Performance Evaluation Parameters Research Articles

Benchmarking Pretrained Models for Speech Emotion Recognition: A Focus on Xception

Speech emotion recognition (SER) is an emerging technology that utilizes speech sounds to identify a speaker’s emotional state. Computational intelligence is receiving increasing attention from academics, health, and social media applications. This research was conducted to identify emotional states in verbal communication. We applied a publicly available dataset called RAVDEES. The data augmentation process involved adding noise, applying time stretching, shifting, and pitch, and extracting the features zero cross rate (ZCR), chroma shift, Mel-Frequency Cepstral Coefficients (MFCC), and a spectrogram. In addition, we used many pretrained deep learning models, such as VGG16, ResNet50, Xception, InceptionV3, and DenseNet121. Out of all of the deep learning models, Xception yielded superior outcomes. Furthermore, we improved performance by changing the Xception model to include hyperparameters and additional layers. We used a variety of performance evaluation parameters to test the proposed model. These included F1-score, accuracy, misclassification rate (MCR), precision, sensitivity, specificity, negative predictive value, false negative rate, false positive rate, false discovery rate, false omission rate, and false discovery rate. The model that we suggested demonstrated an overall accuracy of 98%, with an MCR of 2%. Additionally, it attained precision, sensitivity, and specificity values of 91.99%, 91.78%, and 98.68%, respectively. Additional models attained an F1-score of 91.83%. Our suggested model demonstrated superiority compared to other cutting-edge techniques

Development of soft computing-based models for forecasting water quality index of Lorestan Province, Iran.

The Water Quality Index (WQI) is widely used as a classification indicator and essential parameter for water resources management projects. WQI combines several physical and chemical parameters into a single metric to measure the status of Water Quality. This study explores the application of five soft computing techniques, including Gene Expression Programming, Gaussian Process, Reduced Error Pruning Tree (REPt), Artificial Neural Network with FireFly (ANN-FFA), and combinations of Reduced Error Pruning Tree with bagging. These models aim to predict the WQI of Khorramabad, Biranshahr, and Alashtar sub-watersheds in Lorestan province, Iran. The dataset consists of 124 observations, with input variables being sulfate (SO4), total dissolved solids (TDS), the potential of Hydrogen (pH), chloride (Cl), electrical conductivity (EC), Potassium (K), bicarbonate (HCO), magnesium (Mg), sodium (Na), and calcium (Ca), and WQI as the output variable. For model creation (train subset) and model validation (test subset), the data were split into two subsets (train and test) in a ratio of 70:30. The performance evaluation parameters values of training and testing stages of various models indicate that the ANN-FFA based data-driven model performs better than the other modeling techniques applied with the values of coefficient of correlation 0.9990 & 0.9989; coefficient of determination 0.9612 & 0.9980; root mean square error 0.3036 & 0.3340; Nash-Sutcliffe error 0.9980 & 0.9979; and Mean average percentage error 0.7259% & 0.7969% for the train and test subsets, respectively. Taylor diagram results also suggest that ANN-FFA is the best-performing model, followed by the GEP model. This study introduces a novel model for predicting WQI using advanced soft computing models that have not been previously applied in this study area, highlighting its novelty and relevance. The proposed model significantly enhances predictive accuracy and efficiency, offering real-time, cost-effective WQI predictions that outperform traditional methods in handling complex, nonlinear environmental data.

Predicting High-Flow Nasal Cannula Oxygen Therapy Failure in Patients With Acute Hypoxaemic Respiratory Failure Using Machine Learning: Model Development and External Validation.

To develop and validate a prediction model for high-flow nasal cannula (HFNC) failure in patients with acute hypoxaemic respiratory failure (AHRF). AHRF accounts for a major proportion of intensive care unit (ICU) admissions and is associated with high mortality. HFNC is a non-invasive respiratory support technique that can improve patient oxygenation. However, HFNC failure, defined as the need for escalation to invasive mechanical ventilation, can lead to delayed intubation, prolonged mechanical ventilation and increased risk of mortality. Timely and accurate prediction of HFNC failure has important clinical implications. Machine learning (ML) can improve clinical prediction. Multicentre observational study. This study analysed 581 patients from an academic medical centre in Boston and 180 patients from Guangzhou, China treated with HFNC for AHRF. The Boston dataset was randomly divided into a training set (90%, n = 522) and an internal validation set (10%, n = 59), and the model was externally validated using the Guangzhou dataset (n = 180). A random forest (RF)-based feature selection method was used to identify predictive factors. Nine machine learning algorithms were selected to build the predictive model. The area under the receiver operating characteristic curve (AUC) and performance evaluation parameters were used to evaluate the models. The final model included 38 features selected using the RF method, with additional input from clinical specialists. Models based on ensemble learning outperformed other models (internal validation AUC: 0.83; external validation AUC: 0.75). Important predictors of HFNC failure include Glasgow Coma Scale scores and Sequential Organ Failure Assessment scores, albumin levels measured during HFNC treatment, ROX index at ICU admission and sepsis. This study developed an interpretable ML model that accurately predicts the risk of HFNC failure in patients with AHRF. Clinicians and nurses can use ML models for early risk assessment and decision support in AHRF patients receiving HFNC. TRIPOD checklist for prediction model studies was followed in this study. Patients were involved in the sample of the study.

Elderly care and health monitoring using smart healthcare technology: An improved routing scheme for wireless body area networks

AbstractHypertensive patients need regular checkups and constant monitoring for taking time critical decisions by the medical experts. Unfortunately, it is hard to maintain uninterrupted patient health surveillance due to limited medical staff resulting in an increasing mortality rate annually. Thanks to recent developments in wireless sensor networking, we can monitor constantly and efficiently diverse parameters of a network. Similarly, Wireless Body Area Networks (WBANs) have become a well‐known sub‐branch of Wireless Sensor Networks. Such sensor networks can be leveraged for patient health monitoring, minimising the medical staff workload. Wireless Body Area Networks require tiny sensor nodes with limited battery power. Therefore, it is always desirable to design effective routing schemes that can enhance network lifetime, and reduce packet drop ratio. In this paper, we re‐simulate and explain in detail the results of a selected published journal article for WBANs and provide some modifications to improve the network's overall performance. Based on these amendments, the modified protocol successfully extends the operational time of the network than the original. Our performance evaluation parameters are dead nodes, throughput, residual energy, and path loss versus the number of rounds. These analyses support effective solutions that improve network performance and data delivery ratio.

Noise monitoring in primary schools: comparisons between different time-dependent parameters for acoustic performance evaluation

The A-weighted equivalent sound level is commonly used to describe or regulate environmental noise over a defined time interval. Although it is very convenient and easy to compare noise limits or requirements, it does not provide summary information on how noise evolves. On the other hand, the detailed analysis of the history of the measurements over time is too burdensome in the case of long-term monitoring. As part of a large study that aims to investigate the effects of noise on the cognitive abilities of primary school children, long-term unattended monitoring was carried out using low-cost instrumentation based on smartphone apps and calibrated external microphones. From the data obtained, acoustic indices and parameters were calculated, which allowed the assessment of the time-dependent noise exposure of the students based on the type of activity carried out in class. This analysis can be useful for validating the acoustic design requirements in schools.

Comparative investigation on heat transfer augmentation in a liquid cooling plate for rectangular Li-ion battery thermal management

This work conducted a numerical study with experimental validation to improve the performance of a rectangular multi-channel cooling plate utilised for cooling lithium batteries. Seven different designs on a cold plate for laminar flow conditions were evaluated using numerical modelling simulations in the 3D ANSYS program. Three cooling plate designs were proposed with channel arrangements: arc, convergent–divergent and different channel widths (narrow central channels and wide peripheral channels). The other four proposed cooling plate designs included representations of variable-sized pin fins with different shapes: oval, drop, rhombus and trapezoidal within different channel widths. Real experiments were conducted on a manufactured conventional cooling plate to verify the results of the numerical simulation. Results indicated that all proposed designs improved the heat transfer of the cooling plate. The percentage of enhancement in Nusselt number due to the presence of rhombus-shaped pin fins inside different channel widths is approximately 95.5 %, which is about three times the enhancement in pin fins that used only smooth different channel widths, which is approximately 33.5 %. Amongst the studied designs, incorporating different channel widths with the addition of rhombus-shaped pin fins exhibited the largest heat dissipation capacity and the best thermal–hydraulic performance. The highest temperature uniformity enhancement ratio and performance evaluation parameter for this case reached about 71.96 % and 1.68, respectively.

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising thrombosis, atherosclerosis, cerebral venous thrombosis, or embolic arterial blood clot. In this paper, authors have proposed a robust framework for the detection of cerebrovascular diseases employing two different proposals which were validated by use of other datasets. In proposed model 1, the Discrete Fourier transform is used for the fusion of CT and MR images which was classified them using machine learning techniques and pre-trained models while in proposed model 2, the cascaded model was proposed. The performance evaluation parameters like accuracy and losses were evaluated. 92% accuracy was obtained using Support Vector Machine using Gray Level Difference Statistics and Shape features with Principal Component Analysis as a feature selection technique while Inception V3 resulted in 95.6% accuracy while the cascaded model resulted in 96.21% accuracy. The cascaded model is later validated on other datasets which results in 0.11% and 0.14% accuracy improvement over TCIA and BRaTS datasets respectively.

Performance aware algorithm design for elastic resource workflow management of cluster consolidation to handle enterprise big data

<span lang="EN-US">Integration and deployment of big data and business analytics application with cloud computing are more attractive as a service and are trending practice. This hybrid workflow is rapidly increasing and will trigger a revolution for enterprise data handling, information retrieval and computing. This paper presents hybrid workflow management framework for big data and multi cloud computing systems in a two-step approach. Linear optimization-based resource assessment algorithm is planned in the first step. Cluster oriented elastic resource allocation and workflow management techniques are concentrated in the second step. This paper also focus on performance evaluation parameters includes execution time, through put with multi task work flow optimization model. The proposed framework is efficiently managed the implementation of hybrid workflows by finetuning the evaluation attributes and provides improvement in terms of response time an average of 6%.</span>

Prospect and Challenges of Volatile Organic Compound Breath Testing in Non-Cancer Gastrointestinal Disorders.

Breath analysis, despite being an overlooked biomatrix, has a rich history in disease diagnosis. However, volatile organic compounds (VOCs) have yet to establish themselves as clinically validated biomarkers for specific diseases. As focusing solely on late-stage or malignant disease biomarkers may have limited relevance in clinical practice, the objective of this review is to explore the potential of VOC breath tests for the diagnosis of non-cancer diseases: (1) Precancerous conditions like gastro-esophageal reflux disease (GERD) and Barrett's esophagus (BE), where breath tests can complement endoscopic screening; (2) endoluminal diseases associated with autoinflammation and dysbiosis, such as inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), and coeliac disease, which currently rely on biopsy and symptom-based diagnosis; (3) chronic liver diseases like cirrhosis, hepatic encephalopathy, and non-alcoholic fatty liver disease, which lack non-invasive diagnostic tools for disease progression monitoring and prognostic assessment. A literature search was conducted through EMBASE, MEDLINE, and Cochrane databases, leading to an overview of 24 studies. The characteristics of these studies, including analytical platforms, disorder type and stage, group size, and performance evaluation parameters for diagnostic tests are discussed. Furthermore, how VOCs can be utilized as non-invasive diagnostic tools to complement existing gold standards is explored. By refining study designs, sampling procedures, and comparing VOCs in urine and blood, we can gain a deeper understanding of the metabolic pathways underlying VOCs. This will establish breath analysis as an effective non-invasive method for differential diagnosis and disease monitoring.

Nanoarchitectonics and applications of two-dimensional materials as anodes for lithium-ion capacitors

Lithium-ion capacitors (LICs) represent an innovative hybridization in the energy storage field, effectively combining the best features of supercapacitors and lithium-ion batteries. However, the theoretical advantage of LICs is impeded by the low reaction efficiency of the negative electrode material and significant volume expansion. Two-dimensional (2D) materials, due to their unique morphology, abundant pores, rich active centers, and adjustable composition, have been widely studied and developed as negative electrodes for LICs. Therefore, it is imperative to provide a timely review of the latest advancements in the field. The review initiates with a detailed exploration of the infrastructure, key performance evaluation parameters, and the underlying energy storage mechanisms that define LICs. Subsequently, the focus shifts towards the cutting-edge research surrounding 2D materials, including graphene, MXene, transition-metal dichalcogenides, and transition-metal oxides. The review further elaborates on the typical applications of these 2D materials within LIC frameworks, highlighting their unique properties and contributions to enhanced energy storage solutions. In conclusion, the discussion addresses the significant challenges these materials encounter within LIC applications, such as scalability, cost, and integration issues, while also projecting future development prospects. It outlines both the current limitations and the potential breakthroughs that could pave the way for more advanced and efficient LIC technologies.

Bioanalytical validation and clinical application of a liquid chromatography-tandem mass spectrometry method for the quantification of 3-orthomethyldopa, 5-hydroxytryptophan, 5-hydroxyindolacetic acid and homovanillic acid in human cerebrospinal fluid

Inherited disorders of monoamine neurotransmitters are a subset of inborn errors of metabolism affecting biochemical pathways of catecholamines, serotonin or their enzymatic cofactors. Usually, their clinical presentation is similar to those of other common neurological syndromes. For this reason, they are frequently under-recognized and misdiagnosed. Because cerebrospinal fluid concentration of catecholamine metabolites (3-orthomethyldopa and homovanillic acid) and serotonin metabolites (5-hydroxytryptophan and 5-hydroxyindolacetic acid) presents a direct correlation with their brain levels, analysis of this group of compounds is critical to reach an accurate diagnosis. Although there are several published liquid chromatography-based bioanalytical methods for the quantification of these compounds, most of them present disadvantages, making their application difficult to implement in routine clinical practice. In this study, a rapid and simple UHPLC-MS/MS method for simultaneous quantification of 3-orthomethyldopa, 5-hydroxytryptophan, 5-hydroxyindolacetic acid and homovanillic acid in human cerebrospinal fluid was validated. All the evaluated performance parameters, including linearity, carryover, accuracy and precision (within and between-day), lower limit of quantitation, recovery, matrix effect and stability under different conditions met the acceptance criteria from international guidelines. Additionally, 10 human cerebrospinal fluid samples collected via lumbar puncture from 10 pediatric patients were quantified using the validated method to assess its clinical application and diagnostic utility for inherited monoamine neurotransmitter metabolism.

Certain Studies on Alzheimer's disease: A Comprehensive Review

The brain serves as the central control centre for our body, and as time progresses, an increasing number of new brain diseases are being identified. A brain disease is any medical problem or disorder that interferes with the brain's normal functioning. This review briefs about various types of deep learning models for neurological disorders, in addition to neurodegenerative conditions like Parkinson's and Alzheimer's. In addition to various dataset identifiers commonly used as the primary source of brain disease data in the reviewed studies, forty other methodologies are examined. AUC, sensitivity, specificity, accuracy, and other performance evaluation parameters have also been addressed and recorded. The key findings from the reviewed articles are briefly summarized, and several major issues regarding machine learning and deep learning-based diagnostic approaches for brain diseases are discussed.

UV photodegradation of methylene blue using microstructural carbon materials derived from citrullus colocynthis

A low temperature aqueous growth followed by mild pyrolysis was used in this study to synthesize high-quality carbonized materials from the deserted plant Citrullus Colocynthis. It was found that the carbon material prepared for this study contained an abundance of functional groups and surface active sites. A few microns were evidently the size of the carbon material. This study investigated a variety of photocatalytic performance evaluation parameters, including initial dye concentration of methylene blue, pH effect on dye solution, scavenger stability, and recycle stability via irradiating UV light. Methylene blue degradation was found to be significantly affected by pH and concentration of the dye solution. It has been found that pH five is the most effective pH for the removal of dyes. As a result of the study, we found that methylene blue decays according to pseudo first order kinetics and is estimated to remove dye at an almost 100% rate.

Twisted tape inserts in parabolic trough solar collectors: Assessment of Energy, Exergy, and Environmental impacts

The parabolic trough solar collector is a solar technology extensively employed to focus and harness solar energy for diverse heating purposes. One effective technique to enhance its thermal performance involves the insertion of swirl generators within its absorber tube. In this research, experimental investigation on performance enhancement of parabolic trough solar collector using single twisted tape and dual twisted tape insert across Reynolds number values ranging from 1,000 to 9,000 is performed. Collector efficiency, Nusselt number, friction factor, performance improvement factor, and exergy efficiency are examined for single twisted tape inserts with pitch to width ratios (y/w) of 1, 1.5, 2, 2.5, and 3, and dual twisted tape inserts with y/y0 ratios of 2, 3, and 4. Maximum enhancement in collector efficiency is reported at the lowest flow rate for both inserts. The single twisted tape insert with pitch to width ratio of 1 and the dual twisted tape insert with a pitch ratio of 2 exhibit the most notable enhancements in the evaluated performance parameters. Specifically, at lower flow rates (Re < 4400), the dual twisted tape insert (y/y0 = 2) outperforms the single twisted tape insert (y/w = 1). This dual twisted tape insert configuration provides a thermal efficiency enhancement of 14 %, while the Nusselt number and friction factor enhancement are found to be 372 % and 1600 % compared to bare tube. Additionally, the exergy efficiency exhibits a peak increase of 20.8 % at the lowest flow rate using the dual twisted tape insert (y/y0 = 2). This improvement translates to an annual CO2 reduction between 1.53 tons/year offering a highest enviro-economic benefit of 22.19 US dollars per year.

Experimental evaluation of performance parameters of a domestic freezer under door opening conditions

The current study focuses on a comprehensive experimental assessment of the cooling system parameters of a domestic-type upright freezer that is equipped with shelves and test packages under periodic door opening conditions, referring to real user scenarios. The influence of varying ambient relative humidity levels as well as the load state of the cabinet are taken into account. The study reveals that both heat and moisture have a crucial effect on the average air temperature inside the cabinet, the temperature of the walls, the transient evaporation and condensation pressure behaviour of the refrigerant, as well as the compressor power, the total operation ratio of the compressor, and the overall amount of energy consumption. The specific performance experiments are conducted at 32 °C ambient and 35 %, 65 %, and 85 % relative humidity conditions with unloaded, half-loaded, and full-loaded cases. The obtained data and results are evaluated between frosting, influenced by altering ambient humidity levels, and cabinet load state under periodic door openings where the variation in energy consumption is clearly measured. Consequently, the amounts of frost accumulation in the cabinet under half and full loaded conditions at 32 °C an 85 % relative humidity conditions are observed to be about 220 g and 270 g, respectively, followed by regular door openings and closings which result in a raise in total energy consumption rate of 7.6 % and 11.2 % compared to the period before the door opening. The frost accumulation in the full loaded condition at 32 °C and 85 % RH is found to be 2.4 times greater than the unloaded condition with a relative humidity (RH) of 35 % whereas the energy consumption is 3.7 times higher.

A blockchain-machine learning ecosystem for IoT-Based remote health monitoring of diabetic patients

Diabetes poses a global health challenge, demanding continuous monitoring and expert care for effective management. Conventional monitoring methods lack real-time insights and secure data-sharing capabilities, necessitating innovative solutions that leverage emerging technologies. Existing centralized monitoring systems often entail risks such as data breaches and single points of failure, emphasizing the necessity for a secure, decentralized approach that integrates the Internet of Things (IoT), blockchain, and machine learning for efficient and secure diabetes management. This paper introduces a decentralized, blockchain-based framework for remote diabetes monitoring, IoT sensors, machine learning models, and decentralized applications (DApps). The proposed framework comprises five layers: the IoT Sensor Layer, which collects real-time health data from patients; the Blockchain Layer, leveraging smart contracts on the Ethereum blockchain for secure data sharing and transactions; the machine learning Layer, analyzing patient data to detect diabetes; and the DApps Layer, facilitating interactions between patients, doctors, and hospitals. For intelligent decision-making regarding diabetes based on data collected from different sensors, nine machine learning algorithms, including logistic regression, K-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, Random Forest, AdaBoost, stochastic gradient boosting (SGD), and Naive Bayes, were trained and tested on the PIMA dataset. Based on the performance evaluation parameters such as accuracy, recall, F1-score, and the area under the curve (AUC), it was found that the AdaBoost model achieved the highest predictive accuracy of 92.64%, followed by the Decision Tree with an accuracy of 92.21% in diabetes classification.

An Analytical Method for Timely Predicting of Coal Seam Pressure during Gas Production for Undersaturated Coalbed Methane Reservoirs

Coal seam pressure is an important parameter for production performance evaluation and prediction of coalbed methane (CBM). CBM production from undersaturated CBM reservoirs can be divided into two stages according to critical desorption pressure. At present, few prediction models of coal seam pressure performance consider the comprehensive influence of critical desorption pressure, dissolved gas, matrix shrinkage, and stress sensitivity. For the purpose of accurately predicting coal seam pressure during gas production for an undersaturated coalbed methane reservoir, the material balance principle is used to establish the analytical method for predicting coal seam pressure, considering the comprehensive influence of the critical desorption pressure, dissolved gas, matrix shrinkage, and stress sensitivity. Then, the proposed method is verified against a numerical simulation case using a computer modelling group (CMG) and two actual coalbed methane wells. Finally, the sensitivities of influencing factors on the coal seam pressure are analyzed. The results show that good agreements were obtained between the calculated coal seam pressures using the proposed method and those from the CMG-GEM simulation case and actual CBM wells, with the relative errors all being less than 1%. When ignoring the influence of critical desorption pressure and mistaking pd for pi as well as ignoring Cs, the relative error can reach as high as 31.3%. The main factors affecting the coal seam pressure are the critical desorption pressure and free gas saturation. The proposed method is simple to use, and without shutting-in the well, it can provide an important basis for production performance evaluation and development strategies.

Performance Evaluation of Government Section Employees at The Mappak District Office, Tana Toraja District

The aim of this research is to evaluate the performance of government section employees at the Mappak District office, Tana Torja Regency. In the context of the regional autonomy policy provided by Law Number 32 of 2014, regional governments have a greater responsibility to accelerate regional development and improve community welfare. One important aspect of implementing the regional autonomy policy is improving the performance of public services, which is realized through evaluating the performance of government officials. The Mappak District Office has a significant role in providing administrative and government services at the local level. Evaluation of the performance of the government section is carried out to identify successes, obstacles and improvements in the delivery of public services. These performance evaluation parameters include the smoothness of the administrative process related to service operational standards, the ability of the government section to work in accordance with existing duties and functions, the response from the surrounding community regarding government services, the relationship that exists between the government section and the surrounding community and the availability of facilities to support activities and government section services at the Mappak sub-district office, Tana Toraja Regency. With continuous evaluation and continuous improvement efforts, it is hoped that the performance of government can continue to improve in providing effective and efficient services to the community. The results of this evaluation will be the basis for continuous improvement and development, so that government section employees at the Mappak sub-district office, Tana Toraja Regency can provide better services to the community on an ongoing basis.

Dynamic behavior of three-wheel vehicle with inerter-based dynamic absorber suspension system

Three-wheeled vehicles may be a better choice as compared with four-wheeler cars provided they are properly designed to improve the rollover stabilities and ride comfort. The design modifications need improvement in geometry and suspension. This article presents the dynamic behavior of three-wheeled vehicle equipped with an inerter-based dynamic absorber and compares the same with the conventional suspension system. The performance parameters analyzed are vertical-lateral acceleration, front &amp; rear suspension deflection, and front &amp; rear dynamic tyre loads. The analytical rigid body model is formulated by assigning 9 degrees of freedom to the system for the evaluation of performance parameters to compare the inerter-based absorber suspension with the conventional passive suspension system.

Flow boiling heat transfer performance of R245fa in a vertical enhanced evaporator tube with sintering and electroplating composite coats

The R245fa flow boiling performance was investigated in a vertical evaporator tube of which inner wall surface is coated by a composite sintering and electroplating technology. The effects of vapor quality, heat flux, mass flux, and flow direction were compared and analyzed in the bare tube (BT) and the sintering and electroplating treated tube (SET). The results showed that the heat transfer coefficient increased and then decreased with the rise of vapor quality and heat flux. The heat transfer performance in downward flow was better than that in upward flow, while an opposite trend was observed for frictional pressure drop. With the increased of vapor quality, the frictional pressure drop increased and then decreased, but it monotonically increased with the increasing mass flux. The heat transfer enhancement mechanism was explored through flow pattern visualization. The annular flow pattern conversion line of SET was earlier than that of BT. Due to the strong wettability of SET, the thickness of liquid film was thinner in downward and the mother bubble was bigger in upward, which had a positive effect on flow boiling heat transfer. The maximum the heat transfer enhancement factor (EF) and the performance evaluation parameter (PEC) were 1.83 and 1.75, respectively.