Process Information Research Articles

Applications of artificial intelligence in the field of drones

Drones, now equipped with sophisticated sensors and high-resolution cameras, have evolved into highly advanced tools capable of executing a diverse range of tasks with precision and efficiency. The integration of Artificial Intelligence (AI) has been a game-changer, endowing these aerial vehicles with the ability to process information, make autonomous decisions, and perform complex operations without direct human intervention. This paper delves into the transformative potential of AI-integrated drones across various industries, highlighting their role in revolutionizing the way we approach tasks such as autonomous navigation, package delivery, warehouse inventory management, and disaster response. The paper concludes with a comprehensive summary of the findings, emphasizing the potential benefits of AI-integrated drones in various sectors. It also looks forward to future research directions, which will focus on addressing significant challenges such as ensuring data privacy, improving the robustness and reliability of AI algorithms, and navigating the complex regulatory landscape that governs drone usage.

Application of Information and Communication Technology(ICT) in the Services of Public and Academics Libraries in Pangasinan

The intensifying era of Information communication technology (ICT) really brought great changes, especially in libraries. Both positive and negative effects of the application of such are visible to librarians and to users. Different challenges regarding the application of ICT in libraries are also encountered.Considering the importance of ICT in libraries, the researchers conducted this study to determine the level of application of ICT in the services of public and academic libraries in Pangasinan in order to enhance the access, storage,and process of information and to improve the operation services of these libraries. This study used a descriptive method of research. A descriptive research method is used to describe, as well as explain, or validate some sort of hypothesis or objective when it comes to a specific group of people (McNeill, 2018). The questionnaire was used as the data gathering instrument aided with an interview technique in order to validate the answer. The researchers made use of frequency count, percentage, and mean as statistical tools. Results show that both public and academic libraries have available hardware facilities. Computer servers, workstations, net servers, and printers are 100 percent available in these libraries. Moreover, Online databases are the most available resources for information retrieval. On the other hand, Lack of funds/financial assistance was the most identified challenge by the librarians in the application of ICT. Further, a Pooled mean of 2.86 was rated and interpreted as moderately applied concerning the level of application of ICT in their respective libraries both in technical and reader’s services. Based on the findings of this study, it can be concluded that the application of ICT in the library services of public and academic libraries in Pangasinan is not yet at its highest, but advancing it to its fullest is possible if the school administrators and local government will give and provide their full support on ICT project of libraries.

How to take a comprehensive patient history.

A significant proportion of diagnoses are made based on history taking, often alongside physical assessments and laboratory investigations. Taking a thorough patient history is fundamental for the accurate diagnosis and effective management of health conditions. This article outlines a step-by-step process for taking a comprehensive patient history and discusses the evidence for this procedure. • History taking is a structured but flexible process of gathering relevant information from patients to inform diagnosis and treatment. • Important communication skills for nurses when history taking include active listening, empathetic communication and cultural sensitivity. • By actively engaging the patient in a conversation about their health issues, the nurse facilitates their participation and autonomy. REFLECTIVE ACTIVITY: 'How to' articles can help to update your practice and ensure it remains evidence based. Apply this article to your practice. Reflect on and write a short account of: • How this article might improve your practice when taking a patient history. • How you could use this information to educate nursing students or colleagues on taking a patient history.

Emotionally Tailored Web-Based Instruction: A Catalyst for Improved Performance Across Emotional Intelligence Levels

This study examined the impact of emotionally sound web-based teaching on the learners' achievement regarding English grammar. Emotions play a crucial role in education, influencing students' ability to process information and learn effectively. The education system is not giving importance and attention to emotional intelligence education in schools and colleges. They often stress on IQ and not on EI. This study aims to improve English grammar performance by using emotionally sound web-based instruction, addressing the impact of emotions on learning outcomes. The sample of the population for this study was 183 Form Three students selected from a secondary school in Peninsular Malaysia. The research instruments included Pre-test, Post-test, and Trait Emotional Intelligence — Short Form (TEIQue-SF). The research design for this study is Quasi-Experimental with pre-test and post-test designs. In this study, the independent variable would be the instructional strategies, under two types of treatment conditions as well (Web-based Instruction with Emotional Element (WBIe) and Web-based Instruction without Emotional Element (WBIwe), and a moderator variable (level of emotional intelligence)) in two levels of emotional intelligence (high and low level of emotional intelligence). A simple 2×2 factorial design was employed here. The results indicated that students in WBIe were performing better rather than in WBIwe in terms of student success in learning English Grammar and between students of emotional intelligence with a high level on each of the WBIe and WBIwe types had the same score. It was found that the emotional intelligence level has a role in the WBIe and the WBIwe performance of the students. The emotional intelligence of high EI students scored higher on English grammar than the EI of low EI students. The results of this study established a link between sound web-based instruction and instruction; it also serves as a call for an instructional designer to understand clearly, why and how emotions affect learning. They can use this to determine how to harness their emotions toward the most effective kind of learning.

Intersection-union dual-stream cross-attention Lova-SwinUnet for skin cancer hair segmentation and image repair

Skin cancer images have hair occlusion problems, which greatly affects the accuracy of diagnosis and classification. Current dermoscopic hair removal methods use segmentation networks to locate hairs, and then uses repair networks to perform image repair. However, it is difficult to segment hair and capture the overall structure between hairs because of the hair being thin, unclear, and similar in color to the entire image. When conducting image restoration tasks, the only available images are those obstructed by hair, and there is no corresponding ground truth (supervised data) of the same scene without hair obstruction. In addition, the texture information and structural information used in existing repair methods are often insufficient, which leads to poor results in skin cancer image repair. To address these challenges, we propose the intersection-union dual-stream cross-attention Lova-SwinUnet (IUDC-LS). Firstly, we propose the Lova-SwinUnet module, which embeds Lovasz loss function into Swin-Unet, enabling the network to better capture features of various scales, thus obtaining better hair mask segmentation results. Secondly, we design the intersection-union (IU) module, which takes the mask results obtained in the previous step for pairwise intersection or union, and then overlays the results on the skin cancer image without hair to generate the labeled training data. This turns the unsupervised image repair task into the supervised one. Finally, we propose the dual-stream cross-attention (DC) module, which makes texture information and structure information interact with each other, and then uses cross-attention to make the network pay attention to the more important texture information and structure information in the fusion process of texture information and structure information, so as to improve the effect of image repair. The experimental results show that the PSNR index and SSIM index of the proposed method are increased by 5.4875 and 0.0401 compared with the other common methods. Experimental results unequivocally demonstrate the effectiveness of our approach, which serves as a potent tool for skin cancer detection, significantly surpassing the performance of comparable methods.

Confinement of excited states in two-dimensional, in-plane, quantum heterostructures

Two-dimensional (2D) semiconductors are promising candidates for optoelectronic application and quantum information processes due to their inherent out-of-plane 2D confinement. In addition, they offer the possibility of achieving low-dimensional in-plane exciton confinement, similar to zero-dimensional quantum dots, with intriguing optical and electronic properties via strain or composition engineering. However, realizing such laterally confined 2D monolayers and systematically controlling size-dependent optical properties remain significant challenges. Here, we report the observation of lateral confinement of excitons in epitaxially grown in-plane MoSe2 quantum dots (~15-60 nm wide) inside a continuous matrix of WSe2 monolayer film via a sequential epitaxial growth process. Various optical spectroscopy techniques reveal the size-dependent exciton confinement in the MoSe2 monolayer quantum dots with exciton blue shift (12-40 meV) at a low temperature as compared to continuous monolayer MoSe2. Finally, single-photon emission (g2(0) ~ 0.4) was also observed from the smallest dots at 1.6 K. Our study opens the door to compositionally engineered, tunable, in-plane quantum light sources in 2D semiconductors.

Smart Media Video Cloud Technology in News Communication

Cloud technology revolutionizes the way businesses and individuals store, access, and manage data and applications. It involves the use of remote servers hosted on the internet to store and process information, providing on-demand computing resources. With the cloud, users can access their data and applications from anywhere with an internet connection, fostering increased collaboration, flexibility, and scalability. This research explores the convergence of smart media video cloud technology and news communication, introducing a novel framework named Big Data Analytics Parallel Edge Computing with ECC (PEC-ECC). With the proliferation of video content in news dissemination, there is a growing need for innovative technologies to enhance efficiency, security, and accessibility. PEC-ECC integrates the power of big data analytics, parallel edge computing, and error-correcting code mechanisms to optimize video processing and delivery. This framework not only addresses the challenges of data volume and computational speed but also ensures the integrity and security of transmitted content. By leveraging edge computing capabilities, PEC-ECC minimizes latency, making real-time news communication more responsive and reliable. This research contributes to the advancement of smart media technology, offering a robust solution to elevate the quality and effectiveness of video-based news communication in the era of digital journalism.

Understanding PM2.5 risk information seeking and processing among South Koreans: evidence of desensitization?

ABSTRACT This study examines how South Koreans seek and process information about PM2.5 air pollution, amidst concerns of desensitization to its health impacts due to prolonged exposure. A survey was conducted with a sample of 1,500 respondents. Our findings reveal that exposure to PM2.5-related information is positively correlated with risk perceptions and negative emotions. The effect of exposure to PM2.5 information on current knowledge regarding PM2.5 is mediated by perceived information gathering capacity. Interestingly, respondents perceive themselves to have sufficient knowledge about PM2.5, surpassing the threshold knowledge level. This observation contrasts with earlier studies on risk information, suggesting a unique dynamic in the context of PM2.5 pollution. Our findings support the mediation hypotheses, suggesting that exposure and risk perceptions are positively correlated with information seeking effort and systematic processing by eliciting negative emotions. The mediation analysis results also suggest that perceived severity has a more substantial influence on risk information behaviors than perceived susceptibility.

Beat frequency induced transitions in synchronization dynamics

In neurosciences, the brain processes information via the firing patterns of connected neurons operating across a spectrum of frequencies. To better understand the effects of these frequencies in the neuron dynamics, we have simulated a neuronal network of Izhikevich neurons to examine the interaction between frequency allocation and intermittent phase synchronization dynamics. As the synchronized population of neurons passes through a bifurcation, an additional frequency mode emerges, enabling a match in the mean frequency while retaining distinct most probable frequencies among neurons. Subsequently, the network intermittently transits between two patterns, one partially synchronized and the other unsynchronized. Through our analysis, we demonstrate that the frequency changes on the network lead to characteristic transition times between synchronization states. Moreover, these transitions adhere to beat frequency statistics when the neurons’ frequencies differ by multiples of a frequency gap. Finally, our results can improve the performance in predicting transitions on problems where the beat frequency strongly influences the dynamics.

A Multiwavelength, Multiepoch Monitoring Campaign of Accretion Variability in T Tauri Stars from the ODYSSEUS Survey. I. HST Far-UV and Near-UV Spectra

The classical T Tauri star (CTTS) stage is a critical phase of the star and planet formation process. In an effort to better understand the mass accretion processes, which can dictate future stellar evolution and planet formation, a multiepoch, multiwavelength photometric and spectroscopic monitoring campaign of four CTTSs (TW Hya, RU Lup, BP Tau, and GM Aur) was carried out in 2021 and 2022/2023 as part of the Outflows and Disks around Young Stars: Synergies for the Exploration of ULLYSES Spectra program. Here we focus on the Hubble Space Telescope (HST) UV spectra obtained by the HST Director’s Discretionary Time UV Legacy Library of Young Stars as Essential Standards (ULLYSES) program. Using accretion shock modeling, we find that all targets exhibit accretion variability, varying from short increases in accretion rate by up to a factor of 3 within 48 hr to longer decreases in accretion rate by a factor of 2.5 over the course of 1 yr. This is despite the generally consistent accretion morphology within each target. Additionally, we test empirical relationships between accretion rate and UV luminosity and find stark differences, showing that these relationships should not be used to estimate the accretion rate for an individual target. Our work reinforces that future multiepoch and simultaneous multiwavelength studies are critical in our understanding of the accretion process in low-mass star formation.

ОБЗОР СЕЛЕВОЙ ОПАСНОСТИ ДОЖДЕВОГО ГЕНЕЗИСА ПО ГОРНОЙ ТЕРРИТОРИИ РЕСПУБЛИКИ КАЗАХСТАН ЗА 2023 ГОД

The review was prepared based on the results of monitoring the debris-flow hazard caused by rainfall in the mountainous territory of the Republic of Kazakhstan for 2023. Monitoring was carried out for debris-flow hazard areas of Almaty, Zhetysu, Zhambyl, Turkestan, Mangistau, Abay and East Kazakhstan regions. The Ile, Kungey, Teriskey, Zhetysu, Kyrgyz and Talas Alatau, Ugam, Mangystau, Saur, Tarbagatai and Kazakhstan Altai mountains ridges are debris-flow hazard areas. The Review contains a map of debris-flow hazard on the territory of the Republic of Kazakhstan, information on the hydrometeorological situation and debris f low occurrences during the 2023 debris-flow-hazardous period. In the monitoring process information from the state observation network of the RSE «Kazhydromet» in operational mode, synoptic and numerical weather forecasts of the RSE «Kazhydromet» was used, etc.

MPOCSR: optical chemical structure recognition based on multi-path Vision Transformer

AbstractOptical chemical structure recognition (OCSR) is a fundamental and crucial task in the field of chemistry, which aims at transforming intricate chemical structure images into machine-readable formats. Current deep learning-based OCSR methods typically use image feature extractors to extract visual features and employ encoder-decoder architectures for chemical structure recognition. However, the performance of these methods is limited by their image feature extractors and the class imbalance of elements in chemical structure representation. This paper proposes MPOCSR (multi-path optical chemical structure recognition), which introduces the multi-path Vision Transformer (MPViT) and the class-balanced (CB) loss function to address these two challenges. MPOCSR uses MPViT as an image feature extractor, combining the advantages of convolutional neural networks and Vision Transformers. This strategy enables the provision of richer visual information for subsequent decoding processes. Furthermore, MPOCSR incorporates CB loss function to rebalance the loss weights among different categories. For training and validation of our method, we constructed a dataset that includes both Markush and non-Markush structures. Experimental results show that MPOCSR achieves an accuracy of 90.95% on the test set, surpassing other existing methods.

The role of biochemical processes in formation of waters and bottom sediments after reduction of anthropogenic pollution

The role of biochemical processes in formation of waters and bottom sediments after reduction of anthropogenic pollution

DEVELOPMENT OF MARKERLESS BASED AUGMENTED REALITY INTERACTIVE LEARNING MEDIA OF 3D GEOMETRY SHAPES

Education is one sector that continues to experience significant development, especially in terms of the use of information and communication technology (ICT) in the learning process. This research is intended as a development in terms of creating an Augmented Reality 3D geometry shapes interactive learning based on the previous researches featuring a Markerless Based, that is expected to be more effective to use. The conclusion of this research is that Markerless Based Augmented Reality Interactive Learning media of 3D Geometry Shapes has been be developed succesfully. The application development stage was successfully carried out by applying the waterfall model and obtained successful results in the testing plan on the Black Box Testing of application prototypes on mobile android devices, indicates that the developed application has fulfilled all the functional requirements.

Strategi Komunikasi Tanggap Karawang Dalam Upaya Peningkatan Pelayanan Infrastruktur Publik Menggunakan Instagram @Tanggap.Karawang

Tanggap Karawang is an initiative launched by the Communication and Information Office of Karawang Regency to improve public services through the use of social media, particularly Instagram. The program aims to bridge communication between the government and the community, enabling quick responses to complaints and suggestions related to infrastructure and other social issues. Through Instagram, Tanggap Karawang focuses on disseminating up-to-date information, project transparency, and community involvement in maintenance and oversight processes. This study analyzes the effectiveness of using Instagram as a communication and participation tool by evaluating the program's impact on public satisfaction and infrastructure improvement. The results show that Instagram is effective in enhancing government interaction and responsiveness to social and infrastructure issues, though challenges remain in content management and communication consistency. Recommendations for further improvement include optimizing content strategies, training social media teams, and increasing collaboration among various stakeholders

Scalp Electroencephalogram-Derived Involvement Indexes during a Working Memory Task Performed by Patients with Epilepsy.

Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject's engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients with epilepsy, coming from a public dataset by Boran et al., during a verbal WM task and to identify the most suitable location of the electrodes for this purpose. Cognitive engagement was evaluated by computing 37 engagement indexes based on the ratio of two or more EEG rhythms assessed by their spectral power. Results show that involvement index trends follow changes in cognitive engagement elicited by the WM task, and, overall, most changes appear most pronounced in the frontal regions, as observed in healthy subjects. Therefore, involvement indexes can reflect cognitive status changes, and frontal regions seem to be the ones to focus on when designing a wearable mental involvement monitoring EEG system, both in physiological and epileptic conditions.

Incipient fault detection based on dense feature ensemble net

With modern industrial processes becoming more and more complex, the occurrence of faults may cause unmitigated disaster. Therefore, incipient fault detection is very important and has attracted increasing attention Recently, a feature ensemble net (FENet) has been proposed based on a deep feature ensemble framework, which can detect partial notorious faults (such as faults 3, 9 and 15 in the Tennessee Eastman process (TEP)). However, the feature transformer layers in FENet can only mine the process information from the upper layer’s output rather than all features from previous layers. Therefore, a novel dense feature ensemble net (DenseFENet) is proposed to deeply extract the features of data for incipient fault detection in industrial process. DenseFENet firstly constructs base detectors with capabilities for extracting nonlinear features, such as one-class support vector machine, isolation forest, one-class back propagation neural network, one-class long short term memory network and one-class temporal convolutional network, etc. Then, a dense net structure with short paths between non-adjacent feature transformer layers is developed, improving the reutilization capability for shallow knowledge. In addition, a numerical simulation and TEP are adopted to demonstrate the performance of the proposed method. The fault detection rate of DenseFENet has improved by 5.05% and 3.27% correspondingly, revealing the superiority of this approach.

Adverse childhood experiences, brain efficiency, and the development of pain symptoms in youth.

Adverse childhood experiences (ACEs) are often reported by youths with chronic pain, and both ACEs and chronic pain disrupt how information is processed. However, it is unknown whether changes to shared neural networks underlie the relationship between ACEs and the development of pain symptoms. This study explored the relationships between ACEs, brain efficiency, and pain symptomology in youth. A community sample of youths aged 14-18 years underwent MRIs, answered trauma and pain questionnaires, and underwent pain sensory testing, twice, 3 months apart (Nbaseline = 44; Nfollow-up = 42). Sensory testing determined thresholds for mechanical and thermal stimuli. Global and local network efficiencies were evaluated using graph theory. Generalized estimating equations were applied to examine whether brain efficiency moderated the relationships between ACEs, pain intensity, and pain sensitivity (i.e., mechanical detection, heat pain, and temperature change thresholds). There was a significant interaction between ACEs and global brain efficiency in association with pain intensity (β = -0.31, p = 0.02) and heat pain (β = -0.29, p = 0.004). Lower global brain efficiency exacerbated the relationship between ACEs and pain intensity (θX → Y|W = -1.16 = 0.37, p = 0.05), and heat pain sensitivity (θX → Y|W = -1.30 = 0.44, p = 0.05). Higher global brain efficiency ameliorated the relationship between ACEs and pain intensity (θX → Y|W = 1.75 = -0.53, p = 0.05). The relationship between ACEs and pain symptomology was comparable to chronic pain phenotypes (i.e., higher pain intensity and pain thresholds) and may vary as a function of brain efficiency in youth. This stresses the importance of assessing for pain symptoms in trauma-exposed youth, as earlier identification and intervention are critical in preventing the chronification of pain. This article explores the relationship between ACEs, pain symptomology, and brain efficiency in youth. ACEs may affect how the brain processes information, including pain. Youths with lower brain efficiencies that were exposed to more ACEs have pain symptomology comparable to youths with chronic pain. Understanding this relationship is important for the earlier identification of pain symptoms, particularly in vulnerable populations such as youths exposed to trauma, and is critical for preventing the chronification of pain.

Cognitive and behavioral markers for human detection error in AI-assisted bridge inspection

Integrating Artificial Intelligence (AI) and drone technology into bridge inspections offers numerous advantages, including increased efficiency and enhanced safety. However, it is essential to recognize that this integration changes the cognitive ergonomics of the inspection task. Gaining a deeper understanding of how humans process information and behave when collaborating with drones and AI systems is necessary for designing and implementing effective AI-assisted inspection drones. To further understand human-drone-AI intricate dynamics, an experiment was conducted in which participants’ biometric and behavioral data were collected during a simulated drone-enabled bridge inspection under two conditions: with an 80% accurate AI assistance and with no AI assistance. Results indicate that cognitive and behavioral factors, including vigilance, cognitive processing intensity, gaze patterns, and visual scanning efficiency can influence inspectors' performance respectively in either condition. This highlights the importance of designing inspection protocols, drones and AI systems based on a comprehensive understanding of the cognitive processes required in each condition to prevent cognitive overload and minimize errors. We also remark on the visual scanning and gaze patterns associated with a higher chance of missing critical information in each condition, insights that inspectors can use to enhance their inspection performance.

Emergence of Confidence with Principles of Curiosity and Information Processing

Researchers in cognitive neuroscience, developmental cognitive neuroscience, educational psychology, educational technology, education theory, and other related fields collaborate in the emerging field of educational neuroscience, also known as neuroeducation. This interdisciplinary approach aims to explore the connections between psychological processes and education. By integrating basic discoveries in cognitive neuroscience with educational technology, researchers in neuroeducation strive to enhance curricula to foster curiosity and boost confidence levels in learners. The ultimate objective of neuroeducation is to generate both theoretical insights and practical applications that offer a fresh perspective on learning across various disciplines. The development of confidence is closely related to the principles of curiosity and information processing. When people are interested in a topic, they are more likely to seek out information and engage in learning experiences that can lead to deeper understanding. This information process allows people to analyze and understand the information they encounter, which in turn can increase their confidence in their knowledge and abilities. By embracing curiosity and honing their information-processing skills, people can develop a strong self-confidence that empowers them to take new challenges and pursue their goals with determination. This review study on curiosity have uncovered a fascinating insight into its mechanisms. Researchers found that curiosity aligns with a confidence function resembling an inverted U-shape, peaking when individuals have moderate confidence in their knowledge. Moreover, heightened curiosity drives individuals to actively seek out new information, showcasing the profound impact of curiosity on knowledge acquisition. This revelation holds immense promise for understanding human behavior and learning processes. This study focuses on boosting confidence in individuals through the application of curiosity and information knowledge processing techniques to elevate the standards of education and training across both traditional and modern methodologies. The integration of these principles is poised to revolutionize the learning experience, fostering a more dynamic and effective approach to knowledge acquisition and skill development. This innovative strategy holds immense potential to empower learners, educators, and trainers alike, paving the way for a more enriched and impactful educational landscape.