Testing Purposes Research Articles

The Role of Agency Cost as a Mediator in the Effect of Capital Structure on Company Financial Performance

The objective of this study is to examine the impact of capital structure on agency cost, the impact of agency cost on corporate financial performance, and to assess the indirect impact of capital structure on corporate financial performance through agency cost acting as an intervening variable. The subjects of this study are the cement sub-sector companies publicly traded on the Indonesia Stock Exchange (IDX) during the years 2014 and 2018. This study employed a saturated sampling technique, specifically census sampling, which resulted in a sample of 30 enterprises. In this study, the capital structure is measured using the debt-equity ratio (DER), while the company's financial performance is measured using return on equity (ROE). This research employed SmartPLS 3.0 software for testing purposes. The findings of this study suggest that the capital structure, represented by the debt-to-equity ratio (DER), has a detrimental impact on agency costs. Additionally, agency cost negatively influences corporate financial performance, as measured by return on equity (ROE). However, the study did not find any indirect effect of capital structure on financial performance through agency cost as an intervening variable.

Influence on impact strength and chloride permeability of concrete made with cementitious material and artificial sand

The over usage of natural sand and cement has negative social and ecological effects. To address this, by-products of industrial wastes known as pozzolanic materials, including as fly ash, GGBFS, silica fume, and metakaolin, can be utilised with the manufactured sand in place of the natural fine aggregate and cement. As a result, the current research project discussed in this article examines the viability of replacing cement in concrete with pozzolana and crushed sand. Thus, this will address two issues at once: the large-scale use of pozzolanic materials and the preservation of natural sand quarries. The main objective of this experimental investigation is to find out the impact and durability properties of concrete produced by replacing natural sand by manufactured sand in varying percentages like 0, 10, 20, 30, 40, 50, 60 70, 80, 90, 100, and 20% cement replacing with pozzolanic materials. Cylindrical disc 150 mm diameter and 65 mm thick was tested for impact strength and chloride permeability 50 mm thick and 100 mm diameter samples were cast. The 28-day impact strength and chloride permeability of concrete mixed by partially substituting cement with pozzolan and partially substituting natural fine aggregate with artificial sand were tested. For the various concrete mix proportions, the findings for impact strength and chloride permeability were examined and contrasted with standard concrete. The results of this study show that substituting 20% of the cement with silica fume and 60% of the natural fine aggregate with M-sand results in concrete that has a maximum impact strength and a minimum chloride permeability. An ANN Model was developed using the experimental values. For design of model 400 result values where used, 20% results used for testing purpose and 80% results used for ANN model training. Calculated using the product of 25 input data. The ANN model’s results offer a precise elastic prediction of the impact strength and chloride iron pass ability of concrete mixed with substituting industrial cementitious waste for cement and artificial sand with naturally occurring fine aggregate. The findings of this study help to reduce the extraction of non-renewable natural recourses and the environmental impact of industrial waste by preparing more sustainable concrete.

Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms. Continuous wavelet transform (CWT), empirical Fourier decomposition (EFD), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) were exploited for the acquisition of 2D scalograms from 1D data. The research evaluates the effect of multiple factors, including noisy versus denoised scalograms, different optimizers, learning rates, single versus dual channels, model size, and computational time consumption. The benchmark Bern-Barcelona EEG dataset is used for testing purpose. Results obtained show that the combination of MobileNetV2, Continuous Wavelet Transform (CWT) and Adam optimizer at a learning rate of 10−4, coupled with dual-data channels, provides the best performance metrics. Specifically, these parameters result in optimal sensitivity, specificity, f1-score, and classification accuracy, with respective values of 96.06%, 96.15%, 96.08%, and 96.10%. To further corroborate the efficacy of opted pretrained models on exploited Signal Decomposition (SD) algorithms, the classifiers are also being simulated on Temple University database at pinnacle modeling composition. A similar pattern in the outcome readily validate the findings of our study and robustness of deep learning models on epilepsy EEG scalograms.The conclusions drawn emphasize the potential of pretrained CNN-based models to create a robust, automated system for diagnosing epileptiform. Furthermore, the study offers insights into the effectiveness of varying time-frequency techniques and classifier parameters for classifying epileptic EEG data.

Racial Disparities in Breast Cancer Genetic Testing May be Mitigated by Counseling

BackgroundCurrently, racial disparities exist in access to genetic testing. Recent developments have helped narrow the gap in accessibility. The purpose of this study was to determine whether racial disparities in genetic consultation attendance and completion of genetic testing persist, and, if so, factors that contribute to under-utilization of these resources.MethodsA single-institution retrospective review of breast patients referred for genetic counseling between 2017 and 2019 was performed. Univariate and multivariate logistic regression evaluated factors associated with genetic counseling attendance and genetic testing.ResultsA total of 596 patients were referred for genetic counseling: 433 (72.7%) white; 138 (23.2%) black; and 25 (4.2%) other or unknown. In multivariate analysis, black patients, patients without breast cancer family history, and patients without a current cancer diagnosis, classified as high risk, were significantly less likely to attend their genetics appointment (p = 0.010, p = 0.007, p = 0.005, respectively). Age, insurance type, distance from facility, and need for chemotherapy did not significantly impact consult completion rate. Of the patients who completed a genetic consult, 84.4% (n = 248) had genetic testing and 17.7% (n = 44) had a pathogenic variant. For patients who attended counseling, there were no significant factors that were predictive with receipt of genetic testing.ConclusionsIn this study, there was a significant association between race and attending genetic counseling. Once counseled, most patients went on to receive genetic testing, and racial disparities in testing disappeared, emphasizing the value of providing additional education about the importance and purpose of genetic testing.

Tree-based Machine Learning Ensembles and Feature Importance Approach for the Identification of Intrusions in UNR-IDD Dataset

Detection of intrusions from network data with the use of machine learning techniques has gained great attention in the past decades. One of the key problems in the network security domain is the availability of representative datasets for testing and evaluation purposes. Despite several efforts by researchers to release datasets that can be used for benchmarking attack detection models, some of the released datasets still suffer from one limitation or the other. Thus, some researchers at the University of Nevada released a dataset named UNR-IDD dataset which was argued to be free from some of the limitations of the past datasets. This study proposed Tree-based ensemble approaches for building binary intrusion identification models from the UNR-IDD dataset. Decision Tree algorithms are used as base classifiers in the Extra Trees, Random Forest and AdaBoost-based intrusion detection models. The results of the experimental analyses carried out indicated that the three ensembles performed excellently when feature selection was used compared to when all features were applied. For instance, Extra Trees model achieved an accuracy of 0.97, precision of 0.98, recall of 0.98 and f1-score of 0.98. Similarly, Random Forest model achieved an accuracy of 0.98, precision of 0.98, recall of 0.99 and f1-score of 0.98. Adaboost-based model had an accuracy of 0.96, precision of 0.96, recall of 0.99 and f1-score of 0.98. It was deduced that Random Forest intrusion classification model achieved slight overall best results when compared to the other models built. It is concluded that the three homogeneous ensemble models achieved very promising results while feature importance was used as attribute selection method.

Applying the Milan models to setting analytical performance specifications- considering all the information.

Analytical performance specifications (APS) are used for decisions about the required analytical quality of pathology tests to meet clinical needs. The Milan models, based on clinical outcome, biological variation, or state of the art, were developed to provide a framework for setting APS. An approach has been proposed to assign each measurand to one of the models based on a defined clinical use, physiological control, or an absence of quality information about these factors. In this paper we propose that in addition to such assignment, available information from all models should be considered using a risk-based approach that considers the purpose and role of the actual test in a clinical pathway and its impact on medical decisions and clinical outcomes in addition to biological variation and the state-of-the-art. Consideration of APS already in use and the use of results in calculations may also need to be considered to determine the most appropriate APS for use in a specific setting.

DIAR: a blockchain-based system for generation and verification of academic diplomas

Nowadays, the centralized systems used in higher education institutions are sophisticated and have high security mechanisms, offering secure data transfer and real-time encryption. Despite the prevalent usage of centralized systems in many institutions, particularly those within the realm of higher education, some unresolved concerns persist pertaining to privacy, potential abuse, transparency, and the limited capacity to digitize numerous services. Blockchain systems are considered as a potential solution for addressing these constraints. This study begins by highlighting the significance of implementing blockchain systems in higher education institutions, while also outlining the obstacles encountered by researchers in this domain. Centralized systems and blockchain systems are distinguished, with a description of the challenges related to data transfer and adaptation to different platforms. A thorough explanation of the proposed blockchain system begins with a presentation of the conceptual model, followed by a detailed architecture of the processes that would be executed by the system, with particular emphasis on the generation and authentication of academic credentials. Additionally, an analysis is provided on the significance of smart contracts in the programming of blockchain systems. This includes a detailed explanation of the main smart contract architecture used in the proposed blockchain system. This article aims to explore the development of a proposed blockchain system and its practical implementation for testing purposes using specific scenarios and data in the foreseeable future.

Building an open-source collaborative platform for migration research: A metadata modeling approach using XML

Public access to well-organized migration data repositories is rare. While open migration data are available, most are dispersed, challenging their organization. This work uses a metadata modeling approach to organize migration-related data and research components hosted in an open-source, collaborative research environment. The proposed platform integrates data, computational models, application libraries, and research projects in a unified environment for research. An overview of the metadata modeling process and the XML technology used to integrate the platform components was provided, along with descriptions of the platform’s essential features, such as standardized exchange of information for data organization and sharing and collaborative authoring. The design of a prototype collaborative, open-source migration data and modeling research platform using sample data and software components was built and hosted on GitHub for testing and refinement purposes. It allows the authors to collaborate, improve, and assess the platform’s functionalities and capabilities. Our platform, COSMOS (Collaborative Open-Source Modeling System), adopted the creation and use of open data formats for alignment with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles to ensure that data at the prototype level is consistent with and can be used by the international migration research community in the future. By providing a standardized framework for data organization, data sharing, and collaboration, the platform can help accelerate scientific discovery and advance our understanding of complex social and cultural phenomena such as immigration.

Image Classification of Room Tidiness Using VGGNet with Data Augmentation

Tidiness becomes an essential aspect that everyone should maintain. Tidiness encompasses various elements, and one of the aspects closely related to it is the tidiness of a room. The tidiness of a room creates a comfortable and clean environment. The tidiness of a room is particularly crucial for individuals involved in businesses such as the hospitality industry. Therefore, a solution is needed to address this issue, and one of the approaches is to utilize Deep Learning for automatic room tidiness classification. One popular deep learning method for implementing image classification of room tidiness is the convolutional neural network (CNN), which creates a well-performing model for image classification with data augmentation. This research aims to develop an image classification model using CNN with the VGGNet architecture and data augmentation. This study is a reference for further development, with potential applications in the hospitality industry. The research results in a model that achieves an accuracy of 98.44% with a data proportion of 90% for training and validation, while the remaining 10% is used for testing purposes. The conclusion drawn from this study is that the CNN method, combined with data augmentation, can be utilized for image classification of room tidiness.

Experimental Tensile Strength Analysis of Different Manufacturer Oring

Importance and Purpose of Tensile Testing on ORings The tensile test on rings and especially on O-rings is primarily carried out for quality assurance reasons. The tensile test provides information about the formulation quality (evaluation of the absolute values, strength of the material, and determination of formulation changes). For example, it is not possible to detect an exchange of the polymer of compounds with different molecular weights and/or a different molecular weight distribution (e.g. highviscosity press mix by low-viscosity spray mix) by infrared spectroscopy (IR), but only by tensile testing and possibly by long-term compression set tests. Furthermore, the tensile test provides information on subsequent changes compared to the prototype condition. The Influence of Processing In the case of some O-rings, due to poor processing, it is not the strength value of the material that is determined, but the lower strength of the joining line (= confluence point of the rubber compound in the production of the O-ring in the injection mold) at which the O-ring tears. This problem depends on the material (e.g. certain acrylate rubbers), the tool and the process parameters. The joining line can usually be detected in advance by a visual and strain test or by relatively large scatter in a tensile test. Typical for the scatter in tensile tests of O-rings are 5-10%-points standard deviation related to the mean value of the elongation at break or the tensile strength. An important characteristic of good processing is the achievement of the optimum degree of vulcanization. However, there is no generally valid optimum, but this must be determined depending on the intended application of the elastomeric component. If, for example, the tear resistance is the most important material parameter for the finished product, a different degree of crosslinking or a different cross linking density must be aimed for than if, for example, a low

Goodness–of–fit tests based on the min–characteristic function

Tests of fit for classes of distributions that include the Weibull, the Pareto and the Fréchet families are proposed. The new tests employ the novel tool of the min–characteristic function and are based on an L2–type weighted distance between this function and its empirical counterpart applied on suitably standardized data. If data–standardization is performed using the MLE of the distributional parameters then the method reduces to testing for the standard member of the family, with parameter values known and set equal to one. Asymptotic properties of the tests are investigated. A Monte Carlo study is presented that includes the new procedure as well as competitors for the purpose of specification testing with three extreme value distributions. The new tests are also applied on a few real–data sets.

Pavement Crack Detection Based on the Improved Swin-Unet Model

Accurate pavement surface crack detection is crucial for analyzing pavement survey data and the development of maintenance strategies. On the basis of Swin-Unet, this study develops the improved Swin-Unet (iSwin-Unet) model with the developed skip attention module and the residual Swin Transformer block. Based on the channel attention mechanism, the pavement crack region can be better captured while the crack feature channels can be assigned more weights. Taking advantage of the developed residual Swin Transformer block, the encoder architecture can globally model the pavement crack feature. Meanwhile, the crack feature information can be efficiently exchanged. To verify the pavement crack detection performance of the proposed model, we compare the training performance and visualization results with the other three models, which are Swin-Unet, Swin Transformer, and Unet, respectively. Three public benchmarks (CFD, Crack500, and CrackSC) have been adopted for the purpose of training, validation, and testing. Based on the test results, it can be found that the developed iSwin-Unet achieves a significant increase in mF1 score, mPrecision, and mRecall compared to the existing models, thereby establishing its efficacy in pavement crack detection and underlining its significant advancements over current methodologies.

Smart IoT Irrigation System Based on Fuzzy Logic, LoRa, and Cloud Integration

Natural resources must be administered efficiently to reduce the human footprint and ensure the sustainability of the planet. Water is one of the most essential resources in agriculture. Modern information technologies are being introduced in agriculture to improve the performance of agricultural processes while optimizing water usage. In this scenario, artificial intelligence techniques may become a very powerful tool to improve efficiency. The introduction of the edge/fog/cloud paradigms, already adopted in other domains, may help to organize the services involved in complex agricultural applications. This article proposes the combination of several modern technologies to improve the management of hydrological resources and reduce water waste. The selected technologies are (1) fuzzy logic, used for control tasks since it adapts very well to the nonlinear nature of the agricultural processes, and (2) long range (LoRa) technology, suitable for establishing large distance links among the field devices (sensors and actuators) and the process controllers, executed in a centralized way. The presented approach has been validated in the laboratory by means of a control scheme aimed at achieving an adequate moisture level in the soil. The control algorithm, based on fuzzy logic, can use the weather forecast, obtained as a cloud service, to reduce water consumption. For testing purposes, the dynamics of the water balance model of the soil were implemented as hardware in the loop, executed in a dSPACE DS1104. Experiments proved the viability of the presented approach since the continuous space state output controller achieved a water loss reduction of 23.1% over a 4-day experiment length compared to a traditional on/off controller. The introduction of cloud services for weather forecasting improved the water reduction by achieving an additional reduction of 4.07% in water usage.

A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Convolutional Neural Network

Plant diseases pose a significant challenge to agriculture in India, causing substantial losses in crop production each year. Limited-resource farmers often struggle with diagnosing these diseases accurately, especially when relying on visual observation of leaf symptoms. Hence, there is an urgent need to enhance the detection, monitoring, and prediction of crop diseases to mitigate agricultural losses effectively. To address this challenge, we propose a mobile-based system empowered by Machine Learning (ML) and computer vision tailored for the Indian context. Our system utilizes Convolutional Neural Networks (CNNs) as the core deep learning engine to classify 37 disease categories commonly found in Indian crops. We have curated a comprehensive dataset comprising 96,206 images of plant leaves, encompassing both healthy and infected, for training, validation, and testing purposes. The user interface of our system is designed as an Android mobile application, enabling farmers to capture images of diseased plant leaves effortlessly. Upon image capture, the system swiftly identifies the disease category and presents it to the user along with a confidence percentage. This functionality empowers farmers to take timely action to protect their crops, thereby reducing reliance on incorrect fertilizers that may exacerbate plant stress. Furthermore, we evaluated the performance of our system using various metrics, including classification accuracy and processing time. Our results indicate an impressive overall classification accuracy of 94% across the 37 disease classes prevalent in 14 different crop species commonly cultivated in India. In essence, our ML-powered mobile-based solution offers Indian farmers a robust tool for efficient crop disease diagnosis and management, ultimately contributing to the enhancement of agricultural productivity and sustainability in the country.

Exploring the Potential of an Eye Tissue Donor Reporting App in Enhancing the Procurement of Corneal Donors: Mixed Methods Observational Study.

The availability of donated eye tissue saves and enhances vision in transplant recipients; however, the current demand for tissue surpasses the available supply. Corneal donor shortages lead to increased wait times, delayed surgeries, prolonged visual impairment, and increased inconvenience to patients requiring eye tissue transplantation. A web-based application was previously developed to facilitate easy and intuitive submission of potential donor information. The primary objectives of this study were to assess health care professionals' attitudes toward the potential application and evaluate its effectiveness based on user feedback and donor registrations through the application. Researchers used a mixed methods approach, commencing with a literature review to identify challenges associated with donor procurement. Stakeholder interviews were conducted to gauge health care professionals' perspectives regarding the application. User feedback was collected through questionnaires, surveys, and interviews to assess the application's usability and impact. An assessment of the reported potential donors and questionnaire responses were analyzed. The final version of the application successfully reported 24 real cornea donors. Among 64 health care providers who used the application to communicate about potential donors, 32 of them submitted trial entries exclusively for testing purposes. The remaining 8 health care professionals reported potential donors; however, these individuals did not meet the donor qualification criteria. The majority of participants found the application user-friendly and expressed their readiness to use it in the future. Positive ratings were assigned to the layout, appearance, purpose, and specific features of the application. Respondents highlighted the automatic sending of notifications via SMS text messages and the integration of all necessary documents for donor qualification and tissue collection as the most valuable functions of the application. The study indicates that donor reporting applications offer promising solutions to enhance tissue donor procurement. This application streamlined the reporting process, reduced paperwork, facilitated communication, and collected valuable data for analysis.

APPLICATION OF HYBRID APPROACH FOR WOLAITA LANGUAGE PART OF SPEECH TAGGING

The main purpose of this study is to develop part-of-speech tagger for Wolaita Language using hybrid approach. Part of speech tagger is one of the subtasks in NLP application which is important for other Natural Language Processing (NLP) applications, like parser, machine translator, speech recognizer and search engines. PoST is a process of tagging a corresponding part of speech tag for a word that tag defines how the word is used in a sentence. The PoST for Wolaita language is not enough yet to be used as one vital module in other natural language processing applications. In this study, the development of PoS tagger using hybrid approach that combines HMM and rule based approaches was conducted for Wolaita language. In general HMM model need large data to increase the performance and the rule based model learner rule based on the language features. The HMM tagger, tags the words based on the optimal path for a given sequence of words and transformation based learning (TBL) is a rule based approaches that learns rule directly from the training corpus without expert knowledge. The developed hybrid approach of Wolaita language PoS tagger uses HMM tagger as initial annotators and rule based tagger as a corrector based on fixed threshold values. For implementation and experiment purpose the researcher used python programming and NLTK. For training and testing the models, 1256 sentences or 15,268 words are collected from three different categories (Bible, Social media in Wolaita language (Wogetta FM 96.6 ) and Wolaita language department) and annotated data manually. For tagging purpose 26 PoS tag are identified. From entire corpus, 90% for training and the remaining of entire corpus for testing purpose. The performance of the taggers, are tested by using different experiments. After experiment the researcher found that the performance of HMM, rule based and hybrid taggers shows 88.14%, 93.19% and 94.82% respectively. Generally, hybrid approach showed the better performance to assigning part of speech tag for Wolaita language.

The purpose and effectiveness of doping testing in sport.

Maintaining an effective testing program is critical to the success and credibility of the anti-doping movement. However, a low detection ratio compared to the assumed real prevalence of sport doping has led some to question and criticize the effectiveness of the current testing system. In this perspective article, we review the results of the global testing program, discuss the purpose of testing, and compare benefits and limitations of performance indicators commonly used to evaluate testing efforts. We suggest that an effective testing program should distinguish between preventive testing and testing aimed at detecting the use of prohibited substances and prohibited methods. In case of preventive testing, the volume of the test program in terms of number of samples, tests and analyses is likely to be positively related to the extent of the deterrent effect achieved. However, there is a lack of literature on how the deterrent effect works in the practical context of doping testing. If the primary goal is to detect doping, the testing must be risk- and intelligence-based, and quality in test planning is more important than quantity in sample collection. The detection ratio can be a useful tool for evaluating the effectiveness of doping testing, but for the calculation one should take into account the number of athletes tested and not just the number of collected samples, as the former would provide a more precise measure of the tests' ability to detect doping among athletes.

Characterisation of the neutron sources in use at STUK SSDL through a new transfer instrument with spectrometric capabilities

Characterising neutron sources for calibration or generic testing purposes is a complex task, as the neutron spectrum may depend, to some extent, on the construction characteristics of the source. Bonner spheres (BS) are the traditional spectrometric transfer instruments, but they are very sensitive to room scatter and required measurements are highly time consuming. The recently developed NCT-WES device, belonging to the family of the Single Moderator Neutron Spectrometers, has been proposed as a convenient alternative to BS. This work presents the results of a demonstration campaign organised at the STUK neutron metrology laboratory (Finland) in July 2023, where the spectra from two 252Cf and two 241Am-Be sources were determined. For the 241Am-Be sources, the results were compared to the spectra of two categories recently introduced by ISO 8529-1. Based on the results it was concluded that the “large” source category is an appropriate selection for STUK sources. The campaign also proved the operational advantages of NCT-WES as spectrometric transfer instrument.

RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization.

Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.

Commissioning of the hydrogen cryostat of the cryogenic moderator system of the European Spallation Source (ESS) for test purposes at the ZEA-1

A helium-cooled hydrogen cryostat for the European Spallation Source (ESS) was developed, built and tested at the ZEA-1 of the Forschungszentrum Jülich. In order to be able to carry out the final factory acceptance test (FAT), a nitrogen mixing system was developed with which the cryostat could be cooled down and operated in a controlled manner. During the test phase, the cryostat was operated exclusively with nitrogen instead of helium and hydrogen. Nevertheless, all functions of the cryostat system could be checked and key figures could be determined.