Efficiency Metrics Research Articles

A Review of Comprehensive Guidelines for Computational Fluid Dynamics (CFD) Validation in Solar Chimney Power Plants: Methodology and Manzanares Prototype Case Study

This review provides a comprehensive examination of CFD modeling procedures for SCPP, with an emphasis on the detailed methodologies and a case study of the Manzanares prototype in Spain. The introduction delineates the historical context and physical modeling principles of solar chimneys, while highlighting their potential in industrial applications. The governing equations are meticulously discussed, covering assumptions in both 2D and 3D CFD modeling, the continuity and momentum equations, and the selection and accuracy of turbulence models, particularly the k-ε equations. The review also delves into heat transfer modeling, encompassing the energy equation and radiation modeling. Analytical evaluations of turbine pressure drop ratios and performance metrics for power generation efficiency are critically analyzed. The establishment of boundary conditions in solar chimney applications, including sky temperature assessments and distinctions between 2D and 3D boundary conditions, is extensively explored. Mesh generation techniques for both 2D and 3D CFD models are presented, supported by case studies. Parametric studies and experimental investigations are scrutinized to elucidate their impact on the performance of solar chimneys. The temperature–entropy diagram for an idealized Brayton cycle is introduced as a conceptual framework for efficiency analysis. Validation of the CFD codes, both 2D and 3D, against experimental data is performed to ensure model accuracy. The review further examines energy balance approaches in modeling solar chimneys, presenting state-of-the-art CFD results and discussing their implications in both 2D and 3D contexts. The synthesis of these findings culminates in a comprehensive conclusion, offering insights into the future directions and potential advancements in the CFD modeling of solar chimneys. This work aims to serve as a definitive reference for researchers and practitioners in the field, providing a robust foundation for the development and optimization of SCPP technology.

Iterative Development of a Clinical Decision Support Tool to Enhance Naloxone Co-Prescribing.

Background Opioid overdoses have contributed significantly to mortality in the United States. Despite long-standing recommendations from the Centers for Disease Control and Prevention to co-prescribe naloxone for patients receiving opioids who are at high risk of overdose, compliance with these guidelines has remained low. Objectives The objective of this study was to develop and evaluate a hospital-wide electronic health record (EHR)-based clinical decision support (CDS) tool designed to promote naloxone co-prescription for high-risk opioids. Methods We employed an iterative approach to develop a point-of-order, interruptive EHR alert as the primary intervention and assessed naloxone prescription rates, EHR efficiency metrics, and barriers to adoption. Data was obtained from our EHR's clinical data warehouse and analyzed using statistical process control and Chi-square analyses to assess statistically significant differences in prescribing rates during the intervention periods. Results The initial implementation phase of the intervention, spanning from April 2019 to May 2022, yielded a nearly 3-fold increase in the proportion of high-risk patients receiving naloxone, rising from 13.4% [95% CI, 12.9% - 13.8%] to 36.4% [95% CI, 35.2% - 37.5%; p = 1 x 10-38]. Enhancements to the CDS design and logic during the subsequent iteration's study period, June 2022 and December 2023, reduced the number of CDS triggers by more than 30-fold while simultaneously driving an additional increase in naloxone receipt to 42.7% [95% CI, 40.6% - 44.8%; p = 2 x 10-5]. The efficiency of the CDS demonstrated marked improvement, with prescribers accepting the naloxone co-prescription recommendation provided by the CDS in 41.1% of the encounters in version two, compared to 6.2% in version one (p = 6 x 10-9). Conclusion This study offers a sustainable and scalable model to address low rates of naloxone co-prescription and may also be used to target other opportunities for improving guideline-concordant prescribing practices.

Comparative Analysis of Holmium Laser Enucleation of the Prostate (HoLEP) and Robotic Assisted Simple Prostatectomy (RASP) in BPH Management: A Systematic Review and Meta-Analysis.

As the prevalence of Benign Prostatic Hyperplasia (BPH) increases, the demand for surgical interventions that optimize patient outcomes while minimizing complications grows. This systematic review compares the efficacy, efficiency, and safety of Holmium Laser Enucleation of the Prostate (HoLEP) with Robotic-Assisted Simple Prostatectomy (RASP), providing insights for evidence-based surgical decision-making in BPH treatment. Adhering to PRISMA guidelines, the study protocol was registered with Prospero [CRD42024509627]. Searches were conducted in Medline, Embase, Web of Science, Scopus, and CINAHL up to February 1, 2024, to include studies that compare HoLEP and RASP in BPH patients. Risk of Bias was evaluated using the Newcastle Ottawa Scale. HoLEP and RASP demonstrated equivalent effectiveness in treating BPH, as shown by similar functional outcomes like maximum urinary flow rate and post-void residual volume. However, HoLEP outperformed RASP in several operational efficiency metrics, reducing operative time by 49.48 minutes, hospitalization duration by 1.5 days, and catheterization period by 3.8 days. HoLEP also significantly reduced the risk of blood transfusions by 75%. Patients undergoing RASP were 1.87 times more at risk for grade 2 complications and 3.41 times more at risk for developing grade 3 or above complications. HoLEP and RASP are effective for managing BPH. HoLEP shows advantages in recovery metrics and lower blood transfusion rates, while RASP benefits from ease of implementation in robotic-equipped facilities. Optimizing surgical outcomes will depend on reducing disparities in technique adoption, improving surgical training, and aligning with evidence-based guidelines.

Evaluating the Technological Impact of Unmanned Aerial Vehicles on Efficiency Metrics in Precision Agriculture: A Secondary Data Analysis

Evaluating the Technological Impact of Unmanned Aerial Vehicles on Efficiency Metrics in Precision Agriculture: A Secondary Data Analysis

Structure Design on Thermoplastic Composites Considering Forming Effects.

Carbon fiber reinforced polypropylene (CF/PP) thermoplastics integrate the superior formability of fabrics with the recoverable characteristics of polypropylene, making them a pivotal solution for achieving lightweight designs in new energy vehicles. However, the prevailing methodologies for designing the structural performance of CF/PP vehicular components often omit the constraints imposed by the manufacturing process, thereby compromising product quality and reliability. This research presents a novel approach for developing a stamping-bending coupled finite element model (FEM) utilizing ABAQUS/Explicit. Initially, the hot stamping simulation is implemented, followed by the transmission of stamping information, including fiber yarn orientation and fiber yarn angle, to the follow-up step for updating the material properties of the cured specimen. Then, the structural performance analysis is conducted, accounting for the stamping effects. Furthermore, the parametric study reveals that the shape and length of the blank holding ring exerted minimal influence on the maximum fiber angle characteristic. However, it is noted that the energy absorption and crushing force efficiency metrics of the CF/PP specimens can be enhanced by increasing the length of the blank holding ring. Finally, a discrete optimization design is implemented to enhance the bending performance of the CF/PP specimen, accounting for the constraint of the maximum shear angle resulting from the stamping process. The optimized design resulted in a mass reduction of 14.3% and an improvement in specific energy absorption (SEA) by 17.5% compared to the baseline sample.

Impact of advanced endoscopy training on colonoscopy quality and efficiency.

Few reports have detailed improvements in the quality of colonoscopies with continuous training post-fellowship completion. We examined the changes in colonoscopy performance among trainees during our advanced endoscopy training program. Screening or surveillance colonoscopies performed by 11 trainees who participated in our 3-year advanced endoscopy training program between April 2015 and March 2020 were retrospectively analyzed. Quality and efficiency metrics of colonoscopies were evaluated annually. Altogether, 297, 385, and 438 colonoscopies were enrolled in the first, second, and third training years, respectively. The mean insertion times were 8.6, 7.6, and 6.9 min in the first, second, and third training years, respectively, with significant improvement from the first to second year (p = 0.03) and from the first to third year (p < 0.01). The adenoma detection rate, proximal adenoma detection rate, and mean number of adenomas per patient exhibited a tendency to improve annually; however, the difference was not significant. Polypectomy efficiency was 10.5%, 11.2%, and 13.0%, with significant improvements from the first to third year (p < 0.01) and from the second to third year (p = 0.02). Insertion time and polypectomy efficiency showed significant improvements, especially among trainees experienced with <500 colonoscopies. Through our advanced endoscopy training program, there has been an improvement in the quality and efficiency of colonoscopy for trainees who have completed their fellowships, particularly those with <500 colonoscopies.

Stochastic optimization of a mass-in-mass cell with piecewise hybrid nonlinear–linear restoring force

This article investigates the optimization under uncertainty of a mass-in-mass meta-cell for its potential use within a metamaterial. The specificity of the proposed mass-in-mass system stems from the hybrid nonlinear–linear stiffness at the inner level. It is well known that these systems are highly sensitivity to small perturbations in loading conditions or design parameters. In fact, the sensitivity is such that the system can exhibit discontinuous behaviors. Therefore the proposed optimization approach not only accounts for sources of uncertainties but also can handle discontinuous responses. The objective of the stochastic optimization is to find the stiffness properties of the mass-in-mass system which minimize the expected value of a specific efficiency metric. In order to better understand the system’s dynamic behavior and the origins of the discontinuities, slow invariant manifolds and frequency response curves are provided. The efficiency of the optimized system with hybrid stiffness is compared with that of a similar optimized system featuring pure cubic nonlinearity.

Prediction of Overall Equipment Effectiveness in Assembly Processes Using Machine Learning

Abstract Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the support of various modern technical and Information Technology (IT) tools, the recording, storage and processing of large amounts of data is now a routine activity. Based on machine learning, efficiency metrics including Overall Equipment Effectiveness (OEE), can be partially predicted, but industrial companies need more accurate and reliable methods. The analyzed algorithms can be used in general for all production units or machines where production data is recorded by Manufacturing Execution System (MES) or other Enterprise Resource Planning (ERP) systems are available. This paper presents and determinates which most used machine learning methods should be combined with each other in order to achieve a better prediction result.

Service priority classification using machine learning

This article details a procedure for classifying service cases with various priority levels based on machine learning (ML). It accurately defines the priority level of each service case. The presence of imbalanced datasets in service cases poses a challenge for achieving reliable classification accuracy. To address this, the use of the synthetic minority over-sampling technique (SMOTE) was proposed as the method for balancing the datasets prior to applying the ML method. From these experimental results, an improvement in the precision of the learning process was observed, which led to better outcomes in the test sets. This improvement was measured using the efficiency metrics from the confusion matrix. The experiment involved 6,182 service cases, categorized into four levels: critical, serious, moderate, and low. These were based on test comparisons with other ML methods. The accuracy achieved in the test data was 94.37%. By employing a hybrid technique to address the imbalance in SMOTE and the support vector machine model, it was found to be more effective than the comparative term frequency-inverse document frequency model that was used in conjunction with cosine similarity, which achieved an evaluation score of 70.14%.

Association of genomically enhanced residual feed intake with performance, feed efficiency, feeding behavior, gas flux, and nutrient digestibility in growing Holstein heifers.

Residual feed intake (RFI), a metric of feed efficiency, is moderately heritable and independent of body size and productivity, making it an ideal trait for investigation as a selection criterion to improve feed efficiency of growing cattle. The objective of this study was to examine the differences in performance, feed efficiency, feeding behavior, gas flux, and nutrient digestibility in Holstein heifers with divergent genomically enhanced breeding values for RFI (RFIg). Holstein heifers (n = 55; BW = 352 ± 64kg) with low (n = 29) or high (n = 26) RFIg were selected from a contemporary group of 453 commercial Holstein heifers. Heifers were rotated between 1 of 2 pens, each equipped with four electronic feed bunks and one pen with a GreenFeed gaseous exchange monitoring (GEM) system. Individual dry matter intake (DMI) and feeding behavior data were collected for 84-d. Body weight (BW) was measured weekly and spot fecal samples collected at weighing. Phenotypic RFI (RFIp) was calculated as the residual from regression of DMI on average daily gain (ADG) and mid-test metabolic BW (BW0.75). A mixed model including the fixed effect of RFIg classification and random effect of group was used to evaluate the effect of RFIg classification on response variables. There were no differences (P > 0.05) in BW and ADG for heifers with divergent RFIg; however, low RFIg heifers consumed 7.5% less (P < 0.05) feed per day. Consequently, low RFIg heifers exhibited a more favorable (P < 0.05) RFIp (-0.196 vs 0.222kg/d, respectively). Low RFIg heifers had 8.7% fewer (P < 0.05) bunk visit (BV) events per day and tended to have a 11.2% slower (P < 0.10) eating rate. Low RFIg heifers had 7.7% lower (P < 0.05) methane (CH4) emissions (g/d), 6.1% lower (P ≤ 0.05) carbon dioxide (CO2) production (g/d), and 5.6% lower (P ≤ 0.05) heat production (Mcal/d) than high RFIg heifers. However, CH4 yield and CO2 yield (g/kg DMI), and heat production per unit DMI (Mcal/kg DMI) did not differ (P > 0.05) between heifers with divergent RFIg. Dry matter and nutrient digestibility did not differ (P > 0.05) between heifers with divergent RFIg. Overall, heifers selected to be more feed efficient exhibited more favorable energy efficiencies and feed efficiency phenotypes. Results suggest that selection based on RFIg provides opportunities to select cattle with favorable feed efficiency phenotypes to increase the economic and environmental sustainability of the cattle industry.

Measuring, interpreting and monitoring economic efficiency in South Australia’s Spencer Gulf and West Coast Prawn Fisheries

Fishers and fishing vessels are not homogeneous, with their level of catch affected by the choice of physical inputs such as engine size, boat size and the type of fishing technology employed, as well as less tangible factors such as skipper skill and experience. Economic output also vitally depends upon the value of the product in the market and the cost of inputs. These differences in the ability of individual fishers to catch fish, and create economic returns, can be assessed through the application of efficiency analysis. This paper provides an empirical examination of efficiency indicators in the South Australian Spencer Gulf and West Coast Prawn Fisheries. Applying data envelopment analysis (DEA) to a unique vessel-level dataset that includes both quantity and value information, we estimate measures of efficiency in these input-controlled fisheries where effort decisions are made in real-time using a participative management approach. We find that quantity and value-based efficiency measures are relatively high but that the variability of value-based measures is increasing. This increasing heterogeneity in value, despite the homogeneity in quantities, has potential implications for pressures on cooperative management going forwards as fishers pursue different business strategies. Comparing the measures of efficiency to direct measures of individual profit, we find a divergence: technical and cost measures of efficiency are more strongly positively related to short-run economic performance while revenue-efficiency is more strongly positively correlated with long-run economic performance, suggesting that different efficiency metrics may be required for short- and long-run monitoring.

Experiment on Lung Disease Classification using YOLOv8

Lung diseases are a leading cause of illness and mortality worldwide. Accurate and timely diagnosis is crucial for improving patient outcomes, but manual interpretation of chest X-rays by radiologists may consume a significant amount of time and is susceptible to errors. This study leverages the state-of-the-art YOLOv8 deep learning model to develop an automated system for classifying lung diseases from chest X-ray images. The proposed approach utilizes a large, diverse dataset of 21,165 chest X-ray images categorized into four classes: COVID-19, viral pneumonia, lung opacity, and normal. The YOLOv8-cls model is fine-tuned using transfer learning and advanced data augmentation techniques. The system achieves a high accuracy of approximately 95% across all disease classes while maintaining real-time performance. Confusion matrix analysis demonstrates strong performance in correctly identifying each condition, with COVID-19, normal, viral pneumonia and lung opacity cases correctly identified 97%, 97%, 99% and 93% of the time respectively. The outcomes validate the adaptability and reliability of deep learning, specifically YOLOv8, for automated lung disease detection, offering the potential to improve clinical workflows and patient care by providing efficient screening tools for practitioners. The study addresses limitations of previous works by utilizing a large consolidated dataset, reporting comprehensive computational efficiency metrics, and extending classification complexity without substantially impacting accuracy or speed. Future research should focus on further optimizations and extending the curated image repositories to include under-represented patient groups to enhance the model's inclusiveness and robustness.

Demand-Aware Distributed Pathfinding for Repositioning Vehicles in Shared-use Autonomous Mobility Services

Shared-use autonomous mobility services (SAMS) have the potential to provide accessible and demand-responsive mobility to passengers, while benefitting from autonomous vehicle (AV) technology and bypassing challenges related to supply-side incentives or individual driver goals. SAMS operators typically aim to achieve efficiency and improved service quality in their fleet operations, both of which are further enabled by the use of AVs. Specifically, fleet repositioning decisions in anticipation of future demand can improve service quality, but existing approaches in the literature seldom consider the problem of routing repositioning vehicles in a way that further improves SAMS objectives. This paper presents an approach for demand-aware distributed routing pathfinding for repositioning vehicles, which can supplement existing vehicle repositioning approaches. The problem is formulated with a multi-criteria objective that minimizes the vehicles’ total travel time and maximizes their total demand-serving potential, while distributing that potential equitably among the ride-seeking passengers across the transportation network. We evaluate the proposed approach via numerical experiments using an agent-based simulation of SAMS operations in the network of Manhattan in New York City. The proposed approach is compared to a baseline simple shortest path approach for routing the repositioning vehicles. The results demonstrate that mean passenger waiting times for pick-up can be reduced, while also reducing the total vehicle miles and the empty miles travelled due to repositioning. Thus, the proposed approach can help improve the overall system performance in terms of both service quality and efficiency metrics, relative to the baseline approach.

¿Libre de la Maleza Estatista? Assessing Neoliberal Promises and Water Markets in Chile

AbstractNeoliberal approaches to water governance, pioneered in Chile in the 1980s, are reappearing today on the centerstage of the water policy debate. While advocates claim that strong property rights, limits on government authority, and water markets can enhance environmental sustainability, efficiency, neutrality, and equity in the distribution of water rights, limited empirical evidence exists on whether neoliberal policies have delivered on these key promises. In this paper, we combine hydrological analysis with a nationwide data set on government water rights allocations between 1981 and 2021 to determine when and where water has been allocated beyond sustainable limits. We then integrate water market transaction and agricultural data to assess how allocations and scarcity conditions relate to spatial and temporal patterns in irrigation, crop distribution, and water market activity. Our results indicate that 30% of catchments are overallocated, and that continued government allocations of water rights during scarcity exacerbate already‐high inequalities in the distribution of water. We find no evidence that scarcity or water markets induced improvements in numerous efficiency metrics. Overall, our results support growing claims that the neoliberal water model fails to fulfill its key promises, notably to the detriment of nature and marginalized rural communities.

Functional structure of the natural enemy community of the fall armyworm, Spodoptera frugiperda in the Americas

Ecosystem functions such as biological pest control are mediated by the richness and abundance of service providers i.e., biological control agents (BCAs), relative contributions of individual taxa and community structure. This is especially relevant in the native range of agricultural herbivores, where a speciose community of co-evolved BCAs can prevent them from attaining pest status. Here, we use a powerful graphical approach to assess the functional structure of BCA communities of the fall armyworm (FAW) Spodoptera frugiperda (Lepidoptera: Noctuidae) on maize in the Neotropics. Drawing upon a curated database of all-time field and laboratory studies, we graphed patterns in the functional contribution, abundance and niche breadth for a respective 69, 53 and 3 taxa of resident parasitoids, predators and pathogens. Regardless of varying taxon coverage and rigor of the underlying studies, functional structure follows a saturating relationship in which the first three taxa account for 90–98% of aggregate biological control function. Abundance-functionality matrices prove critically incomplete, as more than 80% of invertebrate taxa miss empirically derived efficiency metrics while associated FAW infestation data are scarce. Despite its methodological shortfalls and data gaps, our work pinpoints Chelonus insularis, Cryptus albitarsis, several taxa of egg parasitoids, Doru spp. and Orius spp. as taxa with outsized functionality and conservation potential. This is also exemplified by the highly variable aggregate function across studies, with dispersion indices of 1.52 and 2.14 for invertebrate BCAs. Our work underlines the critical importance of functional ecology research, networked trials and standardized methodologies in advancing conservation biological control globally

PGY-2 emergency medicine residents are more efficient when paired with an early clinical medical student.

There is a concern that provide increased extraneous cognitive load when paired with residents on shift. However, this may be offset by the decrease in extraneous load they may provide to the residents they are paired with by offloading basic patient care tasks. We hypothesized that these forces may not be balanced. We conducted a retrospective observational analysis of PGY-2 emergency medicine residents and junior medical students at a single academic emergency department (ED) in the Midwest. A series of efficiency metrics (relative value unit [RVUs], patients per hour [PPH], time to note completion, and resident assignment to disposition [RATD]) as well as one quality metric (number of return ED visits; "bouncebacks") were compared for resident shifts in which a student was paired with the resident as well those in which no student was paired utilizing a regression model. A total of 1844 records met the inclusion criteria (214 shifts with a paired medical student and 1630 without). After covariates were adjusted for, medical student shift status was a statistically significant predictor of increases in PPH (p < 0.0001) and RVUs (p = 0.0161) but was not significantly associated with RATD (p = 0.6941), log-time to note completion (p = 0.1604), or bounceback status (p = 0.9840). Shifts where residents were paired with medical students were predicted to see an additional 1.131 (95% confidence interval [CI] 0.660-1.602) PPH and produce an additional 1.923 RVUs (95% CI 1.130-3.273) per shift relative to shifts without medical students. When junior medical students were paired with a PGY-2 resident on ED shifts, there was a significant increase in the PPH and RVUs generated when compared with shifts in which no medical student was paired with them.

Sustainable Chemistry in Synthesis of Heterocyclic Systems: [Atom Economy, Yield Economy and Reaction Mass Efficiency As Metrics of Synthetic Reaction Efficiency

Sustainable Chemistry in Synthesis of Heterocyclic Systems: [Atom Economy, Yield Economy and Reaction Mass Efficiency As Metrics of Synthetic Reaction Efficiency

Efficiency metrics for performance measurement: a review in higher education of main methods and determinants

PurposeThis study aims to provide an overview of the state-of-the-art in efficiency measurement within higher education (HE). Specifically, it seeks to gather all relevant articles on the topic and subsequently categorize these studies using a flowchart based on two core aspects of the topic.Design/methodology/approachThis study employs bibliometric and content analyses to conduct a systematic literature review. The Preferred Reporting Items for Systematic Review (PRISMA) framework is used to identify the search protocol, followed by analyses to classify and categorize articles.FindingsThe bibliometric analysis identifies prominent themes, methodologies and literature gaps. The content findings highlight key insights on higher educational institution (HEI) efficiency, including organizational structures, services and operational activities.Originality/valueThis research contributes to the existing knowledge by synthesizing global literature on HEI’s efficiency. Utilizing the flowchart developed by the authors, the study captures the state-of-the-art based on two critical aspects: methodologies and content. Insights from the analysis and subsequent classification of previous literature provide valuable directions for future research.

Developing correction factors for weather’s influence on the energy efficiency indicators of container ships using model-based machine learning

The International Maritime Organization employs technical and operational indicators to assess ship energy efficiency. Weather conditions significantly impact ship fuel consumption during voyages, necessitating the consideration of this influence in energy efficiency calculations. This study aims to design models for estimating the impact of weather components on fuel consumption and develop correction factors to cope with the weather effect on the fuel consumption of container ships for different sea states. Using model-based machine learning, the study analyzes noon reports and hindcasted weather data from two sister container ships. It quantifies weather-induced fuel consumption across various sea states, ranging from 2% to 20%, with an average of 7%–13% depending on the model used. Correction factors specific to each sea state are derived, and different approaches for their integration into energy efficiency indicators are proposed. This study advocates tailored weather correction factors for energy efficiency metrics tied to specific sea states, emphasizing the need for standardized weather impact assessments. Prior to any formal policy application, future work is needed to address the limitations of the present study and extend this approach to various ship types and sizes and different geographical regions.

Unifying Efficiency Metrics for Solar Evaporation and Thermal Desalination

Unifying Efficiency Metrics for Solar Evaporation and Thermal Desalination