Real-time Issues Research Articles

Mental Health Analysis System Using Large Language Model

Traditional mental health surveys in educational settings often provide a limited perspective on students' mental well-being, relying on standardized questions that fail to capture the complexity and individuality of each student's experience. To address this, a system leveraging Large Language Models (LLMs) has been developed to dynamically generate personalized and adaptive questions in real time, based on students' previous responses. This personalized approach fosters deeper engagement and provides a more nuanced understanding of students’ mental health. The system's adaptive nature ensures that each student’s unique experiences and concerns are addressed, rather than relying on one-size-fits-all questionnaires. The data collected through this system is presented in an accessible, visually intuitive format, allowing educators to interpret the results quickly and effectively. This empowers them to adapt teaching strategies and implement targeted interventions that address specific mental health needs within the classroom. By integrating this system into educational settings, academic institutions can foster a more responsive and inclusive learning environment that prioritizes students' mental health. This approach not only enhances students' academic success but also promotes resilience and engagement by addressing mental health issues in real time. The system demonstrates the transformative potential of personalized AI in education, offering a scalable solution for improving student outcomes. It underscores the critical role of AI in creating healthier, more adaptive educational ecosystems capable of responding to the evolving mental health challenges faced by students today.

Network intrusion detection in big datasets using Spark environment and incremental learning

<p class="Abstract">Internet of things (IoT) systems have experienced significant growth in data traffic, resulting in security and real-time processing issues. Intrusion detection systems (IDS) are currently an indispensable tool for self-protection against various attacks. However, IoT systems face serious challenges due to the functional diversity of attacks, resulting in detection methods with machine learning (ML) and limited static models generated by the linear discriminant analysis (LDA) algorithm. The process entails adjusting the model parameters in real time as new data arrives. This paper proposes a new method of an IDS based on the LDA algorithm with the incremental model. The model framework is trained and tested on the IoT intrusion dataset (UNSW-NB15) using the streaming linear discriminant analysis (SLDA) ML algorithm. Our approach increased model accuracy after each training, resulting in continuous model improvement. The comparison reveals that our dynamic model becomes more accurate after each batch and can detect new types of attacks.</p>

Enhancement of Patient Engagement and Healthcare Delivery Through the Utilization of Artificial Intelligence (AI) Technologies

Background: Patient engagement refers to the actively involvement of individuals in their own treatment, decision-making, and partnering with healthcare providers. Due to the progress of Artificial Intelligence (AI), there is a groundbreaking shift happening in healthcare that is bringing numerous benefits to patients and health systems. AI technologies are not only used as instruments in healthcare but also evolving into collaborators for diagnosing and treating patients, as well as enhancing the quality of services, offering a personalized, timely, and interactive healthcare experience. This article delves into how AI is changing patient engagement by increasing effectiveness and catering to individual patient requirements. Methods: The research made use of previously published materials on artificial intelligence, data, and robotic technologies in healthcare settings to explore ways in which patients can be effectively involved in the advancement of these technologies in healthcare settings. Results: AI-powered decision support systems improve healthcare operations by giving instant access to data analysis and medical advice, ultimately aiding in making decisions based on real-time evidence. AI enables proactive healthcare interventions by detecting potential health issues in real time, allowing for remote patient monitoring. Motivating patients to actively participate can result in improved adherence to the treatment plan. Overall, patient engagement appears to be the most well-developed and progressive concept for enabling patients to participate actively in their healthcare. Conclusions: AI technologies are more than just tools in healthcare; they are evolving into collaborators in patient involvement, providing a personalized, proactive and engaging healthcare journey. Further evidence is required to comprehend how patients engage in the process and if this leads to better quality of care. In the dynamic health sector, change management is essential for continuously updating and adjusting healthcare facilities to address the evolving needs of patients. Health systems that do not adjust to changes in a timely manner are unsuccessful.

FPGA implementation of curve fitting algorithm for image diffusion spectrum

Abstract In recent years, the application of image diffusion spectrum algorithm for high-concentration particle systems such as suspensions, coatings, emulsions, gels, etc. in multiple fields such as pharmaceuticals, energy, chemical industry, and food has become more and more extensive. Although the relevant technology has made significant progress at the algorithm level, the real-time and portability issues of the system are still the key factors restricting its application potential. The traditional image diffusion wave system needs to transfer the data to the host computer for processing after the image processing is completed, which has problems such as clock delay and large system volume. To this end, we proposed a fitting algorithm based on the image diffusion spectrum. By directly processing the data obtained in image processing in FPGA, clock delay is avoided and the miniaturization of the instrument is also achieved. Through comparative experiments, we can see that our algorithm can achieve the same processing effect on the host computer.

Detecting and evaluating surface defects in additive manufacturing composite materials via image processing technique

Purpose This study aims to offer an image-based robust edge detection system that can estimate, identify, locate and label surface flaws during manufacturing for real-time surface issue diagnostics. Of great concern, this methodology extrapolates surface defect information from scanning electron microscopy (SEM) images of composite fracture surfaces. This study predicts changes in topological intensity of composite fracture surfaces and display them as real-time surface intensity values for the first time. Design/methodology/approach This work, however, introduces a novel robust edge detection method – based image processing – as it is shown to be effective in locating defects, as measured by SEM images of composite fracture surfaces created using additive manufacturing (AM). SEM images, obtained in this study, are related to previous study considering the fracture surfaces of reinforced thermoset composites created via the AM method. These SEM images are of two types: fracture surface of AM of carbon fiber reinforced thermoset composites and fracture surface of AM of syntactic foam reinforced thermoset composites. Initially, MATLAB environment is used for analyzing the SEM images; the technique used, as well as the validity are explained more in the methodology section. Findings The robust surface defect inspection approach used herein is found to be capable of predicting, identifying, localizing and labeling surface defects during production, allowing for real-time surface issue diagnosis. Further, this work makes it possible to use image processing and analysis of these surfaces to anticipate fluctuations in the topological intensity of the fracture surfaces of composites and represent them as values of surface intensity in real time. Originality/value Rising worldwide company rivalry requires a fast, accurate component failure diagnostic method. To create an efficient feature set, a surface defect inspection system must identify product flaws in real time. Thus, this study proposed an image-based robust edge detection system – based on MATLAB environment – that is capable of estimating, identifying, locating and labeling surface faults during production. This paves the way for an extensive set of high-quality tools for dealing with a wide range of problems associated with digital image processing in composites. As a result, the ability to define methodologies and rapidly prototype prospective solutions typically minimizes the cost and time required to implement a successful system during the design phase of an image processing system.

Application-Level Evaluation of IEEE 802.1AS Synchronized Time and Linux for Distributed Real-Time Systems

The use of Ethernet and Linux is becoming common in industrial applications, even for those with real-time requirements, although neither of them were originally designed for this purpose. The emergence of Industry 4.0 (also known as Industrial Internet of Things, IIoT) has encouraged the evolution of these technologies to better handle real-time issues. On the one hand, Linux now supports mechanisms to configure certain real-time parameters, as well as core isolation and interrupt allocation facilities in multicore processors. On the other hand, the set of Ethernet standards IEEE 802.1 Time-Sensitive Networking (TSN) includes a high precision clock synchronization protocol (IEEE 802.1AS). The purpose of this work is to outline an execution framework for distributed systems based on TSN and Linux, which allows the execution of time-aware applications. We have studied and evaluated different configurations available for the proposed execution framework. In particular, a detailed characterization of the clock synchronization mechanism, from the application point of view, has been performed. Some conclusions about the current real-time capabilities of these technologies are also presented.

Limits of India–US Relations: Balancing Through Strategic Autonomy and Multi-alignment

The India–US relationship has often been described as a defining partnership. But the nature of the friendship in actuality is yet to assume depth. Despite convergences at multiple levels, it falls short of a true strategic partnership or deep friendship. The India–US relationship is neither a trusted nor an assured economic or security partnership. It is often tested by the vagaries of real-time issues, especially involving other great powers. That is why, despite signing the civilian nuclear deal, foundational agreements, being members of the QUAD and Indo-Pacific Economic Forum, and with the common objective of containing the rise of China, they are unable to work closely together on myriad bilateral, international and regional issues. There are differences over matters of primary interests and the type of partnership that each wants to establish. The United States was insensitive to Indian concerns in its abrupt drawdown from Afghanistan in 2021, sub-continental security threats emanating from the China–Pakistan axis and its continued military support to Pakistan. India’s neutral stand (read in the United States as being pro-Russian) in the Ukraine crisis marked by its abstention from voting against Russia in the UN and purchase of Russian oil despite strong objections from the US-led Western camp are cases in point. We argue that India’s US policy is a case of limited balancing dictated by compulsions and lacks depth. For rising India, a continued desire for strategic autonomy and multi-alignment—the post-non-alignment foreign policy principles—guides its foreign policy orientation. For Washington, a deeper relationship would imply India abandoning its visions of strategic autonomy and joining more formally in Washington’s orbit. These core national objectives of the two countries limit the level of partnership they can forge.

The blind HR: Why do people leave managers not companies? A case of inconsistency in performance appraisal and performance outcome

Learning outcomes The learning outcomes are as follows: to benchmark and compare the theoretical models of the performance management and appraisal processes. (Questions 1 and 2) Remembering-in Bloom’s Taxonomy; to understand the importance of practicing fair performance appraisal process. (Question 4) Understanding-in Bloom’s Taxonomy; to analyze the implementation and effectiveness of 180-degree performance appraisal method and rating system prevalent in the IT Sector. (Question 1) Applying and Analyzing-in Bloom’s Taxonomy; to assess the impact of perceptual biases on human behavior and performance (Questions 2 and 3) Evaluating-in Bloom’s Taxonomy. Case overview/synopsis The case study entitled “Is HR Blind? Why do People Leave Managers Not Companies? A Case of Unfair Performance Appraisal and Biases” is a classic example of a flawed and biased performance appraisal process and perceptual biasness, which resulted in the loss of a valuable and talented resource in a leading Indian IT MNC. The present case had been based upon the real-life experience of an employee (i.e. Rahul Verma), who worked with the company from year 2010 to 2021. It was among the top ten IT MNCs employing about 0.1 million people. The objective of the case was to highlight real time issues existing with HR practices, mainly in IT sector organizations. For example, in the present case, do the HR seek proper justification from the manager before taking a harsh decision like forcibly asking an employee to sign a termination contract without looking at the contributions of his qualitative performance or even performance rating (refer to the transcript) for that matter? Was the job of the HR to only ensure how to fit in employees in the faulty bell curve system? Whether the performance appraisal system being followed at the company is adequately capable of identifying and recognizing the talent. Do the different functions really work cohesively and organically toward achieving the intended goals and objectives of the organization? Was this a failure of the manager in recognizing talent or something went wrong at the employee’s part? Was this a failure of the entire HR system or performance management process at the organization that was unable to filter out the capable and skilled resources out of the crowd? Was this a problem of organizational culture that put on stake its most critical resource – the human capital – by allowing the appraisers to evaluate them just because of the hierarchical structure, and not because they are not being competent enough to perform this most critical job objectively? Who ensures the appraiser is free from any kind of prejudice or bias and is capable of fairly assessing the talent resource? So, the present case was a deliberate attempt to throw out these burning questions to the practitioners and students to ponder upon. Does HR really follow the blind process merely acting on the feedback received from the different units of the organization? With the help of strong theoretical foundation and practical applications, the following objectives and questions have been framed to deliberate and propose the workable solutions for the benefits of the relevant stakeholders. Complexity academic level HR practitioners, HR managers, supervisors, senior management and HR students, IT heads, project managers. Supplementary material Teaching notes are available for educators only. Subject code CSS 6: Human Resource Management.

Impact Tendency Characteristics of Borehole Coal Samples under Real-Time and Uniaxial Loading Conditions: Insights from Physical Experiments

Real-time drilling depressurization technology is widely used in the prevention and control of dynamic disasters, such as deep-seated rock burst. However, current coal- and rock-loading tests under drilling conditions seldom account for real-time issues associated with drilling, thus failing to fully reflect the actual stress state of the surrounding rock during the implementation of drilling depressurization technology. Therefore, this study designed and implemented a uniaxial loading scheme for coal samples incorporating real-time-drilling characteristics. The results indicate a significant reduction in the uniaxial compressive strength (RC), elastic energy index (WET), and impact energy index (KE) of the samples post-drilling. These parameters show a clear decreasing trend with increasing axial stress during real-time drilling. The weakening effect of impact tendency following real-time drilling depressurization is significant, and the depressurization effect is pronounced. The RC, WET, and KE of each real-time-drilled sample exhibit a notable decrease with increasing drilling stress, with the reduction rate significantly diminishing after the drilling stress reaches 20% of the peak strength.

Deep learning-powered visual place recognition for enhanced mobile multimedia communication in autonomous transport systems

The progress of autonomous transport systems (ATS) involves efficient multimedia communication for real-time data tradeoffs and environmental issues. Deep learning (DL) powered visual place recognition (VPR) was developed as an effective tool to improve mobile multimedia communication in ATS. VPR relates to the capability of a method or device to recognize and identify particular places or locations from the visual scene. This procedure involves inspecting visual data, like images or video frames, to control the unique features or features connected with diverse locations. By leveraging camera sensors, VPR allows vehicles to detect their surroundings, enabling context-aware communication and enhancing the entire system's performance. DL-empowered VPR offers a transformative manner to improve mobile multimedia communication in ATS. By identifying and understanding their situation, autonomous vehicles can communicate most effectively and operate reliably and safely, paving the way for a future characterized by seamless and intelligent transportation. This article develops a novel Deep Learning-Powered Visual Place Recognition for Enhanced Multimedia Communication in Autonomous Transport Systems (DLVPR-MCATS) methodology. The main aim of the DLVPR-MCATS methodology is to recognize visual places or not utilize optimal DL approaches. For this purpose, the DLVPR-MCATS approach utilizes a bilateral filtering (BF) based preprocessing model. For the feature fusion model, the DLVPR-MCATS approach follows three models: residual network (ResNet), EfficientNet, and MobileNetv2. Moreover, the hyperparameter tuning method uses the Harris Hawks Optimization (HHO) model. Finally, the bidirectional long short-term memory (BiLSTM) technique is implemented to recognize visual places. A wide range of simulations is executed to validate the solution of the DLVPR-MCATS method. The experimental validation of the DLVPR-MCATS method portrayed a superior performance over other models concerning various aspects.

Digital twin-enabled quality control through deep learning in industry 4.0: a framework for enhancing manufacturing performance

ABSTRACT In the context of Industry 4.0, integrating Digital Twin (DT) technology stands out as a critical challenge for enhancing manufacturing processes and productivity. The combination of DT and Artificial Intelligence (AI) provides a significant benefit for improving processes in real-time. Industries are actively researching these technologies to keep pace with the rapid evolution of technology, utilizing virtual representations for efficient real-time monitoring and control. The present paper proposes a new approach that relies on DT technology for monitoring, optimizing manufacturing processes and enabling quality control through Deep learning (DL). The proposed methodology involves creating a digital replica of the physical system and utilizing DL models for quality control purposes. This approach improves automation and productivity while maintaining high levels of quality assurance in factories. DL is deployed within the DT for gathering data from the physical system and making predictions regarding product quality. The approach is illustrated by considering an experimental industrial prototype. The results obtained are particularly intriguing, demonstrating heightened predictive accuracy in assessing product quality and real-time issue resolution. Overall, the findings underscore the significant and interesting impact of DT technology with DL on manufacturing processes in the context of Industry 4.0.

Design and Implementation of Software-Defined Receiver

Acquisition in software-defined GPS receivers is computationally intensive and time-consuming, especially with the increasing demand for rapid and accurate positioning services driven by globalization and digitization. Software-defined receivers face challenges in satellite signal acquisition, which becomes more demanding with longer signals. This paper aims to enhance the efficiency of GPS signal acquisition, a critical step for determining the code phase of PRN codes and the doppler shift in carrier frequency. Real-time applications encounter issues under dynamic conditions and degraded signals, as conventional computers lack the computational power for the necessary correlation and FFT operations. To address these challenges, a method using GPU acceleration for L1 signal acquisition is proposed. This method combines signal acquisition with GPU parallel computing using the SIMT model. A CPU-GPU platform via CUDA programming allows the CPU to handle data reading and intermediate processing, while the GPU performs the core acquisition algorithm in parallel.

Design and sensitivity analysis of GAA nanowire dopingless FET based label free biosensor

This paper proposes a highly sensitive nanoscale label-free biosensor upon charge plasma based gate-all-around nanowire dopingless field effect transistor (GAA NW DL FET Biosensor) for the detection of biomolecules. The proposed sensor device structure employs charge plasma and dopingless approaches to eliminate the requirement for doping. The thermionic emission of FET structures increases the device’s current sensitivity (SI) for various targeted biomolecules introduced through the nanocavity created at the source side/metal of the proposed biosensor. To test the significant sensing performance of the device proposed herein, the biomolecules streptavidin with a dielectric constant (K) of 2.1, 3-aminopropyltriethoxysilane (APTES) with a K value of 3.57, and protein with a K value of 8 are utilized. The proposed sensor resulted in drain current sensitivity as high as 1.4 of protein biomolecule. An extensive analysis was performed to evaluate the efficiency of the proposed sensor with process-related issues such as cavity length variations ranging from 21 nm to 3 nm and real-time related issues such as fill factor variations ranging from 20% to 100% and interface charges for the targeted biomolecules, and their sensitivity parameters were investigated, demonstrating the promising features of GAA NW DL FET biosensor as an ultrasensitive biosensor for clinical applications.

Object Detection for Unmanned Aerial Vehicles: A Comprehensive Review

Goal: Researchers studying artificial intelligence have focused a lot of emphasis on computer vision in drones. Drones with intelligence can tackle a lot of issues in real time. For the purpose of monitoring particular surroundings, computer vision tasks like object identification, object tracking, and object counting are important. It becomes increasingly difficult to do, though, due to elements like motion blur, occlusion, camera angle, and altitude. Methodology: A thorough assessment of the literature on object identification and tracking with unmanned aerial vehicles (UAVs) in relation to various applications has been done for this research. This study highlights the research gaps and provides a summary of the results of previous studies. Contribution: Detailed and categorized object identification techniques are used in UAV photos. A selection of UAV datasets tailored to object identification tasks is provided. Summaries of current research projects in various applications are provided. In order to alleviate highlighted research limitations, a secure onboard processing system on a strong object detection framework in precision agriculture is finally presented.

Development of a Decision Support System for Animal Health Management Using Geo-Information Technology: A Novel Approach to Precision Livestock Management

Livestock management is challenging for resource-poor (R-P) farmers due to unavailability of quality feed, limited professional advice, and rumor-spreading about animal health condition in a herd. This research seeks to improve animal health in southern Africa by promoting sericea lespedeza (Lespedeza cuneata), a nutraceutical forage legume. An automated geospatial model for precision agriculture (PA) can identify suitable locations for its cultivation. Additionally, a novel approach of radio-frequency identifier (RFID) supported telemetry technology can track animal movement, and the analyses of data using artificial intelligence can determine sickness of small ruminants. This RFID-based system is being connected to a smartphone app (under construction) to alert farmers of potential livestock health issues in real time so they can take immediate corrective measures. An accompanying Decision Support System (DSS) site is being developed for R-P farmers to obtain all possible support on livestock production, including the designed PA and RFID-based DSS.

AI in Healthcare: Revolutionizing Patient Care with Predictive Analytics and Decision Support Systems

This article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imaging enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real-time insights based on patient data and clinical guidelines, facilitating evidence-based decision-making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming patient care but also shaping the future of healthcare delivery.

Development of ETS (E-Ticketing System) Module for Issue Management

This study proposes the integration of an Employee Tracking System (ETS) module within a logistics company's existing web application to streamline internal management processes. Focused on a one-month internship, the research aims to address the inefficiencies of conventional communication methods by facilitating real-time issue tracking and resolution. Through collaboration between academia and industry, the study explores the practical implementation of information technology solutions to enhance organizational communication and problem-solving. Results indicate improved communication efficiency and problem resolution within the organization, highlighting the potential of such systems to contribute to the evolution of information technology in the workplace. Highlights : Implementation of ETS module improves communication efficiency. Collaboration between academia and industry addresses practical challenges. Integration of technology enhances problem-solving within organizations. Keywords: Organizational Communication, Employee Tracking System, Information Technology, Logistics Industry, Collaboration

Detection of Coal and Gangue Based on Improved YOLOv8.

To address the lightweight and real-time issues of coal sorting detection, an intelligent detection method for coal and gangue, Our-v8, was proposed based on improved YOLOv8. Images of coal and gangue with different densities under two diverse lighting environments were collected. Then the Laplacian image enhancement algorithm was proposed to improve the training data quality, sharpening contours and boosting feature extraction; the CBAM attention mechanism was introduced to prioritize crucial features, enhancing more accurate feature extraction ability; and the EIOU loss function was added to refine box regression, further improving detection accuracy. The experimental results showed that Our-v8 for detecting coal and gangue in a halogen lamp lighting environment achieved excellent performance with a mean average precision (mAP) of 99.5%, was lightweight with FLOPs of 29.7, Param of 12.8, and a size of only 22.1 MB. Additionally, Our-v8 can provide accurate location information for coal and gangue, making it ideal for real-time coal sorting applications.

AI in Healthcare: Transforming Patient Care through Predictive Analytics and Decision Support Systems

This article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imaging enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real-time insights based on patient data and clinical guidelines, facilitating evidence-based decision-making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming patient care but also shaping the future of healthcare delivery.

Interval-valued Fermatean Neutrosophic Graph with Grey Wolf Optimization for Sarcasm Recognition on Microblogging Data

Game theory is more popular in competitive situations due to its importance in decision making. Several kinds of fuzzy sets can manage uncertainty in matrix games. Neutrosophic set theory has been instrumental in investigating ambiguity, complexity, inconsistency, and incompleteness in real-time issues. Nowadays, sarcastic comments on social media have become a general tendency. Sarcasm is frequently used by individuals to pester or taunt others. It is often conveyed via inflection, tonal stress in speech, or lexical, hyperbolic, and pragmatic features existing in the text. Sentiment Analysis (SA) is regarded as the data mining targets of sentiment organization of the client's criticisms obtainable in textual form. Sarcasm is a form of speech that states an individual's downside feeling through a positive term. Labeling sarcasm in characters is a dynamic task for Natural Language Processing to evade the misconception of sarcastic speeches as a verbatim declaration. The outcome of these kinds of sarcastic speeches is hard for the people and machines. Sarcasm has a considerable influence on the efficacy of SA techniques that are impacted by mendacious sentiments that frequently belong to sarcastic classes. This study introduces an Interval-valued Fermatean Neutrosophic Graph with Grey Wolf Optimization for Sentiment Analysis (IFeNG-GWOSA) on Microblogging Data. The IFeNG-GWOSA technique includes a sarcasm detection technique that categorizes words in sarcastic or non-sarcastic form. The initial phase is preprocessing, where the tokenization and stop word removal are implemented. Then, the preprocessed data is subjected to feature extraction, where the BERT word embedding is applied. The IFeNG model is used for sarcasm detection, and the grey wolf optimizer (GWO) generates its parameter selection technique. Lastly, the efficiency of the presented technique is compared with existing approaches under different measures