Continuous Performance Improvement Research Articles

Physician Effects in Critical Care: A Causal Inference Approach Through Propensity Weighting with Parametric and Super Learning Methods

Physician performance is critical to caring for patients admitted to the intensive care unit (ICU), who are in life-threatening situations and require high level medical care and interventions. Evaluating physicians is crucial for ensuring a high standard of medical care and fostering continuous performance improvement. The non-randomized nature of ICU data often results in imbalance in patient covariates across physician groups, making direct comparisons of the patients’ survival probabilities for each physician misleading. In this article, we utilize the propensity weighting method to address confounding, achieve covariates balance, and assess physician effects. Due to possible model misspecification, we compare the performance of the propensity weighting methods using both parametric models and super learning methods. When the generalized propensity or the quality function is not correctly specified within the parametric propensity weighting framework, super learning-based propensity weighting methods yield more efficient estimators. We demonstrate that utilizing propensity weighting offers an effective way to assess physician performance, a topic of considerable interest to hospital administrators.

Relationship between Head Teacher's Motivation Strategies and Teacher's Performance in Secondary Schools in Serere District

Globally, teacher performance is a significant challenge for education, necessitating effective strategies to combat low morale and ensure adequate school performance (Gitonga, 2012). This study investigated the relationship between headteacher motivation strategies and Teacher Performance in secondary schools in the Serere district. Head teachers in Serere District are faced challenges such as lack of supervision, low assessment, absenteeism and non-participation in school activities, leading to a 50% decline in performance. Research was needed to understand the relationship between motivation strategies and teachers’ performance. With a sample size of 246 respondents, including head teachers and teachers, a cross-sectional correlation survey methodology was employed.The Spearman Rank Order Correlation Coefficient was utilized to analyze the data that were collected through surveys.The results showed a favorable relationship between head teachers' motivation and secondary school teachers' performance in the Serere district, suggesting that accommodations, salary increases, and capacity building encourage teachers to work hard.The research findings indicated a positive association between the motivational strategies employed by head teachers and the performance of teachers in secondary schools within the Serere District. The study suggested that additional policies should be introduced to bolster and sustain the existing framework of head teachers' motivational strategies. It further recommended revisiting and refining policies related to their implementation to foster continuous improvement in teachers' performance. Additionally, the study proposed the exploration of further research avenues to delve into motivation strategies and teachers' job satisfaction in secondary schools specifically within the Serere District

Public-Private Partnership in Healthcare is a Necessity in Difficult Times: A case study

Healthcare delivery is a risky enterprise for both public and private systems which may face adversity in a volatile, uncertain, complex, and ambiguous world. There has been a global emergence of Public-Private Partnership (PPP) hospitals to enhance delivery of sustainable healthcare, although its existence in developing countries remains limited. Risk management strongly influences PPP success; few PPP hospitals were able to meet contractually specified quality and performance requirements, creating debate regarding merits of the PPP model. Conversely, we present a case study of the first PPP hospital operated by not-for-profit organisation in New South Wales, Australia, to successfully complete the contract period. A Continuous Quality and Performance Improvement Framework was applied at five stages of organisational growth during the contract term. This case study demonstrates adaptive leadership and just organisational culture are equally important in providing high quality healthcare services to the community. We show the future potential of PPP model for service delivery as a third option to pure public or private sector hospitals, even in the post COVID-19 era when there is likelihood of financial instability in many settings.

Review-based method for evaluating key performance indicators: an application on warehouse system

This paper presents a procedure for selecting, evaluating, and ranking the key performance indicators (KPIs) in the context of warehouse systems. The method aims to help warehouse managers identify and prioritize KPIs most relevant to their business objectives and performance goals, ultimately driving continuous improvement in warehouse performance. The paper provides three main scientific contributions: (i) a selection composed of 70 KPIs extracted from the analysis of 203 scientific articles and clustered according to their business and sustainability objectives, (ii) a KPI ranking based on four different metrics measured analyzing 585 scientific papers from Scopus and, finally, (iii) a fifth metric, measured trough a survey submitted to 15 industrial experts from as many different Italian companies, that is added to improve the ranking system. As a part of the method applied to a case study, the paper provides a comprehensive review of the current scientific literature and industrial sector state of the art on warehouse KPIs. This article proposes an overview of the most used KPIs in research and industry, a support for understanding formalizing definitions, and a guide on how they can be measured and used to manage warehouses.

Lateral ring compression test applied to a small caliber steel jacket: Identification of a constitutive model

The evolution of threats and scenarios requires continuous performance improvements of ballistic protections for armed forces. From a modeling point of view, it is necessary to use sufficiently precise material behavior models to accurately describe the phenomena observed during the impact of a projectile on a protective equipment. In this context, the goal of this paper is to characterize the behavior of a small caliber steel jacket by combining experimental and numerical approaches. The experimental method is based on the lateral compression of ring specimens directly machined from the thin and small ammunition. Various speeds and temperatures are considered in a quasi-static regime in order to reveal the strain rate and temperature dependencies of the tested material. The Finite Element Updating Method (FEMU) is used. Experimental results are coupled with an inverse optimization method and a finite element numerical model in order to determine the parameters of a constitutive model representative of the jacket material. Predictions of the present model are verified against experimental results and a parametric study as well as a discussion on the identified material parameters are proposed. The results indicate that the strain hardening parameter can be neglected and the behavior of the thin steel jacket can be described by a modeling without strain hardening sensitivity.

Towards use of life cycle–based indicators to support continuous improvement in the environmental performance of avocado orchards in New Zealand

PurposeA life cycle assessment (LCA) study was undertaken for the orchard stage of the NZ avocado value chain, to guide the development of indicators for facilitating continuous improvement in its environmental profile.MethodsThe functional unit (FU) was 1 kg Hass avocados produced in NZ, up to the orchard gate. The baseline model assessed avocados produced in fully productive orchards, using input data collected from 49 orchards across 281 ha in the three main avocado growing regions of New Zealand. In addition, the non-productive and low production years of avocado orchards were assessed using data from four newly established avocado operations spread across 489 ha. Climate change, eutrophication, water use, freshwater ecotoxicity and terrestrial ecotoxicity results were calculated for each orchard. Finally, national scores were calculated for each impact category from the weighted averages of the individual orchard results in the baseline sample of the three studied regions.ResultsThere was significant variability between orchards in different input quantities, as well as impact scores. The impact assessment results showed that fuel use and fertiliser/soil conditioner production and use on orchard were consistently the main hotspots for all impact categories except water use, where impacts were generally dominated by indirect water use (irrespective of whether the orchards were irrigated or not). When considering the entire orchard lifespan, the commercially productive stage of the orchard life contributed the most to all impact category results. However, the impacts associated with 1 kg avocados, when allocated based on the total impacts across the orchard lifespan, were 13–26% higher than the baseline results which considered only the commercially productive years of the orchard life.ConclusionThe study identified the priority areas for focussed improvement efforts (in particular, fertiliser and fuel use for all impact categories, and agrichemical use for the ecotoxicity impacts). Second, the regional- and national-level impact scores obtained in this study can be used as benchmarks in indicator development to show growers their relative ranking in terms of environmental performance. When using the indicators and benchmarks in a monitoring scheme, consideration should be given to developing separate benchmarks (using area-based functional units) for young orchards. It will also be necessary to develop a better understanding of the reasons for the variability in inputs and impacts so that benchmarks can be tailored to account fairly and equitably for the variability between orchards and regions.

Ergonomics management model based on good practices of large industrial companies: Structured elements through the fuzzy set theory.

Most organizations perceive the concept of ergonomics actions as a local tool used to improve workplace issues. Ergonomics however is not included in global management systems. The paradigm of ergonomics action in large organizations proposed by this study is that of management systems whose primary objective is the pressing need for continuous performance improvement, acquisition of excellence, and integration into all aspects of the business. The general objective of this work is to draw a frame presenting a model of ergonomics management based on good practices in managing this discipline in large industrial companies. In this study, we describe a strategy based on the fuzzy set theory to aggregate a group of good practices expressed by experienced ergonomists attached to large industrial companies for handling the lack of ergonomics in an organization. A set of good practices was compiled as a basis for application in ergonomics in large companies. This study shows that ergonomics should not be considered an isolated phenomenon but in a strategic, proactive, integrated, and perennial way as one of the management subjects.

Microstructural changes in nickel‐ceria fuel electrodes at elevated temperature

AbstractDurability testing of low temperature solid oxide cells is challenging as degradation phenomena related to microstructural changes like nickel‐agglomeration are slow. In the present study, a nickel/gadolinia doped ceria (GDC) fuel electrode with a porous GDC‐interlayer towards the zirconia electrolyte was investigated. The electrode, designed for operating temperatures of 600°C, was tested at an elevated temperature of 900°C for up to 1100 h to accelerate aging. Contrary to every expectation, the electrodes showed continuous improvement in electrochemical performance. Impedance spectroscopy, the distribution of relaxation times analysis, scanning electron microscope and transmission electron microscope were applied to correlate electrochemical and microstructural changes. Structural analysis showed a significant Ni agglomeration accompanied by a decrease in triple phase boundary density. Furthermore, a minor particle growth in the GDC‐phase decreased the volume‐specific double phase boundary GDC/pore. Considering these microstructural changes, the decrease in active reaction sites should have increased the polarization resistance, but a decrease of about 32% was observed. The discrepancy between polarization resistance improvement and microstructural degradation might be attributed to an activation of the GDC‐surfaces in the electrode and the porous GDC‐interlayer.

Wavelet analysis model inspired convolutional neural networks for image denoising

In recent years, many image denoising methods have been proposed based on convolutional neural networks (CNNs). While these methods have shown continuous performance improvement by introducing various mechanisms and structures, their computational cost tends to become increasingly expensive, owing to the resulting complex network architectures. This paper aims at winning the trade-off between computational efficiency and denoising performance for CNN-based image denoisers. Towards this end, we draw inspirations from traditional variational models with wavelet analysis operators for CNN architecture design. A model-inspired CNN is proposed with four key modules: iterative encoding-decoding units inspired by the iterative denoising process, directional convolutions inspired by the separable wavelet filters, inception modules inspired by the multi-scale analysis of wavelets, and stage-wise connections inspired by the adding noise back operation. In experiments, our CNN shows high computational efficiency in both training and test, with competitive results to state-of-the-art approaches.

Real-World Application: Six Sigma Quality Tools for Continuous Performance Improvement in Specific Educational Practices: A Systematic Review

The education sector is continuously seeking new approaches to improve the existing educational process while reducing instances of failure. Recently, the use of Six Sigma quality tools as a continuous improvement tool has proven to be a promising solution. To investigate the effectiveness of this method, a systematic literature review was conducted to summarize relevant empirical data on Six Sigma quality tools in education. This systematic review follows the PRISMA criteria (preferred reporting items for systematic reviews and meta-analysis) and utilizes three major databases: Web of Science (WoS), Scopus, and Dimensions. Eleven articles met the given criteria, and the empirical results revealed that Six Sigma quality tools – including Suppliers, Inputs, Process, Outputs and Customers (SIPOC), Pareto analysis, Fish Bone technique, Failure Mode and Effect Analysis (FMEA), and Statistical Process Control (SPC) – could be used in institutions to meet practitioners’ requirements and achieve the desired quality standards. This study will provide a more comprehensive understanding of the benefits of Six Sigma quality tools in education as well as the ways in which they can be used to improve educational quality for students. Six Sigma quality tools are an innovative and promising approach to improving education. As indicated by the findings, further research is recommended to assess the use of the Six Sigma quality tools in evaluating educational interventions in various educational settings, especially at the school level, in order to verify significant improvements in educational quality and, ultimately, better student outcomes.

Lean and interpretable digital twins for building energy monitoring – A case study with smart thermostatic radiator valves and gas absorption heat pumps

The transition to low carbon energy systems poses challenges in terms of energy efficiency. In building refurbishment projects, efficient technologies such as smart controls and heat pumps are increasingly being used as a substitute for conventional technologies with the aim of reducing carbon emissions and determining operational energy and cost savings, together with other benefits. Measured building performance, however, often reveals a significant gap between the predicted energy use (design stage) and actual energy use (operation stage). For this reason, lean and interpretable digital twins are needed for building energy monitoring aimed at persistence of savings and continuous performance improvement. In this research, interpretable regression models are built with data at multiple temporal resolutions (monthly, daily and hourly) and seamlessly integrated with the goal of verifying the performance improvements due to Smart thermostatic radiator valves (TRVs) and gas absorption heat pumps (GAHPs) as well as giving insights on the performance of the building as a whole. Further, as part of modelling research, time of week and temperature (TOWT) approach is reformulated and benchmarked against its original implementation. The case study chosen is Hale Court sheltered housing, located in the city of Portsmouth (UK). This building has been used for the field-testing of innovative technologies such as TRVs and GAHPs within the EU Horizon 2020 project THERMOSS. The results obtained are used to illustrate possible extensions of the use of energy signature modelling, highlighting implications for energy management and innovative building technologies development.

Aerodynamics-based design of the rear wing of a sports car

The value that people place on a car's performance has increased significantly in recent years. With advances in automotive technology, the performance of racing cars has become a critical factor in their success. The study of aerodynamics has become increasingly important in achieving efficiency and speed in sports cars. Despite the growing interest in a car's appearance, the design of sports cars remains highly focused on aerodynamics. The rear wing, in particular, plays a critical role in the car's overall performance, providing downforce and reducing drag. As a result, designing an effective rear wing is a critical aspect of any sports car design. This article analyzes and compares different rear wing designs to determine which is the most effective. By considering factors such as shape, size, and angle of attack, designers can optimize the rear wing's performance for a particular car and racing conditions. The findings of this analysis can help inform future sports car designs and contribute to continued improvements in automotive performance.

Application of Machine learning techniques in environmental governance: A review

In recent years, based on the continuous improvement of computer hardware performance, machine learning (ML) technology has made rapid development. Due to the strong ability of ML methods to find complex functions between associated data and the low cost of human and material resources, they are increasingly favored by environmental governance practitioners. Current research has confirmed that the application of ML technology to the field of environmental governance is of great significance in overcoming the difficulties of analysis and practice in traditional environmental work, and can greatly promote the long-term development of such directions as air quality prediction, solid waste classification, pollutant distribution mapping, and intelligent management of water environment. Therefore, by organizing the representative literature in the past five years, this paper describes the current status of the application of ML technology in four different elements of environmental governance, namely, atmosphere, water, soil, and solid waste, and aims to summarize the possible directions of the future development of ML technology and the new development brought to the field of environmental governance by exploring the basic concepts of ML, the advantages and disadvantages of the model and the constraints and other issues.

How much convergence exists between registered process measures and patient reported experience measures? A study on Catalan primary healthcare.

The use of quality measures is important for transparency and the continuous improvement of performance. However, we do not know enough about the relationship between registered process measures and patient reported experience measures (PREMs) in primary healthcare. Recent studies point to areas of convergence that run contrary anticipated trends. This is a relevant question for healthcare management and governments as their position is stronger when system's guidelines and targets also matter to patients or, vice versa, when patient-centered measures are used to develop new process measures. Our aim is to study both type of measures, their logic and their level of convergence. We also assess the relationship between PREMs, patients' demographic characteristics and area socioeconomic level. We estimated pairwise correlations with patient experience aggregated at the health-center level and a series of multilevel regression models to assess the adjusted effect of four registered process measures on ratings of patient experience at the patient level. We use patient experience as measured by survey data, two composite quality indices and two single indicators regularly computed by the Catalan Health Service. Continuity of care with the same doctor and accessibility are positively associated with patient experience. No relationship was observed in the index created to measure quality of assistance. The index measuring the quality of prescriptions was positively associated with patient experience but only when analyzed separately. We conclude that registered process measures and PREMs are not entirely independent, hence improvements of the management side have the potential to impact patient experience.

An Environmental Impact Assessment of Largemouth Bass (Micropterus salmoides) Aquaculture in Hangzhou, China

With the rapid increase in aquaculture production, its role in food safety and nutritional security has become increasingly important, but this has also given rise to environmental problems that cannot be ignored. The largemouth bass (Micropterus salmoides) has become a widely cultivated and highly economic freshwater farmed species since it was introduced to China in 1983; however, the environmental impacts of its freshwater pond aquaculture process have not yet been elucidated. Here, life cycle assessment (LCA), a decision-making tool that can evaluate and identify environmental issues during production processes, was used to evaluate the environmental performance of the largemouth bass freshwater pond aquaculture process, and a large-scale, commercial company was selected as an example in Hangzhou, China. The results showed that the pond-farming stage and marine aquatic ecotoxicity potential (MAETP) had the largest environmental impacts on the entire aquaculture process. An environmental contribution analysis indicated that electricity (48%) and emissions (23%) were two key factors in the seed-rearing stage, and electricity (60%) and feeds (26%) were two main impact contributors in the pond-farming stage. Improvement measures based on emerging technologies in aquaculture were discussed, namely, clean energies, industrial pond farming, and intelligent feeding strategies, to help with decision making for continuous improvement in the environmental performance of largemouth bass pond farming. Moreover, suggestions for further aquaculture LCA studies in China were summarized, as they will provide a useful reference for promoting the development of China’s aquaculture LCA research and the enrichment of the world’s aquaculture life cycle inventory databases.

Out-of-core diffraction algorithm using multiple SSDs for ultra-high-resolution hologram generation.

The diffraction calculation is critical in computer-generated holography (CGH). However, it becomes a performance bottleneck when generating ultra-high-resolution holograms due to limited physical memory space. We propose a novel out-of-core (OOC) diffraction algorithm that utilizes multiple solid-state drives (SSDs) to address this issue. Our method employs the implicit diffraction approach and exploits its even-odd separation characteristic to utilize multiple SSDs optimally. We implement our algorithm on two machines, each with four SSDs, and compare it with prior OOC diffraction methods and a RAID-based solution. Our approach achieves up to 2.43 times higher performance than prior OOC methods for large-scale diffraction calculations, with continued performance improvement observed by adding more SSDs. Additionally, our method reduces the generation time for ultra-high-resolution holograms (200K × 200K) by 38% compared to the prior OOC method with multiple SSDs. These results demonstrate the effectiveness of our algorithm for extreme-scale CGH.

Enhancing the Hardware Pipelining Optimization Technique of the SHA-3 via FPGA

Information is transmitted between multiple insecure routing hops in text, image, video, and audio. Thus, this multi-hop digital data transfer makes secure transmission with confidentiality and integrity imperative. This protection of the transmitted data can be achieved via hashing algorithms. Furthermore, data integrity must be ensured, which is feasible using hashing algorithms. The advanced cryptographic Secure Hashing Algorithm 3 (SHA-3) is not sensitive to a cryptanalysis attack and is widely preferred due to its long-term security in various applications. However, due to the ever-increasing size of the data to be transmitted, an effective improvement is required to fulfill real-time computations with multiple types of optimization. The use of FPGAs is the ideal mechanism to improve algorithm performance and other metrics, such as throughput (Gbps), frequency (MHz), efficiency (Mbps/slices), reduction of area (slices), and power consumption. Providing upgraded computer architectures for SHA-3 is an active area of research, with continuous performance improvements. In this article, we have focused on enhancing the hardware performance metrics of throughput and efficiency by reducing the area cost of the SHA-3 for all output size lengths (224, 256, 384, and 512 bits). Our approach introduces a novel architectural design based on pipelining, which is combined with a simplified format for the round constant (RC) generator in the Iota (ι) step only consisting of 7 bits rather than the standard 64 bits. By reducing hardware resource utilization in the area and minimizing the amount of computation required at the Iota (ι) step, our design achieves the highest levels of throughput and efficiency. Through extensive experimentation, we have demonstrated the remarkable performance of our approach. Our results showcase an impressive throughput rate of 22.94 Gbps and an efficiency rate of 19.95 Mbps/slices. Our work contributes to advancing computer architectures tailored for SHA-3, therefore unlocking new possibilities for secure and high-performance data transmission.

An adaptable end-to-end maintenance performance diagnostic framework

PurposeThis paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.Design/methodology/approachThe case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.FindingsTo improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.Originality/valueMethodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

Wave‐based inversion at scale on graphical processing units with randomized trace estimation

AbstractThanks to continued performance improvements in software and hardware, wave‐equation‐based imaging technologies, such as full‐waveform inversion and reverse‐time migration, are becoming more commonplace. However, widespread adaptation of these advanced imaging modalities has not yet materialized because current implementations are not able to reap the full benefits from accelerators, in particular those offered by memory‐scarce graphics processing units. Through the use of randomized trace estimation, we overcome the memory bottleneck of this type of hardware. At the cost of limited computational overhead and controllable incoherent errors in the gradient, the memory footprint of adjoint‐state methods is reduced drastically. Thanks to this relatively simple to implement memory reduction via an approximate imaging condition, we are able to benefit from graphics processing units without memory offloading. We demonstrate the performance of the proposed algorithm on acoustic two‐ and three‐dimensional full‐waveform inversion examples and on the formation of image gathers in transverse tilted isotropic media.

Blood culture contamination in a tertiary care hospital: a retrospective three-year study

BackgroundBloodstream infections (BSI) are a leading cause of morbidity and mortality in hospitalized patients worldwide. A blood culture is the primary tool for determining whether a patient has BSI and requires antimicrobial therapy, but it can result in an inappropriate outcome if the isolated microorganisms are deemed contaminants from the skin. Despite the development of medical equipment and technology, there is still a percentage of blood culture contamination. The aims of this study were to detect the blood culture contamination (BCC) rate in a tertiary care hospital in Palestine and to identify the departments with the highest rates along with the microorganisms isolated from the contaminated blood samples.MethodBlood cultures that were taken at An-Najah National University Hospital between January 2019 and December 2021 were evaluated retrospectively. Positive blood cultures were classified as either true positives or false positives based on laboratory results and clinical pictures. Statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) version 21. A p-value of less than 0.05 was considered statistically significant for all analyses.ResultsOut of 10,930 blood cultures performed in the microbiology laboratory from 2019 to 2021, 1479 (13.6%) were identified as positive blood cultures that showed microbial growth. Of these, 453 were blood culture contaminations, representing 4.17% of total blood cultures and 30.63% of the positive blood culture samples. The highest rate of contamination was in the hemodialysis unit (26.49%), followed by the emergency department (15.89%). Staphylococcus epidermidis was the most prevalent (49.2%), followed by Staphylococcus hominis (20.8%) and Staphylococcus haemolyticus (13.2%). The highest annual contamination rate was observed in 2019 (4.78%) followed by 2020 (3.95%) and the lowest was in 2021 (3.79%). The rate of BCC was decreasing, although it did not reach statistically significant levels (P value = 0.085).ConclusionThe rate of BCC is higher than recommended. The rates of BCC are different in different wards and over time. Continuous monitoring and performance improvement projects are needed to minimize blood culture contamination and unnecessary antibiotic use.