Machine Availability Research Articles

Mass customization using hybrid manufacturing and smart assembly: An optimal configuration and platform design approach

Hybrid Manufacturing (HM) and smart assembly stand as pivotal pillars in advanced smart manufacturing systems, offering manufacturers highly efficient and adaptable solutions for manufacturing. This paper delves into the configuration of a production line that integrates HM and assembly stages, each comprising multiple cells, with each cell housing one or more parallel stations. The objective is to manufacture a family of final assemblies, leveraging the platform concept to defer mass customization to later stages and thereby minimize processing costs. A mathematical programming model is proposed to identify the optimal configuration for such production lines, considering constraints such as an allowable capital cost and machine availabilities. In addition, the precedence, inclusion, and seclusion restrictions imposed on the part family are considered. The proposed mathematical programming model aims to delineate which HM features are processed in the part platform cell versus those processed in the mass customization (part variants) cells. Simultaneously, the model determines the components (variants from the HM stage) of final assemblies processed in the assembly platform cell, as well as components assembled or disassembled in the final assembly cells. Furthermore, the model seeks to determine the required number of stations in each cell to meet periodic demand. The overall objective of the model is to minimize the capital and the processing cost. A detailed case study illustrates the effectiveness of the proposed configuration approach and mathematical model. The proposed model is solvable in a few seconds by using commercial solvers.

Analysis of Metal Fused Filament Fabrication process chain for 316L stainless steel

Metal Fused Filament Fabrication (MFFF) has emerged as a prominent technology in Additive Manufacturing (AM), characterized by its cost-effectiveness, versatility in creating intricate geometries using diverse materials, and widespread availability of AM machines. However, the intricate processes of making filament, printing, debinding, and sintering in MFFF pose unique challenges, with a vast number of parameters influencing each stage. Specialized companies handle these stages for research purposes, yet detailed information on the processes remains limited. The objective of this research is to conduct a meticulous examination of the parameters governing the printing, debinding, and sintering of 316L stainless steel—a material widely employed in various industries. The goal is to provide researchers and manufacturers with a comprehensive understanding, enabling them to achieve high-density metal parts that rival those produced through Selective Laser Melting (SLM). This research employs a systematic approach in producing flawless metal parts through fine-tuning parameters such as printing speed, nozzle diameter, extruder and construction table temperatures, heating rate, nitric acid injection during debinding, and sintering atmosphere and temperature. Despite advancements in MFFF parameters in this study, a comparative analysis with SLM reveals superior mechanical properties and density in SLM-produced parts. Because, the robust bonding facilitated by a powerful energy source (laser) in SLM minimizes porosities, whereas MFFF relies solely on molten filament adherence, potentially leading to small gaps between layers. This research contributes valuable insights for achieving dense, defect-free metal parts through Metal Fused Filament Fabrication (MFFF). It sheds light on the persistent advantages of Selective Laser Melting (SLM) in terms of mechanical performance and density. The comprehensive understanding of parameters provided in this study empowers researchers and manufacturers to optimize MFFF processes for 316L stainless steel, narrowing the gap between the two technologies (MFFF and SLM) and enhancing the competitiveness of MFFF in producing high-quality metal parts.

“It’s [gambling] obviously not regulated that well”: insights into Aboriginal peoples’ views on gambling regulation in the Northern Territory of Australia

Abstract Aim This study is the first in-depth qualitative study that has provided insights into Aboriginal (All participants in this study identified as Aboriginal and thus we have not included any reference to the Torres Strait Islander peoples in this paper.) people’s views on current gambling policy and legislation in the Northern Territory (NT), Australia. Subject and methods In-depth semi-structured qualitative interviews were conducted with a targeted selection of participants. The sample comprised 29 participants, aged 18+ years, weekly and nonregular gamblers, and those negatively affected by others’ gambling. An open coding technique (a combination of inductive and deductive analyses) was used for data analysis. Appropriate ethics approval was also obtained. Results Participants described gambling as a revenue-generating product for the government and the gambling industry. Hence, their role in influencing the regulation of gambling for public benefit was debated. Participants provided practical suggestions that the government and the gambling industry could adopt to inform gambling regulation to minimise harm from gambling among Aboriginal people and the wider community in the NT. The suggestions included establishing stricter regulations relating to the licensing and opening hours of venues, limiting bet size and restricting the availability of gaming machines at venues. Participants also encouraged the reinstatement of mandatory formal dress codes at venues and the creation of health promotion resources such as advertisements and awareness campaigns tailored to the needs of the local Aboriginal people. Conclusion Both community-wide and targeted approaches are required to regulate and reduce gambling harm among Aboriginal people in the NT. Although both are important, having more explicit preventive approaches in place may eliminate the need for downstream interventions and strategies.

Manufacturing floor mapping and presence tracking with a physics-based game engine

The manufacturing shop floor exists in a dynamic environment with constantly changing components regarding position and features. Presence tracking on the manufacturing shop floor is essential to enhance effective operations and safety. Internet of Things technology solutions have been developed to track and monitor machine and operators’ movements and materials on the shop floor to enhance machine availability and worker safety and improve overall operational effectiveness. However, for SMMs with limited financial resources and infrastructure, there is a need for a financially friendly approach to shop floor presence tracking and monitoring. This work presents the use of Internet-of-things and physics-based engines to develop a scalable and customizable solution to track presence on a manufacturing floor. The paper aims to improve and promote the adoption of I-4.0 technologies and digital manufacturing by SMMs with the development of an affordable, repeatable, customizable, open-source solution. The article describes a methodology for mapping, modeling, monitoring, and reporting presence on a manufacturing floor using physics-based engines. The methodology describes mapping the manufacturing floor into zones, the design of the IoT solution, selecting and placing sensors, and presenting the data using dashboards and physics-based engines. The paper investigates the methodology by implementing a case study on the Interdisciplinary Center for Advanced Manufacturing Systems (ICAMS) manufacturing floor using the Unity3D game engine, open-source software, and off-the-shelf motion sensors.

Hub production scheduling problem with mold availability constraints

This research delves into a scheduling challenge inherent in the wheel hub casting process, a critical stage in automotive wheel manufacturing. The process involves shaping molten metal into specific wheel hub designs using dedicated molds. Diverse customer demands necessitate a unique mold for each wheel hub design, posing a scheduling challenge due to the limited availability of these expensive and long-lasting molds. There are 2 machines available for casting. Each wheel hub design requires a specific mold. These molds are expensive, long-lasting, and need periodic maintenance. The objective function is to minimize the total time (makespan) to complete all casting jobs. Only one job requiring a specific mold can be processed at a time (due to mold limitations). Molds are unavailable during maintenance periods. The challenge lies in scheduling jobs considering both machine availability and mold constraints to minimize the overall production time. We formulate a mathematical model using mixed integer programming to precisely represent the problem and its constraints. Inspired by the Longest Processing Time (LPT) rule, we propose a heuristic named LRTPT to efficiently schedule jobs on the two machines. For large-scale problems, we employ a novel SIAIS algorithm to find near-optimal solutions effectively. We also present a dedicated branch and bound algorithm specifically tailored to solve smaller-sized instances of the problem. This algorithm leverages two lower bounds and several dominance properties to expedite the search for the optimal solution. To evaluate the effectiveness of our proposed approaches, we conduct a series of comprehensive experiments. The results demonstrate that the branch and bound algorithm significantly outperforms the widely used optimization solver CPLEX for smaller problems. Furthermore, the SIAIS algorithm proves to be more efficient than existing scheduling heuristics.

Digital Twin-Driven Dynamic Scheduling of Flexible Manufacturing System in the Context of Smart Factory Producing Brass Accessories

This study aims to deal with a dynamic scheduling problem of a real Flow Shop follow-up of an assembly process considering the specific constraints and requirements of a brass accessories manufacturing (BAM) company. We basically set up a Digital Twin-driven dynamic scheduling approach for Industry 4.0 by addressing uncertainties of machine availability. In fact, the setup of this Digital Twin (DT) is the outcome of the combination of both optimization and simulation. For the first step, we have elaborated a Mixed Integer Linear Programming (MILP) scheduling model by taking into account the dedicated requisites of our case study. Concerning simulation, a 3D simulation platform of a workshop producing brass accessories controlled by a Cyber-Physical Production System (CPPS) has been developed, in our previous work, including constraints and stochastic aspects. The simulation constraints are difficult or impossible to be modeled in the MILP model. These designs are integrated with the real workshop to construct the Digital Twin. The proposed tool enables the rescheduling of production orders based on machine disturbances and unpredictability. Validation scenarios have been designed and conducted to highlight the efficacy of the Digital Twin approach utilizing the brass accessories case study. We are confident that this represents the initial endeavor addressing the optimization of production rescheduling in a flow shop follow-up of a mixed assembly system.

Machine learning and deep learning prediction of patient specific quality assurance in breast IMRT radiotherapy plans using Halcyon specific complexity indices

IntroductionNew radiotherapy machines such as Halcyon are capable of delivering dose-rate of 600 monitor-units per minute, allowing large numbers of patients treated per day. However, patient-specific quality assurance (QA) is still required, which dramatically decrease machine availability. Innovative artificial intelligence (AI) algorithms could predict QA result based on complexity metrics. However, no AI solution exists for Halcyon machines and the complexity metrics to be used have not been definitively determined. The aim of this study was to develop an AI solution capable of firstly determining the complexity indices to be obtained and secondly predicting patient-specific QA in a routine clinical setting. MethodsThree hundred and eighteen beams from 56 patients with breast cancer were used. The seven complexity indices named Modulation-Complexity-Score (MCS), Small-Aperture-Score (SAS10), Beam-Area (BA), Beam-Irregularity (BI), Beam-Modulation (BM), Gantry and Collimator angles were used as input to the AI model. Machine learning (ML) and deep learning (DL) models using tensorflow were set up to predict DreamDose QA conformance. ResultsMCS, BI, gantry and collimator angle are not correlated with QA compliance. Therefore, ML and DL models were trained using SAS10, BA and BM complexity indices. ROC analyses enabled to find best predicted probability threshold to increase specificity and sensitivity. ML models did not show satisfactory performance with an area under-the-curve (AUC) of 0.75 and specificity and sensitivity of 0.88 and 0.86. However, optimised DL model showed better performance with an AUC of 0.95 and specificity and sensitivity of 0.98 and 0.97. ConclusionThe DL model demonstrated a high degree of accuracy in its predictions of the quality assurance (QA) results. Our online predictive QA-platform offers significant time savings in terms of accelerator occupancy and working time.

Shaping of the risk of a series-parallel manufacturing structure maintenance according to quasi-coherence and the [formula omitted]-th survival value

The paper presents the authors’ model for determining the probability value of the survival function of the difference of two random variables: TP – the amount of time of correct operation and TB – the amount of time of failure. For this purpose, the concept of K-th survival value is introduced. The K-th survival value defines the probability that the total time of correct operation of the production system will be longer by at least k time units (for k≥0) than the total amount of time of failures. The model was exemplified by a real production system with a series–parallel structure. In the nesting manufacturing structure, the availability of machines determines the number and the type of flow streams of the processed material. To formalise the chains of relations at a given Δt, this paper utilises the quasi-coherence property. The paper identifies four possible cases of system operation: 1) a fully operational system in which every machine is capable of meeting the assumed production schedule; 2) a system which has lost the property of quasi-coherence (certain system objects are in an inoperable state) but it is possible to meet the assumed production schedule; 3) a system which has lost the property of quasi-coherence but the assumed task schedule is possible to meet after redefining the material flow streams; 4) a system which has lost the property of quasi-coherence and it is impossible to meet the assumed production schedule. Based on the definition of the survival function [1] S of the random variable T: ST:=PT>τ=1-FTτ,τ∈R, the concept of K-th survival value was introduced. For possible cases, K-th survival value was defined as: KT1,Ti=PT1-Ti≥k=1-FT1-Tik (for i=2,3,4), where T1 and Ti are random variables with given probability distributions. In turn, k is a deterministic value that determines the total number of time units of correct system operation in relation to the total failure time. The application of the developed algorithm enables the system to be analysed in terms of three aspects: (1) to determine the probability of on-time execution of tasks, taking into account historical values of machine failure and correct operation durations; (2) to determine the stability of the system based on empirical data; (3) to monitor the level of risk in real time. The paper validates the model by determining the stability of the analysed system for real machine failure data. The introduced criterion of stochastic stability of the production system has been verified for data in which distributions of correct operation times and failure times are random variables from the family of Gamma distributions. The presented model can be included in the group of multifactorial risk assessment methods.

Implementation of Total Productive Maintenance in Centrifugal Pump Maintenance At Pt. Fajar Surya Wisesa

PT. Fajar Surya Wisesa specializes in manufacturing packaging paper, with its factory located in the WWTP Department. The company is dedicated to enhancing the productivity and operational efficiency of its Centrifugal Pump machines through effective maintenance strategies. This study employs a quantitative approach to assess the Overall Equipment Effectiveness (OEE), encompassing availability, performance efficiency, and quality rate, alongside a Six Big Losses analysis. The findings revealed that the average machine availability reached 98%, with a notable decrease observed in August due to downtime incidents. Performance efficiency consistently exceeded 90%, although higher production outputs occasionally led to efficiency declines. Quality rates remained stable and high, signaling advancements in production processes and quality control measures. Overall, the average OEE value attained 88%, surpassing the global standard of 85%. To further optimize Centrifugal Pump machine effectiveness, proposed enhancements include operator training, regular performance assessments, improved operator welfare considerations, selection of premium raw materials, updates to standard operating procedures (SOPs), scheduled preventive maintenance, enhanced workplace safety measures, and investments in energy backup systems. In conclusion, these initiatives have effectively elevated the performance and operational quality of the Centrifugal Pump machines at PT. Fajar Surya Wisesa.

Evaluation of rock cutting performance of conical cutting tool based on commonly measured rock properties

Efficiency of rock cutting process plays a critical role in performance of mechanical excavation units. The composition of cutting forces (normal and drag force acting on cutting tools) and the total force (FT), specific energy (SE), and percent of fine material (FM) produced in cutting process are important indicators of efficient cutting process. The other key factors in assessment of machine performance are tool wear, energy consumption, dust production, and machine maintenance, availability, and utilization. In this study, small scale linear cutting experiments were performed with a conical pick on thirteen sedimentary and metamorphic weak to medium strength rock samples at a range of 0.5 to 6 mm cutting depths in unrelieved cutting mode. FT was measured by using a 3D dynamometer and recorded by the data acquisition system, and FM was determined by sieve analysis. Finally, SE was calculated using both the cutting force signal and the volume of the cuttings for each test. Subsequently, an analysis of the effective cutting geometry was performed based on cutting depth, using the specific energy as an indicator of cutting efficiency. Statistical and regression analysis was used to correlate FT, SE, and FM with the rock properties and cutting geometry. The results revealed that the uniaxial compressive strength, Schmidt rebound number, and density are the main parameters that affect FT and SE, and the brittleness index is the main parameter that affects FM. A nonlinear predictive model is introduced that offers a reasonable estimate of FT, SE, and FM to assist engineers in determining the effective operational cutting geometry for a given rock type for unrelieved cuts.

The utilization of renal dialysis: a comprehensive study in Saudi Arabia

BackgroundUnderstanding the trend of utilization of renal dialysis in Saudi Arabia (SA) is fundamental as it provides a general overview of renal care. The practice of renal dialysis assists in identifying challenges, opportunities, and potential areas for improvement in the provision of the services.ObjectivesThis research investigated the utilization of renal dialysis services in SA by exploring the number of renal dialysis centers, hemodialysis machines (HD), and peritoneodialysis patients.MethodsThe dataset for this study was derived from a collaboration between the General Authority of Statistics (GaStat) and the Ministry of Health (MoH), focusing on indicators for renal dialysis centers and patients across health sectors in 2021. Analysis was conducted using MS Excel 365 and IBM SPSS Version 29, incorporating multiple regression techniques. The health sector was treated as the dependent variable. At the same time, the number of hemodialysis (HD) machines and the counts of HD and peritoneal dialysis patients were considered independent variables.ResultsAround 275 renal dialysis centers, over 8000 HD machines, 20,440 HD patients, and 1,861 peritoneal patients were tallied from two resources. The findings revealed a negative relationship between the health sector and several renal dialysis centers and peritoneodialysis patients, as demonstrated by p < 0.05 in multiple regression analysis.ConclusionThe number of renal dialysis centers influences the availability of HD machines, affecting the number of HD and peritoneodialysis patients. Most national patients preferred MoH over other semi-governmental and private sectors, and vice versa for non-Saudis.

EVALUATION OF MAINTENANCE EFFECTIVENESS AND HUMAN FACTORS: CASE STUDY OF A RESEARCH INSTITUTE

Maintenance management is an integral part of any organization’s overall commercial process that develops administrative operational value. Thus, the maintenance function is of immense importance to ensure the safety and effective operation of a facility and equipment in compliance with a statutory obligation, to maximize equipment life, and to reduce the risk of failure. However, for maintenance success work planning, the perception of human factors, and the interrelation of workers and groups in any organisation must be understood. This in turn would determine the allocation of maintenance tasks to team members. This research focused on the evaluation of key performance indicators in relation to the maintenance effectiveness of selected equipment, in a research institution. The impact of human factors on the maintenance was also investigated. Key performance indicators such as Mean Time to Repair (MTTR), Mean Time Between Failure (MTBF), downtime, repair time, and availability were used to quantitatively evaluate the maintenance effectiveness of the equipment, while the systematic approach to empirical research was used to analyse the effect of human factors on maintenance practices. This study was carried out over a period of 6 months on a 4-tonne hydraulic lift, hydraulic hoist jack lift, tire changing machine, fault diagnostic machine, and computerized alignment gauge. Data collected from the maintenance unit was analysed. A total of 11.92 hours was recorded as the MTTR of the five machines. The machines had a high level of availability and a total downtime of 12.01 hours out of operational time of 2369.8 hours over a period of 6 months. The level of motivation and competence among maintenance personnel was assessed to be very good. This is expected to have contributed largely to the effective maintenance practices of workers in the unit. From this study, it can be deduced that the level of motivation and competence is high among maintenance personnel. In terms of productivity, effectiveness, teamwork, decision-making, execution, safety, and responsiveness, the human factor has been shown to have thrived well. This in turn, stimulated the corresponding increase in the availability of machines over the 6 months- of study.

IoT Enabled Condition Monitoring Under Predictive Maintenance Framework of Mines and Steel Plant

This case is about a century-old integrated steel plant combined with mines, having more than 0.42 million capitalintensive assets, ages ranging from 0 to 100 years and spreads across 700+ hectares as shown in Figure 1. Assets having varied levels of automation maturity with different maintenance methodologies. It’s about shifting the whole maintenance paradigm from Time-based &amp; Condition-based to Predictive. Through digital initiatives &amp; optimum maintenance cost, we are trying to develop a Maintenance Transformation Roadmap (MTR) to ensure maximum machine availability/ reliability. For this paradigm shift from conventional to predictive maintenance, critical assets were identified through a systematic approach. Under this MTR Journey, to bridge the sensor gap, SMART Sensor (3 axis vibration &amp; Temperature) was identified as the appropriate solution. Customizable In-house SMART Sensor applicable in Mining &amp; Steel industry application and low-cost solution helped in cross locational horizontal. Presently early warning alerts saved 1000+ potential breakdowns on 1200nos critical assets. The Predictive Maintenance Framework has both tangible and intangible benefits. While safety is the most important intangible benefit of this technology, tangible benefits include approved savings of $20 Million. Further, we expect to save $12.5 Million by deploying our Sensor and $35 Million via prevention of breakdowns

Significance of effluent dose in continuous renal replacement therapy in children

Abstract Continuous renal replacement therapy (CRRT) is one of the commonly used extracorporeal blood purification therapies and its use in the pediatric population has increased in the past two decades due to the availability of safer machines which allow lower blood flow rate and dialysate flow rate. The usual indication for initiation of CRRT is for solute clearance and or fluid removal in oliguric patients with acute kidney injury. Knowledge regarding the principles of solute removal and fluid removal along with the application of these mechanisms in different modes of CRRT helps the physician to decide on the appropriate therapy for the given patient. Understanding the concept of CRRT dose and the factors to be considered in the prescription for achieving the preset targets of CRRT is vital for the efficient utilization of the therapy.

Optimal Strategy of Unreliable Flexible Production System Using Information System

Background: Optimization approaches and a models can be applied for critical production systems that experience equipment failure, repair delays and product quality control in order to maximize the total profit. We can cite, as an example, flexible manufacturing systems. Methods: Our methodology involves developing a decision model integrated with an information system to coordinate various system operations, ensuring timely response to customer requests. The module of the information system is provided to optimally manage the production flow and parts ordering according to machine availability. The objective is to determine the optimal order thresholds of part batches that maximize the total profit. Results: Numerical results are provided to analyze the influence of system reliability and uncertainty on decision variables, offering insights into the system’s performance and robustness. By using our method, the advancement of the flexible production systems is carried out by addressing key operational challenges and optimizing production processes for enhanced efficiency and profitability. Conclusions: To achieve this, an optimization algorithm is employed to identify optimal solutions that enhance profitability.

Technological developments and accelerator improvements for the FRIB beam power ramp-up

The Facility for Rare Isotope Beams (FRIB) began operation with 1 kW beam power for scientific users in May 2022 upon completion of 8 years of project construction. The ramp-up to the ultimate beam power of 400 kW, planned over a 6-year period, will enable the facility to reach its full potential for scientific discovery in isotope science and applications. In December 2023, a record-high beam power of 10.4 kW uranium was delivered to the target. Technological developments and accelerator improvements are being made over the entire facility and are key to completion of the power ramp-up. Major technological developments entail the phased deployment of high-power beam-intercepting systems, including the charge strippers, the charge selection systems, the production target, and the beam dump, along with support systems, including non-conventional utilities (NCU) and remote handling facilities. Major accelerator improvements include renovations to aging legacy systems associated with experimental beam lines and system automation for improved operational efficiency and better machine availability. Experience must be gained to safely handle the increased radiological impacts associated with high beam power; extensive machine studies and advanced beam tuning procedures are needed to minimize uncontrolled beam losses for the desired operating conditions. This paper discusses the technological developments and accelerator improvements with emphasis on major R&D efforts.

Improving household well‐being through entrepreneurship and inclusive finance in the context of the Next‐11 countries

AbstractScholarship brainstorming on household welfare enablers abounds in academic literature. Nonetheless, the perspective of entrepreneurship, particularly for the Next‐11 countries, is underreported. Likewise, prior literature failed to investigate the influence of categorical (gender‐specific) entrepreneurship on household welfare. Thus, this investigation covers this lacuna by examining the predictive effects of aggregate, male and female entrepreneurship and financial inclusiveness on household welfare. With panel series spanning 2004–2021, both the Westerlund and Banerjee &amp; Carrion‐i‐Silvestre cointegration procedures confirmed long‐term convergence among the series. The estimates of the augmented mean group (AMG) and the cross‐sectional autoregressive distributed lag (CSARDL) techniques revealed that entrepreneurship enhances household well‐being. This outcome validates the self‐determination theory since entrepreneurial engagements enhance household welfare. By extension, policies targeting entrepreneurship empowerment are critical for economic prosperity in these countries. In terms of gender variations, it is established that female entrepreneurship, much more than male, enhances household well‐being significantly. Furthermore, although automated teller machine availability promotes subjective well‐being, the number of commercial bank branches enhances only objective well‐being. Meanwhile, commercial bank loans failed to enhance both aspects of welfare at all times. Hence, we have provided relevant policy options that would ensure the maximization of households’ well‐being in these countries and other emerging economies.

A Double Deep Q-Network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions

In the semiconductor manufacturing industry, the Dynamic Flexible Job Shop Scheduling Problem is regarded as one of the most complex and significant scheduling problems. Existing studies consider the dynamic arrival of jobs, however, the insertion of urgent jobs such as testing chips poses a challenge to the production model, and there is an urgent need for new scheduling methods to improve the dynamic response and self-adjustment of the shop floor. In this work, deep reinforcement learning is utilized to address the dynamic flexible job shop scheduling problem and facilitate near-real-time shop floor decision-making. We extracted eight state features, including machine utilization, operation completion rate, etc., to reflect real-time shop floor production data. After examining machine availability time, the machine's earliest available time is redefined and incorporated into the design of compound scheduling rules. Eight compound scheduling rules have been developed for job selection and machine allocation. By using the state features as inputs to the Double Deep Q-Network, it is possible to acquire the state action values (Q-values) of each compound scheduling rule, and the intelligent agent can learn a reasonable optimization strategy through training. Simulation studies show that the proposed Double Deep Q-Network algorithm outperforms other heuristics and well-known scheduling rules by generating excellent solutions quickly. In most scenarios, the Double Deep Q-Network algorithm outperforms the Deep Q-Network, Q-Learning, and State-Action-Reward-State-Action (SARSA) frameworks. Moreover, the intelligent agent has good generalization ability in terms of optimization for similar objectives.

A retrospective study on the delay in three different timescales of CT simulation among patients with pediatric cancer in a tertiary hospital.

Patients with pediatric cancer receive radiotherapy to cure several types of cancer, requiring computed tomography simulation (CT sim) for precise treatment. However, there is currently no suitable framework to reduce the inherent delays in CT sim. The present study aimed to identify the underlying causes of the delays in CT sim regarding three different time periods (duration of patient admission to CT sim, diagnosis to treatment and CT sim to treatment) among patients with pediatric cancer. A total of 58 patients with pediatric cancer who received radiation therapy under anesthesia at King Abdulaziz University Hospital (Jeddah, Saudi Arabia) between 2016 and 2021 (60 months) were included in the current study. The underlying cause of delays regarding three separate time periods was determined according to patient type, diagnosis, therapy type and year of diagnosis. The CT sim processing time averaged 73 days and was received by patients after 28.96±28.5 days. The major delays in terms of frequency and length of duration between different time points such as patient admission and CT sim, interval between diagnosis and treatment, and duration between CT sim and therapy were (mean±SD) 37.13±29.9, 58.08±24.9 and 28.15±7.9 days, respectively. Machine availability, instability of the patients' medical condition and intensity-modulated radiation therapy (IMRT) caused 66.6% of the delays. In conclusion, outpatients may experience CT sim delays. Machine availability, conditions of patients and IMRT treatment were the major reasons to cause the delay in CT sim. Strategies should be employed to prevent CT sim delays and improve patient experience.

Noninvasive respiratory support in the perioperative setting: a narrative review.

The application of preoperative noninvasive respiratory support (NRS) has been expanding with increasing recognition of its potential role in this setting as a physiological optimization for patients with a high risk of developing atelectasis and postoperative pulmonary complications (PPC). The increased availability of high-performance anesthesia ventilator machines providing an easy way for NRS support in patients with reduced lung function should not be under-evaluated. This support can reduce hypoxia, restore lung volumes and theoretically reduce atelectasis formation after general anesthesia. Therapeutic purposes should also be considered in the perioperative setting, such as preoperative NRS to optimize treatment of patients' pre-existing diseases, e.g., sleep-disordered breathing. Finally, the recent guidelines for airway management suggest preoperative NRS application before anesthesia induction in difficult airway management to prolong the time needed to secure the airway with an orotracheal tube. This narrative review aims to revise all these aspects and to provide some practical notes to maximize the efficacy of perioperative noninvasive respiratory support.