Temperature Parameters Research Articles

Clean and efficient preparation of metallic bismuth from methane-sulfonate electrolyte in the membrane electrolysis cell

As a prevailing hydrometallurgical extraction system for bismuth sulfide concentrate, the acidic chloride solution has superior applicability to raw materials and high recovery ratio of metallic bismuth. Nevertheless, it has disadvantages of being easily volatile and strongly corrosive, as well as large energy consumption and poor deposition morphology during chloride electrolysis. Methane-sulfonic acid (MSA) solution is widely considered as the next generation of green hydrometallurgical system, due to its excellent physicochemical properties such as high metal salts solubility, low volatility, and biodegradability. In this study, membrane electrolysis technique was adopted based on the acidic bismuth methane-sulfonate leaching solution, and the influence of electrolyte composition, cathode current density (CCD), and temperature parameters on the bismuth conversion ratio (BCR) and deposition rate (BDR), techno-economic indicators and cathodic bismuth morphology were studied. The results show that the CCD affects the deposits morphology significantly, and the bismuth metal is partially dark and composed of loose ellipsoidal particles at high current density. Under the optimal conditions (70 g/L Bi3+ ions, 5 g/L Fe2+ ions, 110 g/L MSA, 35 °C, 180 A/m2), high-purity metallic bismuth (Bi 99.97 %) with silver-white luster is obtained. The BCR and BDR are 9.43 % and 0.46 kg/(h·m2), respectively. The cathodic current efficiency (CCE) reaches over 98 %, and specific energy consumption (SEC) is less than 715 kWh/t Bi. Compared with the traditional chloride system, the MSA-based membrane electrolysis medium has advantages of environmental friendliness, low occupational risks, low carbon and energy consumption, and can thus provide a sustainable green solution for bismuth hydrometallurgy.

Exact solutions of hydromagnetic convective flow in a microchannel with superhydrophobic slip and temperature jump: Microfluidics applications

AbstractThe researchers explored the free convective flow of a hybrid nanofluid in a vertical microchannel with a rectangular cross‐section. Notably, both channel walls were heated alternately, and a transverse magnetic field was applied across the channel. The channel walls had unique properties, one of which was nonslip and the other was exceedingly hydrophobic. The major purpose was to investigate the effects of magnetism and superhydrophobicity on important flow parameters. The differential equations in the investigation were solved, producing accurate results. The study yielded some significant discoveries. First, when heated, the magnetic parameter reduced skin friction on both sides. Second, in both heating conditions, the magnetic field reduced flow rate and velocity. The flow rates in the two reported situations were similar at a crucial temperature jump coefficient. Furthermore, for low‐temperature jump coefficients, heating the superhydrophobic side reduced the Nusselt number whereas heating the nonslip side had no magnetic effect. The percentage change in the value of Nusselt number and velocity decreases continuously with increase in nonlinear density variation with temperature (NDT) parameter and magnetic parameter. The percentage increases in the value of skin friction with increase in temperature jump and slip length but decrease in the percentage of skin friction for the effect of magnetic term and NDT parameter. As the NDT parameter increases, the velocity percentage rises to 50.59% when the superhydrophobic surface is heated and to 84.30% when the nonslip surface is heated. The temperature jump is statistically significant for the value of the Nusselt number and skin friction for the no‐slip surface condition. These discoveries have practical consequences for the design and management of both tiny and large‐scale systems, with possible applications in microfluidics, microelectronics, nanoscience, and nanotechnology.

Knowledge Distillation Based on Fitting Ground-Truth Distribution of Images

Knowledge distillation based on the features from the penultimate layer allows the student (lightweight model) to efficiently mimic the internal feature outputs of the teacher (high-capacity model). However, the training data may not conform to the ground-truth distribution of images in terms of classes and features. We propose two knowledge distillation algorithms to solve the above problem from the directions of fitting the ground-truth distribution of classes and fitting the ground-truth distribution of features, respectively. The former uses teacher labels to supervise student classification output instead of dataset labels, while the latter designs feature temperature parameters to correct teachers’ abnormal feature distribution output. We conducted knowledge distillation experiments on the ImageNet-2012 and Cifar-100 datasets using seven sets of homogeneous models and six sets of heterogeneous models. The experimental results show that our proposed algorithms improve the performance of penultimate layer feature knowledge distillation and outperform other existing knowledge distillation methods in terms of classification performance and generalization ability.

SCHeMoS -- Smart Cow Health Monitoring System: An IoT based Cow Hoof Detection and Healthcare Alert System by Using LSTM Network

Human life and existence are intertwined with a few domestic animals. One of the most important animals of this kind is the cow. Cows play a vital role in daily activities. Most of the people in India consume cow’s milk as one of their major nutrients. Monitoring the health of a cow’s everyday life is quite challenging. After infertility and mastitis, lameness is typically ranked as the third most economically significant health issue in dairy herds.Lameness are caused due to genetics, lack in nutrition i.e. a diet deficient in essential nutrients such as biotin, which can lead to hoof problems. Due to geographical environments like cows kept in wet, muddy conditions are more likely to develop hoof problems. This investigation analyses the typical characteristics of cow behavior, and a Smart Cow Health Monitoring System (ScHeMoS) using IoT is proposed to identify the cow’s health through the data obtained from Internet of Things (IoT) sensors, including position, body temperature, stability, acceleration, and animal feed. IoT is combined with Deep learning (DL) technique to monitor and diagnose animal health. We used the Long Short Term Memory (LSTM) network to predict cow lameness by capturing the body temperature and other parameters, which will aid in predicting their illness. The accelerometer values are stored so that it will further help to determine which cow is lame and which is pregnant or regular and could be intimated to the care takers in the farms. We utilised a self collected dataset to perform the investigation. By implementing this system, we achieved 92.45\% accuracy and 0.92 as F1 score.

Genotoxicity and safety pharmacology of the rVSVInd(GML)-mspSGtc vaccine against SARS-CoV-2 in Sprague-Dawley rats and Beagle dogs.

The emergence of coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to a pandemic, prompting rapid vaccine development. Although vaccines are effective, the occurrence of rare adverse events following vaccination highlights the necessity of determining whether the benefits outweigh the risks posed by the infection itself. The recombinant Vesicular Stomatitis Virus (rVSV) platform is a promising vector for vaccines against emerging viruses. However, limited studies have evaluated the genotoxicity and safety pharmacology of this viral vector vaccine, which is crucial to ensure the safety of vaccines developed using this platform. Hence, the present study aimed to assess the genotoxicity and safety pharmacology of the rVSVInd(GML)-mspSGtc COVID-19 vaccine using micronucleus and comet assays, as well as neurobehavioral, body temperature, respiratory, and cardiovascular assessments in Sprague-Dawley rats and beagle dogs. The intramuscular administration of rVSVInd(GML)-mspSGtc at doses up to 1.5 × 109 PFU/animal did not increase the number of bone marrow micronucleated polychromatic erythrocytes or cause liver DNA damage. Additionally, it had no significant impact on neurobehavioral functions in rats and showed marginal temporary changes in body temperature, respiratory rate, heart rate, and electrocardiogram parameters in rats and dogs, all of which resolved within 24h. Overall, following genotoxicity and pharmacological safety assessments, rVSVInd(GML)-mspSGtc displayed no notable systemic adverse effects in rats and dogs, suggesting its potential as a vaccine candidate for human clinical trials.

The study of ideal/defected graphene nanosheet roughness after atomic deposition process: Molecular dynamics simulation

In this work, molecular dynamics (MD) approach was performed to study the surface roughness of ideal/defected graphene nanosheet after carbon atoms deposition at various temperatures and pressures. In our calculations, the atomic interactions of nanostructures are based on TERSOFF and Lennard-Jones potential functions. The results show that the temperature of simulated structure is an important parameter in atomic deposition process and initial temperature enlarges, intensifies atomic deposition ratio. Numerically, by temperature increasing to 15 K, the surface roughness amplitude increase to 0.98 Å/0.83 Å after atomic deposition in ideal/defected structure. The roughness power in MD simulations converges to 0.64/0.55 in ideal/defected sample at maximum temperature. Furthermore, the pressure effects on dynamical behavior of simulated samples were reported in our study. We conclude that, by increasing initial pressure from 0 to 2 bar, the surface roughness amplitude in ideal/defected atomic arrangement increases to 1.01 Å/0.84 Å after deposition process and the roughness power of simulated structures reaches to larger value. Numerically, by initial pressure setting at 2 bar, the roughness power value converged to 0.72/0.56 in ideal/defected graphene. Reported numeric results in various temperature and pressures predicted the initial condition can be manipulated the atomic deposition process in ideal/defected graphene nanostructures.

Monitoring cotton water status with microtensiometers

Stem water potential (Ψstem) is a key indicator for assessing plant water status, which is crucial in understanding plant health and productivity. However, existing measurement methods for Ψstem, characterized by destructiveness and intermittency, limit its applicability. Microtensiometers, an emerging plant-based sensor, offer continuous monitoring capabilities and have shown success in certain vine and tree species. In this study, we investigate the efficacy of microtensiometers ability to monitor the Ψstem of cotton (Gossypium hirsutum L.) under three distinct irrigation treatments in Maricopa, Arizona, an extremely hot, arid environment. We analyze the diurnal dynamics of Ψstem across the irrigation regimes and compare these measurements with midday leaf water potentials (Ψleaf) obtained using a dewpoint potentiometer. Our results demonstrate that the microtensiometer-derived Ψstem closely follows known diurnal patterns of Ψleaf, tracking with vapor pressure deficit (VPD) and responding to variations in irrigation levels and soil moisture content. Time cross-correlation analysis reveals an 80-minute lag in Ψstem response to changing VPD under non-water limiting conditions, which shortens under water-limiting conditions. Additionally, we establish a robust linear relationship (R2adj = 0.82) between Ψstem and Ψleaf, with this relationship strengthening as water availability decreases. Notably, we observe mean gradients of 1.2 and 0.06 MPa between soil vs. stem and stem vs. leaf water potentials, respectively. Moreover, Ψstem data proves to be more sensitive in distinguishing between irrigation treatments earlier in the growing season compared to Ψleaf, leaf temperature and leaf gas exchange parameters. These findings highlight the utility of microtensiometers as valuable tools for monitoring water status in smaller-stemmed row crops such as cotton.

A versatile economic strategy by HPLC–CAD for quantification of structurally diverse markers in quality control of Shengmai Formula from raw materials to preparations

BackgroundShengmai Formula (SMF), a classic formula in treating Qi-Yin deficiency, is composed of Ginseng Radix et Rhizoma Rubra (GRR), Ophiopogon Radix (OR), and Schisandra chinensis Fructus (SC), and has been developed into various dosage forms including Shengmai Yin Oral Liquid (SMY), Shengmai Capsules (SMC), and Shengmai Injection (SMI). The pharmacological effects of compound Chinese medicine are attributed to the integration of multiple components. Yet the quality criteria of SMF are limited to monitoring schisandrol A or ginsenosides Rg1 and Re, but none for OR. Since the complexity of raw materials and preparations, establishing a economical and unified method for SMF is challenging. It is urgent to simultaneously quantify multiple components with different structures using a universal method for quality control of SMF. Charged aerosol detector (CAD) overcame the above shortcomings owing to its characteristics of high responsiveness, nondiscrimination, and low cost. PurposeWe aimed to establish a versatile analysis strategy using HPLC–CAD for simultaneously quantifying the structurally diverse markers in quality control of SMF from raw materials to preparations. MethodBy optimizing the column, mobile phase, column temperature, flow rate, and CAD parameters, a HPLC-CAD method that integrated multi-component characterization, authenticity identification, transfer information of raw materials and quantitative determination of Shengmai preparations was established. ResultsIn total 50 components from SMF were characterized (28 in GRR, 13 in SC, and 9 in OR). The differences in raw materials between species of SC and Schisandrae sphenantherae Fructus (SS), processing methods of Ginseng Radix (GR) and GRR, and locations of OR from Sichuan (ORS) and Zhejiang (ORZ) were compared. Fourteen components in 19 batches of SMY, SMC and SMI from different manufacturers were quantified, including 11 ginsenosides and 3 lignans. The multivariate statistical analysis results further suggested that Rb1, Rg1 and Ro were the main differences among Shengmai preparations. ConclusionThe established versatile analysis strategy based on HPLC–CAD was proven sensitive, simple, convenient, overcoming the discriminatory effect of UV detector, revealing the composition and transfer information of SMF and applicable for authentication of the ingredient herbs and improving the quality of Shengmai preparations.

Oxide ceramics of A2M3O12 family with negative and close-to-zero thermal expansion coefficients: Machine learning-based modeling of functional characteristics

Ceramic materials with negative and close-to-zero coefficients of thermal expansion may open the avenue to the technologies that have so far been constrained by physical limitations concerned with the thermal stress or with the insufficient structural stability. Two important characteristics of NTE materials which could be used for the evaluation of the possible area and limitations of the sphere of application for negative thermal expansion (NTE) materials are the linear thermal expansion coefficient and the transition temperature from monoclinic to orthorhombic phase. In this study, the machine learning methods were involved in the analysis of experimental data for NTE oxide ceramics of A2M3O12 family (where M is Mo6+, W6+, V5+ or P5+ while A position may be accommodated by the wide range of metal cations). The models are characterized by the following statistical coefficients: the determination coefficient R2 = 0.81 and prediction error RMSE = 1.170 for linear thermal expansion coefficient; the corresponding parameters for the phase transition temperature were assessed as 0.81 and 82.239, respectively. Ionic conductivity in this class of compounds has been discussed as a tandem functional characteristic, emphasizing the role of anharmonicity in both characteristics. The role of synthesis route and defect chemistry in NTE was analyzed. A conclusion on the expected enhancement of NTE resulted from the intentional introduction of cation A vacancies has been made. The principal possibility of combining two functional characteristics, - an ion conductor and a phase with negative thermal expansion, with some compromise in the characteristics of each of them is substantiated.

Средства реализации языкового смысла «признак предмета» в профессиональной сфере «военное строительство»

<p>The article examines the linguistic means of expressing the linguistic meaning of the «feature of the subject» in the military engineering sublanguage. Relative adjectives describing various properties of building materials and their single-root nouns are relevant for the construction sector. Adjectives denoting hydrophysical, thermophysical, physico-chemical, mechanical, chemical, technological, operational and special properties are used to characterize building materials. Quantitative numerals are less likely than ordinal adjectives to participate in the expression of the linguistic meaning of the «feature of the object» and are inconsistent, non-morphologized definitions, however, in the military engineering field they are active in characterizing the quantitative parameters of temperature, thickness, depth, height and other characteristics of construction objects. The onomasiological correlation between relative adjectives and participles in the construction sector is observed in constructions, «adjective name» is «an object with a certain quality»; «adjective name is an object made of a certain material»; «adjective name is an object built of a certain material». It was revealed that speakers prefer the use of relative adjectives to denote the construction materials used; participles — to denote construction technological processes; quantitative numerals — to denote the nomenclature of construction mechanisms. Adjectives and participles as part of terminological combinations represent a contamination of the linguistic meanings of «subject attribute» and «subject».</p>

Comparison of the Effects of Inhalational Anesthesia with Sevoflurane and Total Intravenous Anesthesia in Open Heart Surgery

Background: The primary objective of this study was to compare sevoflurane inhalation anesthesia with total intravenous anesthesia (TIVA) in terms of its effectiveness in maintaining adequate depth of anesthesia during all open heart surgery procedures, including cardiopulmonary bypass. The study's secondary objective was to compare sevoflurane inhalation anesthesia with TIVA regarding the impact on the time of tracheal extubation and the incidence of postoperative acute kidney injury during open heart surgeries. Methods: A total of 58 patients undergoing open heart surgery were included, with 30 receiving sevoflurane inhalation anesthesia and 28 receiving TIVA. Demographic characteristics, intraoperative parameters, and postoperative outcomes were recorded and analyzed. Statistical analysis revealed no significant differences in Bispectral Index (BIS) monitor values, mean arterial pressure, body temperature, or other intraoperative parameters between the two groups. Notably, the time to tracheal extubation was significantly shorter in the Sevoflurane group compared to the TIVA group, although both groups exhibited similar rates of postoperative acute kidney injury (AKI). Results: None of the patients had complaints of intraoperative awareness. The mean arterial pressure, body temperature, and bispectral index values during and before cardiopulmonary bypass were similar between the groups. Postoperative variables such as intensive care unit stay duration, incidence of acute kidney injury, and immediate and 24-hour post-extubation visual analog scale values were similar between the groups. The tracheal extubation time was found to be statistically shorter in the sevoflurane group. Conclusion: We believe that sevoflurane inhalation anesthesia can achieve adequate depth of anesthesia during the intraoperative period in open heart surgery without increasing the rate of postoperative complications.

Development of a method for monitoring the formation of organic deposits on the inner surface of the oilwell tubing

ABSTRACT Intensive formation of organic deposits in the tubing of an oil production well is one of the most serious complications in oil production. An important task is to correctly assess the effectiveness of the methods used to combat the deposits in question. Field diagnostics of the condition of the inner surface of the production tubing is carried out by evaluating indirect parameters that do not allow evaluating the distribution of deposits along the production tubing. With the aim of solving these problems the authors of this work have developed a method for monitoring the formation of deposits on the inner surface of the tubing on the basis of the ‘thermal analogy’ mechanism. The applicability of the technology was evaluated at the Wax Flow Loop laboratory stand. The developed mathematical model for calculating the dynamics of sediment formation based on the temperature parameters of the flow significantly correlates with the data obtained using the basic barometric method. The use of this technology will make it possible to increase the efficiency of oilfield equipment, reduce the accident rate of production, more correctly select methods for combating wax deposition and evaluate their effectiveness.

Aging effects at room temperature and process parameters on 3D-printed poly (lactic acid) (PLA) tensile properties

Aging effects at room temperature and process parameters on 3D-printed poly (lactic acid) (PLA) tensile properties

Structure optimization design and performance analysis of liquid cooling plate for power battery

The structural design of liquid cooling plates represents a significant area of research within battery thermal management systems. In this study, we aimed to analyze the cooling performance of topological structures based on theoretical calculation and simple structures based on design experience to achieve the best comprehensive performance and analyze their operating environment characteristics. The COMSOL Multiphysics software and topology optimization method were used to calculate the topology structure. To assess performance, the numerical computation method was employed to simulate and calculate the optimized structure, comparing its temperature, velocity, and pressure related parameters and changes. Moreover, the study analyzed the impact of inlet flow velocity, ambient temperature, and coolant temperature on heat dissipation performance. The results revealed a reduction of 0.31 % in the highest temperature and a 21.20 % decrease in pressure drop for the topology-based results compared to the post-processing results. However, the cooling performance advantage offered by the topology structure was not significant, the enhancement in cooling performance did not offset the increase in production cost. Furthermore, we found that variations in service conditions influence the heat dissipation performance of liquid cooling plates, requiring comprehensive consideration when selecting condition parameters.

Study of chlorine removal from shredder residue: Thermal dechlorination and water leaching

Shredder residue (SR) is an inevitable waste in the production of steel by scrap melting, and the presence of chlorine is the main responsible for its non-use of energy. This study aimed to remove chlorine from SR by thermal dechlorination and water leaching. Thermal dechlorination was first studied from a PVC sample to evaluate the optimum parameters of temperature, heating rate, residence time and atmosphere (N2 or steam). Under optimal conditions for PVC dechlorination, chlorine removals of 73.7 (N2) and 68.0% (steam) were achieved. Optimal parameters were applied to thermal dechlorination of a Brazilian SR with a chlorine content of 2.12 wt%. Reductions in chlorine content to 0.95 and 0.51 wt% were observed with N2 and steam atmospheres, respectively. In an additional evaluation (conducted also with the Brazilian SR and in order to investigate the reactor rotation effect), it was observed that the reactor rotation does not influence the release of chlorine during the thermal treatment but modifies the granulometry of the produced solid. Samples obtained from this test were subjected to a water leaching study to further reduce the chlorine content. The parameters studied were the type of water (tap and distilled), granulometry and carbonation (contact with CO2) in a total of five washing cycles. Carbonation and use of distilled water achieved the highest chlorine removal resulting in a reduction of chlorine content from 1.13 to 0.45 wt%. The obtained results show directions for chlorine removal aiming at the energetic use of shredder residue.

Impact of wall slip and jump in the heat transmission enhancement of copper-alumina/water hybrid nanofluid due to a stretched cylinder

The early jump in wall temperature and partial slip all along transfer on thermal energy in hybrid nanofluid was studied simultaneously. In addition, the flow characteristics and heat transfer performances on alumina–copper/water ( A l 2 O 3 − Cu / H 2 O ) hybrid nanofluid through the stretching cylinder were examined below the effect on thermal radiation and Lorentz magnetic forces. This Roseland’s flux model was working on the effect on thermal radiations. Denoting fundamental equations was traced over constructing numerical model results about temperature and velocity distributions although considering variable internal energy and fluid property change. The numerical algorithm bvp4c technique was utilised to obtain convergent solutions. The effect on impending parameters of temperature and velocity distributions was investigated over a graphical illustration. It was noticedthat wall momentum diffused with nanofluid quickly to its dispersion on hybrid nanofluid by the existence of the partial slip. The thermal behaviour of the hybrid mono-nanofluid was higher than that of the nanofluid. Moreover, hybrid particles advance the thermal behaviour of the employing fluid.

Online Measurement for Parameter Discovery in Fused Filament Fabrication

To describe a new method for the automatic generation of process parameters for fused filament fabrication (FFF) across varying machines and materials. We use an instrumented extruder to fit a function that maps nozzle pressures across varying flow rates and temperatures for a given machine and material configuration. We then develop a method to extract real parameters for flow rate and temperature using relative pressures and temperature offsets. Our method allows us to successfully find process parameters, using one set of input parameters, across all of the machine and material configurations that we tested, even in materials that we had never printed before. Rather than using direct parameters in FFF printing, which is time-consuming to tune and modify, it is possible to deploy machine-generated data that captures the fundamental phenomenology of FFF to automatically select parameters.

Analyzing temperature, humidity, and precipitation trends in six regions of Thailand using innovative trend analysis

The change of temperature and weather parameters is a major concern affecting sustainable development and impacting various sectors, such as agriculture, tourism, and industry. Changing weather patterns and their impact on water resources are important climatic factors that society is facing. In Thailand, climatological features such as ambient temperature, relative humidity, and precipitation play a substantial role in affecting extreme weather events, which cause damage to the economy, agriculture, tourism, and livelihood of people. To investigate recent serious changes in annual trends of temperature, relative humidity, and precipitation in Thailand, this study used the Mann–Kendall (MK) test and innovative trend analysis (ITA) methods. The MK test showed that all six regions had an upward trend in temperature and humidity index (humidex, how hot the weather feels to the average person), while relative humidity and precipitation showed both upward and downward trends across different regions. The ITA method further confirmed the upward trend in temperature and humidex and showed that most data points fell above the 1:1 line. However, the upward trend in most variables was not significant at the 5% level. The southern and eastern regions showed a significant upward trend in relative humidity and humidex at a 5% level of significance according to the MK test. The output of this study can help in the understanding of weather variations and predict future situations and can be used for adaptation strategies.

Fuzzy logic method-based stress detector with blood pressure and body temperature parameters

In this study, using the fuzzy logic method, a stress detection tool was created with body temperature and blood pressure parameters as indicators to determine a person's stress level. This tool uses the LM35DZ sensor to detect body temperature, the MPX5100GP sensor to read blood pressure values, and Arduino Uno as a data processor from sensor readings which are then calculated using the fuzzy logic method as a stress level decision-maker. The resulting output measures blood pressure, body temperature, and the stress level experienced by a person, which will be displayed on the liquid crystal display. Based on the results of testing the body temperature parameter, the highest error generated was 1.17%, and for the blood pressure parameter, the highest error was 2.5% for systole and 0.93% for diastole. Furthermore, testing the stress level displayed on the tool is compared to the depression, anxiety, and stress scales 42 (DASS 42), a psychological stress measuring instrument. From the results of testing the tool with the questionnaire, the average conformity level is 74%.

Thermal dynamics assessment for multi-phase flow analysis with motile cilia and electric double layer effects: Application of Levenberg–Marquardt backpropagation NNs

Ciliary movement holds substantial importance in the realm of biomedical research. Disorders arising from impaired ciliary motion, such as primary ciliary dyskinesia and specific respiratory ailments, underscore the critical need to investigate both the normal functioning and dysfunction of cilia. Such research is fundamental to the development of effective treatments and therapies. This work explores the application of Levenberg–Marquardt backpropagation neural networks (LMBP-NNs) for simulating the flow of a non-Newtonian Jeffery fluid in peristaltic motion. Inspired by synchronous ciliary movement, microfluidic systems mimicking cilia functionality have been developed. These artificial cilia can generate controlled fluid flow patterns, enabling various applications such as drug delivery, mixing, and particle sorting in lab-on-a-chip devices. The present study focuses on measuring thermal and mass transfer in beating cilia during multiphase peristaltic pumping of Jeffery fluid in two- and three-dimensional channels using transverse magnetohydrodynamics in a porous medium. The governing equations for the non-Newtonian Jeffery fluid in both the fluid and dusty phases are formulated, and exact solutions are calculated for fluid phase velocity, particulate phase velocity, temperature, concentration, and pressure gradient. The physical behavior is investigated by graphing dimensionless parameters for velocity, temperature, and concentration profiles in both solid and liquid phases. The trapping phenomena is depicted by showing streamlines of various dimensionless characteristics such as porosity velocity and volume percentage. The LMBP-NNs approach is then used to train, test, and verify the neural network models using the reference datasets. The accuracy of the LMBP-NN is assessed using statistical data such as mean square error, curve fitting graphs, regression plots, and error histograms. The study also investigates flow model limits for momentum, temperature, and concentration profiles using visual representations. This study illustrates the computational capability of LMBP-NNs in modeling the peristaltic flow of Jeffery fluid and gives useful insights into fluid flow behavior in microfluidic devices containing artificial cilia. The findings help to better understand and optimize these systems for a variety of fluid manipulation and transportation applications.