Recovery Unit Research Articles

Systematic preoperative approach for bariatric surgery, perioperative results, and economic impact.

Obesity is a complex systemic condition, involving numerous anatomical and metabolic changes. Therefore, a comprehensive preoperative assessment is essential for each patient contemplating bariatric surgery. This study presents the findings of a proposed protocol designed to streamline the pre-anesthesia consultation process. Our aim was to compare the efficiency and costs of consultations guided by the protocol with those conducted without a specific strategy. The secondary outcomes assessed included postoperative (PO) length of hospital stay and surgical duration. We conducted a retrospective cross-sectional analysis involving 206 clinical cases. Statistical analyses, including the chi-squared test, Student's t-test, and Mann-Whitney U test, were utilized based on the type of variables. The results showed a significant reduction in the costs, pre-anesthesia consultation duration, time spent in the recovery unit, and the need for referrals. However, no statistically significant differences were observed in the delay before surgery and length of hospital stays, measured in days. This algorithm offers a promising approach for optimizing perioperative management in bariatric surgery, demonstrating its effectiveness in cutting costs and reducing the need for referrals.

Layer-wise-residual-driven approach for soft sensing in composite dynamic system based on slow and fast time-varying latent variables

Driven by the requirements for a comprehensive understanding of composite dynamic systems in industrial processes, this paper investigates a new soft sensor for quality prediction based on slow and fast time-varying latent variables extraction using layer-wise residuals. First, the slow feature partial least squares were expanded into long-term dependency by introducing explicit expressions of the potential state of the process into the objective function. Then, the multilayer regression model for exploring composite dynamics driven by layer-wise residuals is developed using a serial structure that can extract both slow and fast time-varying latent variables that are completely orthogonal. Finally, the exponential-weighted partial least squares are proposed for extracting fast time-varying dynamic latent variables by learning the exponential decay properties of the time-series data correlation. Case studies on the industrial debutanizer and sulfur recovery unit show that the prediction accuracy of the proposed approach outperforms traditional methods.

Performance Evaluation of CO2 + SiCl4 Binary Mixture in Recompression Brayton Cycle for Warm Climates

This work demonstrates the potential of CO2 + SiCl4 binary mixture as a working fluid for power generation cycle. Recompression Brayton cycle configuration is considered due to its proven record of high performance for medium- to high-temperature sources. The objective of this study is to assess the thermodynamic performance of a recompression Brayton cycle using a CO2 + SiCl4 binary mixture as a working fluid, particularly under warm climate conditions. The cycle is simulated using the Peng–Robinson equation of state in Aspen Hysys (v11) software, and the model is validated by comparing VLE data against experimental data from the literature. The analysis involves the assessment of cycle’s thermal efficiency and exergy efficiency under warm climatic conditions, with a minimum cycle temperature of 40 °C. The results demonstrate a notable improvement in the cycle’s thermodynamic performance with CO2 + SiCl4 binary mixture compared to pure CO2. A small concentration (5%) of SiCl4 in CO2 increases the thermal efficiency of the cycle from 41.7% to 43.4%. Moreover, irreversibility losses in the cooler and the heat recovery unit are significantly lower with the CO2 + SiCl4 binary mixture than with pure CO2. This improvement enhances the overall exergy efficiency of the cycle, increasing it from 62.1% to 70.2%. The primary reason for this enhancement is the substantial reduction in irreversibility losses in both the cooler and the HTR. This study reveals that when using a CO2 + SiCl4 mixture, the concentration must be optimized to avoid condensation in the compressor, which can cause physical damage to the compressor blades and other components, as well as increase power input. This issue arises from the higher glide temperature of the mixture at increased SiCl4 concentrations and the limited heat recovery from the cycle.

Organic Rankine Cycle System Design by Utilizing The Waste Heat of Internal Combustion Engine Based on Numerical Simulation

Abstract Most of the energy supply for today’s human activities still comes from fossil energy. Energy based on fossil fuels can cause an increase in greenhouse gas (GHG) emissions. Therefore, increasing the efficiency of energy conversion systems based on fossil fuels is necessary to mitigate pollutant emissions. The internal combustion engine (ICE) is one energy conversion machine that still consumes fossil energy to work in the transportation sector. Losses during internal combustion engine operation result in reduced engine thermal efficiency. Exhaust gas heat loss is one of the relatively significant losses. It leads to the waste of heat, which could be utilized and converted into valuable energy in the form of electrical energy that the engine can use, resulting in a considerable amount of primary fuel that could be saved. An organic Rankine cycle (ORC) system is considered in this analysis as a heat recovery unit from the Diesel ICE. The Aspen Plus simulation software was chosen to design the system consisting of an ORC unit integrated with a Diesel ICE unit. The working fluid for ORC used in this analysis is R-141B, R-123, and R-245fa. The simulation results show that the heat the ORC unit receives from the ICE via turbocharger is around 366 °C. Thus, the system has a thermal efficiency of 11.11% when using R-141B as the working fluid, 8.41% if R-123 as the working fluid, and 6.63% when using R-245fa as the working fluid. This study also obtained a condenser design as part of the ORC unit. The condenser can be considered a plate heat exchanger, with 53 plates needed to support the process.

Ultrasound-guided transversalis fascia plane block or transversus abdominis plane block for recovery after caesarean section: A randomised clinical trial.

Caesarean section is a widely performed surgical procedure that often results in moderate-to-severe postoperative pain. If left untreated, this pain can lead to short-term and long-term consequences. Transversalis fascia plane (TFP) block and transversus abdominis plane (TAP) block are among the regional anaesthesia techniques employed for managing pain after a caesarean section. We aimed to compare the impact of these two blocks on the quality of recovery in patients undergoing elective caesarean section under spinal anaesthesia. A single-centre, double-blind, randomised trial. Operating room, postanaesthesia recovery unit, and ward in a tertiary hospital. Ninety-three patients (ASA 2 to 3) were recruited. After exclusion, 79 patients were included in the final analysis: 40 in the TFP block group and 39 in the TAP block group. After surgery, participants received either TFP block (20 ml 0.25% bupivacaine for each side) or TAP block (20 ml 0.25% bupivacaine for each side). The primary outcome was the difference in obstetric quality of recovery 11-Turkish (ObsQoR-11T) scores between groups. Secondary outcomes included pain scores, opioid consumption and incidence of opioid-related complications. The mean ObsQoR-11T score was higher in the TFP block group compared with the TAP block group (97.13 ± 6.67 points vs. 87.10 ± 9.84 points, respectively; P < 0.001). The pain scores in the TFP block group were slightly lower between postoperative 4 and 24 h. The mean total morphine consumption was 15.08 ± 2.21 mg in the TFP block group and 22.21 ± 3.04 mg in the TAP block group ( P < 0.001). More patients required rescue analgesia between 4 and 8 h in the TAP block group [2.00 (5.00%) vs. 9.00 (23.08%), P = 0.02]. No significant differences were observed between groups in terms of opioid-related side effects. TFP block used for analgesic purposes yielded a better quality recovery period than TAP block and also reduced opioid consumption. Clinicaltrials.gov (NCT05999981). http://links.lww.com/EJA/B6 .

Comparative Study of LSTM and ANN Models for Power Consumption Prediction of Variable Refrigerant Flow (VRF) Systems in Buildings

The optimized control of variable refrigerant flow (VRF) system requires an accurate time series forecast model for power consumption. Currently, physics-based and black-box models widely used for forecasting power consumption may not be able to capture the dynamic and non-linear behavior of such complex systems. This study presents a long-short-term memory (LSTM), deep learning-based model to accurately predict the power consumption of a VRF system with heat recovery units. The model training used one year of VRF system field test data. The feature selection through the Pearson correlation coefficient was implemented to improve the model's accuracy and computational efficiency. The sensitivity analysis of feature selection was performed by preparing three feature sets, including different levels of relationship with the predicted target. Additionally, the hyperparameters of the models were optimized by Bayesian optimization with the Tree-structured Parzen Estimator algorithm. The deep learning model, LSTM model, was compared to the baseline machine learning model, Artificial Neural Network (ANN) and decision tree. The results show that LSTM-30feat with input time step 4 has the best testing performance of Coefficient of the Variation of the Root Mean Square Error (CvRMSE) 23.3%. The best ANN model is ANN-10feat with input time step 8, which has a CvRMSE of 27.8% in testing and 13,569 trainable parameters. However, LSTM-10feat with input time step 4 has the CvRMSE of 24.8% in testing, and the trainable parameters are 1,809. A higher number of trainable parameters in models might result in increased memory usage on the computer and be computationally expensive.

Global dynamic features and information of adjacent hidden layer enhancement based on autoencoder for industrial process soft sensor application

AbstractThere is a lack of consideration of temporal and spatial correlation in the process variables and adjacent hidden layers correlation in the soft sensor model of stacked autoencoders. To address the issue, a novel global dynamic adjacent layer information enhancement auto encoder (GD‐ALIEAE) method is proposed to improve the poor prediction performance. The gated recurrent unit (GRU) and uniform manifold approximation and projection (UMAP) are applied to the GD‐ALIEAE model for obtaining global dynamic features of the temporal and spatial information of process variables by parallel computation. An adjacent layer information correlation algorithm is proposed to avoid the loss of hidden layers information during the stacking process. The algorithm enhances the features of the low layer through nonlinear mapping, combining the low layer and its adjacent layer as input. The input then is fed to the multi‐head attention mechanism to obtain features that contain adjacent layer correlation. Finally, a prediction model is established through a fully connected layer. Through simulation experiments on two industrial cases of sulphur recovery unit and thermal power plant, and compared with models of stacked autoencoder (SAE), stacked isomorphic autoencoder (SIAE), and target‐related stacked autoencoder (TSAE), the effectiveness of the proposed method was verified.

A Comparison of the Use of Propofol versus Midazolam for Pediatric Magnetic Resonance Imaging Sedation: Retrospective Cohort Study.

The aim of the present study was to do a comparison of the recovery profiles and airway-related adverse events of pediatric magnetic resonance imaging (MRI) sedation patients who received propofol alone to those who received midazolam alone. This retrospective cohort study was approved by the Mutah University Ethical Approval Committee (No. 2378). A search of the patients' medical records was performed between September 2021 and April 2022 to identify children aged 4 months-11 years who received propofol or midazolam for MRI sedation. The patients were subdivided into two groups: Those who had propofol alone (propofol group) and those who received midazolam (midazolam group) for pediatric MRI sedation. In propofol group, a 1-2 mg/kg of propofol bolus was given to have a deep sedation (Ramsay Sedation Scale score of 5). Patients in midazolam group received 0.05 mg/kg of midazolam. During the maintenance state of sedation, the patient received 150 µg/kg/min of propofol, and the infusion rate was adjusted in 25 μg/kg/min increments up or down at the discretion of the anesthesiologists to maintain a state of deep sedation. The major targets of this study were recovery profiles (time to awake and time to discharge) and airway-related intervention ratios in pediatric MRI sedation patients. Patient demographics, MRI sedation, and recovery data, including propofol induction dose, airway intervention, and sedation-related adverse events from the pediatric sedation recovery unit were also collected. The mean (standard deviation [SD]) propofol induction dose was higher compared to midazolam group (2.4 [0.7] mg vs. 1.3 [0.5] mg; mean difference, 1.1 mg; P < 0.001). The mean (SD) infusion rate was higher in propofol group compared to midazolam group (161.3 [37.6] μg/min/kg vs. 116.2 [25.6] μg/min/kg; mean difference 45.1 μg/min/kg; P < 0.001). The mean (SD) propofol total dose was higher in propofol group compared to midazolam group (236.3 [102.4] mg vs. 180.7 [80.9] mg; mean difference, 155.4 mg; P < 0.001). The mean (SD) time to awake was longer in midazolam group compared to propofol group (21.2 [5.6] min vs. 23.0 [7.1] min; mean difference, 1.8 min; P < 0.001). The mean (SD) time to discharge was longer in midazolam group compared to propofol group (34.5 [6.9] min vs. 38.6 [9.4] min; mean difference, 4.1 min; 95% confidence interval, 3.0-5.1; P < 0.001). The administration of midazolam during pediatric MRI sedation can decrease the frequency of airway complications without prolonging the clinically significant recovery profile.

Thermo-enviro-economic analyses of a landfill biogas-fed polygeneration process combined with a liquefied natural gas cold energy utilization unit

Landfill biogas offers a promising opportunity to supply society’s energy requirements, following the waste-to-energy approach, which issuitable for sustainable production. However, structural modification for its utilization is the main gap, requiring additional research efforts. Considering this issue, the current study aims to design an eco-friendly polygeneration method for landfill biogas utilization for a cutting-edge heat recovery system that integrates a cold energy recovery and utilization unit for liquefied natural gas. The liquefied natural gas recovery section, the organic Rankine cycle with a zeotropic mixture, and the Kalina cycle are all started using the flue gas from the combustion of biogas in a cascade process. In addition, the waste heat of the Kalina cycle is recovered by a single-effect desalination cycle and liquefied natural gas recovery section. Also, a water electrolysis process is integrated into the whole system. As a result, the products include hydrogen, electricity, natural gas, hot and cold water, and freshwater. The Aspen HYSYS software is utilized to design the system and investigate its thermodynamic, environmental, and economic aspects. The results demonstrate an energy efficiency of 57.54 % and an exergy efficiency of 18.78 %. Also, the carbon dioxide footprint associated with the system equals 0.34 kg/kWh, and the specific cost of products is 73.96 $/GJ. An optimal exergy efficiency of 25.8 % and a specific cost of products of 69.7 $/GJ are obtained by means of an intelligent optimization approach that ultimately makes use of non-dominated sorting genetic algorithm-II and artificial neural networks.

Potential drivers and implications of a balanced breeding sex ratio in a small population of an imperiled species with environmental sex determination.

Small populations of imperiled species are susceptible to the negative consequences of skewed sex-ratios. In imperiled species with environmental sex determination such as sea turtles, examining sex ratios across a range of environments and population abundance levels can provide insight into factors that influence population resilience, which can then be the foci of management plans for these species. Breeding sex ratios (the ratio of actively breeding males to females during a reproductive season; BSRs) extrapolated from genetic parentage analyses are a common approach for enumerating sex ratios in sea turtles. Such analyses also allow for the characterization of multiple paternity within sea turtle clutches, which should reflect BSRs and breeding behaviors. We characterized the first BSR for a breeding assemblage of loggerhead sea turtles (Caretta caretta) belonging to the temperate, low-abundance Northern Gulf of Mexico Recovery Unit using genotypes of 16 microsatellite loci from nesting females and hatchlings. Unlike prior studies at both more-tropical and more-temperate, and higher-abundance, Recovery Units in this region, we found a balanced BSR of 1.3:1 males:female and a low incidence (~17%) of multiple paternity. This suggests that there are relatively few males breeding at this assemblage and within this Recovery Unit. Beaches in this region are expected to produce substantial numbers of male hatchlings based on sand temperature data. The relative dearth of mature males may then be due to hydrologic disturbances that disproportionately affect the fitness and survival of male hatchlings, or due to demographic stochasticity. More work is needed to study the factors that might influence male hatchling production and fitness in this region, particularly as climate change is predicted to lead to feminization in global sea turtle populations. Our work demonstrates the broad utility of characterizing BSRs and other sex ratios across a range of populations in imperiled, environmentally sensitive species.

Coupling a thermoelectric-based heat recovery and hydrogen production unit with a SOFC-powered multi-generation structure; an in-depth economic machine learning-driven analysis

This study presents a comprehensive technical, environmental, and economic analysis of a thermal power plant utilizing solid oxide fuel cells (SOFC) to meet urban demands for electrical power, fresh water, and hydrogen. The integrated system includes SOFC with anode and cathode recycling, multi-effect desalination, a power generation cycle with a heat recovery unit using a thermoelectric generator, and a hydrogen compression unit. A detailed parametric analysis was conducted to identify optimal conditions for key outputs such as total cost rate and exergy efficiency, employing genetic algorithms and artificial neural networks. According to the economic evaluation, the SOFC stack accounts for 65.11 % of total costs at 139.8 $/h, with the inverter contributing 11.9 %. The environmental analysis shows that the proposed system emits the least CO2 per energy unit compared to SOFC/GT and SOFC/GT/RC systems. The parametric study indicates that increasing the pressure ratio enhances the output power of the SOFC and the production of the gas turbine. However, this also leads to higher compressor consumption, thereby reducing net power. Furthermore, increasing the current density results in greater production of electricity, hydrogen, and freshwater, while also raising the exhaust gas temperature, which aids in the desalination process. The optimization results show an exergy efficiency of 61.38 % and a total cost rate of 132.9 $/h, with artificial neural networks reducing optimization time from 124 h to 14 min.

Evaluating energy balance and environmental footprint of sludge management in BRICS countries

Climate change is driving global endeavours to achieve carbon neutrality and renewable energy expansion. Sludge, a nutrient-rich waste, holds energy potential yet poses environmental challenges that need proper management. We conducted a comprehensive life cycle assessment to evaluate the energy balance and environmental footprint of the most commonly used sludge management scenarios in BRICS countries, namely Brazil, Russia, India, China, and South Africa. Technologies such as incineration and anaerobic digestion with energy recovery units (i.e., cogeneration unit) maximize energy balance and minimize the environmental footprint, with incineration showing a superior performance. Shifting sludge management scenarios from the worst to the best can boost energy production by 1.4–98.4 times and cut the environmental footprint by 1.5–21.4 times. In 2050, these improvements could lead to a 98-fold boost in energy generation and a 25-fold drop in carbon emissions, according to the Announced Pledges Scenarios. Optimizing parameters such as volatile solids and anaerobic digestion efficiency further boosts energy output and minimizes the environmental footprint. This study offers robust evidence to support sustainable sludge management and thus promote energy recovery and carbon neutrality goals, guide technological transitions, and inform policymaking for sustainable development.

Utilizing a optimized method for evaluating vapor recovery equipment control efficiency and estimating evaporative VOC emissions from urban oil depots via an extensive survey

As an important intermediary between upstream refineries and downstream urban gas stations, volatile organic compound (VOC) emissions from urban oil depots were often disregarded, underestimating their environmental and health implications. An extensive investigation of urban depots' fuel composition and operational dynamics was conducted nationwide. We developed a novel approach that integrates theoretical models with easily measurable operational data from the depots to evaluate the efficiency of post-treatment devices in actual situations. Even in well-managed oil depots, the actual control efficiency of vapor recovery units fluctuates between 63 % and 85 %, depending on the concentration of hydrocarbon vapors in the intake of the device. The national emission factors for gasoline, diesel, and aviation kerosene at a national level were 6.64 ± 1.16, 2.07 ± 0.42, and 6.17 ± 1.05 tons per 10,000 tons, respectively. In 2019, China's urban oil depots emitted 165 thousand tons of VOC. Enhancing control strategies by optimizing the physical and chemical parameters of refined oil, improving storage capacity and turnover efficiency, and upgrading storage tanks had the potential to reduce emissions by more than 60 %. However, a 30 % failure rate in these systems could negate the benefits of these improved strategies.

Performance and optimization evaluation for integration of sCO2 power system into the aircraft propulsion system

The aviation industry accounts for part of the CO2 emissions contributing to climate change. The industry has established a target to reduce 2050 net aviation carbon emissions by 50 % relative to 2005 levels. With this in mind, waste heat recovery is a key pathway to achieve reduced emissions and improve system efficiency. The waste heat may potentially be converted to electric power using a supercritical CO2 Brayton power cycle. The sCO2 power system offers the advantage of compactness owing to the high working fluid density, which is an important consideration for aircraft performance. The present work focuses on the integration of the sCO2 power system into the aircraft propulsion system and evaluation of its performance. Detailed optimization of the sCO2 waste heat system will be evaluated with a focus on cycle efficiency and net power under different operating conditions, including ground, takeoff, climb, cruise, and landing operations. The study is divided into two parts with two different turbofan engines, one with a nominal thrust of 30 kN and the other with a nominal thrust of 9 kN. The first part shows the effect and operation of the waste heat recovery unit under the different operating conditions. The second part is focused on cycle optimization and performance evaluation. The results demonstrate the potential of waste heat recovery during a range of operational conditions. The sCO2 cycle efficiency can reach between 25 and 39 % (depending on aircraft engine) with net power output in the range of 100 to 260 kW.

Looking beyond energy efficiency targets: Life cycle sustainability of mechanical and water heating equipment in Canadian homes

The building sector's impacts on climate are widely acknowledged, leading to policies and standards promoting energy-efficient buildings. In order to achieve sustainability in a holistic sense, the environmental, social, and economic impacts of equipment used in buildings need to be evaluated. In this regard, a novel decision support framework is introduced to evaluate the heating and cooling equipment in single-family detached homes. Energy simulation, life cycle environmental, social, and economic analyses, and a multi-criteria decision-making method are used to identify the most sustainable set of equipment. This framework is demonstrated through its application on mechanical and water heating equipment in three existing residential buildings of varying energy efficiencies (low, medium, and high, the latter being net-zero home) over a 50-year study period.The results indicate that the carbon footprint of a natural gas furnace is six times higher than that of other equipment. In contrast, the drain water heat recovery unit showed the lowest impacts attributed to lower material and energy usage. For most equipment, the product stage accounted for more than 50% of the total impacts. When grouped, the equipment in the low-efficiency home showed the highest environmental impacts, while the equipment in the medium-efficiency home had higher social and economic impacts. Overall, the equipment set in a net-zero home is the best choice under equal, pro-social, and pro-economic scenarios and only lags behind the equipment set in a medium-efficiency home for pro-environmental scenario. The work will help improve sustainable building policies beyond immediate energy and operational cost savings.

A multiobjective [formula omitted]-constraint based approach for the robust master surgical schedule under multiple uncertainties

The efficient scheduling of elective surgeries in hospitals is critical for ensuring patient satisfaction, cost-effectiveness, and overall operational efficiency. However, operating theater (OT) managers face complex and competing scheduling problems due to numerous sources of uncertainty and the impact of the proposed schedule on downstream recovery units, such as the intensive care unit (ICU). To address these challenges, this study develops a multiobjective robust planning model for the weekly Master Surgical Schedule (MSS) under multiple uncertainties. The model takes into account patient priority, assignment cost and workload balancing, while also considering the constraints of the OT, surgeon availabilities, downstream resources, and the uncertainty of surgery duration and patients’ length of stay (LOS) in the ICU. To evaluate the robust solutions, a Monte Carlo simulation is used to calculate the risk of constraint violations, and an adapted ϵ-constraint algorithm is used for the four-objective problem to compute the Pareto front and calculate the hypervolume for every degree of uncertainty. This provides a comprehensive decision tool for OT decision makers and allows for the comparison of various scenarios in terms of the number of scheduled patients, canceled patients, and the utilization rate of the OT.

Part-load analysis and preliminary annual simulation of a constant inventory supercritical CO2 power plant for waste heat recovery in cement industry

The present work investigates the part-load performance of a MW-scale sCO2 power plant designed as waste heat recovery unit for an existing cement plant located in Czech Republic, in the framework of the H2020 funded project CO2OLHEAT. The study first presents the selected power plant configuration and then focuses on the evaluation of its part-load operation due to variation of flue gas mass flow rate and temperature. The range of flue gas conditions at the outlet of the upstream process is retrieved from a preliminary statistical analysis of historical trends obtained through the cement plant monitoring. The numerical model developed for this study aims at providing realistic results thanks to the adoption of turbomachinery performance maps provided by the turbomachinery manufacturer of the project. Moreover, heat exchangers have been modelled through a discretized approach which has been validated against manufacturer data, while piping inventory and pressure losses have been assessed through a preliminary sizing that considers the actual distances to be covered in the cement plant. Performance decay is estimated for the whole range of flue gas conditions, reporting the most significant power cycle parameters, and identifying the main causes of efficiency loss. The part-load analysis is carried out considering a constant CO2 inventory, in order to reduce the system complexity and capital cost and simplify plant operation. Results show that the operation entails minor variation of the compressors operative points in the whole range of operating conditions of the cement plant, avoiding the risk of anti-surge bypass activation. Moreover, the plant is able to work close to the nominal thermodynamic cycle efficiency (20.5 %–23.0 %) for most of the year and benefits from part-load operation in terms of overall performance. In the last part of the work, a preliminary techno-economic analysis of the plant is also presented to highlight the potential advantages of sCO2 technology for waste heat recovery applications. The results of the part-load performance of the plant are combined with the flue gases data obtained from the preliminary statistical analysis and the cement plant historical monitoring. An annual electricity production equal to 13′909.7 MWh is obtained, corresponding to 6560 equivalent hours and a system capacity factor of 74.9 %. The investment cost of each CO2OLHEAT plant component is estimated by means of cost correlations obtained from literature and the non-discounted payback time is computed as a function of the electricity selling price. The results show that, even considering electricity prices before 2022, the payback time of the CO2OLHEAT plant is estimated to be lower than 8 years, justifying the industrial interest in the proposed technology.

The Ideal Client: Selecting Candidates for Drug Treatment in Prison

Abstract Therapeutic interventions have been implemented in many prison systems around the world, in an attempt to address high rates of harmful substance use amongst people in prison. This article analyses the criteria for acceptance to drug recovery units in Norwegian prisons, based on an ethnographic study of four high-security prisons and 22 interviews with prison staff. The main criteria that were considered by staff during the assessment of potential candidates, were the timing and type of sentence applicants held; the type of offence applicants were convicted for; the types of medications they used and their perceived motivations for entering treatment.

Postoperative Nausea and Vomiting in the Ambulatory Surgery Center: A Narrative Review.

Postoperative nausea and vomiting (PONV) is a common complication of ambulatory surgery, leading to numerous deleterious effects such as decreased patient satisfaction, prolonged recovery unit stays, and rarely, more serious complications such as aspiration pneumonia or wound dehiscence. In this paper, we present a narrative review of the literature regarding common risk factors for PONV including patient factors, surgical factors, and anesthetic factors. We then will review anesthetic techniques and antiemetic drugs demonstrated to mitigate the risk of PONV. Finally, we discuss the potential economic benefits of PONV prophylaxis in the perioperative ambulatory setting.

Dual temporal attention mechanism-based convolutional LSTM model for industrial dynamic soft sensor

Abstract Deep learning is an appropriate methodology for modeling complex industrial data in the field of soft sensors, owing to its powerful feature representation capability. Given the nonlinear and dynamic nature of the process industry, the key challenge for soft sensor technology is to effectively mine dynamic information from long sequences and accurately extract features of relevance to quality. A dual temporal attention mechanism-based convolutional long short-term memory network (DTA-ConvLSTM) under an encoder-decoder framework is proposed as a soft sensor model to acquire quality-relevant dynamic features from serial data. Considering different influences of process variables for prediction at multiple time steps and various locations, ConvLSTM and temporal self-attention mechanism are utilized as the encoder to adaptively fuse spatiotemporal features and capture long-term dynamic properties of process in order to capture the trends of industrial variables. Furthermore, a quality-driven temporal attention mechanism is employed throughout the decoding process to dynamically select relevant features to more accurately track quality changes. The encoder-decoder model meticulously analyses the interactions between process and quality variables by incorporating dual-sequence dynamic information to improve the prediction performance. The validity and superiority of the DTA-ConvLSTM model was validated on two industrial case studies of the debutanizer column and sulfur recovery unit. Compared to the traditional LSTM model, the proposed model demonstrated a substantial improvement with the accuracy R2 up to 97.3% and 94.9% and the root mean square error reducing to 0.122 and 0.022.