Relevant Parameters Research Articles

Fish stranding in relation to river bar morphology and baseflow magnitude: Combining field surveys and hydrodynamic–numerical modelling

AbstractHydropeaking mitigation based on down‐ramping thresholds is crucial to avoid stranding of fish and ensure sustainable hydropower production. However, the risk of stranding is influenced by several biotic and abiotic factors that interact with the ramping rate, including river morphology and baseflow magnitude. Also, at the river bar scale, the lateral or horizontal down‐ramping velocity may be of even greater importance than the vertical one as the prior integrates bar morphology. This study investigates the connection between ecologically relevant hydraulic parameters (vertical and lateral ramping velocity, dewatered river bar width) and the stranding of young‐of‐the‐year European grayling (Thymallus thymallus) due to down‐ramping, based on field observations of stranded fish at the hydropeaked Drava River in Austria combined with hydrodynamic–numerical modelling. We also examine the interplay between baseflow conditions and river topography in the dewatered areas. Stranding observations in spring and summer found a median rate of 90 individuals per 100 m, ranging from 50 to 500 fish per 100 m shoreline. Stranding rates were three times higher at night than during the day. Our data revealed a positive relationship between stranded fish and down‐ramping hydraulics, with higher intensity resulting in more stranded fish. This association was stronger for the lateral than the vertical ramping velocity, presumably because the former accounts for bar morphology, suggesting that the lateral dewatering velocity is a better indicator to assess fish stranding at the bar scale than the vertical velocity. We also found a negative relationship between the extent of the dewatered area and baseflow magnitude, indicating that the stranding risk is higher during down‐ramping at low flow conditions.

Calculation of optical efficiency of the heliostat field based on Reverse Projection Box Counting Model

A more accurate and reasonable calculation model is proposed for the calculation of total optical efficiency, shadow blocking efficiency, cosine efficiency, atmospheric transmittance, collector truncation efficiency and output thermal power of heliostat field in tower solar photovoltaic power plant. In this paper, under the condition that the relevant parameters of heliostat field are determined, the Monte Carlo Ray Tracing method is used as the basis for the calculation of each optical efficiency. At the same time, the innovative Reverse Projection Box Counting Model is established to accurately identify whether there is a shadow blocking problem or not, and the differential idea is used to effectively solve the problem of repeated calculation of the blocking shadow area and the irregularity of the blocking area difficult to be calculated caused by one heliostat being blocked by multiple heliostats, so as to estimate the shadow blocking efficiency of the heliostat field more accurately. Finally, the heliostat field in Gansu, China, is taken as an example for calculation, and the results are compared with those of other scholars and the real value of the heliostat field to prove the superiority of the model constructed in this paper and the accuracy of the solution results.

Sources of bias in applying close-kin mark-recapture to terrestrial game species with different life histories.

Close-kin mark-recapture (CKMR) is a method analogous to traditional mark-recapture but without requiring recapture of individuals. Instead, multilocus genotypes (genetic marks) are used to identify related individuals in one or more sampling occasions, which enables the opportunistic use of samples from harvested wildlife. To apply the method accurately, it is important to build appropriate CKMR models that do not violate assumptions linked to the species' and population's biology and sampling methods. In this study, we evaluated the implications of fitting overly simplistic CKMR models to populations with complex reproductive success dynamics or selective sampling. We used forward-in-time, individual-based simulations to evaluate the accuracy and precision of CKMR abundance and survival estimates in species with different longevities, mating systems, and sampling strategies. Simulated populations approximated a range of life histories among game species of North America with lethal sampling to evaluate the potential of using harvested samples to estimate population size. Our simulations show that CKMR can yield nontrivial biases in both survival and abundance estimates, unless influential life history traits and selective sampling are explicitly accounted for in the modeling framework. The number of kin pairs observed in the sample, in combination with the type of kinship used in the model (parent-offspring pairs and/or half-sibling pairs), can affect the precision and/or accuracy of the estimates. CKMR is a promising method that will likely see an increasing number of applications in the field as costs of genetic analysis continue to decline. Our work highlights the importance of applying population-specific CKMR models that consider relevant demographic parameters, individual covariates, and the protocol through which individuals were sampled.

Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections

PurposeSigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data.MethodsThis retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC).ResultsThe Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22).ConclusionA Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.

Analysis of the Interaction Mechanism between Preharvest Threshing Device and Rice at Harvesting Period Based on DEM Simulations and Bench Tests

Preharvest threshing is a harvesting method that focuses on collecting rice grains while leaving the rice straw unharvested. Investigating the interaction mechanism between the machine and rice during the operation process and its correlation with harvest losses is crucial for enhancing harvest quality. In this study, structural design and operational mechanism analysis of the combs was conducted through theoretical analysis. By extracting the relevant parameters of rice plants, a model of entire-plant rice during the harvesting period was established based on the discrete element method (DEM). Numerical simulation studies were conducted to clarify the interaction mechanism between the machinery and rice at different operating stages and under various operating parameters, as well as the impact of this interaction on operational quality. The simulation results revealed that various operating parameters had a significant impact on the sliding-cut effect between the combs and rice. A higher cylinder rotation speed enhanced the effect, whereas increased forward velocity hampered it. Additionally, the effect initially improved and then decreased with a higher threshing height. In the bench test, high-speed cameras were used to verify and further analyze the comb–rice interaction mechanism and explore the optimal working parameter combination. The results showed that at a rotation speed of 616 r/min, a forward velocity of 0.91 m/s, and a threshing height of 792 mm, the grain loss rate was 1.997%, and the impurity rate was 4.073%. The harvesting losses were effectively reduced, validating the effectiveness of the study on the interaction between the machinery and rice.

Modeling the dynamics of quantum systems coupled to large-dimensional baths using effective energy states.

The quantum dynamics of a low-dimensional system in contact with a large but finite harmonic bath is theoretically investigated by coarse-graining the bath into a reduced set of effective energy states. In this model, the couplings between the system and the bath are obtained from statistically averaging over the discrete, degenerate effective states. Our model is aimed at intermediate bath sizes in which non-Markovian processes and energy transfer between the bath and the main system are important. The method is applied to a model system of a Morse oscillator coupled to 40 harmonic modes. The results are found to be in excellent agreement with the direct quantum dynamics simulations presented in the work of Bouakline et al. [J. Phys. Chem. A 116, 11118-11127 (2012)], but at a much lower computational cost. Extension to larger baths is discussed in comparison to the time-convolutionless method. We also extend this study to the case of a microcanonical bath with finite initial internal energies. The computational efficiency and convergence properties of the effective bath states model with respect to relevant parameters are also discussed.

First-principle investigation of structural, electronic, and phase stabilities in chalcopyrite semiconductors: insights from Meta-GGA functionals

We undertake a comprehensive first-principles investigation into the factors influencing the optoelectronic efficiencies of P I Q III R 2VI chalcopyrite semiconductors. The structural attributes, electronic properties, and phase stabilities are explored using various meta-GGA exchange-correlation (XC) functionals within the density functional framework. In particular, we assess the relative performance of these XC functionals in obtaining estimates of various relevant parameters. The structural parameter u in chalcopyrite semiconductors is a noteworthy aspect, as it is intrinsically tied to the extent of orbital hybridization between distinct atoms and thereby strongly influences the electronic properties. In general, the application of widely used GGA-PBE XC functional to these chalcopyrites results in unreliable predictions of band gaps and ‘u’ parameter due to delocalization errors that in turn arise due to the inclusion of d and f core electrons. While hybrid functionals offer remarkable accuracy through state-of-the-art methods, their main drawback lies in their computational expense and resource demands. Our findings strongly suggest that in comparison to GGA-PBE, the meta-GGA XC functionals perform quite well and provide results that closely align with experimental values. In particular, the r 2SCAN and rMGGAC XC functionals are preferable and superior for investigating chalcopyrites and other solid-state systems. This preference is rooted in their excellent performance and substantially reduced computational costs compared to hybrid functionals.

Relationship of environmental exposure temperature and temperature extremes on sperm DNA fragmentation index in men with different BMI values and the indirect effect of DNA fragmentation index on semen parameters

Prior studies have established a significant correlation between the DNA fragmentation index (DFI) and infertility. Additionally, certain investigations suggest that environmental exposure may serve as an etiological factor impacting semen quality. This study aimed to explore the impact of season, ambient temperature, and weather extremes on the DFI of sperm, along with other relevant parameters. Furthermore, it sought to assess how ambient temperature affects the DFI of sperm and other semen parameters in populations with varying BMI values. Additionally, the study analyzed the transient indirect effect of DFI on sperm parameters. This retrospective study analyzed semen samples from 11,877 men, selected based on female factor considerations, spanning from January 2016 to December 2021. Participants were grouped according to the season of semen collection. The results showed that samples collected in summer had a lower semen volume and sperm motility, while those collected in autumn had a lower DFI. We analyzed the exposure-response ratio between environmental exposure temperature and semen parameters using a generalized additive model. Results showed that the curve of the exposure-response relationship was U-shaped or inverted U-shaped; when the air temperature exposure was below the threshold, for each degree of temperature increase, the total sperm motility, sperm concentration, and progressive motility increased by 0.16 %, 0.29 × 10 (Levine, 1999)/ml and 0.14 %, respectively, while the DFI and inactivity rate decreased by 0.078 % and 0.15 %, respectively. When the air temperature exposure exceeded the threshold, for each degree of temperature increase, the sperm concentration, total sperm motility, semen volume and progressive motility decreased by 0.42 × 10 (Levine, 1999)/ml, 0.11 %, 0.0078 ml and 0.15 %, respectively, while the DFI and inactivity rate increased by 0.13 % and 0.12 %, respectively. Extremely cold weather during spermatogenesis was positively correlated with DFI, and extremely hot weather was negatively correlated with sperm motility. Subgroup analysis revealed that individuals classified as overweight / obese exhibited more pronounced changes in sperm parameters and the DFI in response to variations in environmental exposure temperature compared to those with a normal BMI. In the analysis of the relationship between DFI and sperm parameters, the results showed an inverted U-shape relationship between DFI and semen volume, and a negative correlation between DFI and sperm concentration and sperm motility. And we found that ambient temperature affects sperm parameters through DFI at low as well as average temperatures, whereas at high temperatures this indirect effect is no longer present.

Parameter Calibration for Discrete Element Simulation of the Interaction between Loose Soil and Thrown Components after Ginseng Land Tillage

Given the lack of accurate and reliable discrete element simulation parameters to study the interactions between soft soil and soil casting components after ginseng land cultivation in Northeast China and the design of ginseng land-specific borders, this paper calibrates the relevant model parameters of ginseng soil using the Hertz–Mindlin with JKR contact model in EDEM to standardize the contact parameters between soil particles and between soil and Q235 steel in soft soil after cultivation in ginseng land. Taking the soil particle accumulation angle as the response value, a Box–Behnken design (BBD) was introduced to establish a regression model for the soil accumulation angle; the surface energy, static friction coefficient, rolling friction coefficient, and coefficient of restoration parameters were obtained, respectively, through the optimization of the model, at which time, the simulated value of the soil accumulation angle was 37°, which is a 4% relative error to the actual measured accumulation angle of 35.5°. Taking the sliding friction angle of the soil on the Q235 steel plate as the response value, the regression model of the soil sliding friction angle was obtained based on the BBD. The static friction factor, rolling friction factor, and coefficient of restitution between the soil particles and the Q235 steel were obtained, respectively. Based on the combination of these parameters, the simulated value of the sliding friction angle was 32.2°, which is a 2% relative error to the measured accumulation angle of 31.5°. To verify the accuracy of the optimized simulation parameters, field and simulation tests of soil-throwing components were conducted. The results show that the maximum relative error between the measured value and the simulation value is 5.6% and 3.4%. The error is within an acceptable range, and the simulation test and field test soil-throwing effects are the same, which verifies the accuracy and reliability of the reference soil parameter calibration. The results of the study can be used for discrete element simulation analysis of the interaction between ginseng loam and touchdown components and their structural optimization.

Investigating the occurrence of flow instabilities in coastal unconfined aquifers using sand tank experiments

Subterranean estuaries form the land-sea transition zone in coastal aquifers and are relevant for coastal freshwater management and coastal ecology. Tidal fluctuations typically lead to the formation of upper saline plumes, where denser saltwater circulates on top of the discharging, less dense freshwater. Extensive numerical simulations and only a few experimental trials previously demonstrated that these saline plumes may become unstable and exhibit salt fingering flow. This work used laboratory sand tank experiments to study the formation and evolution of tide induced flow instabilities in homogenous, unconfined coastal aquifers in a systematic manner, addressing the combined effects of relevant parameters such as sediment type, beach slope, fresh groundwater inflow, tidal amplitude, and tidal period variation. Some of these parameters have never been targeted in laboratory-based studies before and a comparable systematic and comprehensive approach has so far not been conducted. The resulting flow patterns were studied visually through the application of a colour tracer to the saltwater. The results show that the upper saline plume may be stable, unstable, or transitional, whereby the beach slope predominantly defines its state. When salt fingers form, they amplify the mixing zone between ground- and saltwater and force the freshwater to discharge at multiple locations along the beach. The processes were best visible in the coarsest sediment (1.02 × 10-2 m s−1). The finer grained the sediment was, the smaller the upper saline plume that formed. The present study further reveals the possibility of three-dimensional fingering flow, even though the physical model was intended to represent a two-dimensional cross-section. The application of a previously proposed non-dimensional stability diagram yielded reasonably accurate predictions of stability behaviour in coastal unconfined aquifers under laboratory settings. This further supports the plausibility of flow instabilities in subterranean estuaries within specific ranges of influencing hydrogeological factors.

Prediction system for water-producing gas wells using edge intelligence

Fluid accumulation in gas wells lead to a sudden drop in production and is a problem. This paper proposes a prediction system for water-producing gas wells using edge intelligence. The method is characterized by use of edge intelligence to realize automatic collection of gas well data, effusion analysis, data backup, upload, production dynamic monitoring, and other functions. The Pearson correlation coefficient was employed to analyze the relevant parameters of liquid accumulation in gas wells. A collaborative discrimination approach utilizing convolutional neural networks and ensemble learning models was implemented to predict liquid accumulation state of gas wells. The model achieved an accuracy rate as high as 98.2%, which aligns well with actual liquid accumulation state of gas wells compared to traditional physical models. In addition, suggestions for extraction and drainage plans are provided based on the degree of liquid accumulation in gas wells, thereby realizing automated and refined management of liquid accumulation wells in the field. This method provides effective theoretical support and reasonable guidance for the efficient development and overall decision-making in gas fields.

Occupational injuries in orthopedic and trauma surgeons in Austria

BackgroundThe aim of this study was to investigate the frequency and type of injuries during the career of orthopedic and trauma surgeons in Austria. The hypothesis was that the percentage of occupational injuries among orthopedic and trauma surgeons aligns with the incidence reported in the United States, thus indicating the need for a workplace prevention program.MethodsA web-based survey was created to collect all necessary data and was sent to all orthopedic and trauma surgeons in Austria via e-mail. Relevant parameters included the surgeons’ age, work experience, severity of pain, type of injury, and current pain. Descriptive and explorative statistical analysis was performed.ResultsA web-based survey was sent to 1122 board-certified orthopedic surgeons and residents in Austria via e-mail. In total, the response rate was 135 (12%). Seventy-two surgeons (54%) had suffered from one or more occupational injuries during their career. We detected a significant raise of occupational injuries related to the work life duration in which operations were performed and the prevalence of injuries. Most injuries of surgeons were reported between 21 and 30 years of their professional life. According to the frequency at different locations, the distribution in descending order was 25% with injuries of the hand, 22% of finger(s), 12% of the foot, 10% of the spine, 2% of the neck, 3% of the head, and 2% of the abdomen. A causality of incapacity to work through injuries at the workplace was given as 4%. Four percent stated a sick leave of at least 3 weeks. In 7% of the facilities, there was no optimization of preventive measures following an occupational injury. We found no correlation of injuries and resident status.ConclusionOrthopedic surgeons in Austria show a high incidence of occupational injuries in line with the findings of colleagues from the United States. The impact on the health system consists of absenteeism in the workplace of highly specialized health service providers as well as the incapacity to work of a high quality, highly trained workforce of at least 4%. With more preventive measures and more attention and care in the rehabilitation phase after such injuries, a positive effect could be achieved. We believe that residents should be specifically trained on how to avoid such injuries.

Risk-based analysis of squeeze cementing operations

The success rate of a squeeze cementing operation is generally low due to complexities in the range of potential leakage pathways and enormous uncertainty in characterizing these pathways. This work aims first to develop a physically based numerical model of the key part of the squeeze cementing operation, i.e. invasion of cement slurry into a realistic microannuli geometry under operational conditions. Secondly, it aims to account for uncertainties by using statistical tools combined with the deterministic model to deliver probabilistic information regarding outcomes. This paper develops this novel risk-based approach. The first objective of this work is to build upon our previous model, which focused on a single-perforation injection scenario. We reconstruct a multi-perforation injection scenario by collocating radial (single-perforation) invasion flows. We use this model to investigate the invasion of a viscoplastic fluid, representing a cement slurry, into a randomized varying width microannulus channel. The second objective is to investigate the effect of relevant parameters on leakage reduction. This is broken into two parts. First, how effectively the squeeze operation fills the microannulus around a perforation, explored through metrics that quantify penetration/filling. Second, what effect the penetration/filling has on the leakage of the entire well, i.e. given that the squeeze operation is local. We compare the effect of different perforation patterns and rheological parameters on penetration/filling metrics and on the reduction of leakage. We generate a probability distribution of leakage rates before and after the operation. In this way can estimate both the mean reduction in leakage for different perforation patterns, and associated confidence intervals. Such predictions have not been made before. Our results demonstrate the inherent uncertainty of a squeeze operation, which arises from the geometrical complexity. Practically, this suggests that higher perforation density and lower yield stress slurry will lead to higher success rates, as is intuitive. However, the spread of uncertainty in outcomes is not reduced much by such practices, meaning that one can still be unlucky in perforating the wrong part of the annulus.

A dynamic low carbon supplier preference model based on Taguchi method

The dynamic nature of the supply chain due to personalised demand and technological advancements has made supplier selection a challenging issue in supply chain management. This research paper aims to tackle the supplier selection problem in a multi-period, multi-sourced, and multi-product environment by creating a process that places the manufacturer at the centre. Firstly, this paper has constructed a multi-period supplier dynamic preference model that takes into account environmental protection, to minimise the total cost and carbon emissions. Secondly, the Taguchi method is extended to the supplier selection problem in the field of quality engineering. The parameter optimisation process is then designed based on the Taguchi method, aiming to optimise the main key parameters that affect supplier selection and determine the optimisation level combinations upon which the model is solved. Finally, the model's feasibility and evolution are verified through examples of its application. The paper's methodology enhances the model's relevant parameters' robustness, augments decision-making efficiency, and offers suppliers guidance to boost their performance and substantially cut the manufacturer's total procurement expenditure and carbon dioxide emissions.

Towards sustainable process heating at 250 °C: Modeling and optimization of an R1336mzz(Z) transcritical High-Temperature heat pump

Fossil fuel boilers typically produce high-temperature process heating due to the impossibility of current renewable technologies. Vapour compression heat pump systems have been proposed up to 200 °C with subcritical cycles. To increase the heating production level, this paper proposes heating up to 250 °C through a transcritical high-temperature heat pump (THTHP) using R1336mzz(Z), which is not thermally degraded till this temperature, according to existing data. Computational models of a transcritical vapor compression cycle with and without an internal heat exchanger are developed for this work. The input cycle parameters, such as evaporation, production temperature, gas cooler output temperature, and the superheating degree, have been modified through iteration to consider possible scenarios in different industrial sectors. Moreover, the impact of using waste heat to increase evaporation temperature or superheating degree has been studied to optimize the cycle. The internal heat exchanger has also been considered for superheating the compressor suction. Other relevant parameters, such as compression ratio or volumetric mass flow rate, have also been assessed. The results show that a heat pump is feasible to reach 250 °C temperature using waste heat at 120 °C (evaporation at 100 °C and 20 °C of superheating degree), reaching a COP of 3.3. The variation of evaporator temperature from 80 °C to 140 °C increases COP from 2.5 to 5.9, while the increase of superheating degree from 20 °C to 100 °C increases COP from 3.2 to 7. Waste heat use optimization proves that it is more efficient to prioritize the increase of the evaporation temperature. If the industry requires lower production temperatures, the COP can reach a value of 7.0 at 180 °C. The decrease in gas cooler outlet temperature impacts less the COP than other input parameters. At baseline conditions, an optimum superheating degree with an internal heat exchanger of 45 K produces a maximum COP of 4.0. THTHP emissions are less than gas boilers for the same application, and in countries with greener electricity generation, these emissions can be 50 times lower.

Application of artificial intelligence to investigate the performance and flow pattern near staggered piece in V-ribs with aligned gaps roughness in solar air heater using relevant input parameters

The present work findings of an experimental investigation on a rectangular duct used in a solar air heater (SAH) to increase heat transfer and frictional characteristics and improve flow near the roughness. The rough SAH surface is created by staggered V-ribs and aligned gaps. For Reynolds numbers 3000–14,000, relative gap breadth (g/e) is changed from 2 to 5, while other parameters are maintained constant: relative roughness pitch (P/e) = 10, relative staggered rib position (P'/P) = 0.4, relative staggered rib size (w/g) = 1, and relative roughness height (e/Dh) = 0.0434. The Nusselt number (Nu) and friction factor (f) are found as 2.16 and 2.73 times higher than a flat plate under the identical operating and flow conditions. The overall augmented behavior of rough SAH considering frictional characteristics is calculated in terms of Thermal enhancement factor (TEF) as 1.56. The impact of gap width on flow pattern is examined by using ANSYS Academic Research CFD 15.0. ANN outperformed GMDH. The air mass flow rate was revealed to be the most sensitive influencing element in a sensitivity analysis.

Effects of site aridity and species on stand transpiration in high-elevation dryland ecosystems

Climate change is altering regional aridity and species composition in dryland ecosystems. Understanding the effects of long-term increasing aridity and species-specific stomatal behaviors on transpiration is therefore important for water-resource forecasting. To assess the effects of site aridity levels and species on stand transpiration rate (Es), we monitored sap flux density (Js) and relevant environmental parameters for 16 isohydric Picea crassifolia (spruce) and 14 anisohydric Juniperus przewalskii (juniper) trees over three growing seasons at four arid to semi-arid high-elevation sites on the Tibetan Plateau. Our results show that Es was about 5–9 times higher in semi-arid sites than in arid sites, and about 6–9 times higher in spruce than in juniper. Spruce exhibited stronger stomatal regulation of transpiration than juniper. Soil water supply strongly promoted Js only for spruce in the arid environment (R2 = 0.23), while at the other sites, Js was mostly controlled by atmospheric vapor pressure deficit (VPD) (R2 > 0.82). However, increasing site aridity greatly reduced the sensitivities of both Js and canopy conductance to VPD, especially for juniper. We expect that in the transition from semi-arid to arid alpine forests, Es will initially rise due to increasing VPD. However, in the long term, there may be a stronger decline in Es since both the sensitivity of Js to VPD and the stand sapwood area will decrease. These findings could be used to reduce the impacts of climate change on water resources in high-elevation drylands.

Electric vehicle charging design: The factored action based reinforcement learning approach

Charging optimization design for Electric Vehicles (EV) is challenging because it must account for various uncertainties and design aspects such as random EVs’ arrivals and departures, battery degradation, and transformer Loss of Life (LoL). Model-free reinforcement learning (RL) can be employed to tackle such the EV charging design where it does not require to explicitly model the environment dynamics and accurately predict relevant system parameters. However, the high complexity involved in conventional RL-based approaches usually limits its application to only small-scale EV charging settings, which is impractical. To overcome this limitation, we employ the factored action based RL method to transform the formulated Markov Decision Process (MDP). Then, we propose novel reward shaping and hybrid learning methods combining the Convolutional Neural Network (CNN) and Proximal Policy Optimization (PPO) algorithm to extract relevant features from high-dimension state space and efficiently solve the transformed MDP problem. Extensive numerical studies demonstrate that the proposed design can be used to control a charging station (CS) supporting a large number of EVs. Moreover, we show that the proposed framework greatly outperforms other baselines including single-agent and multi-agent RL based strategies and a heuristic power scheduling algorithm in terms of the achieved reward.

An Estimation of Speech Privacy Class Based on ISO Parameter

This paper examines speech privacy in both residential and commercial spaces. The ASTM E2638 standard defines the Speech Privacy Class (SPC) parameter, which measures speech privacy based on the signal-to-noise ratio at the listener’s position. This paper proposes estimating the SPC value using relevant ISO parameters commonly used in European practice: the apparent sound reduction index in dB (defined by ISO 16283-1, 2 standards) and the equivalent ambient noise level in dBA (defined by the ISO 1996-1 standard). The estimated value of the SPC parameter in this paper is referred to as the Speech Privacy Index (SPI). A diverse range of situations, i.e., rooms, was analyzed in the field. These rooms varied in terms of purpose, organization, dimensions, furnishings, isolation from other spaces, and internal and external environments. The results of the experiments demonstrate a strong correlation between the SPC value estimated according to ISO parameters (the proposed method) and the SPC as defined in the ASTM E standard. This indicates that the proposed method can provide an indicator of the state of speech privacy in buildings. The significance of the proposed calculation method (i.e., the STI parameter) lies in its ability to be applied at the building design stage, as well as after its completion, during routine testing.

Investigation of Magneto Hydrodynamics Properties of Reiner–Philippoff Nanofluid with Gyrotactic Microorganism in a Porous Medium

Investigation of Magneto Hydrodynamics Properties of Reiner–Philippoff Nanofluid with Gyrotactic Microorganism in a Porous Medium Nanofluids have many potential applications in engineering, medicine, and biotechnology due to their enhanced thermal, electrical, and optical properties. However, the flow and heat transfer characteristics of nanofluids are influenced by various factors, such as the type and size of nanoparticles, the base fluid, the magnetic field, the radiation, the chemical reaction, and the presence of microorganisms. Therefore, it is important to study the effects of these factors on the nanofluid flow and heat transfer using mathematical models and numerical methods. One of the mathematical models that can describe the nanofluid flow is the Reiner-Philippoff model, which is a classical non-Newtonian fluid model that accounts for the shear-thinning behaviour of some fluids. The Reiner-Philippoff model has been used to study the nanofluid flow over a stretching sheet, which is a simplified model of many industrial processes involving stretching or shrinking surfaces. However, most of the previous studies have neglected the effects of the Arrhenius reaction, thermal radiation, viscous dissipation, and bio-convection on the nanofluid flow over a stretching sheet. The objective of this paper is to fill this gap by conducting a numerical investigation of the effects of the Arrhenius reaction, thermal radiation, viscous dissipation, and bio-convection on a Reiner-Philippoff nanofluid of MHD flow through a stretching sheet. This also considers the effects of thermophoresis and Brownian motion, which are two mechanisms that govern the transport of nanoparticles in nanofluids. The article utilized a similarity transformation to reduce the governing partial differential equations into ordinary differential equations, which are then solved by using the MATLAB computational tool bvp4c technique. The paper also employs a hybrid numerical solution method using Runge-Kutta fourth order with a shooting technique and an optimization technique using the Bayesian regularization method for Runge-Kutta to improve the accuracy of the prediction outcomes. The main finding of this paper is that the Arrhenius reaction, thermal radiation, viscous dissipation, and bio-convection have significant effects on the velocity, temperature, concentration, and motile microorganism profiles of the nanofluid flow over a stretching sheet. The paper also discusses how these effects can be controlled by varying the relevant parameters. This provides graphical results for the profiles of velocity, temperature, concentration, and motile microorganisms for different values of these parameters. The study also compares its results with some existing results in the literature and finds good agreement.