Testing Purposes Research Articles

Proficiency in Personalized Medicine and Health Data Sharing in the EU

Abstract Personalized medicine, leveraging genetic, environmental, and lifestyle data, has already transformed healthcare by tailoring prevention, diagnosis, and treatment to individual patients. The successful development and implementation of personalized approaches rely on the public’s proficiency and awareness of personalized medicine, enabling access to innovative techniques and fostering a willingness to share health-related data. We distributed a survey to 6,581 respondents from 8 EU countries: France, Germany, the Netherlands, Italy, Spain, Poland, Hungary, and Romania. 52.5% of respondents were female (n = 3,458), with a mean age of 48.5 years (range 18-89 years, median = 49 years, SD = 15.96), and 37.91% of the participants reported achieving tertiary education. Our survey investigates the general public’s knowledge of personalized medicine, support for genetic testing, and willingness to share health data among EU citizens while assessing their perception and acceptance of these medical paradigms. Knowledge levels vary among different EU countries. Only 12.11% of respondents had a high knowledge of the topics presented in this survey. Support for implementing genetic testing in healthcare was high, with 81.52% supporting the implementation of genetic testing in their healthcare systems, and nuanced differences in acceptance were observed based on testing purposes. 52.35% reported willingness to share health data. Both support for implementing genetic testing and the willingness to share health data correlated positively with knowledge and education levels. Geographical differences within the EU highlighted variations in attitudes toward personalized medicine and data sharing, with respondents from Southern Europe displaying higher odds than their peers in Central and Eastern Europe. The results emphasize the need for targeted communication and education strategies to enhance public understanding and trust in personalized medicine and health data sharing. Key messages • High support for genetic testing across the EU highlights the need for enhanced public education on personalized medicine to boost understanding and data sharing willingness. • Geographical differences in attitudes toward personalized medicine in the EU suggest targeted strategies to build trust and knowledge, particularly in Central Europe.

Image classification with deconvolution operation and augmentation

Abstract Several image classification approaches have been evolved over the years utilizing convolutional neural network (CNN). In convolution operation of CNN, the shifting of kernels to overlapping regions of the image learns redundant data as the images are strongly correlated in reality. The redundant data make the neural network training a challenging task. Again, Deep Learning methods evaluated on small dataset yields degraded performance. To deal with these issues, a proposal is made in this paper that uses deconvolution operation to minimize correlations from images and data augmentation technique to increase the size of datasets. Plant Village, Tomato, and Covid-19 datasets were used for evaluating the performance of the proposed method. 70% of the datasets were used for training, 10% for validation, and 20% for testing purposes. The CIFAR10, MNIST, and Mini-ImageNet datasets were also considered for performance evaluation. The proposed method performed better than other existing methods in terms of classification accuracy.

Validity and reliability of the Endotracheal Suction Assessment Tool© in adult ICU patients: A methodological study

BackgroundNovice nurses often find endotracheal suctioning signs unfamiliar. The Endotracheal Suction Assessment Tool© (ESAT©) aids pediatric nurses in suctioning but lacks an adult equivalent. A valid and reliable ESAT© for assessing intensive care unit patients’ suction needs across different populations is necessary. AimTo test the validity and reliability of the ESAT© in adult ICU patients. MethodsThis methodological study assessed suction needs in patients at a tertiary adult ICU of a university hospital. Two independent observers used the ESAT© to determine suction needs. Data from 106 suction procedures were collected using a patient characteristics form, the ESAT©, and a suction monitoring form. The ESAT© includes eight respiratory and ventilation parameters and seven clinical consideration parameters. Hemodynamic and respiratory parameters were measured before, and one and five minutes after suction. Linguistic validity, content validity, and pilot testing were conducted. Interobserver agreement was evaluated, and psychometric evaluation was done using content validity index (CVI) and intraclass correlation coefficient (ICC). Data were analyzed with paired samples t-test and ANOVA. ResultsItem CVI ranged from 0.80 to 1.00, and scale CVI was 0.96. Inter-item correlation and ICC for inter-rater reliability were both 0.933 (95 % CI = 0.903–0.954, p < 0.001 for ICC). Cohen’s Kappa coefficients ranged from 0.690 to 1.000 (p < 0.001), indicating high consistency between raters for all tool items. All scale items showed near-perfect agreement except SpO2, which showed substantial agreement. ConclusionThe S-CVI was good, indicating near-perfect agreement among raters. ESAT© is highly reliable and valid for determining suction needs in adult intensive care patients. Implications for PracticeThe ESAT© aids autonomous decision-making for suctioning needs. Originally for novice pediatric nurses, the ESAT© was adapted for adult intensive care nurses. The ESAT© is valid and reliable for adult intensive care patients.

A novel memory usage guided fuzzing based on particle swarm optimization

Fuzzing has become the focus of research in the field of software testing due to its advantages such as simple testing process, easy deployment, and easy reproduction of crashes. However, fuzzing also suffers from the disadvantages of poor test purpose and invalid generated seeds. To solve the above problems, researchers proposed the Memory Usage Guided Fuzzing (MUGF). To further optimize the performance of MUGF, this paper proposes a fuzzing method named Memory Usage Guided Fuzzing based on Particle Swarm Optimization (MUGF-PSO). MUGF-PSO will guide the selection of mutation operators for subsequent testing by learning the effectiveness of each mutation operators during previous testing. Specifically, MUGF-PSO regards each mutation operator as a particle in the particle swarm optimization algorithm, and the coverage change is regarded as an important factor to evaluate the seed’s local and global best position. In addition, the efficient selection probability distribution of mutation operator is constantly iteratively searched to make the MUGF tends to select the mutation operator that is more able to trigger new coverage. Furthermore, the MUGF-PSO is integrated into MemLock developed by MUGF, that is, MemLock-PSO is developed. We conduct a comparison experiment with 5 fuzzers (including AFL, MemLock, EcoFuzz, HavocMAB and Darwin), and the results show that MemLock-PSO is able to find more paths and crashes in 12 widely used program with different functions from 10 tools, with a significant gap of around 12 h. Meanwhile, the MemLock-PSO have a significant difference in the number of paths and crashes compared to other fuzzers with a good stability. Our work also proves the remarkable significance of applying swarm intelligence optimization algorithms in fuzzing in order to solve the problem of selection of mutation operators.

Co-creation of a step-by-step guide for specifying the test-management pathway to formulate focused guideline questions about healthcare related tests

BackgroundGuideline development on testing is known to be difficult for guideline developers. It requires consideration of various aspects, such as accuracy, purpose of testing, and consequences on management and people-important outcomes. This can be outlined in a test-management pathway. We aimed to create and user-test a step-by-step guide for guideline developers for designing a test-management pathway.MethodsDevelopmental design with a co-creative strategy. We created a draft step-by-step guide, that was user tested in a workshop with 19 experts, and by interviewing 7 guideline panel members.ResultsOur proposed guide consists of five blocks of signalling questions: patients/population, index test(s), current practice/comparison/control, people-important outcomes, and the link between testing and outcome(s). The user testing led to refinement of the signalling questions, the use of inclusive terminology, and addition of a test-management pathway figure with detailed explanation.ConclusionsThe step-by-step guide for formulating focused guideline questions regarding healthcare related testing can help in identifying relevant characteristics of the population, tests, and outcomes and to create a test management pathway. This should facilitate the formulation of evidence-based guideline recommendations about healthcare related testing.

PERFORMANCE TESTING ANALYSIS OF BANDUNGTANGINAS APPLICATION WITH JMETER

The bandungtanginas.id application was created to serve the needs of data collection on maternal andchild health at every posyandu in the city of Bandung. This application is planned to be able to servearound 1,982 posyandu in the city of Bandung, around 10,000 users, who are expected to be able toaccess the bandungtanginas.id application. In the current condition of the application supportinfrastructure still has limitations in supporting the bandungtanginas.id application. Withspecifications that tend to be low, an improvement effort is needed through generating applicationusage scenarios so that the services provided can be optimally available using the existinginfrastructure. This research focuses on analyzing the application performance by using load testing.This research has four stages: determining the test's purpose, creating a test scenario, carrying outscenario testing, and analyzing the results. The analysis will be carried out with the results of loadtesting experiments based on the scenarios that have been made. Test scenarios are made to obtainexperimental data, last further analysis and conclusions will be made. Scenarios are created based onan estimate of the optimal number of users the application should serve. The test scenario will use atarget user of between 10 – 200 users who are divided in five groups of load testing scenarios. Fromthis trial, it will be seen how the performance of the bandungtanginas.id application for each scenariogroup that has been created. The result of this study showed that the best scenario forbandungtanginas.id application could serve users in the range of 30-50 users. It is obtained from theresponse time results, which show the average results are under 30 seconds according to the SLA(Service Level Agreement) standard for application users.

Harnessing the host response for precision infectious disease diagnosis.

SUMMARYDetection of the presence of infection and its etiology must be accurate and timely to facilitate appropriate antimicrobial use. Diagnostic strategies that rely solely on pathogen detection often are insufficient due to poor test characteristics, inability to differentiate colonization from infection, or protracted delay to result. Understanding the human response across different pathogens on a clinical and molecular level can provide more accurate, timely, and useful answers, especially in critical illness and diagnostic uncertainty. Improvements in understanding the human immune response including genomics, protein analysis, gene expression, and cellular morphology have led to rapid innovation of new host response-based diagnostic tests. This review describes the limitations of pathogen-focused technology and the benefits of examining the breadth of immune response to diagnose infection. It then explores biomarkers that have been studied for this purpose and scrutinizes the performance of host-based multianalyte testing. Currently cleared diagnostics and those in late-stage development are described in depth, with a focus on the purpose of testing and its utility for clinicians. Finally, it concludes by examining opportunities for further host response-derived diagnostic innovation.

A Retrospective Analysis of Broad-Range Microbial PCR-Sequencing Findings and Culture Results in 101 Bronchoalveolar Lavage Specimens

Abstract Introduction/Objective Pneumonia is a significant cause of illness and death, identifying its cause is challenging and critical for effective antibiotic treatment and for reducing the length of stay. Bronchoalveolar lavage is commonly performed to obtain high-quality lower respiratory tract samples. While traditional culture methods have been the standard for pathogen detection, microbial rRNA broad-range polymerase chain reaction (BR-PCR) has emerged as an alternative method. Methods/Case Report A retrospective analysis was conducted on 101 patients suspected of severe pulmonary infections at Oregon Health and Science University (OHSU). Results (if a Case Study enter NA) When important microbial etiology is considered, culture detected 26%, while BR- PCR detected 20%. Both methods proved effective in detecting common pathogens; however, culture demonstrated superiority in detecting fungal pathogens due to potential sampling biases associated with PCR. Furthermore, culture holds the advantages of being faster and more cost-effective, although it may fail to detect difficult to non-cultivable organisms, like Legionella spp. Culture is indispensable for susceptibility testing and strain typing purposes. More often than not, BR-PCR captures airway microbiota indiscriminately with minimal clinical significance, potentially confusing clinical interpretation. Conclusion Novel technologies like Biofire Pneumonia Panel (BPP), a multiplex PCR, have shown promise in the rapid detection of pneumonia pathogens including bacteria, viruses, and resistance genes (&amp;lt; 2 hours), complementing the traditional culture methods for microbiological diagnosis. Based on this analysis we recommend introducing BPP at OHSU.

Real-time robust liver and gallbladder segmentation during laparoscopic cholecystectomy using convolutional neural networks: an analysis

Aim: Images in different laparoscopic cholecystectomy datasets are acquired using various camera models, parameters, and settings, with the annotation methods varying by institution. These factors result in inconsistent inference performance of the network model. This study aims to identify the optimal network model architecture for liver and gallbladder segmentation from several options. Then, the performance and robustness of the optimal network model are evaluated using an independent dataset that is not included in the training. Methods: The public dataset, CholecSeg8k, was utilized as the input for the network model training, validation, and testing. A local private dataset from KPJ Damansara Hospital, Selangor, Malaysia, was used for testing purposes only. For the implementation of liver and gallbladder segmentation, segmentation models, a public Python library was employed. Results: Among the experiments, highly accurate liver and gallbladder segmentation results were achieved using the feature pyramid network (FPN) architecture as the network model, with the Inception-ResNet-v2 architecture as the network backbone. The best-trained network model resulted in a loss of 0.070955, a mean intersection over union (IoU) score of 0.95896, and a mean F1-score of 0.9773 on the test set. However, visualized results for the private dataset contained considerable false-negative areas. Conclusion: The proposed automated technique has the potential to serve as an alternative to the conventional indocyanine green injection along with near-infrared fluorescence imaging (ICG-NIRF)-based method for liver and gallbladder segmentation during laparoscopic cholecystectomy. Future work will focus on enhancing the results of the private dataset. Additionally, a surgeon-assistant robotic arm that will use the liver and gallbladder segmentation results for camera steering will be analyzed.

P2.16A.02 Improving Patients' Awareness of Biomarker Test Timing and Purpose with 4R Oncology Intervention

P2.16A.02 Improving Patients' Awareness of Biomarker Test Timing and Purpose with 4R Oncology Intervention

The Influence of Coupling-Specific Parameters and Structural Parameters on the Electromechanical Impedance of Acoustic Emission Sensors

Acoustic emission (AE) sensors are widely used for testing and monitoring purposes, necessitating effective field-testing methods to assess their functionality during use, ideally resulting in a method for self-diagnostic. A novel approach for field-testing is to measure the electromechanical impedance (EMI) of the AE sensor. This method requires minimal additional equipment and eliminates human interaction, which is needed for current field-tests like pencil-lead break tests. However, the EMI includes the mechanical impedances of the sensor, the coupling layer, and the structure. Therefore, the measurements contain information about all three and influences of the structure and the coupling layer need to be investigated to be able to establish a method for self-diagnosis in field. Additionally, the piezoelectric effect is temperature-dependent, which affects the measured EMI spectra and may result in erroneous assessments of the sensor's condition if not accounted for. This work presents experimental studies on the influence of measuring equipment, environmental temperature, coupling-specific parameters and structural parameters on the EMI of AE sensors. Moreover, numerical simulations have been conducted to provide additional support and to extend the experimental studies of the latter two. To ensure reproducible sensor coupling to a structure, a setup was constructed to couple the sensor to the structure at the same position and with the same force. The thickness of the coupling layer is determined by a small amount of silicon carbide particles with a specific diameter. This setup is also used to examine the effect of structural parameters.

Analysis Of The Use Of Nazief-Adriani Stemming And Porter Stemming In Covid-19 Twitter Sentiment Analysis With Term Frequency-Inverse Document Frequency Weighting Based On K-Nearest Neighbor Algorithm

Abstract. This system was developed to determine the accuracy of sentiment analysis on Twitter regarding the COVID-19 issue using the Nazief-Adriani and Porter stemmers with TF-IDF weighting, along with a classification process using K-Nearest Neighbor (KNN) that resulted in a comparison of 48.24% for Nazief-Adriani and 48.24% for Porter. Purpose: This research aims to determine the accuracy of the Nazief-Adriani and Porter stemmer algorithms in performing text preprocessing using a dataset from Indonesian-language Twitter. This research involves word weighting using TF-IDF and classification using the K-Nearest Neighbor (KNN) algorithm. Methods/Study design/approach: The experimentation was conducted by applying the Nazief-Adriani and Porter stemmer algorithm methods, utilizing data sourced from Twitter related to COVID-19. Subsequently, the data underwent text preprocessing, stemming, TF-IDF weighting, accuracy testing of training and testing data using K-Nearest Neighbor (KNN) algorithm, and the accuracy of both stemmers was calculated employing a confusion matrix table. Result/Findings: This study obtained reasonably accurate results in testing the Nazief-Adriani stemmer with an accuracy of 50.98%, applied to sentiment analysis of COVID-19-related Twitter data using the Indonesian language. As for the accuracy of the Porter stemmer, it achieved an accuracy rate of 48.24%. Novelty/Originality/Value: Feature selection is crucial in stemmer accuracy testing. Therefore, in this study, feature selection is carried out using the Nazief-Adriani and Porter stemmers for testing purposes, and the accuracy data classification is conducted using the K-Nearest Neighbor (KNN) algorithm

MR Image Fusion-Based Parotid Gland Tumor Detection.

The differentiation of benign and malignant parotid gland tumors is of major significance as it directly affects the treatment process. In addition, it is also a vital task in terms of early and accurate diagnosis of parotid gland tumors and the determination of treatment planning accordingly. As in other diseases, the differentiation of tumor types involves several challenging, time-consuming, and laborious processes. In the study, Magnetic Resonance (MR) images of 114 patients with parotid gland tumors are used for training and testing purposes by Image Fusion (IF). After the Apparent Diffusion Coefficient (ADC), Contrast-enhanced T1-w (T1C-w), and T2-w sequences are cropped, IF (ADC, T1C-w), IF (ADC, T2-w), IF (T1C-w, T2-w), and IF (ADC, T1C-w, T2-w) datasets are obtained for different combinations of these sequences using a two-dimensional Discrete Wavelet Transform (DWT)-based fusion technique. For each of these four datasets, ResNet18, GoogLeNet, and DenseNet-201 architectures are trained separately, and thus, 12 models are obtained in the study. A Graphical User Interface (GUI) application that contains the most successful of these trained architectures for each data is also designed to support the users. The designed GUI application not only allows the fusing of different sequence images but also predicts whether the label of the fused image is benign or malignant. The results show that the DenseNet-201 models for IF (ADC, T1C-w), IF (ADC, T2-w), and IF (ADC, T1C-w, T2-w) are better than the others, with accuracies of 95.45%, 95.96%, and 92.93%, respectively. It is also noted in the study that the most successful model for IF (T1C-w, T2-w) is ResNet18, and its accuracy is equal to 94.95%.

RELATIONSHIP BETWEEN ENERGY AND FOOD PRICES IN THE EURO AREA

Over the past five years, global economic developments have been accompanied by a number of challenges. After a period of economic development, the global economy is facing a significant challenge caused by the COVID-19 crisis. There have been factory closures, border closures and supply chain disruptions. All this is reflected in the EU and the euro area. This was followed by a period of recovery and accelerating inflation. The war on the territory of Ukraine is also having a significant impact on inflationary processes. This further jeopardises the supply of energy resources and their prices begin to rise at a very high rate. The shock of rising energy prices has also been reflected in sharp increases in food prices. In the euro area, there have been periods when food prices have risen by more than 17% on an annual basis and energy products by almost 60% in some months. All these developments highlight the need to examine the links between energy and food prices. This is necessary in order to be able to take quick decisions in the event of new volatility in energy prices. The aim of the study is to establish a causal relationship between the dynamics of energy and food prices and the time lag over which food prices react to increases in energy prices. A correlation analysis is carried out and a causal relationship between the two indicators is established by regression analysis at different time lags. The hypothesis tested is that, over the last five years, there has been a strong month-to-month correlation between energy prices and food prices with a lag of six months. For the purpose of regression analysis and hypothesis testing, monthly data for food inflation and inflation for the group Electricity, gas and other fuels have been used. The data are taken from the structure of the Harmonised Index of Consumer Prices published by Eurostat. The detailed regression analysis shows that there is a statistically significant linear relationship between the price indices of the two product groups with a lag of six months. The Granger causality test confirms the results of the dynamic, correlation and regression analysis. The results of the test show not only the existence of a causal relationship, but also the ability of energy price changes to predict food price dynamics. All this leads to the conclusion that in periods of significant changes in energy prices, food prices can be expected to follow similar dynamics within six months. This relationship could be used both for forecasting and for taking specific economic measures.

Comparative evaluation of plasma biomarkers of Schistosoma haematobium infection in endemic populations from Burkina Faso.

Infection with Schistosoma haematobium causes urogenital disease associated with organ disfunction, bleeding, pain, and higher susceptibility to infections and cancer. Timely and accurate diagnosis is crucial for prompt and appropriate treatment as well as surveillance efforts, and the use of plasma biomarkers offers important advantages over parasitological examination of urine, including increased sensitivity and the possibility to use the same specimen for multiple investigations. The present study aims to evaluate the diagnostic performance of different plasma biomarkers in endemic populations from Burkina Faso, West Africa. Schistosoma spp. Circulating Anodic Antigen (CAA), cell free S. haematobium DNA (cfDNA), class M and G antibodies against S. haematobium Soluble Worm Antigen Preparation (SWAP) and Soluble Egg Antigen (SEA) were measured in 406 plasma samples. Results of each biomarker test were compared to those of CAA, a Composite Reference Standard (CRS) and Latent Class Analysis (LCA). An identical proportion of positive samples (29%) was observed as a result of CAA and cfDNA testing, with a substantial agreement (84%, Cohen k = 0.62) between the results of the two tests, and a comparable agreement with the results of CRS and LCA. A higher positivity was observed, as expected, as a result of specific antibody testing (47%-72%), with IgG showing a higher agreement than IgM with the three references. Also, higher IgG levels were observed in current vs past infection, and ROC analysis identified optimal cutoff values for improved testing accuracy. This study provides compelling evidence that can inform the choice of the most appropriate diagnostic plasma biomarker for urogenital schistosomiasis in endemic areas, depending on the purpose, context, and available resources for testing. Either CAA or cfDNA testing can be used for the diagnosis of patients and for epidemiological investigations, even in absence of urine filtration microscopy, whereas anti-SWAP or anti-SEA IgG can be employed for surveillance and integrated monitoring of control interventions against poverty-associated diseases.

VaxBot-HPV: A GPT-based Chatbot for Answering HPV Vaccine-related Questions.

HPV vaccine is an effective measure to prevent and control the diseases caused by Human Papillomavirus (HPV). This study addresses the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine. We constructed the knowledge base (KB) for VaxBot-HPV, which consists of 451 documents from biomedical literature and web sources on the HPV vaccine. We extracted 202 question-answer pairs from the KB and 39 questions generated by GPT-4 for training and testing purposes. To comprehensively understand the capabilities and potential of GPT-based chatbots, three models were involved in this study : GPT-3.5, VaxBot-HPV, and GPT-4. The evaluation criteria included answer relevancy and faithfulness. VaxBot-HPV demonstrated superior performance in answer relevancy and faithfulness compared to baselines (Answer relevancy: 0.85; Faithfulness: 0.97) for the test questions in KB, (Answer relevancy: 0.85; Faithfulness: 0.96) for GPT generated questions. This study underscores the importance of leveraging advanced language models and fine-tuning techniques in the development of chatbots for healthcare applications, with implications for improving medical education and public health communication.

Optimized Spatial Transformer for Segmenting Pancreas Abnormalities.

The precise delineation of the pancreas from clinical images poses a substantial obstacle in the realm of medical image analysis and surgical procedures. Challenges arise from the complexities of clinical image analysis and complications in clinical practice related to the pancreas. To tackle these challenges, a novel approach called the Spatial Horned Lizard Attention Approach (SHLAM) has been developed. As a result, a preprocessing function has been developed to examine and eliminate noise barriers from the trained MRI data. Furthermore, an assessment of the current attributes is conducted, followed by the identification of essential elements for forecasting the impacted region. Once the affected region has been identified, the images undergo segmentation. Furthermore, it is crucial to emphasize that the present study assigns 80% of the data for training and 20% for testing purposes. The optimal parameters were assessed based on precision, accuracy, recall, F-measure, error rate, Dice, and Jaccard. The performance improvement has been demonstrated by validating the method on various existing models. The SHLAM method proposed demonstrated an accuracy rate of 99.6%, surpassing that of all alternative methods.

Enhancing Cloud Data Security using Artificial Neural Networks for Users’ Account Hijacking Security Threats

Objectives: To ensure the security of passwords of cloud users' accounts that cannot be decrypted easily by any software or hackers. Methods: In this manuscript, we have designed an experimental setup using a feed-forward back-propagation algorithm of Artificial Neural Networks techniques to ensure cloud data security. For this purpose, we have utilized password-based datasets created by us. 70% of the datasets are allocated for training and 30% for testing and validation purposes. In this training, TRAINLM training function, LEARNGDM adaptive function, performance function is MSE, and PURELIN and TANSIG transfer function to transfer the neurons from input to hidden layer and hidden to the output layer has been used. The minimum learning rate is set to 0.001. Findings: A username and password are required to access cloud services. A cloud-based storage system is used to store login credentials. Due to inadequate security, attackers use hacking techniques to gain access to users’ accounts. Attackers steal data, resources, or services from these accounts. To ensure the security of passwords of cloud users' accounts, we have designed an experimental setup using a feed-forward back-propagation algorithm of neural network techniques. Through this approach, the passwords are stored in a weight matrix, which is a multi-dimensional structure. Novelty: The dimensions of the weight matrix are obscured, making it impossible for the hackers to determine its structure. As a result, we conclude that cloud data is effectively secured on the cloud server. Keywords: Artificial Neural Networks, Cloud Computing, Data Security, Feed-Forward Back-Propagation Algorithm, Machine Learning

Pengaruh Budaya Organisasi dan Kepuasan Kerja Terhadap Kinerja Pegawai pada Dinas Pengendalian Penduduk dan Keluarga Berencana Kabupaten Karawang

This research aims to determine the influence of organizational culture and job satisfaction on employee performance at the Karawang Regency Population Control and Family Planning Service. In this particular investigation, a quantitative descriptive approach was used. It is estimated that the total number of employees working at the Karawang Regency DPPKB is 39 people (both population and sample). For the purpose of testing our hypothesis, we used SPSS version 25. Research findings show that organizational culture has an influence on employee performance, as indicated by a t value of 2.028 and a significance value (sig) of 0.002, lower than the acceptable threshold of 0.05. The t-calculated value of the relationship between job satisfaction and employee performance is 2.882, the t-table value is 2.028, and the significance level (sig) of 0.007 is also smaller than 0.05. Apart from that, job satisfaction has a partial influence on employee performance. This shows that organizational culture and satisfaction at work where employees operate have a positive and large influence on employee performance.

Broadband acoustic waves in plate-like structures for acoustic material characterisation

Abstract Guided acoustic waves can be used for a multitude of different testing and material characterisation purposes. The options for evaluation are especially plentiful if a method for broadband excitation, e.g. thermoelastic excitation using focused, pulsed laser radiation, is used, and if the distance between excitation and detection of the acoustic waves can be varied. In this contribution, methods to infer different quantitative material properties from the recorded spatiotemporal measurement data are presented, ranging from isotropic elastic properties to parameters describing absorption for complex absorption models.