of the satellite's dipole moment on the accumulation of the reaction wheels angular momentum has been studied. The formula for determining the magnitude of the reaction wheels angular velocities oscillations was obtained using a model of the Earth's magnetic field in the form of the straight dipole and taking into account a circular orbit. Moreover, its use for calculating the maximum values of angular velocities gives results that are very close to the results of modeling taking into account accurate models of the Earth’s magnetic field and the satellite’s center of mass trajectory. The maximum possible operating time intervals of the control system without forced desaturation of its controls are estimated.

МЕТОДИКА ПРОГНОЗИРОВАНИЯ РЕСУРСА УЗЛОВ И ДЕТАЛЕЙ ЖЕЛЕЗНОДОРОЖНОГО ПОДВИЖНОГО СОСТАВА ПРИ ПОМОЩИ МАШИННОГО ОБУЧЕНИЯ

The study objective is to develop a system for forecasting the service life of components and parts of railway rolling stock.&#x0D; The task to which the paper is devoted is to determine the service life of wheel pairs of railway rolling stock before their next turning.&#x0D; Research methods include a technique for forecasting the resource of components and parts of railway wheel pairs using a system for estimating expected parameters, including wheel run. The study includes the use of three different machine learning algorithms: linear regression, random forest and gradient boosting. The trained models of each algorithm are shown, as well as the convergence of MSE, MAPE an d R-squared metrics at each iteration of learning. &#x0D; The novelty of the work: the study results make it possible to predict the period of wheel pairs operation with high accuracy and include a feedback mechanism for automating and updating the model, which increases the accuracy of forecasting.&#x0D; Research results: on the basis of the proposed method, a system is developed that allows determining the time interval of operation of rolling stock wheel pairs before their next turning.&#x0D; Conclusions: the proposed method will make it possible to predict the period of operation of certain components and parts under the conditions of a given proving ground until their working condition is restored, based on a system for evaluating expected parameters using machine learning.

Artificial Neural Network-Based Fault Diagnosis of Gearbox using Empirical Mode Decomposition from Vibration Response

This paper presents a gearbox defect diagnosis based on vibration behaviour. In order to record the vibration response under various circumstances, an industrial gearbox was used as the basis for an experimental setup. The signals resulting from gear wear were processed using an empirical mode decomposition for two operating time intervals (zero-hour running time and thirty-hour running time). The first three intrinsic mode functions and the corresponding frequency response were detected. The ten statistical parameters most sensitive to gear wear were selected using an evaluation method based on Euclidean distance. Using the identified features, an artificial neural network (ANN) was trained to track the gearbox for the selected future data set. The neural network received its input from the statistical parameters, and its output was the number of gearbox running hours. To achieve faster convergence, the radial basis function and the backpropagation neural network were compared. The superiority of the proposed strategy is demonstrated by comparing the performance of ANN. For monitoring the condition of industrial gears, the proposed strategy is found to be effective and trustworthy.

Effect of Solar Powered Cooling System to Counteract Heat Stress in Stall Fed HF Deoni Crossbred Cows

Background: During summer the environmental conditions exceeds beyond threshold limit, that enhances heat gain beyond that lost from the body and induces heat stress in cows. The higher temperature and heat stress increases respiration rate, rectal temperature and temperature humidity index, which affects health of the cows. The objective of the study was to create favourable environmental conditions to reduce heat stress and to improve the performance of the cows. Methods: The solar powered cooling system was developed and installed at Main Agriculture Research Station, University of Agricultural Sciences, Raichur District of Karnataka. The functional components of the developed cooling system are foggers, timer-switch, solar panels, battery, water tank, charge controller and hosepipe. The solar panels act as an energy source for the operation pump and the 4-way fogger assembly provides the fogging of water and creates a healthy environment by reducing the air temperature. The system assessment was carried out with 3 levels of variables of foggers (6, 12 and 18 foggers), height of fogger (2.25, 2.50 and 2.75 m) and operational time interval (2, 4 and 6 min). These were statistically analysed along with air temperature (AT), relative humidity (RH), and respiration rate (RR) were also measured. Result: The AT, RH and RR was significantly influenced by the treatment combinations of selected variables and it was found that reduction of 9.2 oC in AT and 49 % (29 breaths min-1) in RR and 47 % increase in RH (66 %) were recorded at 18 foggers at 2.75 m height with 4 min operational time interval. Thus, the developed cooling system was significant (P less than 0.001) in creating optimum environmental conditions for the wellbeing of milch animals.

Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC.

Optimal pump-valve coupling operation strategy of complex long-distance water-conveyance systems based on MOC

Transient simulation is crucial for protecting complex pump-valve water conveyance pipeline systems from extreme pressure and water level oscillations. In order to numerically simulate the transient response in a complex pump-valve water conveyance system, a transient flow calculation model was established adopting the method of characteristics (MOC). Based on the proposed model, the transient pressure and the water level fluctuation process were numerically predicted under steady-state operation state, and then the results were compared to the actual measured data. Due to the good agreement between the simulation results and the actual measured data, the model established based on MOC is effective and practical. 41 operational scenario switching schemes were designed according to the uniform experimental design, to study the operation sequence and time differences of pumps and valves during operational scenario switching state. The results show that the operational time interval has a major impact on the transient process under the operational scenario switching state in complex pump-valve systems, and also indicate that delayed pump startup can alleviate the pressure extremum of pipeline systems and effectively control water level fluctuation of such systems. In addition, this study points out the optimal operational sequence and the maximum and minimum allowable time intervals between the pump and the valve under operational scenario switching state in such systems. Finally, an optimized joint operation scheme for pumps and valves with simple operation and small water-level oscillation were proposed under operational scenario switching state.

Inhalational versus Intravenous General Anesthesia for mechanical thrombectomy for stroke: A single centre retrospective study

BackgroundWhen general anesthesia is used for endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), the choice of anesthetic agents for maintenance remains inconclusive. The different effects of intravenous anesthetic and volatiles agents on cerebral hemodynamics are known and may explain differences in outcomes of patients with cerebral pathologies exposed to the different anesthetic modalities. In this single institutional retrospective study, we assessed the impact of total intravenous (TIVA) and inhalational anesthesia on outcomes after EVT. MethodsWe conducted a retrospective analysis of all patients ≥ 18 years who underwent EVT for AIS of the anterior or posterior circulation under general anesthesia. Baseline patient characteristics, anesthetic agents, intra operative hemodynamics, stroke characteristics, time intervals and clinical outcome data were collected and analyzed. ResultsThe study cohort consisted of 191 patients. After excluding 76 patients who were lost to follow up at 90 days, 51 patients received inhalational anesthesia and 64 patients who received TIVA were analyzed. The clinical characteristics between the groups were comparable. Multivariate logistic regression analysis of outcome measures for TIVA versus inhalational anesthesia showed significantly increased odds of good functional outcome (mRS 0–2) at 90 days (adjusted odds ratio, 3.24; 95% CI, 1.25–8.36; p = 0.015) and a non-significant trend towards decreased mortality (adjusted odds ratio, 0.73; CI, 0.15–3.6; p = 0.70). ConclusionPatients who had TIVA for mechanical thrombectomy had significantly increased odds of good functional outcome at 90 days and a non-significant trend towards decrease in mortality. These findings warrant further investigation with large randomized, prospective trials.

Iteration dependent interval based open‐closed‐loop iterative learning control for time varying systems with vector relative degree

AbstractFor linear time varying (LTV) multiple input multiple output (MIMO) systems with vector relative degree, an open‐closed‐loop iterative learning control (ILC) strategy is developed in this article, where the time interval of operation is iteration dependent. To compensate the missing tracking signal caused by iteration dependent interval, the feedback control is introduced in ILC design. As the tracking signal of many continuous iterations is lost in a certain interval, the feedback control part can employ the tracking signal of current iteration for compensation. Under the assumption that the initial state vibrates around the desired initial state uniformly in mathematical expectation sense, the expectation of ILC tracking error can converge to zero as the number of iteration tends to infinity. Under the circumstance that the initial state varies around the desired initial state with a bound, as the number of iteration tends to infinity, the expectation of ILC tracking error can be driven to a bounded range, whose upper bound is proportional to the fluctuation. It is revealed that the convergence condition is dependent on the feedforward control gains, while the feedback control can accelerate convergence speed by selecting appropriate feedback control gains. As a special case, the controlled system with integrated high relative degree is also addressed by proposing a simplified iteration dependent interval based open‐closed‐loop ILC method. Finally, the effectiveness of the developed iteration dependent interval based open‐closed‐loop ILC is illustrated by a simulation example with two cases on initial state.

Functional outcomes after transanal total mesorectal excision (TaTME): a random forest analysis to predict patients' outcomes.

Anorectal, sexual, and urinary dysfunction are common issues after rectal cancer surgery, although seldom explored. The primary aim of this study was to investigate postoperative anorectal functional results. Patients with mid/low-rectal cancer treated with transanal TME (TaTME) with primary anastomosis with/without diverting stoma between 2015 and 2020 were reviewed and selected if they had a minimum follow-up of 6months (from the primary procedure or stoma reversal). Patients were interviewed using validated questionnaires and the primary outcome was bowel function based on Low Anterior Resection Syndrome (LARS) scores. Statistical analyses were performed to identify clinical/operative variables correlated with worse outcomes. A random forest (RF) algorithm was computed to classify patients at a greater risk of minor/major LARS. Ninety-seven patients were selected out of 154 TaTME performed. Overall, 88.7% of the patients had a protective stoma and 25.8% reported major LARS at mean follow-up of 19.0months. Statistical analyses documented that age, operative time, and interval to stoma reversal correlated with LARS outcomes. The RF analysis disclosed worse LARS symptoms in patients with longer operative time (> 295min) and stoma reversal interval (> 5.6months). If the interval ranged between 3 and 5.6months, older patients (> 65years) reported worse outcomes. Finally, no statistical difference was documented when comparing the rate of minor/major LARS in the first 27 cases versus others. One-quarter of the patients developed major LARS after TaTME. An algorithm based on clinical/operative variables, such as age, operative time, and time to stoma reversal, was developed to identify categories at risk for LARS symptoms.

Operative Time Is an Independent Predictor of Postoperative Outcomes in Bilateral DIEP Flap Breast Reconstruction: A Multivariate Analysis of 1000 Flaps

Introduction: Skin-to-skin operative time (OT) as a risk factor for adverse postoperative outcomes in microvascular breast reconstruction has not been thoroughly investigated. This study evaluates OT’s impact on length of stay (LOS), overall morbidity, individual complications, and unplanned reoperation (UR) in Deep Inferior Epigastric Artery Perforator (DIEP) flaps, with a primary objective of identifying a clinically relevant time of decreased odds. Materials and Methods: Patients who underwent bilateral DIEP flaps from 2010-2021 by two senior surgeons with standardized surgical and postoperative protocols were retrospectively reviewed. 1000 flaps (500 patients) were analyzed with extensive multivariate regression equations to adjust for potential confounders, including intraoperative complexity. The odds of postoperative complication, extended LOS (eLOS, defined as ≥5 days) were compared across OT per hour and OT intervals. Results: After risk-adjustment, each hour of OT increased morbidity by 19%, UR by 8.7%, and LOS by 6.5 hours (all p<0.001). For eLOS, procedures ≤5 hours had 9.5 times lower odds than ≥5 hours (p=0.050), 5-7 hours had comparable odds (p=0.540), and 7-9 hours had 5.5 times lower odds than procedures ≥ 9 hours (p<0.001). Lastly, a multivariate linear regression showed that LOS can be calculated from OT: LOS (days) =1.527 + 0.272 x OT (hours) (R2=0.308; p<0.001). Conclusion: Operative time (per hour) independently predicts morbidity, UR and LOS in DIEP flaps. Furthermore, 5 hours and 9 hours are critical cutoffs for eLOS. These findings emphasize the benefits of decreasing OT through efficiency models, such as process analysis, team-based intraoperative protocols, and co-surgery model.