Enhancing Privacy-Preserving Intrusion Detection in Blockchain-Based Networks with Deep Learning

Data transfer in sensitive industries such as healthcare presents significant challenges due to privacy issues, which makes it difficult to collaborate and use machine learning effectively. These issues are explored in this study by looking at how hybrid learning approaches can be used to move models between users and consumers as well as within organizations. Blockchain technology is used, compensating participants with tokens, to provide privacy-preserving data collection and safe model transfer. The proposed approach combines Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) to create a privacy-preserving secure framework for predictive analytics. LSTM-GRU-based federated learning techniques are used for local model training. The approach uses blockchain to securely transmit data to a distributed, decentralised cloud server, guaranteeing data confidentiality and privacy using a variety of storage techniques. This architecture addresses privacy issues and encourages seamless cooperation by utilising hybrid learning, federated learning, and blockchain technology. The study contributes to bridging the gap between secure data transfer and effective deep learning, specifically within sensitive domains. Experimental results demonstrate an impressive accuracy rate of 99.01%

MIPI 2023 Challenge on RGBW Remosaic: Methods and Results

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for an in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). With the success of the 1st MIPI Workshop@ECCV 2022, we introduce the second MIPI challenge, including four tracks focusing on novel image sensors and imaging algorithms. This paper summarizes and reviews the RGBW Joint Remosaic and Denoise track on MIPI 2023. In total, 81 participants were successfully registered, and 4 teams submitted results in the final testing phase. The final results are evaluated using objective metrics, including PSNR, SSIM, LPIPS, and KLD. A detailed description of the top three models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2023/.Comment: CVPR 2023 Mobile Intelligent Photography and Imaging (MIPI) Workshop--RGBW Sensor Remosaic Challenge Report. Website: https://mipi-challenge.org/MIPI2023/. arXiv admin note: substantial text overlap with arXiv:2209.08471, arXiv:2209.07060, arXiv:2209.07530, arXiv:2304.1008

Basic experimental research on the delineation of the evolutionary process of fault water inrush

Coal mining is seriously threatened by fault water inrush, showing the disaster characteristics of hysteresis and concealment. Mastering the evolution mechanism and process law of fault water inrush disaster has important theoretical guiding significance for carrying out deep water prevention and control work. By constructing water inrush evolution analysis model , the conduction path of confined water and the evolution characteristics of fault zone are analyzed. Using true triaxial rock test system of coupled stress-seepage , combined with acoustic emission and digital speckle technology, large-size rock-like specimens containing fault fracture zone fillings were designed. The instability and failure characteristics of specimens with different lithologic fillings and fault occurrences during biaxial loading were studied. The acoustic emission events and specimen deformation characteristics were obtained. Finally, the modified and upgraded mining floor water inrush simulation test system and parallel electrical on-line monitoring, stress and water pressure monitoring subsystems were used to reproduce the whole process of mining fault water inrush evolution in the laboratory, and the evolution characteristics of monitoring parameters were obtained. The results show that the response of fault zone to mining disturbance is stronger, and it is easier to destroy and destabilize to provide space for confined water conduction. To a certain extent, the nature of fault fillings determines the difficulty of fault activation, then affects the temporal and spatial evolution process of fault water inrush. The fault tip is obviously affected by fluid-solid coupling. The cracks generated by the fault zone and the aquiclude undergo the initiation-expansion-through stage, the relative position relationship between the fault and the working face determines the time and space position of the fault evolution zone and the floor failure zone. According to the conduction path of confined water, the process of fault water inrush is divided into three stages : fault activation, water conduction, double zone (fault evolution zone and floor failure zone) docking, the stage delineation of fault water inrush evolution process are realized

Transient flow analysis in a Roots blower: Experimental and numerical investigations

It is widely acknowledged that rotary positive displacement machines exhibit highly unsteady flow fields that affect their performance. The presence of the operational clearances impacts this unsteady flow field and further affects the performance. However, the exact nature of these unsteady flow mechanisms remains largely unknown that necessitates both detailed experimental investigations and computational modelling. Thus, the present study employs both optical visualization and unsteady Reynolds-Averaged Navier Stokes (URANS) computational modelling methods while focussing on investigating the transient flow field inside a Roots blower, a general type of the rotary positive displacement machine. Straight lobes in a Roots blower provide convenient optical access to experimentally analyse internal flow and compare it with the predictions obtained by standard computational models. In the first part of this paper, this study covers the low-speed experimental investigations using i) High-Speed Camera (HC), ii) the continuous High-Speed Particle Image Velocimetry (CPIV) and, iii) the instantaneous PIV (IPIV) obtained with a double pulse laser and a double shutter camera. Relative merits from these techniques are discussed with respect to the Roots blower unsteady flow mechanisms. In addition, computational analyses are performed using a combination of in-house and commercial modelling methods and the results are compared against the experiments. The results confirm the existence of highly three-dimensional and unsteady flow field where certain distinct flow mechanisms originating from the operational clearances impact the performance of the Roots blower. The study also highlights challenges of the experimental and computational methods used for evaluation of positive displacement machines that impact the accuracy of results

Tuning surface properties of amino-functionalized silica for metal nanoparticle loading: The vital role of an annealing process

Metal nanoparticles (NPs) loaded on oxides have been widely used as multifunctional nanomaterials in various fields such as optical imaging, sensors, and heterogeneous catalysis. However, the deposition of metal NPs on oxide supports with high efficiency and homogeneous dispersion still remains elusive, especially when silica is used as the support. Amino-functionalization of silica can improve loading efficiency, but metal NPs often aggregate on the surface. Herein, we report that a facial annealing of amino-functionalized silica can significantly improve the dispersion and enhance the loading efficiency of various metal NPs, such as Pt, Rh, and Ru, on the silica surface. A series of characterization techniques, such as diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), Zeta potential analysis, UV–Vis spectroscopy, thermogravimetric analysis coupled with infrared analysis (TGA–IR), and nitrogen physisorption, were employed to study the changes of surface properties of the amino-functionalized silica before and after annealing. We found that the annealed amino-functionalized silica surface has more cross-linked silanol groups and relatively lesser amount of amino groups, and less positively charges, which could be the key to the uniform deposition of metal NPs during the loading process. These results could contribute to the preparation of metal/oxide hybrid NPs for the applications that require uniform dispersion

Digital twins in mechanical ventilation : models, identification, and prediction of patient-specific response to care.

Mechanical ventilation (MV) is a fundamental, core treatment for both daily and critical care in hospitals. Patient-specific lung condition varies significantly due to patient-specific response to the wide range of lung disease and dysfunction. In response, ventilator settings are currently set primarily by clinician experience around broad guideline approaches. They are thus not standardized, and often non-optimal. Technologically, the response has been to design a far broader array of MV modes promising better treatment, but leading to more uncertainty in care and no big change in patient outcomes. All of these uncertainties result in higher morbidity and mortality than if care was personalized. Personalizing care requires greater insight into time-varying, patient-specific lung condition and response to MV care. Mathematical models offer the opportunity, in combination with clinical measured data, to enhance the understanding of patient-specific lung mechanics. A predictive model would allow optimization of MV settings, while minimizing risk to the patient. However, such models and the data available from the ventilator are relatively limited. This thesis studies a well-validated lung mechanics model and extends it with physiological-relevant basis functions to enable prediction over MV care settings. It yields accurate predictions for lung mechanics responses to changes in ventilator settings and thus enhances understanding of patient-specific lung mechanics. The thesis also addresses over-distension, a leading cause of ventilator induced lung injury, which increases length of MV, length of stay, morbidity, mortality, and thus cost. The mechanics relevant digital-twin model and methods are extended to account for the potential appearance of distension in measured pressure-volume (P-V) loops. The new model with basis functions accurately detects and predicts excessive airway pressure resulting by over-assistance from ventilators. It also accurately captures and predicts the retained volume, denoted the dynamic functional residual volume (Vfrc), as positive end expiratory pressure (PEEP) changes. This model-based approach is further extended to create a non-invasive, predictive over-distension index to assess lung condition and MV settings breath-to-breath, which is also much more intuitively understandable than the only current validated metric. Finally, spontaneous breathing effort and assisted breathing MV modes are considered. The digital-twin model and estimation methods are explored and combined for these partial support MV modes. The results show the model fits well and has the ability to identify physiological features from patient effort during breathing. Thus, the model is taken from fully supported ventilation to include assisted and partial support modes. Overall, accurate predictions in patient-specific lung mechanics are important and promising in MV care. Each area studied in this thesis is validated using clinical data and shows model efficacy in optimizing care, while minimizing risk. The combination between well-validated lung mechanics models and physiological-relevant basis functions provides novel insights for future research advancement for safe and optimal clinical care. The models and methods presented offer capabilities of capturing and predicting lung mechanics response under multiple MV modes no other research has yet realized, and provide a platform for future personalized care and improved outcomes at reduced total cost