Invalid Data Research Articles

A Statewide Surgical Quality Improvement Collaborative’s Opioid Data Collection

Introduction: Our statewide surgical quality improvement (QI) collaborative began collecting opioid data to better understand our patients’ pain management and discharge opioid prescriptions. During our pilot there was invalid data requiring variable revision. Collection resumed in 1/2020 and was encouraged during COVID-19, acknowledging that we faced reduced and highly selective operative case volumes with reduced resources. Methods: Institutions from a statewide surgical QI collaborative participated in the revised custom variable opioid data collection. Utilizing the National Surgical Quality Improvement Program (NSQIP) platform, seven custom variable data were collected for 13 general surgery procedures. Variables were abstracted by Surgical Clinical Reviewers from 1/2020-12/2020. Results: 722 cases were performed. 460 (64%) cases had all variable data submitted, 102 (14%) had at least one and 160 (22%) cases had no variables submitted. Postoperative/in-patient opioids and non-opioids administered were the most likely to be submitted (73% and 73%). 489 cases (68%) had submitted data on use of an opioid sparing strategy. 483 (67%) cases had some discharge opioid prescription data, but only 357 cases (49%) had adequate data to standardize for comparison. Conclusion: A significant decrease in collection of the three minimally revised variables was observed. However, from revising the discharge opioid prescription variable from free text to a pre-populated selection, the quality of data improved significantly. This will allow the collaborative to standardize discharge opioid prescribing. Custom variable opioid data collection is feasible and can be utilized to inform collaborative opioid prescribing practices.

Research on Basketball Teaching Network Course Resource Recommendation Method Based on Deep Learning Algorithm

To solve the problem of invalid resource recommendation data and poor recommendation effect in basketball teaching network course resource recommendation, a basketball teaching network course resource recommendation method based on a deep learning algorithm is proposed. The objective function is applied to eliminate the noise in the basketball teaching network course resource data. The prominent characteristics of basketball teaching network curriculum resources are extracted using a kernel function and combined into a feature set. A convolution neural network (CNN) was employed to realize the basketball teaching network curriculum resources recommendation model. The model was assessed in terms of computation time and recognition error. To validate the performance, the proposed model was compared with two well-known recommendation models such as the learning resource recommendation method based on transfer learning and the personalized learning resource recommendation method based on three-dimensional feature collaborative domination. Experimental results show that the proposed model achieved the lowest computation time of 15 s and recommendation error less than 0.4% as compared with the existing model.

A Position Sensing Glove to Aid Ankle-Foot Orthosis Diagnosis and Treatment

A position sensing glove called SmartScan, which creates a 3D virtual model of a real object, is presented. The data from the glove is processed by a volume minimization algorithm to validate the position sensor data. This allows only data from the object’s surface to be retained. The data validation algorithm allows the user to progressively improve an image by repeatedly moving their hand over the object. In addition, the user can choose their own balance between feature resolution and invalid data rejection. The SmartScan glove is tested on a foot model and is shown to be robust against motion artifacts, having a mean accuracy of 2.9 mm (compared to a 3D model generated from optical imaging) without calibration.

Concerns and recommendations for using Amazon MTurk for eating disorder research.

Our original aim was to validate and norm common eating disorder (ED) symptom measures in a large, representative community sample of transgender adults in the United States. We recruited via Amazon Mechanical Turk (MTurk), a popular online recruitment and data collection platform both within and outside of the ED field. We present an overview of our experience using MTurk. Recruitment began in Spring 2020; our original target N was 2,250 transgender adults stratified evenly across the United States. Measures included a demographics questionnaire, the Eating Disorder Examination-Questionnaire, and the Eating Attitudes Test-26. Consistent with current literature recommendations, we implemented a comprehensive set of attention and validity measures to reduce and identify bot responding, data farming, and participant misrepresentation. Recommended validity and attention checks failed to identify the majority of likely invalid responses. Our collection of two similar ED measures, thorough weight history assessment, and gender identity experiences allowed us to examine response concordance and identify impossible and improbable responses, which revealed glaring discrepancies and invalid data. Furthermore, qualitative data (e.g., emails received from MTurk workers) raised concerns about economic conditions facing MTurk workers that could compel misrepresentation. Our results strongly suggest most of our data were invalid, and call into question results of recently published MTurk studies. We assert that caution and rigor must be applied when using MTurk as a recruitment tool for ED research, and offer several suggestions for ED researchers to mitigate and identify invalid data.

Recovering the Association Between Unlinked Fare Machines and Stations Using Automated Fare Collection Data in Metro Systems

Data quality is the foundation of data-driven applications in transportation. Data problems such as missing and invalid data could sharply reduce the performance of the methods used in these applications. Although there exist plenty of studies related to data quality issues, they only focus on missing or invalid data caused by infrastructure failures (e.g., loop detector malfunction). In general, there is a lack of attention to data quality issues from insufficient data management. This paper proposes a tensor decomposition based framework to tackle a specific missing data problem which occurs when the machine-station dictionary of an automated fare collection system database is incomplete. In such cases, there is a large amount of loss of origin/destination information as the affected machines are not linked to any station. Consequently, all associated transactions may miss the origin/destination information. The proposed framework recovers the dictionary by capturing features of the passenger flow passing through the unlinked fare machine. Evaluation results show that the proposed approach could recover the missing data with high accuracy even when several fare machines are not linked to a station. The framework could also support other beneficial applications.

Effective Classification of Melting Curve in Real-time PCR Based on Dynamic Filter-based Convolutional Neural Network

Background: The effective classification of the melting curve is conducive to measure the specificity of the amplified products and the influence of invalid data on subsequent experiments is excluded. Objective: In this paper, a convolutional neural network (CNN) classification model based on dynamic filter is proposed, which can categorize the number of peaks in the melting curve image and distinguish the pollution data represented by the noise peaks. Methods: The main advantage of the proposed model is that it adopts the filter which changes with the input and uses the dynamic filter to capture more information in the image, making the network learning more accurate. In addition, the residual module is used to extract the characteristics of the melting curve, and the pooling operation is replaced with an atrous convolution to prevent the loss of context information. Result: In order to train the proposed model, a novel melting curve dataset is created, which includes a balanced dataset and an unbalanced dataset. The proposed method uses six classification-based assessment criteria to compare with seven representative methods based on deep learning. Experimental results show that the proposed method not only markedly outperforms the other state-of-the-art methods in accuracy, but also has much less running time. Conclusion: It evidently proves that the proposed method is suitable for judging the specificity of amplification products according to the melting curve. Simultaneously, it overcomes the difficulties of manual selection with low efficiency and artificial bias.

Quantitative comparison of measurement quality of cross-correlation based particle velocity instruments in different gas fluidization regimes

Particle velocity measurement based on cross-correlation algorithm is used widely in various multiphase flow systems. In this study, an optical fiber probe, i.e. Labasys® 100 from MSE Meili was used to systematically compare the qualities of its measured particle velocities in a pair of hydrodynamically distinct regimes, i.e. a fast fluidized bed (FFB) and a bubbling fluidized bed (BFB). Compared to FFBs, the measured particle velocities in BFBs show lower data repeatability, higher percentage of invalid data and higher sensitivity to the selection of the threshold value of maximum cross-correlation coefficient, which corresponds to much lower measurement qualities in this regime. Comparatively, higher measurement qualities were observed in the FFB based on the same evaluating methods. The highest measurement qualities appear in the core region of the riser and under high superficial gas velocities. A matchup relationship between the measurement quality and the shape of the probability density function (PDF) curves of maximum cross-correlation coefficient is found. Narrower PDF curves correspond to higher measurement quality and vice versa.

Component-mixing strategy: A decomposition-based data augmentation algorithm for motor imagery signals

Deep learning has achieved a remarkable success in areas such as brain-computer interface systems (BCI). However, electroencephalography (EEG) signals evoked by motor imagery (MI) are sometimes limited in their amount due to invalid data caused by the subjects’ fatigue, leading to a performance degradation. To this end, in this work we extend empirical mode decomposition into multivariate empirical mode decomposition and intrinsic time-scale decomposition, proposing a component-mixing strategy (CMS) for MI data augmentation. Compared to commonly used data augmentation methods such as generative adversarial networks, CMS can generate artificial trials from a few training samples without any required training. We claim that raw and artificial data generated by CMS are consistent with respect to the distribution and power spectral density. Experiments done on the BCI Competition IV dataset 2b show that CMS can achieve a considerable improvement on the binary classification accuracy and the area under the curve score using EEGNet, wavelet neural networks and a support vector machine.

Valid Inertial Gait Data Recovery for Gait Recognition: A Multi-mode Adaptive Orthogonal Matching Pursuit

Gait recognition based on inertial sensor data develops rapidly. Recently, more and more studies are based on the data collected in-the-wild, where the data quality is limited. Thus, the requirement for data preprocessing is higher. In this work, an adaptive preprocessing algorithm, MAOMP, is proposed to extract the effective components in the gait data. Different from traditional denoising methods, MAOMP recovers valid data from scratch using sinusoidal bases adaptively by projecting signals and bases into the Hilbert space. It can remove invalid data to smooth the signals but highlight the essential extremums at the same time. Finally, MAOMP is evaluated on four publicly available datasets of different grades and three different neural networks. The quantization of SNR shows the data recovered by MAOMP is at a higher level. Compared to the two commonly used preprocessing methods, the performance of MAOMP can be more pronounced as the quality of the datasets decreases. The improvements of the recognition performance are more apparent in the ConvLSTM network compared to the CNN with the data recovered by MAOMP.

Implementasi Program Keluarga Harapan Pada Masa Pandemi Covid-19 di Kabupaten Kampar Provinsi Riau

Tambang Sub-district is one of the sub-districts located in Kampar Regency, Riau Province which also established the Covid-19 Pandemic with the current number of PKH recipients as many as 1,804 Beneficiary Families (KPM). This study aims to determine the implementation of the Program Keluarga Harapan (PKH) during the Corona Virus Disease 2019 (Covid-19) Pandemic in Tambang District, Kampar Regency and the inhibiting factors for the implementation of the Program Keluarga Harapan (PKH) during the Corona Virus Disease 2019 (Covid-19) Pandemic. ) in the Mine District, Kampar Regency. The concepts used by researchers are policy theory, policy implementation theory, and the concept of the Program Keluarga Harapan (PKH). The research method used is qualitative using a descriptive case study method with data collection techniques through interviews and documentation. The results of this study indicate that the implementation of the Program Keluarga Harapan (PKH) during the Corona Virus Disease 2019 (Covid-19) Pandemic in Tambang District, Kampar Regency is carried out by applying standards and targets, resources, relationships between organizations, characteristics of implementing agents, social environmental conditions. and economics and disposition of implementers. Then there are also inhibiting factors for the implementation of the Program Keluarga Harapan (PKH) during the Corona Virus Disease 2019 (Covid-19) Pandemic in Tambang District, Kampar Regency, namely the invalid PKH KPM data during the Covid-19 period, adjustments and calculations for PKH assistance during the Covid-19 period. 19 have not fully provided benefits, KPM PKH has difficulty accessing Health Facilities (Faskes) and Education Facilities (Fasdik) during the Covid-19 period, socialization of PKH policies during the Covid-19 period has not been effective, Standard Operational Procedures (SOP) for reporting problems of KPM PKH which intersect with the unstructured BPNT and problematic KPM PKH administration data in e-PKH.Keywords: Implementationi, Program Keluarga Harapan (PKH)

A review of social media-based public opinion analyses: Challenges and recommendations

Compared with survey polls, social media can yield a better and more comprehensive understanding of public perceptions of special topics in a more scientific manner. However, despite this advantage, there seem to be limited investigations into the challenges in social media-based public opinion analysis. This study offers an understanding of the challenges in this field and some corresponding recommendations. Through a systematic literature review, we identify 54 papers to analyze and discuss issues related to data collection, data quality, and data mining. This paper summarizes a framework for social media-based public opinion analysis as well as the commonly employed data mining methodologies. We found that collecting public opinion data from Facebook and Weibo is difficult because of their restricted application programming interface and measures against Web Crawler. How to effectively and conveniently delete invalid data and how to design data mining methods for social media data, especially for those in Chinese, are still two main challenges in social media-based public opinion analysis. We claim that using multiple data sources, optimizing keyword settings, enhancing interdisciplinary cooperation, and paying more attention to the functional role of social media can benefit the development of social media-based public opinion analysis. This study also highlights the potential risks of releasing the personal information of the public in the use of social media data in research.

A power fluctuation evaluation method of PV plants based on RankBoost ranking

Fluctuation evaluation is an important task in promoting the accommodation of photovoltaic (PV) power generation. This paper proposes an evaluation method to quantify the power fluctuation of PV plants. This consists of an index system and a ranking method based on the RankBoost algorithm. Eleven indices are devised and included in the index system to fully cover diverse fluctuation features. By handling missing and invalid data effectively, the ranking method fuses multiple indices automatically and provides a systematic and comprehensive comparison of power fluctuation. Simulation results based on power data from six PV plants indicate that the evaluation list obtained by the RankBoost ranking method is better represented and more comprehensive than that derived by the equal weight method.

Comparison of conventional and actuarial neuropsychological criteria for mild cognitive impairment in a clinical setting

ABSTRACT Introduction Evidence-based practice in neuropsychology involves the use of validated tests, cutoff scores, and interpretive algorithms to identify clinically significant cognitive deficits. Recently, actuarial neuropsychological criteria (ANP) for identifying mild cognitive impairment were developed, demonstrating improved criterion validity and temporal stability compared to conventional criteria (CNP). However, benefits of the ANP criteria have not been investigated in non-research, clinical settings with varied etiologies, severities, and comorbidities. This study compared the utility of CNP and ANP criteria using data from a memory disorders clinic. Method Data from 500 non-demented older adults evaluated in a Veterans Affairs Medical Center memory disorders clinic were retrospectively analyzed. We applied CNP and ANP criteria to the Repeatable Battery for the Assessment of Neuropsychological Status, compared outcomes to consensus clinical diagnoses, and conducted cluster analyses of scores from each group. Results The majority (72%) of patients met both the CNP and ANP criteria and both approaches were susceptible to confounding factors such as invalid test data and mood disturbance. However, the CNP approach mislabeled impairment in more patients with non-cognitive disorders and intact cognition. Comparatively, the ANP approach misdiagnosed patients with depression at a third of the rate and those with no diagnosis at nearly half the rate of CNP. Cluster analyses revealed groups with: 1) minimal impairment, 2) amnestic impairment, and 3) multi-domain impairment. The ANP approach yielded subgroups with more distinct neuropsychological profiles. Conclusions We replicated previous findings that the CNP approach is over-inclusive, particularly for those determined to have no cognitive disorder by a consensus team. The ANP approach yielded fewer false positives and better diagnostic specificity than the CNP. Despite clear benefits of the ANP vs. CNP, there was substantial overlap in their performance in this heterogeneous sample. These findings highlight the critical role of clinical interpretation when wielding these empirically-derived tools.

Research on the Bank Credit Decision Model Based on Comprehensive Risk Assessment

In real life, due to the relatively small scale of SMEs and the lack of mortgage assets, banks usually assess their credit risk based on credit policies, information on transaction notes of enterprises, upstream and downstream influence, and then determine whether to lend or not and credit strategies such as loan lines, interest rates and maturities based on factors such as credit risk. In this paper, a bank credit decision model is established. Considering the influence of three main factors on bank lending, namely, the strength of the enterprise, the stability of the operation of the enterprise and the credit rating of the enterprise, the code is compiled to perform data cleaning, invalid data is eliminated, the enterprise profit is calculated based on the input and output invoices, and then the enterprise is divided into three categories (general income level, middle income level and high income level) and scored through K-Means clustering analysis, and the enterprise strength rating model is established. Then, it combines with the established enterprise stability rating model to determine the size of the credit line, and then establishes an enterprise comprehensive credit rating model to determine the credit interest rate, forming the final credit decision model.

Analysis of the implementation of property sales administration at PT Satwika Permai Indah Jakarta

Administration is an important activity in managing a business. The role of administration is very influential in supporting business growth, without good administrative handling, business actors will have difficulty knowing the data that the company wants. The purpose of this study was to determine the implementation of property sales administration at PT Satwika Permai Indah Jakarta, and various obstacles and solutions to overcome them. Qualitative descriptive research form with data collection through observation, interviews, and documentation. The results showed that PT Satwika Permai Indah Jakarta in the implementation of cash and credit sales administration, there were 10 parties involved, namely consumers, sales marketing and admins, cashiers, finance, taxes, law, the Home Ownership Credit (HOC) team, banks, directors, as well as a notary. Documents required include Proof of Marketing Receipt, PPJB, SP3K, AJB, SSP, Certificate of Ownership, Certificate. The process starts from the consumer selecting the unit, making the payment, interviewing the bank's credit analyst, until it is ratified by a notary, and PT Satwika Permai Indah handing over the unit that is ready to be occupied by the consumer. In making land deeds, sometimes there are delays due to incomplete or invalid consumer data. This is a concern for legal parties to remind consumers to update the necessary data.

Correction of invalid data based on spatial dimension information of a temporally and spatially modulated spatial heterodyne interference imaging spectrometer.

Considering the distribution characteristics of interference data of a temporally and spatially modulated spatial heterodyne interference imaging spectrometer, an algorithm based on spatial dimension baseline of interference data for the detection and correction of invalid data is proposed. The intensity ratio of the target interference data far away from the zero optical path difference has been extracted and calculated, and the intensity of the interference data is compensated; the baseline of the compensated interference data is carried out along the spatial dimension direction, and the deviation matrix is calculated, and combined with the 3σ method for the detection and correction of invalid data. A simulation experiment is carried out in the laboratory to obtain the target push-sweep imaging interference data, and the spectrum is the output spectrum of the integrating sphere halogen tungsten lamp. The experimental results show that the invalid data in the interference data (including invalid data near zero optical path difference) can be effectively identified and corrected using the invalid data detection and correction algorithm described in this paper, which has a good effect on the correction of fixed invalid data and flash pixel.

Firearm-related internet searches as a correlate of future firearm suicides: Cross-correlation analyses of monthly Google search volumes and method-specific suicide rates in the United States

• This is a cross-correlation analysis of monthly Google search volumes and rates of suicide by the three most common methods in the USA. • Box-Jenkins transfer function models, the benjamini-hochberg method, and sensitivity analysis were applied. • The search volume of one firearm-related term ( gun suicide ) correlated with the rate of firearm suicides. • The search volume of one term ( “laid off” ) correlated with the rate of poisoning- and asphyxiation-specific suicide. • Cross-correlations elucidated in this study may provide guidance on development of novel suicide-prevention strategies. No previous study has investigated correlations between monthly Google search volumes (MGSVs) of suicide-related search terms and suicide-method specific monthly suicide rates (MSRs). This study examined if the trends in MGSVs of suicide-related terms preceded the variations in method-specific MSRs. MGSVs of 97 candidate suicide-related terms were obtained by averaging 10 timeseries data per term retrieved from Google Trends. Robust time-series analysis methods were applied to MGSVs and firearm-, poisoning-, and asphyxiation-specific MSRs in the United States between 2004 and 2017. Cross-correlation coefficients between MGSVs and methodspecific MSRs were calculated at lags of −3 to −1 (months). In the main analysis, the Benjamini-Hochberg procedure was applied to determine significant correlations while minimizing falsepositive findings. Afterwards, a sensitivity analysis identified the cross-correlations reproducible in two different time spans. Fifty-six search terms with no invalid MGSV data were analyzed. MGSVs of 14 terms correlated with firearm-, poisoning-, or asphyxiation-specific MSRs in one or more lags. In the sensitivity analysis, two terms consistently showed significant positive cross-correlations: gun suicide (with firearm-specific suicides; lag -3) and “laid off” (with poisoning- and asphyxiationspecific suicides; lag -2). Age- or gender-specific search volumes, lags outside the 1- to 3-month range, non-English searches, and confounding factors of MGSV and MSR were not explored. MGSVs of one firearm-related term ( gun suicide ) correlated with future firearmspecific MSRs. MGSVs of one method-neutral term (“ laid off” ) correlated with future poisoning- and asphyxiation-specific MSRs. These terms may be incorporated in novel nowcasting or predictive models for method-specific suicides.

PENEGAKAN HUKUM PIDANA TERHADAP TINDAK PIDANA PEMALSUAN DATA PERSONAL MENURUT UNDANG-UNDANG NOMOR 6 TAHUN 2011 TENTANG KEIMIGRASIAN

The criminal investigation of Immigration against foreign citizen who is intentionally provides invalid data in the form of falsify the personal data that is under the process in filling an application of Travel Document of The Republic of Indonesia occur at Palembang Class I Immigration office in 2018 by Ling Lee Tiong as known as Muslim. He has perpetrates violates Article 126C of Law of The Republic of Indonesia Number 6 of 2011 concerning Immigration. Under the process of law enforcement of Immigration, the determination of a violation case is solved with the criminal law or administrative is an authority of Immigration Officers (discretion). This study discusses about law enforcement and the barrier of law enforcement against the criminal act of falsify the personal data based on Law of The Republic of Indonesia Number 6 of 2011 concerning Immigration and policy for the future. This type of research used in this research is empirical juridical law. This research was conducted at Palembang State Court and Palembang Class I Immigration office. Criminal law enforcement against the criminal act of falsify the personal data based on Law of The Republic of Indonesia Number 6 of 2011 concerning Immigration at Palembang Class I Immigration office in the form of criminal law enforcement as set forth in laws and regulations (In Abstracto) and actual law enforcement (In Concreto). There are two (2) efforts on criminal law enforcement, namely preventive law effort and repressive law effort. The most dominant factors that hinder law enforcement are legal enforcement factor and facilities factor. The Policy for the future of criminal law shall still refer to the policy of criminal law formulated in the concept of Criminal Code.

Development and Preliminary Validation of LoAD Calc, a Mobile App for Calculating the Maximum Safe Single Dose of Local Anesthetics.

Local anesthetics systemic toxicity can lead to life-threatening situations. Correct calculation of the maximum safe dose is therefore paramount in preventing such complications. Different solutions have already emerged to support anesthesiologists but are seldom used in clinical practice as they require either access to a computer or specific documents to be at hand. A mobile app could provide an easy and practical solution; however, the few apps already created for this purpose often lack key elements, allowing invalid data to be entered and suggesting doses that might exceed the maximum safe dose. We describe the development of LoAD Calc, a mobile health (mHealth) app developed using a modified version of the Information Systems Research framework, which adds design thinking modes to the original framework. The app was enhanced through multiple iterations and developed with the aid of contextual observations and interviews, brainswarming sessions, prototyping, and continuous feedback. The design process led to the creation of two prototypes which underwent thorough testing by a sample of eight anesthesiologists. The final version of the app, LoAD Calc, was deployed on Apple and Android mobile test platforms and tested again by the same sample until deemed fit for release.

Detecting Invalid Associations between Fare Machines and Metro Stations Using Smart Card Data

Data quality is essential for its authentic usage in analysis and applications. The large volume of automated collection data inevidently suffers from data quality issues including data missing and invalidity. This paper deals with an invalid data problem in the automated fare collection (AFC) database caused by the erroneous association between the fare machines and metro stations, e.g., a fare machine located at Station A is wrongly associated with Station B in the AFC database. It could lead to inappropriate fare charges in a distance-based fare system and cause analysis bias for planning/operation practice. We propose a tensor decomposition and isolation forest-based approach to detect and correct the invalid associated fare machines in the system. The tensor decomposition extracts features of passenger flows and travel times passing through fare machines. The isolation forest coupled with a neural network (NN) takes these features as inputs to detect the wrongly associated fare machines and infer the correct association stations. Case studies using data from a metro system show that the proposed detection approach achieves over 90% accuracy in detecting the invalid associations for up to 35% invalid associations. The inferred association has a 90% accuracy even when the invalid association ratio reaches 40%. The proposed data-driven invalid data detection method is useful for large-scale data management in terms of data quality check and fix.