Search

In the post and beam structure, the post and beam play a crucial role in directly supporting and transmitting loads, thus making them essential elements in structural design. In cases where the moment resistance performance of the post-beam joint is inadequate, a shear wall and bracing are frequently installed to provide support for horizontal load on the posts and beams. The structural insulated panel (SIP) is increasingly utilized and studied as shear walls alongside light-frame timber construction, owing to its insulation properties as well as its high shear strength. In this study, when using SIP as a shear wall between post and beams, three composite wall structures were considered based on the installation location of the SIPs on the post-beam structure and lateral resistance performance of the hybrid shear wall was evaluated according to the SIP installation location. Depending on the installation location SIPs on the posts and beams, shear strength for I-SIP 150, E-SIP 150, and M-SIP 150 were 23.2, 24.6, and 26.0 kN, respectively. In case of the shear stiffness, I-SIP 150, E-SIP-150, and M-SIP 150 were found to be 4.32, 3.99, and 2.74 kN/mm, respectively.

Tomatoes are a widely cultivated crop globally, and according to the Food and Agriculture Organization (FAO) statistics, tomatoes are the third after potatoes and sweet potatoes. Tomatoes are commonly used in kitchens worldwide. Despite their popularity, tomato crops face challenges from several diseases, which reduce their quality and quantity. Therefore, there is a significant problem with global agricultural productivity due to the development of diseases related to tomatoes. Fusarium wilt and bacterial blight are substantial challenges for tomato farming, affecting global economies and food security. Technological breakthroughs are necessary because existing disease detection methods are time-consuming and labor-intensive. We have proposed the T-Net model to find a rapid, accurate approach to tackle the challenge of automated detection of tomato disease. This novel deep learning model utilizes a unique combination of the layered architecture of convolutional neural networks (CNNs) and a transfer learning model based on VGG-16, Inception V3, and AlexNet to classify tomato leaf disease. Our suggested T-Net model outperforms earlier methods with an astounding 98.97% accuracy rate. We prove the effectiveness of our technique by extensive experimentation and comparison with current approaches. This study offers a dependable and understandable method for diagnosing tomato illnesses, marking a substantial development in agricultural technology. The proposed T-Net-based framework helps protect crops by providing farmers with practical knowledge for managing disease. The source code can be accessed from the given link.

This study was conducted to investigate the feasibility of using yellow poplar as a structural member by determining allowable bending properties. Full-size lumber was classified by machine grading and the knot diameter ratio on the wide surface of lumber. The majority of machine grades of yellow poplar lumber with a cross-section of 38 × 89 mm2 were E8, E9, and E10. It was confirmed that the size of the knot diameter ratio tended to be smaller for higher machine grades. In the lowest grade, E8, of most machine grades, the allowable bending strength was lower than the corresponding design value in Korean standards. Application of 0.5 knot diameter ratio to the E8 grade lumber increased the bending strength to 3 MPa of the allowable value to suit the design value. All the allowable modulus of elasticities values of the majority of machine grades were higher than the design values. From the results of this study, it was expected that Korean yellow poplar could be utilized for structural bending members.

The shear resistance of a structural particleboard (PB) wall was evaluated as a means to utilize recycled wood and low-grade logs for long periods. The wall was composed of spruce-pine-fir (SPF) lumber, nails, and structural PB, which was developed by improving the water resistance of default PB. Structural PB has a higher density than OSB, which is a commonly used covering material for light wooden structural walls, so a longer end distance for nailing at edge of sheathing is needed for structural PB. In this study, it was confirmed that structural PB walls sufficiently resisted lateral load when the end distance was 15 mm. In this case, the shear strength, shear modulus, and ductility of the structural PB wall for lateral cyclic load were confirmed as 9.2 kN/m, 2.0 kN/mm, and 6.2, respectively. When compared to the design value, the shear strength value was higher than that of the OSB wall and the plywood wall. The shear modulus was lower than that of OSB walls, but higher than plywood walls. Based on the above results, it was considered that structural PB light-frame wall could be used in residential load-bearing applications.

This study aimed to strengthen the security of autonomous vehicles by analyzing the current status of autonomous vehicle security, such as autonomous vehicle features, security threats, and compliance, and deriving security-level check items. Based on this, the relative importance could be obtained by applying it to the AHP (Analytic Hierarchy Process) model. The results of the empirical analysis showed that the order of priority was the establishment/implementation of a cybersecurity management system, encryption, and risk assessment. The significance of this study is that by deriving security-level check items related to autonomous vehicles and verifying the research model, we can reduce cyber security accidents that can cause loss of life and improve the level of autonomous vehicle security management of related companies. Additionally, by applying AHP evaluated by security experts to the autonomous vehicle field for the first time, it will contribute to the market expansion of the autonomous vehicle industry, which is concerned with security. Furthermore, major automobile companies have to manage the security levels of numerous tier companies due to the nature of the industry. Therefore, if they perform a Quick Check (QC) considering the relative importance of the autonomous vehicle security-level check items presented in this paper, they will be able to effectively identify the security levels of tier companies early.

Despite the increasing attention on smart farms as a solution to rural issues such as aging agricultural populations, a shortage of young farmers, decreased production area, and reduced investment leading to stagnant income, exports, and growth rates, many farms still rely on traditional methods like cultivating tangerines in open fields. Despite this, increasing farm income requires producing high-quality tangerines and selling them at premium prices, with fruit sweetness being a crucial factor. Therefore, there is a need to examine the close correlation between tangerine quality and sweetness. In this paper, we use deep learning with the PyCaret library to predict and analyze tangerine sweetness using data from seven regions in Jeju and 13 comprehensive factors influencing sweetness, including terrain, temperature, humidity, precipitation, sunlight, wind speed, acidity, sugar-acid ratio, and others. Although applying all 13 factors could achieve over 90% accuracy, our study, limited to seven factors, still achieves a respectable 82.4% prediction accuracy, demonstrating the significant impact of weather data on sweetness. Moreover, these optimistic predictions enable the estimation of tangerine quality and price formation in the market for the coming year, allowing tangerine farmers and related agencies to respond to market conditions proactively. Furthermore, by applying these data to smart farms to control factors influencing tangerine sweetness, it is anticipated that high-quality tangerine production and increased farm income can be achieved.

The Korean water deer (WD), a predominant wildlife species in South Korea, is listed as vulnerable by the IUCN Red List. Despite belonging to the same family, Cervidae, WD show significantly fewer adult ixodid tick infestations compared to roe deer (RD). Ticks, which cannot fly, engage in questing behavior in natural environments to latch onto hosts. They detect signals like body temperature and host skin chemicals to navigate through the hair coat to the preferred epidermis. In light of this, we performed an extensive comparative study of the skin tissue and hair characteristics of both deer species, focusing on elements contributing to the reduced tick bite incidence in WD. Remarkably, WD exhibited more prominent blood vessels, sebaceous glands, and sweat glands, which are crucial for skin barrier functions (p < 0.005). Moreover, WD had irregular scale patterns on their hair cuticles and possessed hair that was significantly stiffer and 2.83 times thicker than that of RD (p < 0.001). These characteristics potentially impede ticks from reaching the epidermis hair in WD and RD in the context of tick bite prevention. Further investigations in this area could enhance our understanding of tick–host dynamics and contribute to developing preventive measures against tick-borne diseases in other deer species.

The reform of digitalization has deeply influenced the development mode and production methods of traditional production in China. However, the impact of digitalization on the management performance of Chinese listed companies are not well investigated and there is a gap in the establishment of digitalization effective measurement variable. In order to fill this gap, this article constructs a variable to measure the application degree of “digitalization” at the company level. By analyzing the text of the annual reports of Chinese listed companies from 2009 to 2020, the effective variables to measure “digitalization” are determined. Based on this, the development status and determinants of enterprise digital applications and the impact of digitalization on enterprise performance are discussed. Thus, by analyzing the panel data of these companies, the relationship among the age of the enterprise, the knowledge level of the board of directors, the proportion of fixed assets, the company’s assets, and the degree of enterprise digitalization are evaluated. At last, according to the evaluated results, the relationship between the degree of digitalization and the performance of enterprises is analyzed. In this article, our analysis helps to solve the “IT production paradox” problem, and also to provides data and theoretical support for digitalization to improve corporate performance to a certain extent. Furthermore, the digitalization measurement variable laid out in this article provides a foundation for future research on the relationship between digitalization and corporate performance.

Methods for obtaining valuable knowledge from the vast amount of mixed-quality information have become a top priority for knowledge demanders. As an online knowledge-sharing channel, the socialized question and answer (Q&A) platform provides important support services for knowledge payment. Based on the personal psychological dimensions of users and social capital theory, this paper aims to study the behavior mechanisms of knowledge payment users and examine the significant factors affecting user payment. Our research was conducted in two steps: a qualitative study to find these factors and a research model based on a quantitative study for testing the hypothesis. The results show that the three dimensions of individual psychology are not all positively correlated with cognitive and structural capital. Our results fill a gap in the literature on the formation of social capital in the knowledge payment environment by showing how individual psychological dimensions affect cognitive and structural capital differently. Thus, this study offers effective countermeasures for knowledge producers on social Q&A platforms to better amass their social capital. This research also makes practical recommendations for social Q&A platforms to strengthen the knowledge payment model.

Korean native cattle are highly valued for their rich marbling and flavor. Nonetheless, endeavors to enhance marbling levels can result in obesity, a prevalent contributor to fat necrosis. Fat necrosis is characterized by the formation of necrotic fat masses in the abdominal cavity, which physically puts pressure on affected organs, causing physical torsion or obstruction, resulting in death and consequent economic loss. Pancreatic injuries or diabetes mellitus were reported as factors of fat necrosis in humans; however, the pathogenesis in animals has not been established. In this study, we identified fat necrosis in a 6-month-old Korean native cow and investigated its potential underlying causes. Serum samples were utilized for a microarray analysis of bovine miRNA. Comparative examination of miRNA expression levels between cattle afflicted with fat necrosis and healthy cattle unveiled notable variances in 24 miRNAs, such as bta-miR-26a, bta-miR-29a, bta-miR-30a-5p and bta-miR-181a. Upon conducting miRNA-mediated KEGG pathway analysis, several pathways including the prolactin signal pathway, insulin resistance, autophagy, the insulin-signaling pathway and the FoxO-signaling pathway were found to be significantly enriched in the calf affected by fat necrosis. As a result, this study potentially indicates a potential connection between fat necrosis and diabetes in Korean native cattle.

Newborn piglets are susceptible to a highly contagious enteritis caused by the porcine epidemic diarrhea virus (PEDV), associated with high levels of mortality worldwide. There is pressing need for a rapid, safe, and cost-effective vaccine to safeguard pigs from getting infected by PEDV. PEDV belongs to the coronavirus family and is characterized by high levels of mutability. The primary goal of a PEDV vaccine is to provide immunity to newborn piglets through vaccination of sows. Plant-based vaccines are becoming more popular because they have low manufacturing costs, are easily scalable, have high thermostability, and a long shelf life. This is in contrast to conventional vaccines which include inactivated, live, and/or recombinant types that can be expensive and have limited ability to respond to rapidly mutating viruses. The binding of the virus to host cell receptors is primarily facilitated by the N-terminal subunit of the viral spike protein (S1), which also contains several epitopes that are recognized by virus-neutralizing antibodies. As a result, we generated a recombinant S1 protein using a plant-based vaccine platform. We found that the recombinant protein was highly glycosylated, comparable to the native viral antigen. Vaccination of pregnant sows at four and two weeks before farrowing led to the development of humoral immunity specific to S1 in the suckling piglets. In addition, we noted significant viral neutralization titers in both vaccinated sows and piglets. When challenged with PEDV, piglets born from vaccinated sows displayed less severe clinical symptoms and significantly lower mortality rates compared to piglets born from non-vaccinated sows.

Renewable energy sources produce electricity without causing increment in pollution, and solar energy is one of the primary renewable sources. Switching to renewable electricity is particularly impactful for companies whose emissions from purchased energy are the primary source. The Renewable Energy (RE100) initiative provides awareness to governments and the general public. Therefore, organizations must now move from renewable energy sources to clean energy sources. Solar panels are the primary source of renewable energy. However, a harsh environment or solar panel malfunction can lead to missing data, which causes various problems, such as data processing complexity, severe biases, and commitment to data quality. Optimal orientation and tilt angle for solar panels effectively get more energy from the solar panels. We have used machine learning to predict the optimal angle for a solar panel according to the season and time. This article studies solar panel data’s photovoltaic energy generation value and proposes a machine learning model based on the stacking ensemble learning technique. Three ML models, including catboost, XGboost, and random forest, are ensebmled. Experimental data are obtained by setting up sixteen solar panels with different combinations of tilt and direction. The performance of the proposed method is compared with other ML and statistical models. We obtained a regression score (R2) of 0.86 and a mean absolute percentage error (MAPE) of 2.54%.

Demand for electric kickboards is increasing specifically in tourist-centric regions worldwide. In order to gain a competitive edge and to provide quality service to customers, it is essential to properly deploy rental electric kickboards (e-kickboards) at the time and place customers want. However, it is necessary to study how to divide the region to predict electric mobility demand by region. Therefore, this study is made to more accurately predict future demand based on past regional customers’ electric mobility demand data. We have proposed a novel electric kickboard demand prediction in spatiotemporal dimension using clustering-aided bagging regressor. We have used electric kickboard usage data from a Jeju, South Korea-based company. As a result of the experiment, it was found that the accuracy before using clustering-based bagging regressor and when the region was divided by the clustering method, the performance was improved, and we have achieved a regression score R2 of 93.42 using our proposed approach. We have compared our proposed approach with other state-of-the-art models, and we have also compared our model with different other combinations of bagging regressors. This study can be helpful for companies to meet the user’s demand for a better quality of service.

The battery management system in an electric vehicle must be reliable and durable to forecast the state of charge. Considering that battery degradation is generally nonlinear, state of charge (SOC) estimation with lower degradation can be challenging. Lithium-ion batteries are highly dependent on the knowledge of aging, which is usually costly or not available online. In this paper, we suggest the state of charge estimation of lithium-ion battery systems by using an extreme gradient boosting algorithm for electric vehicles application, which acquires the nonlinear relationship model can with offline training. The extreme gradient boosting algorithm is the tree on based learning, which effectively performs and speeds. Voltage-time data used as an input of this system from the partial constant current phase; the proposed algorithm improves the accuracy of predicting the relevant. Additionally, no initial state of charge is required in our proposed method; thus, estimating the state of charge can consider each battery state.

With the growing awareness regarding the importance of personal data protection, many countries have established laws and regulations to ensure data privacy and are supervising managements to comply with them. Although various studies have suggested compliance methods of the general data protection regulation (GDPR) for personal data, no method exists that can ensure the reliability and integrity of the personal data processing request records of a data subject to enable its utilization as a GDPR compliance audit proof for an auditor. In this paper, we propose a delegation-based personal data processing request notarization framework for GDPR using a private blockchain. The proposed notarization framework allows the data subject to delegate requests to process of personal data; the framework makes the requests to the data controller, which performs the processing. The generated data processing request and processing result data are stored in the blockchain ledger and notarized via a trusted institution of the blockchain network. The Hypderledger Fabric implementation of the framework demonstrates the fulfillment of system requirements and feasibility of implementing a GDPR compliance audit for the processing of personal data. The analysis results with comparisons among the related works indicate that the proposed framework provides better reliability and feasibility for the GDPR audit of personal data processing request than extant methods.

Modern computing resources, including machine learning-based techniques, are used to maintain stability between the demand and supply of electricity. Machine learning is widely used for the prediction of energy consumption. The researchers present several artificial intelligence and machine learning-based methods to improve the prediction accuracy of energy consumption. However, the discrepancy between actual energy consumption and predicted energy consumption is still challenging. Various factors, including changes in weather, holidays, and weekends, affect prediction accuracy. This article analyses the overall prediction using error curve learning and a hybrid model. Actual energy consumption data of Jeju island, South Korea, has been used for experimental purposes. We have used a hybrid ML model consisting of Catboost, Xgboost, and Multi-layer perceptron for the prediction. Then we analyze the factors that affect the week-ahead (WA) and 48 h prediction results. Mean error on weekdays is recorded as 2.78%, for weekends 2.79%, and for special days it is recorded as 4.28%. We took into consideration significant predicting errors and looked into the reasons behind those errors. Furthermore, we analyzed whether factors, such as a sudden change in temperature and typhoons, had an effect on energy consumption. Finally, the authors have considered the other factors, such as public holidays and weekends, to analyze the significant errors in the prediction. This study can be helpful for policymakers to make policies according to the error-causing factors.

Background and Purpose The primary mechanism underlying paramedian pontine infarction (PPI) is atheroma obliterating the perforators. Here, we encountered a patient with PPI in the post-stenotic area of basilar artery (BA) without a plaque, shown by high-resolution magnetic resonance imaging (HR-MRI). We performed an experiment using a 3D-printed BA model and a particle image velocimetry (PIV) to explore the hemodynamic property of the post-stenotic area and the mechanism of PPI. Methods 3D-model of a BA stenosis was reconstructed with silicone compound using a 3D-printer based on the source image of HR-MRI. Working fluid seeded with fluorescence particles was used and the velocity of those particles was measured horizontally and vertically. Furthermore, microtubules were inserted into the posterior aspect of the model to measure the flow rates of perforators (pre-and post-stenotic areas). The flow rates were compared between the microtubules. Results A recirculating flow was observed from the post-stenotic area in both directions forming a spiral shape. The velocity of the flow in these regions of recirculation was about one-tenth that of the flow in other regions. The location of recirculating flow well corresponded with the area with low-signal intensity at the time-of-flight magnetic resonance angiography and the location of PPI. Finally, the flow rate through the microtubule inserted into the post-stenotic area was significantly decreased comparing to others (P

Malicious codes may cause virus infections or threats of ransomware through symmetric encryption. Moreover, various bypassing techniques such as steganography, which refers to the hiding of malicious code in image files, have been devised. Unknown or new malware hidden in an image file in the form of malicious code is difficult to detect using most representative reputation- or signature-based antivirus methods. In this paper, we propose the use of ImageDetox method to neutralize malicious code hidden in an image file even in the absence of any prior information regarding the signatures or characteristics of the code. This method is composed of four modules: image file extraction, image file format analysis, image file conversion, and the convergence of image file management modules. To demonstrate the effectiveness of the proposed method, 30 image files with hidden malicious codes were used in an experiment. The malicious codes were selected from 48,220 recent malicious codes purchased from VirusTotal (a commercial application programming interface (API)). The experimental results showed that the detection rate of viruses was remarkably reduced. In addition, image files from which the hidden malicious code had previously been removed using a nonlinear transfer function maintained nearly the same quality as that of the original image; in particular, the difference could not be distinguished by the naked eye. The proposed method can also be utilized to prevent security threats resulting from the concealment of confidential information in image files with the aim of leaking such threats.

In today’s world, renewable energy sources are increasingly integrated with nonrenewable energy sources into electric grids and pose new challenges because of their intermittent and variable nature. Energy prediction using soft-computing techniques plays a vital role in addressing these challenges. As electricity consumption is closely linked to other energy sources such as natural gas and oil, forecasting electricity consumption is essential for making national energy policies. In this paper, we utilize various data mining techniques, including preprocessing historical load data and the load time series’s characteristics. We analyzed the power consumption trends from renewable energy sources and nonrenewable energy sources and combined them. A novel machine learning-based hybrid approach, combining multilayer perceptron (MLP), support vector regression (SVR), and CatBoost, is proposed in this paper for power forecasting. A thorough comparison is made, taking into account the results obtained using other prediction methods.

The ongoing upsurge of deep learning and artificial intelligence methodologies manifest incredible accomplishment in a broad scope of assessing issues in different industries, including the energy sector. In this article, we have presented a hybrid energy forecasting model based on machine learning techniques. It is based on the three machine learning algorithms: extreme gradient boosting, categorical boosting, and random forest method. Usually, machine learning algorithms focus on fine-tuning the hyperparameters, but our proposed hybrid algorithm focuses on the preprocessing using feature engineering to improve forecasting. We also focus on the way to impute a significant data gap and its effect on predicting. The forecasting exactness of the proposed model is evaluated using the regression score, and it depicts that the proposed model, with an R-squared of 0.9212, is more accurate than existing models. For the testing purpose of the proposed energy consumption forecasting model, we have used the actual dataset of South Korea’s hourly energy consumption. The proposed model can be used for any other dataset as well. This research result will provide a scientific premise for the strategy modification of energy supply and demand.

During 2001–2007, to determine incidence of all hantavirus infections, including those without pulmonary syndrome, in western Panama, we conducted 11 communitywide surveys. Among 1,129 persons, antibody prevalence was 16.5%–60.4%. Repeat surveys of 476 found that patients who seroconverted outnumbered patients with hantavirus pulmonary syndrome by 14 to 1.

Abstract Background In general, prognosis and impact of prognostic/predictive factors are assessed with Kaplan-Meier plots and/or the Cox proportional hazard model. There might be substantive differences from the results using these models for the same patients, if different statistical methods were used, for example, Boag log-normal (cure-rate model), or log-normal survival analysis. Methods Cohort of 244 limited-stage small-cell lung cancer patients, were accrued between 1981 and 1998, and followed to the end of 2005. The endpoint was death with or from lung cancer, for disease-specific survival (DSS). DSS at 1-, 3- and 5-years, with 95% confidence limits, are reported for all patients using the Boag, Kaplan-Meier, Cox, and log-normal survival analysis methods. Factors with significant effects on DSS were identified with step-wise forward multivariate Cox and log-normal survival analyses. Then, DSS was ascertained for patients with specific characteristics defined by these factors. Results The median follow-up of those alive was 9.5 years. The lack of events after 1966 days precluded comparison after 5 years. DSS assessed by the four methods in the full cohort differed by 0–2% at 1 year, 0–12% at 3 years, and 0–1% at 5 years. Log-normal survival analysis indicated DSS of 38% at 3 years, 10–12% higher than with other methods; univariate 95% confidence limits were non-overlapping. Surgical resection, hemoglobin level, lymph node involvement, and superior vena cava (SVC) obstruction significantly impacted DSS. DSS assessed by the Cox and log-normal survival analysis methods for four clinical risk groups differed by 1–6% at 1 year, 15–26% at 3 years, and 0–12% at 5 years; multivariate 95% confidence limits were overlapping in all instances. Conclusion Surgical resection, hemoglobin level, lymph node involvement, and superior vena cava (SVC) obstruction all significantly impacted DSS. Apparent DSS for patients was influenced by the statistical methods of assessment. This would be clinically relevant in the development or improvement of clinical management strategies.

The community of inquiry (Col) framework identifies three essential elements--social, cognitive, and teaching presences--that are crucial for a successful online learning experience. This study utilized the Col framework to examine whether differences existed in students' perceived Col presences between online and face-to-face sections of an introductory undergraduate IT course. The results indicated significant differences in social and teaching presences between the two delivery modes. Furthermore, the analysis revealed that social, cognitive, and teaching presences were significant predictors of students' course satisfaction, and different instructors significantly influenced social presence. However, a close comparison of both delivery modes taught by the same instructor showed no differences in the Col presences. These findings suggest that while students experience different levels of presence across different delivery modes, teachers have the potential to mitigate these differences and contribute to successful online learning experiences. [ABSTRACT FROM AUTHOR]