Information Input Research Articles

Amplitude-assisted tagging of longitudinally polarised bosons using wide neural networks

Extracting longitudinal modes of weak bosons in LHC processes is essential to understand the electroweak-symmetry-breaking mechanism. To that end, we propose a general method, based on wide neural networks, to properly model longitudinal-boson signals and hence enable the event-by-event tagging of longitudinal bosons. It combines experimentally accessible kinematic information and genuine theoretical inputs provided by amplitudes in perturbation theory. As an application we consider the production of a Z boson in association with a jet at the LHC, both at leading order and in the presence of parton-shower effects. The devised neural networks are able to extract reliably the longitudinal contribution to the unpolarised process. The proposed method is very general and can be systematically extended to other processes and problems.

Mapping Seagrass Biodiversity Indicators of Pari Island using Multiple WorldView-2 Bands Derivatives

Comprehensive information on seagrass biodiversity indicators, such as species composition, percentage cover, and biomass carbon stock, remains limited across various regions globally. Mapping these indicators using remote sensing images requires extracting maximum information from the input images to achieve effective results. This study aims to map seagrass distribution, percent cover (PC), and aboveground carbon stock (AGC) as biodiversity indicators in the optically shallow waters surrounding Pari Island. We integrate WorldView-2 (WV2) derivatives, field seagrass data, and RF classification and regression algorithms to accomplish this objective. The WV2 image derivatives encompass surface reflectance bands, band ratios, mean and variance co-occurrence texture bands, and principle component bands. These inputs are used individually and collectively for mapping, employing a random forest algorithm trained with field seagrass data. Our results demonstrate that the most accurate benthic habitat map achieves an overall accuracy (OA) of 65.2%, with a user's accuracy of 65.2% and a producer's accuracy of 72.8% for the seagrass-dominated class. Seagrass PC mapping yields a root mean square error (RMSE) of 17.1%, with an average PC of 47.4 ± 9.9%. Seagrass AGC mapping achieves an RMSE of 5.0 g C m-2, with an average AGC range of 6.2 – 29.1 g C m-2, estimating the study area's aboveground biomass carbon stock at 27.9 tons C. Combined inputs produce the most accurate results for all biodiversity indicators, emphasizing the importance of utilizing combined bands from SR band derivatives to maximize information input for training mapping algorithms, instead of using derivative bands individually or as replacements for the initial SR bands.
 Keywords : Seagrass; Biodiversity; Mapping; WorldView-2; Pari Island
 
 Copyright (c) 2023 Geosfera Indonesia and Department of Geography Education, University of Jember
 This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

Orthographic influence in the distributional learning of non-native speech sounds

This study investigated the role of orthographic information in the acquisition of non-native speech sounds by monolingual English listeners. Two potentially important orthographic variables were explored: Orthographic compatibility (whether the orthographic information supports or contradicts the distributional information) and orthographic familiarity (whether the native and target languages share the same orthography). Ten groups of learners were trained on either a unimodal or bimodal distribution of two length continua. Out of the 10 groups, eight groups were also exposed to orthographic cues that varied in their compatibility with the distributional information (compatible vs. incompatible) and familiarity with the orthography of learners’ native language (Roman vs. Arabic). Following training, all participants performed an AX discrimination task to test their discrimination of the length contrast. The results revealed that, in general, the availability of either familiar or unfamiliar orthographic input which signaled the existence of a single length category significantly lowered learners’ discrimination of the length contrast regardless of the auditory distribution. Further, the exposure to orthographic input that supported a two-category length distinction enhanced the discrimination of the length contrast irrespective of the distribution. However, the most significant improvement occurred when both distributional information and familiar orthographic input were compatible. Overall, these findings indicate that orthographic input, regardless of its level of compatibility or familiarity, may influence the acquisition of non-native speech sounds.

Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction.

As a biological characteristic, gait uses the posture characteristics of human walking for identification, which has the advantages of a long recognition distance and no requirement for the cooperation of subjects. This paper proposes a research method for recognising gait images at the frame level, even in cases of discontinuity, based on human keypoint extraction. In order to reduce the dependence of the network on the temporal characteristics of the image sequence during the training process, a discontinuous frame screening module is added to the front end of the gait feature extraction network, to restrict the image information input to the network. Gait feature extraction adds a cross-stage partial connection (CSP) structure to the spatial-temporal graph convolutional networks' bottleneck structure in the ResGCN network, to effectively filter interference information. It also inserts XBNBlock, on the basis of the CSP structure, to reduce estimation caused by network layer deepening and small-batch-size training. The experimental results of our model on the gait dataset CASIA-B achieve an average recognition accuracy of 79.5%. The proposed method can also achieve 78.1% accuracy on the CASIA-B sample, after training with a limited number of image frames, which means that the model is more robust.

Advancing Health Equity in Neurologic Disorders and Stroke: Stakeholder Insights Into Health Disparities, Research Gaps, and Potential Interventions.

The purpose of this study was to analyze the National Institute of Neurological Disorders and Stroke (NINDS) Request for Information (RFI) input from the public-including health care providers, researchers, patients, patient advocates, caregivers, advocacy organizations, professional societies, and private and academic stakeholders with an interest in health disparities (HDs) in neurologic disease. RFI questions were structured to solicit input on what stakeholders believe are neurologic disease HD research priorities, drivers of health inequity, and potential interventions. Furthermore, these stakeholder insights were examined within the context of contemporary scientific literature and research frameworks on health equity and health disparities. The NINDS published a RFI from March 31 to July 15, 2020. The RFI analysis presented here is part of a larger strategic planning process aimed to guide future NINDS efforts in neurologic disorder health equity (HE) research and training. The public commented on facilitators of HDs, populations that experience HDs (HDPs), potential interventions, and research opportunities related to HDs in neurologic disease and/or care in the United States across the lifespan. Responses were analyzed using qualitative methodology. Frequently suggested interventions were thematically clustered using the interpretive phenomenological analysis methodology and are presented in this article to provide a stakeholder-identified roadmap for advancing HE. Respondents identified socioecological factors as driving HDs in 89% of determinants reported. Stakeholder-reported HD determinants and subsequent interventions could be classified into the following conceptual categories: HDP neurospecialty care access, innovative HDP engagement and research inclusion strategies, and development of a well-trained clinician-scientist HD workforce. Clustering of the feedback from patient and patient-adjacent respondents (i.e., caretakers and patient advocates) highlighted the prevalence of patient-provider interpersonal factors and limited resources driving access-to-care barriers among their sentiments. Respondent sentiments suggest prioritization of social determinants of health (SDOH) research, shifting away from the common target of biological and behavioral themes addressed in the existing body of HE research provided by the stakeholder. Overall, respondents suggest focusing research prioritization on access to care, engagement across the HE research and care landscape, and HE workforce development.

The Relationship Between Perceived Organizational Support And Work Engagement In The Inpatient Room Of RS X Lamongan

To provide good health services, health workers in a hospital must have good work engagement. Work engagement is defined as a positive, satisfying, work-related state of mind characterized by passion, dedication, and absorption. High-perceived organizational support, employees are more satisfied with their jobs, feel closer to the organization, are more driven to see the goals of the organization as their own, and are more loyal and committed to the organization. To determine the relationship between perceived organizational support and work engagement in nurses and midwives in the Inpatient Installation of X Hospital Lamongan. Research Method: The research design is quantitative observational with cross-sectional analytics. The statistical test uses the Spearman correlation test. The results of the study: there is a relationship between perceived organizational support and work engagement among employees at the Inpatient Installation of X Hospital Lamongan indicated by P <0.05 (0.001 <0.05). In the correlation coefficient, the result is 0.728 which shows that this relationship is strong. Suggestion: the results of this study can be used as information material and input for X Hospital Lamongan.

Sistem Informasi Geografis Berbasis Web Pada Industri Pariwisata

Geographic Information System (GIS) is a type of software that can be used for input, storage, manipulation, display, and output of geographic information and its attributes (Prahasta). Geographic Information Systems have a very important role in combining data, organizing data, conducting data analysis which can ultimately produce data and can be used as a reference for decision making on issues related to geography.

Physiologically-Informed Gaussian Processes for Interpretable Modelling of Psycho-Physiological States.

The widespread popularity of Machine Learning (ML) models in healthcare solutions has increased the demand for their interpretability and accountability. In this paper, we propose the Physiologically-Informed Gaussian Process (PhGP) classification model, an interpretable machine learning model founded on the Bayesian nature of Gaussian Processes (GPs). Specifically, we inject problem-specific domain knowledge of inherent physiological mechanisms underlying the psycho-physiological states as a prior distribution over the GP latent space. Thus, to estimate the hyper-parameters in PhGP, we rely on the information from raw physiological signals as well as the designed prior function encoding the physiologically-inspired modelling assumptions. Alongside this new model, we present novel interpretability metrics that highlight the most informative input regions that contribute to the GP prediction. We evaluate the ability of PhGP to provide an accurate and interpretable classification on three different datasets, including electrodermal activity (EDA) signals collected during emotional, painful, and stressful tasks. Our results demonstrate that, for all three tasks, recognition performance is improved by using the PhGP model compared to competitive methods. Moreover, PhGP is able to provide physiological sound interpretations over its predictions.

RDA-Genebank and Digital Phenotyping for Next-Generation Research on Plant Genetic Resources.

The National Agrobiodiversity Center under the Rural Development Administration (RDA) in Jeonju, Republic of Korea stands as the foremost national genebank in the country. Over the years, the National Agrobiodiversity Center has remained committed to enriching its collection with foreign genetic resources, elevating its status to a world-class plant genetic resources (PGR)- holding genebank. Currently, several steps are being undertaken to improve the accessibility of the collection to national as well as international researchers, improve the data available on the resources, and amend the passport information for the accessions. With the implementation of the Nagoya Protocol, the origin of genetic resources is being highlighted as an important input in the passport information. The RDA-Genebank actively responds to the Nagoya Protocol by supplementing passport data for resources lacking information on their origin. In addition, a large number of conserved resources are continuously multiplied, and agronomic traits are investigated concurrently. With the traditional methods of characterization of the germplasm requiring a significant amount of time and effort, we have initiated high-throughput phenotyping using digital techniques to improve our germplasm data. Primarily, we have started adding seed phenotype information followed by measuring root phenotypes which are stored under agronomic traits. This may be the initial step toward using largescale high-throughput techniques for a germplasm. In this study, we aim to provide an introduction to the RDA-Genebank, to adopted international standards, and to the establishment of high-throughput phenotyping techniques for the improvement of passport information.

ANALISIS BIAYA TRANSAKSI USAHA BUDIDAYA UDANG VANAME DI KECAMATAN KETAPANG, KABUPATEN LAMPUNG SELATAN

Vaname shrimp is a commodity that has high economic value so that it is widely cultivated by Indonesian people. Apart from the production costs incurred, there are also transaction costs that are often not taken into account by cultivators. The purpose of this study was to identify components and calculate transaction costs and analyze the economic efficiency of vannamei shrimp farming in Ketapang District based on transaction costs incurred. This study uses secondary and primary data with samples selected by cluster proportional simple random sampling. The selected respondents were vannamei shrimp cultivators from six vannamei shrimp pond center villages with traditional, semi-intensive and intensive technology as many as 30 people. The data analysis method used is descriptive quantitative and qualitative. The data obtained is then analyzed by transaction cost analysis. The results showed that the components of the transaction costs of the vannamei shrimp farming business and their ratios consisted of market information costs (0.02), input and output costs (0.01), implementation costs (0.19), licensing fees (0.23) , and levy fees (0.55). The economic efficiency of aquaculture can be seen from the ratio of transaction costs to production costs and revenues of 0.08 and 0.04 respectively.

Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama

Long short-term memory (LSTM) models have been shown to be efficient for rainfall-runoff modeling, and to a lesser extent, for groundwater depth forecasting. In this study, LSTMs were applied to quantify the spatiotemporal evolution of surface and subsurface hydrographs in Alabama in the Southeastern United States, where water sustainability has not been fully quantified across spatiotemporal scales. First, the surface water LSTM model with extensive dynamic (precipitation and other weather variables) and static (basin characteristics) inputs predicted the main characteristics of streamflow for six years at 19 gauged basins in Alabama. The model tended to underestimate extremely high streamflow but adding drainage density as an input feature slightly improved the predictions of extreme events. Second, to predict the groundwater depth evolution, a groundwater LSTM (GW-LSTM) model was proposed and applied using static inputs capturing the aquifers' hydrogeological properties and dynamic inputs of meteorological information. Three precipitation scenarios were also explored to evaluate the groundwater hydrograph evolution in the next two decades. The GW-LSTM model predicted the general trend of daily groundwater depth fluctuations (at 21 wells distributed across Alabama from 1990 to 2021) including most extremely high groundwater levels, and recovered groundwater depth for locations withheld from model training and validation. This study, therefore, extended the application of LSTMs in quantifying the spatiotemporal evolution of surface water and groundwater, two manifestations of a single integrated resource.

How Do Higher Educated Employees Affect Firms’ Investment Efficiency in China?

ABSTRACT China’s higher education expansion increases firms’ human capital input in terms of the number of educated employees, while its quality undergoes questioning. It is unknown what employees with higher education bring to the firm in China. Considering that investment decision is one of the most important drivers of firm value, we examine how employees with higher education affect firms’ investment efficiency in this paper. We document that the number of employees with higher education increases by 89% from 2008 to 2021. We further find that more employees with higher education are associated with higher investment efficiency through mitigating over- and underinvestment. This association is stronger for firms with high product market competition, low customer concentration, state-owned enterprises, and high senior manager education. Collectively, the results suggest that the information inputs channel, monitoring channel and up-and-down communications channel are likely underlying mechanisms through which higher educated employees affect firms’ investment decisions. Overall, our paper sheds light on the real effect of higher educated employees on firms’ investment efficiency.

Numeric and nonnumeric information input to predict adsorption amount, capacity and kinetics of tetracyclines by biochar via machine learning

The detection of tetracyclines (TCs) adsorption performance by specific adsorbent is a time-consuming process. Thus, developing prediction framework based on existing data to quickly evaluate adsorption performance is necessary especially in the urgent scenario. Herein, we employ machine learning to deliver the accurate prediction of TCs adsorption performance by biochar via grouping numeric/nonnumeric information over physiochemical properties of biochar and environmental pressures. We find the TCs adsorption by biochar is a purely physical behavior where porous filling is the primary mechanism. Porous structure of biochar and environmental pressures co-determine the adsorption amount, and the synergism of physiochemical properties of biochar and environmental pressures determines adsorption capacity. Adsorption kinetics is subject to physical properties and environmental pressures. Chemical properties of biochar have a limited influence on adsorption performance and its main contribution is delivered via the interactions between C-contained or O-contained functional groups and TCs. Simplifying model only with reduced variables input can also accurately predict TCs adsorption performance by biochar. This strategy enables the portable prediction in the urgent scenario since expensive instruments and complex detections can be avoided. These findings provide a comprehensive understanding of the way of physiochemical characteristics and environmental pressures on adsorption performance, and offer useful tips in designing biochar-based adsorbents for TCs removal.

Imagining risk taking: The valence of mental imagery is related to the declared willingness to take risky actions

AbstractThe aim of the present research was to investigate the involvement of mental imagery in people's choices under risk. We tested the general idea that decision makers can use visual mental images (visual mental simulations) to pre‐experience how rewarding or threatening future outcomes of risky behavior will be and try out the potential consequences of their risky activities. The paper reports the results of three preregistered studies (including one experiment) showing that the valence of mental imagery is related to the willingness to take risky actions and that people spontaneously use mental imagery as an informative decision input. In Study 1, we found that the more positive mental images people produced when faced with risk, the more willing they were to take risky actions representing different risk domains. Study 2 extended the results of Study 1, indicating that the valence of mental imagery has a causal effect on participants' risk taking willingness. Qualitative analysis based on independent judges' evaluations conducted in Studies 1 and 2 documented that, when requested, participants could easily generate visual mental images illustrating the consequences of their risky choices. Finally, with Study 3, we found that participants declared using mental imagery as a decision input (i.e., a source of information that helps them make choices) even when they were not instructed to do so. However, the frequency of reporting images as decision inputs differed across risky activities.

Methods used to evaluate usability of mobile clinical decision support systems for healthcare emergencies: a systematic review and qualitative synthesis.

The aim of this study was to determine the methods and metrics used to evaluate the usability of mobile application Clinical Decision Support Systems (CDSSs) used in healthcare emergencies. Secondary aims were to describe the characteristics and usability of evaluated CDSSs. A systematic literature review was conducted using Pubmed/Medline, Embase, Scopus, and IEEE Xplore databases. Quantitative data were descriptively analyzed, and qualitative data were described and synthesized using inductive thematic analysis. Twenty-three studies were included in the analysis. The usability metrics most frequently evaluated were efficiency and usefulness, followed by user errors, satisfaction, learnability, effectiveness, and memorability. Methods used to assess usability included questionnaires in 20 (87%) studies, user trials in 17 (74%), interviews in 6 (26%), and heuristic evaluations in 3 (13%). Most CDSS inputs consisted of manual input (18, 78%) rather than automatic input (2, 9%). Most CDSS outputs comprised a recommendation (18, 78%), with a minority advising a specific treatment (6, 26%), or a score, risk level or likelihood of diagnosis (6, 26%). Interviews and heuristic evaluations identified more usability-related barriers and facilitators to adoption than did questionnaires and user testing studies. A wide range of metrics and methods are used to evaluate the usability of mobile CDSS in medical emergencies. Input of information into CDSS was predominantly manual, impeding usability. Studies employing both qualitative and quantitative methods to evaluate usability yielded more thorough results. When planning CDSS projects, developers should consider multiple methods to comprehensively evaluate usability.

The Relationship of Various Forms of Accommodative Asthenopia with the Characteristics of the Professional Activity in Patients with Visually Intense Work and the Phenomena of Computer Visual Syndrome

Purpose: to study the relationship of various forms of accommodative asthenopia (AA) with the characteristics of the professional activity of patients with visually stressful work (VSW) with the phenomena of computer visual syndrome (CVS).Methods. There were 150 patients with VSW — professional users of personal computers (mean age 28.8 ± 1.4 years) with myopic refraction (mean value of spherical equivalent 2.8 ± 1.1 diopters) under observation. The patients were divided into three groups: “normal” (50 patients); asthenic form of accommodative asthenopia (AFAA, pathology, 50 patients); habitual excessive tension of accommodation (HETA), pathology, 50 patients). All patients underwent a comprehensive ophthalmological examination for 13 parameters. As a basic diagnostic method, a specially developed questionnaire aimed at assessing professional activity was used. Statistical analysis of the results of the study was performed on the basis of an assessment of the average values of the correlation coefficients (ACC) between the patient’s responses (in points) and the studied indicators of the visual system.Results. High ACC were determined on issues characterizing the amount of daily visual load. However, no differences were found between APAA and HETA. The occurrence of AFAA is more often associated with the input of information, HETA — with reading. Evaluation of issues related to the regularity of the use of optical correction revealed differences in ACC (p < 0.05) between the “normal” group and the “pathology” groups, while in the “normal” group, optical correction was used in most cases “often” or “constantly”, in the “pathology” groups — “sometimes”.Conclusion. The results of the correlation assessment of the relationship between various types of AA in patients with VSW with the characteristics of professional activity and the parameters of the visual analyzer indicate the leading role of the duration and nature (input, reading of information) of daily work at a personal computer. One of the leading risk factors for the development of AA is inadequate optical correction. At the same time, there were no significant differences between AFAA and PINA. The most informative parameters of the accommodative system of the eye are the volume of absolute and relative accommodation (by the subjective method), as well as the coefficients of microfluctuations and stability of accommodogram (by the objective accommodation method).

Technology adoption behaviors of farmers during crises: What are the key factors to consider?

The Covid-19 pandemic required economic agents, in this case farmers, to make immediate decisions with an eye on a future characterized by high level of uncertainty. Using a multivariate probit, this paper examines the factors that farmers, as economic agents, considered at the peak of the pandemic in their digital (e-service) technology adoption decisions. The results indicate that the key factors that influence digital technology adoption behaviors of farmers are age, gender, education, level of dependency, experience in farming, access to credit and perceived possible impact of the pandemic on production activities. These factors were found to have different levels of influences on the adoption of different digital technologies that could help reduce physical contacts in line with nationally determined Covid-19 protocols whiles sustaining agricultural production activities. Digital technologies that facilitate easy access to good agronomic practices, weather information services, input and output market information services and financial services were identified as crucial. The general conclusion of this paper is that farmers are willing to adopt technologies that add value to their welfare through timely resolution of problems that confront them. Thus, value propositions – such as relevance, effectiveness, efficiency, sustainability, and impact – must be the key considerations in policy interventions that promote positive technology adoption behaviors.

Gaze behavior data in the vitrine of human movement science: considerations on eye-tracking technique

Background: The eyes are the main gateway of visual information input. Moving the eyes is essential to extract visual information from scenes while performing motor actions. This has helped to explain motor behavior, especially in relation to visual attention mechanisms, gaze training and learning, and the relevance of visual information in controlling actions. Thus, collecting data on gaze behavior has become important for explaining motor behavior. Aim: We present the main video-based eye-tracking techniques, briefly describing the anatomy of the eyes, explaining the operation of the eye-tracker (eye capture techniques, calibration, and data analysis), and proposing interpretations of the main variables extracted by the technique. In this way, we carry out considerations (limitations and advantages) on the eye-tracking technique that placed gaze behavior data in the vitrine of human movement science. Interpretation: Eye-tracking has become an excellent tool to assist in the analysis of human movement through gaze behavior. Mainly by combining sensory information, such as visual information, with performance during motor tasks, it is possible to infer about perception, cognition, and human behavior during the most diverse day-to-day activities. Eye-tracker systems have been employed in different majors related to motor behavior, such as medicine, commerce, and game development.

Towards a Better Tradeoff between Quality and Efficiency of Community Detection: An Inductive Embedding Method across Graphs

Many network applications can be formulated as NP-hard combinatorial optimization problems of community detection (CD) that partitions nodes of a graph into several groups with dense linkage. Most existing CD methods are transductive , which independently optimized their models for each single graph, and can only ensure either high quality or efficiency of CD by respectively using advanced machine learning techniques or fast heuristic approximation. In this study, we consider the CD task and aims to alleviate its NP-hard challenge. Motivated by the efficient inductive inference of graph neural networks (GNNs), we explore the possibility to achieve a better tradeoff between the quality and efficiency of CD via an inductive embedding scheme across multiple graphs of a system and propose a novel inductive community detection (ICD) method. Concretely, ICD first conducts the offline training of an adversarial dual GNN structure on historical graphs to capture key properties of a system. The trained model is then directly generalized to new graphs of the same system for online CD without additional optimization, where a better tradeoff between quality and efficiency can be achieved. Compared with existing inductive approaches, we develop a novel feature extraction module based on graph coarsening, which can efficiently extract informative feature inputs for GNNs. Moreover, our original designs of adversarial dual GNN and clustering regularization loss further enable ICD to capture permutation-invariant community labels in the offline training and help derive community-preserved embedding to support the high-quality online CD. Experiments on a set of benchmarks demonstrate that ICD can achieve a significant tradeoff between quality and efficiency over various baselines.