Physical Distribution Research Articles

Free music streaming for Kenyan independent artists: A blessing in disguise?

The Fourth Industrial Revolution (4IR) has left an indelible mark on the music industry, driven by technological innovations. In Kenya, on-demand music streaming and downloading platforms have emerged as potent tools, empowering independent artists to access broader audiences without the traditional reliance on record labels or physical distribution channels. However, this transformative model has ignited debates within the industry. While some artists thrive in this access-based ecosystem, concerns loom large over the platforms' intermediary roles and the meagre royalties disbursed to artists, underscoring an urgent call for equitable compensation. This study takes a qualitative approach, utilising in-depth interviews and secondary data analysis, to delve into the impact of free music streaming and downloading platforms on independent artists in Kenya. It unravels a complex tapestry of both positive and negative impacts on these artists. The discussion concludes by shedding light on the opportunities that have arisen for artists due to the prevalence of free music streaming services. Ultimately, this study puts forth recommendations geared toward fostering a sustainable and fair music industry. It seeks to deepen our comprehension of the evolving dynamics within the Kenyan music industry, inform policies and strategies tailored to the digital age, and attempt to address the persistent concerns surrounding artist compensation and the role of technology in the field.

Numerical investigation on design of soil nailed slopes by limit equilibrium method

ABSTRACT This paper introduces a numerical investigation into the stability analysis of soil nailed slopes. It establishes a simplified framework within the limit equilibrium method, focusing on the physical distribution of tension force along nails. MATLAB® was adapted for the Analysis of Soil Nailed Walls (ASNW) based on Bishop’s method, with major command loops optimized through ‘array operations’. Employing K-fold cross-validation, five regression models predicted the maximum tensile force (Tmax), revealing Gaussian process regression (GPR) as the best model based on root mean square error and r-squared metrics. To capture the amount of uncertainty in the nail load model, we have used a clustering method for estimating the measured Tmax. Self Organizing Map (SOM) was employed for clustering the nail load dataset. We randomly generated Tmax between the measured Tmax obtained by SOM and the predicted Tmax gained by the GPR model for each soil nail. We apply these loads in ASNW program to compute the Factor of Safety, and finally estimate system failure probability for 35 different nail layouts. A parametric study with a uniform nail layout explores the impact of nail length on system failure probability. Results show diminishing significance of nail length on failure probability beyond a nominal length.

Justifying calculations of snow load distribution on snow retaining structures in avalanche protection system

Snow load estimation is one of the most important tasks in designing snow-retaining structures for avalanche protection of railways and motorways in mountainous areas. The situation is complicated by the fact that avalanches are subdivided, depending on the consistency of snow, into dry, wet and damp avalanches. This paper analyses the existing foreign and domestic snow load calculation methods, based on which was concluded that there is no dependence of the load value on the physical and mechanical parameters distribution of the snow cover in all the considered methods of calculation. Moreover, none of these methods makes it possible to estimate the snow pressure distribution on a snow-retaining structure by its depth. The article proposes an alternative way of calculating the snow layer pressure using the developed discrete model for calculating the static snow pressure distribution on a barrier by its depth. The discrete model peculiarity is to take into account the change of physical and mechanical characteristics of snow — density, specific adhesion and angle of internal friction, by layers of natural deposit. Discrete model verification was performed by comparing the calculated and experimental data obtained as a result of both natural and laboratory experiments. The experiments were conducted in laboratory conditions on a special model, using a material with stable physical and mechanical characteristics that do not change during the course of the experiment. Also, for verification, the field experiment results on determining the naturally deposited snow pressure on an obstacle, taken from the open press, were used.

Bound states of Dirac fermions in the presence of a Fermi velocity modulation

We investigate the effect of a position-dependent Fermi velocity on the electronic properties of two-dimensional Dirac materials. A physical Fermi velocity distribution, which approaches a finite value at infinity and experiences a modulation near x = 0, is considered. Such a position-dependent Fermi velocity could be realized in the curved graphene or by applying strain. It is shown that the bound states are absent in the presence of a pure Fermi velocity modulation without an electrostatic potential well. However, an extra electrostatic potential modulation could produce the bound states. A set of discrete energy level spectrum and the corresponding wave functions are obtained by solving the Dirac equation exactly. Local probes such as scanning tunnel microscopy should be able to observe the predicted bound states in two-dimensional materials.

A Data-Driven Approach to Rapidly Identify the Critical Current of Fast-Charging Lithium-Ion Batteries

Traditional evaluation of battery charging protocols typically requires hundreds of electrochemical cycles and months of experimentation to select charging schemes that maximize the battery performance without compromising the cycle life. In this work, by nesting clustering and classification algorithms, a data-driven method using only data within a few tens of cycles is proposed to accurately classify constant-current charging protocols and rapidly identify the critical current, beyond which rapid degradation tends to occur within a specified lifetime. Specifically, by utilizing unsupervised clustering to process early-stage features and generate prediction labels, a model for early-stage prediction of the rapid degradation is established with an accuracy higher than 92.75%. Subsequently, the critical current is determined by intersecting the classification boundary with the physical distribution domain of the features. The reliability and generalizability of the proposed method is also discussed, which suggests that only ∼30 cycles and ∼40 samples are required to accomplish acceptable identification. The method is also proven to suitable for different battery systems. Therefore, the data-driven method proposed in this work provides a novel pathway to rapidly evaluate fast-charging batteries and charging protocols.

Costa Rican students’ proportional reasoning and comparing probabilities in spinners

This research aimed to relate Costa Rican students (11-16-year-olds) competence to compare probabilities in spinners and proportional reasoning in the comparison of ratios. We gave one of two questionnaires to a sample of 292 students (grade 6 to grade 10) with three probability comparison and three ratio comparison problems each. Globally both questionnaires cover six different proportional reasoning levels for each type of problem. Additionally, each questionnaire contains two comparison probabilities items intended to discover a specific bias. We analyze the percentages of correct responses to the items, strategies used to compare probabilities per school grade, and students’ probabilistic reasoning level. The results confirm more difficulty in comparing ratio than in comparing probability and suggest that the reasoning level achieved is lower than established in previous research. The main bias in the students’ responses was to consider the physical distribution of colored sectors in the spinners. Equiprobability and outcome approach were very scarce.

Effect of biochar and hydrochar of pistachio residues on physical quality indicators of a sandy loam soil

The carbonization of agricultural residues into carbon-rich biochars can be considered as one of the practical methods in sustainable management. The objectives of this study were to: (i) investigate and compare the chemical, physical and micromorphological properties of biochar and hydrochar produced from pistachio (Pistacia vera) residues, and (ii) compare the effects of these chars on the physical quality of a sandy loam soil (classified as Typic Haplocalcids) in pistachio orchards. The physical and chemical characteristics of the biochar and hydrochar were determined. Scanning electron microscopy images showed that hydrochar had a spherical pore structure while the biochar had elliptical pores. The chars were different in terms of functional groups, ratio of elemental composition, and electrical conductivity. Both chars were added at the rates of 0, 1 and 2%w/w to the soil and incubated for nine months under wet and dry cycles. Then, soil physical indicators including bulk density (ρb), dry and wet aggregate stability, water retention curve, saturated water content (θs), field capacity (θFC) and plant available water (PAW) were determined. The results showed that the hydrochar and biochar application had no significant effect on the ρb and dry aggregate stability. However, the percentage of water-stable aggregates larger than 0.25 mm increased at the rate of 1%w/w of chars in comparison with the control. The θs, θFC and PAW increased at both rates of chars when compared with the control. The residual water content (θr) increased at the rate of 1%w/w of chars compared to the untreated soil. It can be concluded that chars of pistachio residues can affect soil physical quality indicators and pore size distribution by the means of different mechanisms like the effect of morphological characteristics of chars, forming complexes between the soil particles, and creating hydrophobic coatings on the soil particles and aggregates.

Power Supply Reliability Analysis of Distribution Systems Considering Data Transmission Quality of Distribution Automation Terminals

A distribution automation system is the integration of physical power distribution systems and information systems. Its information system guarantees the safe operation and reliable power supply of physical systems by monitoring, collecting and transmitting information. In the information system, the remote terminal unit of distribution automation is the hub of the information system, connecting it to the physical power system. Considering the unreliability of terminal information transmission in the information system, this paper aims to build a model to quantitatively evaluate the impact of unreliable transmission information on the power supply reliability of distribution systems. Firstly, the m-segment and n-connection unit model of distribution feeders is established, and then, the power supply reliability indices in the process of handling feeder terminal unit error are analyzed and calculated under the configuration modes of “three-remote” and “two-remote” of remote terminals. Then, considering the impact of a transmission error in the information system, the reliability index calibration model under the condition of unreliable information transmission is established. Finally, a case study is presented to illustrate how the proposed model is implemented.

Geomorphic features of Lake Havasu with impacts on its water resource capacity

Wilson DC. 2024. Geomorphic features of Lake Havasu with impacts on its water resource capacity. Lake Reserv Manage. 40:93–108. Determining a reservoir’s available storage capacity and its possible rate of change over time through in-filling are 2 of many parameters needed to conduct efficient and flexible water delivery operations, particularly in cascading river-reservoir systems. This study focused on this issue through generating the first bathymetric elevation map of Lake Havasu on the Colorado River (CR) with enough detail to identify geomorphic features, including areas of deposition, to assess the current storage capacity of the reservoir. Two main areas of deposition were identified, the CR inlet delta and the Bill Williams River (BWR) delta at the reservoir’s northern and southern ends, respectively. The physical distribution of the CR inlet deposits well into the main reservoir and to some extent those of the BWR delta also may have been influenced by one episode of drastic water level lowering early in the reservoir’s history that probably re-mobilized sediments when fluvial dynamics temporarily resumed. High resolution sonar images compared with the CR channel position on a pre-reservoir topographic map, a sonar-generated reservoir bottom hardness map coupled with grain-size analyses of sediment samples, and modeling reservoir water level changes support this notion. Post-reservoir dam construction up both rivers has cut off sediment sources that historically reached Lake Havasu, effectively reducing sedimentation rates. Determined thicknesses of the sediment packages indicate that Lake Havasu has lost more than 7% of its available storage capacity since inception, which is an important parameter for adjusting management of water deliveries and storage in the Colorado River system.

Fundament for a methodological standard to process hip accelerometer data to a measure of physical activity intensity in middle-aged individuals.

There is a lack of a methodological standard to process accelerometer data to measures of physical activity, which impairs data quality and comparability. This study investigated the effect of different combinations of settings of multiple processing components, on the measure of physical activity and the association with measures of cardiometabolic health in an unselected population of middle-aged individuals. Free-living hip accelerometer data, aerobic fitness, body mass index, HDL:total cholesterol ratio, blood glucose, and systolic blood pressure were achieved from 4391 participants 50-64 years old included in The Swedish CArdioPulmonary bioImage Study (SCAPIS) baseline measurement (cross-sectional). Lab data were also included for calibration of accelerometers to provide comparable measure of physical activity intensity and time spent in different intensity categories, as well as to enhance understanding. The accelerometer data processing components were hardware recalibration, frequency filtering, number of accelerometer axes, epoch length, wear time criterium, time composition (min/24 h vs. % of wear time). Partial least regression and ordinary least regression were used for the association analyses. The setting of frequency filter had the strongest effect on the physical activity intensity measure and time distribution in different intensity categories followed by epoch length and number of accelerometer axes. Wear time criterium and recalibration of accelerometer data were less important. The setting of frequency filter and epoch length also showed consistent important effect on the associations with the different measures of cardiometabolic health, while the effect of recalibration, number of accelerometer axes, wear time criterium and expression of time composition was less consistent and less important. There was a large range in explained variance of the measures of cardiometabolic health depending on the combination of processing settings, for example, 12.1%-20.8% for aerobic fitness and 5.8%-14.0% for body mass index. There was a large variation in the physical activity intensity measure and the association with different measures of cardiometabolic health depending on the combination of settings of accelerometer data processing components. The results provide a fundament for a standard to process hip accelerometer data to assess the physical activity in middle-aged populations.

A new way of reducing negative weights in MC@NLO

We introduce a new technique, that we dub Born spreading, aimed at reducing the number of negative-weight S\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {S}}$$\\end{document} events in the MC@NLO matching of NLO calculations with parton-shower simulations. We show that such a technique, based on a re-distribution of Born matrix elements in the radiative phase space, achieves a sizeable reduction of negative-weight events at little computational cost. The method does not induce any biases in physical distributions.

Tolerance Analysis of Cyber-Manufacturing Systems to Cascading Failures

In practical cyber-manufacturing systems (CMS), the node component is the forwarder of information and the provider of services. This dual role makes the whole system have the typical physical-services interaction characteristic, making CMS more vulnerable to cascading failures than general manufacturing systems. In this work, in order to reasonably characterize the cascading process of CMS, we first develop an interdependent network model for CMS from a physical-service networking perspective. On this basis, a realistic cascading failure model for CMS is designed with full consideration of the routing-oriented load distribution characteristics of the physical network and selective load distribution characteristics of the service network. Through extensive experiments, the soundness of the proposed model has been verified and some meaningful findings have been obtained: (1) attacks on the physical network are more likely to trigger cascading failures and may cause more damage; (2) interdependency failures are the main cause of performance degradation in the service network during cascading failures; and (3) isolation failures are the main cause of performance degradation in the physical network during cascading failures. The obtained results can certainly help users to design a more reliable CMS against cascading failures.

A medium-density genotyping platform for cultivated strawberry using DArTag technology.

Genomic prediction in breeding populations containing hundreds to thousands of parents and seedlings is prohibitively expensive with current high-density genetic marker platforms designed for strawberry. We developed mid-density panels of molecular inversion probes (MIPs) to be deployed with the "DArTag" marker platform to provide a low-cost, high-throughput genotyping solution for strawberry genomic prediction. In total, 7742 target single nucleotide polymorphism (SNP) regions were used to generate MIP assays that were tested with a screening panel of 376 octoploid Fragaria accessions. We evaluated the performance of DArTag assays based on genotype segregation, amplicon coverage, and their ability to produce subgenome-specific amplicon alignments to the FaRR1 assembly and subsequent alignment-based variant calls with strong concordance to DArT's alignment-free, count-based genotype reports. We used a combination of marker performance metrics and physical distribution in the FaRR1 assembly to select 3K and 5K production panels for genotyping of large strawberry populations. We show that the 3K and 5K DArTag panels are able to target and amplify homologous alleles within subgenomic sequences with low-amplification bias between reference and alternate alleles, supporting accurate genotype calling while producing marker genotypes that can be treated as functionally diploid for quantitative genetic analysis. The 3K and 5K target SNPs show high levels of polymorphism in diverse F. × ananassa germplasm and UC Davis cultivars, with mean pairwise diversity (π) estimates of 0.40 and 0.32 and mean heterozygous genotype frequencies of 0.35 and 0.33,respectively.

Analysis of improved digital filter inflow generation methods for compressible turbulent boundary layers

We propose several enhancements to improve the accuracy and performance of the digital filter turbulent inflow generation technique and assess their efficacy in the context of wall-resolved large-eddy simulations of a compressible turbulent boundary layer. Improvements of accuracy include a more realistic correlation function for the transversal directions, target length scales that vary with wall-distance, and a counter-intuitive approach that involves the suppression of streamwise velocity fluctuations at the inflow. For improving the computational performance, we propose to generate the inflow data in parallel in single precision and at a prescribed time interval based on the turbulence time scale, and not at every time-step of the simulation. Based on the results of 7 wall-resolved large-eddy simulations, we find that the new correlation functions and the considered performance improvements are beneficial and therefore desired. Suppressing streamwise velocity fluctuations at the inflow leads to the fastest relaxation of the pressure fluctuations; however, this approach increases the adaptation length defined in terms of compliance with the von Kármán integral equation. The adaptation length can be shortened by artificially increasing the wall-normal Reynolds stresses, thereby preserving the desired turbulence kinetic energy level. A detailed inspection of the Reynolds stress transport budgets reveals that the observed spurious spatial transients are largely driven by pressure-related terms. For instance, increased values of u′p′¯ are found throughout the computational domain when a physical Reynolds stress distribution is prescribed at the inflow. Therefore, efforts to enhance digital filter techniques should aim at modeling pressure fluctuations as well as their correlation with the velocity components.

Improving the performance of baitul maal wat tamwil (BMT) employees: Do the principles of good corporate governance (GCG) matter?

Research aims: The role of human resources in Baitul Maal wat Tamwil (BMT) is vital as it is the main driver of all company activities in achieving its goals. This study, therefore, aims to examine the influence of corporate governance (CG) principles on employee performance in BMT.Design/Methodology/Approach: The object of this study was BMTs in Yogyakarta Province, with the respondents being BMT employees. The data collection technique employed a questionnaire measured by five Likert scales. The data in this study were then processed using the multiple linear regression method.Research findings: The study uncovered that all CG principles, i.e., transparency, accountability, responsibility, independence, and fairness, positively influenced employee performance.Theoretical contribution/Originality: This study contributes to the development of BMT management in Indonesia to start or improve the implementation of corporate governance since it positively impacts employee performance.Practitioner/Policy implication: BMT has challenges related to human resource competence. The organization (BMT) can evaluate the implementation of GCG in its operational activities because it can affect employee performance.Research limitation/Implication: The study only took samples from Yogyakarta Province. The physical distribution of questionnaires also limited the number of samples that could be collected.

American society of anesthesiologists physical status classification significantly affects the performances of machine learning models in intraoperative hypotension inference

Study objectiveTo explore how American Society of Anesthesiologists (ASA) physical status classification affects different machine learning models in hypotension prediction and whether the prediction uncertainty could be quantified. DesignObservational Studies SettingUofL health hospital PatientsThis study involved 562 hysterectomy surgeries performed on patients (≥ 18 years) between June 2020 and July 2021. InterventionsNone MeasurementsPreoperative and intraoperative data is collected. Three parametric machine learning models, including Bayesian generalized linear model (BGLM), Bayesian neural network (BNN), a newly proposed BNN with multivariate mixed responses (BNNMR), and one nonparametric model, Gaussian Process (GP), were explored to predict patients' diastolic and systolic blood pressures (continuous responses) and patients' hypotensive event (binary response) for the next five minutes. Data was separated into American Society of Anesthesiologists (ASA) physical status class 1– 4 before being read in by four machine learning models. Statistical analysis and models' constructions are performed in Python. Sensitivity, specificity, and the confidence/credible intervals were used to evaluate the prediction performance of each model for each ASA physical status class. Main resultsASA physical status classes require distinct models to accurately predict intraoperative blood pressures and hypotensive events. Overall, high sensitivity (above 0.85) and low uncertainty can be achieved by all models for ASA class 4 patients. In contrast, models trained without controlling ASA classes yielded lower sensitivity (below 0.5) and larger uncertainty. Particularly, in terms of predicting binary hypotensive event, for ASA physical status class 1, BNNMR yields the highest sensitivity of 1. For classes 2 and 3, BNN has the highest sensitivity of 0.429 and 0.415, respectively. For class 4, BNNMR and GP are tied with the highest sensitivity of 0.857. On the other hand, the sensitivity is just 0.031, 0.429, 0.165 and 0.305 for BNNMR, BNN, GBLM and GP models respectively, when training data is not divided by ASA physical status classes. In terms of predicting systolic blood pressure, the GP regression yields the lowest root mean squared errors (RMSE) of 2.072, 7.539, 9.214 and 0.295 for ASA physical status classes 1, 2, 3 and 4, respectively, but a RMSE of 126.894 if model is trained without controlling the ASA physical status class. The RMSEs for other models are far higher. RMSEs are 2.175, 13.861, 17.560 and 22.426 for classes 1, 2, 3 and 4 respectively for the BGLM. In terms of predicting diastolic blood pressure, the GP regression yields the lowest RMSEs of 2.152, 6.573, 5.371 and 0.831 for ASA physical status classes 1, 2, 3 and 4, respectively; RMSE of 8.084 if model is trained without controlling the ASA physical status class. The RMSEs for other models are far higher. Finally, in terms of the width of the 95% confidence interval of the mean prediction for systolic and diastolic blood pressures, GP regression gives narrower confidence interval with much smaller margin of error across all four ASA physical status classes. ConclusionsDifferent ASA physical status classes present different data distributions, and thus calls for distinct machine learning models to improve prediction accuracy and reduce predictive uncertainty. Uncertainty quantification enabled by Bayesian inference provides valuable information for clinicians as an additional metric to evaluate performance of machine learning models for medical decision making.

Detecting New Visual Binaries in Gaia DR3 with Gaia and Two Micron All Sky Survey (2MASS) Photometry. I. New Candidate Binaries within 200 pc of the Sun

We present a method to identify likely visual binaries in Gaia eDR3 that does not rely on parallax or proper motion. This method utilizes the various point-spread function sizes of Two Micron All Sky Survey (2MASS)/Gaia, where at <2.″5 two stars may be unresolved in 2MASS but resolved by Gaia. Due to this, if close neighbors listed in Gaia are a resolved pair, the associated 2MASS source will have a predictable excess in the J band that depends on the ΔG of the pair. We demonstrate that the expected relationship between 2MASS excess and ΔG differs for chance alignments, as compared to true binary systems, when parameters like magnitude and location on the sky are also considered. Using these multidimensional distributions, we compute the likelihood of a close pair of stars to be a chance alignment, resulting in a total(clean) catalog of 68,725(50,230) likely binaries within 200 pc with a completeness rate of ∼75%(∼64%) and contamination rate of ∼14%(∼0.4%). Within this, we find 590 previously unidentified binaries from Gaia eDR3 with projected physical separations <30 au, where 138 systems were previously identified, and for s < 10 au we find that 4 out of 15 new likely binaries have not yet been observed with high-resolution imaging. We also demonstrate the potential of our catalog to determine physical separation distributions and binary fraction estimates, from this increase in low-separation binaries. Overall, this catalog provides a good complement for the study of local binary populations by probing smaller physical separations and mass ratios, and provides prime targets for speckle monitoring.

Negative stiffness metamaterial with directional stability in uniform fields

Negative stiffness metamaterials have been widely studied due to their unusual properties and potential applications in shape reconfiguration, shock isolation, and reusable energy absorption, but their stability in fields has been less studied. In this work, a theoretical framework for determining the stability of negative stiffness metamaterials in a uniform field is established. More importantly, the concepts of directional stability and semi-directional stability are proposed for the metamaterials that have different stability in different field directions; metamaterials with different physical feature distributions are designed and prepared to achieve such ability, and the finite element simulation is used for theory verification and parameter study. The proposed metamaterial has the potential to be applied as field direction detectors and field-sensitive electrical relays.

A novel flow channel design to achieve high temperature homogenization in solid oxide fuel cell

The thermal stress, which has a significant impact on the stable and prolonged operation of Solid Oxide Fuel Cells (SOFCs), can be further aggravated by the presence of local hot spots. Improving the distribution uniformity of physical quantities is of great significance to improving the internal temperature distribution of the SOFC. In this study, to improve the uniformity of temperature distribution, the thermal and flow distribution of three kinds of flow fields are investigated including Z, U, and L flow fields. The results show that the temperature distribution is dominated by the flow rate distribution. To optimize the physical quantity distribution of the designed flow channel, a novel, and easy-to-manufacture thermal management is proposed. This method is to adopt the extended ridge for controlling the flow rate into each flow channel. Moreover, a multi-population genetic algorithm is adopted to optimize the length of each extended ridge. The optimized results show that the temperature deviation in the Z, U, and L flow fields are reduced by about 64.20 %, 26.06 %, and 23.45 % respectively. This novel thermal management method enables more uniform temperature distribution and it is expected to be used in large-scale manufacturing due to its simple but subtle design.

Rapid Prediction of the In Situ Pyrolysis Performance of Tar-Rich Coal Using the POD Method

In this paper, a POD reduced-order interpolation model for solving the in situ pyrolysis process of tar-rich coal is employed to predict the flow and heat transfer performance in the porous media region so as to save computational resources and realize fast calculations. Numerical simulation using the finite volume method (FVM) is firstly used to obtain sample data, based on the samples through the primary function and spectral coefficients of the solutions. The physical field information and parameter distribution under different conditions of inlet temperature, inlet velocity and permeability are predicted. The results are compared with those of FVM to verify the accuracy of the calculated results. The relative mean deviation (RME) of the results of the POD prediction of each parameter for each working condition was synthesized to be no more than 5%. The performance of in situ pyrolysis of tar-rich coal is then investigated, and the oil and gas production are predicted. As the inlet velocity increases from 0.3 m/s to 0.9 m/s, the fraction of high-quality oil and gas production reaches 0.47 and then decreases to 0.38. Increasing the inlet temperature and permeability has a negative effect on the fraction of high-quality hydrocarbon production, after which the quality fraction of high-quality oil and gas dropped sharply to about 0.22. Porosity has a positive impact on the oil and gas production. When the porosity reaches 0.3, the quality fraction of high-quality oil and gas can reach 0.27.