Processing Domain Research Articles

Possible evidence for the Law of General Intelligence in honeybees (Apis mellifera)

Finke, Scheiner, Giurfa, and Avarguès-Weber (2023) published correlational data on the performance of honeybees (Apis mellifera) in three learning tasks (associative, reversal, and negative patterning, capturing the domains of operant conditioning, executive-functioning-like ability, and inhibition plus configural processing, respectively) evaluated under both visual and olfactory stimulus conditions. They speculate that general cognitive ability (GCA) may be weakly causing all-positive correlations between performance in these different learning modalities, but do not formally test this possibility. A factor-analytic model applied to Finke et al. (2023) data revealed the presence of two perfectly congruent GCA factors (one for each stimulus condition). Both exhibited all-positive loadings, with the visual factor accounting for 46.8% of the performance variance and the olfactory factor accounting for 52.3%. Diagnostic statistics confirmed that in both stimulus conditions, the correlation matrices were adequate for factor analysis. These findings support extant hypotheses that GCA influences covariation between cognitive measures in honeybees, and constitute the first formal potential demonstration of GCA in an invertebrate. It is argued that GCA might be ubiquitous with respect to metazoans possessing organized nervous systems, perhaps because it convergently evolved multiple times in independent phylogenies, this being a key prediction of Christopher Chabris' Law of General Intelligence. Indeed, GCA has now been identified in insect, avian, mammal, and fish taxa. Some “primordial” aspects of GCA may even by basal to metazoans, and experiments employing Caenorhabditis elegans are suggested that could potentially shed light on such aspects. The findings are also strikingly inconsistent with evolutionary and comparative psychological theories positing a “modules first” understanding of cognitive evolution, such as one recent proposal that smaller brains cannot accommodate structures that give rise to GCA. Other theoretical implications of these findings are discussed.

Microstructural evolution and hot tensile deformation behaviors below δ-solvus temperature of IN718 alloy fabricated by plasma arc additive manufacturing

Plasma arc additive manufacturing (PAAM) stands out for its remarkable efficient utilization and good performance reliability, making it a highly favored additive manufacturing technique. This study delves into the tensile deformation below δ-solvus temperature of PAAM IN718 alloy with complete heat treatment. The results show that the peak stresses are decreased from 1069 to 1189 MPa at 550 °C to 620–1059 MPa at 750 °C. Through the orientation distribution function, the domain textures for hot deformation were identified as cube texture <100> and Rt-Goss texture {110}<110>. The results of the observations on the substructure and grain boundary components of the deformed alloy confirmed that the onset temperature of recrystallization in PAAM alloy is approximately 750 °C. The recrystallization nucleation mechanism of PAAM IN718 alloys was identified as discontinuous dynamic recrystallization. A deformation constitutive equation for hot deformation was established using the Zener-Hollomon parameter, with an average activation energy of 508.93 kJ/mol. A processing map for the hot working of PAAM alloy was developed, and the reasonable processing domains are located at a temperature 700–750 °C/strain rate 10−2–10−1 s−1 with a power dissipation efficiency range of 0.41–0.47. The high-temperature fracture mechanism of PAAM alloy was discussed, identifying the important inducements for high-temperature fracture failure including carbides, residual Laves phase, and δ phase in the PAAM alloy.

LM4OPT: Unveiling the potential of Large Language Models in formulating mathematical optimization problems

In the fast-paced domain of natural language processing, converting linguistic descriptions into mathematical optimization problems is a complex task, requiring profound comprehension and processing skills from Large Language Models (LLMs). In this study, various LLMs were evaluated, including GPT-3.5, GPT-4, and smaller variants with seven billion parameters: Llama-2, Falcon, Mistral, and Zephyr. This research investigated their performance in both zero-shot and one-shot settings for this task, revealing that GPT-4 outperformed others, particularly in the one-shot scenario. A core contribution of this study is the development of LM4OPT, a progressive fine-tuning framework specifically designed for smaller LLMs. This framework leverages noisy embeddings and specialized datasets to enhance the performance of the models. Regardless of the inherent limitations of smaller models in processing complex and lengthy input contexts, our experimental results indicate a significant reduction in the performance disparity between smaller and larger models when the former are fine-tuned using LM4OPT. Our empirical study, utilizing the NL4Opt dataset, unveils that GPT-4 surpasses the baseline performance established by previous research, achieving an accuracy of 63.30 % , solely based on the problem description in natural language, and without relying on any additional named entity information. GPT-3.5 follows closely, both outperforming the progressively fine-tuned smaller models.

A noveliterationscheme withconjugategradient forfaster pruningon transformermodels

Pre-trained models based on the Transformer architecture have significantly advanced research within the domain of Natural Language Processing (NLP) due to their superior performance and extensive applicability across multiple technological sectors. Despite these advantages, there is a significant challenge in optimizing these models for more efficient deployment. To be concrete, the existing post-training pruning frameworks of transformer models suffer from inefficiencies in the crucial stage of pruning accuracy recovery, which impacts the overall pruning efficiency. To address this issue, this paper introduces a novel and efficient iteration scheme with conjugate gradient in the pruning recovery stage. By constructing a series of conjugate iterative directions, this approach ensures each optimization step is orthogonal to the previous ones, which effectively reduces redundant explorations of the search space. Consequently, each iteration progresses effectively towards the global optimum, thereby significantly enhancing search efficiency. The conjugate gradient-based faster-pruner reduces the time expenditure of the pruning process while maintaining accuracy, demonstrating a high degree of solution stability and exceptional model acceleration effects. In pruning experiments conducted on the BERTBASE and DistilBERT models, the faster-pruner exhibited outstanding performance on the GLUE benchmark dataset, achieving a reduction of up to 36.27% in pruning time and a speed increase of up to 1.45× on an RTX 3090 GPU.

A User-Centered Framework for Data Privacy Protection Using Large Language Models and Attention Mechanisms

This paper introduces a user-centered data privacy protection framework utilizing large language models (LLMs) and user attention mechanisms, which are tailored to address urgent privacy concerns in sensitive data processing domains like financial computing and facial recognition. The innovation lies in a novel user attention mechanism that dynamically adjusts attention weights based on data characteristics and user privacy needs, enhancing the ability to identify and protect sensitive information effectively. Significant methodological advancements differentiate our approach from existing techniques by incorporating user-specific attention into traditional LLMs, ensuring both data accuracy and privacy. We succinctly highlight the enhanced performance of this framework through a selective presentation of experimental results across various applications. Notably, in computer vision, the application of our user attention mechanism led to improved metrics over traditional multi-head and self-attention methods: FasterRCNN models achieved precision, recall, and accuracy rates of 0.82, 0.79, and 0.80, respectively. Similar enhancements were observed with SSD, YOLO, and EfficientDet models with notable increases in all performance metrics. In natural language processing tasks, our framework significantly boosted the performance of models like Transformer, BERT, CLIP, BLIP, and BLIP2, demonstrating the framework’s adaptability and effectiveness. These streamlined results underscore the practical impact and the technological advancement of our proposed framework, confirming its superiority in enhancing privacy protection without compromising on data processing efficacy.

The investigation of traditional models and machine learning models in dynamic facial expression recognition

In everyday life, dynamic facial expressions are merely continuous human responses to external events. However, in human-computer interaction, rapidly recognizing changes in facial expressions from video streams is a relatively complex process. This complexity renders Dynamic Facial Expression Recognition (DFER) a critical research task in the domains of computer vision and image processing. This paper analyses the correlations and contrasts between static and dynamic facial expression research, highlighting key issues in the study of dynamic facial expressions, such as dynamic feature extraction and frame extraction. After that, it enumerates significant algorithms in both traditional models and deep learning models, providing an analysis of the advantages and disadvantages of these two major approaches. At the same time, it investigates the reasons behind the transition of research models for DFER from traditional methods to deep learning approaches. The paper focuses on two notable models from each approach: Histogram of Oriented Gradient (HOG) for processing raw images, Support Vector Machine (SVM) for data classification in traditional models. Convolutional Neural Network (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) for temporal feature extraction in deep learning models. These models are discussed in detail concerning their strengths and weaknesses, operational processes, and performance outcomes. In the concluding section, the author summarizes the main factors influencing research in this field and the current challenges encountered. By focusing on future research directions, the paper also presents a review of recent methodologies and offers insightful research directions for further investigation.

Laser-Induced Breakdown Spectroscopy in Mineral Exploration and Ore Processing

Laser-induced breakdown spectroscopy (LIBS) is a type of optical emission spectroscopy capable of rapid, simultaneous multi-element analysis. LIBS is effective for the analysis of atmospheric gases, geological fluids, and a broad spectrum of minerals, rocks, sediments, and soils both in and outside the traditional laboratory setting. With the recent introduction of commercial laboratory systems and handheld analyzers for use outside the laboratory for real-time in situ analysis in the field, LIBS is finding increasing application across the geosciences. This article first overviews the LIBS technique and then reviews its application in the domain of mineral exploration and ore processing, where LIBS offers some unique capabilities.

Ant Colony Optimization Based Edge Detection Algorithm

The problem of edge detection represents one of the most elementary assignments in image processing, providing an essentialbase for all further study and interpretation of the visual data analysis. This paper proposed an enhanced version of the Ant Colony Optimization (ACO) algorithm for edge detection. The following paper tries to compare the Proposed ACO method with the conventional techniques of edge detection like Canny, Prewitt, and Sobel, using various quantitative metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Entropy, Natural Image Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) applied over different images. The datasets for this evaluation are considered as a standard Cameraman, a biological cell, and MRIimage, with and without noise, considering the ranges of complexities and textures.The results of our study prove the competencies of ACO's algorithm. In some cases, it stands out against standard algorithms for MSE and PSNR values and maintains high Entropy values, suggesting the robustness of detail-keeping in an image. Further, the quality assessment of the images by using NIQE and BRISQUE shows the ability of ACO to maintain a natural appearance post-edge detection. In this regard, the studyhighlights that the proposed ACO isan effective method for edge detection in varying image conditions,and, in doing so, it even validates the effectiveness of bio-inspired algorithms in image processing domains.

Implementation of Environmental Education Based on Madura Local Wisdom to Increase Student Scientific Literacy

Environmental Education is a lecture process that provides insight to students regarding the understanding and awareness of the environment as a means of sustainable development. Environmental education lectures can be used as a means to improve students' science literacy skills. Science literacy skills can increase in learning associated with local wisdom. Local wisdom has an important relationship with education, this is important for the development of education, so the educational process must be conducive to connecting learning with the environment. Therefore, local wisdom needs to be associated with learning, one of which is with environmental education lectures. The purpose of this research is to improve students' science literacy skills applied to Environmental Education lectures based on Madura local wisdom. This research is a quantitative descriptive research. Data collection techniques include observation, interviews, documentation and tests. Then, the data obtained was analyzed using quantitative descriptive data analysis. The results showed that science literacy in the science process domain for indicators identified scientific problems by 7.6%, explained scientific phenomena by 7.4%, and used scientific evidence by 66%. The achievement of science literacy in the science content domain was 78% (good category). Science literacy in the realm of students' attitudes towards science as a whole reached 76.3% (good category). The average result of students' overall science literacy achievement was 72% (sufficient category). Based on the results of the study, it can be concluded that research on the Implementation of Environmental Education Lectures Based on Madura Local Wisdom to Improve Student Scientific Literacy can improve students' science literacy skills and teach environmental conservation and local wisdom on Madura Island

Analysis of neural networks in text sentiment

The proliferation of digital media and online platforms has led to a significant increase in the production and distribution of textual data. Consequently, the study of text sentiment analysis has emerged as a crucial area of research within the domain of natural language processing. The objective of sentiment analysis is to detect and categorize emotional inclinations in text, which has substantial practical importance in comprehending public sentiment, monitoring brand reputation, and other related areas. This research does a comprehensive investigation of the utilization of neural network models in text sentiment analysis using literature review methods. It examines the difficulties encountered and the approaches employed to overcome these difficulties. The research encompasses the fundamental principles and techniques of text sentiment analysis utilizing neural networks, as well as the utilization of convolutional neural networks (CNN), recurrent neural networks (RNN)/long short-term memory networks (LSTM), attention mechanisms, and pre-trained models in the field of sentiment analysis. Furthermore, this study delves into the complexities associated with data noise, the interpretability of models, the analysis of long text and multimodal sentiment, as well as the utilization of transfer learning and domain adaptation techniques. The study's importance is in establishing a theoretical basis and practical recommendations for the advancement of sentiment analysis technology, particularly in enhancing model precision, comprehensibility, and range of applications, thereby delivering useful insights.

Preliminary Validation of the Children's Auditory Performance Scale (CHAPS) and the Auditory Processing Domain Questionnaire (APDQ) in Greek Cypriot Children.

Identification of auditory processing disorders is achieved using questionnaires along with linguistic, non-linguistic, and auditory processing tests. Notably, the questionnaires "Children's Auditory Performance Scale" (CHAPS) and "Auditory Processing Domain Questionnaire" (APDQ) are widely recognized and used. The current study investigated the psychometric properties of the CHAPS and APDQ in Greek Cypriot children. The CHAPS and APDQ questionnaires were completed by parents of 40 Greek Cypriot children, 16 typically developing (TD) children, and 24 children with a history of Speech Sound Disorders (SSDs). There were significant differences between the two groups on both questionnaires. Cronbach's alpha was calculated at α = 0.922 for the CHAPS total score and α = 0.926 for the APDQ total score. The Receiver Operating Curve (ROC) analysis provided a cut-off point equal to -0.30 (AUC 0.849, p < 0.001) for CHAPS and a cut-off point equal to 90.00 (AUC 0.820, p < 0.001) for APDQ. Significant positive Spearman ρ correlations were observed between the CHAPS and APDQ (ρ = 0.639, p = 0.001). The CHAPS and APDQ can identify distinct auditory processing characteristics between in children with SSDs and TD children.

Mapping Quality Indicators to Assess Older Adult Health and Care in Community-, Continuing-, and Acute-Care Settings: A Systematic Review of Reviews and Guidelines.

Quality indicators (QIs) play a vital role in enhancing the care of older adults. This study aimed to identify existing QIs relevant to the health and care of older adults in community-care, continuing-care, and acute-care settings, along with available information such as definitions and calculation methods. A systematic review of published review studies, grey literature, and guidelines was undertaken, utilizing six electronic databases searched for materials dated from 2010 to 2 June 2023. To be included in this study, the literature had to provide data on QIs in a setting involving older adults. This study included 27 reviews and 44 grey literature sources, identifying a total of 6391 QIs. The highest number of indicators (37%) were relevant to continuing care; 32% and 28% were pertinent to community- and acute-care settings, respectively. The process domain had the highest number of QIs (3932), while the structure domain had the fewest indicators (521). A total of 39 focus areas were identified, with the five most common areas being, in descending order, orthopedics/hip fractures, end-of-life/palliative care, appropriate prescribing, neurocognitive conditions, and cardiovascular conditions; these areas ranged between 10% and 6%. When mapped against the Quadruple Aim framework, most QIs (85%) were linked to improving health outcomes. This inclusive compilation of QIs serves as a resource for addressing various focus areas pertinent to the Quadruple Aims. However, few quality indicators have been designed to provide a comprehensive and thorough evaluation of a specific aspect, taking into account all three key domains: structure, process, and outcomes. Addressing the description and psychometric properties of QIs is foundational for ensuring their trustworthiness and effective application.

RETRACTED: Systematic theoretical research towards industrial application of high-speed microparticle impact resulting material modifications

Microscale particle-target interactions and the resulting modifications of the target material play a crucial role in the domains of industrial manufacturing and application process. As know that the processing and manufacturing, failure prevention, and even the spacecraft protection against hypervelocity micrometeorites and orbital microdebris. Based on such an interesting, noteworthy, and profoundly applied research, it quickly followed up and compiled a series of relevant studies for particle-target interactions of high-speed microparticle impact. Here, it discussed the gas-based, laser-based, and electrostatic-based of the high-speed microparticle impacts. Among these, laser-induced particle impacts stand out for their high throughput and the suitability for operation in small facilities or even on standard laboratory optical benches. Various behaviors have been observed with smaller projectiles, relatively high velocities, and extreme strain rates, which involved the description of launching system, dynamic capturing of high-speed videography, triggering and characterization of material response, and resulting material modification. Subsequently, it conducted a summary and future prospect of the focused topics. As expected that the particle-target interactions will become an effective tool for the study of microprocessing, multi-field coupling, material strengthening and modification, it will bridge multidisciplinary to understand the scientific phenomena involved in the impact process, also provides a novel strategy for the development of next-generation of ballistic impact testing.

Machine learning system for commercial banana harvesting

The conventional process of visual detection and manual harvesting of the banana bunch has been a known problem faced by the agricultural industry. It is a laborious activity associated with inconsistency in the inspection and grading process, leading to post-harvest losses. Automated fruit harvesting using computer vision empowered by deep learning could significantly impact the visual inspection process domains, allowing consistent harvesting and grading. To achieve the goal of the industry-level harvesting process, this work collects data from professional harvesters from the industry. It investigates six state-of-the-art architectures to find the best solution. 2,685 samples were collected from four different sites with expert opinions from industry harvesters to cut (or harvest) and keep (or not harvest) the banana brunch. Comparative results showed that the DenseNet121 architecture outperformed the other examined architectures, reaching a precision, recall, F1 score, accuracy, and specificity of 85%, 82%, 82%, 83%, and 83%, respectively. In addition, an understanding of the underlying black box nature of the solution was visualized and found adequate. This visual interpretation of the model supports human expert’s criteria for harvesting. This system can assist or replace human experts in the field.

Feature Selection and Classification with the Annealing Optimization Deep Learning for the Multi-Modal Image Processing

This paper investigates and compares various feature selection algorithms within the context of image processing across multiple datasets. The study evaluates Seahorse Annealing Optimization for Feature Selection (SAO-FS), Genetic Algorithms (GA), CNN + Feature Fusion Network, and Lasso Regression on distinct image datasets—medical images, satellite images, MRI scans, and microscopy images. Performance metrics including accuracy, precision, recall, and computational time are analyzed to assess the efficacy of each algorithm in optimizing feature subsets for classification tasks. SAO-FS demonstrates superior performance in medical image classification with an accuracy of 92.5%, showcasing its ability to achieve high precision and recall rates critical for medical diagnostics. GA proves effective for satellite imagery with an accuracy of 87.3%, while the CNN + Feature Fusion Network excels in MRI scans with 89.8% accuracy. Lasso Regression, though slightly less accurate at 85.6%, efficiently selects features for microscopy images within a shorter computational time. These findings highlight the strengths and trade-offs of each algorithm across different image processing domains, providing insights for selecting appropriate feature selection methods tailored to specific imaging applications.

A New Program to Foster Inclusion: Unraveling Language Teachers’ Pedagogical Practices to Differentiated Instruction

The "Guru Penggerak" program is Indonesia's Merdeka Belajar initiative, aiming to enhance the quality of education by equipping teachers to implement differentiated instruction (DI). This study investigates its implementation focusing on Indonesian language teachers who have been qualified as the Guru Penggerak. Drawing on a qualitative case study conducted in three Indonesian senior high school teachers, this research explores how teachers integrate DI across content, process, and product domains in language education. Findings reveal that teachers adeptly differentiate content by curating diverse learning materials and collaboratively establishing classroom agreements. Process differentiation entails tailoring instructional delivery to accommodate varied learning styles and readiness levels, employing specialty grouping and facilitating student engagement. Moreover, teachers differentiate product by offering multiple avenues for demonstrating learning outcomes and leveraging technology for flexible assignment submission. Through DI, teachers create inclusive and engaging learning environments, promoting deeper comprehension, critical thinking, and student autonomy. This study underscores the transformative potential of DI in language teaching, advocating for pedagogical approaches that prioritize individual student needs and foster linguistic proficiency.

Identifying Terms to Represent Concepts of a Work Process Ontology

Objectives: To identify ontology concepts from text documents for the construction of work process ontology. Methods: This study proposes a methodology to identify terms representing a work process ontology concept from a document. The methodology encompasses several key steps: document collecting, text pre-processing, term weighting and analysis, terms mapping, and domain expert relevance judgments. A comparison between the results of three different term weighting schemes, namely the Term Frequency (TF), Term Frequency-Inverse Document Frequency (TFIDF), and Mutual Information (MI) is made with the ontology concept that the domain expert has judged. Findings: The approaches adopted in this study have managed to extract ontological concepts from the targeted domain knowledge source. The findings of the comparison analysis suggest that the TFIDF term weighting scheme exhibits better results compared to the TF and MI weighting schemes. Novelty: A work process ontology is a structured knowledge describing daily operations in the government sector. However, there has been little to no effort in building the work process ontology. This study presents an integrated approach for identifying ontology concepts from documents within the domain of the work process. To the utmost extent of our understanding, this research initiative is the initial attempt to introduce a structured methodology for the semi-automatic extraction and evaluation of concepts and relationships within this domain. The findings can be utilised as a foundation for developing an ontology in the specific field. Keywords: Ontology, Work process, Text extraction, Natural language processing, Term weighting

Facilitators and Barriers to Implementing High-Intensity Gait Training in Inpatient Stroke Rehabilitation: A Mixed-Methods Study.

(1) Background: High-intensity gait training (HIT) is a recommended intervention that improves walking function (e.g., speed and distance) in individuals who are undergoing stroke rehabilitation. This study explored clinicians' perceived barriers and facilitators to implementing HIT utilizing a mixed-methods approach comprising a survey and exploratory qualitative research. (2) Methods: Clinicians (n = 13) who were implementing HIT at three facilities participated. We collected and analyzed data using the consolidated framework for implementation research. Three focus groups were recorded and transcribed, and data were coded and thematically categorized. (3) Results: Survey results identified that the facilitators with a strong impact on implementation were access to knowledge/resources and intervention knowledge/beliefs. The only agreed-upon barrier with a strong impact was lack of tension for change. The focus groups resulted in 87 quotes that were coded into 27 constructs. Frequently cited outer setting facilitators were cosmopolitanism and peer pressure, and the only barrier was related to the patient needs. Innovation characteristics that were facilitators included relative advantage and design quality and packaging, and complexity was a barrier. Inner setting facilitators included networks and communication, learning climate, leadership engagement, and readiness for implementation. However, communication, leadership engagement, and available resources were also barriers. Regarding characteristics of individuals, knowledge and beliefs were both barriers and facilitators. In the implementation process domain, common facilitators were formally appointed implementation leaders and innovation participants. Barriers in this domain were related to the patients. (4) Conclusions: Clinicians identified many barriers and facilitators to implementing HIT that often varied between facilities. Further research is warranted to deepen our understanding of clinicians' experiences with HIT implementation.

A Novel Web-Based Application for Influenza and COVID-19 Outbreak Detection and Response in Residential Aged Care Facilities.

The use of innovative digital health technologies in public health is expanding quickly, including the use of these tools in outbreak response. The translation of a digital health innovation into effective public health practice is a complex process requiring diverse enablers across the people, process, and technology domains. This paper describes a novel web-based application that was designed and implemented by a district-level public health authority to assist residential aged care facilities in influenza and COVID-19 outbreak detection and response. It discusses some of the challenges, enablers, and key lessons learned in designing and implementing such a novel application from the perspectives of the public health practitioners (the authors) that undertook this project.

Digital Aided Analog Data Acquisition for Telemetry

This paper tackles the limitations in the current data acquisition system through the integration of a equiripple Finite Impulse Response (FIR) filter. The inclusion of this digital filter eliminates the necessity for physical component alterations, thereby increasing the system’s adaptability and versatility in telemetry applications. The FIR filter’s ability to adjust passband, stopband, and sampling frequencies allows for seamless reconfiguration without the need for hardware modifications, representing a substantial enhancement compared to the constraints of the previous system. Additionally, the equiripple design guarantees minimal variations in passband and stopband ripples, thereby optimizing overall filter performance. An important aspect of the proposed solution is the implementation of shift-and-add multiplier architecture for the FIR filter, which contributes to a reduction in hardware complexity and resource requirements, ultimately leading to a more efficient system. The adoption of MATLAB’s robust filter design tools, coupled with subsequent verification through SIMULINK simulations, has played a pivotal role in successfully realizing the proposed design. To sum up, this project has efficiently addressed the identified issues by introducing a reconfigurable FIR filter solution that not only enhances the flexibility, performance, and user friendliness of the data acquisition system in telemetry applications but also showcases the potential for broader applications in various signal processing domain.