Advanced and Secure Laboratory Management System

Advanced & Secure Laboratory Information Management System, TRLIMS is the management system which has live tracking system for all the testing and research conducted at the laboratory. This system is developed to achieve diverse functionality for the disciplines such as mechanical, chemical, environmental, microbiology and non-destructive fields. The basic features of this application are that it can manage the data related to client, employees and testing results of the laboratory. Apart from that since the application is fully hosted on server which offers flexibility, providing future scope for more hardware and operating system configuration. This application provides very enhanced turn-around-time (TAT) for the material testing laboratory It aims to manage the employees, clients and associated testing data to improve the lab productivity. The application allows clients to track their improvement in sample testing from time to time, the data is updated on server by employees who perform tests at the premises. This paper could provide guidance to understanding the operation mechanism of Laboratory Information Management System.

State of the art in wind speed in England using BATS ,TBATS , Holt’s Linear and ARIMA model

Wind energy is one of the most important renewable energy sources in the world. Hence, the prediction of wind speed is a highly significant subject with respect to both protecting the environment and economic development. England is among the countries with an increasing interest in the potential for wind energy systems. In this study, various time series models, including BATS, TBATS, Holt’s Linear Trend, and ARIMA models were applied for wind speed prediction in England, and their performance was compared. The available wind speed data between 1994-07-07 and 2015-12-31 were divided into two parts: training data that is used to build up the models and testing data that is used to measure the validity of a model forecast. The results of the testing data indicate that the BATS and ARIMA outperform the other time series models according to the root mean square errors.

Analysis of Information Technology (IT) Goods Sales Patterns Using the FP-Growth Algorithm

Determination of sales patterns is very important in marketing. Sales pattern serves to conduct an effective analysis in improving marketing. Sales analysis aims to explore new knowledge that can help design effective strategies by utilizing sales transaction data. This study processes sales data for 12 transaction days containing 47 items using the Fp-Growth algorithm. The results of this study are items with a minimum value of support > 0.10 and confidence 0.60 and will be compared with testing data using RapidMiner to test whether the results are valid so that the test results can help in designing sales strategies.

Utility of Diffusion Modeling of Cogstate Brief Battery Test Performance in Detecting Mild Cognitive Impairment

Cognitive testing data are essential to the diagnosis of mild cognitive impairment (MCI), and computerized cognitive testing, such as the Cogstate Brief Battery, has proven helpful in efficiently identifying harbingers of dementia. This study provides a side-by-side comparison of traditional Cogstate outcomes and diffusion modeling of these outcomes in predicting MCI diagnosis. Participants included 257 older adults (160 = normal cognition; 97 = MCI). Results showed that both traditional Cogstate and diffusion modeling analyses predicted MCI diagnosis with acceptable accuracy. Cogstate measures of recognition learning and working memory accuracy and diffusion modeling variable of decision-making efficiency (drift rate) and nondecisional time were most predictive of MCI. While participants with normal cognition demonstrated a change in response caution (boundary separation) when transitioning tasks, participants with MCI did not evidence this change.

Alpha particle microdosimetry calculations using a shallow neural network

A shallow neural network was trained to accurately calculate the microdosimetric parameters, <z1> and <z1 2> (the first and second moments of the single-event specific energy spectra, respectively) for use in alpha-particle microdosimetry calculations. The regression network of four inputs and two outputs was created in MATLAB and trained on a data set consisting of both previously published microdosimetric data and recent Monte Carlo simulations. The input data consisted of the alpha-particle energies (3.97–8.78 MeV), cell nuclei radii (2–10 µm), cell radii (2.5–20 µm), and eight different source-target configurations. These configurations included both single cells in suspension and cells in geometric clusters. The mean square error (MSE) was used to measure the performance of the network. The sizes of the hidden layers were chosen to minimize MSE without overfitting. The final neural network consisted of two hidden layers with 13 and 20 nodes, respectively, each with tangential sigmoid transfer functions, and was trained on 1932 data points. The overall training/validation resulted in a MSE = 3.71×10-7. A separate testing data set included input values that were not seen by the trained network. The final test on 892 separate data points resulted in a MSE = 2.80×10-7. The 95th percentile testing data errors were within ±1.4% for <z1> outputs and ±2.8% for <z1 2> outputs, respectively. Cell survival was also predicted using actual vs. neural network generated microdosimetric moments and showed overall agreement within ±3.5%. In summary, this trained neural network can accurately produce microdosimetric parameters used for the study of alpha-particle emitters. The network can be exported and shared for tests on independent data sets and new calculations.

Protective effect of a first SARS-CoV-2 infection from reinfection: a matched retrospective cohort study using PCR testing data in England

The duration of immunity after first SARS-CoV-2 infection and the extent to which prior immunity prevents reinfection is uncertain and remains an important question within the context of new variants. Using a retrospective population-based matched observational study approach, we identified cases with a first PCR positive test between 01 March 2020 and 30 September 2020 and cases were matched by age, sex, upper tier local authority of residence and testing route to individuals testing negative in the same week (controls) by PCR. After a 90-day pre-follow up period for cases and controls, any subsequent positive tests up to 31 December 2020 and deaths within 28 days of testing positive were identified, this encompassed an essentially vaccine-free period. There were 517,870 individuals in the matched cohort with 2,815 reinfection cases and 12,098 first infections. The protective effect of a prior SARS-CoV-2 PCR-positive episode was 78% (OR 0.22, 0.21-0.23). Protection rose to 82% (OR 0.18, 0.17-0.19) after a sensitivity analysis excluded 934 individuals with a first test between March and May and a subsequent positive test between June and September 2020. Amongst individuals testing positive by PCR during follow-up, reinfection cases had 77% lower odds of symptoms at the second episode (adjusted OR 0.23, 0.20-0.26) and 45% lower odds of dying in the 28 days after reinfection (adjusted OR 0.55, 0.42-0.71). Prior SARS-CoV-2 infection offered protection against reinfection in this population. There was some evidence that reinfections increased with the Alpha variant compared to the wild-type SARS-CoV-2 variant highlighting the importance of continued monitoring as new variants emerge.

GFAP: ultra-fast and accurate gene functional annotation software for plants

Sequence alignment is the basis of gene functional annotation for unknow sequences. Selecting closely related species as the reference species should be an effective way to improve the accuracy of gene annotation for plants, compared with only based on one or some model plants. Therefore, limited species number in previous software or website is disadvantageous for plant gene annotation. Here, we collected the protein sequences of 236 plant species with known genomic information from 63 families. After that, these sequences were annotated by pfam, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to construct our databases. Furthermore, we developed the software, Gene Annotation Software for Plants (GFAP), to perform gene annotation using our databases. GFAP, an open-source software running on Windows and MacOS systems, is an efficient and network independent tool. GFAP can search the protein domain, GO and KEGG information for 43000 genes within 4 minutes. In addition, GFAP can also perform the sequence alignment, statistical analysis and drawing. The website of https://gitee.com/simon198912167815/gfap-database provides the software, databases, testing data and video tutorials for users. GFAP contained large amount of plant-species information. We believe that it will become a powerful tool in gene annotation using closely related species for phytologists.

WAVELET DETAIL COEFFICIENT AS A NOVEL WAVELET-MFCC FEATURES IN TEXT-DEPENDENT SPEAKER RECOGNITION SYSTEM

Speaker recognition is the process of recognizing a speaker from his speech. This can be used in many aspects of life, such as taking access remotely to a personal device, securing access to voice control, and doing a forensic investigation. In speaker recognition, extracting features from the speech is the most critical process. The features are used to represent the speech as unique features to distinguish speech samples from one another. In this research, we proposed the use of a combination of Wavelet and Mel Frequency Cepstral Coefficient (MFCC), Wavelet-MFCC, as feature extraction methods, and Hidden Markov Model (HMM) as classification. The speech signal is first extracted using Wavelet into one level of decomposition, then only the sub-band detail coefficient is used as the feature for further extraction using MFCC. The modeled system was applied in 300 speech datasets of 30 speakers uttering “HADIR” in the Indonesian language. K-fold cross-validation is implemented with five folds. As much as 80% of the data were trained for each fold, while the rest was used as testing data. Based on the testing, the system's accuracy using the combination of Wavelet-MFCC obtained is 96.67%. ABSTRAK: Pengecaman penutur adalah proses mengenali penutur dari ucapannya yang dapat digunakan dalam banyak aspek kehidupan, seperti mengambil akses dari jauh ke peranti peribadi, mendapat kawalan ke atas akses suara, dan melakukan penyelidikan forensik. Ciri-ciri khas dari ucapan merupakan proses paling kritikal dalam pengecaman penutur. Ciri-ciri ini digunakan bagi mengenali ciri unik yang terdapat pada sesebuah ucapan dalam membezakan satu sama lain. Penyelidikan ini mencadangkan penggunaan kombinasi Wavelet dan Mel Frekuensi Pekali Cepstral (MFCC), Wavelet-MFCC, sebagai kaedah ekstrak ciri-ciri penutur, dan Model Markov Tersembunyi (HMM) sebagai pengelasan. Isyarat penuturan pada awalnya diekstrak menggunakan Wavelet menjadi satu tahap penguraian, kemudian hanya pekali perincian sub-jalur digunakan bagi pengekstrakan ciri-ciri berikutnya menggunakan MFCC. Model ini diterapkan kepada 300 kumpulan data ucapan daripada 30 penutur yang mengucapkan kata "HADIR" dalam bahasa Indonesia. Pengesahan silang K-lipat dilaksanakan dengan 5 lipatan. Sebanyak 80% data telah dilatih bagi setiap lipatan, sementara selebihnya digunakan sebagai data ujian. Berdasarkan ujian ini, ketepatan sistem yang menggunakan kombinasi Wavelet-MFCC memperolehi 96.67%.

Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer

To realize a machine learning (ML) model to estimate the dose of low molecular weight heparin to be administered, preventing thromboembolism events in COVID-19 patients with active cancer. Methods: We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logistic regression, decision tree, random forest, support vector machine and Gaussian naive Bayes. We decided to implement the logistic regression model for our study. A model with 19 variables was analyzed. Data were randomly split into training (70%) and testing (30%) sets. Model performance was assessed by confusion matrix metrics on the testing data for each model as positive predictive value, sensitivity and F1-score. Results: We showed that the five selected models outperformed classical statistical methods of predictive validity and logistic regression was the most effective, being able to classify with an accuracy of 81%. The most relevant result was finding a patient-proof where python function was able to obtain the exact dose of low weight molecular heparin to be administered and thereby to prevent the occurrence of VTE. Conclusions: The world of machine learning and artificial intelligence is constantly developing. The identification of a specific LMWH dose for preventing VTE in very high-risk populations, such as the COVID-19 and active cancer population, might improve with the use of new training ML-based algorithms. Larger studies are needed to confirm our exploratory results.