Performance Deviation Research Articles

DRiFT current mode, trigger settings and flexible detector specifications applied to scintillator arrays

MCNP radiation transport output is post-processed by DRiFT, a Detector Response Function Toolkit to simulate detailed nuclear instrumentation response. DRiFT can be used to assess the performance and potential limitations of scintillator, gas, and semiconductor detectors under a variety of simulated conditions not easily achievable in a laboratory setting. This work describes new updates in DRiFT for scintillator simulations which focus on the capability to simulate scintillators in current mode, an expansion of trigger options, and the ability to customize individual detector properties in a simulation. These improvements are designed to facilitate the ability to model large arrays of scintillator detectors with higher fidelity than was previously possible and are demonstrated in three examples. The first shows the difference between operating DRiFT in current and pulse mode. In the second example, which is intended to demonstrate deviations in individual detector performance, each detector has properties (PMT gain, optical transport, scintillation yield, etc.) that vary between detectors and are specified in DRiFT. A final example examines how DRiFT could be used to optimize digitizer settings in high rate measurements with split signals using the new common trigger option.

Strategies for Changing the Approach of Health Promoting Hospitals Based on the Importance-Performance Model: A Case Study in Yazd Hospitals in 2022

Background: Considering the key role of hospitals in improving the level of health, staff and community, implementing standards and promoting activities in hospitals is necessary to realize this important goal. Therefore, the study purpose to examine the strategies for changing the approach of health hospitals based on the importance-performance model. Methods: The present descriptive and cross-sectional study was conducted in 2022 in selected hospitals of Yazd province, Iran. 56 hospital managers and officials were selected by census method. Data was collected using the World Health Organization (WHO) questionnaire regarding HPH (health promoting hospital) standards which contains 69 questions. The data was analysed using SPSS26, and mean score and standard deviation of importance and performance of HPH standards were calculated. The status of HPH standards in the studied hospitals was investigated using the importance-performance analysis matrix. Results: The results showed that HPH standards had a moderate importance from the point of view of the studied people (3.89±0.76) and in the examination of the performance of the studied hospitals regarding HPH standards, the researchers implemented the standards in a moderate level (3.28±0.51). All the standards were identified as the points for keeping up the good work points based on importance-performance analysis matrix in the studied hospitals. Conclusion: In order to fully achieve the standards and improve the condition of hospitals, changing the views of hospital managers and health service policy-makers towards the HPH plan, creating specific and consistent policies and guidelines in the field of training and interventions provided to patients and employees is necessary and effective.

Collaborative robust topology optimization of FGMs considering hybrid bounded uncertainties based on the distance to ideal solution

In this article, an efficient collaborative robust topology optimization (CRTO) method is proposed for functionally graded materials (FGMs) considering hybrid bounded uncertainties (HBU). Firstly, to realize the collaborative optimization of the structural topology and volume fraction of reinforcement in FGMs, two sets of design variables are defined following the SIMP framework. Secondly, with the uncertain material properties and external loads mathematically modeled as random and interval uncertain parameters respectively, the objective performance of FGMs are described as the implicit function of two sets of design variables and uncertain parameters. In contrast to the classical method of weighting the mean and standard deviation of objective performance, the robust objective function is constructed based on a novel distance to ideal solution to avoid the manually setting of weight parameters. The sensitivity analysis of the objective and constraint function with regard to the two sets of design variables is derived explicitly and a realization vector set is defined for bounded probabilistic uncertainties to parallelize the sensitivity analysis. In addition, an adaptive density shift algorithm is proposed to produce a clear topological profile and accelerate the convergence of the optimization. The effectiveness of the proposed method is demonstrated by both numerical and engineering examples.

Improving ion rejection by optimizing the structure and charge distribution of the Janus membrane

Due to the asymmetry of charge distribution and geometric structure, the Janus membrane can simultaneously intercept counter-ions and co-ions. This work constructed four Janus membrane configurations using numerical simulation methods. Due to the different potential within the membrane and the selectivity level, there is a significant deviation in the interception performance. The results found that the change in the height of the Janus membrane affects the distribution of anion and cation concentration inside the nanochannel, and the asymmetry of the nanochannel height is more conducive to interception than a uniform nanochannel. The effect of the nanochannel length of the first section on the rejection rate is higher than that of the second section. Considering the design of the membrane, we recommend that the pore length of the first section be set to about 50 nm. Since the flow potential balances the electroviscous force, the optimization of the flow rate for ion transport is often opposite to the interception effect. The Janus membrane has asymmetric pore size and charge distribution characteristics and has excellent retention ability for divalent salts under low-pressure intensity and high-concentration conditions.

Performance of different nebulizers in clinical use for Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC).

Technical ex-vivo comparison of commercial nebulizer nozzles used for Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC). The performance of four different commercial nebulizer nozzles (Nebulizer; HurriChemTM; MCR-4 TOPOL®; QuattroJet) was analysed concerning: i) technical design and principle of operation, ii) operational pressure as function of the liquid flow rate, iii) droplet size distribution via laser diffraction spectrometry, iv) spray cone angle, spray cone form as well as horizontal drug deposition by image-metric analyses and v) chemical resistance via exposing to a cytostatic solution and chemical composition by means of spark optical emission spectral analysis. The Nebulizer shows quasi an identical technical design and thus also a similar performance (e.g., mass median droplet size of 29 μm) as the original PIPAC nozzles (MIP/ CapnoPen). All other nozzles show more or less a performance deviation to the original PIPAC nozzles. The HurriChemTM has a similar design and principle of operation as the Nebulizer, but provides a finer aerosol (22 μm). The principle of operation of MCR-4 TOPOL® and QuattroJet differ significantly from that of the original PIPAC nozzle technology. The MCR-4 TOPOL® offers a hollow spray cone with significantly larger droplets (50 μm) than the original PIPAC nozzles. The QuattroJet generates an aerosol (22 μm) similar to that of the HurriChemTM but with improved spatial drug distribution. The availability of new PIPAC nozzles is encouraging but can also have a negative impact if their performance and efficacy is unknown. It is recommended that PIPAC nozzles that deviate from the current standard should be subject to bioequivalence testing and implementation in accordance with the IDEAL-D framework prior to routine clinical use.

Procedure excellence: Changing paradigms to enable human reliability

AbstractA multi‐year review of process safety event learning data revealed that procedure usability and human factors were leading the trends of performance deviations by front‐line personnel. These insights prompted an enterprise‐wide Agile project to build capability focused on effective written guidance using science‐based tools and advanced error reduction techniques. Application of the Agile methodology was critical to gaining stakeholder commitment and establishing a comprehensive procedure integrity lifecycle to drive continuous improvement. Chemours partnered with an external consultant to create a holistic framework applicable to vital process safety roles with an orientation toward operational excellence. We share preliminary learnings and best practices leveraged from the nuclear industry, including systemic drivers to procedural deviations, the “top five error drivers” in written guidance, human performance modes (mental models), and key principles to enable human reliability.

Analysis of driver behavior at grade-separated intersections to support design

Understanding driver behaviors in varied traffic scenarios is critical to the design of safe and efficient roadways and traffic control device. This research presents an analysis of driver cognitive workload, situation awareness (SA) and performance for three different scenarios, including a standard intersection and contraflow grade-separated intersections (C-GSI) and quadrant GSI (Q-GSI) with lane assignment sign manipulations. The study used a simulator-based driving experiment with application of the NASA Task Load Index and Situation Awareness Global Assessment Technique to assess the influence of the scenarios on driver behavioral responses. The findings reveal challenges for drivers navigating the C-GSI, characterized by diminished SA and elevated workload. These states were associated with behaviors such as delayed lane changes, missed opportunities for appropriate lane changes, heightened acceleration behavior within deceleration segments, and frequent speeding. In contrast, while drivers in the Q-GSI scenario faced elevated workloads, their SA remained steady, largely due to lane-specific signs facilitating early lane changes. Although the Q-GSI led to increased speed variability and slight increases in deceleration, the use of supplementary speed signage revealed a promising alternative to the S-intersection. Correlation analysis highlighted a significant relationship between mental workload and acceleration responses, indicating that increased acceleration was associated with higher mental workload. In addition, a significant negative correlation between driver perceived performance and absolute lane deviations indicated that drivers with higher self-assessed performance were more accurate in lane-keeping. The study underscores the need for GSIs and signage designs that support driver SA, manage cognitive workload to improve driver performance and increase road safety.

Aging abnormality detection of lithium-ion batteries combining feature engineering and deep learning

The safety of battery packs is greatly affected by individual abnormal cells. However, it is challenging to diagnose abnormal aging batteries in the early stages due to the low abnormality rate and imperceptible initial performance deviations. This paper proposes a feature engineering and deep learning (DL)-based method for abnormal aging prognosis and end-of-life (EOL) prediction. The mathematical model of dimensionless indicators (DIs) is applied to partial voltage-capacity (V-Q) of one cycle to construct the optimal DIs with high sensitivity to abnormal aging. Taking the optimal DIs and partial V-Q as inputs, an abnormal aging prognosis model is established, and an ablation study is designed to verify the necessity of the selected DIs. Finally, the gated recurrent unit (GRU) is utilized to establish EOL prediction models for normal and abnormal degradation batteries, respectively. The proposed method is verified by a Lithium Cobalt Oxide (LiCoO2) battery dataset. The results indicate that the 100% prognosis of abnormal degradation batteries is achieved and the EOL prediction accuracy is less than 3.7%. This work highlights the rapid abnormal battery detection using data of one cycle without excessive battery testing, which contributes to the rational deployment of batteries and reduces the probability of failures during operation.

Perch use in 11-wk-old turkey hens: impact on performance, health, and behavior

Commercial turkeys are traditionally reared in floor barns, however, like most poultry species, turkeys display a natural desire to perch when given the opportunity. Perch use has typically been evaluated in laying hens, however due to size, weight, and postural differences, turkeys may have different preferences in perch design. The objectives were to examine perching preferences of turkey hens reared to 11 wk, as well as to examine the impact of perch use on hen health and performance. Nicholas Select (n=620) 11-d old turkey hens were randomly allocated to 20 pens (4 pen replicates/treatment) and assigned 1 of 5 perch design treatments (no perch (NP), 5 cm round dowel (5Rnd), 5 × 5 cm (5Sq), 10 × 5 cm (10Rec), or 15 × 5 cm (15Rec)). Data were analyzed via ANOVA as a complete randomized design, with significance declared when P≤0.05. Perch design did not affect hen performance or keel bone deviations and no fractures were found. Perching treatment had no effect on gait or footpad score at wk 7. At wk 11 gait scores were poorer in NP hens compared with 5Rnd (P=0.04) and footpad scores were poorer in NP hens compared with 15Rec (P=0.02). There were more turkey hens on the perch, as perch width increased at both 9 and 11 wk of age, demonstrating a preference for wider and flat perches. The results of this study demonstrate that turkeys will utilize perches when given the opportunity, with no detrimental effects seen on performance or bird health.

Explicit Model Predictive Cascade-Free Direct Force and Torque Control of Quadrotors

High-performance control of quadrotors' force and torques is the basis for achieving excellent trajectory tracking. However, the conventional method indirectly controls the force and torques by controlling four permanent magnet synchronous motors' speeds, resulting in poor dynamic performance and static deviations when pneumatic parameters mismatch. Dedicated to achieving high-dynamic and high-accuracy control of force and torques, an explicit model predictive (EMP) cascade-free direct force and torque control method is proposed. Firstly, defining force current and torque currents, whereby a new structure is proposed. Then, dynamics of the quadrotor and motor are fused to design the linear robust predictive models of force and torques. Accordingly, the EMP controller is constructed to replace the traditional cascaded speed solution module and four speed controllers uniformly. The EMP controller generates the optimal current commands directly from the force and torque commands, realizing direct control without the speed loop. The new structure makes it possible to solve the pneumatic parameter mismatch problem with an auxiliary extended state observer, obtaining offset-free control. The proposed method optimizes the dynamic performance and steady-state accuracy of force and torques and improves the system's robustness. Finally, the proposed method's feasibility is verified by experiments.

An improved high-fidelity adaptive model for integrated inlet-engine-nozzle based on mechanism-data fusion

Nowadays, there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system. Traditional component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model, failing to meet the accuracy requirements of supersonic conditions. This paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental calibration. Additionally, a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm (SSA-ELM) is proposed. The study reveals the inlet model's efficacy in reflecting installed performance, flow matching, and mitigating pressure distortion, while the nozzle model accurately predicts flow coefficients and thrust coefficients, and identifies various operational states. The model's output closely aligns with typical experimental parameters. By combining offline optimization and online adaptive correction, the mechanism-data fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation, and effectively handles gradual degradation within a single flight cycle. Notably, the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location correction. This approach significantly curbs performance deviations in supersonic conditions. For example, at Ma = 2.0, the system error impressively drops from 34.17% to merely 6.54%, while errors for other flight conditions consistently stay below the 2.95% threshold. These findings underscore the clear superiority of the proposed method.

Accelerated formulation of optimal control law for adaptive cycle engines: A novel design methodology

Adaptive cycle engines (ACEs) represent a sophisticated class of power units, embodying numerous variable geometry components, marking them as pivotal for the advancement of next-generation high-speed civil and military engines. Nonetheless, this variability introduces substantial complexity into the engineering of control laws. Contemporary research predominantly relies on either manual methodologies or global optimization algorithms. The former rarely achieves peak performance, while the latter incurs substantial computational expenses. This research introduces an accelerated methodology by using variable-step gradient with boundary constraints (VGB) algorithm tailored for the nuanced optimization challenges posed by multi-variable, multi-constraint nonlinearities inherent in ACEs. Recognizing that ACE's peak performance is typically boundary-constrained, the algorithm strategically manipulates variable geometries to widen the operational gap from the most disadvantageous boundary (MDB), thereby inching closer to the engine's ideal operational nexus. This strategy culminates in a decease in computational overhead, diverging significantly from conventional global optimization approaches. Empirical findings represent the method's efficacy, with a maximal performance deviation of just 3.78 % across three steady-state missions. Post transient control law refinement, engine's starting time contracted by 5.93 s via VGB algorithm, opposed to baseline design point configurations. Collectively, this approach serves as a versatile and efficient blueprint for designing control laws.

Optical functionality simulation through traceable characterization of optical components

The production of high-value components containing functional micro- and submicron-scale features requires the implementation of high-quality in-process inspection technologies to guarantee zero-defect manufacturing, boosting the transition to digital manufacturing. In the present study, the impact of the measurement uncertainty assigned to surface micro- and submicron-structures on the optical performance of a one-dimensional diffraction grating was determined. Thus, 3D confocal microscopy was selected as an inspection technology, introducing measuring data and uncertainty values on an optical simulation model to evaluate the influence of those magnitudes on the irradiance profile of a light beam at a target plane after passing through the diffraction grating. To achieve this goal, the calibrated areal standards have been used in addition to good practice guides for instrument calibration combined with optical modeling to simulate the functional behavior of the optical device. Results proved that changes of hundreds of nanometers in the lateral dimensions of the grating profile lead to drastic deviations in the irradiance profile and, thus, deviation in the optical performance. Hence a change in the period and the structure width of the step grating to the limits of the calculated uncertainty (Nominal Period ± ux,y) supposes a change of down to a 50 % of decrease in the maximum peak of the irradiance profile detected. Thanks to these results, it is possible to define a range in the grating device's performance depending on the dimensional surface characterization and its uncertainty. Moreover, an in-situ measurement approach has been designed for further product quality control.

Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines.

An integrated automatic optical inspection (iAOI) system with a procedure was proposed for a printed circuit board (PCB) production line, in which pattern distortions and performance deviations appear with process variations. The iAOI system was demonstrated in a module comprising a camera and lens, showing improved supportiveness for commercially available hardware. The iAOI procedure was realized in a serial workflow of image registration, threshold setting, image gradient, marker alignment, and geometric transformation; furthermore, five operations with numerous functions were prepared for image processing. In addition to the system and procedure, a graphical user interface (GUI) that displays sequential image operation results with analyzed characteristics was established for simplicity. To demonstrate its effectiveness, self-complementary Archimedean spiral antenna (SCASA) samples fabricated via standard PCB fabrication and intentional pattern distortions were demonstrated. The results indicated that, compared with other existing methods, the proposed iAOI system and procedure provide unified and standard operations with efficiency, which result in scientific and unambiguous judgments on pattern quality. Furthermore, we showed that when an appropriate artificial intelligence model is ready, the electromagnetic characteristic projection for SCASAs can be simply obtained through the GUI.

Super capacitor and SSSC-based coordinated strategy for performance improvement of diverse source interconnected power system

In this work, the dynamical behavior of the dual area thermal-hydro-wind (DATHW) model of the interconnected power system (IPS) is investigated. For analysis, the area-1 of the DATHW system is laid with a disturbance in step load (SLD) of 10%. The investigation is carried out on the DATHW system under the supervision of the fractional order proportional-integral-derivative-filter (FOPIDN) controller optimized with the water cycle algorithm (WCA). However, the dominance of FOPIDN in regulating the DATHW system performance deviations is validated by other regulators. To get the research near practicality, the DATHW system is deliberated with communication time delays (CTDs). The CTDs is the inherit nature of the communication peripherals that are employed in the IPS network for providing the data exchange among various devices. Further, the supplementary control approach of super capacitors (SC) and static synchronous series compensators (SSSC) is adopted with the DATHW system for performance improvement. The supplementary control is implemented by integrating the SC in the control areas and the SSSC with the tie-line. Finally, we conducted the sensitivity test to reveal the efficacy of the suggested controller mechanism in this work.

Experimental Investigation to Elucidate the Temporal Effect of Nanofluids on the Thermal Performance of Heat Pipes

The perplexity associated with the relevant practical application of the nanofluid lies with the alteration of the surface morphology. In the context of a heat pipe, it is established that the nanofluids, when used for a longer duration, are prone to agglomerate, and using them as working fluid can influence the heat pipe’s performance. The current work aims to examine the heat pipe performance deviation charged with Al2O3/de-ionized water nanofluid for extended operation periods of 0, 3, 6 and 12 months. The fabricated heat pipes charged with 40% of evaporator volume with various concentrations of Al2O3 base nanofluid (0.1%, 0.5%, and 0.8% volume fraction) were tested for various heat inputs (10, 30, and 60 W). The temporal effect of nanofluid on heat pipe performance was analyzed after successive intervals. The results propose that the heat pipe charged with nanofluid exhibits superior thermal performance in lieu of de-ionized water. Also, the performance characteristics of the heat pipe were more consistent and reliable for higher heat input of 60 W compared to 10 W when tested after a longer duration. The nanoparticles’ sedimentation and accumulation on the mesh clog adversely affect the contact angle and thus help in increasing the boiling limit.

Using Various Models for Predicting Soil Organic Carbon Based on DRIFT-FTIR and Chemical Analysis

Soil organic carbon (SOC) is a crucial factor influencing soil quality and fertility. In this particular investigation, we aimed to explore the possibility of using diffuse reflectance infrared fourier transform spectroscopy (DRIFT-FTIR) in conjunction with machine-learning models, such as partial least squares regression (PLSR), artificial neural networks (ANN), support vector regression (SVR) and random forest (RF), to estimate SOC in Sohag, Egypt. To achieve this, we collected a total of ninety surface soil samples from various locations in Sohag and estimated the total organic carbon content using both the Walkley-Black method and DRIFT-FTIR spectroscopy. Subsequently, we used the spectral data to develop regression models using PLSR, ANN, SVR, and RF. To evaluate the performance of these models, we used several evaluation parameters, including root mean square error (RMSE), coefficient of determination (R2), and ratio of performance deviation (RPD). Our survey results revealed that the PLSR model had the most favorable performance, yielding an R2 value of 0.82 and an RMSE of 0.006%. In contrast, the ANN, SVR, and RF models demonstrated moderate to poor performance, with R2 values of 0.53, 0.27, and 0.18, respectively. Overall, our study highlights the potential of combining DRIFT-FTIR spectroscopy with multivariate analysis techniques to predict SOC in Sohag, Egypt. However, additional studies and research are needed to improve the accuracy or predictability of machine-learning models incorporated into DRIFT-FTIR analysis and to compare DRIFT-FTIR analysis techniques with conventional soil chemical measurements.

The impact of imputation methods on the performance of Phase I Hotelling’s T 2 control chart

The objective of this study was to evaluate the impact of three different methods of handling missing data on the performance of Phase I Hotelling’s T 2 multivariate control chart. Using a Monte Carlo simulation, we studied the average, median, and standard deviation of the run length performance of multivariate data imputed using mean substitution, regression imputation, and predictive mean matching at three different levels of missingness ( 1 % , 10 % , and 25 % ) and three levels of variable correlation coefficients (0.2, 0.4, and 0.8). We found that predictive mean matching has average run length performance results comparable to that of the complete in-control data set at all levels of missingness and variable correlation, while the performance of mean substitution was adversely affected by high levels of missingness and by strong variable correlation. Based on the simulation (multivariate normal data), we concluded that predictive mean matching is superior to both regression imputation and mean substitution as a method for imputing missing values for the analysis of Phase I Hotelling’s T 2 control chart. Two applications were presented using the Altenrhein wastewater treatment plant and Olive oil datasets.

Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial Vehicle

Plant height and biomass are important indicators of rice yield. Here we combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass under different irrigation and fertilizer treatments. The study included two treatments: I—water availability factor (i.e., three drought objects, optimal, and excess water); and II—two levels of deep percolation and five nitrogen fertilization doses. The introduced model is extreme learning machine (ELM), back propagation neural network (BPNN), and particle swarm optimization-ELM (PSO-ELM), respectively. The results showed that: (1) Proper water level regulation (3~5 cm) significantly increased the accumulation of spike biomass, which was about 6% higher compared to that under flooded conditions. (2) For plant height inversion, the ELM model was optimal with a mean coefficient of determination of 0.78, a mean root mean square error of 0.26 cm, and a mean performance deviation rate of 2.08. For biomass inversion, the PSO-ELM model was optimal with a mean coefficient of determination of 0.88, a mean root mean square error of 3.8 g, and a mean performance deviation rate of 3.29. This study provided the possible opportunity for large-scale estimations of rice yield under environmental disturbances.

Effect of Eccentricity Difference on the Mechanical Response of Microfluidics-Derived Hollow Silica Microspheres during Nanoindentation.

Hollow microspheres as the filler material of syntactic foams have been adopted in extensive practical applications, where the physical parameters and their homogeneity have been proven to be critical factors during the design process, especially for high-specification scenarios. Based on double-emulsion droplet templates, hollow microspheres derived from microfluidics-enabled soft manufacturing have been validated to possess well-controlled morphology and composition with a much narrower size distribution and fewer defects compared to traditional production methods. However, for more stringent requirements, the innate density difference between the core-shell solution of the double-emulsion droplet template shall result in the wall thickness heterogeneity of the hollow microsphere, which will lead to unfavorable mechanical performance deviations. To clarify the specific mechanical response of microfluidics-derived hollow silica microspheres with varying eccentricities, a hybrid method combining experimental nanoindentation and a finite element method (FEM) simulation was proposed. The difference in eccentricity can determine the specific mechanical response of hollow microspheres during nanoindentation, including crack initiation and the evolution process, detailed fracture modes, load-bearing capacity, and energy dissipation capability, which should shed light on the necessity of optimizing the concentricity of double-emulsion droplets to improve the wall thickness homogeneity of hollow microspheres for better mechanical performance.