Desain Proportional Integral Derrivative (Pid) Controller Pada Model Arm Robot Manipulator

Dalam rangka menuju proses industrialisasi modern di negara Indonesia, harus didukung dengan teknologi yang canggih, contoh nya adalah arm robot manipulator. sebagai pelaku proses produksi sehingga dihasilkan ketepatan,kepresisian, dan kefektifan pada proses produksi. Dengan hal tersebut dibuat sebuah desain kontrol PID pada arm robot manipulator dengan tujuan menghasilkan tingkat presisi dan kestabilan yang lebih baik. Kontroler tersebut didesain, disimulasikan, dan diaplikasikan pada hardware dengan menggunakan software MATLAB/Simulink, kemudian dianalisa kestabilannya dengan metode root locus. Akrilik digunakan sebagai material pada body arm robot. Selanjutnya komponen utama seperti, potensiometer sebagai analog input, motor DC sebagai penggerak setiap link, serta Arduino Mega 2560 sebagai mikrokontroler. Akrilik dipilih karena ringan, kuat dan tahan lama.Dalam penelitian ini akan dilakukan penyempurnaan konstruksi mekanik arm robot manipulator yang sudah ada, pemasangan hardware elektronik, dan pemrograman mikrokontroler dengan menggunakan software MATLAB pada Arduino Toolbox. Dari penelitian yang telah dilakukan, model arm robot manipulator dapat bergerak lebih smooth sesuai dengan input pergerakan potensiometer, dan berada posisi kestabilan saat dianalisa dengan menggunakan root locus

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics

Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics

EIS test conditions.

<p>EIS test conditions.</p

Flow chart of parameter identification.

<p>Flow chart of parameter identification.</p

ECM based on the EIS response of the lithium-ion battery with 2855 mAh capacity and 60% SOC.

<p>ECM based on the EIS response of the lithium-ion battery with 2855 mAh capacity and 60% SOC.</p

Voltage step response test.

<p>(a) Battery voltage and current response; (b) Tracing voltage and tracing error; (c) Voltage tracing error probability.</p

EIS curves of lithium-ion batteries with different SOC and maximum discharge capacities.

<p>EIS curves of lithium-ion batteries with different SOC and maximum discharge capacities.</p

Voltage tracing in UDDS drive cycle test.

<p>(a) Battery current and voltage response in UDDS; (b) Voltage tracing of the UDDS test; (c) Voltage tracing error of the UDDS test; (d) Voltage tracing error probability distribution of the UDDS test.</p

Data_Sheet_1_Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification.PDF

<p>Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique.</p

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student’s t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics