Sophisticated Features Research Articles

Presentation matters: Surface text features and text quality in written narratives of Dutch high school students with and without dyslexia.

Presentation features such as spelling, punctuation and handwriting can influence the evaluation of general text quality. High school students with dyslexia might therefore be at a disadvantage, as at least their spelling performance is typically poor(er). Furthermore, these students might show less sophisticated linguistic features of texts, such as word length and sentence complexity, that might also be related to text quality. We compared narratives written by Dutch high school students (mean age 13.7 years) with (n = 28) and without (n = 29) dyslexia. Students with dyslexia's texts contained more spelling errors and poorer handwriting quality, but not more punctuation errors. Teacher-rated general text quality was lower for the texts of students with dyslexia in uncorrected versions. When spelling and punctuation errors were corrected, no teacher-rated text quality differences emerged. No differences in linguistic text features were found. Furthermore, spelling, punctuation and, to a lesser extent, number of words per sentence clause were related to ratings of text quality across participants. These results confirm the influence of presentation features on text quality rating. They encourage teachers to be aware of this effect and emphasize the importance of spelling and writing support and interventions for students with dyslexia throughout education.

Imprisoned at Work: The Impact of Employee Monitoring on Physical Privacy and Individual Liberty

There are now a wide range of tools available to monitor employees, boasting sophisticated features like keystroke logging, geolocational tracking and video surveillance. From a legal perspective, employee monitoring is most commonly analysed in terms of the impact it has on an employee's control over their information and data, ie their ‘informational privacy’. This article argues that monitoring is a more complex and layered issue, requiring the consideration of other values as well. Specifically, we argue that there are two (by no means exhaustive) reasons why employee monitoring is concerning which go beyond the use of information and data. First, monitoring affects physical and spatial, not just informational, components of privacy. Second, monitoring can be so severe in extreme cases that it curtails individual liberty and constitutes a form of imprisonment. The article draws out the significant legal implications of each of these claims, while addressing counterarguments to our position based on employee consent and the idea that work has always involved constraints on individual freedom.

How technical and situational cues affect impulse buying behavior in social commerce? Evidence from bored consumers.

With the rise of social media and web technologies, users are increasingly spending time on browsing and purchasing on social commerce, particularly during idle moments of casual scrolling. Social commerce applications with sophisticated social features and security measures may tend to attract a significant number of highly engaged users. The purpose of this study is to find out whether customers will be interested in the content posted on the applications and generate impulse consumption when they are bored. Drawing on stimulus-organism-response framework, this paper explores how technical cues and situational cues affect impulse buying behavior in social commerce applications and the mediating impact of consumer-perceived values. Data were gathered from 395 respondents who frequently utilize and have shopping experience on social commerce applications. The PLS-SEM and fsQCA were applied to formulate and test the proposed hypotheses. The results of PLS-SEM reveal technical cues (ease of use, visual appeal and security) and situational cues (passing time and serendipity) positively influenced impulse buying. The results of fsQCA offer six solutions of different combinations of constructs which can lead to high impulse buying. These findings may extend existing research on impulse buying behavior and consumer psychology, offering valuable insights for marketers. They also point towards strategies for more effectively encouraging impulse purchase in digital retail environments, particularly among consumers who are browsing out of boredom.

FacePsy: An Open-Source Affective Mobile Sensing System - Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings

Depression, a prevalent and complex mental health issue affecting millions worldwide, presents significant challenges for detection and monitoring. While facial expressions have shown promise in laboratory settings for identifying depression, their potential in real-world applications remains largely unexplored due to the difficulties in developing efficient mobile systems. In this study, we aim to introduce FacePsy, an open-source mobile sensing system designed to capture affective inferences by analyzing sophisticated features and generating real-time data on facial behavior landmarks, eye movements, and head gestures - all within the naturalistic context of smartphone usage with 25 participants. Through rigorous development, testing, and optimization, we identified eye-open states, head gestures, smile expressions, and specific Action Units (2, 6, 7, 12, 15, and 17) as significant indicators of depressive episodes (AUROC=81%). Our regression model predicting PHQ-9 scores achieved moderate accuracy, with a Mean Absolute Error of 3.08. Our findings offer valuable insights and implications for enhancing deployable and usable mobile affective sensing systems, ultimately improving mental health monitoring, prediction, and just-in-time adaptive interventions for researchers and developers in healthcare.

AUTOMATIC MICROSLEEP DETECTION BASED ON KNN CLASSIFIER UTILIZING SELECTED AND EFFECTIVE EEG CHANNELS

Annually, the global economy suffers significant financial losses due to decreased productivity of work, accidents, and crashes in traffic resulting from microsleep. To reduce the adverse impacts of microsleep, it is necessary to have a discreet, dependable, and socially acceptable method of detecting microsleep episodes consistently throughout the day, every single day. Regrettably, the current solutions fail to match these specified criteria. Moreover, by utilizing sophisticated features and employing machine learning techniques, it is possible to process electroencephalogram (EEG) information in a highly efficient manner, enabling the rapid and successful detection of microsleep. The selection of an optimum channel and the use of a competent classification algorithm are crucial for effective microsleep detection. One unique channel selecting strategy has been introduced in the current study to evaluate the classifying accuracy of microsleep detection based on EEG. This strategy is based on correlation coefficients and utilizes the K-Nearest Neighbor (KNN) method. Furthermore, the Fast Fourier Transform (FFT) was employed for extracting the feature, so validating the endurance of the proposed technique. In order to enhance the speed of the microsleep detecting system, the study was performed using 3 distinct time windows: 0.5s, 0.75s, and 1s. The study revealed that the suggested approach achieved a classification accuracy of 98.28% within a time window of 0.5 seconds to detect microsleep using EEG signal. The exceptional effectiveness of the given system can be efficiently utilized in detecting microsleep using EEG signal.

Temporal Dynamics and Physical Priori Multimodal Network for Rehabilitation Physical Training Evaluation.

Sensor-based rehabilitation physical training assessment methods have attracted significant attention in refined evaluation scenarios. A refined rehabilitation evaluation method combines the expertise of clinicians with advanced sensor-based technology to capture and analyze subtle movement variations often unobserved by traditional subjective methods. Current approaches center on either body postures or muscle strength, which lack more sophisticated analysis features of muscle activation and coordination, thereby hindering analysis efficacy in deep rehabilitation feature exploration. To address this issue, we present a multimodal network algorithm that integrates surface electromyography (sEMG) and stress distribution signals. The algorithm considers the physical knowledge a priori to interpret the current rehabilitation stage and efficiently handles temporal dynamics arising from diverse user profiles in an online setting. Besides, we verified the performance of this model using a learned-nonuse phenomenon assessment task in 24 subjects, achieving an accuracy of 94.7%. Our results surpass those of conventional feature-based, distance-based, and ensemble baseline models, highlighting the advantages of incorporating multimodal information rather than relying solely on unimodal data. Moreover, the proposed model presents a network design solution for rehabilitation physical training that requires deep bioinformatic features and can potentially assist real-time and home-based physical training work.

Cyberbullying issues on a transgender Instagram account

Critical Discourse Analysis (CDA) of Fairclough was employed to analyze discourse regarding cyberbullying on Millen Cyrus's Instagram account, which has claimed herself as transgender. Most of the comments show negative responses. Some linguistic features are analyzed, such as 'bro,' 'laki,' 'tulen,' 'banci,' and many more, which refer to cyberbullying. With the sophisticated features of Instagram, it is no wonder that news could get about quickly. In the context of the situation, Millen could not avoid any attention from the media and the public as she is a part of Ashanty's family, an Indonesian singer and socialite. At the instructional level, it is easy to get more exposure from others since her account has been verified by Instagram, including Millen's followers and non-followers, as people quickly found her posts on the explore feature. The act of some people who put comments on a transgender account, like what they have done to Millen Cyrus, has also evidenced that their discourse is influenced by the existence of their culture, religion, family background, and so on.

Advancements in Research on the Classification of Benign and Malignant Breast Tumors Utilizing Ultrasound Radiomics and Deep Learning

This paper examines the applications of traditional machine learning and deep learning in the analysis of breast ultrasound images for tumor diagnosis and explores recent developments in multimodal imaging. Traditional machine learning efficiently classifies breast ultrasound images through preprocessing, feature extraction, and selection, utilizing classifiers such as support vector machines. However, the design of features is highly dependent and its application scope is limited. Deep learning methods, particularly convolutional neural networks, autonomously extract sophisticated features, demonstrating enhanced classification performance and generalization capabilities. For instance, they achieve diagnostic accuracies exceeding 90% in large-scale datasets, with some studies outperforming clinicians. Moreover, this study highlights that the multimodal analysis strategy, integrating breast ultrasound with shear wave elastography, compensates for the limitations of unimodal images and enhances diagnostic accuracy and reliability, signifying a significant advancement in the technology for early breast cancer diagnosis.

Comprehensive characterization of a novel small-molecule activator for the nuclear receptor Nur77: Chemical, molecular, and biological insights

The nuclear receptor Nur77 is a transcription factor belonging to the NR4A subfamily. Upon activation, it regulates a wide array of biological and pathophysiological processes by modulating the expression of its target genes. Previous findings have classified Nur77 as an orphan receptor because of the discovery of a structurally atypical ligand-binding domain and the lack of identification of an endogenous ligand. Nevertheless, recent studies have uncovered several endogenous, natural, and small synthetic molecules that can bind to and activate Nur77. However, developing selective and potent Nur77 activators remains a significant challenge. The discovery of novel and potential small synthetic molecules that modulate Nur77 activity will facilitate therapeutic interventions of Nur77 against several human diseases. In this study, we have reported the development of a novel and effective Nur77 ligand. Our data show that (1E,4E)-1,5-di(pyrazin-2-yl)penta-1,4-dien-3-one (PB) induces Nur77 transcriptional activity by interacting with a putative Nur77 ligand binding site by forming solid hydrogen bonding. Calculated chemical parameters denote that PB has sophisticated chemical features that will enhance its interaction with the Nur77 ligand-binding domain. Molecular docking simulations showed that PB fits in the Nur77 putative ligand binding site with solid hydrogen bonding, and molecular dynamics simulations indicate that PB binding would stabilize the Nur77 ligand binding domain. Further, in vitro studies revealed that PB increases Nur77 nuclear expression and activity, inhibits cigarette smoke-induced inflammatory phenotype of airway epithelial cells, and protects against apoptosis. These findings provide insights into developing an effective Nur77 small-molecule activator which could be developed into a therapeutic agent against inflammatory diseases.

An Appraisal Of Large-Scale Particle Tracking With Uncrewed Aerial Vehicles

Camera technology is rapidly enhancing within the last years, which led to the availability of sophisticated technical features within consumer cameras, that could only be found in scientific set-ups earlier. The present work explores the possibilities of utilizing the potentials of commercially-available camera drones as moving cameras for large-scale particle tracking velocimetry. We made use of the glare-point particle tracking approach introduced by Kaiser & Rival (2023), where only a single camera and natural illumination, e.g. the sun, could be used. The set-up demonstrated full suitability for large volumes in the order of 10-100 m³. The frame-to-frame camera movement, caused by the slight movement of the drone could be quantified and corrected by an imaged-based approach. The calibration strategy could be simplified due to the fixed set-up in the camera drone. The limits introduced through the tracer size and uncertainty caused by the glare-point approach are discussed accordingly. For the presented magnification and camera set-up given by the DJI Mini 3 pro, the limit in height can be determined when the two most dominant glare points collapse to one, which happened well above 10 m for the present set-up. The equipment together with the glare-point particle tracking approach is best suited for flow information extraction at large-scale facilities or difficult accessible terrain, where the current set-up could depict its strengths.

Enhancing Autonomous Vehicle Technology with YOLOv8

The system's products and features form the basis of the product search process. By splitting up and recording huge photos of low-quality images in high resolution, its performance may be easily balanced As machine learning advances quickly, powerful tools are capable of to take on more intricate, sophisticated, or profound features to address issues with legacy tools.. This project offers a new way to detect vehicles, pedestrians and traffic signs using only publicly available data. Because research requires long-term photographs (such as images shot in direct sunlight), it is challenging to incorporate research into the data, and confidence training is uncommon due in part to the nature of the data. We presents modification of the YOLOv8 model for training to improve accuracy. In that model, a number of constants and lossy components were employed. The reason behind this is that YOLOv8 works well utilizing mobile gadgets and requires less RAM management. Unity also provides additional support to simplify the conversion process.

Impact Analysis of using Natural Language Processing and Large Language Model on Automated Correction of Systems Engineering Requirements

AbstractThe increasing complexity of Electronic Control Units (ECUs) in the Automotive Industry due to the integration of more sophisticated vehicle features has made the Systems Engineering (SE) application a necessity to define and implement efficient solutions. In this context, requirements emerge as a critical part of the communication between cross‐functional teams. Thus, the more complex systems become, the more requirements are needed to define them. However, lack of information, misalignment and ambiguity on requirements impact the entire development process, resulting in issues later, harder to be fixed. Some studies are being applied to evaluate techniques using Natural Language Processing (NLP) and how it can replace extensive peer reviews, identifying weaknesses in requirements earlier in the process, avoiding wasted time and large financial losses. Normally, NLP is combined with templates such as Easy Approach Requirements to Syntax (EARS), or other rule‐based techniques such as INCOSE's requirements writing best practice rules to define metrics and assess the compliance of the requirements syntax automatically. The focus of this work is to enhance the use of requirements syntax assessment algorithm by combining NLP techniques with Large Language Models (LLMs) to provide automatically corrected requirements.

RFID-GSM Automatic Gate Control for Highly Secure Organizations

Abstract: In the current dynamic global landscape, security is paramount for organizations, particularly those entrusted with safeguarding sensitive data and valuable assets. Automatic gate control systems are pivotal in fortifying security and optimizing the operational efficiency of access points for highly secure organizations. By integrating Radio-Frequency Identification (RFID) and Global System for Mobile Communications (GSM) technologies, these systems provide sophisticated features that bolster security measures while enabling seamless access for authorized personnel. These automatic gate control systems provide a multi-layered approach to security, addressing various vulnerabilities and potential threats highly secure organizations face. By leveraging RFID technology, these systems enable efficient and reliable authentication of authorized personnel, minimizing the risk of unauthorized access or breaches. Additionally, the integration of GSM technology enhances the system's responsiveness and remote management capabilities, allowing administrators to monitor gate activities in real time and respond promptly to any security incidents or anomalies.

Medical Record Monitoring an Administration System

Abstract— A state-of-the-art software program called the Medical Record Monitoring and Administration System offers a consolidated platform for monitoring and managing medical data, hence streamlining the management of healthcare. Healthcare personnel can effectively handle patient data, keep track of medical histories, and guarantee regulatory compliance using this system. Medical records are kept accurate, current, and easily accessible thanks to the system’s sophisticated features, which also include safe data storage, automatic alarms, and real-time monitoring. This solution improves data management, lowers mistakes, improves patient care, and boosts operational efficiency by automating administrative activities. The Medical Record Monitoring and Administration System is a vital tool for healthcare businesses looking to increase the caliber and effectiveness of their services because of its user-friendly interface and strong security features. Keywords – Pregnancy care management, Monitoring of scheduled visits, Maternal health informatics.

Extraction of water bodies from high-resolution remote sensing imagery based on a deep semantic segmentation network

The precise delineation of urban aquatic features is of paramount importance in scrutinizing water resources, monitoring floods, and devising water management strategies. Addressing the challenge of indistinct boundaries and the erroneous classification of shadowed regions as water in high-resolution remote sensing imagery, we introduce WaterDeep, which is a novel deep learning framework inspired by the DeepLabV3 + architecture and an innovative fusion mechanism for high- and low-level features. This methodology first creates a comprehensive dataset of high-resolution remote sensing images, then progresses through the Xception baseline network for low-level feature extraction, and harnesses densely connected Atrous Spatial Pyramid Pooling (ASPP) modules to assimilate multi-scale data into sophisticated high-level features. Subsequently, the network decoder amalgamates the elemental and intricate features and applies dual-line interpolation to the amalgamated dataset to extract aqueous formations from the remote images. Experimental evidence substantiates that WaterDeep outperforms its existing deep learning counterparts, achieving a stellar overall accuracy of 99.284%, FWIoU of 95.58%, precision of 97.562%, recall of 95.486%, and F1 score of 96.513%. It also excels in the precise demarcation of edges and the discernment of shadows cast by urban infrastructure. The superior efficacy of the proposed method in differentiating water bodies in complex urban environments has significant practical applications in real-world contexts.

Semantic precision: crafting RDF-based digital editions for unveiling the layers of historical correspondence

Abstract This article proposes that Resource Description Framework (RDF) technology is well suited for representing and storing inherently connected epistolary data. We justify this proposition by creating an RDF-based digital edition of the correspondence of members of the Bernoulli dynasty and Leonhard Euler and depicting the benefits this type of edition provides, which are hardly achievable otherwise. We describe the ontologies defined to fully represent letters and illustrate how we formulated letters’ historical, scientific content, rich with mathematical formulae, markups, figures, and references, as RDF statements to facilitate complex queries common in the humanities field. Next, we outline the tools we developed for presenting these editions in an efficient and user-friendly form, hiding the complexity of the data structure from non-technical users while offering sophisticated analysis features to explore atoms of knowledge and their relations. Additionally, since the citability and durability of the studied digital sources play a crucial role for scholars who publish research on the editions, we explain the steps we undertook to ensure the sustainability of the RDF-based editions and to offer persistent citation possibility.

SJCIT Transport Management System

Innovative software developed with great care to maximize transportation inside of educational institution called the SJCIT Transport Management System (TMS). Acting as a single point of contact for all student and faculty transportation-related information, including records, routes, diversions, and more, this system includes sophisticated features like effective database management, user-friendly interfaces, and aesthetically pleasing design elements. Its rapid adaptation minimizes transition times by guaranteeing quick user onboarding. A key instrument in coordinating the smooth movement of people across multiple locations and schedules is the SJCIT TMS, which is centred around the promotion of safe, efficient, dependable, and sustainable transportation practices. Its adaptability makes it possible for it to function seamlessly in both standalone and integrated configurations, which makes it an essential tool for improving transportation management in learning environments

Smart Agrobot: An Integrated Approach to Seed Sowing, Pesticide Application

Abstract: A state-of-the-art multifunctional autonomous agricultural robotic vehicle intended to transform farming methods is presented by the suggested system. This robotic vehicle is mostly operated via Bluetooth, but it also has sophisticated navigation features like GPS and object detection sensors, which allow it to navigate precisely and cautiously over fields of crops. The autonomous irrigation system with soil sensors and intelligent seeding mechanisms for different crops are just two of the robotic vehicle's many features that make it stand out. Farmers have freedom thanks to the Bluetooth control capability, which enables remote operation and monitoring. Safety features protect workers' health and prevent equipment damage, such as emergency stop buttons and collision avoidance procedures. This robotic system also performs exceptionally well in drip irrigation management in places that receive rain, optimizing fertilizing depending on soil conditions, and controlling weeds using computer vision. The suggested method, which reduces human intervention and increases total production, is a comprehensive and intelligent approach to automated and sustainable agriculture with an emphasis on energy conservation, data logging, and analytics.

From Niche Narrative to Audio Blockbusters

From Niche Narrative to Audio Blockbusters

IoT Based Pipe- inspection, and Cleaning Robot

Abstract: The "IOT Based Pipeline Inspection and Cleaning Robot" is a new addition to the infrastructure maintenance lineup! Pipelines are vital for moving necessary materials in the field of infrastructure maintenance. Our "IOT-based pipeline inspection and cleaning robot" is an inventive IoT-based rover outfitted with an ESP32 camera for real-time streaming and a motor propeller for effective exploration and operation. Its purpose is to improve maintenance procedures and tackle the difficulties associated with pipeline inspection. The major goal of the IOT-based pipeline cleaning and inspection robot is to transform pipeline maintenance by offering an adaptable, affordable, and successful solution. Real-time pipeline exploration and inspection are made possible by the rover's sophisticated features, which improve the infrastructure's functioning and integrity.