Optimization Program Research Articles

BLOOD PRESSURE MONITORING USING TELEMEDICINE TECHNOLOGIES IN PATIENTS WITH HEART FAILURE

An urgent task of modern cardiology is to ensure continuous monitoring of cardiovascular patients' condition indicators. Monitoring of patients with heart failure (HF) is especially important due to the instability of the condition, extremely high prevalence of pathology and life-threatening. Currently, active search and development of optimal devices and programs for medical telemonitoring continues. The review presents the results of Russian and foreign studies on the introduction of invasive and non-invasive technologies for blood pressure (BP) monitoring and demonstrates their clinical significance. The comparison of clinical data of several studies using wireless remote monitoring of pulmonary artery pressure with the help of CardioMEMS sensor was carried out. Mobile applications and telephone monitoring are also considered as an alternative to invasive devices, and information about their effectiveness and impact on the quality of life of patients is given. The article also compares the effectiveness of treatment of hypertension and HF using remote monitoring with standard practice and demonstrates the dependence of the risk of hospitalization for HF and the presence of monitoring of the condition.

Mem‐Transistor‐Based Gaussian Error–Generating Hardware for Post‐Quantum Cryptography Applications

AbstractQuantum computing can potentially hack the information encrypted by traditional cryptographic systems, leading to the development of post‐quantum cryptography (PQC) to counteract this threat. The key principle behind PQC is the “learning with errors” problem, where intentional errors make encrypted information unpredictable. Intentional errors refer to Gaussian distributed data. However, implementing Gaussian distributed errors is challenging owing to computational and memory overhead. Therefore, this study proposes a Gaussian error sampler that employs the intrinsic Gaussian properties of nanometer‐scale semiconductor devices. The proposed Gaussian error sampler significantly reduces computational and memory overhead. This work comprehensively evaluates the effectiveness of the proposed device by conducting statistical normality tests and generating quantile–quantile plots. The optimal programming voltage is identified to be −5.25 V, and the experimental results confirmed the Gaussian distribution of error data generated by the proposed module, aligning closely with software‐generated Gaussian distributions and distinct from uniform random distributions.

Operational optimization of a rural multi-energy system supported by a joint biomass-solid-waste-energy conversion system and supply chain

Biomass, as one of the most accessible renewable resources, could reduce dependency on fossil fuels and mitigate associated environmental impacts. With economic development and improved living standards, biomass in rural areas is converted into various forms of energy to meet high energy demands. However, existing studies on rural multi-energy system operations assume energy production from biomass is a given input parameter and consider it to be a fixed constant or subject to upper and lower bounds, independent of the biomass supply chain. Indeed, biomass energy production is significantly influenced by the collection, transportation, storage, and other processes within the supply chain. This work proposes a mixed integer linear program (MILP) for the joint optimization of an integrated rural multi-biomass-solid waste energy conversion system with a source-grid-demand-based biomass-solid waste supply chain (IRMBS-BSC system). The IRMBS-BSC system encompasses three sub-systems: the biomass-solid waste source sub-system, the biomass-solid waste grid sub-system, and the biomass-solid waste demand sub-system. Various biomass-solid wastes are classified, and a source model is established to evaluate their availability. Subsequently, a supply chain model is developed within the biomass-solid waste grid sub-system. Based on the available and transported quantity from the biomass-solid waste source sub-system and grid sub-system, a multi-energy system operation model, considering refined biomass conversion processes, is proposed. A case study involving a multi-energy system that integrates multiple rural community biomass-solid waste supplies is conducted. The results demonstrate that the proposed MILP, which considers the biomass-solid waste supply chain, can reduce energy imports during the load peak periods and can also decrease the overall operational costs of the multi-energy system.

Building material reuse: An optimization framework for sourcing new and reclaimed building materials

The process of sourcing Reclaimed Construction Materials (RCMs) is predominantly manual and hindered by the limited digital presence of RCMs. However, the advancing technological landscape provides an opportunity to create a modular framework that can assess the value proposition of New Construction Materials (NCMs) and RCMs for a project before acquiring them. A field study in the Kitchener/Waterloo region inspired the development of a framework that has three underlying systems encompassing digital tools for data collection and analysis: (1) The Real Environment uses 3D scanners and a spreadsheet software, (2) The Model Environment uses Building Information Modeling (BIM) software and a Life Cycle Assessment (LCA) tool, and (3) The Core Engine uses an optimization program. Real-world data collected from RCM stores (The Habitat for Humanity) and NCM stores (The Home Depot) are used for a realistic demonstration of the framework. By practically applying the framework to source window and door components for a modeled multi-residential building design, an integrated selection of 35% RCMs and 65% NCMs was proposed for the building design. Furthermore, sensitivity analysis is also performed for validation. The framework may disrupt the ongoing building design practices that deem material reuse problematic by enabling flexible sourcing of used and new building materials. A modular and iterative framework for facilitating reuse is thus contributed.

Mathematical modeling of containing the spread of heroin addiction via awareness program

As many countries are hit by the social economic impact of heroin addiction, there is an urgent need to have an effective awareness program that focuses on educating the population on the danger of heroin addiction and helping the heroin‐users quit. This paper aims to study the effect of the awareness program on the spread of heroin dependence using a mathematical model with distributed delay. First, we show the existence threshold parameter , which we call the basic reproduction number of the spread of heroin use. We prove, via the Lyapunov direct method, that the drug‐free equilibrium is globally asymptotically stable if . If , the drug dependence persists, and the drug equilibrium is globally asymptotically stable. We give three possible scenarios to find the optimal awareness program strategy that puts the heroin epidemic under control. These scenarios take into consideration the reachability of the population, the immunity against heroin addiction, and the effectiveness of the program to contain the use of heroin in the population and bring below one.

Reservation and allocation model considering the user cost and the utilization of parking space

In order to alleviate the problem of imbalance between parking supply and demand by improving the utilization rate of parking resources, and considering the game relationship between user benefits and system benefits in parking allocation, the optimization objective function of maximum parking space utilization and minimum user cost in parking reservation mode is established from the two aspects which are system optimization and user optimization respectively. Then, the optimal allocation parking integer programming model (OPA) considering users' preferences is established. An Augmented Lagrangian-Alternating Direction Method of Multipliers Algorithm is designed to solve the optimal solution of the model. Finally, in order to test the validity of the model, the proposed model and the classical distribution model are compared and analyzed under different supply and demand conditions. The results show that the performance of the proposed model in three performance metrics which are parking utilization, average user cost and request acceptance rate is significantly better than that of the classical allocation model. The research results of this paper can provide theoretical reference for the management of parking reservation platform.

Taking a Team Approach: Keep up with the EHR with a Training and Optimization Program.

This study aimed to describe the current landscape of electronic health record (EHR) training and optimization programs (ETOPs) and their impact on healthcare workers' (HCWs) experience with the EHR. A 72-question electronic survey was developed and distributed to healthcare organization (HCO) leaders in clinical informatics through various channels such as national informatics conferences, social media, and email distribution lists of vendors and informatics associations. The survey collected data on the characteristics, resourcing, approach, and outcomes of ETOPs. Descriptive statistics were applied to analyze the data. There were 193 responses from 147 distinct HCOs. Of these, 69% offer ongoing EHR training, and 52% offer some version of an ETOP. Offered ETOPs vary in their timing, modality, audience, team composition, duration, and EHR build strategy. The most commonly measured outcomes were EHR satisfaction, efficiency, and provider burnout, and most ETOPs reported improvement in these areas. The findings suggest that ETOPs are inconsistently implemented across HCOs, and while there are some commonalities, there is a wide variety of design and methods of evaluation for the programs. Though the problems to solve for (EHR efficiency, proficiency, and satisfaction) are the same, the organizational structure and culture of HCOs varies widely, which may partially explain the variability seen in reported ETOPs. When considering the measured outcomes, ETOPs may have direct and indirect effects on HCW burnout by improving EHR efficiency and satisfaction, as well as driving organizational culture towards teamwork and flexible problem solving. For this reason, ETOPs may also serve as a model for addressing other challenges in healthcare delivery. ETOPs are a promising intervention to enhance HCW experience with the EHR and reduce burnout. More research is needed to identify the optimal features, methods, and outcomes of ETOPs, and to disseminate them across HCOs.

Evidence-Based Recommendations for Effective Enrichment to Improve the Welfare of Caged Hens Used for Research and Teaching Purposes

Currently, there are no guidelines on housing or recommendations for practical environmental enrichments for chickens used for research and teaching purposes. This study was conducted to identify optimal enrichment programs to improve the health and welfare of caged hens used for teaching and research purposes. The 24-week-old Hy-line Brown laying hens (n = 168) were allocated at random to six enrichment programs/treatments with 14 replicate cages of two birds per cage during a 5-week experiment. Hens had physical access to the following treatments: no enrichment provided (control); round, wooden perches provided (perch); scratch pads made of wood and covered with sandpaper provided (scratch pad); hanging CDs provided (hanging CD); all three enrichment forms provided (complex environment); and each enrichment form provided on a 2-week rotation (novelty). While the egg quality was consistent, the complex group had a significantly lower feed intake (p < 0.001) but similar egg mass; thus, they also had a lower feed conversion ratio (p < 0.001). Additionally, hens in the complex treatment group had the leanest average body weight at week 5 (p < 0.01). It should be noted that while some measures of welfare did show significant differences, the biological or practical difference may be negligible, as in the case of the body condition score in this study. The results show that providing three different forms of environmental enrichment had positive effects on the welfare, health condition, and laying performance of laying hens relative to no enrichment or only one enrichment item.

Experimental Study on Proportion Optimization of Rock-like Materials Based on Genetic Algorithm Inversion.

It is very important to clarify the optimization method of the rock-like material ratio for accurately characterizing mechanical properties similar to the original rock. In order to explore the optimal ratio of rock-like materials in gneissic granite, the water-paste ratio, iron powder content and coarse sand content were selected as the influencing factors of the ratio. An orthogonal test design and sensitivity analysis of variance were used to obtain the significant influencing factors of the ratio factors on seven macroscopic mechanical parameters, including compressive strength σc, tensile strength σt, shear strength τf, elastic modulus E, Poisson's ratio ν, internal friction angle φ and cohesion c. A multivariate linear regression equation was constructed to obtain the quantitative relationship between the significant ratio factors and the macroscopic mechanical parameters. Finally, a rock-like material ratio optimization program based on genetic algorithm inversion was written. The results show that the water-paste ratio had extremely significant effects on σc, σt, τf, E, ν and c. The iron powder content had a highly significant effect on σc, σt, τf and c, and it had a significant effect on ν and φ. Coarse sand content had a significant effect on σc, E and c. The multiple linear regression model has good reliability after testing, which can provide theoretical support for predicting the macroscopic mechanical parameters of rock-like materials to a certain extent. After testing, the ratio optimization program works well. When the water-paste ratio is 0.5325, the iron powder content is 3.975% and the coarse sand content is 15.967%, it is the optimal ratio of rock-like materials.

12420 Effects Of A Continuous Glucose Monitoring (CGM) Optimization Program On Prescribing Behavior And Glycemic Outcomes

Abstract Disclosure: A. Sterbenz: None. J. Fuentes Rosales: None. S. Abraham: None. P. Siroya: None. R. Middelbeek: None. J. Mitri: None. F. Rossitto: None. E. Cabana: None. A. Gharse: None. L. Collins: None. L. Aliaga: None. A. Millan Ferro: None. J. Cifuni: None. A. Schwartz: None. E. Kelley: None. C. Gomez: None. M. Aballay: None. Introduction: Continuous glucose monitoring (CGM) improves glucose control in patients with diabetes, as demonstrated in clinical trials. There is limited information available on the real-world application of CGM adoption in a resource-limited setting or safety net outpatient hospital setting. This intervention's aim was to assess the impact of a multifaceted approach on improving CGM utilization and on glycemic control among patients with diabetes. Methods: Wyckoff Heights Medical Center, located in Brooklyn, NY is a resource-limited setting that in partnership with the Joslin Diabetes Center, Boston, MA launched a CGM Clinical Care Optimization Program. This quality improvement (QI) initiative included establishing a diabetes technology team (DTT) which met weekly (n=24 sessions) to establish a highly specialized interprofessional group. The DTT activities included: 1) Establishing a CGM workflow; 2) Expanding Medical Assistant responsibilities related to diabetes technology and training; 3) Creating five bilingual (English-Spanish) patient handouts; 4) Building CGM vendor relationships; 5) Developing a CGM manual for data download, billing and coding, and education to increase CGM knowledge across the system. Data were collected prior to CGM initiation and at most recent visit. Results: The intervention occurred between February and November 2023. Prior to this intervention, 49 patients had a CGM prescription. After 9 months, CGM prescriptions were written for 333 patients (680% increase) with the following baseline characteristics (mean ± SD): age (61.2 ± 14.7 yrs.); 56% female; 44% male; 71% Latino; Race: 67% Other (63% Hispanic or Latino), 25% Black or African American, 4% White, Asian 2%, American Indian or Alaska Native 2%; 38% Spanish-speaking, 59% English-Speaking, 3% Other; 81% government insurance (63% Medicaid, 18% Medicare), 19% commercial insurance. The majority of patients had been diagnosed with type 2 diabetes (91%) and 9% had type 1 diabetes. Baseline HbA1C was 9.7 ± 1.5%. Of note, prior to CGM, 34% of patients had A1C>9%. The impact of CGM use was evaluated on 116 patients that met the following criteria: baseline A1C value within 60 days before or up-to 30 days after prescription, and a A1C >90 days after. After an average duration of 154 days of CGM use, HbA1c decreased significantly by -0.51% (p=0.001), demonstrating improved glycemic control post-CGM use. In those with A1c >9%, HbA1c decreased from 10.5% to 8.8% (p<0.01). Conclusion: Creating a CGM optimization program in a resource-limited setting is critical in improving patient self-awareness and provider insight in glycemic control in patients diagnosed with type 1 and type 2 diabetes. These data demonstrate the feasibility of implementing a CGM optimization program in a resource-limited setting, which is critically important in improving clinical care for people with diabetes. Presentation: 6/2/2024

Passive Mobilization With Place-and-Hold Versus Active Mobilization Therapy After Flexor Tendon Repair: 5-Year Minimum Follow-Up of a Randomized Controlled Trial

There is no consensus on the optimal postoperative rehabilitation program following flexor tendon repair. Some studies suggest a faster recovery after active mobilization, whereas other studies have failed to find any differences between active and passive mobilization at 12 months. To our knowledge, no prior randomized controlled trial has compared the long-term effects of these two approaches. This randomized controlled trial compared the long-term outcomes of active mobilization with those of passive mobilization in combination with place-and-hold. Sixty-four patients with a flexor tendon injury in zones I or II were included in the study. After surgery, patients were randomized to either active mobilization or passive mobilization with place-and-hold. Forty-seven patients were available for the 5-year minimum follow-up. Assessments included range of motion, grip strength, key pinch, as well as the Disabilities of the Arm, Shoulder, and Hand (DASH) and ABILHAND questionnaires. At the 5-year minimum follow-up, range of motion was significantly better in the group treated with passive mobilization with place-and-hold compared with the active mobilization group. Furthermore, there was a significant deterioration in the range of motion and an increased flexion contracture in the active mobilization group compared with 1 year after surgery. Grip strength deteriorated significantly in both groups from the 1-year to the 5-year minimum follow-up, but key pinch did not change. In both groups, DASH and ABILHAND scores improved from the 1-year to the 5-year minimum follow-up. Passive mobilization with place-and-hold following flexor tendon repair results in superior long-term outcomes compared with active mobilization. Therapeutic I.

Exercising with virtual reality is potentially better for the working memory and positive mood than cycling alone

Background and aimsAlthough virtual reality (VR) exercise has attracted attention as a factor in exercise habituation due to its mood-enhancing effects, its impact on brain function remains unclear. This study aimed to explore how VR exercise affects working memory, a key executive function, and its underlying neural mechanisms. MethodsThe study involved 23 healthy university students who engaged in exercise in a VR environment. Outcomes included cardiorespiratory and psychological assessments, as well as functional magnetic resonance imaging (fMRI). ResultsOur findings indicate that a 10-min VR exercise session improved mood (arousal and vitality level) and working memory task performance (3-back task) more effectively than exercise or rest alone. Furthermore, the results confirmed that increased vitality from both exercise and VR exercise interventions was associated with improved 3-back task performance. However, specific brain regions contributing to this enhancement remain unidentified. ConclusionThese results highlight VR exercise as an optimal exercise program for enhancing working memory function by increasing vitality levels. These insights underscore the potential of VR as a novel exercise modality, with benefits extending beyond exercise adherence to potentially prevent dementia and depression.

Machine-directed Gravitational-wave Counterpart Discovery

Joint observations in electromagnetic and gravitational waves shed light on the physics of objects and surrounding environments with extreme gravity that are otherwise unreachable via siloed observations in each messenger. However, such detections remain challenging due to the rapid and faint nature of counterparts. Protocols for discovery and inference still rely on human experts manually inspecting survey alert streams and intuiting optimal usage of limited follow-up resources. Strategizing an optimal follow-up program requires adaptive sequential decision-making given evolving light curve data that (i) maximizes a global objective despite incomplete information and (ii) is robust to stochasticity introduced by detectors/observing conditions. Reinforcement learning (RL) approaches allow agents to implicitly learn the physics or detector dynamics and the behavior policy that maximizes a designated objective through experience. To demonstrate the utility of such an approach for the kilonova follow-up problem, we train a toy RL agent with the goal of maximizing follow-up photometry for the true kilonova among several contaminant transient light curves. In a simulated environment where the agent learns online, it achieves 3× higher accuracy compared to a random strategy. However, it is surpassed by human agents by up to a factor of 2. This is likely because our hypothesis function (Q that is linear in state-action features) is an insufficient representation of the optimal behavior policy. More complex agents could perform at par or surpass human experts. Agents like these could pave the way for machine-directed software infrastructure to efficiently respond to next generation detectors, for conducting science inference and optimally planning expensive follow-up observations, scalably and with demonstrable performance guarantees.

Dedicated Patient Optimization Program Improves Lives of Vulnerable Patients

Dedicated Patient Optimization Program Improves Lives of Vulnerable Patients

A non randomised pilot study to investigate the acceptability and feasibility of LivR Well: a multifaceted 28-day home based liver optimisation program for acute-on-chronic liver failure.

Background and AimsAcute-on-chronic liver failure (ACLF) has a 22-74% 28-day mortality rate and 30-40% 30-day re-admission rate. We investigated the acceptability and feasibility of a multimodal community intervention for ACLF. MethodsA single-arm, non-randomized pilot study of consecutive participants with ACLF was conducted in a tertiary health service. Participants received weekly medical and nursing reviews, dietetics, physiotherapy, pharmacy, social work, addiction medicine, and/or neuropsychiatry. A digital platform included remote weight monitoring, and online surveys. The primary outcome was acceptability/feasibility. Secondary outcomes included safety, mortality, re-admission, liver disease severity, and costs. ResultsFifty-nine patients were enrolled with median age 51 years (IQR: 45-59); majority alcohol etiology (74%),and median Model for End-Stage Liver Disease Sodium (MELD-Na) score 16 (IQR: 12-21). LivR Well was acceptable with low attrition (8/59), adherence to the program including home visits (mean 8.4±4.2) and consultations (mean 2.4±1.5) per patient. This was supported by positive feedback and themes identified through a qualitative sub-analysis. Feasibility was demonstrated by recruitment rate of 4.94 patients/month and 86% completion. Mortality was lower than expected at 3%, 30-day readmission rate was 15%, and median MELD-Na score reduced to 15 (p = 0.01). Median 6-month costs reduced from $30,454 (IQR: 21953-$65657) to $17, 657 ($4249-42876) (p=0.009). The total 6-month healthcare cost was $1,868,859 (95% CI 1,081,821-2,655,897) compared to $2,518,227 (95% CI 1,959,610-3,076,844). ConclusionLivR Well was acceptable, feasible and safe with low short-term mortality and readmission rates. Healthcare costs were reduced by 26% driven by a 40% reduction in 30-day re-admission. Further evaluation includes a randomized, controlled trial of LivR Well compared to standard care.

Automatic Optimal Design Method for Minimum Total Resistance Hull Based on Enhanced FFD Method

Hull optimization plays a crucial role in enhancing ship performance and efficiency. This study aimed to improve ship performance, particularly by reducing total resistance, through automated optimization methods. To this end, an integrated, fully automated optimization program was developed based on Python, incorporating Enhanced Free-Form Deformation (FFD) technology, scripted CFD numerical evaluation, and the Particle Swarm Optimization (PSO) algorithm. This program allowed precise control of hull form, improving efficiency while reducing costs. The KCS hull, known for its excellent resistance performance, was chosen as the optimization target, with the goal of minimizing total resistance by adjusting the bulbous bow line plan. The research results indicated that the optimized scheme exhibited lower resistance characteristics compared to the original design while satisfying design constraints. This study not only provides a new optimization strategy for ship design but also lays a foundation for future hull optimization research.

Learning-based Pareto-optimum routing of ships incorporating uncertain meteorological and oceanographic forecasts

In modern shipping logistics, multi-objective ship route planning has attracted considerable attention in both academia and industry, with a primary focus on energy conservation and emission reduction. The core challenges in this field involve determining the optimal route and sailing speed for a given voyage under complex and variable meteorological and oceanographic conditions. Typically, the objectives revolve around optimizing fuel consumption, carbon emissions, duration time, energy efficiency, and other relevant factors. However, in the multi-objective route planning problem involving variable routes and speeds, the extensive solution space contains a substantial number of unevenly distributed feasible samples. Traditional heuristic optimization techniques, such as multi-objective evolutionary algorithms, which serve as the core component of optimization programs, suffer from inefficiencies in exploring the solution space. Consequently, these algorithms may tend to converge toward local optima during population iteration, resulting in a solution set characterized by sub-optimal convergence and limited diversity. This ultimately undermines the potential benefits of routing optimization. To address such challenging problem in route planning tasks, we propose a self-adaptive intelligent learning network aiming at capturing the potential evolutionary characteristics during population iteration, in order to achieve high-efficiency directed optimization of individuals. Additionally, an uncertainty-driven module is developed by incorporating ensemble forecasts of meteorological and oceanographic variables to form the Pareto frontier with more reliable solutions. Finally, the overall framework of the proposed learning-based multi-objective evolutionary algorithm is meticulously designed and validated through comprehensive analyses. Optimization results demonstrate its superiority in generating routing plans that effectively minimize costs, reduce emissions, and mitigate risks.

An intervention to determine the effectiveness of the Sanubari optimal health program (OHP) in improving mental well-being among junior doctors in Malaysia: a quasi-experimental study

BackgroundMental health problems among junior doctors in Malaysia pose a significant concern, as they not only adversely affect their overall well-being but also impact the quality of health services they provide. Therefore, it is important to implement interventions aimed at preserving their mental health. This study aimed to evaluate the effectiveness of the Sanubari Optimal Health Program (OHP) intervention in enhancing the mental well-being of junior doctors in Malaysia.MethodsThe study utilised a quasi-experimental design involving 204 junior doctors who participated in the intervention. The Sanubari OHP Intervention Package was employed alongside self-administered questionnaires for mental health and well-being assessment. To evaluate the effects over time, repeated measures ANOVA was employed to analyse within-group and between-group changes in various endpoints, as measured at different assessment time points.ResultsThe study revealed that self-efficacy and adaptive coping behaviours scores increased over time among the intervention group and decreased among the control group. Yet, depression and anxiety scores decreased significantly over time among the intervention group but not in the control group. No significant differences were observed in well-being and maladaptive coping strategies among the groups.ConclusionsThis study showed promising results regarding the effectiveness of Sanubari OHP in improving mental well-being among junior doctors.

Do modifiable patient factors increase the risk of postoperative complications after total joint arthroplasty?

Numerous studies demonstrate that modifiable lifestyle risk factors can influence patient outcomes including survivability, quality of life, and postoperative complications following orthopaedic surgery. The purpose of this study was to determine the impact of modifiable lifestyle risk factors on postoperative medical and surgical complications following a total joint arthroplasty (TJA) in a large national healthcare system. A retrospective chart review of a large national health system database was performed to identify patients who underwent TJA between 2017 and 2021. TJA included total knee arthroplasty, total hip arthroplasty, and total shoulder arthroplasty. Modifiable lifestyle risk factors were defined as tobacco use, narcotic drug abuse, hypertension, and diabetes mellitus. Postoperative medical complications and postoperative surgical complications were collected. Logistic regression and odds ratio point estimate analysis were conducted to assess for associations between postoperative complications and modifiable lifestyle risk factors. Of the 16,940 patients identified, the mean age was 71 years, mean BMI was 29.7kg/m2, and 62% were women. We found that 3.5% had used narcotics, 8.7% were past or current smokers, 24% had diabetes, and 61% had hypertension; in addition, 5.4% experienced postoperative medical complications and 6.4% experienced postoperative surgical complications. Patients who used narcotics were 90% more likely to have postoperative complications (p < 0.0001) and 105% more likely to experience prosthetic complications (p < 0.0001). Similarly, patients with tobacco use were 65% more likely to have postoperative complications (p < 0.0001) and 27% more likely to experience prosthetic complications. Our results demonstrate critical rates of increased postoperative medical and surgical complications after TJA for patients with narcotic abuse, tobacco use, or diabetes mellitus. Furthermore, adopting preoperative interventions and optimization programs informed by our findings on specific modifiable risk factors could aid orthopaedic surgeons in optimizing patient health. III; Retrospective study.

Optimizing multivariate alarm systems: A study on joint false alarm rate, and joint missed alarm rate using linear programming technique

In modern complex industrial systems, multiple process variables interact with one another. The role of alarm systems in ensuring the safety of these systems is of utmost importance. Consequently, there is an increasing value placed on the assessment of the performance of multivariate alarm systems. As the dimensions of the system and the number of variables grow, designing optimal parameters for the multivariate alarm system using traditional approaches such as probability density function estimation becomes increasingly convoluted. In this paper, an approximate method is proposed for calculating two indices known as the Joint False Alarm Rate (JFAR) and Joint Missed Alarm Rate (JMAR), which are used to evaluate the performance of multivariate alarm systems. These indices are computed using the multivariate Markov chain method. The Markov chain is constructed by solving an optimal Linear Programming (LP) problem. Subsequently, joint indices are defined based on steady state estimations of a multivariate Markov chain. To validate the theoretical results obtained on the JFAR and JMAR and to demonstrate the proposed performance assessment and alarm system design procedures, numerical example and an industrial case study are provided.