Inspection Of Data Research Articles

BLOOD PRESSURE SCREENING THROUGH FREE HEALTH SERVICE PROGRAM IN AMAHAMI, BIMA CITY, NTB

Blood pressure is one of the most commonly measured clinical parameters. One of the preventive steps that data takes is to carry out free checks. This activity is an implementation of the tri dharma of higher education in the form of community service carried out by the nutrition study program, faculty of health, Bima Muhammadiyah University. The aim of the activity is to diagnose and detect early health symptoms in people's blood pressure to ensure superior human resources and Sustainable Development Goals (SDGs). The Free Examination Activity was attended by 55 people. The activity method consists of 3 stages including the preparation stage, data collection stage, inspection stage and consultation. The hope is that with a program like this, it can reduce and minimize the risk of blood pressure disorders in the people of Bima City. It is hoped that programs like this can reduce and minimize the risk of blood pressure disorders in the people of Bima City, the formation of healthy living behaviors, and the fulfillment of balanced nutrition that encourages the quality of superior human resources.

Povandeninio roboto taikymas vandens transporto ir infrastruktūros povandeninės dalies techninės apžiūros duomenų bazės sukūrimui

This paper examines the application of the CHASING M2 PRO MAX underwater robot for the creation of databases for the technical inspection of the underwater components of water transport vehicles and their infrastructure. The paper defines and analyzes specific areas of application for the underwater robot, detailing the characteristics of its control systems that are essential for efficiently and accurately collecting data on water transport vehicles and underwater port infrastructure. Special attention is given to the operations of the technical inspection process of watercraft and port infrastructure. The study analyzes how this robot can improve the data collection process and inspection efficiency. Finally, the underwater inspection process is modeled, demonstrating how the CHASING M2 PRO MAX can be integrated into existing procedures to optimize data collection and analysis. This research opens new possibilities in the field of water transport safety and infrastructure maintenance, providing valuable insights into the application of underwater robot technologies. Keywords: CHASING M2 PRO MAX, ROV, technical inspection of ships and port infrastructure, remotely operated underwater drone, technical inspection, hull inspection, IMO, SQL.

EDIT: A data inspection tool for smart contracts temporal behavior modeling and prediction

Modeling and predicting the behavior of nodes and users in blockchains provide opportunities for business strategy optimization. Indeed, the number of interactions of a node is strictly related to its balance and its prediction may be used for analytics purposes and investment strategies. However, the amount and diversity of information stored on the blockchain demand advanced tools for the modeling and analysis of blockchain data. Such tools should be able to capture the dynamicity and interaction of multiple independent actors, considering a large number of variables and dynamic interaction graph topologies. This is exacerbated by the use of smart contracts, programs stored in blockchain blocks that bring automation to blockchain’s operations and thus increasing the variability of the resulting interaction graphs. Existing modeling methodologies are unable to keep track of all these details, as they are not able to capture the temporal variability of the network.In this paper, we propose a novel framework for modeling and predicting the behavior of smart contracts on a blockchain. We propose the concept of temporal smart contracts networks, i.e., graphs representing the temporal evolution of interactions and data flow. Our framework allows the creation of temporal smart contract networks with different granularity levels by considering different interaction patterns between smart contracts, externally owned accounts, and internal transactions. Thanks to these graphs, we are able to model features such as the node in degree and amount of ether received by a smart contract, which are directly related to its behavior. We incorporate our modeling approach in Ethereum Data Inspection Tool (EDIT), a novel tool able to model interactions and predict them based on historical data. We test different machine learning models to predict features extracted by EDIT, hence allowing for the prediction of the overall behavior of the smart contract. We test EDIT on the Ethereum blockchain and model several temporal smart contracts networks, which represent the interactions and the data flow resulting from about 4 000 000 consecutive blocks. The evaluation of different real case studies shows that the proposed framework is able to predict, with a mean absolute error close to 1%, the evolution of several interesting properties (e.g., amount of received ether) related to both accounts and smart contracts.

The verification of periodicity with the use of recurrent neural networks

Abstract The ability to automatically and robustly self-verify periodicity present in time-series astronomical data is becoming more important as data sets rapidly increase in size. The age of large astronomical surveys has rendered manual inspection of time-series data less practical. Previous efforts in generating a false alarm probability to verify the periodicity of stars have been aimed towards the analysis of a constructed periodogram. However, these methods feature correlations with features that do not pertain to periodicity, such as light-curve shape, slow trends, and stochastic variability. The common assumption that photometric errors are Gaussian and well determined is also a limitation of analytic methods. We present a novel machine learning based technique which directly analyses the phase-folded light curve for its false alarm probability. We show that the results of this method are largely insensitive to the shape of the light curve, and we establish minimum values for the number of data points and the amplitude to noise ratio.

Monitoring internal swelling reactions in concrete dams

Alkali-silica reaction (ASR) and delayed ettringite formation (DEF) are the most common internal swelling reactions (ISR) in concrete dams. The swelling effects and concrete deterioration caused by ISR can be observed in several forms at the structural level, including relative movements between blocks, displacements and deformations, cracking, surface discoloration around the cracks, scaling or spalling as well as surface “pop-outs”.The detection and the assessment of these structural symptoms in large concrete dams is usually made through visual inspection and interpretation of the monitoring data, such as stress free strainmeters embedded in concrete, geodedic levelling, pendulums, rod extensometers and internal or external jointmeters. The strainmeters and the levelling data are also used for providing estimations of the dam concrete expansion.The Portuguese information system, designed to serve as a national database, has its own data structure and includes the monitoring systems data for large concrete dams of all structural types, including dams with over 60 years old. This information system is used by both the dam owners and the National Laboratory for Civil Engineering (LNEC) for dam safety assessment over the years.This work discusses the importance of the dam observed behaviour data provided by the monitoring system as a tool for ISR assessment. The progression of concrete expansion for several dams located in Portugal is presented. The presented observed results were based on data from stress free strainmeters, while others were calculated from geodetic levelling.

Self-supervised contrastive learning of radio data for source detection, classification and peculiar object discovery

Abstract New advancements in radio data post-processing are underway within the Square Kilometre Array (SKA) precursor community, aiming to facilitate the extraction of scientific results from survey images through a semi-automated approach. Several of these developments leverage deep learning methodologies for diverse tasks, including source detection, object or morphology classification, and anomaly detection. Despite substantial progress, the full potential of these methods often remains untapped due to challenges associated with training large supervised models, particularly in the presence of small and class-unbalanced labelled datasets. Self-supervised learning has recently established itself as a powerful methodology to deal with some of the aforementioned challenges, by directly learning a lower-dimensional representation from large samples of unlabelled data. The resulting model and data representation can then be used for data inspection and various downstream tasks if a small subset of labelled data is available. In this work, we explored contrastive learning methods to learn suitable radio data representations by training the SimCLR model on large collections of unlabelled radio images taken from the ASKAP EMU and SARAO MeerKAT GPS surveys. The resulting models were fine-tuned over smaller labelled datasets, including annotated images from various radio surveys, and evaluated on radio source detection and classification tasks. Additionally, we employed the trained self-supervised models to extract features from radio images, which were used in an unsupervised search for objects with peculiar morphology in the ASKAP EMU pilot survey data. For all considered downstream tasks, we reported the model performance metrics and discussed the benefits brought by self-supervised pre-training, paving the way for building radio foundational models in the SKA era.

HUBUNGAN PSN DAN PERAN KADER JUMANTIK DENGAN KEJADIAN DI PUSKESMAS PONCOKUSUMO MALANG

Dengue Hemorrhagic Fever (DBD) is a disease caused by a virus which is transmitted through mosquitoes. Mosquito nest eradication or PSN is an activity to eradicate the eggs, larvae and the pupae of Aedes aegepti as the mosquitos which cause Dengue Hemorrhagic Fever in their breeding habitat. Jumantik cadres have the task of assisting officers. health centers and carry out data collection or inspection of mosquito larvae and provide education in people's homes. The aim of this research is to analyze the relationship between eradicating mosquito nests (PSN) and the role of Jumantik cadres with the incidence of dengue Hemorragic fever in Poncokusumo working area, M. ang Regency. 
 This type of research was observational analytic he a cross-sectional approach design. The population in this study were all dengue fever sufferers at the Poncokusumo Health Center, Malang Regency from January-December 2022 January to January-March 2023. Number of samples was 60 respondents. Research data was obtained through questionnaires and interview.and analyzed by chi square tes.
 Based the research and data analysis, the results show that there is no relationship between the behavior of Mosquito Nest Eradication (PSN) and the incidence of Dengue Hemorrhagic Fever, while the role of Jumantik shows that there is no relation between their role and the incidence of Dengue Hemorragic Fever. 
 Keywords : dengue hemorrhagic fever, PSN, jumantik cadres

Chinas Urban and Rural CPI Prediction Based on ARIMA Model

Inflation represents the continuous rise of the overall price level of a country. In severe cases, it may cause an imbalance between social supply and demand and lead to a crisis of currency confidence. Therefore, it is necessary to measure and predict the level of inflation. The CPI index is an important indicator to measure the level of inflation, which can largely reflect the national economic situation in a certain period. This paper conducts research by selecting the urban and rural CPI data of the National Bureau of Statistics from January 2007 to June 2023, a total of 198 months. After data processing and inspection, this paper use ARIMA model to forecast. The experimental research results show that the ARIMA (12,0,1) model and the ARIMA (12,0,0) model have good predictive effects on the CPI index of cities and villages respectively. In the short term, the ARIMA model can accurately predict the changing trend of the CPI index, with an error rate of less than 0.5%. The model predicts that China's urban and rural inflation from June 2023 to June 2024 will be stable and improving overall.

Inovasi Pembelajaran Bahasa Arab Melalui al-Thoriqoh al-Intiqo’iyah dalam Menumbuhkan Minat Belajar Bahasa Arab Siswa di MA. Nurul Hikmah Besuki Situbondo

As time goes by, the phenomenon of learning often changes in educational institutions in the country. This phenomenon is often faced by subject teachers, especially by Arabic language subject teachers. This variant learning phenomenon is a challenge for Arabic language teachers to anticipate the possibilities that cause mental degradation and failure to achieve students' understanding of Arabic language subject matter. The openness of Arabic teachers in choosing learning methods that must be applied every time they carry out teaching activities can project the achievement of basic educational goals that can be realized perfectly. Thus, Arabic language teachers must be able to develop good learning strategies, one of which can be done is to use effective and fun learning methods. One of the methods referred to is al-Thoriqoh al-Intiqo'iyah (electic method). The aim of this research was to identify Arabic Language Learning Innovations through al-Thoriqoh al-Intiqo'iyah in Growing Students' Interest in Learning Arabic at MA. Nurul Hikmah Besuki Situbondo. The data collection techniques used were observation, interviews and documentation. In this research, the researcher used a descriptive method with a qualitative approach with a case study type of research. The data collection techniques used were observation, interviews and documentation. Apart from that, researchers in analyzing data use an interactive model which consists of elements in the form of data reduction, data presentation, and data analysis. Meanwhile, the data inspection technique for this research uses triangulation. The research results confirmed that al-Thoriqoh al-Inriqo'iyah was implemented in the Supreme Court. Nurul Hikmah repeated the positive results, students were able to grow interested in learning Arabic through modifying the learning method. The justification for the success of using al-Toriqoh al-Intiqo'iyah in the school is illustrated by the enthusiasm for learning which continues to increase in addition to the participation of students in various language activities

The Complex Structural Variant Chromothripsis Can be Defined on Targeted Sequencing Panels, Allowing Direct Clinical Translation in Order to Improve Multiple Myeloma Prognostication

Introduction: the complex structural variant (SV) chromothripsis is an independent predictor of progression-free (PFS) and overall survival (OS) in newly diagnosed multiple myeloma (NDMM); predictive in multivariate analysis when considering ISS, IgH translocations and TP53 status (Rustad BCD 2020). Chromothripsis is not included in current prognostic scores, despite a prevalence of 24% in NDMM. While gold-standard definition requires whole genome sequencing (WGS), we've previously shown that copy-number (CN) signatures are also able to accurately define chromothripsis in whole exome sequencing (WES, Maclachlan Nat Com 2021). As neither WGS nor WES are currently used in clinical medicine, we applied CN signatures to targeted sequencing, aiming for direct clinical translation of this key biological feature into clinical practice. Memorial Sloan Kettering (MSK) now offers the MSK-Heme-IMPACT targeted sequencing panel to all MM patients having a bone marrow biopsy (BMBx), as well as offering retrospective analysis for current patients having leftover DNA from prior BMBx. Methods: We analyzed 420 samples having either myTYPE or MSK-Heme-IMPACT targeted sequencing at MSK prior to July 2023. We excluded those with whole genome duplication, applied CN signatures, then for samples from NDMM performed multivariate analysis for PFS prediction by backwards stepwise regression. In samples with CN signatures predictive for chromothripsis and with DNA available from the same BMBx, we performed parallel WGS in order to check for chromothripsis. WGS sequencing was at 60-80x for tumor, 30-40x for normal, with analysis including Battenberg for CN analysis, with SvABA, BRASS and GRIDSS for SV calling. Additional parallel WGS is in progress for 45 samples with NDMM prior to DVRd or DKRd, in order to examine chromothripsis prediction and PFS impact in the context of highly effective induction. Results: From 420 samples, 48 were collected at the precursor stage, 205 with NDMM, and 167 with relapsed disease. 39 patients had 2 assays performed, while the remainder were unique patient samples. Within NDMM, the range of induction regimens received included lenalidomide and dexamethasone with bortezomib (VRd), carfilzomib (KRd) or daratumumab (DRd), along with quadruplet therapy (DVRd or DKRd). Targeted sequencing data detected all 6 key CN features observed in WGS and WES analysis. Similar to WES, there was a lower contribution from very high breakpoint counts per 10mB and longer lengths of oscillating CN segments, due to the lower resolution of targeted sequencing data. De novo extraction of CN signatures defined 1 signature containing multiple features consistent with chromothripsis; high breakpoint count per 10mB, more jumps between adjacent CN segments, longer lengths of oscillating CN segments and a predominance of small CN segments. When examining samples from NDMM, contribution from this signature was predictive of PFS in multivariate analysis when considering age, ISS, t(4;14), TP53 status and gain1q21 (hazard ratio > 4, p=0.003). 33 samples had a predictive probability for chromothripsis > 0.6 (the cut-off in WGS / WES having the highest sensitivity and specificity for prediction), with also having further DNA available. 30/33 (91%) were confirmed to harbor chromothripsis on manual inspection of WGS data. 19/30 (63%) with chromothripsis were BMBx from NDMM, while 11/30 (37%) were from relapsed disease. Discussion: the complex SV chromothripsis is a key genomic risk factor predicting for poor PFS and OS in MM, which is currently not being captured in routine practice or in clinical trials. Here, we demonstrate that chromothripsis can be predicted from targeted sequencing data, confirmed by gold-standard WGS analysis. Ongoing studies are expanding our cohort of NDMM with concurrent targeting sequencing and WGS, to define the sensitivity and specificity of targeted-sequencing-based CN signature prediction of chromothripsis. The additional cohort largely comprises patients treated with daratumumab-quadruplet induction therapy, aiming to confirm that chromothripsis and CN signatures retain prognostic significance in the context of this therapy. With this data is in hand, we will develop a CN signature fitting tool, similar to mmsig for mutational signatures (Rustad Comm Biol 2021), to allow easily accessible incorporation of chromothripsis assessment into clinical practice.

A new computational workflow to guide personalized drug therapy

Objective:Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics. Methods:GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations. Results:To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months. Conclusion:GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions.

Digitalizing the TIM-1 Model using Computational Approaches-Part One: TIM-1 Data Explorer.

The TIM-1 gastrointestinal model is one of the most advanced in vitro systems currently available for biorelevant dissolution testing. This technology, the initial version of which was developed nearly 30 years ago and has been subject to a number of significant updates over this period, simulates the dynamic environment of the human gastrointestinal tract, including pH, transfer times, secretion of bile, enzymes, and electrolytes. In the pharmaceutical industry, the TIM-1 system is used to support drug product design and provide a biopredictive assessment of drug product performance. Typically, the bioaccessibility data sets generated by TIM-1 experiments are used to qualitatively compare formulation performance, and the use of bioaccessibility data as inputs for physiologically based pharmacokinetic (PBPK) modeling for quantitative predictions is limited. To expand the utility of the TIM-1 model beyond standard bioaccessibility measurements (which define the fraction available for absorption), we have developed a computational tool, TIM-1 Data Explorer, to describe the fluid and mass balance within the TIM-1 system. The use of this tool allows a detailed inspection and in-depth interpretation of the experimental data. In addition to mass balance calculation, this model also can be used to describe the critical processes a drug substance would undergo during a TIM-1 experiment, such as dissolution, precipitation on transfer from the stomach to duodenum, and redissolution. The TIM-1 Data Explorer was validated in two case studies. In the first case study with paracetamol, we have shown the ability of the simulator to adequately describe mass transfer events within the TIM-1 system, and in the second study with a weakly basic in-house compound, PF-07059013, the TIM-1 Data Explorer was successfully used to describe dissolution and precipitation processes.

Local defect formation in short glass fibre reinforced polymers – micro-mechanical simulations and interrupted in-situ experiments

Discontinuous fibre reinforced polymers are widely used in various industry sectors and often replace conventional materials, due to lower production costs and their lightweight structure. For improvement of the component design, detailed knowledge of the failure mechanisms are necessary. To better understand the defect formation and thus the micro-mechanics, the strain behaviour in single fibres was analysed by micro-mechanical simulations of Representative Volume Elements (RVE). Therefore, selected fibres – similar orientated as in the real structure – were chosen for detailed analysis. Additionally, the defect formation next to selected fibres was investigated by X-ray computed tomography (CT). Furthermore, the critical fibre length was estimated based on the protruding fibre length of the fracture surface. Overall the simulation results correspond to theory. However, the detailed local inspection of the experimental volume data showed a rather strong influence of neighbouring fibres.

Mucuna and Avocado-Seed Residues as Sustainable Fertilizers and Biostimulants for Cherry Tomatoes

The global demand for sustainable agricultural practices is increasing, necessitating the preference for fertilizers and organic stimulants with minimal chemical transformation. This study investigates the potential use of Mucuna (Mucuna pruriens sp.) and avocado (Persea americana Mills) seed residues in the cultivation of cherry-tomato crops (Lycopersicon esculentum Mill.) var. cerasiforme. After extracting L-dopa, the Mucuna ground residual seeds were incorporated into the soil substrate as an edaphic fertilizer. In contrast, the hydrolyzed avocado seed was mixed with water or the nutrient Long Ashton and applied as a foliar biostimulant to cherry plants grown in a greenhouse. We report the nutrients and amino acid content in hydrolysates of the avocado and Mucuna’s residue seeds and experiment with their effect in plants employing a completely blocked random design of eight treatments with four replicates. Data inspection involved analysis of variance, and mean differences were determined using Fisher’s least significant difference test. Significant differences (p < 0.05) were observed among the treatments regarding the number of flowers (70%), fruits (23%), and dry weight fruits (25%) in favor of those using these seed residues. A second experiment revealed that treatments containing both seed residues slightly increased the °Brix in fruits. This study supports with evidence that residual seeds benefit tomatoes and probably other important plants, contributing to the path to sustainable agriculture.

Graphical reduction and analysis small-angle neutron scattering program: GRASP.

GRASP is a scientific software application designed for the graphical inspection, reduction and analysis of multidetector data produced by the small-angle neutron scattering (SANS) instruments at the Institut Laue-Langevin and other neutron sources around the world. The first developments of GRASP began more than 20 years ago and were written in MATLAB, allowing rapid development of scientific code, with much of the data handling, matrix manipulation, mathematical tools, user interface and graphical tools integrated at a high level in the underlying MATLAB platform. By their very nature, multidimensional data are often best appreciated in graphical form. GRASP deals with many of the diverse requirements for data reduction and analysis of SANS data using a general set of tools and reduction algorithms suited to 2D multidetector data. A further fundamental architectural inclusion is a third dimension of data manipulation, thereby easily allowing parametric analysis and cross referencing of series data such as composition, kinetic measurements, temperature, magnetic field, angle or time of flight, often considered as a single 'measurement'. This article serves as a reference document for users of the software, and outlines the architecture and strategy of the program. An overview of some of the features, capabilities, peripheral user modules and neutron scattering tools is presented.

Based on the Big Data Audit Risk Model Audit Process Research

With the gradual application of big data technology in enterprises, the way and connotation of audit have changed greatly. This paper is based on the impact of big data technology on auditing, Break down the audit risk into four risk elements, including inherent risk, control risk, big data technology risk and inspection risk, Building an audit risk model adapted to the big data environment, Summarize the new connotation of the four risk elements in the new model; Then, analyze and explore the impact of big data audit risk model on the audit implementation process from four stages: audit objective, audit risk identification and evaluation, audit plan and audit risk response, Combined with the audit practice, the big data audit process is sorted out again, Provide thinking guidance and solutions for big data audit.

Fast and efficient identification of anomalous galaxy spectra with neural density estimation

ABSTRACT Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up subtle variations in rich data sets and are fast to apply once trained. Here, we study the applicability of an unsupervised and probabilistic deep learning framework, the probabilistic auto-encoder, to the detection of peculiar objects in galaxy spectra from the SDSS survey. Different to supervised algorithms, this algorithm is not trained to detect a specific feature or type of anomaly, instead it learns the complex and diverse distribution of galaxy spectra from training data and identifies outliers with respect to the learned distribution. We find that the algorithm assigns consistently lower probabilities (higher anomaly score) to spectra that exhibit unusual features. For example, the majority of outliers among quiescent galaxies are E+A galaxies, whose spectra combine features from old and young stellar population. Other identified outliers include LINERs, supernovae, and overlapping objects. Conditional modelling further allows us to incorporate additional information. Namely, we evaluate the probability of an object being anomalous given a certain spectral class, but other information such as metrics of data quality or estimated redshift could be incorporated as well. We make our code publicly available.

Don't hold PAT: watch for and correct oximetry artifact.

Plante DT, Rumble ME. Don't hold PAT: watch for and correct oximetry artifact. J Clin Sleep Med. 2023;19(12):2113-2116.

DEVELOPMENT OF ALOE VERA BASED FUNCTIONAL DRINK

Background: Aloe vera (Aloe barbadensis miller) is composed of minerals, vitamins, polysaccharides, enzymes, phenolic compounds, amino acids, and antioxidants which provide numerous health benefits such as anti-inflammatory, antibacterial and antiviral. Grapes (Vitis vinifera) are high in polyphenols has a substantial impact on preventing cancer and heart disorders. In the beverage sector, recent work is based on the combination of functional ingredients. Aloe vera and grapes drinks are highly nutritious functional drinks that prevent different diseases. Objectives: This study investigated how Aloe vera juice improved the nutritional profile of grape juice and its sensory properties. Several parameters of the functional drink were investigated, including its physiological characteristics, oxidative stability, microbial load, and organoleptic properties. Methodology: Statistix 8.1 was used for the application of statistics, Tukey HSD Post Hoc analysis, and Two-way ANOVA was utilized for the inspection of data obtained after analysis. Different concentrations of grape juice and Aloe vera were mixed to prepare the functional beverage which is stored at 4oC temperature for 28 days. Results: the study outcomes revealed significantly improved effects in microbial analysis; maximum load of 7.68 ± 0.19, and non-significantly increasing effects were observed in titratable acidity (0.9900±0.01), ash content (0.223±0.01), dissolved solids (0.1450±0.001), and total solids (3.90±0.1) and non-significantly decreasing effects were observed in pH, total flavonoid content, and viscosity. Conclusions: Functional drink contained more nutrients when grapes and Aloe vera were combined. Among the treatments evaluated, treatment T3 with 60 percent grape juice and 40 percent Aloe vera juice produced the best sensory results.

Multivariate control charts for calibration of hydrophones using the Mahalanobis statistic

Statistical analysis of data to ensure the validity of calibrations can be challenged when the measurement model describes a process and result that spans a high-dimensional parameter space. Calibration of hydrophones presents one such case whereby the sensitivity to acoustic pressure is calculated using measured values for several different quantities and the result is needed at many excitation frequencies distributed over a large bandwidth. We addressed this challenge by developing a multivariate statistical analysis procedure that supports efficient and effective process control for hydrophone calibrations. We first characterize the in-control condition by the mean and covariance of measurements expressed in a p-dimensional parameter space using a curated training data set. We then test subsequent measurements against a control limit using Hotelling’s T 2-test to compare the current measurement process to the training data. We report a case study for secondary calibration of hydrophones spanning a frequency range of 1 kHz–200 kHz where we detected an emergent defect in the laboratory’s calibrated reference hydrophone by inspection of data presented in multivariate control charts. Process control data collected during calibration services exceeded the control limits in intermediate and high-frequency bands by factors of 5.1 and 11.2, respectively. The process remained in control for the low-frequency band. These observations were consistent with misalignment of the reference hydrophone’s sensitive element, which we confirmed upon disassembly, inspection and repair of the instrument.