Multiple Sources Research Articles

The effects of input flood and input enhancement techniques in teaching collocations to EFL learners

Input flood and input enhancement are two prominent techniques to develop learners’ writing competence. While the former refers to offering multiple sources to increase students’ gains, the latter implies highlighting certain linguistic items to attract attention. A plethora of studies have been conducted to measure the effectiveness of both techniques. However, a gap in the literature has been noticed in comparing input flood with input enhancement technique in terms of writing improvement in Iraq. Thus, the present study was conducted to measure the impacts of the input flood technique to teach collocations on 48 EFL learners who joined an intensive language learning program at the Language Preparatory School of Tishk International University in Erbil, Iraq. Participants, chosen by convenience sampling method, were exposed to input flood or input enhancement techniques to expand their colloquial expression knowledge within six weeks in the 2022–2023 academic year spring semester. Data were collected via writing exams and interviews as a part of the explanatory sequential research design. The findings, analyzed by SPSS 27 and MAXQDA, revealed that experimental group students who received the instruction via input flood increased their writing marks more significantly than control group students whose instruction was via input enhancement. Likewise, students who received the instruction with input flood had more positive views than students receiving input enhancement. The findings of the study may have several implications for educators who think about teaching collocations via input flood and input enhancement techniques professionally.

Ebselen: A Review on its Synthesis, Derivatives, Anticancer Efficacy and Utility in Combating SARS-COV-2.

Ebselen is a selenoorganic chiral compound with antioxidant properties comparable to glutathione peroxidase. It is also known as 2-phenyl-1,2-benzisoselenazol-3(2H)-one. In studies examining its numerous pharmacological activities, including antioxidant, anticancer, antiviral, and anti-Alzheimer's, ebselen has demonstrated promising results. This review's primary objective was to emphasize the numerous synthesis pathways of ebselen and their efficacy in fighting cancer. The data were collected from multiple sources, including Scopus, PubMed, Google Scholar, Web of Science, and Publons. The starting reagents for the synthesis of ebselen are 2-aminobenzoic acid and N-phenyl benzamide. It was discovered that ebselen has the ability to initiate apoptosis in malignant cells and prevent the formation of new cancer cells by scavenging free radicals. In addition, ebselen increases tumor cell susceptibility to apoptosis by inhibiting TNF-α mediated NF-jB activation. Ebselen can inhibit both doxorubicin and daunorubicin-induced cardiotoxicity. Allopurinol and ebselen administered orally can be used to suppress renal ototoxicity and nephrotoxicity. Due to excessive administration, diclofenac can induce malignancy of the gastrointestinal tract, which ebselen can effectively suppress. Recent research has demonstrated ebselen to inhibit viral function by binding to cysteine-containing catalytic domains of various viral proteases. It was discovered that ebselen could inhibit the catalytic dyad function of Mpro by forming an irreversible covalent bond between Se and Cys145, thereby altering protease function and inhibiting SARS-CoV-2. Ebselen may also inhibit the activation of endosomal NADPH oxidase of vascular endothelial cells, which is believed to be required for thrombotic complications in COVID-19. In this review, we have included various studies conducted on the anticancer effect of ebselen as well as its inhibition of SARS-CoV-2.

Multi-Source Domain Adaptation for Medical Image Segmentation.

Unsupervised domain adaptation(UDA) aims to mitigate the performance drop of models tested on the target domain, due to the domain shift from the target to sources. Most UDA segmentation methods focus on the scenario of solely single source domain. However, in practical situations data with gold standard could be available from multiple sources (domains), and the multi-source training data could provide more information for knowledge transfer. How to utilize them to achieve better domain adaptation yet remains to be further explored. This work investigates multi-source UDA and proposes a new framework for medical image segmentation. Firstly, we employ a multi-level adversarial learning scheme to adapt features at different levels between each of the source domains and the target, to improve the segmentation performance. Then, we propose a multi-model consistency loss to transfer the learned multi-source knowledge to the target domain simultaneously. Finally, we validated the proposed framework on two applications, i.e., multi-modality cardiac segmentation and cross-modality liver segmentation. The results showed our method delivered promising performance and compared favorably to state-of-the-art approaches.

Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller

A micro grid system with renewable source operation control is a complex part as each source operates at different parameters. This renewable micro grid with multiple sources like solar plants, wind farm, fuel cell, battery backup has to be operated in both grid connected and standalone condition. During grid connection the micro grid, inverter has to inject power to the grid and compensate load in synchronization to the grid voltages. And during standalone condition the inverter is controlled with droop control module which stabilizes the voltage and frequency of the system even during grid disconnection. The droop control module is further updated with new advanced controllers like fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) replacing the traditional proportional integral derivative (PID) and proportional integral (PI) controllers improving the response rate and for achieving better stabilization. This paper has comparative analysis of the micro grid system with different droop controllers under various operating conditions. Parameters like voltage magnitude (Vmag), frequency (F), load and inverter powers (Pload and Pinv) of the test system are compared with different controllers. A numeric comparison table is given to determine the optimal controller for the inverter operation. The analysis is carried out in MATLAB/Simulink software with graphical and parametric validations.

The Next Era of Assessment: Can Ensuring High-Quality, Equitable Patient Care Be the Defining Characteristic?

Previous eras of assessment in medical education have been defined by how assessment is done, from knowledge exams popularized in the 1960s to the emergence of work-based assessment in the 1990s to current efforts to integrate multiple types and sources of performance data through programmatic assessment. Each of these eras was a response to why assessment was performed (e.g., assessing medical knowledge with exams; assessing communication, professionalism, and systems competencies with work-based assessment). Despite the evolution of assessment eras, current evidence highlights the graduation of trainees with foundational gaps in the ability to provide high-quality care to patients presenting with common problems, and training program leaders report they graduate trainees they would not trust to care for themselves or their loved ones. In this article, the authors argue that the next era of assessment should be defined by why assessment is done: to ensure high-quality, equitable care. Assessment should place focus on demanding graduates possess the knowledge, skills, attitudes, and adaptive expertise to meet the needs of all patients and ensuring that graduates are able to do this in an equitable fashion. The authors explore 2 patient-focused assessment approaches that could help realize the promise of this envisioned era: entrustable professional activities (EPAs) and resident sensitive quality measures (RSQMs)/TRainee Attributable and Automatable Care Evaluations in Real-time (TRACERs). These examples illustrate how the envisioned next era of assessment can leverage existing and new data to provide precision education assessment that focuses on providing formative and summative feedback to trainees in a manner that seeks to ensure their learning outcomes prepare them to ensure high-quality, equitable patient outcomes.

It takes two: Dual Branch Augmentation Module for domain generalization

Although great success has been achieved in various computer vision tasks, deep neural networks (DNNs) suffer dramatic performance degradation when evaluated on out-of-distribution data. Domain generalization (DG) is proposed to handle this problem by learning domain-agnostic information from multiple source domains to generalize well on unseen target domains. Several methods resort to Fourier transform due to its simplicity and efficiency. They argue that amplitude spectra imply domain-specific information, which should be suppressed, while phase counterparts imply domain-agnostic information, which should be preserved. However, these methods only suppress the domain-specific information in source domains and neglect the relationship with target domains, leading to the persistence of the domain gap. Besides, these methods preserve domain-agnostic information by keeping phase components unchanged, causing them to be underutilized. In this paper, we propose Dual Branch Augmentation Module (DBAM) by leveraging Fourier transform and taking advantage of both amplitude and phase spectra. For the amplitude branch, we propose Inner-domain Amplitude Distribution Rectification (IADR) and Cross-domain Amplitude Dirichlet Mixup (CADM) to stabilize the training process and explore more feature space. In addition, we propose Test-time Amplitude Prototype Calibration (TAPC) to construct the connection between source and target domains during evaluation to further mitigate the domain gap. For the phase branch, we propose Random Symmetric Phase Perturbation (RSPP) to enhance the robustness for recognizing domain-agnostic information. With the combined contributions of the two branches, DBAM significantly surpasses other state-of-the-art (SOTA) methods. Extensive experiments on four benchmarks and further analysis demonstrate the effectiveness of DBAM.

Development of a novel machine learning-based approach for brain function assessment and integrated software solution

Industry 5.0 emphasizes human-centered solutions. However, bridging the gap between human conditions and engineering systems remains a challenge in this era. Promoting the development of cybernetics applications in the field of rehabilitation is imperative. In the healthcare sector, the evaluation of rehabilitation products’ ergonomics and the dynamic assessment of the human body are of utmost importance. Amid the burgeoning prominence of data-driven product design concepts, ergonomics methods have emerged as indispensable evaluative tools. Nevertheless, the integration of data from multiple sources remains a contemporary challenge; one must extract meaningful information from diverse data sets while enhancing data analysis and fusion techniques. In this study, we collect multimodal neurobiological signals using electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) to devise a robust multimodal rehabilitation data fusion method based on graph convolutional network (GCN), particularly focusing on the motor imagery (MI) task. To achieve a more holistic evaluation, we incorporate granger causality (GC) and brain region adjacency matrix as supplementary features, integrating electrophysiological and hemodynamic perspectives. This integration aims to enhance information complementarity and foster a comprehensive understanding of the neural processes involved in MI. Our results demonstrate the superiority of the multimodal fusion method: it achieves higher average accuracy and improved stability, as indicated by the reduced standard deviation of accuracy. This enhanced performance suggests its potential regarding broader research applications. Building upon our successful findings, we establish an intelligent algorithmic rehabilitation platform based on multimodal neural data. This platform not only holds significant clinical value, facilitating precision and personalized medicine, but also propels the advancement of medical practices, paving the way for a more tailored and effective approach to patient care. Our research makes progresses in the areas of rehabilitation modeling, equipment design, intelligence, and interdisciplinary collaboration through the in-depth study and extension of cybernetic principles and techniques.

Fuzzy convolutional neural network model applied to classification problems

This paper presents an approach incorporating fuzzy logic techniques inside a convolutional neural network to manage uncertainty present in the multiple data sources that the model handles when training. The implementation considers the use of information and filters in the fuzzy spectrum, as well as the creation of a new layer to replace the traditional convolution layer with a fuzzy convolutional layer. The aim is to design artificial intelligence algorithms that combine the potential of deep convolutional neural networks and fuzzy logic to create robust systems that allow modeling the uncertainty present in the sources of data and that are applied to classification problems. The fuzzification process is developed using three membership functions, including the Triangular, Gaussian, and S functions. The work was tested in databases oriented to traffic signs, due to the complexity of the different circumstances and factors in which a traffic sign can be found.

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

The reliability of a pressurized water reactor power plant’s control rod drive mechanism (CRDM) is affected by many factors, such as operation states, unit performance, and dynamic environments. Multiple sources of uncertainties, including random, interval, and fuzzy, exist when analyzing the reliability of CRDMs. Modeling reliability without considering the fusion of multisource uncertainties may result in model distortion and may not provide realistic assessment results. This paper proposes a multisource uncertainty fusion method powered by the margin-based variable transformation and the Bayesian network propagation. The interval and fuzzy variables are transformed into random variables to obtain the corresponding generalized probability density function of the CRDM’s feature parameters. After that, the CRDM’s reliability can be evaluated via probability quantization of the Bayesian network inference result through sampling algorithms. A magnetic-jack type CRDM case is presented to verify the proposed method, and the results show that this method can fuse three types of uncertainty variable in a unified way and effectively obtain reliability evaluation results.

Polyphyletic origin of saxitoxin biosynthesis genes in the marine dinoflagellate Alexandrium revealed by comparative transcriptomics

The marine dinoflagellate Alexandrium is known to form harmful algal blooms, and at least 14 species within the genus can produce saxitoxins (STXs). STX biosynthesis genes (sxt) are individually revealed in toxic dinoflagellates; however, the evolutionary history remains controversial. Herein, we determined the transcriptome sequences of toxic Alexandrium (A. catenella and A. pacificum) and non-toxic Alexandrium (A. fraterculus and A. fragae) and characterized their sxt by focusing on evolutionary events and STX production. Comparative transcriptome analysis revealed higher homology of the sxt in toxic Alexandrium than in non-toxic species. Notably, non-toxic Alexandrium spp. were found to have lost two sxt core genes, namely sxtA4 and sxtG. Expression levels of 28 transcripts related to eight sxt core genes showed that sxtA, sxtG, and sxtI were relatively high (>1.5) in the toxic group compared to the non-toxic group. In contrast, the non-toxic group showed high expression levels in sxtU (1.9) and sxtD (1.7). Phylogenetic tree comparisons revealed distinct evolutionary patterns between 28S rDNA and sxtA, sxtB, sxtI, sxtD, and sxtU. However, similar topology was observed between 28S rDNA, sxtS, and sxtH/T. In the sxtB and sxtI phylogeny trees, toxic Alexandrium and cyanobacteria were clustered together, separating from non-toxic species. These suggest that Alexandrium may acquire sxt genes independently via horizontal gene transfer from toxic cyanobacteria and other multiple sources, demonstrating monocistronic transcripts of sxt in dinoflagellates.

Development of method using language processing techniques for extracting information on drug-health food product interactions.

Health food products (HFPs) are foods and products related to maintaining and promoting health. HFPs may sometimes cause unforeseen adverse health effects by interacting with drugs. Considering the importance of information on the interactions between HFPs and drugs, this study aimed to establish a workflow to extract information on Drug-HFP Interactions (DHIs) from open resources. First, Information on drugs, enzymes, their interactions, and known DHIs was collected from multiple public databases and literature sources. Next, a network consisted of enzymes, HFP, and drugs was constructed, assuming enzymes as candidates for hubs in Drug-HFP interactions (Method 1). Furthermore, we developed methods to analyze the biomedical context of each drug and HFP to predict potential DHIs out of the DHIs obtained in Method 1 by applying BioWordVec, a widely used biomedical terminology quantifier (Method 2-1 and 2-2). 44,965 DHIs (30% known) were identified in Method 1, including 38 metabolic enzymes, 157 HFPs, and 1256 drugs. Method 2-1 selected 7401 DHIs (17% known) from the DHIs of Method 1, while Method 2-2 chose 2819 DHIs (30% known). Based on the different assumptions in these methods where Method 2-1 specifically selects HFPs interacting with specific enzymes and Method 2-2 specifically selects HFPs with similar function with drugs, the propsed methods resulted in extracting a wide variety of DHIs. By integrating the results of language processing techniques with those of the network analysis, a workflow to efficiently extract unknown and known DHIs was constructed.

Spatial patterns in recent forest growth trends across the Czech Republic

ABSTRACT The radial growth of trees significantly contributes to climate change mitigation by sequestering carbon into woody biomass. Radial growth trends observed in European temperate forests during the recent period of climate warming vary between growth acceleration due to longer growing seasons and growth declines due to amplified drought stress. Assessing the spatial variation of growth trends is challenging due to the point relevance of available empirical data including forest inventories and tree-ring width chronologies. Here, we used a database of tree-ring width chronologies from 596 sites and spatial models to describe the growth trends of five tree species across the Czech Republic between 1990 and 2018. The resulting map highlights multiple sources of variation in growth trends including differences between species and prominent spatial gradients along elevation, latitude, and longitude. The knowledge of spatially explicit growth trends is essential for the adaptation of the forestry sector to ongoing climate change.

RSS Feeds Filtering from multiple sources using automated techniques of Natural Language Processing

The Internet's rapid growth has resulted in a surge of information, posing challenges for users trying to stay updated. The World Wide Web, with its diverse websites updated at varying intervals, necessitates efficient categorization of news articles. As the number of online news sources continues to rise, categorization becomes crucial for users to find information easily. Given the overwhelming volume of digital media information, categorizing news using algorithms and assigning multiple tags based on their category becomes essential. In the modern digital era, users face difficulties filtering and accessing relevant content tailored to their preferences due to the abundance of information from various sources. This study employs Natural Language Processing (NLP) methods for automated content filtering of RSS feeds. The developed system extracts feeds via RSS, generates headings and summaries, and employs NLP for categorization. This research has broad implications, offering opportunities for improved information management, personalized recommendations, and informed decision-making across diverse domains.

A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment

AbstractWith the proliferation of digital devices in internet of things (IoT) environment featuring advanced visual capabilities, the task of Image Source Identification (ISI) has become increasingly vital for legal purposes, ensuring the verification of image authenticity and integrity, as well as identifying the device responsible for capturing the original scene. Over the past few decades, researchers have employed both traditional and machine‐learning methods to classify image sources. In the current landscape, data‐driven approaches leveraging deep learning models have emerged as powerful tools for achieving higher accuracy and precision in source prediction. The primary focus of this research is to address the complexities arising from diverse image sources and variable quality in IoT‐generated multimedia data. To achieve this, a Hybrid Data Fusion Approach is introduced, leveraging multiple sources of information to bolster the accuracy and robustness of ISI. This fusion methodology integrates diverse data streams from IoT devices, including metadata, sensor information, and contextual data, amalgamating them into a comprehensive data set for analysis. This study introduces an innovative approach to ISI through the implementation of a Twin Convolutional Neural Network Architecture (TCA) aimed at enhancing the efficacy of source classification. In TCA, the first CNN architecture, referred to as DnCNN, is employed to eliminate noise from the original data set, generating 256 × 256 patches for both training and testing. Subsequently, the second CNN architecture is employed to classify images based on features extracted from various convolutional layers using a 3 × 3 filter, thereby enhancing prediction efficiency. The proposed model demonstrates exceptional accuracy in effectively classifying image sources, showcasing its potential as a robust solution in the realm of ISI.

CSDMS Data Components: data–model integration tools for Earth surface processes modeling

Abstract. Progress in better understanding and modeling Earth surface systems requires an ongoing integration of data and numerical models. Advances are currently hampered by technical barriers that inhibit finding, accessing, and executing modeling software with related datasets. We propose a design framework for Data Components, which are software packages that provide access to particular research datasets or types of data. Because they use a standard interface based on the Basic Model Interface (BMI), Data Components can function as plug-and-play components within modeling frameworks to facilitate seamless data–model integration. To illustrate the design and potential applications of Data Components and their advantages, we present several case studies in Earth surface processes analysis and modeling. The results demonstrate that the Data Component design provides a consistent and efficient way to access heterogeneous datasets from multiple sources and to seamlessly integrate them with various models. This design supports the creation of open data–model integration workflows that can be discovered, accessed, and reproduced through online data sharing platforms, which promotes data reuse and improves research transparency and reproducibility.

LinChemIn: Route Arithmetic─Operations on Digital Synthetic Routes.

Computational tools are revolutionizing our understanding and prediction of chemical reactivity by combining traditional data analysis techniques with new predictive models. These tools extract additional value from the reaction data corpus, but to effectively convert this value into actionable knowledge, domain specialists need to interact easily with the computer-generated output. In this application note, we demonstrate the capabilities of the open-source Python toolkit LinChemIn, which simplifies the manipulation of reaction networks and provides advanced functionality for working with synthetic routes. LinChemIn ensures chemical consistency when merging, editing, mining, and analyzing reaction networks. Its flexible input interface can process routes from various sources, including predictive models and expert input. The toolkit also efficiently extracts individual routes from the combined synthetic tree, identifying alternative paths and reaction combinations. By reducing the operational barrier to accessing and analyzing synthetic routes from multiple sources, LinChemIn facilitates a constructive interplay between artificial intelligence and human expertise.

Sentiment Analysis on the Quality of Public Services with User Satisfaction Prediction of YuhSinau Application Managed by BKPSDM Kabupaten Kebumen Using LSTM Method

The quality of public services is critical in providing effective and responsive governance in an increasingly digital society. The development of the YuhSinau application by the Personnel and Human Resource Development Agency (BKPSDM) of Kebumen Regency offers an innovative response to the growing need for e-learning solutions for local government civil servants (Pegawai Negeri Sipil or PNS). However, determining the app’s effectiveness and user satisfaction is critical. This demands a thorough sentiment analysis in order to acquire insights into users’ thoughts and opinions about the quality of public services supplied by YuhSinau. The Long Short-Term Memory (LSTM) approach is used in this article to examine feelings and forecast customer pleasure. Data collection from multiple sources, initial data preprocessing, LSTM model construction, training, validation, and prediction are all part of the process. The results show that the model has some drawbacks, most notably its failure to appropriately explain variation in target data due to a negative R-squared value. Enhancements to the LSTM architecture, hyperparameter adjustment, and the use of more diverse and representative training data are proposed to improve the model. Continuous review and responsiveness to user comments are critical for improving the quality of public services via the YuhSinau application.
 Keywords: sentiment analysis, public service quality, user satisfaction, YuhSinau application,LSTM method

Modeling, design and optimization of integrated renewable energy systems for electrification in remote communities

Integrated renewable energy systems are becoming a promising option for electrification in remote communities. Integrating multiple renewable energy sources allows the communities to counteract the weaknesses of one renewable energy source with the strengths of another. This study aims to model, design and optimize integrated renewable energy systems consisting of solar photovoltaic (PV) panels, wind turbines, a biomass power generator, and storage batteries for applications in remote communities in Canada. Biomass is used as a fuel to produce electricity during periods when solar power and wind power are not capable of meeting the power demand. A methodology is developed to optimize the integrated renewable energy systems design, with the aim of minimizing the net present cost (NPC) and the levelized cost of electricity (LCOE) of the energy systems. Results show that the NPC is $3.61 M and the LCOE is $0.255/kWh for an optimized integrated renewable energy system in a sample remote community that has a peak power consumption of 238.7 kW and an average load demand of 2230 kWh/day. Through the present research, the integrated energy systems are evidenced to be an effective option for electrification in remote communities.

DGAMDA: Predicting miRNA-disease association based on dynamic graph attention network.

MiRNA (microRNA)-disease association prediction has essential applications for early disease screening. The process of traditional biological experimental validation is both time-consuming and expensive. However, as artificial intelligence technology continues to advance, computational methods have become efficient tools for predicting miRNA-disease associations. These methods often rely on the combination of multiple sources of association data and require improved feature mining. This study proposes a dynamic graph attention-based association prediction model, DGAMDA, which combines feature mapping and dynamic graph attention mechanisms through feature mining on a single miRNA-disease association network. DGAMDA effectively solves the problems of feature heterogeneity and inadequate feature mining by previous static graph attention mechanisms and achieves high-precision feature mining and association scoring prediction. We conducted a five-fold cross-validation experiment and obtained the mean values of Accuracy, Precision, Recall, and F1-score, which were .8986, .8869, .9115, and .8984, respectively. Our proposed model outperforms other advanced models in terms of experimental results, demonstrating its effectiveness in feature mining and association prediction based on a single association network. In addition, our model can also be used to predict miRNAs associated with unknown diseases.

The use of practice evidence by Australian occupational therapists: Perspectives and actions.

Evidence-based practice supports clinical decision-making by using multiple sources of evidence arising from research and practice. Research evidence develops through empirical study while practice evidence arises through clinical experience, client preferences, and the practice context. Although occupational therapists have embraced the paradigm of evidence-based practice, some studies have identified limits in the availability and use of research, which can lead to reliance on other forms of evidence. This study aimed to understand how Australian occupational therapists use practice evidence, manage potential bias, and enhance trustworthiness. Potential use of a critical appraisal tool for practice evidence was also explored. A 42-item questionnaire was developed to address the study aims. It consisted of a 7-point Likert scale, ordinal and free text questions. Likert scales were collapsed into binary scales and analysed using SPSS. Ordinal data were graphed and free text responses were analysed using manifest content analysis. Most respondents (82%) indicated that practice evidence was an important informant of practice and is used alongside research evidence. Almost all respondents (98%) expressed confusion when reconciling discrepancies between research and practice evidence. There was general acknowledgement that practice evidence is prone to bias (82%), yet 92% were confident in trusting their own practice evidence. Most respondents (74.5%) undertook some measures to appraise practice evidence, and almost all respondents (90%) agreed they would refer to a critical appraisal tool that helped them evaluate practice evidence. Occupational therapists in this study routinely use practice evidence arising from their own experience, client perspectives, and their practice context to inform clinical decision-making. While they agreed that practice evidence was prone to bias and misinterpretation, they generally trusted their own practice evidence. Participants indicated they needed guidance to critically appraise their practice evidence and supported the development of a critical appraisal tool for this purpose.