Operational Settings Research Articles

Well Pattern optimization as a planning process using a novel developed optimization algorithm

Determination of optimum well location and operational settings for existing and new wells is crucial for maximizing production in field development. These optimum conditions depend on geological and petrophysical factors, fluid flow regimes, and economic variables. However, conducting numerous simulations for various parameters can be time-consuming and costly. Also, due to the high dimension of the possible solutions, there is still no general approach to address this problem. The application of searching algorithm as a general approach to solve such problems has received much attention in recent years. In this study, the efficiency, and reliability of genetic algorithm, particle swarm optimization and in particular a newly developed algorithm was analyzed and compared. The novelty of this work is the integrated algorithm, which improves searching performance by leveraging the memorizing characteristics of the particle swarm optimization algorithm to enhance genetic algorithm efficiency. In traditional genetic algorithms, solutions lacking adequate qualifications are deleted from the algorithmic process; however, the new algorithm provides these solutions with additional opportunities to prove themselves by acquiring new velocities from particle swarm optimization. The results indicate that while the genetic algorithm and particle swarm optimization do not guarantee optimal outcomes, the newly developed algorithm outperforms both methods. This performance was tested across various scenarios focused on well pattern optimization, highlighting its innovative contribution to the field development.

Implementation of WRF‐Urban Asymmetric Convective Model (UACM) for Simulating Urban Fog Over Delhi, India

AbstractAccurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF‐Urban Asymmetric Convective Model (WRF‐UACM) for Delhi, India, integrating explicit urban physics with Sentinel‐updated USGS land‐use and urban morphological parameters derived from the UT‐GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF‐BACM) using Winter Fog Experiment (WiFEX) data, WRF‐UACM significantly improved urban meteorological variables such as diurnal variations in 10‐m wind speed, 2‐m air temperature (T2), and 2‐m relative humidity (RH2) during a fog event. UACM also demonstrates improved accuracy in simulating temperature and significantly reducing biases for wind speed and daytime RH2 under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF‐BACM, WRF‐UACM reduces the cold bias soon after sunset, thus improving the fog onset error by ∼3 hr. This study highlights the UACM's potential to improve fog prediction and its application in operational settings. With further investigation into different fog types, the UACM can provide crucial insights for preventive measures and reducing disruptions in urban areas.

Optimal Design and Analysis of Wide-Band Near-Infrared Hybrid Dielectric Gratings with High Transmission Efficiency

Since surface relief transmission gratings have very strict requirements on operators and use environment, according to the semiconductor laser external cavity spectral beam combining system, this paper proposes a design scheme for a semiconductor laser array spectral beam combining system based on the grating-external cavity. The finite element approach was used to create a wideband, high-efficiency fill-in multilayer dielectric transmission grating structure for a high-power spectrum beam combining system. The incidence angle, ridge height, duty cycle, and sidewall inclination angle of the transmission grating were tuned and evaluated, and a link between the transmission grating’s diffraction efficiency and grating characteristics was discovered. The calculated design of the high-power fused silica transmission grating has a negative first-order peak diffraction efficiency of 99.5% in the 800 nm range. In the spectral region of 765–872 nm, the transmission grating’s diffraction effectiveness exceeds 92%. The filled ultra-high diffraction efficiency multilayer dielectric transmission grating design addresses the issue of resistance to high-power lasers under complicated operating settings. It is intended to maintain a high diffraction efficiency even after several cleaning cycles, and it is an ideal component for high-power spectrum beam combining systems.

An exploration of community food pantries in Scotland: strategic and operational perspectives on addressing food insecurity and health inequalities

Introduction: Food insecurity and health are inextricably linked. Since 2008, Scotland has witnessed a proliferation of both food insecurity and emergency food provision. There is a recognised commitment from Scottish Government to ‘end the need for food banks’, however, the food aid landscape was ‘turbo-charged’ during COVID-19 leading to intense expansion and diversification of food-based projects, including the development of community food pantries (CFPs). These ‘new’ models are relatively under-researched, meaning we do not adequately understand their potential or realised impacts on food insecurity and health. This study aims to fill that gap.MethodsA qualitative methodology was used to collect and analyse data from in-depth interviews with 10 representatives from both operational and policy settings related to food insecurity in Scotland. In addition, we conducted an analysis of policy documentation from Scottish Government related to tackling food insecurity to understand how CFPs fit into its overall strategy to transition away from food bank use.ResultsWe found there were variations in conceptualisations of CFPs and how they operate, challenges related to addressing food insecurity at a community level and varied narratives around the role of community level interventions in tackling health inequalities. Choice and access to services were viewed as central components to the pantry model. However, there were significant challenges faced by CFPs, including territorialism, funding and food supply. Articulations of health were often multi-layered and complicated with strong recognition of the social determinants as well as acknowledgement of the limitations of tackling food insecurity and health inequalities solely at the community level.ConclusionsDespite a commitment to transition away from emergency food provision, CFPs in Scotland appear to face many of the same issues as food banks, particularly those which impact health. Urgent critique of their reliance on surplus food redistribution is required alongside investigation of how these ‘new’ models are experienced by the people who access them. Further expansion of these models should be viewed with caution and in the same vein as traditional emergency food provision: a symptom of, rather than a solution to, the problem of food insecurity.

Neurosurgical Microvascular Anastomosis: Systematic Review of the Existing Simulators and Proposal of a New Training Classification System.

The literature lacks a combined analysis of neurosurgical microvascular anastomosis training models. We performed a systematic literature search to provide an overview of the existing models and proposed a classification system based on the level of simulation and reproducibility of the microvascular anastomosis. The systematic literature search followed the PRISMA guidelines. We consulted MEDLINE, Web of Knowledge, and EMBASE independently for papers about bypass training models. Every training model was analyzed according to six tasks supposed to esteem their fidelity to the real operative setting by using a scoring system from zero to two. Finally, authors classified the models into five classes, from A to E, by summing the individual scores. This study included 109 papers for analysis. Training models were grouped into synthetic tubes, ex vivo models (animal vessels, fresh human cadavers, human placentas) and in vivo simulators (live animals-rats, rabbits, pigs). By applying the proposed classification system, live animals and placentas obtained the highest scores, falling into class A (excellent simulators). Human cadavers and animal vessels (ex vivo) were categorized in class B (good simulators), followed by synthetic tubes (class C, reasonable simulators). The proposed classification system helps the neurosurgeon to analyze the available training models for microvascular anastomosis critically, and to choose the most appropriate one according to the skills they need to improve.

Perfluorooctanoic Acids (PFOA) removal using electrochemical oxidation: A machine learning approach

The urgent need to eliminate Perfluorooctanoic Acid (PFOA) has positioned electrooxidation (EO) as a key solution for pollutant degradation. This study evaluates several machine learning (ML) models, including K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosted Decision Trees (GBDT), and Deep Learning (DL), to predict EO efficiency in PFOA removal. Using 10-fold cross-validation, the RF model outperformed others with a root mean square error (RMSE) of 7.7 and a correlation coefficient of 0.965, demonstrating its robustness and accuracy across diverse operational settings. Feature importance within the RF model was analyzed using Gini impurity and Mean Decrease in Accuracy (MDA). Electrolysis Time consistently emerged as the most influential factor in both analyses, underscoring its pivotal role in providing extended exposure of PFOA molecules to reactive species at the electrode surfaces. The study also found strong agreement between Gini and MDA in identifying Current Density and Anode Material as critical factors, although MDA placed slightly more emphasis on Anode Material. Differences between Gini and MDA were more pronounced in the ranking of Electrolyte Type and Concentration, with MDA assigning higher importance to Electrolyte Concentration. In contrast, the Water Matrix was consistently ranked as the least important factor. The strong concordance between Gini and MDA highlights the reliability of the RF model in identifying key drivers of electrochemical degradation. Overall, this work contributes significantly to the advancement of pollutant degradation technologies, presenting a reliable ML-based tool for environmental remediation efforts.

Direct Membrane Filtration (DMF) of municipal wastewater – A study on the prevention and remediation of fouling

Direct membrane filtration (DMF) has gained a lot of attention in recent years because of its potential to treat low-strength and low temperature wastewater, where biological based treatments are inefficient. However, fouling is a major concern which keeps this technique from being more widely used. The main aim of this study was to investigate membrane fouling, its mitigation approaches and the organic/nutrient retention during DMF of municipal wastewater by adjusting operational parameters and pretreating the sewage. In a laboratory-scale setup employing crossflow to mitigate fouling, both ultrafiltration and microfiltration membranes were utilized. Physicochemical pre-treatment of sewage with polyaluminum chloride and polyamide significantly inhibited membrane fouling and improved organics/nutrient retention. Subsequent experiments with physiochemically pre-treated sewage, using microfiltration and ultrafiltration separately, revealed that (i) an increase in crossflow velocity from 1 to 2 m s −1 improved filtration performance by reducing irreversible fouling, (ii) elevating transmembrane pressure from 0.18 to 0.4 bar increased both reversible and irreversible fouling, and (iii) periodic relaxation increased the extent of irreversible fouling, contributing to higher overall fouling in ultrafiltration. When employing DMF for sewage concentration, ultrafiltration demonstrated superior filtration performance and organics retention compared to microfiltration. Furthermore, through pre-precipitation and microsieving (100 μm) followed by DMF with ultrafiltration membranes, a remarkable removal efficiency of 92 % for chemical oxygen demand (COD), 98 % for total phosphorus (TP), and 100 % for total suspended solids (TSS) was achieved. By cleaning the fouled membranes using an alkaline cleaning solution (Ultrasil-10) at 50 °C, combined with a crossflow of 1 m s−1, the permeability was completely restored. Overall, this study demonstrates that by optimizing operational settings and cleaning conditions, a sustainable DMF process for wastewater treatment with controlled membrane fouling and improved resource recovery can potentially be developed for upscaling. Short abstractThis study investigated the effectiveness of direct membrane filtration for municipal wastewater treatment. It was found that physicochemical pre-treatment with polyaluminum chloride and polyamide significantly reduced membrane fouling and improved nutrient retention. Ultrafiltration membranes demonstrated superior performance and nutrient retention compared to microfiltration membranes. By optimizing operational parameters and cleaning conditions, a sustainable direct membrane filtration process with controlled fouling and improved resource recovery can be developed for wastewater treatment.

Forecasting Solar Energetic Particle Events During Solar Cycles 23 and 24 Using Interpretable Machine Learning

The prediction of solar energetic particle (SEP) events garners increasing interest as space missions extend beyond Earth’s protective magnetosphere. These events, which are, in most cases, products of magnetic-reconnection-driven processes during solar flares or fast coronal-mass-ejection-driven shock waves, pose significant radiation hazards to aviation, space-based electronics, and particularly space exploration. In this work, we utilize the recently developed data set that combines the Solar Dynamics Observatory/Space-weather Helioseismic and Magnetic Imager Active Region Patches and the Solar and Heliospheric Observatory/Space-weather Michelson Doppler Imager Active Region Patches. We employ a suite of machine learning strategies, including support vector machines (SVMs) and regression models, to evaluate the predictive potential of this new data product for a forecast of post-solar flare SEP events. Our study indicates that despite the augmented volume of data, the prediction accuracy reaches 0.7 ± 0.1 (experimental setting), which aligns with but does not exceed these published benchmarks. A linear SVM model with training and testing configurations that mimic an operational setting (positive–negative imbalance) reveals a slight increase (+0.04 ± 0.05) in the accuracy of a 14 hr SEP forecast compared to previous studies. This outcome emphasizes the imperative for more sophisticated, physics-informed models to better understand the underlying processes leading to SEP events.

Simulation of a System to Simultaneously Recover CO2 and Sweet Carbon-Neutral Natural Gas from Wet Natural Gas: A Delve into Process Inputs and Units Performances

The growing need for carbon-neutral energy solutions necessitates the development of efficient systems for carbon dioxide (CO2) recovery and the production of sweet carbon-neutral natural gas (CNNG) from wet natural gas. Despite existing approaches, limitations in process optimization, solvent efficiency, and output purity persist. This study aims to address these gaps by simulating a system for simultaneous recovery of CO2 and CNNG using an integrated three-stage process, modeled in Aspen Plus V8.8. The unique aspect of this work lies in employing the ENRTL-RK base model, coupled with sensitivity analyses to optimize input parameters across 13 interconnected process units, including compressors, heat exchangers, and extraction columns. Key innovations include the novel configuration of units, yielding a recovery efficiency of 95.94% for CNNG and a CO2 purity of 93.185% at optimal conditions, surpassing conventional methods. The performance of the monoethanolamine (MEA) solvent was enhanced by careful adjustment of input parameters, improving its absorption efficiency by 12% compared to standard operational settings. Sensitivity analysis revealed critical parameters such as feed pressure and solvent flow rate as primary drivers for maximizing output efficiency. This study also provides a detailed quantitative assessment of power requirements, with a compressor brake horsepower (BHP) of 18,2605 watts at 110 bar discharge pressure. It addresses the existing research gap by introducing a systematic approach to process optimization, significantly improving the purity and recovery of CNNG and CO2 while minimizing energy consumption. The results not only demonstrate the viability of this process but also provide a foundation for further refinement in sustainable gas processing technologies.

Exploring Adherence to Operational Guidelines Set by Rural Women’s Savings and Investment Groups in Kashari County, Mbarara District

Most Civil Society Organisations (CSOs) in developing countries all over the world are fighting poverty through various strategies such as employment promotion, agricultural and rural development among others. Microcredit has been proven as one of the most effective strategies in poverty alleviation especially through giving small loans to vulnerable populations particularly women without requiring collateral security. This study delved into exploring the adherence to operational guidelines set by the rural women savings and investment groups in Kashari County, Mbarara district. This is a qualitative study that was conducted among rural women in Kashari county Mbarara district. Key findings revealed that all members including those who can’t read and write are aware of the operational guidelines. However, only members who are involved in the day today running of the groups’ activities are more knowledgeable about the operational guidelines, lending rules and procedures and they go ahead to adhere to them. Findings further revealed that members who are not involved in daily running of the groups have not adequately adhered to the operational guidelines as expected. It was further found out that the guidelines which were easily adhered to by most members were mainly to do with subscription as members for instance paying their annual, monthly and weekly subscription and contributions. The lending guidelines were also reported to be easily and keenly followed during the loan appraisal process. This paper concludes that operational guidelines are crucial ingredients in the smooth running of rural women to the savings and investment groups. We recommend that concerted efforts must be directed towards ensuring that both active and non-active members, educated and less or non-educated members are kept abreast with operational guidelines and reminded of their obligations on regular basis.

Waste to energy: Enhancing biogas utilization in dual-fuel engines using machine learning based prognostic analysis

Alternative fuels derived from organic matter like biomass can be a viable solution in the present scenario of increasing greenhouse gases. In the present study, waste food and animal refuse-derived biogas were tested as fuel in a diesel engine. To further enhance the waste-to-energy perspective waste cooking oil biodiesel − diesel blends were used as pilot fuel. A comprehensive set of operational settings including fuel injection timing, pressure, compression ratios, different flow rates of biogas, and engine loads were tested. Four machine learning approaches Linear Regression (LR), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Gaussian Process Regression (GPR) were employed to develop prognostic models. The developed models for engine performance and emission were predicted with high accuracy with R2 values as high as 0.9962 for brake-specific fuel consumption and 0.9948 for brake thermal efficiency. The prediction errors were low 0.06 for BTE during model training and 0.18 during the model testing phase while it was almost negligible in the case of BSFC. All models were compared using statistical metrics and violin plots. The DT-based forecasting models were observed to be the best among all the models both based on statistical measures as well as violin plots.

Response Surface Methodology for Ni-Zeolite Catalyst Optimization in Syngas Production.

This work addresses the problem of converting waste methane, a significant greenhouse gas, using customized nickel-zeolite catalysts to produce profitable syngas. The investigation employs 5 wt % of Ni on various zeolite supports with Si/Al ratios ranging from 13 to 25. Comprehensive characterization methods, including temperature-programmed reduction, N2 adsorption-desorption, and X-ray diffraction, were used to identify critical structural characteristics that greatly impact the catalyst's performance. The study indicates that the reducibility and basicity of the catalyst, the type of zeolite support, and the kind of carbon deposits formed during the reaction at 800 °C all influence the efficiency of methane conversion to syngas. The best catalyst was found to be 5Ni-Z3, which at 800 °C produced high conversion rates of carbon dioxide (60%) and methane (50%). Lastly, the response surface methodology, in conjunction with numerical simulation, was used to determine the best operating settings for maximizing syngas production with the 5Ni-Z3 catalyst. Reaction temperature, space velocity, and the methane-to-carbon dioxide feed ratio were considered in this analysis. With a methane conversion rate exceeding 92%, a carbon dioxide conversion rate exceeding 90%, and a hydrogen-to-carbon monoxide ratio of 1.00, the catalyst produced experimental results very similar to the SRM predictions when the reaction was conducted at conditions close to the predicted values [temperature around 845 °C, space velocity around 22,000 mL/(h·gcat), and feed ratio close to 0.94]. The effectiveness of the identified operating conditions for the dry reforming process is validated by the near alignment of expected and experimental outcomes.

Complete CP-eigen bases of meson-baryon chiral lagrangian up to p5-order

Chiral perturbation theory describes the low energy dynamics of mesons and baryons in terms of the nonlinear Goldstone boson and fermion degrees of freedom. Through the Young tensor technique, we construct the on-shell operator bases for the meson-baryon system up to p5-order, using the chiral dimension power counting and heavy baryon expansion. For the Lorentz structure, additional treatments on off-shell external sources and operators with higher derivatives are necessarily considered, while for the internal structure, the invariant tensor basis is converted into the trace basis equivalently, and Cayley-Hamilton relations are utilized to classify different CP eigen-operators. Finally we present the complete operator set of C+P+, C+P-, C-P+, and C-P- eigen-operators at the p5-order, and obtain the operator counting from the Hilbert series.

Standing on FURM Ground: A Framework for Evaluating Fair, Useful, and Reliable AI Models in Health Care Systems

The impact of using AI to guide patient care or operational processes depends on the interplay between the AI model’s output, the decision-making protocol based on that output, the capacity of the stakeholders involved to take the necessary subsequent action, and the benefits and harms of the action taken. Estimating the effects of this interplay before deployment and studying it in real time after deployment are essential for bridging the chasm between AI model development and achievable benefits. To accomplish this, the Data Science Team at Stanford Health Care has developed a testing and evaluation mechanism to identify Fair, Useful, and Reliable AI Models (FURMs) by conducting an ethical review to identify potential value mismatches, running simulations to estimate usefulness, making financial projections to assess sustainability, conducting analyses to determine IT feasibility, designing a deployment strategy, and recommending a prospective monitoring and evaluation plan. The authors report on FURM assessments carried out to evaluate six AI model–guided solutions for potential adoption, spanning both clinical and operational settings, each with the potential to impact up to tens of thousands of patients each year. The authors describe the assessment process, summarize the six assessments, and share their framework, to enable others to conduct similar assessments. Of the six solutions assessed, two have moved into an implementation phase. The novel contributions of this effort — usefulness estimates determined by simulation, financial projections to quantify sustainability, and a process for carrying out ethical assessments — as well as the underlying methods and open-source tools, are available for other health care systems to use to conduct actionable evaluations of candidate AI solutions.

Investigation of operational settings, environmental conditions, and faults on the gas turbine performance

Abstract Gas turbine engines are complex mechanical marvels widely employed in diverse applications such as marine vessels, aircraft, power generation, and pumping facilities. However, their intricate nature renders them susceptible to numerous operational faults, significantly compromising their performance and leading to excessive emissions, consequently incurring stringent penalties from environmental regulatory bodies. Moreover, the deterioration of gas turbine performance is exacerbated by variations in working conditions based on operational settings and environmental conditions. Past studies have focused on certain working conditions that limit effectiveness in real-world applications where operational settings and environmental conditions vary during operations. The influence of these working conditions on the performance of gas turbines also needs to be assessed, as they can lead to different fault patterns resulting in unplanned maintenance, unnecessary maintenance costs, unsafe conditions and stringent penalties. This study uses the gas turbine simulation program to simulate a high-bypass turbofan engine inspired by Pratt & Whitney PW-4056, analysing the combined effects of operational settings and environmental conditions on engine performance while also incorporating simulations of common gas turbine faults like fouling and erosion in various locations and severities along the gas path. The model’s accuracy is confirmed by low mean absolute percentage errors of 0.004% of thrust at the cycle reference point and 0.15% and 0.28% at 2 km and 7 km altitudes, respectively, demonstrating the model’s robustness across varying operational scenarios. In conclusion, this research highlights the significant effects of operational settings and environmental factors on gas turbine performance, particularly impacting specific fuel consumption and thrust. The study reveals that operational settings and environmental factors significantly impact fuel consumption and thrust. Specifically, compressor fouling and low-pressure turbine erosion increase nitrogen oxide (NOx) emissions by 4.5% and 11.1%, while fouling of nozzle guide vanes and high-pressure turbine erosion raise unburned hydrocarbon by 10.0% and 20.2%, and carbon monoxide (CO) by 3.2% and 5.2%, respectively, compared to a healthy engine. These insights highlight the importance of component-specific degradation in influencing gas turbine performance and emissions.

A novel intervention for acute stress reaction: exploring the feasibility of ReSTART among Norwegian soldiers

ABSTRACT Background: Soldiers in combat may experience acute stress reactions (ASRs) in response to trauma. This can disrupt function, increasing both immediate physical danger and the risk for post-trauma mental health sequelae. There are few reported strategies for managing ASRs; however, recent studies suggest a novel peer-based intervention as a promising approach. Objectives: This study assesses the feasibility of ReSTART training, a peer-based course designed to prepare soldiers to manage ASRs. ReSTART builds on programmes established by US and Israeli militaries. The current study evaluates the ReSTART programme in a Norwegian setting, across distinct groups of soldiers, professionals and conscripts. Methods: Participants included professional soldiers deploying to Mali and conscripts with 6 months of service, who completed the ReSTART training course and surveys administered pre- and post-training. These surveys assessed attitudes and programme acceptability. Analyses included 74 soldiers who provided complete survey responses. Results: ReSTART training received high ratings in terms of usefulness, relevance, and importance in managing ASRs. From pre- to post-training, respondents had significant increases in positive attitudes towards ASR management and confidence in handling ASRs personally, and at the unit level; decreases in stigma-related attitudes associated with ASRs; and increased perception of leadership emphasizing ASR management. Conclusions: ReSTART training shows potential as an effective tool when preparing soldiers to manage ASRs in high-risk environments, enhancing military units’ capacity to support each other and effectively respond to stress-induced functional disruptions. This study adds evidence supporting the utility of peer-based ASR management in operational settings and highlights the need for broader implementation and systematic evaluation.

Optimal Salesforce Compensation with General Demand and Operational Considerations

Problem definition: We investigate the optimal salesforce compensation scheme in the context of private information and unobservable actions, considering common operational factors encountered in practice, including inventory costs, contractible versus censored demand information, and controlled versus delegated ordering. Methodology/results: Based on an agency model with general demand and cost functions, we derive optimality conditions for implementable contracts that can achieve the second-best outcome in all scenarios. The contracts are in the forms of a menu with linear compensation for demand or sales, incorporating inventory costs. Moreover, the contracts feature adjustments in compensation corresponding to the ordering level if it is delegated. Managerial implications: Our study reveals that, under reasonably mild conditions, optimal salesforce contracts can still maintain relatively simple forms, even when confronted with common operational factors and generalized demand and cost functions. However, the contracts must be tailored to suit the operational settings. Intriguingly, neither the loss of demand information nor the delegation of inventory decisions would compromise system efficiency at optimum. Funding: H. Song is partially supported by the Key International Cooperation and Exchange Projects of the NSFC [Grant W2411062] and the Foundation for Innovative Research Groups of the NSFC [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0400 .

Compliance analysis of transversely asymmetric flexure hinges for use in a piezoelectric Scott-Russell microgripper

Notch flexure hinges with longitudinal/transverse asymmetries can be widely found in compliant mechanisms to balance the performance trade-offs. However, the transverse asymmetry often leads to difficult analyses of kinetostatics and dynamics. In this paper, a miniaturized piezoelectric gripper featuring reversed Scott-Russell compliant amplifier with transversely asymmetric single-notched flexure hinges is designed for use in confined spaces. The compliance and vibration characteristics of the transversely asymmetric single-notched flexure hinges are quantitatively analyzed by a new transfer matrix method. The proposed theoretical methodology involves discretizing the transversely asymmetric flexure hinge into a series of constant beam segments with non-coaxial nodes, which enables a straightforward modeling process and hence simplifies the kinetostatic and dynamic analyses of compliant mechanisms comprised of complex flexure hinges. Comparative validations with respect to the finite element simulation and experiments confirm the advantages of easy operation and small-scale equation sets of the proposed modeling method. As to the designed piezoelectric microgripper with single-notched flexure hinges, the jaw displacement amplification ratio of 20 and resonance frequency of 1250 Hz has been experimentally tested with a small size of 38 mm × 15 mm × 7 mm.

Association between surgeon training grade and the risk of revision following unicompartmental knee replacement: An analysis of National Joint Registry data.

Unicompartmental knee replacements (UKRs) are performed by surgeons at various stages in training with varying levels of supervision, but we do not know if this is a safe practice with comparable outcomes to consultant-performed UKR. The aim of this study was to use registry data for England and Wales to investigate the association between surgeon grade (consultant, or trainee), the senior supervision of trainees (supervised by a scrubbed consultant, or not), and the risk of revision surgery following UKR. We conducted an observational study using prospectively collected data from the National Joint Registry for England and Wales (NJR). We included adult patients who underwent primary UKR for osteoarthritis (n = 106,206), recorded in the NJR between 2003 and 2019. Exposures were the grade of the operating surgeon (consultant, or trainee) and whether or not trainees were directly supervised by a consultant during the procedure (referred to as "supervised by a scrubbed consultant"). The primary outcome was all-cause revision surgery. The secondary outcome was the number of procedures revised for the following specific indications: aseptic loosening/lysis, infection, progression of osteoarthritis, unexplained pain, and instability. Flexible parametric survival models were adjusted for patient, operation, and healthcare setting factors. We included 106,206 UKRs in 91,626 patients, of which 4,382 (4.1%) procedures were performed by a trainee. The unadjusted cumulative probability of failure at 15 years was 17.13% (95% CI [16.44, 17.85]) for consultants, 16.42% (95% CI [14.09, 19.08]) for trainees overall, 15.98% (95% CI [13.36, 19.07]) for trainees supervised by a scrubbed consultant, and 17.32% (95% CI [13.24, 22.50]) for trainees not supervised by a scrubbed consultant. There was no association between surgeon grade and all-cause revision in either crude or adjusted models (adjusted HR = 1.01, 95% CI [0.90, 1.13]; p = 0.88). Trainees achieved comparable all-cause survival to consultants, regardless of the level of scrubbed consultant supervision (supervised: adjusted HR = 0.99, 95% CI [0.87, 1.14]; p = 0.94; unsupervised: adjusted HR = 1.03, 95% CI [0.87, 1.22]; p = 0.74). Limitations of this study relate to its observational design and include: the potential for nonrandom allocation of cases by consultants to trainees; residual confounding; and the use of the binary variable "surgeon grade," which does not capture variations in the level of experience between trainees. This nationwide study of UKRs with over 16 years' follow up demonstrates that trainees within the current training system in England and Wales achieve comparable all-cause implant survival to consultants. These findings support the current methods by which surgeons in England and Wales are trained to perform UKR.

Effects of procurement risk management strategies on public procuring entities' performance

Organizations implement risk management to mitigate the effects of unforeseen events in their operation settings. The current study aims to investigate the relationship between procurement risk-management strategies and the performance of public higher-learning institutions registered by the National Council for Technical Education and Vocation Training in Tanzania. The findings of the study provide valuable insights that can influence the risk management strategies and performance management policies of public procuring entities. The Principal-Agent Theory guided the study. A convergent parallel mixed-method design was employed, utilizing a census method that included all 16 public high-learning institutions in Dar es Salaam as the sample. Primary data were collected through structured questionnaires and interviews. Quantitative data analysis involved descriptive statistics to compute percentages and means of school respondents. Multiple Regressions was employed for inferential statistics analysis. Qualitative data analysis was conducted using thematic analysis. The study identified risks affecting the performance of PEs including non-compliance risks, planning risks, managerial risks, contract management risks, and project delays. To address these challenges, the study recommends that entities should develop and adhere to their procurement plans, invest in capacity building to strengthen ethical practices to avoid unethical behavior, and address non-compliance issues. Entities should use a combination of strategies based on internal policies, experience, and the preferences of the procurement assignment.