Miracle-dz/UniFAR-FTUs huggingface dataset:
Disaster loss and social media: Can online information increase flood resilience?

When confronted with natural disasters, individuals around the world increasingly use online resources to become informed of forecasted conditions and advisable actions. This study tests the effectiveness of online information and social media in enabling households to reduce disaster losses. The 2011 Bangkok flood is utilized as a case study since it was one of the first major disasters to affect a substantial population connected to social media. The role of online information is investigated with a mixed methods approach. Both quantitative (propensity score matching) and qualitative (in‐depth interviews) techniques are employed. The study relies on two data sources—survey responses from 469 Bangkok households and in‐depth interviews with internet users who were a subset of the survey participants. Propensity score matching indicates that social media enabled households to reduce flood losses by an average of 37% (USD 3708 per household), using a nearest neighbor estimator. This reduction is substantial when considering that household flood losses for the matched sample averaged USD 8278. Social media offered information not available from other sources, such as localized and nearly real‐time updates of flood location and depth. With this knowledge, households could move belongings to higher ground before floodwaters arrived. These findings suggest that utilizing social media users as sensors could better inform populations during disasters. Overall, the study reveals that online information can enable effective disaster preparedness and reduce losses.
Earthquake supercycles as part of a spectrum of normal fault slip styles

Like thrust boundaries in subduction zones, some normal faults show evidence of temporally transient slip patterns, such as temporally clustered earthquakes or periods of elevated creep rate. We create rate‐and‐state friction numerical models to assess whether typical distributions of frictional parameters can explain transient slip on normal faults. We constrain our models with geometry from the Wasatch Fault zone, where 5–10 kyr timescale earthquake clusters are recorded on fault scarps. We use numerical models to test whether these earthquake clusters could be the geologic representation of “supercycles” which we define as long‐period stress cycles containing multiple seismic cycles. Field observations, laboratory data, and analogue experiments suggest that the ratio of velocity‐weakening to velocity‐strengthening material varies with the ratio of brittle to ductile material in the shear zone. We therefore employ a linear transition of frictional parameters with depth to simulate bulk frictional properties of the shear zone. We find that for generalized normal faults, rate‐and‐state friction predicts a spectrum of slip behaviors including steady aseismic creep, aseismic transients, clustered earthquakes, and regular seismic cycle earthquakes. The dominant control on slip behavior is the degree of weakening material in the potentially seismogenic fault patch. Earthquake clusters occur with higher proportions of velocity‐weakening material which correspond to more competent, less ductile shear zone assemblages. We find that rate‐and‐state friction predicts a wider spectrum of slip behavior on active normal faults than has been previously recognized, potentially due to geologic preservation and identification issues.
Design and Harmonic Elimination of sinusoidal pulse width modulation (SPWM) Based Five Level Cascaded H-Bridge Multilevel Inverter for Photovoltaic System for Educational Purposes

In this modern era, the electrical power system faces a shortage of fossil fuel-based energy sources. To overcome this problem, people more focus on giving towards renewable energy sources like solar energy, wind energy, and nuclear energy sources. This also can be used to overcome global warming and reduce pollution by fossil fuels. This study used a photovoltaic (PV) array to give supply to the Multilevel Inverter (MLI) with different irradiance values, which can be used for educational purposes. The sinusoidal pulse width modulation (SPWM) method is used here to control the converter. SPWM technique is the easiest technique than the other types of pulse width modulation (PWM) technique. In this technique the switching loss is very low, therefore efficiency is high. After the simulation, the output waveforms are similar to the sinusoidal waveform for the resistive load. In this topology, after simulation, the total harmonic distortion (THD) contents were found to be less by using an LC filter. The model is simulated in MATLAB/Simulink. The analysis of output voltage waveforms is discussed with the reduced THD.
Enhancing Structural Engineering Education: Integrating Artificial Intelligence for Continuous Improvement

The study aims to analyze the impact of artificial intelligence (AI) usage on structural learning through student-developed programming in open-source software languages: Python, Octave, and OpenSees. The research collaborates with 90 undergraduate students in the early courses of civil engineering at the Universidad Nacional de Chimborazo. The ADDIE methodology is employed in the initial phase for planning, development, and monitoring. A survey on students' perceptions regarding effectiveness, satisfaction, recommendation, and feedback is conducted, followed by academic performance evaluation using a grading rubric to verify the achievement of set objectives. An analysis of factors contributing to AI-focused learning is then performed. Initial results revealed outliers, some deviating from study parameters and others discarded for a comprehensive view of study behavior. Regarding the survey data analysis, efficiency and satisfaction exhibited the highest reliability. Subsequently, variables were correlated considering their normality, showing a relationship between effectiveness and satisfaction; however, a strong connection cannot be guaranteed for these or other variables. Therefore, ANOVA tests, indicating positive linear relationships, and hypothesis testing were employed, demonstrating that students achieved objectives with a moderately high degree of effectiveness and satisfaction. The use of technological options and consideration of innovative learning methods can positively enhance the learning experience, contingent on prior education. Exploring artificial intelligence may prove challenging without guided information search based on predefined criteria and constraints.
GSAtt-CMNetV3: Pepper Leaf Disease Classification Using Osprey Optimization

Nowadays, the demand for pepper keeps on increasing with the increase in human population. Accurate diagnosis, flawless identification, and early detection of the lesions will improve the income of farmers. At present, deep learning (DL) based techniques assist farmers in identifying plant diseases with low cost and minimal time complexity. Hence, this study proposes a novel optimized DL model for classifying the presence and absence of pepper leaf disease using an effective feature learning process. The proposed study undergoes four major stages namely Pre-processing, Segmentation, Feature extraction, and Classification. In the pre-processing stage, initially, the input images are resized and the Improved Contrast Limited Adaptive Histogram Equalization (ICLAHE) technique is introduced to enhance the quality of the pepper leaf images. Then, the Kernelized Gravity-based Density Clustering (KGDC) technique is conquered to segment the diseased portions from the leaf images. Finally, the Gated Self-Attentive Convoluted MobileNetV3 (GSAtt-CMNetV3) technique is proposed to extract the features and classify the pepper leaf disease accurately. Moreover, a novel osprey optimization algorithm (Os-OA) is introduced to tune the parameters of the proposed DL model for enhancing the classification performance. The proposed study is implemented via the Python platform, and a publicly available Plant-Village dataset is utilized for the simulation process. Accuracy, precision and recall values achieved by the proposed pepper leaf disease classification for training percent 80 is 97.87%, 96.87% and 97.08% respectively.
Injuries and Associated Factors in Swedish Sporting and Utility Trial Dogs—A Cross-Sectional Study

Simple Summary Canine sporting competitions, e.g., agility, obedience, rally obedience, and utility trials, e.g., protection, tracking, search, and messenger, are physically and mentally demanding disciplines. There is a growing concern within the dog sports community that some disciplines and breeds may be at higher injury risk. This study aimed to describe the frequencies and types of injuries experienced amongst Swedish sporting and utility trial dogs and to explore associations between discipline, breed, sex, neuter status, age, and injury history. Through a questionnaire survey, information about 1582 dogs was collected. During their lifetime, more than half of the dogs (n = 928, 58.7%) had suffered from an injury whether it appeared during training, competition, or outside of sport. Key findings were that muscular, joint, and dermatologic injuries were most commonly reported and the most common injury locations were the thoracic, lumbar and lumbosacral spine, paw, head, shoulder, and stifle. In a multivariate analysis, Border Collies, Belgian Malinois, and higher age at the time of report increased the odds of injury history. Our results provide more understanding of injuries in sporting and utility dogs and contribute to injury prevention strategies that potentially enhance canine welfare. Abstract Canine sporting competitions, e.g., agility, obedience, rally obedience, and utility trials, e.g., protection, tracking, search, and messenger, are physically and mentally demanding disciplines. This study aimed to describe the types and frequencies of injuries experienced amongst Swedish sporting and utility trial dogs and to explore associations between discipline, breed, sex, neuter status, age, and injury history. Dog handlers provided information on competition-level dogs (n = 1582) through a cross-sectional survey. The overall proportion of dogs sustaining any injury during their lifetime, whether it was during competition, training, or outside of sport, was 58.7% (n = 928). Muscular, joint, and dermatologic injuries were most commonly reported and the most common injury locations were the thoracic, lumbar and lumbosacral spine, paw, head, shoulder, and stifle. According to multivariate analysis, Border Collie (adjusted OR 1.93), Belgian Malinois (adjusted OR 2.51) and higher age at the time of report (adjusted OR 1.81–9.67) increased the odds of injury history. Our results provide more understanding of injuries in sporting and utility dogs and contribute to injury prevention strategies that potentially enhance canine welfare.
The Relationship Between Motivation and Job Satisfaction

A multitude of factors can influence job performance. Workplace motivation and satisfaction might be some of the most important ones. This paper investigates the ways in which these former variables influence each other in regards to a workers performance; whether workplace satisfaction increases after five years of working in the same domain; and lastly how the five factors of personality from the Big Five model influence workplace satisfaction. In order to test our hypotheses we applied three questionnaires: The Workplace Satisfaction Scale, The Multidimensional Workplace Motivation Scale and The Five Factor Personality Inventory. These tests were completed in an online format by 60 consenting adult, half of which had a working experience over five years and half under five years. In our findings workplace satisfaction and motivation influenced each other positively likely due to the quality of external or internal sources of validation. We also found that extraversion, agreeableness and emotional stability have an impact on workplace satisfaction prabably because of the way in which high scores in these facets facilitate good work conduit, communication and integrations in workgroups. Unfortunetely, we did not see significant differences in regards to workplace experience and workplace satisfaction, posibly because of the small sample size of this study. In the end, the biggest takeaway from this paper is that good workplace relationships and proper financial compensation are the true benefactors of high workplace satisfaction that can in turn boost motivation and job performance.
Functional Data Clustering Method Based on Shape Information and Functional Mahalanobis Distance

Function-type data can provide insights into the internal structure of the data and facilitate the extraction of data features from the perspective of interactive derivative functions. The paper proposes a nonparametric clustering method for function-based data that incorporates first-order and second-order derivative function information into the Marginal distance for clustering function-based data. The method is based on the traditional K-means algorithm and is designed to cluster function-based data that contains rich shape information. The paper evaluated the performance of the algorithm of the text by comparing its purity and adjusted Rand coefficients against six other clustering algorithms on three different datasets. The results show that the algorithm of the text outperforms the other algorithms, demonstrating its outstanding performance, wide applicability, and practical significance in solving real-world problems.
Crop Segmentation of Unmanned Aerial Vehicle Imagery Using Edge Enhancement Network

Crop segmentation enables agricultural producers to comprehensively understand the state of their farmland, make more informed management decisions and thereby ensure food security. Unmanned aerial vehicle (UAV) remote sensing technology offers cost-effective high-resolution imaging for crop segmentation. Deep learning-based methods have continued to improve the accuracy of crop segmentation over time, but accurate edge segmentation remains a challenge. In this letter, we introduce a convolutional neural network (CNN) termed edge enhancement network (EENet) to improve the representation of edge information for crop segmentation in UAV RGB images. Our approach centers on an innovative edge enhancement (EE) Strategy that augments the learning of edge information during the training phase and refines the representation of edge details during the generation phase. To facilitate the evaluation of our method, we have produced a publicly available dataset for experimentation. Our results, as demonstrated on this self-constructed dataset, illustrate that our proposed approach surpasses competing methods in critical metrics such as mean intersection over union (mIoU), F1_score, and model complexity.
The Up to Date Progress of Prostate Cancer Vaccines

The prostate cancer vaccine has significant influences on males, and the cancer among American men has second highest prevalence among all cancers. Both the prevalence and mortality rates of prostate cancer in America are increasing each year. Researchers have been working to improve and innovate the way prostate cancer is diagnosed and treated. One promising direction is immunotherapy. Scientists have been working on a vaccine for prostate cancer for decades, and FDA only passes Sipuleucel-T as prostate cancer vaccine, but many other prostate cancer vaccines have been developed. Therefore, after synthesizing many papers, this paper explains the principles, latest progress, the advantages and disadvantages of all the vaccines developed so far, and proposes that future researchers should improve the prostate cancer vaccine in the direction of combination therapy, and conduct in-depth research to explore the prostate cancer vaccines that are currently showing an excellent trend, and to optimize the Sipuleucel-T.
Design of Identification and Compensation Methods for Space Rider GNC Algorithms

The ESA Space Rider System is designed to provide Europe with the first reusable and independent end-to-end orbital platform, to deliver an uncrewed orbital laboratory able to de-orbit, re-enter, land and be relaunched after limited refurbishment. In this context, AVIO is the prime contractor of the support module that must guarantee the guidance, navigation and control functions during the orbital phase of the system. High versatility and reliability are thus requested for the AVUM Orbital Module since the Space Rider mission foresees various in-orbit experiments, ranging from payload for commercial and institutional applications to In Orbit Verification/Demonstration missions, microgravity experiments, and Earth/Space observation. From this perspective, it becomes of paramount importance to manage all the operative conditions for which the GNC must guarantee the best performance in terms of accuracy and operative duration. Attitude sensors must guarantee high data reliability despite being subjected to stresses during launch, a harsh operating environment and not least any assembly errors during integration. Constant in-orbit calibration is, therefore, necessary to meet requirements. Actuators, on the other hand, must be able to compensate for system and external uncertainties and manage any non-nominal condition. In particular, the malfunctioning or degraded behaviours of actuators are to be taken into account. The objective of this thesis is to investigate the counteract action that the Space Rider’s GNC must implement to identify and compensate for all the effects that could degrade the mission performance. Specifically, both sensor and actuator issues are addressed. To achieve this, sliding mode techniques are used for both identification and compensation phases, combined with closed-loop analysis of sensors and actuators. The performance and robustness of these algorithms are then demonstrated using simulations of real operating conditions that Space Rider will face in future missions.
Impacts and effects of ocean warming on intertidal rocky habitats

• Intertidal rocky habitats comprise over 50% of the shorelines of the world, supporting a diversity of marine life and providing extensive ecosystem services worth in the region of US$ 5-10 trillion per year. • They are valuable indicators of the impacts of climate change on the wider marine environment and ecosystems. • Changes in species distributions, abundance and phenology have already been observed around the world in response to recent rapid climate change. • Species-level responses will have considerable ramifications for the structure of communities and trophic interactions, leading to eventual changes in ecosystem functioning (e.g. less primary producing canopy-forming algae in the North-east Atlantic). • Whilst progress is made on the mitigation1 required to achieve goals of a lower-carbon world, much can be done to enhance resilience to climate change. Managing the multitude of other interactive impacts on the marine environment, over which society has greater potential control (e.g. overfishing, invasive non-native species, coastal development, and pollution), will enable adaptation1 in the short and medium term of the next 5-50 years.
Ability of the European League Against Rheumatism-Outcomes Measures in Rheumatology combined scoring system for grading dorsal joint space synovitis to accurately evaluate ultrasound-detected hand synovitis

Background The European League Against Rheumatism-Outcomes Measures in Rheumatology (EULAR-OMERACT) recommend only scanning dorsal spaces for scoring ultrasound-detected hand synovitis. This study evaluated the efficiency of the combined scoring system only depending on dorsal joint spaces synovitis in diagnosing and evaluating ultrasound-detected hand synovitis. Methods The data of 56 patients who underwent hand joint ultrasonography exams in the Ultrasound Department of West China Hospital, Sichuan University were prospectively collected. The participants formed a random series. The images of each patient included gray-scale (GS) and power Doppler (PD) images of bilateral first to fifth metacarpophalangeal joints (MCP) and the thumb and second to fifth proximal interphalangeal joints (IP). The synovial thickness was measured quantitatively in GS images, and the synovial GS scores in the dorsal joint spaces and PD scores in the dorsal and volar joint spaces were calculated according to the combined EULAR-OMERACT scoring system. Results The detection rate of synovitis in the first to fifth MCP, thumb and second to fifth proximal IP synovitis were 41.4% (232/560) and 33.9% (190/560), respectively. The sensitivity of only inspecting the dorsal joint spaces with GS ultrasound was 79.3% for MCP and 52.6% for the thumb and second to fifth proximal IPs. The PD scores were higher in the dorsal joint spaces than in the volar joint spaces (P value <0.001). The combined scores were higher than either the GS or PD scores alone in the dorsal joint spaces (P value of the combined scores vs. GS scores =0.001; P value of the combined scores vs. PD scores <0.001). Conclusions Adopting the EULAR-OMERACT combined scoring standard is recommended to evaluate ultrasound-detected hand synovitis, as determined by the highest value of the GS scores or the PD scores. More specifically, PD scores can mainly be used to appraise the dorsal joint spaces. However, GS scores should be used to evaluate both the dorsal joint spaces and the volar joint spaces.
A Review of Three Methods of Artificial Intelligence in Smart Grid Cyber Security: Machine Learning, Reinforcement Learning, Ensemble Methods

Renewable energy is gradually replacing traditional fossil fuels. The change of power generation energy structure brings new challenges to the traditional power grid. Through the efficient bidirectional movement of electricity and information, smart grids might include renewable energy. For the complex informational and financial operations required by smart grid, communication systems are crucial, but they also make smart grid vulnerable to numerous cyber attacks. Smart grid cyber security has been widely concerned. The purpose of this paper is to explore the use of artificial intelligence technology in smart grid cyber security. Three methods in the field of artificial intelligence are highlighted: Machine Learning, Reinforcement Learning, and Ensemble Methods. This paper summarizes the benefits and drawbacks of their use of smart grid cyber security, and further makes a qualitative comparison of the three methods from multiple performance indicators.
New SiC Kicker Power Supply for J-PARC

A new kicker power supply using silicon carbide metal–oxide semiconductor field-effect transistors (SiC-MOSFETs) is under development at the Japan Proton Accelerator Research Complex (J-PARC). SiC-MOSFETs fabricate compact high-speed pulse power supplies to replace the thyratron switch power supply. The base circuit of the new power supply uses the linear transformer driver (LTD) system, and the semi-conductor module circuit comprises a radial symmetry type that achieves low noise. The three main components of the current kicker power supply, i.e., the thyratron, pulse-forming network (PFN) circuit, and end clipper, can be assembled on a single board as the new module circuit board. The new power supply contains a 1.25 kV/2 kA main board that forms a trapezoidal pulse and a 0.1 kV/2 kA correction board, compensating for the flat section droop. The 32 main boards and 20 correction boards are hierarchically connected in series to achieve the waveform specifications required for the J-PARC rapid cycling synchrotron (RCS) kicker power supply: output voltage of 40 kV, output current of 2 kA, and pulse width of 1.2 μs. Furthermore, a 25 % power saving has been confirmed and an insulating cylinder for the conductor has been developed to suppress corona discharge and withstand continuous operation for a long time.