Simple Knowledge Research Articles

Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering

In China, fruit tree diseases are a significant threat to the development of the fruit tree industry, and knowledge about fruit tree diseases is the most needed professional knowledge for fruit farmers and other practitioners in the fruit tree industry. Research papers are the primary sources of professional knowledge that represent the cutting-edge progress in fruit disease research. Traditional knowledge engineering methods for knowledge acquisition require extensive and cumbersome preparatory work, and they demand a high level of professional background and information technology skills from the handlers. This paper, from the perspective of fruit tree industry knowledge dissemination, aims at users such as fruit farmers, fruit tree experts, fruit tree knowledge communicators, and information gatherers. It proposes a fast, cost-effective, and low-technical-barrier method for extracting fruit tree disease knowledge from research paper abstracts—K-Extract, based on large language models (LLMs) and prompt engineering. Under zero-shot conditions, K-Extract utilizes conversational LLMs to automate the extraction of fruit tree disease knowledge. The K-Extract method has constructed a comprehensive classification system for fruit tree diseases and, through a series of optimized prompt questions, effectively overcomes the deficiencies of LLM models in providing factual accuracy. This paper tests multiple LLM models available in the Chinese market, and the results show that K-Extract can seamlessly integrate with any conversational LLM model, with the DeepSeek model and the Kimi model performing particularly well. The experimental results indicate that LLM models have a high accuracy rate in handling judgment tasks and simple knowledge Q&A tasks. The K-Extract method is simple, efficient, and accurate, and can serve as a convenient tool for knowledge extraction in the agricultural field.

Proses Pemahaman Pembuatan Ketupat Pada Pelaksanaan Upacara Agama Hindu Di Sindu Cakranegara Mataram

Community service activities are carried out in providing simple knowledge and understanding of the concept of local wisdom in the implementation of religious activities. The selection of the venue for the Ida Pedanda Nyoman Sebali Keniten Mangkuh Ceremony will be held on Thursday, September 19, 2024. The main thing that is socialized in this is to use the form of the meaning function of the ketupat, with the existence of the birth ketupat which is identical to the food of local wisdom, from various customs and customs or traditions. The annual tradition carried out by the majority of the population of Indonesia, namely the Eid al-Fitr tradition community, the Eid al-Fitr tradition is one of the traditions that usually occurs in the community, the Muslim community. This is a harmony that needs to be studied and revealed in order to have the right understanding. The result of this service is open insight in religious moderation, tolerance, and harmony in sitting together to make ketupat.

Optimising children's movement assessment batteries through application of motivational and attentional manipulations

An external focus of attention, enhanced expectancies, and autonomy support (i.e., OPTIMAL factors) are key factors to optimise motor performance and uncover latent movement capabilities. However, research on the combination of OPTIMAL factors, particularly in children's dynamic movement settings is limited. Therefore, this study examined the combined effects of OPTIMAL factors on children's performance on a dynamic movement assessment battery, hypothesising higher performance scores in the optimised version of the assessment battery versus standardised version of the assessment. Forty-nine children (15 boys, 34 girls; mean age 10.61 ± 1.38 years) completed the Dragon Challenge (DC) dynamic movement assessment battery. Performance was measured via a summation of movement process (technique), outcome, and time-to-completion scores (max score N = 54) with higher scores representing better performance. Participants completed a standardised and an optimised version of the DC in a counterbalanced fashion. For the latter, DC protocols were optimised via the provision of choice (autonomy support); external focus instructions augmented by simple knowledge statement, positive feedback and promotion of a growth mindset (Enhanced expectancies). Results indicate that motor performance (DC score) was better in the optimised (M = 31.08 ± 6.66) vs. standardised (M = 29.04 ± 5.88). The findings indicate that the combination of OPTIMAL factors can improve children's motor performance in dynamic movement settings and that standardised motor assessment may not reveal children's true movement capabilities.

论印尼本土汉语教师与中国志愿者教师的教学焦虑对比研究

<p>Teaching anxiety is a problem that teachers feel deeply in teaching practice. Whether it is local Chinese teachers in Indonesia or Chinese volunteer teachers, they will feel anxious when teaching, coupled with the characteristics of students, different classroom teaching situations, language and cultural communication barriers, etc. In Chinese teaching, perhaps very simple knowledge for teachers, students have a lot of difficulties to learn, teachers will inevitably have a kind of anxiety, which will affect the smooth implementation of teaching work. When Chinese teachers face pressure, how to find the cause of teaching anxiety and take measures to overcome anxiety is of great significance to carry out teaching work. This article explores the teaching anxiety problems and performance comparisons of local Chinese teachers in Indonesia and Chinese volunteer teachers, promoting the physical and mental health development of teachers to improve teaching effectiveness.<strong></strong></p>

The Impact of Epistemological Beliefs and School Climate on the Sustainability of Critical Thinking Dispositions in Middle School Students

The objective of this study is to ascertain whether school climate and students’ epistemological beliefs serve as predictors of sustainable critical thinking dispositions in middle school students. This research is designed as a correlational study. A total of 585 students from four middle schools in Çanakkale, Turkey, participated in the research. To this end, the researchers administered the Critical Thinking Disposition Scale, the Middle School Students’ Epistemological Belief Scale, and the School Climate Survey, gradually collecting the raw data. Following the collection of the data, they were subjected to analysis using multiple linear regression. The results of the statistical analysis demonstrated that the students’ critical thinking dispositions were significantly predicted by both epistemological beliefs and school climate. In other words, belief in the omniscient power of authority and quick learning were found to have a negative correlation with critical thinking, whereas belief in simple knowledge was observed to have an inverse positive relationship. The dimension of the school climate, relating to positive student–teacher relations, appeared to constitute a prospective positive predictor, while authoritarian student–teacher relations and the lack of resources were identified as the main negative classroom environment factors. The study highlights the complex mutual influence of individual beliefs and the learning environment in the development of critical thinking dispositions.

Strategies for Enhancing Legal Application Skills among College Students

In modern society, improving legal application abilities is crucial to college students' personal development and career planning. With the progress of society and the continuous changes in the country's legal application environment, college students, as the main force leading social changes, need to improve their legal application capabilities to solve many legal problems that arise in social development and promote the country's legal environment and rule of law. Capacity building. Improving the legal application ability of college students is an urgent requirement for building a socialist society ruled by law and an inevitable requirement for implementing the basic strategy of governing the country according to law. The current actual situation is that college students have low legal application ability and there are some problems, mainly lack of legal knowledge, weak legal awareness, and lack of legal practical experience. In today's era, except for professional law students, the legal studies of ordinary college students are mainly concentrated in the political field, and they only learn some simple basic legal knowledge. In the learning process, they pay too much attention to theoretical knowledge, lack practical application capabilities, and lack of practical experience, which makes them Insufficient ability to understand and apply legal knowledge. This article mainly discusses the legal application ability of ordinary college students other than law students. By analyzing the reasons for the problems in college students' legal application ability, it proposes corresponding strategies: First, improve classroom teaching links and optimize legal education content. The second is to establish scientific training concepts and strengthen the cultivation of practical experience. The third is to create a three-dimensional training environment and enhance the cultivation of legal awareness. The fourth is to construct effective practice links and cultivate college students’ legal application abilities. Through the above-mentioned strategies, we can improve the legal application ability of college students, so that they will pay more and more attention to and become accustomed to resolving disputes through legitimate legal channels and protect their legitimate rights and interests. At the same time, they can also contribute to the progress of society and the construction of the rule of law in the country. Make a contribution.

A conceptual model for tracking the provenance of activities in knowledge organization systems

PurposeThe purpose of this study is to develop a conceptual model, ProvKOS, for tracking the provenance of change activities in a knowledge organization system (KOS). By extending current provenance practices, this model represents dynamic changes in a KOS more effectively.Design/methodology/approachWe take a five-step approach to develop the conceptual model, including content analysis of KOS editorial data, environmental scan of existing provenance models, development of persona-specific provenance questions and a participatory design with stakeholders to ensure the model’s utility.FindingsWe introduce (1) a taxonomy of editorial activities for a KOS; (2) a conceptual model ProvKOS, which extends existing models PROV and Simple Knowledge Organization Systems (SKOS). We also provide detailed data dictionaries for the entities, activities and warrants classes proposed in the model. A use case on “gender dysphoria” in Dewey Decimal Classifications (DDCs) is provided to illustrate the implementation of ProvKOS. This shows ProvKOS’s ability to capture KOS changes effectively and to link external resources relating to the changes.Research limitations/implicationsFurther validation may be needed to implement the ProvKOS model across various types of KOSs.Practical implicationsProvKOS can help improve machine readability, querying and analysis of a KOS. Especially within the linked data environment, the enhanced provenance documentation through ProvKOS can enable a network of KOSs, which will then inform better linked data or knowledge graph designs.Social implicationsBy facilitating better tracking of changes within a KOS and across KOSs, ProvKOS can enhance the accessibility and usability of knowledge bases across different cultural and social contexts, thus better supporting inclusive information practices.Originality/valueThe proposed model is novel in two ways: one, its ability to represent dynamic change activities in a KOS, which has not been discussed anywhere else; two, it supports the interconnectivity across KOSs by providing a “warrant” class to substantiate the context of changes.

Distilling BERT knowledge into Seq2Seq with regularized Mixup for low-resource neural machine translation

Pre-trained language models, such as Bidirectional Encoder Representations from Transformers (BERT), have demonstrated state-of-the-art performance in many Natural Language Processing (NLP) downstream tasks. Incorporating pre-trained BERT knowledge into the Sequence-to-Sequence (Seq2Seq) model can significantly enhance machine translation performance, particularly for low-resource language pairs. However, most previous studies prefer to fine-tune both the large pre-trained BERT model and the Seq2Seq model jointly, leading to costly training times, especially with limited parallel data pairs. Consequently, the integration of pre-trained BERT contextual representations into the Seq2Seq framework is limited. In this paper, we propose a simple and effective BERT knowledge fusion approach based on regularized Mixup for low-resource Neural Machine Translation (NMT), referred to as ReMixup-NMT, which constrains the distributions of the normal Transformer encoder and the Mixup-based Transformer encoder to be consistent. The proposed ReMixup NMT approach is able to distill and fuse the pre-trained BERT knowledge into Seq2Seq NMT architecture in an efficient manner with non-additional parameters training. Experiment results on six low-resource NMT tasks show the proposed approach outperforms the state-of-the-art (SOTA) BERT-fused and drop-based methods on IWSLT’15 English→Vietnamese and IWSLT’17 English→French datasets.

LearnMSA2: deep protein multiple alignments with large language and hidden Markov models.

For the alignment of large numbers of protein sequences, tools are predominant that decide to align two residues using only simple prior knowledge, e.g. amino acid substitution matrices, and using only part of the available data. The accuracy of state-of-the-art programs declines with decreasing sequence identity and when increasingly large numbers of sequences are aligned. Recently, transformer-based deep-learning models started to harness the vast amount of protein sequence data, resulting in powerful pretrained language models with the main purpose of generating high-dimensional numerical representations, embeddings, for individual sites that agglomerate evolutionary, structural, and biophysical information. We extend the traditional profile hidden Markov model so that it takes as inputs unaligned protein sequences and the corresponding embeddings. We fit the model with gradient descent using our existing differentiable hidden Markov layer. All sequences and their embeddings are jointly aligned to a model of the protein family. We report that our upgraded HMM-based aligner, learnMSA2, combined with the ProtT5-XL protein language model aligns on average almost 6% points more columns correctly than the best amino acid-based competitor and scales well with sequence number. The relative advantage of learnMSA2 over other programs tends to be greater when the sequence identity is lower and when the number of sequences is larger. Our results strengthen the evidence on the rich information contained in protein language models' embeddings and their potential downstream impact on the field of bioinformatics. Availability and implementation: https://github.com/Gaius-Augustus/learnMSA, PyPI and Bioconda, evaluation: https://github.com/felbecker/snakeMSA.

Epidemiology of CVD Risk Factors and Gender Differences

Background: Cardiovascular diseases (CVDs) can either be symptomatic or asymptomatic for many people. Until now, they are still unaware of their manifestations. Thus, risk factors can help to strategise in managing and controlling the signs and symptoms of CVDs. Men are more prone to the adversity of health outcomes, including CVD. However, women tend to have a worse condition if the symptoms prevail. Therefore, taking into account the gender-related differences in the incidences of CVD might help in risk management and profiling the specific attributes that may contribute to the development of CVD itself. The Aim of Study: To enlist and give an overview of the gender differences in pursuing CVD health equality and prevent them from developing CVD. Method: A narrative review based on general statements and simple knowledge to give them the overall picture of CVD among men and women. Results: Overall, the CVD classifications for men and women can be differentiated by their hormonal changes, daily nutrient intakes, physical exercises, and treatment strategies. Conclusion: In conclusion, early detection may prevent the progression of CVD and minimise the risk of developing it.

Research on Enhancing Students’ Intercultural Awareness and Competence in English Teaching

With the deepening of globalization, intercultural communication has become an important part of people’s daily lives and work. English, as an international language, plays a vital role in intercultural communication. However, simple language knowledge can no longer meet the needs of modern society. Students need to have intercultural awareness and competence to better adapt to the development of globalization. Therefore, it is particularly important to improve students’ intercultural awareness and competence in English teaching. This paper will discuss how to improve students’ intercultural awareness and competence in English teaching, which includes three parts. The first part introduces the definition of intercultural awareness and competence. The second part explores the importance of intercultural awareness and competence to students, which can help students better understand and respect different cultures, stimulate students’ innovative thinking and creativity, and cultivate critical thinking skills, and is also of great significance to students’ professional development, personal growth, and social responsibility. The third part discusses the effective methods and strategies to improve students’ intercultural awareness and competence in teaching, including carrying out cultural discussion and debate, introducing diverse materials, creating intercultural experience teaching activities, and using virtual learning tools. To a certain extent, this study can enrich the theoretical research in the field of intercultural awareness and competence, help to improve students’ intercultural awareness and competence, and provide certain practical guidance for educators to teach intercultural courses.

Context-aware reinforcement learning for cooling operation of data centers with an Aquifer Thermal Energy Storage

Data centers are often equipped with multiple cooling units. Here, an aquifer thermal energy storage (ATES) system has shown to be efficient. However, the usage of hot and cold-water wells in the ATES must be balanced for legal and environmental reasons. Reinforcement Learning has been proven to be a useful tool for optimizing the cooling operation at data centers. Nonetheless, since cooling demand changes continuously, balancing the ATES usage on a yearly basis imposes an additional challenge in the form of a delayed reward. To overcome this, we formulate a return decomposition, Cool-RUDDER, which relies on simple domain knowledge and needs no training. We trained a proximal policy optimization agent to keep server temperatures steady while minimizing operational costs. Comparing the Cool-RUDDER reward signal to other ATES-associated rewards, all models kept the server temperatures steady at around 30 °C. An optimal ATES balance was defined to be 0% and a yearly imbalance of −4.9% with a confidence interval of [−6.2, −3.8]% was achieved for the Cool 2.0 reward. This outperformed a baseline ATES-associated reward of 0 at −16.3% with a confidence interval of [−17.1, −15.4]% and all other ATES-associated rewards. However, the improved ATES balance comes with a higher energy consumption cost of 12.5% when comparing the relative cost of the Cool 2.0 reward to the zero reward, resulting in a trade-off. Moreover, the method comes with limited requirements and is applicable to any long-term problem satisfying a linear state-transition system.

Subpocket Similarity-Based Hit Identification for Challenging Targets: Application to the WDR Domain of LRRK2.

We herewith applied a priori a generic hit identification method (POEM) for difficult targets of known three-dimensional structure, relying on the simple knowledge of physicochemical and topological properties of a user-selected cavity. Searching for local similarity to a set of fragment-bound protein microenvironments of known structure, a point cloud registration algorithm is first applied to align known subpockets to the target cavity. The resulting alignment then permits us to directly pose the corresponding seed fragments in a target cavity space not typically amenable to classical docking approaches. Last, linking potentially connectable atoms by a deep generative linker enables full ligand enumeration. When applied to the WD40 repeat (WDR) central cavity of leucine-rich repeat kinase 2 (LRRK2), an unprecedented binding site, POEM was able to quickly propose 94 potential hits, five of which were subsequently confirmed to bind in vitro to LRRK2-WDR.

A Forest Fire Smoke Monitoring System Based on a Lightweight Neural Network for Edge Devices

Forest resources are one of the indispensable resources of the earth, which are the basis for the survival and development of human society. With the swift advancements in computer vision and artificial intelligence technology, the utilization of deep learning for smoke detection has achieved remarkable results. However, the existing deep learning models have poor performance in forest scenes and are difficult to deploy because of numerous parameters. Hence, we introduce an optimized forest fire smoke monitoring system for embedded edge devices based on a lightweight deep learning model. The model makes full use of the multi-scale variable attention mechanism of Transformer architecture to strengthen the ability of image feature extraction. Considering the needs of application scenarios, we propose an improved lightweight network model LCNet for feature extraction, which can reduce the parameters and enhance detecting ability. In order to improve running speed, a simple semi-supervised label knowledge distillation scheme is used to enhance the overall detection capability. Finally, we design and implement a forest fire smoke detection system on an embedded device, including the Jetson NX hardware platform, high-definition camera, and detection software system. The lightweight model is transplanted to the embedded edge device to achieve rapid forest fire smoke detection. Also, an asynchronous processing framework is designed to make the system highly available and robust. The improved model reduces three-fourths of the parameters and increases speed by 3.4 times with similar accuracy to the original model. This demonstrates that our system meets the precision demand and detects smoke in time.

Design and Implementation of an Ontology for Measurement Terminology in Digital Calibration Certificates.

Digital Calibration Certificates (DCCs) are a key focus in metrology digitalization, necessitating that they satisfy the criteria for machine readability and understandability. Current DCCs are machine-readable, but they are still missing the essential semantic information required for machine understandability. This shortfall is particularly notable in the lack of a dedicated semantic ontology for measurement terminologies. This paper proposes a domain ontology for measurement terminologies named the OMT (Ontology for Measurement Terminology), using a foundation of metrological terms from standards like the International Vocabulary of Metrology (VIM), the Guide to the Expression of Uncertainty in Measurement (GUM), and JJF1001. It also incorporates insights from models such as the SI Reference Point, the Simple Knowledge Organization System (SKOS), and the DCC Schema. The methodology was guided by Stanford's Seven-Step Method, ensuring a systematic development process tailored to the needs of metrological semantics. Through semantic expression capability verification and SPARQL query validations, the OMT has been confirmed to possess essential machine readability and understandability features. It has been successfully integrated into version 3.2.1 of DCCs across ten representative domains. This integration demonstrates an effective method for ensuring that DCCs are machine-readable and capable of interoperating within digital environments, thereby advancing the research in metrology digitization.

Implications of Entrepreneurship and Simple Accounting Knowledge of Endek Crafting MSMEs on Economic Improvement and Preservation of Bali Culture

Riches Indonesian culture is abundant has push development various product in almost all area with varied styles.? One of from inheritance valuable Balinese culture historical, cultural, and economic is cloth endek. However, with changing times and styles modern living, fabric endek start threatened extinct. Study This highlights two problems main in industry endek in Bali: lack of Skills entrepreneurship and knowledge good accounting, as well low ability recording finance. Research methods applied? is qualitative, with focus on the cause decline amount craftsman endek in Klungkung and efforts regeneration through interaction with generation young. Research result show that potency industry endek in Klungkung For increase economy local and preserving Balinese culture is huge, however lack of participation generation young become challenge main. Skills entrepreneurship and knowledge accountancy identified as solution potential for overcome challenge this. Collaboration between government, community local, and craftsmen required for give support, training, and promotion necessary for the industry endek can Keep going develop and become asset important in preservation Balinese culture.

Information accessibility and knowledge creation: the impact of Google’s withdrawal from China on scientific research

ABSTRACT How important is Google for scientific research? This paper exploits the exogenous shock represented by Google’s sudden withdrawal of its services from mainland China to assess the importance of access to information for the knowledge production function of scientific scholars in the field of economics. For economists, a type of scholar with a simple knowledge production function, results from difference-in-difference analyses, which compare their scientific output to scholars located in the neighbouring regions, show that the scientific productivity declines by about 28% in volume and 30% in terms of citations. These results are consistent with the view that information accessibility is an important driver of scientific progress. Considering that the negative effect of the shock is stronger for top scholars located in China, Google’s sudden exit bears the risk that researchers lose touch with the research frontier and persistently lag behind their foreign peers.

ChatGPT's Accuracy on Magnetic Resonance Imaging Basics: Characteristics and Limitations Depending on the Question Type.

Our study aimed to assess the accuracy and limitations of ChatGPT in the domain of MRI, focused on evaluating ChatGPT's performance in answering simple knowledge questions and specialized multiple-choice questions related to MRI. A two-step approach was used to evaluate ChatGPT. In the first step, 50 simple MRI-related questions were asked, and ChatGPT's answers were categorized as correct, partially correct, or incorrect by independent researchers. In the second step, 75 multiple-choice questions covering various MRI topics were posed, and the answers were similarly categorized. The study utilized Cohen's kappa coefficient for assessing interobserver agreement. ChatGPT demonstrated high accuracy in answering straightforward MRI questions, with over 85% classified as correct. However, its performance varied significantly across multiple-choice questions, with accuracy rates ranging from 40% to 66.7%, depending on the topic. This indicated a notable gap in its ability to handle more complex, specialized questions requiring deeper understanding and context. In conclusion, this study critically evaluates the accuracy of ChatGPT in addressing questions related to Magnetic Resonance Imaging (MRI), highlighting its potential and limitations in the healthcare sector, particularly in radiology. Our findings demonstrate that ChatGPT, while proficient in responding to straightforward MRI-related questions, exhibits variability in its ability to accurately answer complex multiple-choice questions that require more profound, specialized knowledge of MRI. This discrepancy underscores the nuanced role AI can play in medical education and healthcare decision-making, necessitating a balanced approach to its application.

Simple knowledge graph completion model based on PU learning and prompt learning

Simple knowledge graph completion model based on PU learning and prompt learning

FF-LPD: A Real-Time Frame-by-Frame License Plate Detector With Knowledge Distillation and Feature Propagation.

With the increasing availability of cameras in vehicles, obtaining license plate (LP) information via on-board cameras has become feasible in traffic scenarios. LPs play a pivotal role in vehicle identification, making automatic LP detection (ALPD) a crucial area within traffic analysis. Recent advancements in deep learning have spurred a surge of studies in ALPD. However, the computational limitations of on-board devices hinder the performance of real-time ALPD systems for moving vehicles. Therefore, we propose a real-time frame-by-frame LP detector focusing on real-time accurate LP detection. Specifically, video frames are categorized into keyframes and non-keyframes. Keyframes are processed by a deeper network (high-level stream), while non-keyframes are handled by a lightweight network (low-level stream), significantly enhancing efficiency. To achieve accurate detection, we design a knowledge distillation strategy to boost the performance of low-level stream and a feature propagation method to introduce the temporal clues in video LP detection. Our contributions are: (1) A real-time frame-by-frame LP detector for video LP detection is proposed, achieving a competitive performance with popular one-stage LP detectors. (2) A simple feature-based knowledge distillation strategy is introduced to improve the low-level stream performance. (3) A spatial-temporal attention feature propagation method is designed to refine the features from non-keyframes guided by the memory features from keyframes, leveraging the inherent temporal correlation in videos. The ablation studies show the effectiveness of knowledge distillation strategy and feature propagation method.