Performance Deviation Research Articles

Standardisation of the Brazilian version of Lucerne ICF-based multidisciplinary observational scale (LIMOS) into a patient-reported outcome measure (PROM)

Purpose The LIMOS is a validated observational tool designed to assess stroke rehabilitation within hospital settings. To enable its application in outpatient settings in Brazil and similar low- and middle-income countries, it is necessary to adapt LIMOS into a Patient-Reported Outcome Measure (PROM). Accordingly, this study aimed to translate, cross-culturally adapt, and standardise LIMOS for the Brazilian context (LIMOS-Br) and transform it into a PROM. Material and methods The adaptation process included translation, synthesis, expert evaluation, back-translation, target population evaluation and re-evaluation by experts. The study involved 100 adult stroke survivors. We assessed content validity, reliability, internal consistency, Criterion-related validity with the Functional Independence Measure (FIM), item and participant calibration, and accuracy. Results The LIMOS-Br demonstrated good internal consistency (Cronbach’s alpha = 0.8), and excellent intra- and inter-rater reliability (ICC2.1=0.97; 95%CI = 0.94–0.98 and ICC2.1=0.85; 95%CI = 0.74–0.91, respectively). Criterion-related validity with FIM was high (rho = 0.85; p < 0.01). Rasch analyses indicated high reliability indices (item: 0.96, people: 0.94), good item and person variance (separation indices: items 5.18, people 4.06), and adequate performance deviations. Accuracy for functional independence was optimal with a cut-off score of 180 points (sensitivity 83.3% and specificity 88.9%). Conclusions The LIMOS-Br maintained its clinimetric integrity, validating its use as a PROM.

Адаптация к новой реальности: обзор международных исследований специфики развития детей, оставшихся без попечения родителей, в замещающих семьях и при усыновлении

&lt;p&gt;This article analyzes the challenges encountered during the adoption of children or their placement in substitute families across various countries, with a focus on their adaptation, psychosocial development, cognitive abilities, and social integration. To achieve the objectives of this research, the scope was not limited to adopted children but extended to children deprived of parental care who were placed in alternative forms of family care (substitute families&amp;mdash;such as guardianship families, including foster families). Foreign researchers differentiate between foster children: &amp;ldquo;substitute family&amp;rdquo; &amp;mdash; raised in a foster or substitute family, &amp;ldquo;adopted children&amp;rdquo; &amp;mdash; adopted, &amp;ldquo;looked after children&amp;rdquo; &amp;mdash; under guardianship, &amp;ldquo;foster families&amp;rdquo; &amp;mdash; temporary stay in families, (an analogue is the system of state care in Russia), who are waiting for a suitable family for upbringing and long-term residence. The possibility of studying this category of children is associated with the practice of open adoption prevalent in most countries. In contrast, the secrecy of adoption in Russia impedes the ability to track the developmental dynamics of children, particularly those adopted at an early age. The study examines key factors influencing the well-being of adopted children and children under kinship guardianship, including the impact of cultural differences, models of adoption (open and closed), early traumatic experiences, and the level of support provided to foster families. Special attention is paid to medical aspects, academic performance, psychological issues, and potential behavioral deviations. An analysis of international data identifies the primary challenges faced by adopted children, such as legal troubles, emotional instability, and difficulties in social integration. It is revealed that the source of psychological trauma in most cases lies in the pre-adoption period of the child's life, which often includes factors collectively referred to as adverse childhood experiences. Parental attention and warmth in relationships can significantly mitigate the challenges faced by children. The article underscores the importance of an interdisciplinary approach, including collaboration between government institutions, NGOs, and educational organizations, to minimize the negative effects of adverse childhood experiences and improve the quality of life for children.&lt;/p&gt;

Optimizing Cost Efficiency through Cost Control in Construction Projects: A Review

This study explores various methodologies and factors influencing budget performance across different sectors, particularly in construction and public budgeting. Construction projects are inherently complex and prone to cost overruns, making cost control a critical aspect of project management. This study investigates the impact of cost control on cost efficiency in construction projects. A neural network model was utilized to identify key management factors affecting project budget performance, focusing on critical aspects such as project manager experience, team turnover, and budget updates. Several monitoring techniques for cost and performance deviations, including Activity-Based Ratios, were also analyzed, revealing their varying effectiveness. In the public sector, performance-based budgeting systems were discussed, with an emphasis on integrating planning and performance metrics for more informed decision-making. Challenges in implementing such systems were highlighted, particularly regarding political and organizational barriers. In construction, various approaches were examined, including cost estimation frameworks and performance-based budgeting tools. The research underlined the importance of accurate budgeting and cost control measures in both construction projects and public administration, suggesting that strategic adjustments, better forecasting, and active stakeholder participation could improve overall budget performance. This research paper presents a comparative analysis of various process-based cost control models utilized in construction projects. The objective is to evaluate each model’s effectiveness, advantages, and limitations to provide insights for project managers in selecting the most suitable approach for their specific project needs. Key Words: Cost Efficiency, Cost Control, Construction, Projects

Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis

Many natural signals exhibit quasi-periodic behaviors and are conveniently modeled as combinations of several harmonic sinusoids whose relative frequencies, magnitudes, and phases vary with time. The waveform shapes of those signals reflect important physical phenomena underlying their generation, requiring those parameters to be accurately estimated and modeled. In the literature, accurate phase estimation and modeling have received significantly less attention than frequency or magnitude estimation. This paper first addresses accurate DFT-based phase estimation of individual sinusoids across six scenarios involving two DFT-based filter banks and three different windows. It has been shown that bias in phase estimation is less than 0.001 radians when the SNR is equal to or larger than 2.5 dB. Using the Cramér–Rao lower bound as a reference, it has been demonstrated that one particular window offers performance of practical interest by better approximating the CRLB under favorable signal conditions and minimizing performance deviation under adverse conditions. This paper describes the development of a shift-invariant phase-related feature that characterizes the harmonic phase structure. This feature motivates a new signal processing paradigm that greatly simplifies the parametric modeling, transformation, and synthesis of harmonic signals. It also aids in understanding and reverse engineering the phasegram. The theory and results are discussed from a reproducible perspective, with dedicated experiments supported by code, allowing for the replication of figures and results presented in this paper and facilitating further research.

Validating genomic predictions for economic traits in purebred U.S. dairy heifers

Most genotypes in the National Cooperator Database now originate from cows, but most previous studies validating genomic predictions have primarily focused on bulls. This study paired official within-breed genomic predicted transmitting ability (GPTA) and parent average (PA) for genotyped heifer calves born between 2019 and 2021 using the August 2021 database with their corresponding performance deviations (PDEV) for 17 different traits. The PDEV data became available when the heifers completed their first lactation and were extracted from the August 2023 database in which at least one PDEV value for those 17 traits existed for each genotyped heifer record. The separate breed analyses included records for 219 Ayrshires (AY), 2,715 Brown Swiss (BS), 1,055 Guernseys (GU), 949,904 Holsteins (HO), and 125,275 Jerseys (JE). These validation cows were heifer calves born between 2019 and 2021. However, due to timing or recording patterns, each trait had missing or incomplete PDEV data, leading to unbalanced distributions of records across traits. The squared accuracy of genomic prediction, or genomic reliability (r2), was divided by the corresponding heritability for each trait, as only the heritable portion of cow records could be predicted, and this reliability varied across different traits and breeds. For HO and JE, the predictive ability of GPTA outperformed PA in predicting cow PDEV for yield, productive life, somatic cell score, fertility, and health traits. The improvement ranged from 33% to 142% compared with the predictive ability of the PA. However, the results for AY, BS, and GU breeds were less consistent due to the smaller number of genotyped heifers. The r2 gains in those breeds were smaller and aligned with the published reliabilities of GPTA. Weighted and unweighted regressions of PDEV on GPTA and PA traits mostly exceeded the expected value of 2.00 when predicting the future trait PDEV using GPTA or PA. The larger number of observations and lower standard error of the weighted regression coefficient prediction in HO and JE breeds contributed to more stable and consistent regression coefficients for all traits except milk fever and heifer livability. Our study suggests that herd owners may experience greater benefits from genomics than originally forecast.

Performance evaluation of irrigation outlets with physical model study in Indus basin irrigation system of Pakistan.

The efficiency of water distribution at primary, secondary, and tertiary levels in the Indus Basin Irrigation System (IBIS) has historically suffered due to poor design, suboptimal operation, and water scarcity. To address these issues, the system has been designed with ungated irrigation outlets to ensure equitable water allocation at secondary and tertiary levels. This research evaluates the hydraulic performance of three irrigation outlets: adjustable proportional module (APM), adjustable orifice semi-module (AOSM), and open flume (OF) using a physical model study. A distributary channel model with these outlets was constructed at the Centre of Excellence in Water Resources Engineering, where discharge coefficients (Cd) were calibrated and measured under various hydraulic and geometric conditions, including free and submerged flow conditions, and with adjustments to flow depths and outlet settings. The results showed variability in Cd values under free flow and submerged flow conditions with APM and AOSM ranging from 6.07 to 8.20 and 0.56 to 0.74, respectively, and OF between 2.46 and 4.31. Additionally, the behavior of outlet tampering on Cd values was also assessed under three scenarios: tampering with the half wing wall (1st), tampering with the full wing wall (2nd), and lowering the bed level downstream of the outlet (3rd). The increase in Cd values for APM and AOSM was + 10.84% and + 14.49% under 1st scenario, + 17.12% and + 22.36% under 2nd scenario, and + 24.25% and + 26.30% under 3rd scenario, respectively. The results reveal that even minor tampering with outlet structures can lead to significant deviations in performance, highlighting the importance of maintaining stringent control over outlet configurations to ensure equitable and efficient water distribution. There is a critical need for rigorous, site-specific calibration of irrigation outlets to optimize their performance under local conditions and redesigning outlet structures to minimize the impacts of tampering, thereby enhancing the overall sustainability of water use in large-scale irrigation systems. The findings from this study provide essential insights for irrigation engineers and policymakers tasked with upgrading and maintaining irrigation infrastructure. By adopting a more customized approach to outlet design and management, it is possible to significantly improve water use efficiency and achieve more sustainable irrigation practices.

Cognitive performance in ISS astronauts on 6-month low earth orbit missions.

Current and future astronauts will endure prolonged exposure to spaceflight hazards and environmental stressors that could compromise cognitive functioning, yet cognitive performance in current missions to the International Space Station remains critically under-characterized. We systematically assessed cognitive performance across 10 cognitive domains in astronauts on 6-month missions to the ISS. Twenty-five professional astronauts were administered the Cognition Battery as part of National Aeronautics and Space Administration (NASA) Human Research Program Standard Measures Cross-Cutting Project. Cognitive performance data were collected at five mission phases: pre-flight, early flight, late flight, early post-flight, and late post-flight. We calculated speed and accuracy scores, corrected for practice effects, and derived z-scores to represent deviations in cognitive performance across mission phases from the sample's mean baseline (i.e., pre-flight) performance. Linear mixed models with random subject intercepts and pairwise comparisons examined the relationships between mission phase and cognitive performance. Cognitive performance was generally stable over time with some differences observed across mission phases for specific subtests. There was slowed performance observed in early flight on tasks of processing speed, visual working memory, and sustained attention. We observed a decrease in risk-taking propensity during late flight and post-flight mission phases. Beyond examining group differences, we inspected scores that represented a significant shift from the sample's mean baseline score, revealing that 11.8% of all flight and post-flight scores were at or below 1.5 standard deviations below the sample's baseline mean. Finally, exploratory analyses yielded no clear pattern of associations between cognitive performance and either sleep or ratings of alertness. There was no evidence for a systematic decline in cognitive performance for astronauts on a 6-month missions to the ISS. Some differences were observed for specific subtests at specific mission phases, suggesting that processing speed, visual working memory, sustained attention, and risk-taking propensity may be the cognitive domains most susceptible to change in Low Earth Orbit for high performing, professional astronauts. We provide descriptive statistics of pre-flight cognitive performance from 25 astronauts, the largest published preliminary normative database of its kind to date, to help identify significant performance decrements in future samples.

Transient gas path fault diagnosis of aero-engine based on domain adaptive offline reinforcement learning

Real-time measurement parameters are crucial for diagnosing faults in aero-engine gas path performance, ensuring engine reliability, and mitigating potential economic losses. Traditional aero-engines performance diagnosis was mainly based on the measurements of steady-state condition and lacked the utilization of data under transient conditions. Gas path diagnosis of aero-engines under transient conditions is crucial for early fault detection and safety of flight within the envelope. The challenge lies in the inconsistent distribution of performance deviations caused by variable operating conditions, especially with complex fault types, which can undermine diagnostic credibility. To improve reliability of gas path diagnosis under transient conditions, an offline reinforcement learning fault diagnosis framework based on a transient aero-engine performance model is proposed. To address the issue of variable operating conditions during transient states, a domain adaptive approach is utilized to reconstruct the measurement baseline and facilitate the transfer of different performance deviation distributions. Additionally, by adding spool acceleration as a measurement parameter, the multi-component fault coupling is solved. Finally, validation with actual operating data simulates fault cases, demonstrating the proposed method's efficacy in quantitatively detecting gradual, sudden, and multiple component faults under transient conditions with high accuracy and efficiency. The method proposed in this study achieves a computational speed improvement by 64% compared to the conventional method, achieving a time of 0.13 seconds, with an average error of less than 0.00389%. Additionally, it demonstrates strong robustness in the presence of noise, with an average error of less than 0.03125%. This proposed method improves real-time fault detection under transient conditions for its higher accuracy and efficiency, and therefore significantly enhance gas path health monitoring and diagnosis capability.

The Influence of Temperature and Stoichiometry on the Optical Properties of CdSe Nanoplatelets.

Colloidal quasi-two-dimensional cadmium chalcogenide nanoplatelets have attracted considerable interest due to their narrow excitonic emission and absorption bands, making them promising candidates for advanced optical applications. In this study, the synthesis of quasi-two-dimensional CdSe NPLs with a thickness of 3.5 monolayers was investigated to understand the effects of synthesis temperature on their stoichiometry, morphology, and optical properties. The NPLs were synthesized using a colloidal method with temperatures ranging from 170 °C to 210 °C and optimized precursor ratios. Total reflection X-ray fluorescence (TXRF) analysis was employed to determine stoichiometry, while high-resolution transmission electron microscopy (HRTEM) and UV-Vis spectroscopy and photoluminescence spectroscopy were used to analyze the structural and optical characteristics. The results showed a strong correlation between increasing synthesis temperature and the enlargement of nanoscroll diameters, indicating dynamic growth. The best results in terms of uniformity, stoichiometry, and optical properties were achieved at a growth temperature of 200 °C. At this temperature, no additional optical bands associated with secondary populations or hetero-confinement were observed, indicating the high purity of the sample. Samples synthesized at lower temperatures exhibited deviations in stoichiometry and optical performance, suggesting the presence of residual organic compounds.

The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines

Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind energy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to predict wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, which were evaluated using metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were taken into consideration as well. After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their capabilities and responses to preprocessing, as well as each model’s reaction in terms of statistical performance deviation indicators. Ultimately, the data analysis and the results from metrics and criteria show that Random Forest regression is more suitable for weather condition datasets than the other two regression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy prediction.

The effects of government policy on performance planning for implementation of performance contracting in the government ministries in Kenya

The study sought to analyse the effects of government policy on performance planning for the implementation of performance contracting in government ministries in Kenya. A mixed-method type of design was adopted. Stratified sampling was used to sample four government ministries. In sampling staff to be interviewed, top and middle-level management totalling 831 were identified. Thirty per cent of the 831 (249 staff members) were considered to be representative enough for the study. The study adopted descriptive and inferential statistics. The study used ANOVA, regression analysis, and correlation to analyse the relationship between variables. The study finding observed that there was a strong correlation coefficient of 0.866 at p-value of 0.035 between government policy and performance planning, which implied that employees' performance planning is significant in the implementation of performance contracting; a unit standard deviation increase in effective training and development was likely to increase the standard deviation of performance monitoring by 0.096 on implementation of performance contracting; a unit standard deviation increase in top management commitment was likely to increase the standard deviation of performance reporting on performance implementation by 0.337. The study concludes that government policies have an effect on performance contracting in government ministries in Kenya. Further, the conclusion was that government policy has minimally addressed issues of training and development. Therefore, it is recommended that transfer of training be included in the next reviews of the government policy and specifically addressed in the workplace that often affect the impact of training and development.

Investigation on Effect of Nonlinear Parameters in torque accuracy of Permanent Magnet Synchronous Machines

EVs and PHEVs are mainly driven by inverter control topologies which will be driving permanent magnet synchronous machines. PMSM is widely used for high drive performance. However, due to parameter variations high torque accuracy is not achieved. The performance of the PMSM drives is widely affected which is caused mainly due to environmental factors and aging. This would include component tolerance, input signal tolerance, machine parameters, software algorithms, sensor accuracy and calibration. It is important to understand these factors and make the tuning throughout the lifecycle since it may show performance deviations over the time and may lead to field complaints. As a step-by-step procedure, the factors which affects the torque accuracy are identified as part of the literature review. This is given as input to a cause effect relationship model which could demonstrate the deviation points. Simulation is performed by varying the operating points at different regions in the e-drive system modeled in Matlab/Simulink. Simulation results are used with different operating points to find out the influential parameters contributing to the deviation. The outcomes derived from the simulations are analyzed and dominant factors are determined. It is observed that this behavior will vary according to the changes in speed and torque. Correlation coefficient is derived from the statistical analysis. The future scope will be applying the optimization techniques and verification of improvements.

Microstructure and compressive behaviour of PLA/PHA-wood lattice structures processed using additive manufacturing

This study investigates the microstructure and compressive behaviour of PLA/PHA-wood lattice structures manufactured using fused filament fabrication (FFF). The primary objective is to evaluate the effects of defects, such as porosity and surface roughness, on the mechanical properties of these lattice structures. X-ray micro-tomography (XRT) and finite element analysis are employed to compare CAD models with real lattice structures, offering insights into defect-induced performance deviations. The novel approach integrates experimental and numerical methods to better understand damage accumulation in porous structures. The methodology includes uniaxial compression testing and image processing for microstructural characterization. Results reveal significant differences in relative density and stress concentration due to manufacturing defects. In particular, 3D printed lattice structures display typical cellular material behaviour with a primary bending deformation mechanism. X-ray tomography reveals that process-induced porosity generate stress heterogeneity, which is not captured from CAD-based model resulting in an overestimation of the stiffness. The study concludes that accounting for these defects can be quantified by shifting the target relative density to compensate lack of performance in 3D-printed lattice materials.

Uncertainty quantification based on active subspace dimensionality-reduction method for high-dimensional geometric deviations of compressors

Compressors are inevitably exposed to diverse geometric deviations from manufacturing errors and in-service degradation. Consequently, the evaluation of performance uncertainties becomes of utmost importance for compressors in engineering application. However, the presence of high-dimensional and strongly nonlinear geometric deviations poses significant challenges in efficiently and accurately assessing the performance uncertainties of compressors. This study proposes an active subspace-based dimensionality-reduction method for high-dimensional uncertainty quantification (UQ) of compressors. Based on the active subspace (AS) method, a dimensionality-reduction high-precision artificial neural network is raised to solve the dimension disaster problem for high-dimensional UQ. Additionally, a data-driven approach is used to calculate the gradient of the quantity of interest, addressing the issue of high computational cost during the AS dimensionality reduction process. Furthermore, the Shapley method is applied to explore the influence mechanism of geometric uncertainties on performance deviations of compressors. The UQ of one transonic compressor stage at design point and near stall point is conducted by the proposed method. The findings show that the original 24-dimensional uncertainties are reduced to three-dimensional uncertainties by using this method. Consequently, the required sample size is reduced by 75% while maintaining almost unchanged model accuracy. The findings reveal that the sweep and stagger deviation of the rotor are key uncertainties on the performance of the compressor. The dispersion in efficiency is attributed to variations in shock wave position and intensity, while the dispersion in total pressure ratio is primarily affected by changes in rotor work capacity. Moreover, the dispersion at near stall is 50% higher than that at design point. Therefore, when studying UQ, it is important to pay closer attention to the performance dispersion at near stall conditions.

Fuel transport mechanisms and power generation enhancement in air-breathing microfluidic fuel cells with discrete-hole anode

Current challenges in air-breathing microfluidic fuel cells (AMFCs) technology include insufficient fuel transfer and unsatisfactory power density. To address these issues, this study develops a three-dimensional computational model to optimize the performance of AMFC with a discrete-hole anode. This model reveals that the "petal-shaped" non-uniform fuel distribution is due to the shearing and dilution effects of the electrolyte. Additionally, discrete-hole structures such as size have a slight influence on fuel transport and current production, with performance deviations under 1.6%. Significant improvements are achieved by reducing the electrolyte flow rate and electrode length. Transitioning from a single cell with a long anode to a series/parallel stack of cells with short electrodes significantly boosts net power output and fuel utilization. Under optimal conditions, a maximum power density of 349.9 mW cm−3 and an output of 19.2 mW are obtained. This research provides valuable insights into fuel transport mechanisms and power enhancement strategies in the AMFCs, guiding the future design and optimization of microfluidic reactors for energy conversion.

Monitoring of Artificial Intelligence in Hospitals.

Monitoring of artificial intelligence (AI)-based algorithms is necessary for safe implementation and will be required in upcoming regulations. This study investigates the potential for monitoring of AI in hospitals. First, by reviewing regulatory requirements and state of the art of monitoring. Then, by conducting a gap analysis of ISO42001, containing industry agnostic requirements harmonized with the EU AI Act. The analysis illustrates the need for comprehensive monitoring capable of capturing deviations in input, performance drifts and unintended interactions. However, hospitals often suffer from a technical debt, and the gap analysis provides qualitative indications on implementation challenges, including data quality, infrastructure and limitations in continuous improvement.

Study of effects of perforation layouts on wave energy dissipation caused by a submerged perforated breakwater in front of a vertical seawall

Perforated structures are a promising alternative to traditional breakwaters for constructing harbors and protecting coastlines. Perforated structures can effectively remove wave energy from ocean waves by the energy dissipation associated with the turbulence generated by the flow through the perforations in the structure. Understanding the factors that may affect the hydrodynamic characteristics of flow through a perforated plate is important for designing perforated marine structures. The purpose of this research was to determine the wave energy dissipation performance of thin-walled plates with various perforation configurations using OpenFOAM-based CFD simulation. Ten different plate configurations were designed and tested with a constant porosity while varying the distribution, shape, and size of the perforations, to determine the impacts to energy dissipation performance. The energy dissipation was quantified by CFD simulations of wave-interaction with a submerged perforated plate placed vertically in front of a vertical backwall (“seawall”). Physical wave flume experiments were also conducted to verify and validate the numerical models. The results showed that the perforation distribution, shape, and size play a minimal role in the energy dissipation performance when porosity is held constant. The minimal performance deviation between the perforation configurations was consistent across a range of wave periods and amplitudes.

Determination of Bioactive Compounds in Buriti Oil by Prediction Models Through Mid-infrared Spectroscopy

Buriti oil is a vegetable oil extracted from the pulp and seeds of buriti (Mauritia flexuosa L.), a palm commonly found in the Amazon region, and is used both in popular medicine and in the cosmetic and food industries. This work aimed to develop a faster and more accessible procedure to quantify the content of carotenoids, polyphenols, and total flavonoids in buriti oils, where predictive models emphasize figures of merit. The study was carried out with 50 buriti oil samples from the state of Pará, Brazil, which were sampled by combining attenuated total reflection (ATR) spectroscopy with mid-infrared Fourier transform (FT-MIR) together with partial least squares regression (PLSR). The confidence and validation matrix were obtained from ultraviolet–visible spectroscopy. The PLSR model regarding the total carotenoid content presented values ​​between 335.33 and 1557.05 μg/g was validated by the concentration demonstration coefficient (R2cal) equal to 0.9556, prediction demonstration coefficient (R2pred) equal to 0.85642, bias = 5.68.10−13, performance deviation ratio value (RDP) of 2.0135, and range error rate (RER) equal to 4.3747. Concentrations of phenolic compounds were predicted between 96.2964 and 121.857 GAE/100 g, where the model presented R2cal = 0.9762, R2pred = 0.8198, bias = 3.38.10−10, RDP = 5.9028, and RER = 5.7578. The flavonoid prediction model contains concentrations between 86.844 and 133.852 mg EC/100 g that circulate R2cal = 0.9445, R2pred = 0.8536, bias = 6.98.10−8, RDP = 6.7085, and RER = 6.7085. Buriti oil showed high levels of b-carotene. Prediction models are overwhelming and can be used for screening and quality control of natural products.

Resist and recover: Introducing a spring theory for modeling disaster resilience.

This paper presents a new approach for quantitatively modeling the resilience of a system that has been disrupted by a sudden-impact event. It introduces a new theoretical model that explicitly incorporates representations of the enabling and inhibiting forces that are inherent within postdisruption recovery behavior. Based on a new, more comprehensive measure of resilience that is able to capture both negative and positive deviations in performance, a generic mass-spring system is then used to illustrate the applicability of the theoretical model. The interplay between the enabling and inhibiting forces that is revealed by the new model provides a new theoretical basis for understanding the complexity of resilience and disaster recovery. With the addition of the new resilience measure, it lends support for defining and characterizing a new type of resilient behavior: unstableresilience.

Position Control of Electro-hydraulic Servo System Based on Repetitive Control Strategy

Background: When performing repetitive work in an electro-hydraulic servo system, the expected tracking signals are often periodic signals, such as trigonometric functions. For this kind of electro-hydraulic servo system, repetitive control is one of the most ideal control strategies. Objective: The objective of this patent technology is to improve the position-tracking performance of the electro-hydraulic servo system and minimize tracking errors by designing and implementing a repetitive control strategy. Methods: The study models an electro-hydraulic servo system, designs a stabilizing controller, and develops a plug-in repetitive controller to enhance EHSS tracking. The regeneration spectrum is used as a stability criterion, and performance is evaluated using statistical metrics like Mean Square Error (MSE), Root Mean Square Error (RMSE), and standard deviation of the tracking error along with tracking performance and steady-state error. Results: The developed controller, validated through simulation analysis and real-time experiments, significantly reduces tracking error and enhances system position tracking accuracy, demonstrating its effectiveness. For instance, the repetitive control strategy outperforms PID and backstepping controllers at 30 mm with 0.5 Hz, achieving an error of 0.2 with an RMSE of 0.0924 and σ of 0.0878. Similar trends are observed at various test conditions, highlighting the consistent and robust performance of the designed repetitive controller. Additionally, the designed repetitive controller demonstrates an average improvement of 75.175% and 62.97% compared to the proportional- integral-derivative and backstepping controllers, respectively. Conclusion: The designed controller provides technical support for position control of the electrohydraulic servo system, achieves position control requirements, and significantly improves positioning accuracy and response.