Hot Problem Research Articles

Remote sensing image road network detection based on channel attention mechanism

Extracting and detecting road network consistency from high-resolution remote sensing images has been a hot and difficult problem in the computer vision. Although it has made significant progress, there is still a phenomenon of high training accuracy but unsatisfactory actual extraction and detection results. The attention mechanism is one of the efficient and practical mechanisms in deep learning. It improves the performance of deep learning by selectively focusing on a portion of all information while ignoring other visible information, while effectively utilizing computing resources. Numerous experiments have also confirmed that the attention mechanism is resource-saving and effective. Its plug and play feature brings great convenience to programmers. In order to provide better road network detection results and solve the above problem, this paper combines the channel attention mechanism with ResNet and proposes SE-ResNet and ECA-ResNet for remote sensing image road network detection, making networks extract and learn road network features and ignore some non-road network features. The experimental results show that on the Massachusetts roads (MR) and CHN6-CUG roads datasets, ECA-ResNet and SE-ResNet based on channel attention mechanism perform similar to LeNet7 and ResNet in terms of accuracy, loss, accuracy convergence, and loss convergence, and even increase a certain computational burden. However, their final road network detection results (including road network detection pixel count, precision, recall, accuracy, IOU, F1 score, and actual road network detection result) of the former are significantly better than those of the latter. The channel attention mechanism makes the deep neural network pay more attention to the extraction and learning of road network features, while ignoring the extraction and learning of some non-road network features, which improves the accuracy of containing road network samples and reduces the accuracy of not containing road network samples. Therefore, the performance of ECA-ResNet and SE-ResNet is similar to that of LeNet7 and ResNet in the accuracy, loss, accuracy convergence and loss convergence, but the final road network detection results of ECA-ResNet and SE-ResNet are significantly better than those of LeNet7 and ResNet. Therefore, the proposed ECA-ResNet and SE-ResNet have broad application prospects in road network detection, especially ECA-ResNet.

Nonlinear analysis and recognition of epileptic EEG signals in different stages.

It is a hot problem in epilepsy research to detect and predict seizures by EEG signals. Clinically, it is generally observed that there are only sudden abnormal signals during the ictal stage, but there is no significant difference in the EEG signal between the interictal and preictal stages. To solve the problem that preictal signals are difficult to recognize clinically, and then effectively improve the recognition efficiency of epileptic seizures, so, in this paper, some nonlinear methods are comprehensively used to extract the hidden information in the EEG signals in different stages, namely, phase space reconstruction (PSR), Poincaré section (PS), synchroextracting transform (SET), and machine learning for EEG signal analysis. First, PSR based on C-C method is used, and the results show that there are different diffuse attractor trajectories of the signals in different stages. Second, the confidence ellipse (CE) is constructed by using the scatter diagram of the corresponding trajectory on PS, and the aspect ratio and area of the ellipse are calculated. The results show that there is an interesting transitional phenomenon in preictal stage. To recognize ictal and preictal signals, time-frequency (TF) spectrums, which are processed by SET, are fed into the convolutional neural network (CNN) classifier. The accuracy of recognizing ictal and preictal signals reaches 99.7% and 93.7%, respectively. To summarize, our results based on nonlinear method provide new research ideas for seizure detection and prediction.NEW & NOTEWORTHY Our results based on nonlinear method have better practical significance and clinical application value and improved the prediction efficiency of epileptic EEG signals effectively. This work provides direct insight into the application of these biomarkers for seizure detection and prediction.

Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm for feature selection

Feature selection is a hot problem in machine learning. Swarm intelligence algorithms play an essential role in feature selection due to their excellent optimisation ability. The Chimp Optimisation Algorithm (CHoA) is a new type of swarm intelligence algorithm. It has quickly won widespread attention in the academic community due to its fast convergence speed and easy implementation. However, CHoA has specific challenges in balancing local and global search, limiting its optimisation accuracy and leading to premature convergence, thus affecting the algorithm’s performance on feature selection tasks. This study proposes Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm (SOSCHoA). SOSCHoA enhances inter-population interaction through social coevolution, improving local search. Additionally, it introduces sine chaotic opposition learning to increase population diversity and prevent local optima. Extensive experiments on 12 high-dimensional classification datasets demonstrate that SOSCHoA outperforms existing algorithms in classification accuracy, convergence, and stability. Although SOSCHoA shows advantages in handling high-dimensional datasets, there is room for future research and optimization, particularly concerning feature dimensionality reduction.

Analysis of Students' Physics Literacy Skills in Solving HOTS Problems and Self-Regulated Learning

The purpose of this research is to examine the physics literacy skills of students solving HOTS questions and self-regulated learning (SRL) on measurement material at private high schools in Maju Village, through descriptive research using a qualitative approach. The instruments used include questionnaires, and HOTS test questions on Measurement material. The results showed that the low SRL category (score 98) was unable to reach the questions at C4. The medium group (score 120), could not fulfill all level 4 indicators. In high SRL, students are able to fulfill the indicators, by solving the problem completely, using the right information, applying numeracy skills, and being able to provide conclusions and their reasons, but cannot fulfill the indicators at level 5 and 6. The learning independence factor plays a significant role in determining the level of HOTS student.

IGWO-VINC Algorithm Applied to MPPT Strategy for PV System

As a kind of inexhaustible renewable energy, solar energy is popular, and photovoltaic power generation has been paid attention to by all circles. However, determining the global maximum power point (GMPP) is difficult under external ambient temperature and light intensity change, and MPPT control technology becomes the key to research. Fast, accurate, and stable GMPP capture has become a hot research problem in PV power generation systems. The GWO algorithm incorporating the Levy flight function and the INC using the vertex as the dividing point with different step sizes on the left and right sides are combined and applied to the MPPT control strategy of PV systems. The global search is completed by IGWO first, and then the exact search is completed by the improved INC when it is close to the global optimum. The final tracking accuracy is above 99%, and compared with the GWO, INC, and ICS-IP&O algorithms, respectively, in the case of abrupt changes, the tracking time is accelerated by 0.021 s on average. The oscillation amplitude is smaller, and the voltage is more stable.

Students’ Thinking Process in Solving HOTS Problem in Derivative Application Materials

Students’ Thinking Process in Solving HOTS Problem in Derivative Application Materials

Abstract PO3-01-10: Changes in Patient-Reported Outcomes with 3 Months of Pre-operative Endocrine Therapy in the POWER Trial

Abstract Background: The Pre-Operative Window of Endocrine Therapy to Inform Radiation Therapy Decisions Trial (POWER, NCT04272801) aims to determine whether 3 months of pre-operative endocrine therapy (pre-ET) affects decision-making regarding adjuvant radiation therapy (RT) among older women with early-stage, estrogen receptor (ER) positive breast cancer. The goal of the present study is to evaluate changes in patient-reported outcomes (PROs) during the course of pre-ET. We hypothesize that PROs will change over the course of pre-ET and that those changes can be used to identify patients who are at-risk for long-term noncompliance to adjuvant ET and who may benefit from RT. Methods: Participants in the POWER Trial receive 3 months of pre-ET prior to breast-conserving surgery and final treatment decisions regarding adjuvant RT. Health-related quality of life (HRQOL) (EORTC QLQ-C30 and QLQ-BR23), depression (Center for Epidemiologic Studies Depression Scale Revised), and general symptom burden (Breast Cancer Prevention Trial Symptom Checklist (BCPT-SCL)) are assessed at baseline and at day 90 after pre-ET. Perceived sensitivity to medicine (Perceived Sensitivity to Medicine (PSM) survey) is assessed at baseline. The BCPT-SCL was used to examine common ET side effects, including hot flashes, musculoskeletal symptoms, cognitive problems, weight gain, and bladder control. Graphical methods are used to display results. Results: The first 46 participants who completed pre-ET in the POWER trial were included in this preliminary assessment. From baseline to day 90, there was notable variability in HRQOL and no definitive shift towards worsening HRQOL with a median change (IQR) of 0 (-8.33, 8.33). Similarly, the median change in symptom profiles for the whole cohort was 0 over the 90 days of pre-ET. However, a subset of patients experienced changes in symptom burden throughout the 90-day period. Approximately one-fourth of participants reported an increase in hot flashes and musculoskeletal symptoms from baseline to day 90. There does not appear to be worsening fatigue, depression scores, cognitive problems, or weight gain. After stratifying by self-perceived sensitivity to medicine, patients with high PSM scores have a greater median change (0.5 and 0.33) in symptoms compared to those with low PSM scores (0 and 0) for hot flashes and musculoskeletal symptoms, respectively (Figure 1). Conclusions: When looking at this cohort, there was no change in symptoms or quality of life after the 3-month course of pre-ET. Evaluation of the changes in PROs identified a subset of participants with worsening symptoms after pre-ET. Future analyses, when trial accrual is complete, will examine the relationship between PROs and long-term adherence to adjuvant ET and will elucidate if the patients who experience worsening BCPT-SCL symptoms during the 90-day period are more likely to opt for RT. Figure 1 Change in symptoms after pre-ET Change in hot flash burden and musculoskeletal symptom burden is greater in patients with high perceived sensitivity to medicine. Citation Format: Trish Millard, Lena Turkheimer, Christiana Brenin, David Brenin, Patrick Dillon, Einsley Janowski, Anneke Schroen, Gina Petroni, Shayna Showalter. Changes in Patient-Reported Outcomes with 3 Months of Pre-operative Endocrine Therapy in the POWER Trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-01-10.

Constructing The Bridge of Understanding: The Role of Habit of Mind, Resilience, and Mathematical Self-Confidence in Solving HOTS Problems


 
 
 This study aims to analyze the influence of Habit of Mind, Mathematical Resilience, and Self-Confidence on the ability of fifth-grade students at SDN Gugus Gajah Mungkur, Gajah Mungkur District, Semarang City, to solve HOTS mathematics problems. Employing a quantitative approach with a correlational design, the research sample consists of 110 students, selected through proportional random sampling from a population of 136 students across five elementary schools within the Gajah Mungkur cluster. The research instruments include a mathematical reasoning ability test and a questionnaire to measure the independent variables. Data collection techniques involve tests and questionnaires, while data analysis employs descriptive analysis using Microsoft Excel 2016 and inferential analysis using statistical software such as SPSS. This includes conducting correlation tests, and regression tests, after verifying analysis requirements like normality, linearity, homoscedasticity, and autocorrelation. The results reveal a positive and significant relationship between Habit of Mind, Mathematical Resilience, and Self-Confidence, and the students' ability to solve HOTS Mathematics problems. Thus, this study highlights the importance of developing these three psychological aspects to enhance high-level mathematical problem-solving skills. Consequently, Habit of Mind, Mathematical Resilience, and Self-Confidence significantly contribute to improving students' abilities in solving HOTS Mathematics problems.
 
 

Tristable TaOx-based memristor by controlling oxygen vacancy transportion based on valence transition mechanism

It is always a hot and difficult problem of how to realize multi-bit data storage by preparing multi-resistance memristors by a simple process compatible with standard CMOS processes. In this work, a tristable TaOx-based memristor with tunable oxygen vacancies is fabricated by physical vapor deposition, consisting of a high-resistance state and two low-resistance states. Its set and reset voltages are low −1.1V, −2.1V, and -1V, 3.3V, respectively. The XPS characterization demonstrates the presence of three valence states within the TaOx film. It has been suggested that different barriers lead to different switching voltages during the transition of the trivalent state, resulting in the formation of stable states. Interestingly, when increasing the concentration of oxygen during preparation to decrease the concentration of oxygen vacancies in TaOx film, the volt-ampere properties of memristor change from tristable to bistable, which further proves the above point. This work provides a viable strategy for switching between bistable and tristable states in memristors prepared from transition metal oxides.

Analysis of Student Ability Solving Hots Problems Material Exponent Equation Reviewed From's Beginning Ability

This study aims to describe the ability of high school students in solving HOTS-type questions in terms of initial abilities. The research method used in this research is descriptive qualitative. The subjects of this study were three students of class X consisting of 1 high-ability student, 1 medium-ability student, and 1 low-ability student. Collecting data or information in this study using written tests and interviews. The written test was conducted twice, namely the initial ability test and the problem-solving test. The instrument used was a validated test and interview guide. The result of this study is that students who have high abilities turn out to have similarities with students who have moderate abilities, which are only able to solve problem number 1 with an analytical level (C4) even though they use different methods of solving. While students who have low abilities are not able to respond to the three questions correctly. In general, the ability of SMA Negeri 1 Baureno students in solving HOTS category questions is still below average. This is because they are not used to working on HOTS category questions at school. So far, teachers only give ordinary questions found in textbooks or worksheets.

Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road

How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road was taken as the research object and the impacts of the students back to school on the volatility and reliability of the travel speed of road sections were qualitatively and quantitatively analyzed based on the road section travel speed data during the weekday morning peak (7:00–8:59). The results showed that the travel speed of the Beijing 3rd Ring Road had cyclicity, time variability, large-scale volatility, and light congestion during the weekday morning peak, and the volatility and reliability indexes of the travel speed of road sections significantly decreased under the impact of the students back to school. The data showed that after the students back to school, the maximum reduction ratio of average travel speed was larger than 55%, and the maximum travel speed reliability reduction value was larger than 0.85 based on the evaluation model of travel speed reliability of car commuters. The research results provide data and theoretical support for urban road traffic congestion mitigation and governance.

Analysis HOTS Problem Solving: Student Learning Outcomes from Student Facilitator and Explaining and Geogebra-Assisted Discovery Learning Models

Educators need to think about the difficulties that students often face when faced with high-order thinking skills in order to improve the teaching and learning system through the use of the right learning methods. This study aims to compare learning outcomes between using the Student Facilitator and Explaining the learning model with Discovery Learning powered by Geogebra to help students be critical, creative, and innovative in solving problems in the form of High Order Thinking Skills questions to think about in the context of science and everyday life. This experiment uses quantitative methods and is conducted in two classes XI with a total of 32 samples, namely 13 students of Experiment A and 19 students of Experiment B. This study uses instruments in the form of HOTS questions, consisting of 5 descriptive questions with pre-test and post-test designs so that the generated data is tested with JASP and SPSS software. In the Geogebra-powered Discovery Learning model, the p-value is 0.059 with an average of 20.000, while in the Geogebra-powered Student Facilitator and Explaining learning model, the p-value is 0.276 with an average of 52.947. Thus, the learning model suitable for the application of Geogebra software is Student Facilitator and Explaining based on the generated data and has a higher learning outcome value than the pre-test and post-test of the Discovery Learning learning model. This research can be a reference for education in education because of the need for proper technology to support the teaching and learning activities for students.

Cool and hot executive function problems in young children: linking self-regulation processes to emerging clinical symptoms

Self-regulation (SR) difficulties are implicated in a wide range of disorders which develop in childhood, including attention deficit hyperactivity disorder (ADHD), oppositional defiance disorder (ODD), anxiety and depression. However, the integration of the existing research evidence is challenging because of varying terminology and the wide range of tasks used, as well as the heterogeneity and comorbidity within and across diagnostic categories. The current study used the Research Domain Criteria (RDoC) framework to guide the examination of different SR processes in young children showing a wide range of symptomatology. Children (aged 4–8) referred by teachers for moderate-to-high conduct, hyperactivity and/or emotional problems at school (assessed using the Strengths and Difficulties Questionnaire (SDQ) subscales; n = 212), and children in SDQ typical ranges (n = 30) completed computerised cognitive control and decision-making tasks. Parents completed questionnaires to assess ADHD, ODD, anxiety and depression symptoms (n = 191). Compared to children with no teacher-reported difficulties, those with moderate-to-high problems showed poorer visuomotor control and decision-making. A factor analysis revealed that task variables adhered to RDoC dimensions and predicted variance in specific disorders: difficulties in cognitive control predicted ADHD symptoms, low reward-seeking was associated with depression and high reward-seeking was associated with ODD. This study highlights how the assessment of cognitive processes positioned within the RDoC framework can inform our understanding of disorder-specific and transdiagnostic difficulties in SR which are associated with diverse clinical symptoms in children.

EDTBERT: Event Detection and Tracking in Twitter using Graph Clustering and Pre-trained Language Model

The identification of events from social media platforms such as Twitter (now known as X) is a hot research problem. It has applications in diverse domains such as journalism, marketing, public safety, crisis management and disaster response. The process includes the identification, monitoring, and analysis of events or incidents while they are being discussed or reported on Twitter. When it comes to identifying events from tweets (i.e. feeds from Twitter), many of the currently available event detection methods mainly rely on keyword burstiness features or structural changes in the network. However, due to the intricate characteristics of tweets and the ever-changing nature of events, they frequently fail to recognise noteworthy occurrences before they become trending. Moreover, these methods face difficulties when it comes to capturing the evolving characteristics of events with limited or insufficient contextual information. In this paper, we propose a window-based tweet-processing method called EDTBERT for detecting events and tracking the evolution of events over time. Our proposed method utilizes the structural and semantic affinities that exist among words in tweets. The method starts by generating graph of tweets, where tweets are represented as nodes, and edges are the similarities between tweets. The method utilizes overlapping hashtags and named entities to capture the structural relationship between tweets. Additionally, a pre-trained sentence transformer model, specifically BERT, is employed to collect contextual knowledge and find semantically similar tweets. Next, the graph clustering technique is employed to identify optimized event clusters. Subsequently, our method generates chain of event clusters for each event to track the evolving variation of the event over time by utilising the ”Maximum-Weight Bipartite Graph Matching” (MWBGM) algorithm. The effectiveness of our approach is assessed using standard Tweet Datasets. Our evaluation demonstrates that our approach outperforms the baseline approaches.

Student Reasoning Based on Mathematical Ability in Solving HOTS Problems through Differentiated Learning

The mathematical abilities of students with reasoning are closely interrelated. The HOTS-type problem is an effort to improve reasoning ability. The ability of students to solve HOTS questions is still relatively low. Through a differentiated learning model, it is expected to help students in the learning process that suits their needs. The purpose of this study is to describe the application of differentiated learning models and describe the mathematical reasoning ability of students who have low, medium, and high abilities. This type of research is qualitatively descriptive with instruments in the form of observation sheets, questionnaires, test questions and interviews. Data analysis techniques include data reduction, data presentation, and conclusions. Test the validity of data using triangulation techniques. The results of this study show that differentiated learning can improve students' ability to reason. Four indicators of mathematical reasoning are, students' ability to present mathematical problems in the form of written statements, students' ability to make conjectures, students' ability to manipulate mathematics, and students' ability to draw conclusions from problems. Subjects with a high level of reasoning meet two indicators. Whereas subjects with moderate levels of reasoning met only three indicators and subjects with low levels of reasoning met only two indicators. Mathematics teachers should provide learning that is in accordance with the learning style of students and students should also do HOTS type questions more often.

An information-decision framework for the multilevel co-design of products, materials, and manufacturing processes

Realizing products that meet targeted performance requires careful consideration of the material processing to identify appropriate material microstructures and associated mechanical properties. The integrated design of such systems involving products, materials, and manufacturing processes necessitates facilitating co-design - a collective and coordinated effort by the product, materials, and process designers at multilevel to share their resources, information, and knowledge for making effective design decisions using the Processing-microStructure-Property-Performance (PSPP) relations. Goal-oriented Inverse Design (GoID) is one approach to co-designing these systems. In GoID, multilevel decisions are targeted at meeting the goals propagated inversely from the top level in the design hierarchy. Nevertheless, achieving the inversely propagated goals from one level may not be feasible at another level owing to the goal targets, established constraints, and available bounds. This results in design conflicts between multilevel decisions, leading to a loss in multilevel and system performance.In this paper, we propose an information‐decision framework to model goal-directed, multilevel decision-making and interactions for products, materials, and manufacturing processes, detect potential conflicts between the multilevel decisions, and regulate the decisions to achieve improved multilevel and system performance. Decision regulation is achieved by studying the sensitivity of the goals to dominant design variables and constraints and making appropriate design modifications. We use a hot rod rolling problem to showcase the efficacy of the proposed framework in systematically detecting and managing conflicts while co-designing the product, material, and manufacturing processes involved. The framework is generic and facilitates the top-down co-design of multilevel systems involving products, materials, and manufacturing processes.

Structural optimization design of BIW using NSGA-III and entropy weighted TOPSIS methods

Multi-objective optimization is always a difficult and hot problem for the static and dynamic performance and lightweight optimization design of a body-in-white (BIW). In this regard, a hybrid method coupled with NSGA-III and entropy weighted TOPSIS methods was put forward here. Effects of each design variable on the BIW performance was studied by means of the contribution method so as to optimize various design variables. Further, an experimental design was carried out by means of a radial basis function model to approximate each performance index of the automotive body. On the base of that, a comprehensive method where the Pareto frontier is searched by means of NSGA-III while the optimal solution is selected by means of the entropy weighted TOPSIS method was proposed based on lightweight, static and dynamic performances to analyze BIW performances to determine the contradictory optimal solution among various BIW factors. Afterward, our numerical verifications demonstrate that the BIW performances are improved significantly so that it can be feasible to optimize static and dynamic BIW performances and the lightweight performance of the BIW can be guaranteed accordingly.

Analysis of high school students' difficulties in solving hots problems using the two-tier test method

High Order Thinking Skills (HOTS) are high-order thinking skills that should be owned and improved by Indonesian students to catch up with other countries, especially in the field of mathematics. This study aims to identify and describe students' difficulties in solving HOTS questions through the two-tier test method at MAN 3 Aceh Tengah. This research is a qualitative-research with document data sources on test answers, questionnaires, and interviews. The research subjects in this study were 3 subjects from class XII at MAN 3 Central Aceh. Analysis of the validity of the data used triangulation techniques, namely identifying and describing the difficulties faced by students when solving HOTS questions by analyzing the results of test answers, questionnaires and interviews. The results of the research show specifically and descriptively that students when solving analyzing indicator questions feel anxious, lack self-confidence and are unable to process information into the form of solving mathematics. In the evaluating indicator, students are not sure they can solve the questions given. So that students fail to understand the concept of solving the problems presented. Meanwhile, in the creating indicator, students still have difficulty developing their own ideas. In the indicator of creating, students are only monotonous in what the teacher teaches, without developing their creativity to solve the given problem.

Connecting the SST Pattern Problem and the Hot Model Problem

AbstractIn the equatorial and subtropical east Pacific Ocean, strong ocean‐atmosphere coupling results in large‐amplitude interannual variability. Recent literature debates whether climate models reproduce observed short and long‐term surface temperature trends in this region. We reconcile the debate by reevaluating a large range of trends in initial condition ensembles of 15 climate models. We confirm that models fail to reproduce long‐term trends, but also find that many models do not reproduce the observed decadal‐scale swings in the East to West gradient of the equatorial Pacific. Models with high climate sensitivity are less likely to reproduce observed decadal‐scale swings than models with a modest climate sensitivity, possibly due to an incorrect balance of cloud feedbacks driven by changing inversion strength versus surface warming. Our findings suggest that two not well understood problems of the current generation of climate models are connected and we highlight the need to increase understanding of decadal‐scale variability.

Current Botanical and Agriculture Aspects in Plants and Physiological Mechanism

The molecular epigenetics study in the plants plays a vital role in plant gene regulations, since the early descriptions of the non-Mendelians plant-based gene activities to pivotal detections of chromatin amending proteins necessary for plant growth and the RNAs which facilitates the silencing of genes in human and in the eukaryotes. Different factors playing important role in gene regulation in plants through cellular signaling pathways. Different genes show over expression and repression in response to different conditions in plants as a result of environmental stresses. While on the other hand, plants have haploid (gametophyte) development stage which occurs after meiosis and before fertilization. Genomic screening in Arabidopsis thaliana, the model plant, have been mainly worthwhile, yielding more than one hundred and thirty epigenetic regulators so far. The major contribution of plant science to current global hot problems like sustainability and climate change makes the expansion of plant science research capacity crucial. The objective is to highlight that reflect these developments in the creation of new biotechnological tools (NBTs) and the creative uses of plant genetic engineering. Studies that concentrate on the creation of NBT for resistant or previously non-transformable species to enable the unlocking of these species' biology are of great relevance to this collection. In addition, the use of cutting-edge genetic engineering techniques such as genome/gene editing and protein-domain specific technology (such as K-Domain technology).