Acceptable Range Research Articles

A Low-Cost Microstrip Patch Antenna Based Metamaterials for Non-Invasive Breast Tumor Detection

Microstrip patch antennas have been used extensively in broadband telecommunication applications. Despite their countless promises, their narrow bandwidth and the loss at high-frequency bands have limited their usage in medical applications. The purpose of this work is to design a patch antenna sensor that is a low-cost microstrip sensor which is suitable for biomedical application to detect a breast cancer tumor. The proposed antenna sensor is comprised of three layers namely ground, substrate and microstrip patch sensor that can be easily fabricated by using standard printed circuit board technique. The comparison study between two resonance frequency at 1.8 GHz and 2.9 GHz has been performed and investigated by especially accurate simulation with the presence and absence of tumor cell. Results obtained using computer simulation technology CST Studio Suite 3D EM simulation and analysis software indicates that the design can detect tumor by using phase shift detection and depth of the return loss. The result shows that the antenna return loss is getting lower in -39 dB at 1.8 GHz and -12 dB at 2.9 GHz and the phase shift detected with the presence of the tumor cell. Specific absorption rate has been also calculated (0.746 and 0.934 W/kg) and found to be in acceptable range and not exceed the standard value of <1.6 W/kg, which mean that the patch sensor is compatible for human and biomedical application. The breast phantom models without/with a tumor have been numerically simulated by using the antenna operating as a transceiver for the detection of cancer tumor cells. Two parameters have been observed, the frequency phase shift and the deep amount of reflection return loss. In summary, this study concludes that a lower frequency band will result in higher penetration depth but a lower resolution. Meanwhile, higher frequency band will provide a better resolution, but the penetration depth will be lesser as seen in the comparison study between 1.8 GHz and 2.9 GHz. The proposed work could provide a pathway on the design of electromagnetic sensors for biomedical applications.

Application of Chitosan Coating and Liquid Smoke as Antimicrobial Agent in Tuna Fish Pempek

The goal of this study was to see the effect utilizing chitosan coating and liquid smoke had on pempek as an antibacterial. This study was carried out in an exploratory method, with differences in tuna pempek storage period, namely: control=0 days, A=1 day, B=2 days, C=5 days, D=7 days, and E=9 days. The results showed that the longer pempek tuna was stored using chitosan coating with the addition of 1% liquid smoke, the lower the total plate number was compared to without the addition of 1% liquid smoke. The storage time of pempek tuna without liquid smoke yielded results on chitosan coating, with water content ranging from 6.24 to 11.99% and protein content ranging from 3.68 to 11.8%. The Total Plate Count (TPC) test result was ranging from 5x10-4 to 13x10-4 colonies/g. With liquid smoke added, the water content increased to 4.56−10.48%, the protein content increased to 5.43−12.39, and the TPC test resulted in 2.7x10-4-7.3x10-4 colonies/g. According to SNI 7661.1:2013, a storage time of 3 days in the chitosan coating treatment with the addition of liquid is in the acceptable range

Mechanistic enhanced cell voltage based on halides doped metal oxide fullerenes for use in Li-ion batteries: Insights from DFT intuition

Designing of anode nanomaterials with an optimum cell voltage is an important challenge for high-performance lithium (Li) ion batteries. In this study, we have investigated the electrochemical potential of pristine and Li-doped magnesium oxide (Mg12O12) and zinc oxide (Zn12O12) fullerenes through density functional theory (DFT) simulations, with a focus on their potential applications in lithium-ion batteries. Specifically, we investigated the halide ion encapsulated oxide nanocages as a means to achieve this improvement. We examined both unaltered and halide encapsulated Mg12O12 and Zn12O12 with Li atom/lithium cation (Li/Li+). Our investigation delves into the geometric and electrochemical characteristics of Li/Li+ interactions with Mg12O12 and Zn12O12 nanocages. We analyzed adsorption energies (Ead) and frontier molecular orbitals (FMOs) of Li/Li+ ions with these nanocages to gain insights into how neutral or ionic Li affects their geometric and electrochemical properties. The initial cell voltages of bare Mg12O12 and Zn12O12 are found to be 0.61 and 0.57 V, primarily due to marginal distinctions in the adsorption energies of Li and Li+ bound systems. Notably, the encapsulation of various halides into Mg12O12 and Zn12O12 nanocages significantly enhanced the cell voltage. Among the considered halides, the encapsulation of fluoride anion (F−) into Mg12O12 and Zn12O12 stands out, leading to a remarkable increase in cell voltage of 4.05 and 3.55 V for lithium-ion batteries, respectively. Moreover, the use of toluene solvent further increased the Ead values of Li cation with pure and halides encapsulated oxides (Mg12O12 and Zn12O12) nanocages. Ultimately, the Gibb's free also decreased along with the decrease in the cell voltage which is encountered in the acceptable range. The most suitable anode nanomaterial is suggested to be Li doped F−@Mg12O12 which has the most acceptable cell voltage of 1.59 V. The work also leads to more research work on the designing of halides encapsulated oxides fullerenes based secondary batteries for use in electrochemical energy storage devices. This work is a full-length guideline for the experimentalist to synthesize such systems to overcome the energy crises.

Health Risk Assessment of Some Heavy Metals in Groundwater Samples in Rada'a City, Yemen

Groundwater is an important source of domestic drinking water supply, and groundwater quality assessment is necessary to reduce pollution to acceptable levels. Therefore, this study aims to determine the concentrations of heavy metals (HM) (Cr, Cd, Cu, Ni, Mn, Fe, and Zn) in the groundwater of Rada'a City and to evaluate the health risk. Groundwater samples were analyzed by inductively coupled plasma optical emission spectrometry (ICP-OES). The following average concentrations (mg/l) were determined in the groundwater 0.01035, 0.01702, 0.043718, and 0.03652 for Cu, Ni, Mn, and Zn were concentration values lower than the World Health Organization acceptable limit (WHO) 2, 0,07,0.4, and 4 for Cu, Ni, Mn, and Zn, respectively, but at some locations in the study area, concentration values were found to be above the acceptable limit 0.3 (mg/l) for Fe, the average concentrations were 0.42567 (mg/l). Based on the HM concentrations, the health risk was classified as non-carcinogenic. The health risk indices (HRI) were < 1 for all HMs in the groundwater samples, indicating no health risk. In addition, a high carcinogenic risk (Probability of Cancer Risk, PCR) of 4.42 × 10−4 on average was found from drinking water consumption, while the acceptable range for cancer risk is ≤ 1 × 10−6 to 1 × 10−4.

Spatial and variety distributions, risk assessment, and prediction model for heavy metals in rice grains in China.

In this study, 6229 brown rice grains from three major rice-producing regions were collected to investigate the spatial and variety distributions of heavy metals in rice grains in China. The potential sources of heavy metals in rice grains were identified using the Pearson correlation matrix and principal component analysis, and the health risks of dietary exposure to heavy metals via rice consumption were assessed using the hazard index (HI) and total carcinogenic risk (TCR) method, respectively. Moreover, 48 paired soil and rice samples from 11 cities were collected to construct a predicting model for Cd accumulation in rice grains using the multiple linear stepwise regression analysis. The results indicated that Cd and Ni were the main heavy metal pollutants in rice grains in China, with approximately 10% of samples exceeding their corresponding maximum allowable limits. The Yangtze River basin had heavier pollution of heavy metals than the Southeast Coastal Region and Northeast Plain, and the indica rice varieties had higher heavy metal accumulation abilities compared with the japonica rice. The Cu, Pb, and Cd mainly originated from anthropogenic sources, while As, Hg, Cr, and Ni originated from both natural and anthropogenic sources. The mean HI and TCR values of dietary exposure to heavy metals via rice consumption ranged from 2.92 to 4.31 and 9.74 × 10-3 to 1.44 × 10-2, respectively, much higher than the acceptable range, and As and Ni were the main contributor to the HI and TCR for Chinese adults and children, respectively. The available Si (ASi), total Cd (TCd), available Mo (AMo), and available S (AS) were the main soil factors determining grain Cd accumulation. A multiple linear stepwise regression model was constructed based on ASi, TCd, AMo, and AS in soils with good accuracy and precision, which could be applied to predict Cd accumulation in rice grains and guide safe rice production in contaminated paddy fields.

Monitoring of Cotton Boll Opening Rate Based on UAV Multispectral Data

Defoliation and accelerating ripening are important measures for cotton mechanization, and judging the time of defoliation and accelerating the ripening and harvest of cotton relies heavily on the boll opening rate, making it a crucial factor to consider. The traditional methods of cotton opening rate determination are time-consuming, labor-intensive, destructive, and not suitable for a wide range of applications. In this study, the relationship between the change rate of the vegetation index obtained by the unmanned aerial vehicle multi-spectrum and the ground boll opening rate was established to realize rapid non-destructive testing of the boll opening rate. The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) had good prediction ability for the boll opening rate. NDVI in the training set had an R2 of 0.912 and rRMSE of 15.387%, and the validation set performance had an R2 of 0.929 and rRMSE of 13.414%. GNDVI in the training set had an R2 of 0.901 and rRMSE of 16.318%, and the validation set performance had an R2 of 0.909 and rRMSE of 15.225%. The accuracies of the models based on GNDVI and NDVI were within the acceptable range. In terms of predictive models, random forests achieve the highest accuracy in predictions. Accurately predicting the cotton boll opening rate can support decision-making for harvest and harvest aid spray timing, as well as provide technical support for crop growth monitoring and precision agriculture.

CONCEPTUAL DESIGN, FABRICATION AND VALIDATION ON PROTOTYPE OF INDUSTRIAL ERGONOMIC TROLLEY-LIFTER FOR WOOD FURNITURE INDUSTRY

Over the past several decades, ergonomics design has been demanded and considered as a main factor of efficiency and effectiveness in optimising production. Recently, a lot of research has paid attention to the risk factors on WMSDs and its prevention strategies. However, little attention has been paid to physical ergonomic intervention and its validation. This study was conducted among ten woodworkers in a medium-sized woodworking workshop in Selangor, Malaysia. Through this study, demographic surveys, QEC, and REBA were examined before and after the ergonomic intervention of the industrial ergonomic trolley-lifter. The results of QEC and REBA as well as the survey indicated that comfort perception improved when postural measurement in task performance was at an acceptable range. In addition, work time with external load was reduced by 75% due to the proposed trolley. The trolley-lifter offers an alternative solution to lifting activity by providing a lifting product to assist the handling of wood board in efficiency and occupational safety and health conditions.

On the relative value of clustering techniques for Unsupervised Effort-Aware Defect Prediction

Unsupervised Effort-Aware Defect Prediction (EADP) uses unlabeled data to construct a model and ranks software modules according to the software feature values. Xu et al. (JSS 2021) conducted an exploration of clustering techniques for unsupervised defect prediction and found that several clustering methods exhibit better performance on the F1@20% effort-aware metric. However, their conclusion may not be convincing, as they did not take into account the impact of the Initial False Alarms (IFA) metric on unsupervised EADP. Furthermore, their study did not compare with the state-of-the-art supervised EADP models. To further investigate clustering techniques for unsupervised EADP more comprehensively, we explore the performance of 22 clustering techniques for unsupervised EADP using three classification metrics and six effort-aware metrics. The experimental results demonstrate that (1) the best clustering technique for unsupervised EADP, K-medoids, can significantly reduce the IFA of the ManualUp method to an acceptable range. In contrast, the clustering techniques recommended by Xu et al. exhibit a high IFA value that cannot be deemed acceptable by testing teams; (2) K-medoids performs better than some supervised EADP methods, especially on metrics such as IFA and PMI@20% (Proportion of Modules Inspected when inspecting the top 20% lines of code); (3) better classification performance of clustering techniques could lead to better effort-aware performance. In summary, we recommend using the K-medoids clustering technique for unsupervised EADP and suggest that future research devote more effort to exploring better-unsupervised clustering techniques. In support of reproducibility and future research, we provide the source code used in our study (https://github.com/Andre-Yang816/Clustering4UEADP).

Validation of a Targeted LC-MS/MS Method for Cereulide and Application in Food and Faeces.

Cereulide is an emetic toxin produced by some strains of Bacillus cereus. This bacterial toxin, a cyclic 1.2 kDa dodecadepsipeptide, is stable to heat and acids and causes nausea and vomiting when ingested via contaminated food. This work aimed to develop and validate a targeted analytical method applying liquid chromatography-tandem mass spectrometry (LC-MS/MS) to quantify this toxin in food and human faeces. Samples were extracted with acetonitrile in the presence of 13C6-cereulide, a labelled internal standard, and purified by centrifugation and filtration. The limits of quantification were 0.5 and 0.3 µg kg-1 for food and faeces, respectively. The linearity of the method was very good, with calculated R2 values above 0.995. The mean recovery of the method was within the acceptable range of 70.0%-120.0%, the repeatability was not higher than 7.3%, and the highest intra-laboratory reproducibility was 8.9%. The estimated range for the expanded measurement uncertainty was between 5.1% and 18.0%. The LC-MS/MS method was used to analyse one food sample (rice) from a Belgian foodborne outbreak and five faecal samples from patients with clinical symptoms after consumption of the contaminated rice. The levels of cereulide were 12.22 µg g-1 for food and between 6.32 and 773.37 ng g-1 for faecal samples.

Bianchi Type V Cosmological Scenario In f(R,T) Gravity Theory With Special Form Of Scale Factor

In this study, we have examined the exact solutions of the field equations for a Bianchi type V universe filled with bulk viscous fluid within the framework of theory, where and and represent the Ricci scalar and the trace of the energy momentum tensor, respectively. We used a combination of exponential and hyperbolic scale factors to determine the physical parameters and metric potentials in the space-time. We also investigated the geometrical and physical parameters of the model, as well as the energy conditions. Additionally, we found that the state finder diagnostic pair falls within an acceptable range.

Quality development of vocational education in the context of big data

Abstract Taking vocational education as an entry point, this paper utilizes factor analysis to deal with the problem of multicollinearity of vocational education indicators so that the synthesized factors are no longer correlated and lay a good foundation for vocational education cluster analysis. The distance measure and similarity coefficient describe the relationship between vocational education indicators. To increase computational efficiency and handle large and multidimensional data sets more effectively, the advantages of K-mean and grid clustering algorithms are combined to create the GBKM cluster analysis algorithm. Higher education institutions in Province H are chosen as representative samples in order to examine the variables influencing the development of high-quality vocational education, categorize the degree of that development, and forecast the trend of that development. The findings demonstrate that the GBKM cluster analysis algorithm’s accuracy difference between the predicted and actual data on the development trend of vocational education ranges from [0,0.1]. The algorithm’s absolute error value is 0.182, which falls within an acceptable error range and suggests that the algorithm proposed in this paper is capable of more accurately predicting the development trend of vocational education and may have some theoretical and practical implications for fostering high-quality development of vocational education. It has theoretical and practical implications for advancing the development of vocational education to a high standard.

Evaluation of a hybrid protocol using continuous glucose monitoring and point-of-care testing in non-critically ill patients in a community hospital.

Inpatient glycemic management typically involves use of point-of-care (POC) glucose measurements to inform insulin dosing decisions. This study evaluated a hybrid monitoring protocol using real-time continuous glucose monitoring (rtCGM) supplemented with POC testing at a community hospital. Adult inpatients receiving POC glucose testing were monitored using rtCGM in a telemetry unit. The hybrid monitoring protocol required a once-daily POC test but otherwise primarily relied on rtCGM values for insulin dosing decisions. Outcomes assessment included surveillance error grid (SEG) and Clarke Error Grid (CEG) analysis results, the mean absolute relative difference (MARD) for available rtCGM-POC value pairs before and after study protocol application, the number of POC tests avoided, and the number of hypoglycemic events involving a blood glucose value of <70 mg/dL identified by rtCGM and POC values. Data were collected from 30 inpatients (the mean age was 69.4 years, 77% were female, 80% had type 2 diabetes, and 37% were at-home insulin users). With the protocol applied, a total of 202 rtCGM-POC pairs produced a MARD of 12.5%. SEG analysis showed 2 pairs in the "moderate" risk category, with all other pairs in the "none" or "slight" risk categories. CEG analysis showed 99% of paired values to be in the clinically acceptable range. Six hypoglycemic events in 5 patients were resolved without incident. Three hundred three POC tests were avoided, a 60% reduction for the study duration. Use of a hybrid monitoring protocol of rtCGM and POC testing in a community hospital demonstrated sustained rtCGM accuracy and was found to reduce the frequency of POC testing to manage inpatient glycemia.

Development of Stability Indicating Method for the Quantification of Futibatinib in K2EDTA Human Plasma by LC-MS/MS

sensitive, accurate, and linear LC-MS/MS method for the quantification of futibatinib in human plasma K2 ethylene diamine tetraacetic acid (EDTA) was developed. A Phenomenex C18, 150×4.60 mm, 2.1 μ column, 0.1% HCCOH, ACN and methyl alcohol (13/67/20, v/v/v) mobile solvent system at 0.8 mL/min was employed for the isolation of components.10 μL. of volumes were employed to isolate the peak responsess within 2.0 minutes at 40 ± 5ºC of oven temperature. Analyte retention was 1.261 minutes and ISTD of 1.292 minutes. Throughout the process of validation, each of the four calibration curves exhibited linear behavior for standards with concentrations ranging from 0.16 to 3250 ng/mL. Validation showed an r2 = 0.9997. At MQC, HQC, and LQC concentrations, futibatinib had 98.49, 99.35, and 98.47% mean recovery. Every QC level had a mean recovery of 90.51% and a %CV of 3.06. All of the control solutions had back-calculated concentration values that were accurate between 94.61 and 98.86% of the time. The range of %CV of back-calculated values for all quality control samples were in the range of 0.6 and 4.63, which is within the acceptable range of 15%. The developed method can be applied successfully for the quantification of the Futibatinib in biological matrices.

On the Generation of Digital Data and Models from Point Clouds: Application to a Pedestrian Bridge Structure

This study investigates the capture of digital data and the development of models for structures with incomplete documentation and plans. LiDAR technology is utilized to obtain the point clouds of a pedestrian bridge structure. Two different point clouds with varying densities, (i) fine (11 collection locations) and (ii) coarse (4 collection locations), collected via terrestrial LiDAR, are analyzed to generate geometry and structural sections. This geometry is compared to the structural plans, which are then converted into numerical models (finite element—FE model) based on the point cloud data. Point cloud-based FE models (based on fine and coarse data) are compared with the structural plan-based FE model. It is observed that the static and dynamic responses are comparable within an acceptable range of a maximum difference of 5.5% for static deformation and an 8.23% frequency difference, with an average difference of less than 5%. Additionally, the dynamic properties of the fine and coarse point cloud FE models are compared with the operational modal analysis data obtained from the bridge. The fine and course point-cloud-based FE models, without model calibration, achieve an average accuracy of 8.76% and 9.94% for natural frequencies and a 0.89 modal assurance criterion value. The research found that the digital data generation yields promising results in this case for a bridge if documentation or plans are unavailable. With recent technologies and approaches such as digital twins, the connection between physical and virtual entities needs to be established by fusing digital models, sensorial information, and other data forms for better infrastructure management. Models such as those investigated and discussed in this paper can assist engineers with structural preservation in conjunction with monitoring data and utilization for digital twins.

Enhancing thermal comfort and natural ventilation in residential buildings: A design and assessment of an integrated system with horizontal windcatcher and evaporative cooling channels

In this study, researchers investigated the efficacy of natural cooling in a residential building employing a horizontal windcatcher alongside a direct evaporative cooling system featuring a cross-flow pattern. ANSYS Fluent was used to model the three-dimensional airflow, while MATLAB measured the system's thermal performance. The study assessed thermal comfort and natural ventilation in accordance with the Adaptive Thermal Comfort Standard (ATCS) and ISO/EN7730 Standard. The impact of environmental conditions and window aperture on the hybrid system's functionality was scrutinized, culminating in design guidelines dictating the acceptable range of window openings to ensure compliance with thermal comfort conditions. The passive system demonstrated the capability to maintain thermal comfort within the test building under a maximum cooling load of 12,000 W (ATCS) and 6000 W (ISO/EN7730). Moreover, the natural cooling system decreased hourly electricity consumption during hot seasons in Tehran, Iran, by 0.0155 (kW/m2) compared to split air conditioners and 0.00087 (kW/m2) compared to evaporative coolers. The study also investigated the influence of neighboring buildings positioned at specific distances relative to the test building on the system's performance. Furthermore, adopting the suggested horizontal windcatcher instead of conventional vertical windcatchers resulted in a 50 % reduction in energy consumption.

Research on Flood Risk Control Methods and Reservoir Flood Control Operation Oriented towards Floodwater Utilization

Since improving floodwater utilization may increase flood risk, flood risk control methods for trade-offs between these factors have research value. This study presented a flood risk control method oriented towards floodwater utilization which considers multiple main flood risk factors. The proposed method not only achieves the boundaries of the flood limited water level (FLWL) under various acceptable risks but also dynamically controls the water level to enhance floodwater utilization. A case study conducted on the Danjiangkou reservoir yielded the following results: (1) The proposed method provides FLWL dynamic control boundaries under various acceptable risks. (2) The proposed method reveals the potential to raise the FLWL, with a possibility to raise it by 1.00 m above the present FLWL under the absence of flood risk. (3) The available flood resources in both the wet and dry seasons increase, on average, by 0.83 and 0.81 billion m3, and the flood risk remains within the acceptable range after raising the FLWL by 1.00 m, which contributes to enhancing floodwater utilization.

Transient Tip Clearance Prediction Model Considering Transient Radial Temperature Distribution of Discs in a Gas Turbine Engine

Abstract The tip clearance of turbomachinery components, such as compressor and turbine, significantly impacts the gas turbine engine performance, including the thrust and fuel consumption. Therefore, an appropriate prediction of the turbomachinery tip clearance is indispensable in improving the accuracy of engine performance evaluations. The traditional 1D transient tip clearance prediction model does not consider the radial temperature distribution of the disk (also called disc). However, as the engine performance keeps improving, the radial temperature gradient of the disk also increases. In order to maintain the prediction accuracy within an acceptable range, a generic transient tip clearance prediction model is developed in this paper. Based on the special treatment of the axial and radial convective heat transfer boundary regions of the disk, a 2D transient heat transfer model is established, and the radial temperature of the disk is predicted. At the same time, the interaction effect of the thermal expansion between the two different layers of the shroud is studied to improve the prediction accuracy further. In addition, the centrifugal deformation of the blade and disk with variable-thickness characteristics is considered in the new transient model. Compared with the traditional 1D transient prediction model, the model developed in this paper further improves the predicting accuracy of the transient tip clearance, which is verified by the high-fidelity fluid–solid–thermal coupling method. The prediction model provides a useful tool for evaluating the transient tip clearance. It may also benefit the future design strategy of active clearance control.

Development of a bio-inspired optical system that mimics accommodation and lighting regulation like the human eye.

Bio-inspired optical systems have recently been developed using polarizers and liquid or rigid lenses. In this work, we propose a bio-inspired opto-mechatronic system that imitates the accommodation and regulation of light intensity as the human eye does. The system uses a polymeric lens as a cornea, an adjustable diaphragm as an iris, a tunable solid elastic lens as a crystalline lens, and a commercial sensor as a retina. We also present the development of the electronic control system to accommodate and regulate the amount of light that enters the system, for which two stepper motors, an Arduino control system, and light and movement sensors are used. The characterization of the system is presented together with the results obtained, where it can be seen that the system works in an acceptable range as the human eye does.

The influence of parallel curb parking on traffic capacity at an intersection

In this paper, we propose a model based on traffic flow theory to quantify the influence of curb parking on traffic capacity at a road intersection. The model takes into account vehicles parked at the curb and those that perform parking maneuvers, under three different saturation scenarios at the intersection. We apply the model to three input data sets and compare the results with those obtained from a traffic simulation. The model calculation and traffic simulation errors (MAPE and RMSE) are within an acceptable range (smaller than 10 %), which shows the effectiveness of the proposed model.

Pitch-classifier model for professional pitchers utilizing 3D motion capture and machine learning algorithms

IntroductionA pitcher's ability to achieve pitch location precision after a complex series of motions is of paramount importance. Kinematics have been used in analyzing performance benefits like ball velocity, as well as injury risk profile; however, prior utilization of such data for pitch location metrics is limited. ObjectiveTo develop a pitch classifier model utilizing machine learning algorithms to explore the potential relationships between kinematic variables and a pitcher's ability to throw a strike or ball. MethodsThis was a descriptive laboratory study involving professional baseball pitchers (n = 318) performing pitching tests under the setting of 3D motion-capture (480 Hz). Main outcome measures included accuracy, sensitivity, specificity, F1 score, positive predictive value (PPV), and negative predictive value (NPV) of the random forest model. ResultsThe optimized random forest model resulted in an accuracy of 70.0 %, sensitivity of 70.3 %, specificity of 48.5 %, F1 equal to 80.6 %, PPV of 94.3 %, and a NPV of 11.6 %. Classification accuracy for predicting strikes and balls achieved an area under the curve of 0.67. Kinematics that derived the highest % increase in mean square error included: trunk flexion excursion(4.06 %), pelvis obliquity at foot contact(4.03 %), and trunk rotation at hand separation(3.94 %). Pitchers who threw strikes had significantly less trunk rotation at hand separation(p = 0.004) and less trunk flexion at ball release(p = 0.003) compared to balls. The positive predictive value for determining a strike was within an acceptable range, while the negative predictive value suggests if a pitch was determined as a ball, the model was not adequate in its prediction. ConclusionsKinematic measures of pelvis and trunk were crucial determinants for the pitch classifier sequence, suggesting pitcher kinematics at the proximal body segments may be useful in determining final pitch location.