Hasil untuk "Information technology"

Junshi Chen, Xuhong Li, Russ Whiton et al.

Radio frequency (RF) signal-based localization using modern cellular networks has emerged as a promising solution to accurately locate objects in challenging environments. One of the most promising solutions for situations involving obstructed-line-of-sight (OLoS) and multipath propagation is multipath-based simultaneous localization and mapping (MP-SLAM) that employs map features (MFs), such as virtual anchors. This paper presents an extended MP-SLAM method that is augmented with a global map feature (GMF) repository. This repository stores consistent MFs of high quality that are collected during prior traversals. We integrate these GMFs back into the MP-SLAM framework via a probability hypothesis density (PHD) filter, which propagates GMF intensity functions over time. Extensive simulations, together with a challenging real-world experiment using LTE RF signals in a dense urban scenario with severe multipath propagation and inter-cell interference, demonstrate that our framework achieves robust and accurate localization, thereby showcasing its effectiveness in realistic modern cellular networks such as 5G or future 6G networks. It outperforms conventional proprioceptive sensor-based localization and conventional MP-SLAM methods, and achieves reliable localization even under adverse signal conditions.

Ahmed Métwalli, Fares Fathy, Esraa Khatab et al.

Ant Colony Optimization (ACO) is a widely adopted metaheuristic for solving complex combinatorial problems; however, performance is often deteriorated by premature convergence and limited exploration in later iterations. Eclipse Randomness–Ant Colony Optimization (ER-ACO) is introduced as a lightweight ACO variant in which an exponentially fading randomness factor is integrated into the state-transition mechanism. Strong early-stage exploration is enabled, and a smooth transition to exploitation is induced, improving convergence behavior and solution quality. Low computational overhead is maintained while exploration and exploitation are dynamically balanced. ER-ACO is positioned within real-time healthcare logistics, with a focus on Emergency Medical Services (EMS) routing and hospital resource scheduling, where rapid and adaptive decision-making is critical for patient outcomes. These systems face dynamic constraints such as fluctuating traffic conditions, urgent patient arrivals, and limited medical resources. Experimental evaluation on benchmark instances indicates that solution cost is reduced by up to 14.3% relative to the slow-fade configuration (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>γ</mi><mo>=</mo><mn>1</mn></mrow></semantics></math></inline-formula>) in the 20-city TSP sweep, and faster stabilization is indicated under the same iteration budget. Additional comparisons against Standard ACO on TSP/QAP benchmarks indicate consistent improvements, with unchanged asymptotic complexity and negligible measured overhead at the tested scales. TSP/QAP benchmarks are used as controlled proxies to isolate algorithmic behavior; EMS deployment is treated as a motivating application pending validation on EMS-specific datasets and formulations. These results highlight ER-ACO’s potential as a lightweight optimization engine for smart healthcare systems, enabling real-time deployment on edge devices for ambulance dispatch, patient transfer, and operating room scheduling.

Aleksandr I. Minakov, Svetlana V. Zenkina

Problem statement. The integration of artificial intelligence (AI) into the field of education has become one of the key factors transforming pedagogical activities worldwide. The proliferation of generative AI tools (ChatGPT, DeepSeek, GigaChat) is accompanied by numerous discussions about their impact on the learning process and teachers’ professional activities. Among the main challenges highlighted in the global academic literature are: 1) the lack of unified attitudes towards AI use; 2) insufficient digital literacy among participants in the educational process; and 3) ethical and long-term risks of applying AI in education. The aim of this study is to explore future teachers’ attitudes towards the use of generative AI in solving professional tasks and to determine the impact of additional training on their perception of AI tools. Methodology. The empirical study involved 32 students pursuing a pedagogical profile. Surveys were conducted before and after completing an elective course on the use of AI in teachers’ professional activities. Methods included self-assessment (attitude survey), analysis of survey data, and statistical processing of results using the Student’s t-test to assess the significance of changes in future teachers’ attitudes towards AI. Results. The significance of additional training for improving future teachers’ attitudes towards AI has been confirmed. It was found that generative AI is perceived most positively in text generation tasks, while tasks involving assignment grading and generating video and audio materials inspire the least trust. The training helped reduce negative perceptions and improved the attitude towards using AI in solving professional tasks. Conclusion. The findings confirm the need for targeted training for future teachers in the fundamentals of AI to minimize negative aspects and ensure effective use of the technology. The developed principles could form the basis for creating educational disciplines and professional development courses, enabling more rational and safe applications of AI in education.

Daniel Cristóbal Andrade-Girón, Juana Sandivar-Rosas, William Joel Marin-Rodriguez et al.

Cardiovascular disease (CVD) is a major cause of mortality around the world. This underscores the critical need to implement effective predictive tools to inform clinical decision-making. This study aimed to compare the predictive performance of ensemble learning algorithms, including Bagging, Random Forest, Extra Trees, Gradient Boosting, and AdaBoost, when applied to a clinical dataset comprising patients with CVD. The methodology entailed data preprocessing and cross-validation to regulate generalization. The performance of the model was evaluated using a variety of metrics, including accuracy, <i>F</i>1 score, precision, recall, Cohen’s Kappa, and area under the curve (<i>AUC</i>). Among the models evaluated, Bagging demonstrated the best overall performance (accuracy ± SD: 93.36% ± 0.22; <i>F</i>1 score: 0.936; <i>AUC</i>: 0.9686). It also reached the lowest average rank (1.0) in Friedman test and was placed, together with Extra Trees (accuracy ± SD: 90.76% ± 0.18; <i>F</i>1 score: 0.916; <i>AUC</i>: 0.9689), in the superior statistical group (group A) according to Nemenyi post hoc test. The two models demonstrated a high degree of agreement with the actual labels (Kappa: 0.87 and 0.83, respectively), thereby substantiating their reliability in authentic clinical contexts. The findings substantiated the preeminence of aggregation-based ensemble methods in terms of accuracy, stability, and concordance. This underscored the prominence of Bagging and Extra Trees as optimal candidates for cardiovascular diagnostic support systems, where reliability and generalization were paramount.

Rawan Amr Elmasri, Alaa A. Rashwan, Sarah Hany Gaber et al.

A non-negligible part of our DNA has been proven to be transcribed into non-protein coding RNA and its intricate involvement in several physiological processes has been highly evidenced. The significant biological role of non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) has been variously reported. In the current review, the authors highlight the multifaceted role of myocardial infarction-associated transcript (MIAT), a well-known lncRNA, in hepatocellular carcinoma (HCC). Since its discovery, MIAT has been described as a regulator of carcinogenesis in several malignant tumors and its overexpression predicts poor prognosis in most of them. At the molecular level, MIAT is closely linked to the initiation of metastasis, invasion, cellular migration, and proliferation, as evidenced by several in-vitro and in-vivo models. Thus, MIAT is considered a possible theranostic agent and therapeutic target in several malignancies. In this review, the authors provide a comprehensive overview of the underlying molecular mechanisms of MIAT in terms of its downstream target genes, interaction with other classes of ncRNAs, and potential clinical implications as a diagnostic and/or prognostic biomarker in HCC.

Yingjie Ji, Weiguo Wu, Guangtong Wan et al.

With rapid urbanization, retrieving information about residential complexes in a timely manner is essential for urban planning. To develop efficiency and accuracy of building extraction in residential complexes, a Segment Anything Model-based residential building instance segmentation method with an automated prompt generator was proposed combining LiDAR data and VHR remote sensing images in this study. Three key steps are included in this method: approximate footprint detection using LiDAR data, automatic prompt generation for the SAM, and residential building footprint extraction. By applying this method, residential building footprints were extracted in Pukou District, Nanjing, Jiangsu Province. Based on this, a comprehensive assessment model was constructed to systematically evaluate the spatial layout of urban complexes using six dimensions of assessment indicators. The results showed the following: (1) The proposed method was used to effectively extract residential building footprints. (2) The residential complexes in the study area were classified into four levels. The numbers of complexes classified as Excellent, Good, Average, and Poor were 10, 29, 16, and 1, respectively. Residential complexes of different levels exhibited varying spatial layouts and building distributions. The results provide a visual representation of the spatial distribution of residential complexes that belong to different levels within the study area, aiding in urban planning.

Armand Fréjuis Akpa, Augustin Foster Chabossou

The introduction of information and communication technology (ICT) has altered the way society operates things. ICT is used in various sectors, including agriculture. It can be used in the agricultural sector to distribute pricing and encourage agricultural commodity exports. The study aims to investigate the effect of ICT on cashew nut export in Benin using an autoregressive distributed lag (ARDL) approach. Data were collected over the period of 31 years (1990–2020) in Benin. The estimated results showed that mobile cellular telephone subscription is negatively and significantly correlated with cashew nut export in the short-run. However, in the long-run, it exhibits a positive and significant correlation. On the other hand, internet usage had no significant effect on cashew nut export in the short-run, but negatively influenced cashew nut export in the long-run. These results suggest that to increase its cashew nut export, the Beninese government should invest in technological infrastructure to improve internet access by reducing the cost of internet and increasing education that will allow farmers to better understand and use ICT.

Jing Chen, Ming-Yuan Sun, Zhen-Hua Wang et al.

Highlights The review provides a comprehensive summary of performance limits of the single two-dimensional transition metal dichalcogenide (2D-TMD) transistor. The review details the two logical expressions of the single 2D-TMD logic transistor, including current and voltage. The review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices.

Lukasz Szymula, Krzysztof Dyczkowski

Li-Chen Cheng, Wei-Ting Lu, Benjamin Yeo

Abstract In 2021, the abnormal short-term price fluctuations of GameStop, which were triggered by internet stock discussions, drew the attention of academics, financial analysts, and stock trading commissions alike, prompting calls to address such events and maintain market stability. However, the impact of stock discussions on volatile trading behavior has received comparatively less attention than traditional fundamentals. Furthermore, data mining methods are less often used to predict stock trading despite their higher accuracy. This study adopts an innovative approach using social media data to obtain stock rumors, and then trains three decision trees to demonstrate the impact of rumor propagation on stock trading behavior. Our findings show that rumor propagation outperforms traditional fundamentals in predicting abnormal trading behavior. The study serves as an impetus for further research using data mining as a method of inquiry.

Olusola Bamisile, Dongsheng Cai, Humphrey Adun et al.

The need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic nature of wind and solar energy resources is still a major challenge. These can affect the safety, stability, and reliability of the energy grid. In existing works of literature, the forecast and prediction of wind energy, solar power, wind power, and solar energy with various models have been considered independently. However, with the rise in solar power and wind power penetration, there exists a gap in literature on the development of models that can simultaneously forecast solar and wind power production. In this paper, two deep hybrid neural networks (DHN-Net) models are developed for the simultaneous forecast of wind and solar power. The novelty of this study is further strengthened as a minute-level timestep is considered for the application of the models developed. The models are trained and tested with data collected from Zone 1 of four different power system operators in the USA. The two DHN-Net models are built on the foundation of artificial, convolutional, and recurrent neural networks (ANN, CNN, and RNN). Results from this study show that the two DHN-Nets can accurately forecast solar and wind power with an R-squared (r2) value of 0.9915, RMSE of 0.01920, and MAE of 0.00736 for data collected from PJM_Zone1. The DHN-Net models recorded a better performance when compared to the benchmark results in literature.

Chun-Wei Chang, Takeshi Miki, Hao Ye et al.

Tasmilah Tasmilah Tasmilah, Devanto Pratomo, Wildan Syafitri

The Covid-19 pandemic has decreased the number of entrepreneurs assisted by permanent workers in Indonesia. Using SAKERNAS Data of August 2019 and 2020, this study uses multinomial logistic regression to estimate the impact of digital technology in the entrepreneurial transition during the Covid-19 pandemic. The results show that digital technology and the internet have a negative and significant effect on the transition of formal entrepreneurship in Indonesia. The use of digital technology and the internet will prevent formal entrepreneurs from transitioning into informal entrepreneurs or leaving entrepreneurship. Increasing digital technology usage and the internet for promotional and sales purposes will enable entrepreneurs to survive amid a pandemic. In addition, building information technology infrastructure such as Base Transceiver Station (BTS), especially in rural areas, is necessary to increase internet coverage and encourage business scale-up in rural areas.

Ying Yan, Bin Suo, Ziwei Li

Failure mode and effects analysis (FMEA) is a widely used, powerful tool to identify and assess potential failure modes in products and to make products more reliable. Due to the complexity of products and lack of knowledge, FMEA involves many uncertainties in practice. In previous studies, numerous modified FMEA methods based on fuzzy logic and Dempster-Shafer (D-S) evidence theory have been employed to address these uncertainties. These studies focus on how to handle uncertainties and to identify a more reliable prioritization of risk priority numbers (RPNs). However, studies have not sufficiently examined how many uncertainties are present in resulting RPNs. To better model and process various types of uncertainties in FMEA, two new area-based metrics are constructed in this paper. One is the interval area metric (IAM), which is used in RPN representation. The other is the dimensionless uncertainty metric (DUM), which is used to measure how many uncertainties there are in RPN. IAM is used to rank the risks in failure modes, and DUM is used to rank the uncertainties in failure modes. Then, an expert system is presented to qualitatively evaluate the DUM, which can help FMEA users intuitively judge whether further investigation should be performed to alleviate the epistemic uncertainties in each failure mode. Finally, a practical risk evaluation case regarding the grinding wheel system of a numerically controlled (NC) machine is provided to demonstrate the application and effectiveness of the proposed FMEA. The case study shows that the calculation programs of IAM and DUM do not require any assumptions or need to address conflict among experts. In addition, proposed method can not only give a more accurate rating of each failure mode, but also help designers intuitively see the uncertainty grade of each RPN, which is useful to help them understand FMEA results.

Johanna Freund, Claudia Buntrock, Lina Braun et al.

Introduction: Farmers, forest workers and gardeners have a higher risk of developing depression compared to other occupational populations. As part of the German pilot project “With us in balance”, the potential of six guided internet- and mobile-based interventions (IMIs) to prevent depression among their insurants is examined. The IMI program is tailored to various risk factors of depression, individual symptoms, and needs. Although IMIs have been shown to be effective in reducing depressive symptoms, there is little qualitative research about the acceptance of digital preventive IMIs. The aim of this qualitative study is to gain insights into participants' experiences with the guided IMIs by focusing on determinants for acceptance and satisfaction. Methods: Semi-structured interviews were conducted with 22/171 (13 %) intervention group (IG) participants of a randomized controlled trial. The interview guide was developed based on theoretical models of user acceptance (Unified Theory of Acceptance and Use of Technology) and patient satisfaction (evaluation model, discrepancy theory). The interviews were evaluated independently by two coders performing a deductive-inductive content analysis and attaining a substantial level of agreement (K = 0.73). Results: The qualitative analysis revealed 71 determinants for acceptance and satisfaction across ten dimensions: performance expectancy, organisation, e-coach, usability, training content and structure, training usage, training outcome, financing, social influence, and behavioural intention. The most frequently identified drivers for the IMI use include “location independence”, “positive relationship to the e-coach” (each n = 19, 86 %), “personal e-coach guidance”, “expertise of the e-coach”, “target group specific adaptation” (each n = 18, 82 %), “flexibility”, “high willingness for renewed participation” (each n = 17, 77 %), “fast and easy availability”, “training of health enhancing attitudes and behaviours” and “content with figurative expressions” (each n = 16, 73 %). Discussion: The qualitative findings predominantly suggest the acceptance of and satisfaction with the IMI program for the prevention of depression in famers and related lines of work. Many identified positive drivers are related to the e-coach guidance, which emphasizes its importance in the preventive setting from the perspective of the participants. Nevertheless, some negative aspects have been identified which help to understand potential weaknesses of the IMI program. Participants indicated different needs in terms of IMI content and usage, which points towards the potential benefit of individualisation. The possibility of being able to use IMIs anonymously, flexibly and independently of location might be highly relevant for this specific target group.

Yu-Chieh Huang, Pierre Tremouilhac, An Nguyen et al.

Abstract ChemSpectra, a web-based software to visualize and analyze spectroscopic data, integrating solutions for infrared spectroscopy (IR), mass spectrometry (MS), and one-dimensional 1H and 13C NMR (proton and carbon nuclear magnetic resonance) spectroscopy, is described. ChemSpectra serves as web-based tool for the analysis of the most often used types of one-dimensional spectroscopic data in synthetic (organic) chemistry research. It was developed to support in particular processes for the use of open file formats which enable the work according to the FAIR data principles. The software can deal with the open file formats JCAMP-DX (IR, MS, NMR) and mzML (MS) proposing these data file types to gain interoperable data. ChemSpectra can be extended to read also other formats as exemplified by selected proprietary mass spectrometry data files of type RAW and NMR spectra files of type FID. The JavaScript-based editor can be integrated with other software, as demonstrated by integration into the Chemotion electronic lab notebook (ELN) and Chemotion repository, demonstrating the implementation into a digital work environment that offers additional functionality and sustainable research data management options. ChemSpectra supports different functions for working with spectroscopic data such as zoom functions, peak picking and automatic peak detection according to a default or manually defined threshold. NMR specific functions include the definition of a reference signal, the integration of signals, coupling constant calculation and multiplicity assignment. Embedded into a web application such as an ELN or a repository, the editor can also be used to generate an association of spectra to a sample and a file management. The file management supports the storage of the original spectra along with the last edited version and an automatically generated image of the spectra in png format. To maximize the benefit of the spectra editor for e.g. ELN users, an automated procedure for the transfer of the detected or manually chosen signals to the ELN was implemented. ChemSpectra is released under the AGPL license to encourage its re-use and further developments by the community.

Sidrah Liaqat, Kia Dashtipour, Adnan Zahid et al.

The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.

Zhenxue He, Limin Xiao, Zhisheng Huo et al.

Logic minimization has recently attracted significant attention because in many applications it is important to have a compact representation as possible. In this paper, we propose a fast minimization algorithm (FMA) of fixed polarity Reed-Muller expressions (FPRMs). The main idea behind the FMA is to search the minimum FPRM with the fewest products by using the proposed binary differential evolution algorithm (BDE). The BDE can efficiently maintain population diversity and achieve a better tradeoff between the exploration and exploitation capabilities by use of proposed binary random mutation operator and improved selection operator. The experimental results on 24 MCNC benchmark circuits demonstrate that the FMA outperforms the genetic algorithm-based and simulated annealing genetic algorithm-based FPRMs minimization algorithms in terms of accuracy of solutions and solving efficiency. To the best of our knowledge, we are the first to use differential evolution algorithm to minimize FPRMs. The FMA can be extended to derive a minimum mixed polarity Reed-Muller expression.

M. Parker, R. Benson, H. E. Trainor

Yi Yang, Meirun Chen, Hao Li et al.

This paper mainly focuses on routing in two-dimensional mesh networks. We propose a novel faulty block model, which is cracky rectangular block, for fault-tolerant adaptive routing. All the faulty nodes and faulty links are surrounded in this type of block, which is a convex structure, in order to avoid routing livelock. Additionally, the model constructs the interior spanning forest for each block in order to keep in touch with the nodes inside of each block. The procedure for block construction is dynamically and totally distributed. The construction algorithm is simple and ease of implementation. And this is a fully adaptive block which will dynamically adjust its scale in accordance with the situation of networks, either the fault emergence or the fault recovery, without shutdown of the system. Based on this model, we also develop a distributed fault-tolerant routing algorithm. Then we give the formal proof for this algorithm to guarantee that messages will always reach their destinations if and only if the destination nodes keep connecting with these mesh networks. So the new model and routing algorithm maximize the availability of the nodes in networks. This is a noticeable overall improvement of fault tolerability of the system.