Reviewerly/exHarmony huggingface dataset:
Assessing the relative importance of dry-season incoming solar radiation and water storage dynamics during the 2005, 2010 and 2015 southern Amazon droughts: Not all droughts are created equal

Abstract Three severe droughts impacted the Amazon in 2005, 2010, and 2015, leading to widespread above-average land surface temperature (LST) (i.e., positive thermal anomalies) over the southern Amazon in the dry season (Aug–Sep) of these years. Below-average dry-season incoming solar radiation (SW↓) and terrestrial water storage (TWSA) were simultaneously observed in 2005 and 2010, whereas the opposite was observed in 2015. We found that anomalies in precipitation (P), SW↓, and TWSA combined can well explain dry-season thermal anomalies during these droughts (average R 2 ~0.51). We investigated the causes for opposing anomalies in dry-season SW↓ and TWSA, and found different hydro-meteorological conditions preceding the drought-year dry seasons. In 2005 and 2010, P was considerably below average during the wet-to-dry transition season (May–Jul), causing below-average TWSA in dry season that was favourable for fires. Increased atmospheric aerosols resulting from fires reduced solar radiation reaching the ground. In 2015, although below-average dry-season P was observed, it was above the average during the wet-to-dry transition season, leading to reduced fires and aerosols, and increased dry-season SW↓. To further examine the impact of opposite hydro-meteorological processes on the drought severity, we compared dry-season LST during droughts with the maximum LST during non-drought years (i.e., LST max ) for all grid cells, and a similar analysis was conducted for TWSA with the minimum TWSA (i.e., TWSA min ). Accordingly, the regions that suffered from concurrent thermal and water stress (i.e., LST &gt; LST max and TWSA &lt; TWSA min ) were identified. These regions are mainly observed over the southeast in 2005 and southern Amazon in 2010. In 2015, large-scale dry-season thermal stress was found over central and southeast Amazon with little water stress. This study underlines the complex interactions of different hydrological components and the importance of understanding the evolution of droughts to better predict their possible impacts on the Amazon rainforest.
The pedagogical approach of grade 7 teachers in teaching the learning competency of integers

This research addresses the intricate challenges of teaching integers in middle school mathematics. Focusing on grade 7 teachers, the study explores strategies to enhance integers' teaching-learning competency, revealing nuanced approaches employed by three teachers. Through qualitative methods, specifically in-depth interviews, the research uses an exploratory approach to unravel the underlying strategies shaping the teaching of integers. Key findings underscore integers as a formidable learning competency for grade 7 students, particularly in operations like addition and subtraction. The most effective pedagogical approach, encapsulated in the 3R — Reaching, Representing, Recommending — involves drill exercises for foundational concepts, integrating manipulatives like algebra tiles and coloured tiles, and advocating for continuous manipulative use with persistent drill exercises and integrating technology. These identified themes emphasize the need for a uniform understanding among teachers during discussions to ensure a cohesive and effective teaching strategy. Recognizing these strategies is crucial for addressing the existing learning gap and advancing grade 7 students' competency in dealing with integers. The research contributes valuable insights to the academic discourse, offering effective pedagogical practices and fostering a more robust foundation for mathematical understanding at the middle school level.
Maximum entropy inverse reinforcement learning-based trajectory planning for autonomous driving

With the rapid advancement of autonomous driving technology, effective trajectory planning has become crucial for ensuring road safety and driving efficiency. Traditional trajectory planning methods often rely on preset rules and models, making them ill-suited for the complex and dynamic traffic environment. To address this, a Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) based trajectory planning method is proposed in this paper, aiming at learning from expert driving behaviors to infer an efficient reward function, which in turn guides decision-making and path planning for autonomous vehicles. This study begins by analyzing expert driving data to extract key state and action features. Then, the MaxEnt IRL algorithm is applied to learn the reward function underlying these features, reflecting the decision-making logic of expert drivers. The learned reward function is subsequently used to guide the trajectory planning of the autonomous driving system, generating safe and efficient driving paths. A series of experiments conducted in a simulated environment demonstrate that the MaxEnt IRL-based method proposed in this paper exhibits higher adaptability and efficiency in handling complex traffic scenarios compared to traditional trajectory planning methods.
Research on the Method of Artistic Image Restoration Based on Artificial Intelligence

In order to improve the current inpainting algorithm for natural art images, an artistic image inpainting model is proposed by integrating artificial intelligence and embedding multi-scale attention expansion convolution. According to the uniqueness of artistic images, the network structure of DMFB is improved, and its repair model combines two plug-and-play optimization modules: extended convolution block and coordinate attention mechanism. The extended convolution module is used to capture multi-scale context information, and the coordinate attention mechanism is used to improve the remote migration ability of features of the repaired network. The combination of the two makes the repair results meet the visual visibility and semantic rationality. The experimental results show that ablation experiments are carried out to evaluate the effectiveness of each module in this model. In this paper, the hybrid extended convolution and coordinated attention mechanism are used to train the network, and the effectiveness of MADC is verified. When the expansion rate changes to 2, 4, 6 and 8, the method in this paper will lead to moderately distorted structure and fuzzy texture. On the contrary, there is no coordination attention. In the branch of mechanism, the output of this method shows texture defects and discontinuities. By using these two branches, the method in this paper has achieved good results in structure and texture. The quantitative evaluation of branches without coordinated attention mechanism is given when the expansion rate is fixed at 2, 4, 6 and 8. Conclusion: The results restored by this method are more consistent with the real image in human senses, and the objective evaluation of PSNR, SSIM and MSE has also been improved to some extent.
Research on Intelligent Assistant Teaching System for National Musical Instruments Based on PSS

In order to improve the efficiency and quality of the teaching service of the national musical instruments, a design method of product service system (PSS) for national musical instruments is proposed to build a tool for integrating teaching resources, autonomous practice feedback, and social interaction. The basic characteristics of the national musical instruments of the balance between the improvement and the inheritance of traditional music is summarized, the shortcomings of digital transformation of national musical instruments under the condition of smart technology are compared and pointed out, there is still room for development in precision teaching services. Based on the design method of PSS, the musical instrument products and teaching services are integrated into an overall solution, a model of teaching service and value co-creation is constructed. A service process is formed with the song as the basic teaching unit and gesture skill training as the key. The human-smart system collaboration mode is analyzed. The smart product subsystem and teaching service subsystem are established. The relationship among the stakeholders is discussed. The prototype scheme of the smart assistant teaching system of Pipa based on this method is built up to verify.
The Design of Undergraduate Students’ Learning Product Service System Based on User Behavioural Persuasion

The article investigates the application of user behavior and persuasive design theories in product design and develops a design model based on user behavioral persuasion. The research collects data on study habits observed from undergraduate students and subsequently examines the patterns and variations within this cohort. The investigation leads to the development of a product service system that integrates motivational education to encourage undergraduate students to cultivate positive study habits and enhance the overall quality of their learning experience. The study builds a logical and efficient design model and hits the pain points of undergraduate students learning behaviors, delivering effective design solutions and improving the match between product strategies and design requirements by combining with motivational education.
Research on Improving the Quality of Japanese Chinese Machine Translation Based on Deep Learning

In order to improve the quality of Japanese-Chinese machine translation, the research on improving the quality of Japanese-Chinese machine translation based on deep learning is put forward. The maximum entropy model is used to effectively classify Japanese possible states and passive states, and then the features of Japanese possible states and passive states are effectively integrated into the log-linear model to improve the translation model, so as to improve the accuracy of selecting possible states and passive states in translation rules. The experimental results show that this method can effectively improve the translation quality of Japanese possible sentences and passive sentences. On large-scale Japanese Chinese materials, the maximum translation BLEU value can be increased from 41.50 to 42.01, and the overall intelligibility of translation results has been improved by 2.71% in manual evaluation. Conclusion: This method can improve the accuracy of rule selection.
Spillover Effect of Network Public Opinion on Market Prices of Small-Scale Agricultural Products

Network public opinion plays a crucial role in the behavior and decision making of various stakeholders, including farmers, middlemen, and consumers. It also affects the price fluctuations of small-scale agricultural products. Understanding the transmission path and spillover effect of network public opinion on the price fluctuations of these products is essential for ensuring their sustainable development and price stability. This paper selects the monthly data of network public opinion and related market prices of small-scale agricultural products from January 2014 to December 2021, constructs a network public opinion value through the sentiment classification results of deep learning models, and uses the trivariate VAR-BEKK-GARCH(1,1) model and spillover index model to study the spillover effect and spillover index of network public opinion on the market prices of small-scale agricultural products (national average price and origin price). The results show that: (1) There is a bidirectional volatility spillover effect between public opinion sentiment and the market prices of small-scale agricultural products. Additionally, this two-way volatility spillover effect is also evident between the average market prices and the origin prices of these commodities. (2) The influence of network public opinion on the market prices of small-scale agricultural products is substantial, with the spillover index being more pronounced for origin prices than for national average prices and reaching its zenith earlier. Consequently, based on these results, recommendations are provided to adapt planting and inventory strategies, enhance vigilance towards price risk transmission amongst small-scale agricultural product markets, and improve the comprehensive information platform encompassing the entire industry chain.
Success Factors in Management of IT Service Projects: Regression, Confirmatory Factor Analysis, and Structural Equation Models

Although there have been some studies on the success factors for IT software projects, there is still a lack of coherent research on the success factors for IT service projects. Therefore, this study aimed to identify and understand the factors and their relationships that contribute to the success of IT service projects. For this purpose, multivariate regressions and structural equation models (SEMs) were developed and analyzed. The regression models included six project management success criteria used as dependent variables (quality of the delivered product, scope realization and requirements, timeliness of delivery, delivery within budget, customer satisfaction, and provider satisfaction) and four independent variables (agile techniques and change management, organization and people, stakeholders and risk analysis, work environment), which had been identified through exploratory factor analysis. The results showed that not all success factors were relevant to all success criteria, and there were differences in their importance. An additional series of exploratory and confirmatory factor analyses along with appropriate statistical measures were employed to evaluate the quality of these four factors. The SEM approach was based on five latent constructs with a total of twenty components. The study suggests that investing in improving people’s knowledge and skills, using agile methodologies, creating a supportive work environment, and involving stakeholders in regular risk analysis are important for project management success. The results also suggest that the success factors for IT service projects depend on both traditional and agile approaches. The study extensively compared its findings with similar research and discussed common issues and differences in both the model structures and methodologies applied. The investigation utilized mathematical methods and techniques that are not commonly applied in the field of project management success modeling. The comprehensive methodology that was applied may be helpful to other researchers who are interested in this topic.
ESARS: A Situation-Aware Multi-Agent System for Real-Time Emergency Response Management

This paper describes ESARS, a real-time situation-aware social media- enabled emergency situation alert and reporting system, as a decision support system built on multi-agent software design architecture for emergency situation management. The impact of an incident or disruption due to the incident could be minimized by implementing real-time intervention strategies that involve event monitoring, detection and situation identification via classification and prediction, notification, visualization and reporting that culminate in providing emergency support within time. The nature of agent behavior, which is autonomous, proactive and cooperative, makes them a suitable method for the design and deployment of a dynamic system of this nature. The system relies on historical and streamed real-time geolocation-enabled Twitter data stream for the target emergency events to provide decision-makers with dynamic, comprehensive, and timely information specific to the emergency situation.
A Syntactic Proof of the Decidability of First-Order Monadic Logic

Decidability of monadic first-order classical logic was established by Löwenheim in 1915. The proof made use of a semantic argument, but a purely syntactic proof has never been provided. In the present paper we introduce a syntactic proof of decidability of monadic first-order logic in innex normal form which exploits G3-style sequent calculi. In particular, we introduce a cut- and contraction-free calculus having a (complexity optimal) terminating proof-search procedure. We also show that this logic can be faithfully embedded in the modal logic T.
Ant Colony Optimization for Efficient Distance and Time Optimization in Swarm Drone Formation

One of the challenges in swarm drone formation is achieving fast and effective formation with optimal distances. In this paper, we propose a swarm drone formation approach utilizing Ant Colony Optimization (ACO) for achieving it. We conducted simulations involving the formation of three or more drones, aiming to identify the best formation based on distance, acceleration, and time criteria. Simulation results demonstrate that formation time is significantly reduced when employing ACO optimization compared to non-optimized methods. Additionally, the optimized formations exhibit shorter inter-drone distances compared to non-optimized formations. By implementing this approach, swarm drone formations can be rapidly established with minimized distances, resulting in substantial battery savings. The simulation encompassed various patterns formed by 3, 5, 10, 15, 20, and 25 drones. The findings indicate that the approach can reduce formation time by varying degrees, ranging from 12% to 51%, across 66% of the conducted experiments, notably for patterns created with a substantial drone count. The degree of diversity observed among the proposed solutions reached 60%, with minimal variances of less than 1% for each.
Analyzing Facebook Mobile Usage: Efficacy and ESL Learners’ Writing Proficiency

Facebook has leveraged rapid technological and societal changes over the past decade and has emerged as the largest social networking platform. However, research on Facebook has been limited, particularly when examining its potential to improve English as a second language (ESL) writing in comparison to a control group. The study aims to explore the morphological, syntactic, and orthographical aspects of ESL writing through an experimental group [N = 30] using Facebook on mobile and a control group [N = 30] with a traditional teaching approach. The experimental and control groups undergo observation through twice-weekly, in-class-focused free writing sessions for 10 weeks. The progress of the learners’ writing was tracked using pre- and post-tests to assess its impact. The analysis of variance (ANOVA) test revealed that the experimental group made more significant advancements in writing by reducing morphological, syntactic, and orthographical errors compared to the control group. These results validate previous studies that support the use of Facebook on mobile devices in ESL courses and emphasize the need for further research comparing Facebook with other writing platforms.
Convolutional Neural Network Architectures for Gender, Emotional Detection from Speech and Speaker Diarization

This paper introduces three system architectures for speaker identification that aim to overcome the limitations of diarization and voice-based biometric systems. Diarization systems utilize unsupervised algorithms to segment audio data based on the time boundaries of utterances, but they do not distinguish individual speakers. On the other hand, voice-based biometric systems can only identify individuals in recordings with a single speaker. Identifying speakers in recordings of natural conversations can be challenging, especially when emotional shifts can alter voice characteristics, making gender identification difficult. To address this issue, the proposed architectures include techniques for gender, emotion, and diarization at either the segment or group level. The evaluation of these architectures utilized two speech databases, namely VoxCeleb and RAVDESS (Ryerson audio-visual database of emotional speech and song) datasets. The findings reveal that the proposed approach outperforms the strategy level in terms of recognition results, despite the real-time processing advantage of the latter. The challenge of identifying multiple speakers engaging in a conversation while considering emotional changes that impact speech is effectively addressed by the proposed architectures. The data indicates that the gender and emotion classification of diarization achieves an accuracy of over 98 percent. These results suggest that the proposed speech-based approach can achieve highly accurate speaker identification.
The Effectiveness of a Program Based on Blended Learning in Developing National Identity and Concepts of Intellectual Security Among Kindergarten children in the Najran Region

The goal of this study was to determine how well a blended learning program develops national identity and concepts of intellectual security in kindergarten students in the Najran region. Blended learning is an integrated system that combines e-learning with traditional methods to guide and help the child in acquiring concepts and skills. It is one of the modern approaches based on the use of educational technology in designing new educational situations, and it has become an urgent necessity, especially because of its characteristics that make it the most suitable alternative for developing the educational system. In order to do this, a one-group quasi-experimental design was employed along with a blended learning program that was suggested, along with measures of intellectual security and national identification for kindergarteners. Twenty-seven kids made up the research sample, and they were chosen at random. The outcomes demonstrated how well the suggested program worked to help the study sample's national identification and ideas of intellectual security grow. The study made a number of recommendations, the most significant of which are to use blended learning to help children develop their thoughts and abilities and to incorporate national identity and ideas of intellectual security into educational units and kindergarten activities.
Decentralised Storage Network by Using Block-Chain

This paper explores the intersection of network security and blockchain technology in the context of today's digital era. It begins by acknowledging the vulnerabilities and threats contemporary networks face, including data breaches and denial-of-service attacks. The paper then delves into the foundational principles of blockchain, highlighting its decentralization, consensus mechanisms, cryptographic encryption, and immutability as key attributes that can enhance network security The central idea presented is the potential of blockchain to revolutionize network security. It discusses how blockchain can be integrated into network architecture to achieve secure and transparent identity management, combat identity fraud, automate network processes through smart contracts, and enhance real-time threat detection and response. However, the paper also recognizes challenges such as scalability, interoperability, and energy efficiency when implementing blockchain in secure networks and suggests ongoing research efforts and potential solutions to make this integration more practical.
Computer-Aided Putonghua Test: Problems and Strategies

The implementation of mandarin test is one of the most important measures to popularize Putonghua in China. With the rapid development of modern information technology, especially the effective integration of computer, network and speech recognition technology, the model of computer-aided Putonghua proficiency test was born in a new form, which provides a good opportunity to promote the standardization and modernization of Putonghua proficiency test. Based on a brief introduction of the advantages of computer-aided Putonghua test, this paper mainly discusses the typical problems in the process of Putonghua test, and then puts forward the corresponding solutions in order to better promote the development of this test.