Non-continuous Data Research Articles

Efficacy and safety of pharmacogenomic-guided antidepressant prescribing in patients with depression: an umbrella review and updated meta-analysis.

To determine the efficacy and safety of pharmacogenomics (PGx)-guided antidepressant prescribing in patients with depression through an umbrella review and updated meta-analysis. A comprehensive systematic search was conducted on PsycINFO, PubMed, Embase and the Cochrane databases. The pooled effect sizes of randomized controlled trials (RCTs) were expressed as mean differences for continuous data and risk ratios for noncontinuous data. Patients who received PGx-guided medications were 41% to 78% more likely to achieve remission and 20% to 49% more likely to respond to antidepressants than patients receiving treatment-as-usual (TAU). PGx-guided antidepressant prescribing improves the treatment of depression. However, the significance and magnitude of the benefit varies widely between studies and different PGx testing panels. https://www.crd.york.ac.uk/prospero/, identifier CRD42022321324.

The Effects of Climate Change on Streamflow, Nitrogen Loads, and Crop Yields in the Gordes Dam Basin, Turkey

The Mediterranean region is highly vulnerable to climate change. Longer and more intense heatwaves and droughts are expected. The Gordes Dam in Turkey provides drinking water for Izmir city and irrigation water for a wide range of crops grown in the basin. Using the Soil and Water Assessment Tool (SWAT), this study examined the effects of projected climate change (RCP 4.5 and RCP 8.5) on the simulated streamflow, nitrogen loads, and crop yields in the basin for the period of 2031–2060. A hierarchical approach to define the hydrological response units (HRUs) of SWAT and the Fast Automatic Calibration Tool (FACT) were used to reduce computational time and improve model performance. The simulations showed that the average annual discharge into the reservoir is projected to increase by between 0.7 m3/s and 4 m3/s under RCP 4.5 and RCP 8.5 climate change scenarios. The steep slopes and changes in precipitation in the study area may lead to higher simulated streamflow. In addition, the rising temperatures predicted in the projections could lead to earlier spring snowmelt. This could also lead to increased streamflow. Projected nitrogen loads increased by between 8.8 and 25.1 t/year. The results for agricultural production were more variable. While the yields of poppy, tobacco, winter barley, and winter wheat will increase to some extent because of climate change, the yields of maize, cucumbers, and potatoes are all predicted to be negatively affected. Non-continuous and limited data on water quality and crop yields lead to uncertainties, so that the accuracy of the model is affected by these limitations and inconsistencies. However, the results of this study provide a basis for developing sustainable water and land management practices at the catchment scale in response to climate change. The changes in water quality and quantity and the ecological balance resulting from changes in land use and management patterns for economic benefit could not be fully demonstrated in this study. To explore the most appropriate management strategies for sustainable crop production, the SWAT model developed in this study should be further used in a multi-criteria land use optimization analysis that considers not only crop yields but also water quantity and quality targets.

Analysis of total lip score system and total groove score for gender identification: A cross-sectional study.

Endless ink and pixel space have been spent to validate the use of cheiloscopy classification systems in the field of identification. It is becoming evident that lip-print patterns may provide indications about an individual's gender, race, occupation, habits, blood type, and paternity. Over the course of time, much data have been collected on a substantial population in this field. In the present study, we strive to see how well a noble total lip scoring (TLS) system works in identifying a person's gender among the College of Dentistry students. We also quantified the total groove score (TGS) and compared it with each quadrant for better authentication of the set study design. A cross-sectional descriptive study was conducted among 60 dental students (30 males and 30 females), with an age range of 18-30 years. Lip prints were recorded using the writing pad method and subsequently digitalized by capturing the images using Adobe Photoshop CS 6 Extended Version, counted and summed up, quadrant-wise, in both upper and lower lips. The TLS and TGS between males and females were statistically calculated using the Jamovi project. The mean value for TLS for males (n = 402; SD ± 92.1) is higher than that for females (n = 348; SD ± 86.1). The correlation matrix applying a non-parametric test for non-continuous data using Spearman's ratio inferred a statistically significant correlation (-0.354) with a P-value of 0.005 for TLS, whereas a correlation (-0.162) of TGS with gender was not so significant with a P-value of 0.217. Our study proved the uniqueness of applying TLS as a novel approach to gender identification. The study revealed that males had a statistically higher lip score than females. Being unique, our study inferred the importance of TLS, which needs to be explored in detail for applying, evaluating, and validating its accuracy in forensic research that can help for errorless investigations as full-proof evidence in court.

An inversion model of microplastics abundance based on satellite remote sensing: a case study in the Bohai Sea

Nowadays, microplastics (MPs) as emerging contaminants have posed great risks to marine ecosystems and human health. However, non-continuous field sampling data makes it difficult to meet the needs of scientific research and pollution control of marine MPs. Consequently, the development of rapid monitoring techniques for marine MPs to achieve efficient acquisition of data is increasingly essential. Remote sensing technology provides a convenient and effective tool for monitoring and mapping marine MPs pollution. Therefore, we established an inversion model based on multiple regression by combining the remote sensing data and the measured data to predict the MPs pollution status in the Bohai Sea. The feature variables of a model are crucial to the prediction, and we proposed three methods of variable selection, namely successive projections algorithm (SPA), band combination method, and remote sensing index method. By comparing accuracy evaluation metrics, an approach based on SPA was selected to analyze the abundance and spatio-temporal distribution of MPs in the Bohai Sea in 2022. The determination coefficient of the SPA model is 0.75, and the root mean square error is 0.38 items/m3. The error of the model is within an acceptable range. It was found that the MPs abundance on the sea surface of the Bohai Sea varied significantly in different seasons and regions. This study indicates that satellite remote sensing technology has great potential in monitoring marine MPs.

A future location prediction method based on lightweight LSTM with hyperparamater optimization

In this study, we presented a method for future location prediction based on machine learning over geopositioning data sets. There are large amounts of geopositioning data sets collected by mobile devices mainly due to modern geopositioning systems such as GPS, GLONASS and Galileo. Based on these geopositioning data sets, it is possible to have a wide variety of location-based services. These data sets can be used for future location prediction of objects, especially humans. Additionally, they have a high possibility for further applications. The purpose of this research is to present a simple and lightweight method that can be applicable to devices with lower computing capability devices, such as AIoT (Artificial Intelligence of Things) or EdgeML (Edge Machine Learning) devices. We introduced a basic LSTM (Long Short Term Memory) model with hyperparameter optimization, especially on window size of continuous geopositioning data, using limited previous geopositioning data for location prediction purposes. We found that the results of using our method for future location prediction are considerably fast and accurate compared with existing neural network-model-based approaches. We also applied our method to non-continuous geopositioning data sets and found it to be equally effective.

Pharmacy stewardship to reduce recurrent hypoglycemia

BackgroundInpatient hypoglycemia is associated with increased morbidity and mortality. After a hypoglycemic event, the likelihood of additional episodes increases. The Joint Commission recommends evaluating all episodes of hypoglycemia for root-cause analysis. Studies have shown that pharmacists’ involvement with glycemic control protocols can prevent hypoglycemia. ObjectivesThis study aimed to assess whether the implementation of pharmacists’ real-time assessment of hypoglycemic events using an electronic alert messaging system contributes to the reduction of the number of recurrent hypoglycemia during hospitalization. Practice descriptionA community hospital that provides a wide range of health care services. The pharmacy department provides fully decentralized clinical services and team-based specialist services. Practice innovationThe pharmacist-led hypoglycemia stewardship initiative included a comprehensive review of hypoglycemic alerts received via an automated message. The alerts generated in the electronic health record (EHR) every time a patient’s blood glucose resulted in less than 70 mg/dL if there was a documented administration of a hypoglycemic agent 48 hours before the hypoglycemia event. Once the alert was received by the pharmacists via an EHR in-basket, a real-time review was conducted to identify the potential causes of the event and opportunities for therapy modification. Evaluation methodsA single-center retrospective observational study including a pre- and post-implementation phase from January 1 to June 3, 2020, and January 1 to June 30, 2021, respectively. Continuous data were analyzed using paired and equal variance t test. Noncontinuous data were analyzed using Fisher exact and chi-square test. Descriptive statistics were used to describe distribution and frequency of data. ResultsThere was a 5.1% absolute reduction in recurrent hypoglycemic events (P < 0.001) and a 0.6% reduction of severe hypoglycemic days (P = 0.269) in the postimplementation group. The average time to pharmacist intervention was 4 (± 3.5) hours with a 92% acceptance rate. ConclusionThis study demonstrated the utility of pharmacist-led hypoglycemia reviews in the reduction of recurrent hypoglycemic events in the inpatient setting.

Confirmatory Factor Analyses in Psychological Test Adaptation and Development

Confirmatory Factor Analyses in Psychological Test Adaptation and Development

Thermal Removal Method for Chang’e-5 Multispectral Data Using a Heat Conduction Model and Real Terrain

The lunar mineral spectrometer (LMS) onboard the Chang’e-5—China’s lunar exploration mission—collected in-situ multispectral data up to 3 μm over a large area of the lunar surface for the first time. As Chang’e-5 operated near the Moon’s noon local time, the spectral data are influenced by thermal radiation. In particular, the LMS multispectral data are in the form of characteristic spectral bands with discontinuities that make it difficult to directly apply existing empirical models for thermal removal. Here, we present a thermal removal method based on the one-dimensional heat conduction model and real terrain data. A centimeter-scale temperature distribution map of the multispectral measurement region is calculated, which ranged from 330 to 362 K. We calculated the temperatures of four hyperspectral regions using an empirical model, compared them with those of the same region using the proposed method, and obtained a relative error of less than 1%. Afterwards, we removed the effects of thermal radiation in the multispectral data using a thermal removal model with the temperature of the multispectral region. We compared multispectral data after thermal removal using our proposed method with hyperspectral data after thermal removal using an empirical model at similar locations. The results showed good consistency between the data corrected by both methods. This paper proposed method does not depend on the setting of the spectral band, it can provide a reference for thermal removal of Chang’e-5 multispectral data and that of subsequent in-situ non-continuous spectral detection data.

Does the Reduction Matter? Non-Emergent Ankle Fractures and the Effect of Near-Anatomic Reduction

Category: Ankle; Other Introduction/Purpose: Ankle fractures are a common orthopedic injury observed in a wide variety of patient populations. Prior to being seen by an orthopedic specialist, patients with ankle fractures may have already undergone several procedures in the field or in the emergency department to stabilize or reduce the injured bone. While a paradigm that near-anatomic reduction improves outcomes in non-urgent ankle fractures exists, no study to date has shown objective evidence of this in postoperative patient- reported outcomes. The present study aims to evaluate whether adequate reduction of non-emergent ankle fractures within 24 hours of inciting injury influenced postoperative outcomes. Methods: All non-emergent ankle fractures treated by a single foot and ankle, fellowship-trained surgeon were queried over a period of 5 years and retrospectively evaluated through the electronic medical record. 97 patients over the age of 18 years with traumatic ankle fracture etiologies were considered. Plain film radiographs from initial presentation to the emergency department or first visit to orthopedics clinic were evaluated by the attending physician for adequate reduction based on AP and lateral tibiotalar congruency. Patient demographics and surgical outcomes data were collected from electronic medical record review. In the cases deemed to be dislocated or subluxated on initial injury films, the days until adequate reduction were calculated along with patient-reported VAS and SF-36 scores. Non-continuous data were compared using chi-squared tests, while continuous, non- normally distributed data were compared utilizing a Mann-Whitney U test. Alpha and beta for this study were assumed to be 0.05 and 0.8, respectively. Results: The average age was 46, with 68.9% of the patient population that was female. 51 patients were found to be adequately reduced within 24 hours, while 39 were not. The average time to final PRO follow-up was 12.6 months +- 1.9. No differences in preoperative VAS, SF-36 physical nor mental scores were noted between adequately and inadequately reduced cohorts (p= 0.654, p=0.262, p=0.760 respectively). Similarly, no differences in final postoperative follow up VAS, SF-36 physical nor mental scores were observed (p=0.503, p=0.252, p=0.296 respectively). VAS pain scores in both cohorts also improved to a similar degree over the preoperative to final postoperative follow up time point (p=0.261). Conclusion: These data show no difference in patient-reported pain or physical and mental functioning scores over 1 year after surgical fixation of the ankle, regardless of whether the ankle joint was adequately reduced within 24 hours of injury. We assert that anatomic reduction of the ankle joint following traumatic, non-emergent ankle fracture does not play a significant role in overall patient outcomes over 1 year following surgical correction.

Particulate matter exposure during pregnancy and infancy and risks of autism spectrum disorder in children: A systematic review and meta-analysis

PurposeThis meta-analysis aimed to clarify the relationship between particulate matter (PM) and autism spectrum disorder (ASD) in detail. MethodsA systematic literature search was performed using eight databases before April 9, 2022. The estimated effects were combined separately according to the PM type. Subgroup analyses were conducted in terms of the study design type, study location, exposure window, birth year, and sex. ResultsPM2.5 was associated with an increased risk of ASD, while PM10 was not. PMc, PM1, and diesel particulate matter (DPM) were also associated with an increased risk of ASD. Specifically, a 10 μg/m3 increase in PM2.5 was associated with a 1.337-fold increased risk of ASD in children, and a 10 μg/m3 increase in PMc and PM1 may increase the risk of ASD by 1.062 and 3.643 times, respectively. PM2.5 exposure may increase the risk of ASD in boys. Exposure to PMc might increase the risk of ASD in children born after the year 2000. The combined results of different PM differed between studies with continuous and non-continuous data for different study design type, study location, and birth year. The sensitive window for PM2.5 exposure to increase the risk of ASD may be from the first, second, and third trimesters to the first year of the postnatal period. Exposure to PMc during pregnancy was significantly associated with ASD. ConclusionExposure to PM2.5 may increase the risk of ASD in boys. Exposure to PM2.5 during the first, second, and third trimesters and postnatally increased the risk of ASD.

564 The Impact of COVID-19 on the Provision of Pediatric Burn Care

IntroductionThe WHO declared the outbreak of COVID-19 a pandemic in the spring of 2020 which led to widespread restrictions on daily life activities as people were instructed to isolate at home. Given that 75 – 85% of pediatric burns occur in the home, it is likely that these measures had an impact on pediatric burn care. Thus, the aim of this study was to investigate the impact of the COVID-19 pandemic on the provision of pediatric burn care at an American Burn Association-verified pediatric burn center.MethodsData was retrospectively extracted from all new burn patients aged 0-18 years during a pre-pandemic period (April 2019 – August 2019) and a pandemic period (April 2020 – August 2020). Continuous data was examined using 2 tailed t-tests (p < 0.05), while non-continuous data was examined using Pearson chi-squared tests (p < 0.05). These analyses were used to analyze burn demographics and examine changes in the delivery of acute and follow-up burn care before and during the pandemic.ResultsDuring the pre-pandemic period, 213 new burns were identified, compared to 172 new burns during the pandemic period. No clinically significant changes were observed in patient age at presentation (p = 0.54), total body surface area of burn (p = 0.85), and time to presentation following the injury (p = 0.24). Interestingly, a significant increase in friction burns (p = 0.023) was observed, which mainly consisted of treadmill burns. During the pandemic, burn operating room utilization remained high and represented approximately 25% of the hospital's total surgical capacity. In addition, there were no significant changes to inpatient and outpatient encounters (p = 0.56 and p = 1.00) between the two periods thereby highlighting the need for these essential services during the pandemic.ConclusionsBurn-related service needs remained consistent across the pre-pandemic and pandemic cohorts as demonstrated by the number of new burns as well as the continued provision of burn care. Overall, no clinically significant changes to patient demographics, aside from the increase in friction burns, were observed. Furthermore, the ability to provide all aspects of pediatric burn care at this tertiary pediatric hospital remained consistent across the pre-pandemic and pandemic cohorts. Although this study presents data from the first five months on the pandemic, further analysis of the entire year will be carried out in order to identify additional trends.

Regression-Based Approach to Test Missing Data Mechanisms

Missing data occur in almost all surveys; in order to handle them correctly it is essential to know their type. Missing data are generally divided into three types (or generating mechanisms): missing completely at random, missing at random, and missing not at random. The first step to understand the type of missing data generally consists in testing whether the missing data are missing completely at random or not. Several tests have been developed for that purpose, but they have difficulties when dealing with non-continuous variables and data with a low quantity of missing data. Our approach checks whether the missing data are missing completely at random or missing at random using a regression model and a distribution test, and it can be applied to continuous and categorical data. The simulation results show that our regression-based approach tends to be more sensitive to the quantity and the type of missing data than the commonly used methods.

Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets

Clustering mixed data presents numerous challenges inherent to the very heterogeneous nature of the variables. A clustering algorithm should be able, despite of this heterogeneity, to extract discriminant pieces of information from the variables in order to design groups. In this work we introduce a multilayer architecture model-based clustering method called Mixed Deep Gaussian Mixture Model that can be viewed as an automatic way to merge the clustering performed separately on continuous and non-continuous data. This architecture is flexible and can be adapted to mixed as well as to continuous or non-continuous data. In this sense we generalize Generalized Linear Latent Variable Models and Deep Gaussian Mixture Models. We also design a new initialisation strategy and a data driven method that selects the best specification of the model and the optimal number of clusters for a given dataset. Besides, our model provides continuous low-dimensional representations of the data which can be a useful tool to visualize mixed datasets. Finally, we validate the performance of our approach comparing its results with state-of-the-art mixed data clustering models over several commonly used datasets.

Simulation-based validation of activity logger data for animal behavior studies

Bio-loggers are widely used for studying the movement and behavior of animals. However, some sensors provide more data than is practical to store given experiment or bio-logger design constraints. One approach for overcoming this limitation is to utilize data collection strategies, such as non-continuous recording or data summarization that may record data more efficiently, but need to be validated for correctness. In this paper we address two fundamental questions—how can researchers determine suitable parameters and behaviors for bio-logger sensors, and how do they validate their choices? We present a methodology that uses software-based simulation of bio-loggers to validate various data collection strategies using recorded data and synchronized, annotated video. The use of simulation allows for fast and repeatable tests, which facilitates the validation of data collection methods as well as the configuration of bio-loggers in preparation for experiments. We demonstrate this methodology using accelerometer loggers for recording the activity of the small songbird Junco hyemalis hyemalis.

Multimodal Emotion Recognition Using Non-Inertial Loss Function

Automatic understanding of human emotion in a wild setting using audiovisual signals is extremely challenging. Latent continuous dimensions can be used to accomplish the analysis of human emotional states, behaviors, and reactions displayed in real-world settings. Moreover, Valence and Arousal combinations constitute well-known and effective representations of emotions. In this paper, a new Non-inertial loss function is proposed to train emotion recognition deep learning models. It is evaluated in wild settings using four types of candidate networks with different pipelines and sequence lengths. It is then compared to the Concordance Correlation Coefficient (CCC) and Mean Squared Error (MSE) losses commonly used for training. To prove its effectiveness on efficiency and stability in continuous or non-continuous input data, experiments were performed using the Aff-Wild dataset. Encouraging results were obtained.

The Impact of Different System Call Representations on Intrusion Detection

Abstract Over the years, artificial neural networks have been applied successfully in many areas including IT security. Yet, neural networks can only process continuous input data. This is particularly challenging for security-related, non-continuous data like system calls of an operating system. This work focuses on five different options to preprocess sequences of system calls so that they can be processed by neural networks. These input options are based on one-hot encodings and learning word2vec, GloVe or fastText representations of system calls. As an additional option, we analyse if mapping system calls to their respective kernel modules is an adequate generalization step for (i) replacing system calls or (ii) enhancing system call data with additional information regarding their context. When performing such preprocessing steps it is important to ensure that no relevant information is lost during the process. The overall objective of system call analysis in the context of IT security is to categorize a sequence of them as benign or malicious behavior. Therefore, this scenario is used to evaluate different system call representations in a classification task. Results indicate that a broader range of attacks can be detected when enriching system call representations with corresponding kernel module information. Prior learning of embeddings does not achieve significant improvements. This work is an extension of the work by Wunderlich et al. [1] published in Advances in Intelligent Systems and Computing (AISC, volume 951).

Feasibility of Rapid Development and Deployment of a Telemedicine Program in a Foot and Ankle Orthopedic Practice.

A lack of access to care is predictably associated with negative outcomes in foot and ankle surgery. Despite recent advances in telecommunication technologies, the field of orthopedics has been slow to adopt these resources in offsetting barriers to care. The COVID-19 pandemic has forced departments to change their clinical approach, lending unprecedented opportunity to better understand how telehealth may bridge this care gap in foot and ankle practices. The purpose of this study was to assess patient-reported outcomes of telemedicine encounters, including comfort and patient satisfaction. Our hypothesis was that patients would be significantly less satisfied with telemedicine when compared with in-office appointments for all nonemergency visit types. Telemedicine satisfaction was assessed via phone survey with a modified 1 to 5 Likert scale. Patients who had completed a telemedicine visit between April 13, 2020, and June 19, 2020, were eligible to participate. Patient demographics were recorded, and data were analyzed using paired and independent t tests for parametric continuous data and Fisher's exact and chi-square tests for noncontinuous data. A total of 216 patients completed the telemedicine questionnaire. The overall mean satisfaction for telemedicine visits (4.7) was significantly lower than that for in-office visits (4.9) (P < .001). However, the majority (90.3%) of patients reported they would use telemedicine again in the future. When compared, patients seeking fracture care had significantly higher telemedicine satisfaction (4.9, n = 38) than those receiving nonfracture care (4.6, n = 178) (P = .001), and those greater than 50 miles from the clinic had higher satisfaction (5.0, n = 14) than patients living within 50 miles of the clinic (4.7, n = 202) (P < .001). Patients were more satisfied with their in-office clinic visit than telemedicine, although the vast majority of patients endorsed a willingness to utilize telemedicine in the future. Patients with trauma and greater barriers to foot and ankle care were more satisfied with their telemedicine visits. Level III, retrospective cohort study.

Is Telemedicine Suited for Lower Extremity Pathologies? An Analysis of Non-Urgent Follow-Up in a Foot and Ankle Practice

Category:Other; Ankle; Hindfoot; Midfoot/ForefootIntroduction/Purpose:A lack of access to care is predictably associated with negative outcomes in foot and ankle surgery. Despite recent advances in telecommunication technologies, the field of orthopaedics has been slow to adopt these resources in offsetting barriers to care. The COVID-19 pandemic has forced departments to change their clinical approach, lending unprecedented opportunity to better understand how telehealth may bridge this care gap in foot and ankle practices. The purpose of this study was to assess patient-reported outcomes of telemedicine encounters, including comfort and patient satisfaction. Our hypothesis was that patients would be significantly less satisfied with telemedicine when compared to in-office appointments for all non-emergent visit types.Methods:Retrospective analysis of patients seen via telemedicine between April 13, 2020, to June 19, 2020, by one surgeon in the Emory Orthopedics Foot and Ankle department was conducted. Patients were contacted by one of the study authors via telephone to complete a questionnaire; satisfaction and other visit characteristics were assessed with a modified Likert scale from 1 to 5. Patients were designated as either ‘New’ or ‘Established’ based on whether or not they had completed an in-office clinic visit within the last year. Anatomy and pathology of disorder were determined by chief complaint and billing code. Patient demographics were recorded, and data were analyzed using paired and independent t-tests for parametric continuous data, Fisher’s exact and chi-square tests for non-continuous data.Results:338 eligible patients were contacted via telephone, with 216 (63.9%) completing the telemedicine questionnaire. The patient cohort had an average age of 50.6 (19-84) years old and was 73.6% female (n=159). Overall mean satisfaction for telemedicine visits (4.69) was significantly lower than in-office visits (4.86) (p<0.001). In a subgroup analysis of patient satisfaction, patients seeking fracture care had significantly higher telemedicine satisfaction when compared to those receiving non-fracture care (4.90 vs. 4.64, p=0.001). Telemedicine satisfaction was also significantly greater in patients traveling more than 50 miles from their home to clinic (4.96 vs. 4.67, p<0.001). Patients with median household income less than the Georgia state median ($55,679) were more satisfied with their telemedicine visits than those with greater income, though the difference was non-significant.Conclusion:our data suggest those with significantly hindered mobility such as increased distance from clinic, lower socioeconomic status, and those seeking care for fractures had higher telemedicine satisfaction than their peers. Further study is needed to detail the precise and safe use of telemedicine in practice, but these data illuminate the high ceiling telemedicine offers in expanding patient care. Our hope is that this study aids as a supportive rationale for the continued use of telehealth visits past the period of the pandemic and encourages a more nuanced view of what visit types may be best-suited for telehealthcare

Python TensorFlow Big Data Analysis for the Security of Korean Nuclear Power Plants

The Republic of Korea also suffered direct and indirect damages from the Fukushima nuclear accident in Japan and realized the significance of security due to the cyber-threat to the Republic of Korea Hydro and Nuclear Power Co., Ltd. With such matters in mind, this study sought to suggest a measure for improving security in the nuclear power plant. Based on overseas cyber-attack cases and attacking scenario on the control facility of the nuclear power plant, the study designed and proposed a nuclear power plant control network traffic analysis system that satisfies the security requirements and in-depth defense strategy. To enhance the security of the nuclear power plant, the study collected data such as internet provided to the control facilities, network traffic of intranet, and security equipment events and compared and verified them with machine learning analysis. After measuring the accuracy and time, the study proposed the most suitable analysis algorithm for the power plant in order to realize power plant security that facilitates real-time detection and response in the event of a cyber-attack. In this paper, we learned how to apply data for multiple servers and apply various security information as data in the security application using logs, and match with regard to application of character data such as file names. We improved by applying gender, and we converted to continuous data by resetting based on the risk of non-continuous data, and two optimization algorithms were applied to solve the problem of overfitting. Therefore, we think that there will be a contribution in the connection experiment of the data decision part and the optimization algorithm to learn the security data.

Decomposing the Causes for Niche Differentiation Between Species Using Hypervolumes

Hutchinson’s n-dimensional hypervolume concept holds a central role across different fields of ecology and evolution. The question of the amount of hypervolume overlap and differentiation between species is of great interest to understand the processes that drive niche dynamics, competitive interactions and, ultimately, community assembly. A framework is proposed to decompose overall differentiation among hypervolumes into two distinct components: niche shifts and niche contraction / expansion processes. Niche shift corresponds to the replacement of space between the hypervolumes occupied by two species, whereas niche contraction / expansion processes correspond to net differences between the amount of space enclosed by each hypervolume. A procedure to implement non-continuous trait data in the estimation of n-dimensional hypervolumes is proposed. Hypervolumes were constructed for three Darwin' finches, Geospiza conirostris, Geospiza magnirostris and Geospiza difficilis using intraspecific trait data. Results showed that significant niche shifts, not niche contraction, occurred between G. conirostris and G. magnirostris in Genovesa island, where they live in sympatry. This means that G. conirostris occupied a different niche space and not a reduced space on Genovesa. G. difficilis was well differentiated from the other two species. The proposed framework allows disentangling different processes underlying niche partitioning between coexisting species. This framework offers novel insights to understand the drivers of niche partitioning strategies among coexisting species.