Step Change Research Articles

Deconvolution-based correction of pre-strike arc voltage measurement in medium voltage switches

Abstract Switching arc characterization, e.g. measuring the arc voltage and current, is crucial for assessing the performance of power switchgears under different working conditions. This is especially challenging for short circuit current making as one of the most severe operations for medium voltage load break switches. By closing the switch, a short circuit current flows through the arc which is ignited by the electrical breakdown in the gap between contacts before the contacts touch. This results in an abrupt change from the rated voltage of the power network, i.e. tens of thousands of volts, to the switching arc voltage, i.e. a few tens of volts and ultimately zero when the contacts touch. The inherent response of the measurement system to this very large step preceding the arc formation overlaps with the switching arc voltage and results in unrealistic arc voltages which makes correction of the distorted voltage waveform necessary. In this paper, a post processing method based on deconvolution correction technique, i.e. deconvolution of the measured voltage in time and frequency domains, is examined using experimentally determined impulse responses of the measurement system. To demonstrate its effectiveness, this method is applied to three different measurement system configurations with various step responses. The results show practically identical deconvoluted (corrected) voltage waveforms in all three measurement systems, although the directly measured waveforms have significant differences. The corrected arc voltage waveforms are in agreement with the indirect measurements proposed in previous studies.

Non-stationary models for hydrological extremes in the mountain rivers of the Argentinean Central Andes

ABSTRACT The assessment of extreme hydrological events relies on the assumption that time series are independent and identically distributed. However, empirical evidence contradicts this assumption, indicating the presence of nonstationarity, including trends, step changes, or both, in the river records of the Argentinean Central Andes. This study proposes the use of generalized additive models in location, scale and shape (GAMLSS) to model the annual maximum and minimum flow of seven rivers. Firstly, the presence of trends and step changes was assessed. Subsequently, the models were formulated considering time and various climatic indices as covariates. The findings reveal declining trends and negative jumps over the past 50 to 70 years. The location parameters of the models exhibit linear or smoothing dependence with time or climate indices covariates. Different modes of climate variability are associated with hydrological extremes. The nonstationary models provide a novel complementary framework for water resource planning in the region.

Composition tracking of natural gas–hydrogen mixtures in pipeline flow using high-resolution schemes

A transient pipeline flow model with gas composition tracking is solved for studying the operation of a natural gas pipeline under nonisothermal flow conditions in a hydrogen injection scenario. Two approaches to high-resolution pipeline flow modeling based on the WENO scheme are presented and compared with the implicit finite difference method. The high-resolution models are capable of capturing fast fluid transients and tracking the step changes in the composition of the transported mixture. The implicit method assumes the decoupling of the flow model components in order to enhance calculation efficiency. The validation of the composition tracking results against actual gas transmission pipeline indicates that both models exhibit good prediction performance, with normalized root mean square errors of 0.406% and 1.48%, respectively. Under nonisothermal flow conditions, the prediction response of the reduced model against a high-resolution flow model, with respect to the mass and energy linepack, is at most 3.20%.

Respiration rate and intestinal microbiota as promising indicators for assessing starvation intensity and health status in Pacific oyster (Crassostrea gigas)

Implementing early disease warning is vital to prevent large-scale mortality of mollusks cultured in open waters. Starvation-induced emaciation syndrome is an undeniable prerequisite for mollusk mortality. Nevertheless, the indicators of mollusk response to starvation remain poorly understood. In this study, the effects of a 44-day starvation period on the physiological performance and symbiotic microbiota of oysters were investigated. Starvation resulted in reduced growth and survival and notable damage to intestinal structure, including shrinking lumens and shortened intestinal walls and villi. Analyses of the respiration rate and intestinal microbiota, including OTUs, taxonomy, and predicted functions, revealed a stepped change pattern with a tipping point on day 10, indicating that oysters employed distinct strategies to adapt to prolonged starvation. Biomarkers associated with nutrient digestion and uptake, such as Firmicutes and Staphylococcus abundance, as well as the Firmicutes/Bacteroidetes ratio, progressively declined with increasing starvation. Conversely, Proteobacteria, an indicator of energy disequilibrium and unstable gut microbiota, and pathogenic Vibrio, showed significant enrichment. As starvation progressed, bacterial competition intensified, as evidenced by the reduced network connectivity and the increasing percentage of negative correlations. Functional predictions demonstrated an early-stage enrichment of degradation functions followed by later-stage suppression during prolonged starvation. In contrast, biosynthesis functions exhibited the opposite trend, suggesting that intestinal microbiota adjusted their specific functions to cope with food deprivation. Collectively, a conceptual model of starvation stress tolerance limits (SSTL) was introduced to define critical thresholds and candidate markers for evaluating starvation intensity and oyster health. This study offers valuable insights into non-feeding mollusks' adaptation to prolonged starvation and provides potential indicators for mollusk mortality events.

Compound Attitude Control Strategy for Reusable Launch Vehicle Based on Improved Particle Swarm Optimization Algorithm

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers’ weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system’s responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios.

Compound Attitude Control Strategy for Reusable Launch Vehicle Based on Improved Particle Swarm Optimization Algorithm

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers’ weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system’s responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios.

Research on the characteristics of electro-hydraulic position servo system of RBF neural network under fuzzy rules

A radial basis function neural network PID controller under fuzzy rules (FUZZY-RBF-PID) was designed for the electro-hydraulic position servo system under the influence of uncertain factors such as load mutation, and load stiffness change. Firstly, the mathematical model of the system is established, and the frequency domain and time domain analysis of the system are carried out. Secondly, based on the analysis results, a radial basis function (RBF) neural network PID controller is designed, and fuzzy rules are innovatively used to adjust the learning rate of PID parameters in the RBF neural network learning algorithm in real time. Thirdly, the simulation results show that under the action of the FUZZY-RBF-PID controller, the unit step response of the system has high steady-state accuracy, fast response speed, and under the condition of large load stiffness, the system can recover to the steady-state value faster after being disturbed. At the same time, when the input signal is the sinusoidal signal of 10 HZ, the system under the action of the FUZZY-RBF-PID controller has no obvious phase lag phenomenon, and the tracking error is minimal. The proposed method can effectively improve the comprehensive performance of the electro-hydraulic position servo system under the influence of uncertain factors.

Family Medicine Clerkship Directors' Perspectives on USMLE Pass/Fail Scoring: A CERA Study.

Reports on the effects of changing the United States Medical Licensing Exam (USMLE) Step 1 examination scoring to pass/fail are evolving in the medical literature. This Council of Academic Family Medicine Educational Research Alliance family medicine clerkship directors' study seeks to describe family medicine clerkship directors' perceptions on the impact of incorporation of Step 1 pass/fail score reporting on students' family medicine clerkship performance. Ninety-six clerkship directors responded (56.8% response rate). After exclusion of Canadian schools, we analyzed 88 clerkship directors' responses from US schools. We used descriptive statistics for demographics and responses to survey questions. We used ꭓ2 analysis to determine statistically significant associations between survey items. Clerkship directors did not observe changes in students' overall clinical performance after Step 1 pass/fail scoring (60.8%). Fifty percent of clerkship directors reported changes in Step 1 timing recommendations in the past 3 years. Reasons included curriculum redesign (30.5%), COVID (4.5%), change in Step 1 to pass/fail (11.0%), and other reasons (3.7%). Forty-five percent of these clerkship directors did not observe a change in students' clinical medical knowledge after Step 1 went to pass/fail. Eighty-four percent of these clerkship directors did not compare student performance on clerkship standardized exams before and after Step 1 score changes. We found no significant relationship between Step 1 timing and student performance. This study represents an early description of family medicine clerkship directors' perceived observations of the impact of Step 1 scoring changes on student performance. Continued investigation of the effects of USMLE Step 1 pass/fail scoring should occur.

One-dimensional dynamic model of a PEM fuel cell for analyzing through-plane species distribution and irreversible losses under various operating conditions

This study develops a one-dimensional dynamic model of a proton-exchange membrane fuel cell (PEMFC), using species mole fractions as shared variables to examine through-plane water and oxygen distribution. During dynamic operation, the species mole fraction difference between the cathode channel/cathode gas distribution media (cCH/cGDM) interface and the cGDM/cathode catalyst layer (cGDM/cCL) interface varies with current density. When relative humidity (RH) changes from 20 % to 90 % at 1 A/cm2, the water mole fraction difference between cCH/cGDM and cGDM/cCL increases by 33.21 %, while the oxygen mole fraction difference increases by only 3.29 %. Current density step changes cause overshoot and undershoot behavior in the water mole fraction, affecting membrane water content, especially at low RH, where it remains low and sensitive to current and temperature changes. Beyond 50 % RH, membrane water content stabilizes, with fluctuations mainly observed on the anode side. The impact of species distribution on irreversible losses was then examined.Furthermore, oxygen transport resistance increases with operating pressure, while oxygen molecular diffusion remains stable. Conversely, rising temperatures substantially improve oxygen molecular diffusion. An analysis of FC loss profiles under various temperatures (333.15K–363.15K) and RH (0%–100 %) at constant loads (0.2&1 A/cm2) shows the model's capability in determining optimized operating conditions for PEMFCs.

The impact of Covid-19 vaccine spring boosters on Covid-19 hospital admissions in England 2022/23

BackgroundIn the spring of 2022 and 2023 Covid-19 vaccine boosters were recommended for those aged ≥75 years in England as well as those in an immunosuppression risk group. The aim was to reduce severe Covid-19 disease in these groups. MethodsThe large difference in coverage between those above and below age 75 years was the basis for applying an age-discontinuity approach for measuring the impact of vaccination on Covid-19 hospitalisations in both spring 2022 and 2023. Hospitalisations in individuals positive by PCR for Covid-19 were obtained from the national secondary user service hospital dataset. The ratio of hospital counts by each year of age in 8-week periods after compared to before the roll out was modelled using negative binomial regression to estimate the discontinuity at age 75 years. ResultsA clear discontinuity was seen at age 75 years of 17.0% (95% CI: 6.1%-26.6%) in 2022 and 18.0% (3.3%-30.4%) in 2023. If applied to those aged ≥75 years this translates to 1302 and 418 averted hospitalisations in the 8-week period in 2022 and 2023, respectively. ConclusionsThis study shows a clear impact of vaccination on preventing Covid-19 hospitalisations and compliments other epidemiological methods assessing the impact of Covid-19 vaccines. Plain Language SummaryOne way to see if the booster vaccines doses given to protect against Covid-19 disease are working is to compare hospital admissions in groups of people who were and were not eligible for the dose. In England the spring booster doses were recommended for those aged 75 years and above. We could therefore compare hospitalisations in those above this age to those just below (aged 65-74) and see if there is a step change in rates from age 74 to 75 in the time after the vaccine was given. The results showed hospitalisations were about 18% lower in the group that were eligible, which is evidence that the vaccine is protecting against severe Covid-19.

Archaeological evidence for long-term human impacts on sea turtle foraging behaviour.

Early conservation efforts to prevent the loss of green sea turtles (Chelonia mydas) from the Caribbean Sea jumpstarted marine habitat and biodiversity protection. However, even there, limitations on historical observations of turtle ecology have hampered efforts to contextualize foraging behaviours for conservation management. We integrate isotopic and zooarchaeological evidence from green sea turtles harvested at the Miskito Cays (Nicaragua) to assess foraging behaviour before and after a step change in harvesting intensity. Highly structured isotopic evidence shows greater foraging adaptability in earlier populations. This provides a counterpoint to recent synthesis, suggesting the ecological non-exchangeability of sea turtles, which complicates conservation planning focused on genetic-stock-based repopulation. In contrast, our results suggest future populations would have a capacity for higher degrees of ecological exchangeability than current perspectives allow. This highlights a need to consider the kinds of longer term perspectives, such as those offered by archaeological materials, when planning for future sea turtle recovery.

Rational engineering of triazine-benzene linked covalent-organic frameworks for efficient CO2 photoreduction

Three large π-conjugated and imine-based COFs, named TFP-TAB, TFP-TTA, and TTA-TTB, were synthesized via the ordered incorporation of benzene and triazine rings in the same host framework to study how the structural units affect the efficiency of CO2 photoreduction. Results from both experiments and density-functional theory (DFT) calculations indicate the separation and transfer of the photoinduced charges is highly related to the triazine-N content and the conjugation degree in the skeletons of COFs. High-efficiency CO2 photoreduction can be achieved by rationally adjusting the number and position of both benzene and triazine rings in the COFs. Specifically, TTA-TTB, with orderly interlaced triazine-benzene heterojunctions, can suppress the recombination probability of electrons and holes, which effectively immobilizes the key species (COOH) and lowers the free energy change of the potential-determining step, and thus exhibits a superior visible-light-induced photocatalytic activity that yields 121.7 μmol HCOOH g−1 h−1. This research, therefore, helps to elucidate the effects of the different structural blocks in COFs on inherent heterogeneous photocatalysis for CO2 reduction at a molecular level.

Adaptive member adding for truss topology optimization: application to elastic design

This paper provides a rigorous and computationally efficient means of identifying compliance-based optimal truss topologies from high-resolution ground structures—problems involving over 30 million potential elements can be solved in under 1 h on a typical laptop. The adaptive ‘member adding’ approach is shown to provide significant savings in computational time and memory usage compared to directly solving the full optimization problem, whilst obtaining the same optimal solution; this paper presents the first application of this powerful principle to the well-known linear-elastic design problem. The computational advantages are particularly notable for multiple load-case problems, as the numerical structure of these cannot be effectively exploited without understanding of the physical nature of the problem. For such cases, the member adding process reduces the computational time required from approximately O(m2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(m^2)$$\\end{document} to O(m), where m is the number of potential elements; here, the time required is reduced by a factor of up to 60 for the relatively small problems that could be solved with both approaches. By using the member adding approach, compliance-optimized structures are obtained at a significantly higher resolution than has previously been possible. The findings of this paper have the potential to deliver a step change in the size of problems that can be solved in compliance-based truss topology optimization, and an accompanying Python code is provided to facilitate this.

Adaptive member adding for truss topology optimization: application to elastic design

This paper provides a rigorous and computationally efficient means of identifying compliance-based optimal truss topologies from high-resolution ground structures—problems involving over 30 million potential elements can be solved in under 1 h on a typical laptop. The adaptive ‘member adding’ approach is shown to provide significant savings in computational time and memory usage compared to directly solving the full optimization problem, whilst obtaining the same optimal solution; this paper presents the first application of this powerful principle to the well-known linear-elastic design problem. The computational advantages are particularly notable for multiple load-case problems, as the numerical structure of these cannot be effectively exploited without understanding of the physical nature of the problem. For such cases, the member adding process reduces the computational time required from approximately O(m2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(m^2)$$\\end{document} to O(m), where m is the number of potential elements; here, the time required is reduced by a factor of up to 60 for the relatively small problems that could be solved with both approaches. By using the member adding approach, compliance-optimized structures are obtained at a significantly higher resolution than has previously been possible. The findings of this paper have the potential to deliver a step change in the size of problems that can be solved in compliance-based truss topology optimization, and an accompanying Python code is provided to facilitate this.

Instrumentation of Novel Optical Sensor Technology to Detect the Real-Time Electrolyte Colour Change in Li-on Pouch Cells

The need for new and improved diagnostic tools becomes paramount for Li-ion batteries in order to ensure their optimum performance along with their longevity and safety and there remain aspirational targets for Li-ion batteries regarding fast-charging, energy density, safety and lifetime that must be met to achieve further growth. The study of degradation mechanisms (such as electrolyte degradation, thermal runaway, gassing and cycling aging) at the cell level contributes significantly to the understanding of the aging phenomena and will enable improvements for future generations of LIBs. Among various indicators, the colour change of the electrolyte within Li-ion cells presents a largely unexplored avenue for assessing state of health and predicting early signs of degradation. This research proposes a new methodology for the development of an innovative optical sensor technology and its incorporation into a pouch cell. The sensor comprises of a photodiode and RGB LED, mounted on a single flexible PCB, capable of the real-time detection of electrolyte colour changes inside Li-ion pouch cells. Previous studies have identified electrolyte colour change as a potential marker for battery condition through various invasive testing methods; however, the proposed optical technique represents a step change in in-situ real-time diagnostics. This study elaborates on the process of developing and incorporating the sensors precisely into pouch cells and seeks to demonstrate the capability of these sensors to accurately detect alterations in the colour of the electrolyte as the cell ages, without having adverse effects on the cell's performance and offers the potential for direct correlation of electrolyte change with battery state of health.

Adaptive configuration control of combined UAVs based on leader-wingman mode

Modular Unmanned Aerial Vehicles (UAVs) can adapt to rapidly changing payload requirements based on the shape and weight of the load by adding or subtracting units, reconfiguring, or changing the type of units. The existing research has addressed aerial docking and hover control post-docking but fails to achieve coordinated flight following combination, leading to delayed response and oscillations as the number of UAV units increases. Moreover, the configuration of modular UAVs is complex and variable, making it challenging to adjust the controller parameters of each unit online. Therefore, this paper presents: (A) Adaptive attitude allocation method for different combined UAV configurations: establishing a mapping relationship between constant controller parameters of the unit and the combination angular acceleration. The desired torque of the combination is allocated based on the size of the lever arm, enabling adaptive attitude control of the combination for varying configurations by controlling the attitude of the local unit; (B) A power allocation strategy based on a leader-wingman mode: employing a leader to control the entire combination, distributing the combination’s force and torque to wingman units according to the mapping relationship of the attitude allocation method. This transforms the complex control of the combination into unit control in the leader-wingman mode. Compared to current average allocation methods, the step response of attitude angle improves by about 60% on average, and spatial trajectory tracking increases by an average of 11.5%. As the number of units grows, the response of the combination becomes similar to that of a single, independently flying UAV, resolving the oscillation issue in combined flight. Additionally, this approach eliminates the need to change the controller parameters of all units, facilitating convenient reconfiguration and coordinated flight for modular UAVs post-combination.

Optimal design of diversion dams based on Upgraded Multi-Objective Particle Swarm Optimization (UMOPSO)

In the present research, the application of the Upgraded Multi-Objective Particle Swarm Optimization (UMOPSO) algorithm is investigated with the aim of reducing concreting costs through minimizing the diversion dam weight function. At the time of using optimization algorithms, regarding the nature of such a;gorithms and the research problem, making changes in algorithm running steps is necessary. Hence, in the current study, making some changes in the UMOPSO algorithm develops it to solve multi-objective optimization problems by considering their problem restrictions. In this paper, the study diversion dam is the Nazelian dam located in Kermanshah, Province, Iran. The decision variables in the optimization problem include the height of upstream and downstream cut off walls, the length and the thickness of the upstream concrete bed and the thickness of the stilling basin. By considering the initial population (n = 15), the optimal answer is achieved in iteration 100 and after algorithm run 1500. In this study, considering that the objective function should be evaluated for five decision variables with infinite possible values on a discrete range for each variable, the general result of the present study emphasizes on the desirable efficiency and speed of the UMOPSO algorithm in finding the optimal answer of the diversion dam optimal design problem which shows the strength of this algorithm.

Nastawy regulatora kursu dla autopilota statku

Selected methods of PID settings for the heading controller in the ship’s autopilot are presented. The consistency of the design is possible due to the fact that the Nomoto model was used for the ship and thanks to the elimination of the time constant of the object by the controller, so that the closed system becomes a 2nd order system. In two cases, it was additionally assumed that the system is to have a double time constant, differing in a given ratio r from the time constant of the object. It has been shown that the heading controller designed in this way provides critical aperiodic waveforms at a step change in the setpoint value and suppresses environmental disturbances better than standardly tuned controllers.

Oral administration of liposome-encapsulated thymol could alleviate the inflammatory parameters in serum and hippocampus in a rat model of Alzheimer's disease

BackgroundNeuroinflammation is closely related to Alzheimer's Disease (AD) pathology, hence supplements with anti-inflammatory property could help attenuate the progression of AD. This study was conducted to evaluate the potential anti-inflammatory effects of liposome encapsulated thymol (LET), administered orally, in prevention of Alzheimer in a rat model by anti-inflammatory mechanisms. MethodsThe rats were grouped into six groups (n = 10 animals per group), including Control healthy (Con), Alzheimer's disease (AD) model, AD model treated with free thymol in 40 and 80 mg/kg body weight (TH40 and TH80), AD model treated with LET in 40 and 80 mg/kg of body weight (LET40 and LET80). The behavioral response of step through latency (Passive Avoidance Test), concentrations of interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) and cyclooxygenase-2 (COX-2) and brain-derived neurotrophic factor (BDNF) were assessed in serum and hippocampus. ResultsThe results showed that significant increase in concentrations of IL-1β (P = 0.001), IL-6 (P = 0.001), TNF-α (P = 0.001) and COX-2 (P = 0.001) in AD group compared with healthy control rats. AD induction significantly reduced step through latency and revealed deficits in passive avoidance performance. The results also showed the treatment with free thymol especially in higher concentrations and also LTE could decrease serum concentrations of IL-1β (P < 0.05), IL-6 (P < 0.05), TNF-α (P < 0.05), and COX-2 (P < 0.05) and increase BDNF (P < 0.05) compared with control Alzheimer rats in hippocampus and serum. There were also significant correlations between serum and hippocampus concentrations of IL-1β (r2 = 0.369, P = 0.001), IL-6 (r2 = 0.386, P = 0.001), TNF-α (r2 = 0.412, P = 0.001), and COX-2 (r2 = 0.357, P = 0.001). It means a closed and positive relation between serum and hippocampus concentrations of IL-1β, IL-6, TNF-α, and COX-2. ConclusionsLET demonstrates its ability to attenuate neuroinflammatory reaction in AD model through suppression of IL-1β, IL-6, and TNF-α and COX-2 indicators. Hence, it can ameliorate AD pathogenesis by declining inflammatory reaction.

Model based control of dissolved oxygen in paper mill effluent using response surface methodology

In this work, for analysis, one and half liters of (effluent) waste water from paper mill industry was used in batch reactor, to study the variations in DO values under three operating conditions are speed, concentration of effluent in water and time with respect to step change in oxygen feed. The experiment was done 270 trails under specified operating conditions. Experimental data were collected, many trials were done using response surface method (RSM) and respective optimum values of three operating parameters were obtained for DO value ranging from 5 to 6 ppm. In trials, RSM predicted 82.732 % effluent to water concentration, stirrer speed at 150 rpm and at 0.091 min predicted DO is 5.7 ppm, from obtained experimental results shown in figure 3 for 80 % and 90 % concentration. RSM predicted optimal conditions are considered for experimental study and three control strategies such as particle swarm optimization (PSO) based PID control and state feedback controller with Integral action and state observer were used to achieve desired DO value. A comparative study was done among three controllers and it was concluded that state feedback controller with Integral action based control strategy yield better closed loop results in-terms of time-domain and stability analysis.