Processing Model Research Articles

Dissipative and thermal aspects in cyclic loading of additive manufactured AISI 316L

This study presents a comprehensive understanding of the fatigue behavior of 316L stainless steel specimens produced using the laser powder bed fusion (PBF-LB/M) method. The investigation has been conducted through a multifaceted approach that includes surface roughness analysis, density measurements, microstructural examination, fatigue testing, strain measurements, and thermographic analysis. Thermographic data processing models, i.e. the bi-power law (TCM-modified) model and the TCM method, are applied to fatigue test results of standard samples to evaluate fatigue limit values. The fatigue limit of the samples was estimated using the Murakami Method (MM), Constant Amplitude Loading (CAL) and Step Loading (SL) tests. The proposed methodology allows exploration of the entire stress level range within a single test, which could allow evaluation of the fatigue limit of components within only one test. This study is the starting point for a rapid evaluation method for estimating the fatigue limit using thermography, offering a cost-effective, time-efficient, and non-destructive means of assessing the fatigue performance of materials produced using additive manufacturing processes.

Trie and LOUDS hybrid model for efficient e-commerce processing in cloud environment

Set superset query is widely used in e-commerce processing and many other domains, particularly in cloud computing environments. Indexing is an efficient way to model e-commerce data. Many existing indexes, however, primarily focus on enhancing either query performance or space efficiency, often neglecting the need to strike a balance between these two factors. We have observed that upper nodes closer to the root of a tree are frequently accessed, while lower nodes near the leaves tend to entail expensive storage costs. To address this issue, we introduce TLI model, a trie and level-ordered unary degree sequence (LOUDS) hybrid model. The upper part of TLI is a trie, which is optimized for superior query performance. The lower part of TLI uses the LOUDS structure. TLI strikes a good balance between query performance and space utilization. To seamlessly integrate these two parts, we have developed efficient connecting strategies. Our simulation results on localhost demonstrate that TLI outperforms its competitors in terms of both space and time efficiency. Remarkably, it enhances query performance by up to 1.89 times, with a modest 6.72% increase in space overhead compared to LOUDS-based alternatives.

Decolonisation: meaning, sentiments and implications for heritage

ABSTRACT Within the printed and online newspaper media in the UK, notions of ‘decolonisation’ referring to various contexts, such as historical, relating to museums and institutions, cultural decolonisation and in relation to modern independence discussions, can be traced. In this article, we have applied topic modelling and natural language processing methods to carry out a classification of, and sentiment analysis on, newspaper headlines and texts from leading British newspapers covering decolonisation over the past decade. The results show an abrupt change in the meaning of decolonisation starting in the middle of the 2010s with an increased focus on cultural and institutional matters, particularly in right-leaning media. Surprisingly, the editorial slant of broadsheets seemingly only had at best moderate effects on tone, while headlines in right-wing tabloids were significantly more negative. Articles covering cultural aspects of decolonisation were substantially more negative than those applying a more traditional, territorial definition of decolonisation. Given the influence of newspaper media on public and private opinions, we discuss the heritage implications of these findings and suggest avenues for further investigation.

Bolstering the adaptive information processing model: a narrative review.

In recent years, several theoretical models have been suggested as complementary to the adaptative information processing model of eye movement desensitization and reprocessing therapy. A narrative review of such models was conducted to assess the contributions of each, as well as their convergences, contradictions, and potential complementarity. Seven theoretical models were identified. All focus on the effects of EMDR therapy as a comprehensive psychotherapy approach with its principles, procedures, and protocols. Several refer to concepts related to propositional or predictive processing theories. Overall, the contribution of these proposals does appear to bolster Shapiro's original AIP model, potentially offering additional depth and breadth to case conceptualization and treatment planning in clinical practice, as well as a more precise theoretical understanding. The current exploratory comparative analysis may serve as a preliminary baseline to guide research into the relative merit of suggested theoretical proposals to enhance current standards for the clinical practice and teaching of EMDR therapy.

Accurate wire rope defect MFL detection using improved Hilbert transform and LSTM neural network

ABSTRACT Wire rope is playing a significant role in various engineering fields. Although a great number of non-destructive testing methods are applied to wire rope defect detection, the results are greatly influenced by the signal noises and the quantitative defect detection of wire rope is consequently limited. Therefore, a new accurate and quantitative wire rope defect magnetic flux leakage (MFL) recognition method based on improved Hilbert transform and long short-term memory (LSTM) neural network is proposed. The theoretical background of the proposed signal processing models and preliminary results, as well as the influence of the main model parameters are analyzed first. Then, different wire rope defect classification principles through LSTM neural network and performance evaluation based on the improved signal processing and machine learning combined method are presented. Finally, the defect inspection and classification comparison results between the proposed method and other deep learning neural networks and pattern recognition strategies are given, which manifest that the proposed method has higher classification accuracy and shorter running time for five different wire rope defects under various servicing conditions, and is promising in accurate defect inspection for multi-scenario wire rope. Additionally, the main conclusions, disadvantages of the proposed method are summarized.

Cellular automata-based MapReduce design: Migrating a big data processing model from Industry 4.0 to Industry 5.0

A successful deployment of Industry 5.0 is significantly dependent on the synergetic integration of several advanced technologies such as big data processing, Artificial Intelligence (AI) integration, and several effective digitization techniques that emphasize the uses of Robotics, Internet of Things (IoTs), Cloud Computing, etc. with active participations from human workers. Several researchers have explored the importance of big data processing in view of Industry 4.0 as it facilitated enhanced production at any smart manufacturing line by ensuring efficient process management, typically involving big data processing. Researchers presented several MapReduce-based data processing models at smart manufacturing lines to facilitate big data processing. Among several others, the Elementary Cellular Automata (ECAs)-based MapReduce model was introduced as an energy-efficient low-cost model for big data processing in view of the Industry 4.0 scenario. In the present research, an investigation is further proposed to explore the true potential (if any) for the ECAs-based MapReduce model with reference to available several MapReduce models, to migrate an existing big data processing model from Industry 4.0 into the future i.e., Industry 5.0. Investigation results as achieved from the comparison among several MapReduce models and further examinations about the inherent quality of shuffle in those MapReduce blocks, explore the inherent advantages of ECAs-based MapReduce model towards its choice for big data processing in Industry 5.0 scenario.

Pengaruh Direct Marketing terhadap Keputusan Pembelian pada Live Streaming Tiktok dengan Kepercayaan sebagai Variabel Moderasi dalam Perspektif Bisnis Islam

The development of the times is constantly changing, marked by the existence of various applications that have online promotion and sales features. competition in online sales is getting tighter, making online business people look for the right strategy to market their products, such as in the TikTok strategy application so that consumers are easy to make a purchasing decision, namely with its latest feature, namely live streaming, which can be seen from direct marketing and trust. The purpose of this study was to determine the effect of direct marketing on purchasing decisions with trust as a moderating variable in an Islamic business perspective. This research uses quantitative methods. Data collection techniques in literature review, questionnaires and Likert scales. The population used in this study is generation z in Bandar Lampung City. Sampling in this study using purposive sampling technique. The number of samples used, determined by the formula according to Widianto, amounted to 97 respondents. The data analysis method uses Partial Last Scructural Equation Modeling (PLS-SEM) and data processing using smartPLS software. The results of this study indicate that there is a positive and significant influence between direct marketing on purchasing decisions. And trust does not moderate the relationship between direct marketing and purchasing decisions. In the perspective of Islamic business direct marketing on purchasing decisions on live streaming tiktok has implemented the principles of Islamic business ethics, including unity, balance, free will, responsibility, and benevolence so that it is something that Muslim business people need to pay attention to.

Efficient Scheduling of Data Transfers in Multi-tiered Storage

Multi-tiered persistent storage systems integrate many types of persistent storage devices, such as different types of NVMes, SSDs, and HDDs. This integration provides a multi-level view of persistent storage, where each tier has a different data transmission speed and capacity. Data transfer processes operating on multi-tiered persistent storage allow for the parallelisation of data transfers among the partitions of data at the same or different tiers. This work considers the problem of efficient scheduling of parallel data transfers between the tiers of persistent storage. We consider a data processing model where several data transfer processes move or copy data from one tier to another through the buffers in transient memory. We propose a new model for data processing over multi-tiered persistent storage and new algorithms to minimise both the overall time spent on parallel data transfers and the idle time of data transfer processes. We also describe how the scheduling algorithms dynamically apply different procedures to assign data transfers to the processes. Finally, we present the outcomes from the experiments that confirm the correctness and efficiency of the scheduling algorithms.

Application and Challenges of the Technology Acceptance Model in Elderly Healthcare: Insights from ChatGPT

The Technology Acceptance Model (TAM) plays a pivotal role in elderly healthcare, serving as a theoretical framework. This study aimed to identify TAM’s core components, practical applications, challenges arising from its applications, and propose countermeasures in elderly healthcare. This descriptive study was conducted by utilizing OpenAI’s ChatGPT, with an access date of 10 January 2024. The three open-ended questions administered to ChatGPT and its responses were collected and qualitatively evaluated for reliability through previous studies. The core components of TAMs were identified as perceived usefulness, perceived ease of use, attitude toward use, behavioral intention to use, subjective norms, image, and facilitating conditions. TAM’s application areas span various technologies in elderly healthcare, such as telehealth, wearable devices, mobile health apps, and more. Challenges arising from TAM applications include technological literacy barriers, digital divide concerns, privacy and security apprehensions, resistance to change, limited awareness and information, health conditions and cognitive impairment, trust and reliability concerns, a lack of tailored interventions, overcoming age stereotypes, and integration with traditional healthcare. In conclusion, customized interventions are crucial for successful tech acceptance among the elderly population. The findings of this study are expected to enhance understanding of elderly healthcare and technology adoption, with insights gained through natural language processing models like ChatGPT anticipated to provide a fresh perspective.

ОДНОВИМІРНА ЗГОРТКОВА МОДЕЛЬ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ ОБРОБЛЕННЯ СИГНАЛІВ З АМПЛІТУДНОЮ МОДУЛЯЦІЄЮ БАГАТЬОХ СКЛАДОВИХ

The processing of radio signals using artificial neural networks (ANNs) has great potential for research, which can be explained by the adaptability of ANNs to various transmission conditions and the ability to detect abstract patterns of changes in signal parameters. The article reviews the works of other authors devoted to different ways of using ANNs for processing radio signals. Taking into account the information in the reviewed works, the research task was formed, which consists in developing an optimized ANN model for radio signal processing. Signals with amplitude modulation of many components (AMMC) were chosen to form training samples for ANN. The choice of modulation type is justified by greater energy efficiency compared to other widely used digital modulation types, such as quadrature amplitude modulation. Mathematic basis of AMMC signal generation is described. The process of finding the coordinates of three component 8-AMMC signal constellation is explained, the formation of signals in the time plane based on the found coordinates is explained as well as their discretization and the addition of white noise. An iterative algorithm for generating initial data for ANN based on the described ratios is proposed. The general structure of one-dimensional convolutional neural network is considered. Functions of individual neurons, connections between them, the formation of layers and the convolution operation are described mathematically. On the basis of the previously given ratios, a final display of the network was formed. Specific dimensions and activation functions for layers are selected. The use of convolutional layers is justified by time invariance. Based on the reviewed mathematical models, selected activation functions and dimensions, a neural model was formed. The process of validating the effectiveness of the formed neural model is described, which is based on comparing the symbolic error probabilities of the proposed and reference models at different signal-to-noise ratios. The validation results are presented. The advantages of the obtained model over the previously proposed purely recurrent model and the AMMC reference receiver are explained.

INAPPROPRIATE CONTENT DETECTION IN IMAGE

The proliferation of social media platforms has led to a significant increase in the sharing of memes and other image-based content. However, some of these images may contain inappropriate or offensive text, which can be challenging to detect using traditional methods. Because the open-source existing solutions does not classify the text content in images. This project aims to develop a robust solution for detecting Not Safe for Work (NSFW) text content in images, with a particular focus on social media memes. The objectives of this project are to accurately extract text from images using optical character recognition (OCR) techniques, and to classify the extracted text as either NSFW or Safe for Work (SFW) using a fine-tuned natural language processing (NLP) model. The methodology employed in this project involves several key steps. First, the Keras OCR library is utilized to extract text from input images, as it has demonstrated superior performance compared to other OCR tools for social media images. Next, a preprocessing step is performed to align the extracted text in the correct sequence using coordinates, distance from the image origin, and the Pythagorean theorem. The pre-processed text is then passed to a fine-tuned BERT-BASE-UNCASED language model, which has been trained on a dataset of Reddit posts (NSFW and SFW) to classify the text as either NSFW or SFW. Key Words: OCR, Inappropriate, BERT, NSFW, SFW, Image, Text

Zero-Shot Strike: Testing the generalisation capabilities of out-of-the-box LLM models for depression detection

Depression is a significant global health challenge. Still, many people suffering from depression remain undiagnosed. Furthermore, the assessment of depression can be subject to human bias. Natural Language Processing (NLP) models offer a promising solution. We investigated the potential of four NLP models (BERT, Llama2-13B, GPT-3.5, and GPT-4) for depression detection in clinical interviews. Participants (N = 82) underwent clinical interviews and completed a self-report depression questionnaire. NLP models inferred depression scores from interview transcripts. Questionnaire cut-off values for depression were used as a classifier for depression. GPT-4 showed the highest accuracy for depression classification (F1 score 0.73), while zero-shot GPT-3.5 initially performed with low accuracy (0.34), improved to 0.82 after fine-tuning, and achieved 0.68 with clustered data. GPT-4 estimates of symptom severity PHQ-8 score correlated strongly (r = 0.71) with true symptom severity. These findings demonstrate the potential of AI models for depression detection. However, further research is necessary before widespread deployment can be considered.

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

As strawberries are a widely grown cash crop, the development of strawberry fruit-picking robots for an intelligent harvesting system should match the rapid development of strawberry cultivation technology. Ripeness identification is a key step to realizing selective harvesting by strawberry fruit-picking robots. Therefore, this study proposes combining deep learning and image processing for target detection and classification of ripe strawberries. First, the YOLOv8+ model is proposed for identifying ripe and unripe strawberries and extracting ripe strawberry targets in images. The ECA attention mechanism is added to the backbone network of YOLOv8+ to improve the performance of the model, and Focal-EIOU loss is used in loss function to solve the problem of imbalance between easy- and difficult-to-classify samples. Second, the centerline of the ripe strawberries is extracted, and the red pixels in the centerline of the ripe strawberries are counted according to the H-channel of their hue, saturation, and value (HSV). The percentage of red pixels in the centerline is calculated as a new parameter to quantify ripeness, and the ripe strawberries are classified as either fully ripe strawberries or not fully ripe strawberries. The results show that the improved YOLOv8+ model can accurately and comprehensively identify whether the strawberries are ripe or not, and the mAP50 curve steadily increases and converges to a relatively high value, with an accuracy of 97.81%, a recall of 96.36%, and an F1 score of 97.07. The accuracy of the image processing method for classifying ripe strawberries was 91.91%, FPR was 5.03%, and FNR was 14.28%. This study demonstrates the program’s ability to quickly and accurately identify strawberries at different stages of ripeness in a facility environment, which can provide guidance for selective picking by subsequent fruit-picking robots.

Are You Watching or Warning? the Role of Comprehension, Warning Lead Time and Prior Experience On Individual Preparation of Tornadic Events

This study examines how prior experience with tornados and predicted warning lead times of severe weather may influence one’s willingness to take protective action when a potential tornadic event is imminent. Using theoretical constructs from the Protective Action Decision Model and Risk Information Seeking and Processing model, the project examines how individuals make decisions about taking protective action and what factors motivate them during severe weather. The overall focus of the study is the impact of prior experience with tornados, geographic location and amount of warning lead time on an individual’s likelihood to prepare for potential severe weather. A survey of 679 mid-south residents provided insight into their perceptions of warning language and events. Results indicated that individuals who live in rural areas and those who have more prior experience with tornadic events are more likely to engage in protective behavior. Further, an interaction was noted, indicating those with more prior experience with tornados reporting that they needed less warning lead time to prepare when compared to those with less prior experience, who reported they wished for more lead warning time. Finally, definitions of “tornado warning” noted that more than half of participants did correctly identify its meaning.

Predicting the Utility of Scientific Articles for Emerging Pandemics Using Their Titles and Natural Language Processing.

Not all scientific publications are equally useful to policy-makers tasked with mitigating the spread and impact of diseases, especially at the start of novel epidemics and pandemics. The urgent need for actionable, evidence-based information is paramount, but the nature of preprint and peer-reviewed articles published during these times is often at odds with such goals. For example, a lack of novel results and a focus on opinions rather than evidence were common in coronavirus disease (COVID-19) publications at the start of the pandemic in 2019. In this work, we seek to automatically judge the utility of these scientific articles, from a public health policy making persepctive, using only their titles. Deep learning natural language processing (NLP) models were trained on scientific COVID-19 publication titles from the CORD-19 dataset and evaluated against expert-curated COVID-19 evidence to measure their real-world feasibility at screening these scientific publications in an automated manner. This work demonstrates that it is possible to judge the utility of COVID-19 scientific articles, from a public health policy-making perspective, based on their title alone, using deep natural language processing (NLP) models. NLP models can be successfully trained on scienticic articles and used by public health experts to triage and filter the hundreds of new daily publications on novel diseases such as COVID-19 at the start of pandemics.

Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness

BackgroundThe Center for Medicare and Medicaid Services (CMS) imposes payment penalties for readmissions following total joint replacement surgeries. This study focuses on total hip, knee, and shoulder arthroplasty procedures as they account for most joint replacement surgeries. Apart from being a burden to healthcare systems, readmissions are also troublesome for patients. There are several studies which only utilized structured data from Electronic Health Records (EHR) without considering any gender and payor bias adjustments.MethodsFor this study, dataset of 38,581 total knee, hip, and shoulder replacement surgeries performed from 2015 to 2021 at Novant Health was gathered. This data was used to train a random forest machine learning model to predict the combined endpoint of emergency department (ED) visit or unplanned readmissions within 30 days of discharge or discharge to Skilled Nursing Facility (SNF) following the surgery. 98 features of laboratory results, diagnoses, vitals, medications, and utilization history were extracted. A natural language processing (NLP) model finetuned from Clinical BERT was used to generate an NLP risk score feature for each patient based on their clinical notes. To address societal biases, a feature bias analysis was performed in conjunction with propensity score matching. A threshold optimization algorithm from the Fairlearn toolkit was used to mitigate gender and payor biases to promote fairness in predictions.ResultsThe model achieved an Area Under the Receiver Operating characteristic Curve (AUROC) of 0.738 (95% confidence interval, 0.724 to 0.754) and an Area Under the Precision-Recall Curve (AUPRC) of 0.406 (95% confidence interval, 0.384 to 0.433). Considering an outcome prevalence of 16%, these metrics indicate the model’s ability to accurately discriminate between readmission and non-readmission cases within the context of total arthroplasty surgeries while adjusting patient scores in the model to mitigate bias based on patient gender and payor.ConclusionThis work culminated in a model that identifies the most predictive and protective features associated with the combined endpoint. This model serves as a tool to empower healthcare providers to proactively intervene based on these influential factors without introducing bias towards protected patient classes, effectively mitigating the risk of negative outcomes and ultimately improving quality of care regardless of socioeconomic factors.

The Impact of ChatGPT on Finance Technology

With the rapid development of science and technology, financial technology has risen rapidly around the world and has become a key driving force in the financial field. The emergence of natural language processing models such as ChatGPT, has brought new possibilities to financial services. This paper mainly studies the role and impact of ChatGPT in financial technology. Focus includes its applications in customer service, investment advice and risk management, as well as the potential impact of these applications on financial operations and user experience. The purpose of the research is to gain an in-depth understanding of the application of ChatGPT in the financial field, and reveal its potential contribution to improving financial service efficiency. This study uses literature analysis, case analysis and data analysis. Through systematic combing of relevant literature, we can understand the development status of ChatGPT in financial services. The research of this paper found that ChatGPT has a significant influence in the field of financial technology. Its application in customer service can improve service efficiency and provide users with faster and more personalized services. However, to ensure information security and privacy protection, financial institutions need to carefully weigh the pros and cons when adopting ChatGPT technology.

Automating literature screening and curation with applications to computational neuroscience.

ModelDB (https://modeldb.science) is a discovery platform for computational neuroscience, containing over 1850 published model codes with standardized metadata. These codes were mainly supplied from unsolicited model author submissions, but this approach is inherently limited. For example, we estimate we have captured only around one-third of NEURON models, the most common type of models in ModelDB. To more completely characterize the state of computational neuroscience modeling work, we aim to identify works containing results derived from computational neuroscience approaches and their standardized associated metadata (eg, cell types, research topics). Known computational neuroscience work from ModelDB and identified neuroscience work queried from PubMed were included in our study. After pre-screening with SPECTER2 (a free document embedding method), GPT-3.5, and GPT-4 were used to identify likely computational neuroscience work and relevant metadata. SPECTER2, GPT-4, and GPT-3.5 demonstrated varied but high abilities in identification of computational neuroscience work. GPT-4 achieved 96.9% accuracy and GPT-3.5 improved from 54.2% to 85.5% through instruction-tuning and Chain of Thought. GPT-4 also showed high potential in identifying relevant metadata annotations. Accuracy in identification and extraction might further be improved by dealing with ambiguity of what are computational elements, including more information from papers (eg, Methods section), improving prompts, etc. Natural language processing and large language model techniques can be added to ModelDB to facilitate further model discovery, and will contribute to a more standardized and comprehensive framework for establishing domain-specific resources.

Preface: 2nd International Conference on Algorithms, Mathematical Modeling and Machinery Processing (AMMMP 2024)

We sincerely invite you to participate in the 2024 2nd International Conference on Algorithms, Mathematical Modeling and Machinery Processing (AMMMP 2024). This conference was successfully held in Mumbai, India during March 23-24, 2024 for researchers, engineers, etc. on topics related to algorithmic intelligence, mathematical modeling, computer simulation, control technology, and machinery processing. AMMMP 2024 aims to elaborate basic and applied researches from all over the world. As our theme for this conference, we encourage researchers to present their researches on algorithms and mathematical modeling. The participants attending this two-day conference reach 50 people. We design that the participants can present their work either in oral or poster types. Furthermore, the crucial factor for AMMMP 2024 is that we facilitate the works from the participants that can be published at a journal with the peer-reviewed process. We have worked hard to process all accepted manuscripts reviewed by professionals and experts in related science fields. At least, one manuscript is under reviewed by two reviewers, and the handling editor decided whether a manuscript can be accepted or rejected mainly based on the reviewer's comments. With best regards, Technical Program Committees of AMMMP Mumbai, India

The Review of Modeling and Rendering 3D Environment: A Survey

This paper explores several topics in the field of 3D Environments, including ray tracing, rendering techniques, algorithms and optimization, data processing, real-time rendering, modeling, physical simulation, and material modeling. The paper covers a large variety of applications and techniques that are widely used in computer graphics and visualization. This paper also points out that significant progress has been made in ray tracing, texture filtering, image processing, motion synthesis, geometric modeling, and physical simulation. These advances have improved the visual quality, performance, and fidelity of computer-generated images. In addition, the article mentions the trend of integrating machine learning, deep learning, and data analysis techniques into computer graphics and visualization, especially in data processing and analysis models, to improve the results of rendering and simulation. Overall, this article explores details of the current state of the computer graphics and visualization field, highlighting the various technologies and applications which shapes the field.