Entropy Optimized Second Grade Fluid with MHD and Marangoni Convection Impacts: An Intelligent Neuro-Computing Paradigm

Artificial intelligence applications based on soft computing and machine learning algorithms have recently become the focus of researchers’ attention due to their robustness, precise modeling, simulation, and efficient assessment. The presented work aims to provide an innovative application of Levenberg Marquardt Technique with Artificial Back Propagated Neural Networks (LMT-ABPNN) to examine the entropy generation in Marangoni convection Magnetohydrodynamic Second Grade Fluidic flow model (MHD-SGFM) with Joule heating and dissipation impact. The PDEs describing MHD-SGFM are reduced into ODEs by appropriate transformation. The dataset is determined through Homotopy Analysis Method by the variation of physical parameters for all scenarios of proposed LMT-ABPNN. The reference data samples for training/validation/testing processes are utilized as targets to determine the approximated solution of proposed LMT-ABPNN. The performance of LMT-ABPNN is validated by MSE based fitness, error histogram scrutiny, and regression analysis. Furthermore, the influence of pertinent parameters on temperature, concentration, velocity, entropy generation, and Bejan number is also deliberated. The study reveals that the larger β and Ma, the higher f′(η) while M has the reverse influence on f′(η). For higher values of β, M, Ma, and Ec, θ(η) boosts. The concentration ϕ(η) drops as Ma and Sc grow. An augmentation is noticed for NG for higher estimations of β,M, and Br. Larger β,M and Br decays the Bejan number.

Anti-drug Antibody Validation Testing and Reporting Harmonization

Evolving immunogenicity assay performance expectations and a lack of harmonized anti-drug antibody validation testing and reporting tools have resulted in significant time spent by health authorities and sponsors on resolving filing queries. Following debate at the American Association of Pharmaceutical Sciences National Biotechnology Conference, a group was formed to address these gaps. Over the last 3 years, 44 members from 29 organizations (including 5 members from Europe and 10 members from FDA) discussed gaps in understanding immunogenicity assay requirements and have developed harmonization tools for use by industry scientists to facilitate filings to health authorities. Herein, this team provides testing and reporting strategies and tools for the following assessments: (1) pre-study validation cut point; (2) in-study cut points, including procedures for applying cut points to mixed populations; (3) system suitability control criteria for in-study plate acceptance; (4) assay sensitivity, including the selection of an appropriate low positive control; (5) specificity, including drug and target tolerance; (6) sample stability that reflects sample storage and handling conditions; (7) assay selectivity to matrix components, including hemolytic, lipemic, and disease state matrices; (8) domain specificity for multi-domain therapeutics; (9) and minimum required dilution and extraction-based sample processing for titer reporting. Graphical Abstract

Genome-wide Imputation Using the Practical Haplotype Graph in the Heterozygous Crop Cassava

Genomic applications such as genomic selection and genome-wide association have become increasingly common since the advent of genome sequencing. The cost of sequencing has decreased in the past two decades, however genotyping costs are still prohibitive to gathering large datasets for these genomic applications, especially in non-model species where resources are less abundant. Genotype imputation makes it possible to infer whole genome information from limited input data, making large sampling for genomic applications more feasible. Imputation becomes increasingly difficult in heterozygous species where haplotypes must be phased. The Practical Haplotype Graph is a recently developed tool that can accurately impute genotypes, using a reference panel of haplotypes. We showcase the ability of the Practical Haplotype Graph to impute genomic information in the highly heterozygous crop cassava (Manihot esculenta). Accurately phased haplotypes were sampled from runs of homozygosity across a diverse panel of individuals to populate PHG, which proved more accurate than relying on computational phasing methods. The Practical Haplotype Graph achieved high imputation accuracy, using sparse skim-sequencing input, which translated to substantial genomic prediction accuracy in cross validation testing. The Practical Haplotype Graph showed improved imputation accuracy, compared to a standard imputation tool Beagle, especially in predicting rare alleles.

Perancangan Spiral Kondensor untuk Pengolahan Sampah Plastik Menjadi Bahan Bakar Minyak dengan Proses Pirolisis

This study aims to determine the dimensions of the spiral groove condenser to convert plastic waste into fuel and determine the material's effectiveness for making spiral groove condensers. This research was conducted in stages: potential identification, data collection, equipment design and calculation, design validation, testing, and equipment feasibility test. In the testing and equipment feasibility test, namely by inserting plastic waste into the pyrolysis process reactor, then heated to a temperature of 180 oC and an evaporation process occurs, the vapors obtained are then condensed to be fuel. The spiral groove condenser design is made with a length of 3 m, a diameter of 30 cm, and a height of 34 cm use ½ inch galvanized iron material and a plate thickness of 0.0127 mm. The cooling water circulation process uses a spiral iron pipe, with a temperature of steam entering the condenser 180 oC and the temperature of the water in the condenser is 40 oC. From 1000 gr of plastic waste can be produced as much as 100 ml of fuel.ABSTRAKPenelitian ini bertujuan untuk mengetahui dimensi kondensor alur spiral untuk merubah sampah plastik menjadi bahan bakar minyak, untuk mengetahui efektifitas bahan pembuatan kondensor alur spiral. Penelitian ini dilakukan dengan tahapan : identifikasi potensi, pengumpulan data, desain peralatan dan perhitungan, validasi desain, pengujian dan uji kelayakan alat. Pada proses pengujian dan uji kelayakan alat yaitu dengan memasukkan sampah plastik kedalam reaktor proses pirolisis, selanjutnya dipanaskan sampai temperatur 180 oC dan terjadi proses penguapan, uap yang yang diperoleh selanjutnya di kondensasi menjadi bahan bakar minyak. Rancangan kondensor alur spiral yang telah dibuat dengan panjang 3 m, berdiameter 30 cm dan tinggi 34 cm menggunakan bahan besi galfanis ½ inch dan tebal plat 0.0127 mm, proses sirkulasi air pendingin menggunakan pipa besi spiral, dengan suhu uap yang masuk ke dalam kondensor 180 oC dan temperatur air pada kondensor 40 oC. dari 1000 gr sampah plastik dapat dihasilkan sebanyak 100 ml bahan bakar minyak.

Hybrid Permanent Magnet Motor Application to Electric Submersible Pumps in SAGD wells

A Permanent Magnet Motor (PMM) designed to break the 300°C barrier was previously presented that included many advancements to greatly improve the operating temperature and reliability beyond the ability of current equipment [1]. A key design element is the inclusion of a squirrel cage in the PMM rotor that results in a hybrid construction. This paper will delve into the rationale for the hybrid configuration and will assess motor performance using electromagnetic simulations and validation testing. PMMs are used in many industrial applications and have recently started to gain traction in oil and gas upstream production applications. A significant issue is the PMM compatibility with existing motor drive equipment and their need for special provisions to operate at the end of long cables without position sensors. A hybrid configuration help overcome these limitations and allows operation with conventional variable speed drives using a standard scalar controller as used with induction motors. The design, development, and qualification of the hybrid PM rotor construction were undertaken using a rigorous analytical approach combined with extensive validation testing. The motor is designed to maintain stability under the severe transient conditions in the SAGD environment, where the produced emulsion rich in gas and solids creates highly variable conditions for the motor and controller. A detailed electromagnetic model of the motor for configurations with or without the squirrel cage was undertaken to demonstrate the effectiveness of the hybrid configuration to maintain speed control stability. A time stepped method was used to simulate the motor start with simulated loading conditions, reflecting the starting and operating conditions with breakaway torques up to full load torque condition and 50% transient loads. The squirrel cage was successfully integrated within the rotor structure of a 150hp PM motor. Extensive design and thermal-structural analysis ensured the construction was acceptable for operation in the ranges −40°C to 350°C. Validation testing was then performed to demonstrate the hybrid PM motor construction functioned for use with conventional and legacy variable frequency drives.

Translation, Cross-Cultural Adaptation, And Validation of The Japanese Version of The Patient Education Materials Assessment Tool (PEMAT)

Background: The Patient Education Materials Assessment Tool (PEMAT) systematically evaluates the understandability and actionability of patient education materials. This study aimed to develop a Japanese version of PEMAT and verify its reliability and validity.Methods: After assessing content validation, experts scored healthcare-related leaflets and videos according to PEMAT, to verify inter-rater reliability. In validation testing with laypeople, the high-scoring material group (n=800) was presented with materials that received high ratings on PEMAT, and the low-scoring material group (n=799) with materials that received low ratings. Both groups responded to understandability and actionability of the materials and perceived self-efficacy for the recommended actions.Results: The Japanese version of PEMAT showed strong inter-rater reliability (PEMAT-P: % agreement= 87.3, Gwet’s AC1=0.83. PEMAT-A/V: % agreement=85.7%, Gwet’s AC1=.80). The high-scoring material group had significantly higher scores for understandability and actionability than the low-scoring material group (PEMAT-P: understandability 6.53 vs. 5.96, p<.001; actionability 6.04 vs. 5.49, p<.001; PEMAT-A/V: understandability 7.65 vs. 6.76, p<.001; actionability 7.40 vs. 6.36, p<.001). Perceived self-efficacy increased more in the high-scoring material group than in the low-scoring material group.Conclusions: Our study showed that materials rated highly on PEMAT were also easy for laypeople to understand and action. The Japanese version of PEMAT can be used to assess and improve the usability of patient education materials.

Testing a Quaternion Conversion Method to Determine Human 3D Tibiofemoral Angles During an in Vitro Simulated Jump Landing

Lower limb joint kinematics have been measured in laboratory settings using fixed camera-based motion capture systems; however, recently inertial measurement units (IMUs) have been developed as an alternative. The purpose of this study was to test a quaternion conversion (QC) method for calculating the three orthogonal knee angles during the high velocities associated with a jump landing using commercially available IMUs. Nine cadaveric knee specimens were instrumented with APDM Opal IMUs to measure knee kinematics in one-legged 3-4x bodyweight simulated jump landings, four of which were used in establishing the parameters (training) for the new method and five for validation (testing). We compared the angles obtained from the QC method to those obtained from a commercially available sensor and algorithm (APDM Opal) with those calculated from an active marker motion capture system. Results showed a significant difference between both IMU methods and the motion capture data in the majority of orthogonal angles (p&lt;0.01), though the differences between the QC method and Certus system in the testing set for flexion and rotation angles were smaller than the APDM Opal algorithm, indicating an improvement. Additionally, in all 3 directions both the limits of agreement and root mean square error between the QC method and the motion capture system were smaller than between the commercial algorithm and the motion capture.

Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators

Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25% accuracy improvement by tuning the hyperparameter ‘k’ to 9 with the chi-squared attribute set.