Data Transfer Research Articles

A copula-based approach for multi-modal demand dependence modeling: Temporal correlation between demand of subway and bike-sharing

As a representative mode of shared mobility, bike-sharing serves not only as a convenient way to conduct short-distance trips in urban areas, but also as a feeder mode to public transit, forming the Bike and Ride (BnR) system. Conducting management for such a hybrid multi-modal system faces various challenges, including the complex interactions between bike-sharing and other modes, highly dynamic passenger demand, and the difficulty of accessing direct transfer data. To overcome such difficulties, our study proposes a framework for assessing the dependency between the two usage modes. Firstly, a Dynamic-Time-Warping-based (DTW) method is utilized to determine the catchment area (CA) between the two modes, allowing the BnR-related tendency similarity under a given time scale to be considered. Then, the patterns of probabilistic dependence between travel demand of the two modes are obtained by a copula-based approach, which separates correlations under specific usage levels from single modal demands. A case study on the multi-modal system formed by docked bike-sharing and subway in New York is conducted to validate the proposed framework. The tendency similarity is found to be most pronounced within 500 m on average under a 4-hour interval. For each formed station group (SG), the best-fitted copula type is selected, capturing the strong tail correlations present only at specific usage levels. The results show a variety of different correlation patterns within SGs, despite the close geographic locations they may share. Areas of potential transfer resistance between the two modes are identified, which is more evident in first-mile-related (FMR) activities. In contrast, the two modes display more weak connections in last-mile-related (LMR) activities. The obtained results can be utilized by bike-sharing service providers to analyze demand distributions and conduct efficient station-level rebalancing. Compared to previous methods, our proposed framework is computationally inexpensive since no direct transfer of data or complex inference network is required. It incorporates statistically significant spatial–temporal information, allowing for a more accurate determination of the bi-modal assessment range. Moreover, considering that single-mode influences are mathematically removed, the resulting correlation in principle links to the strength of the connections between the two modes. Therefore, it can be assessed as an indicator of the reliability of the multi-modal system.

The Future in Journalism and Mass Communication India

Journalism is assembling, handling and spread of news and data identified with the news to a group of people. The word applies to both the technique for inquisitive for news and the abstract style which is utilized to spread it. Mass communication is the investigation of how people and elements transfer data through broad communications to vast fragments of the populace in the meantime. International Journal of Media Journalism and Mass Communications (IJMJMC) is an internationally peer reviewed academic journal that publishes recent advancements in the field of Media Journalism and Mass Communications. We publish articles on topics like Media studies, Media influence, Communication studies, Social science, Citizen Journalism, Creative nonfiction, Journalism ethics and standards, Journalism genres, Non-profit journalism, Corporate media, Journalistic professionalism, Media balance, Media bias Peace Journalism, Social dynamics, Political commentary etc.

Single image dehazing method based on knowledge transfer and multi-branch data augmentation correction

Single image dehazing method based on knowledge transfer and multi-branch data augmentation correction

Monitoring The Effort Curve In Physical Education For Normal And Overweight Students Using Smartwatches And Mobile Applications

Study purpose. In physical education and sports lessons, correctly dosing effort is essential for optimising physical performance and adapting effort parameters, including breaks, in line with physiological principles. The study hypothesises that using smart-watches and mobile apps to monitor effort in middle school physical education lessons will reveal significant differences in effort and heart rate between overweight and average BMI students. Modern technology is expected to enhance understanding of students' physical exertion, facilitating adjustments to programs based on health status and fitness level. Material and method. The study involved 45 students (average age 13.5 years) using 15 Xiaomi 8 smart-watches, phones, Wi-Fi, the Mi Fit app, and other devices to analyse body composition. Data on heart rate and BMI were collected during lessons, which followed the same theme and effort dosage. The Mi Fit app provided detailed aerobic and anaerobic exertion analyses. Parental consent was obtained, and data collection was done under routine lesson conditions. The technology aimed to improve performance and customise programs, offering a personalised approach to physical education. Real-time data transfer enabled accurate analysis of physical effort, promoting healthier lifestyles and better lesson adaptation by teachers. Results. A significant interaction between heart rate and BMI steps (p < 0.01) shows different cardiovascular adaptations to exercise based on BMI. MANOVA confirmed the significant impact of these variables on heart rate, and post hoc comparisons showed differences between weight categories, except between overweight and extremely obese groups. These results highlight the need to tailor physical education programs to BMI categories to optimise cardiovascular health and performance. Conclusion. Results suggest that monitoring the effort curve using modern technologies, such as smart-watches and mobile apps, provides valuable information for adjusting and optimising physical education lessons, especially for overweight and obese students.

B-112 Regression Analysis Techniques for Assessing Calibration Reproducibility Within and Between Instruments. A Practical Example with Blood Ca²⁺ Assayed with Two cobas® b 221Analyzers

Abstract Background Reproducibility of the calibration curve within the analytical measurement range (AMR) is crucial for ensuring reliable patient values within and between laboratory instruments to detect shifts and trends of clinical significance. In this practical example for ionized calcium (Ca²⁺) assayed with two cobas b221® instruments, the equality of calibration lines and the verification with patient specimen comparison study was assessed with regression analysis techniques. Methods Instruments: Two cobas b 221 (Roche Diagnostics). Calibration material: COOX/MSS™ (Five levels, Roche Diagnostics). This was independently assayed either three or five times in parallel with both instruments. The patient specimens were obtained by venipuncture in Na/Li-heparin BD™ tubes (Becton-Dickinson). Upon receipt, specimens were assayed with both instruments within two minutes. The data were transferred to Minitab® (Version 21, Minitab Inc) statistical software and analyzed with polynomial ordinary least squares (POLS), orthogonal and locally weighted scatterplot smoother (lowess) regression analysis models. The aptness of the model was assessed with standardized residuals (std. res.) diagnostics. For acceptable total error the CLIA’s criterion target value ± 10% was adopted. Results While the POLS model did not show probabilistically significant differences between instruments (P=0.8), it showed probabilistically significant differences between years (P=0.003). However, this was due to a small MSE (= 0.003 mg/dL), which is not clinically significant. The test for linearity showed probabilistically significant lack-of-fit (P<0.0001) due to a small MS pure error (<0.0001 mg/dL). The lowess for the standardized deleted residuals by the fitted value showed a quasi-linear relationship. The multiple comparisons (P=0.3) and the Levene’s (P=0.4) tests did not detect probabilistically significant heteroscedasticity of the variance by reference value. The plot of the std. res. by the leverage showed two possible outliers (std. res.>|3|) and no influential observations (Hi<0.5). These diagnostics proved the aptness of the POLS model. The harmonization between the two instruments was verified with patient specimens. The orthogonal regression model showed in the interval 3-6 mg/dL a quasi-linear relationship between the values as obtained with the two instruments. The orthogonal regression line (y=0.1+1.02 n) and the OLS regression line (y=0.03+601 n) were very similar. There was no probabilistically significant lack-of-fit (P=0.5). The plot of the std. res. by the leverage showed one possible outlier (std.res.>|3|) and no influential observations. Additionally, the plot of the relative differences between the values as obtained with b221#9371 and b221#24796 by the values as obtained with b221#9371 showed only one value exceeding half of the total allowable error. Conclusions The POLS model clearly showed that the calibration lines for the two instruments were quasi-linear in the interval 2-10 mg/dL and reproducible within and between instruments. Additionally, this reliable performance was verified with patient specimens in the interval 3-6 mg/dL. The differences between values as obtained with each instrument were within half of the CLIA’s total allowable error. This clearly showed that equality of calibration lines ensured interchangeable patient values between instruments. Finally, electronic transfer of data and the availability of statistical software like Minitab was crucial for conducting this study.

Comparative analysis of data protection regulations in East African countries

Purpose Protection of personal data is integral to the digital economy, ensuring trust and privacy as its foundational elements. The purpose of this study is to analyze data protection laws in Tanzania, Kenya, Uganda and Rwanda to understand their legal frameworks and identify challenges hindering their effective implementation. Design/methodology/approach This study uses a comparative exploratory case study approach, analyzing legal frameworks of four East African (EA) countries through examination of legal documents, official reports and academic articles. The dimensions of analysis include registration, supervisory authority, data subject rights and cross-border data transfer regulations. Findings While all four EA countries are in the process of enacting data protection acts, they differ in scope, provisions and enforcement; more needs to be done to ensure mature data protection in these countries. The commonalities and distinctions in the legal frameworks are underscored, providing a mapping of data protection regulations in the EA region. Moreover, this study reports implementation constraints and areas for improvement. Practical implications The findings of this study provide valuable insights for policymakers, highlighting areas where data protection regulations can be improved. The results of this study can guide harmonizing regional data protection laws, ensuring consistent and effective enforcement. This study offers a foundation for future policy development and regional cooperation on data protection issues. Social implications The social implications of this research lie in its potential to shape public attitudes on data protection and privacy rights. By highlighting these concerns, this study may influence societal norms and values, encouraging a more informed and conscientious public discourse on inclusive policies that consider the diverse needs of different regional populations. Originality/value This study provides a pioneering comparative analysis of data protection regulations across four EA countries, offering unique insights into the regional variations and commonalities in legal frameworks. Its value lies in informing future policy development, enhancing regional cooperation and contributing to the harmonization of data protection practices in the selected EA countries, which remains an under-explored area in existing literature.

B-146 Unlocking the potential of large-cohort proteomics studies with the Orbitrap Astral mass platform

Abstract Background Large-cohort proteomics analysis using mass spectrometry is a powerful approach to discover and validate new biomarkers. In combination with clinical data and computational analysis, large-cohort proteomics study brings opportunities in improving early diagnosis, refining patient stratification, and predicting/monitoring treatment response. Yet, to achieve meaningful biological insights in large-cohort studies, robust, reproducible, and comprehensive proteome profiling in a high-throughput manner still remains challenging. Here, we use a high-resolution accurate mass (HRAM) Oribtrap Astral platform to enable high-quality and robust protein quantification across thousands of LC-MS/MS analyses. With a throughput of 100 samples/day, we can reproducibly profile ∼9000 proteins from human cell line and ∼800 proteins from undepleted plasma across multiple instruments and more than 10 consecutive days in a 24/7 operation mode. Methods Experiments were performed on multiple Orbitrap Astral mass spectrometers with and without FAIMS Pro interface. Chromatographic separations were performed using a trap/elute method on Vanquish Neo system at a 11-minute gradient and a flow rate of 1ul/min, resulting in a throughput of 100 samples/day. Undepleted plasma digest was analyzed in a 24/7 mode for > 1000 injections on each LC-MS/MS setup. In addition, HeLa digest as quality control (QC) was analyzed every 12hours. The Oribtrap Astral platform was operated in data-independent acquisition (DIA) mode using a full scan with m/z 380-980, and DIA MS/MS scans with an isolation window of 2Th. Resulting DIA raw files were automatically transferred and processed using Chimerys in a beta version of Proteome Discoverer software. Results Multiple LC-MS/MS systems were operated in DIA mode either with or without FAIMS Pro device in a 24/7 operation mode at a throughput of 100SPD. Undepleted plasma digest was analyzed with >1000 injections on each LC-MS/MS setup. To monitor the baseline performance, HeLa digest served as QC were analyzed periodically every 12 hours with 3 technical replicates each time. To effectively manage and analyze these thousands of data files generated, we developed an automated data transfer and analysis pipeline. Automatically, the resulting DIA raw data files were immediately transferred to a server, then processed by state-of-the-art intelligent search algorithm Chimerys. Benefiting from the ultra-high scan speed of up to 200Hz on the Orbitrap Astral mass spectrometer, a much narrower isolation window width of 2Th was applied in the DIA method, comparing to 10-20Th on the classic DIA method. As a result, ∼9000 proteins from HeLa digest and ∼800 proteins from undepleted plasma digest were identified within only a 11-minutes gradient, respectively. More than 80% of the plasma proteins were reproducibly identified and quantified from all the runs on each LC-MS/MS setup, indicating a great reproducibility from run-to-run longitudinally. Stable and robust peptide quantitation was observed by extracting peptides with high, medium, and low abundant across the runs. Importantly, QC showed no performance degradation throughout the entire study, indicating high robustness of the entire LC-MS/MS setup. Conclusions Orbitrap Astral mass platform can comprehensively analyze the proteome of >1000s of sample robustly and reproducibly in a high-throughput manner, addressing the needs in large-cohort studies.

Data Sharing in Deutschland: Theorie, Empirie und europäische Gesetzgebung

Abstract Based on empirical surveys on the use and sharing of data in the German economy in 2021, 2022 and 2023, a number of current European laws, such as the General Data Protection Regulation of 2018, the Digital Markets Act of 2023, and the Data Regulation of 2024, are presented as instruments for increasing legal certainty in the transfer of data and the enforcement of protection of costly acquired data.

A hybrid framework for Detection of Multivariate porphyry Cu Signatures and Anomaly Enhancement: Incorporation of SFA, GMPI, and Grey Wolf Optimization

Geochemical data from stream sediment are commonly employed for regional scale mineral exploration, as they can be utilized to detect geochemical anomalies associated with mineralization processes. However, recognizing efficient multielement geochemical signatures related to target mineralization using stream sediment geochemical data can be challenging due to the intricate nature of mineralization events. To achieve this goal, this article proposes a combination of statistics, logistic functions and machine learning techniques. Initially, the staged factor analysis (SFA) approach was applied to the data to determine the geochemical footprints associated with porphyry copper mineralization in the Varzaghan area. Subsequently, the geochemical mineralization probability index (GMPI) was utilized to facilitate appropriate separation, more precise identification, and the transfer of data into a fuzzy space. By using a combination of SFA and GMPI, it was feasible to enhance the detection of geochemical halos and concealed anomalies. Ultimately, the output of these two approaches was subjected to an improved clustering technique known as GWO-K-means to detect geochemical anomalies. The findings indicated that there is a strong relationship between known mineral occurrences (KMOs) and the identified anomalous areas. The success rate curve demonstrated that the optimized model outperformed the K-means model, proposing that optimization can boost the accuracy in identifying geochemical targets. Overall, the outcomes of this study can be beneficial for proper planning of future exploration activities.

High precision aerodynamic heat prediction method based on data augmentation and transfer learning

Data-driven modeling methods have become one of the main technologies for predicting aerodynamic heat in hypersonic conditions. However, due to the limitations of wind tunnel experimental conditions, the spatial distribution of aerothermal wind tunnel experimental data is often sparse, and the sample size is relatively small. Furthermore, there is a lack of direct correlation in the aerodynamic heat distribution data among different shapes of vehicles, which poses challenges for constructing high-performance data-driven aerodynamic heat prediction models. To address these issues, this paper proposes a high-precision aerodynamic heat modeling and prediction method based on data augmentation and transfer learning. First, integrating the concept of data fusion, we propose to enhance the sparse aerothermal wind tunnel experimental data by using deep neural networks and introducing low-precision numerical computation results. Next, based on the close physical correlation between boundary layer outer edge information and wall surface aerodynamic heat, we construct the aerodynamic heat prediction model ED-ResNet using a double-series residual neural network. Finally, by fine-tuning the ED-ResNet model for transfer learning, high-precision predictions of aerothermal wind tunnel experimental results for different shaped vehicles are achieved under small sample conditions. Verification using hypersonic double-ellipsoid, blunt cone, and blunt bicone shows that after data augmentation, the prediction error of the aerodynamic heat prediction model is significantly reduced to 1/3 of that when data augmentation is not used. Moreover, through transfer learning, the model effectively leverages existing hypersonic double-ellipsoid aerothermal wind tunnel experimental data to achieve high-precision predictions of aerodynamic heat distribution for blunt cone and blunt double cone under different incoming flow conditions, with normalized root mean square error(NRMSE) maintained below 10%.

A Multi-Agent Reinforcement Learning Blockchain Framework for Improving Vehicular Internet of Things Cybersecurity

The Vehicular Internet of Things (VIoT) is a novel idea in the field of connected transportation systems that defines a new paradigm. Nevertheless, even the most modern and complex system will require additional and more powerful layers to protect the conversation from interception and the data from leakage. The centralized models have problems like trust problems and that there might be vulnerabilities and that is why there are attempts to integrate decentralization in operation. The first challenge in the VIoT networks is that the security and openness of such a connection and data transfer are still not well developed. Another issue is the security and dependability of the interaction between the vehicles and the infrastructure, although this issue is magnified by the size of the VIoT network. This research is a blockchain and game theory base research that uses Multi-Agent Reinforcement Learning (MARL) to improve the security and efficiency of the VIoT ecosystem. The technology of blockchain gives a distributed ledger where data cannot be altered or erased. Moreover, the MARL architecture allows for the realisation of better decisions for each of the members of the network. To this, the set that was made up of the smart contracts, Vehicle Units (VUs) and the decentralized servers that form the proposed architecture would be added to allow for the right flow and processing of the data. The blockchain’s decentralized nature provides a guarantee for all secure, immutable data transfers and transparent transactions throughout the network of the VIoT. MARL enables agents to learn and acquire the best strategies as they pass through time, which leads to secure and effective communication among entities. Besides, the implementation of lightweight cryptography techniques and strategic selections according to game theory help to protect and improve the performance of the security system of the VIoT ecosystem.

Patient Experience and Feasibility of a Remote Monitoring System in Parkinson's Disease.

Remote monitoring systems have the potential to measure symptoms and treatment effects in people with Parkinson's disease (PwP) in the home environment. However, information about user experience and long-term compliance of such systems in a large group of PwP with relatively severe PD symptoms is lacking. The aim was to gain insight into user experience and long-term compliance of a smartwatch (to be worn 24/7) and an online dashboard to report falls and receive feedback of data. We analyzed the data of the "Bringing Parkinson Care Back Home" study, a 1-year observational cohort study in 200 PwP with a fall history. User experience, compliance, and reasons for noncompliance were described. Multiple Cox regression models were used to identify determinants of 1-year compliance. We included 200 PwP (mean age: 69 years, 37% women), of whom 116 (58%) completed the 1-year study. The main reasons for dropping out of the study were technical problems (61 of 118 reasons). Median wear time of the smartwatch was 17.5 h/day. The online dashboard was used by 77% of participants to report falls. Smartphone possession, shorter disease duration, more severe motor symptoms, and less-severe freezing and balance problems, but not age and gender, were associated with a higher likelihood of 1-year compliance. The 1-year compliance with this specific smartwatch was moderate, and the user experience was generally good, except battery life and data transfer. Future studies can build on these findings by incorporating a smartwatch that is less prone to technical issues.

Developing Low-Cost LoRaWAN Internet of Things Devices for Water Resources Monitoring in Bali

In this study, we developed a solar-powered prototype using an ESP-32 MCU, commercially available sensors, and a LoRaWan communication module. The components cost less than $30 USD. The prototype has been running on solar power for two months in room conditions, repeating the sleep-wake cycle and transmitting sensor data - temperature, battery %, light color, and accelerometric data - every ten minutes over LoRaWAN to a cloud data storage. While the data only reflect room settings, and not real environmental data, the operating record demonstrates steady behavior, power autonomy, and data transfer, which is a necessity for IoT devices that monitor water supplies in the field. In the future, the developed devices will be used in Bali, Indonesia, to monitor the hydrological status during an impending water crisis.

Performance Analysis of Compression Algorithms on Matrix Data: Data Transfer Optimization in Microservices Architectures

With the rapid proliferation of microservices architectures these days, the efficient and fast transfer of large matrix data between services has become a significant challenge. This study presents an analysis aimed at finding solutions to this challenge. The analysis addresses the compression and decompression of large matrix data, focusing on lossless compression algorithms to optimize data transfer without data loss. The study is implemented on an example scenario. This scenario is taken from a project with a microservice architecture. In the example scenario, an image processing service developed in Python programming language generates 640x480 matrix data. After going through a compression algorithm, this data is periodically transferred to a backend service developed in C# programming language. This data is then stored in a database. In the final stage, decompression operations are performed so that this data can be used for reporting. The performance of various compression algorithms in the data compression, database storage and report generation stages is extensively tested. Within the scope of the study, tests were performed using five different compression algorithms (Gzip, Zlib, Deflate, Brotli and Bz2). The results are obtained through performance tests aimed at determining the most optimized end-to-end solution. Analyzing the performance of the compression algorithms on the example scenario, the Brotli algorithm gives the most optimal result in terms of both speed and compression size. This work makes an important contribution to data transfer optimization in microservice architectures and provides a reference for research in this area by presenting the performance analysis of various compression algorithms.

An initial approach to industry 5.0

Industry 5.0 focuses on human-system collaboration and a more integrated approach to manufacturing and services. Digital twin is a technological advancement positioned to propel Industry 5.0 by making way for newer implementations and greater cyber-physical connections within a system. Digital twins will lead the way as Industry 5.0 encourages a closer integration of digital and physical systems. They will act as a link between the digital and physical worlds, giving each, real-time information and insights. Within Industry 5.0, digital twins are essential for establishing communication between the digital and physical realms as well as for facilitating human-autonomous system cooperation and cohabitation. Industry 5.0 highlights the requirement for adaptable manufacturing to satisfy specific client needs. Manufacturing processes can be swiftly and effectively adjusted with the use of digital twins. Industry 5.0 relies heavily on digital twins to help businesses adapt to the changing manufacturing environment and use data-driven insights to increase productivity, flexibility, and human-robot collaboration. The aspects of industry such as the commissioning and the automation of the Industrial Work floor processes are to be integrated with the digital twin. The digital twin allows for the virtual commissioning and integration of humans through interactive mixed reality that transcends the user experience making the process of integration and data (instruction) transfer seamless. The main focus of the paper was on utilizing the digital twin for the connection of various aspects of an industrial factory to establish control and real time monitoring for a Cyber-physical system. The digital Twin of a material handling system was developed and used for virtual commissioning and Mixed Reality integration and exploration.

The concept of effective coverage radius use of the unlicensed high-frequency range in the operation of the 5G network

Economic expediency encourages mobile operators to deploy 5G networks in places with a high concentration of speed-demanding subscribers. In such conditions, sharp fluctuations in the volume of traffic with regulated requirements for the quality of service are inevitable. Note that 5G operates in the millimeter range. Accordingly, the quality of traffic service is affected both by the number of subscribers simultaneously initiating requests from one sector of coverage, and by the appearance of obstacles opaque to radio radiation in the space between the subscriber device and the base station. Effective smoothing of 5G traffic fluctuations, taking into account these disturbing factors, is an urgent task. The goal of this research is to evaluate the service quality parameters in a target area characterized by a specific user density. It takes into account that if the declared QoS requirements for connection speed for users in a network segment deployed within the licensed frequency range for 5G are not met, they can utilize a network segment deployed in the unlicensed high-frequency range for 5G under conditions of free competition. The metric being studied is the probability of session loss in the licensed network segment and the achievable transmission speed in the unlicensed network segment. Based on this, a method for assessing the density of base station deployment in the unlicensed network segment necessary to support the specified user density in the licensed network segment with defined QoS guarantees in terms of bandwidth is formalized. The experiment results showed that the probability of losing sessions with regulated requirements for the quality of service in both network segments, in addition to the base station placement density and subscriber devices, is significantly affected by the minimum data transfer rate, the intensity of obstacles, and the value of the Contention Window.

Integration of Inventory Data from Cohort and Registry Studies into an Existing Research Network: National Pandemic Cohort Network (NAPKON)

In the early phase of the COVID-19 pandemic, many local collections of clinical data on patients infected with SARS-CoV-2 were initiated in Germany. As part of the National Pandemic Cohort Network (NAPKON) of the University Medicine Network, the "Integration Core" was established to design the legal, technical and organisational requirements for the integration of inventory data into ongoing prospective data collections and to test the feasibility of the newly developed solutions using use cases (UCs). Detailed study documents of the data collections were obtained. After structured document analysis, a review board evaluated the integrability of the data in NAPKON according to defined criteria. Of 30 university hospitals contacted, 20 responded to the request. Patient information and consent showed a heterogeneous picture with regard to the pseudonymised transfer of data to third parties and re-contact. The majority of the data collections (n=13) met the criteria for integration into NAPKON; four studies would require adjustments to the regulatory documents. Three cohorts were not suitable for inclusion in NAPKON. The legal framework for retrospective data integration and consent-free data use via research clauses (§27 BDSG) was elaborated by a legal opinion by TMF - Technology, Methods and Infrastructure for Networked Medical Research, Berlin. Two UCs selected by the NAPKON steering committee (CORKUM, LMU Munich; Pa-COVID-19, Charité- Universitätsmedizin Berlin) were used to demonstrate the feasibility of data integration in NAPKON by the end of 2021. Quality assurance and performance-based reimbursement of the cases were carried out according to the specifications. Based on the results, recommendations can be formulated for various contexts in order to create technical-operational prerequisites such as interoperability, interfaces and data models for data integration and to fulfil regulatory requirements on ethics, data protection, medical confidentiality and data access when integrating existing cohort data. The possible integration of data into research networks and their secondary use should be taken into account as early as the planning phase of a study - particularly with regard to informed consent - in order to maximise the benefits of the data collected.

A human-centered intelligent platform for holographic control and management of 3D printers

Under the wave of emerging human-centric concepts such as Industry 5.0 and Human-Centered Intelligent Manufacturing, Mixed Reality (MR) has emerged as a pivotal enabling technology with profound impacts on human–machine interaction. Simultaneously, the affordability of Fused Deposition Modeling (FDM) 3D printers has expanded their accessibility beyond industrial applications to homes and public spaces. However, the operational and management complexities of different 3D printers and their varying human–machine interfaces (HMI) present challenges for users, especially beginners. In this paper, we presented a human-centered intelligent platform for FDM 3D printer control and management enabled by Mixed Reality. A flexible system architecture is developed in the paper with two essential communication protocols, MQTT and WebSocket, for stable data transfer. In addition, a task-oriented HMI is explored on the MR headset to simplify the operation procedure with little training effort and allow parallel supervision of multiple machines. By leveraging the advantages of our architecture, real-time monitoring, failure detection, and hologram visualization were able to be integrated into our platform for intelligent operation and management. Importantly, the flexibility of the architecture via a holographic interface enables our platform to easily expand to various machining operations in the future.

Advancing Iot Security: A Novel Intrusion Detection System for Evolving Threats In Industry 4.0 Using Optimized Convolutional Sparse Ficks Law Graph Point Trans-Net

With the rapid advancement of Industry 4.0, the integration of Internet of Things (IoT) strategies in industrial environments has increased exponentially. While this integration enhances productivity and efficiency, it also introduces significant security vulnerabilities. Previous research has employed several deep learning approaches for intrusion detection; however, these methods often suffer from insufficient accuracy, increased computational time, complexity, and higher error rates. To address these issues, this work proposes an innovative solution: "Advancing IoT Security: A Novel Intrusion Detection System (IDS) for Evolving Threats in Industry 4.0 using optimized Convolutional Sparse Fick's Law Graph Pointtrans-Net (CSFLGPtrans-Net)." The proposed system utilizes a comprehensive intrusion detection dataset composed of four different datasets: ToN-IoT, NSL-KDD, CSE‑CIC‑IDS2018, and IoT_bot. Initially, the input data undergoes a pre-processing stage that includes cleaning columns and rows, encoding features, and normalizing data. Following this, a hybrid optimization method, combining the Fire Hawk Optimizer with the Spider Wasp Optimizer, is applied for feature selection. This step is crucial for identifying the most significant features to enhance classification accuracy. The refined data is then classified using the CSFLGPtrans-Net model. To ensure secure data transfer, Fuzzy-based Elliptic Curve Cryptography (FECC) is employed. Experimental simulations conducted on the Python platform demonstrate that the proposed method outperforms existing approaches across various performance metrics, achieving a higher accuracy of 98% and a recall of 0.993. These results highlight the method's superior efficiency and potential for further advancement in securing Industry 4.0 environments.

A EVALUASI KINERJA CWDM 8 CHANNEL DENGAN VARIASI PANJANG GELOMBANG: FOKUS POWER LINK BUDGET, SNR DAN BER

The need for internet connection continues to grow and is the main priority of the Indonesian population. The use of fiber optic as a transmission system aims to increase data transfer speed and is resistant to interference. The use of CWDM technology can overcome high traffic volumes by dividing the wavelength. PT. Surya Citra Media has a network that connects the SCTV tower office with the IVM office in Daan Mogot. This study examines the performance of the CWDM fiber optic network link SCTV Tower - IVM Daan Mogot. The CWDM used has a capacity of 8 channels with a wavelength of 1310nm to 1450nm with a channel spacing of 20nm. This study was conducted to evaluate the 8-channel CWDM fiber optic network through performance measurements. The performance of the fiber optic network is focused on the parameters of the power link budget, SNR and BER. The results showed a power link budget of -9.407 dB and an SNR value with the smallest value of 37.0864 and the largest of 37.6150 dB. While the BER value ranges from 10-10 to 10-11. So that the network can be declared feasible based on the evaluation results.