The Kanta Patient Data Repository contains healthcare data from the population of Finland for more than a decade. The repository is a continuously expanding real world dataset produced by many information systems and healthcare service providers. Kanta data has been accessible for secondary uses such as scientific research since 2019. The data can be requested from the Finnish authority Findata. However, before a request has been accepted, it is difficult to assess if the accumulated data allows answering a specific research question. Publicly available descriptions of data structures in Kanta do not tell how much they are used in practice. This publication enables future data use cases by providing a view on the overall availability of types of structured health data in the Kanta patient data repository based on a sample of 96 200 medical histories of over 18-year-old patients. We conclude that Kanta PDR is a promising source of real world data for development and evaluation of medical risk calculators within the Finnish population. The wide coverage of the Finnish population and timeliness of the data are its strengths as a source of research data also outside of Finnish context. However, the limitations on data availability in variable level need to be considered on a case-by-case basis. Main challenges in the use of Kanta data are multiple code systems for laboratory results, short durations of recorded data for specific data types, and missing or very rarely used structured format e.g., in cases of tobacco and alcohol use.

This work delves into the exploration of optimizing Multilayer Perceptrons (MLP) or the dense layers of other sorts of Deep Neural Networks when they are aimed at edge computing applications such as Internet of Things (IoT) devices, very limited in resources at the edge. The proposed optimization approach consists of generating a pruning mask for the hidden dense layers of the original neural network by using auxiliary dense Morphological Neural Networks (MNN). These MNN have shown a notable efficiency when it comes to the process of pruning, resulting in a significant decrease in the overall number of connections and a low cost in terms of accuracy degradation. The effectiveness of this new pruning methodology has been explained in detail and validated for two widely used datasets as MNIST and Fashion MNIST and two very well-known neural networks such as LeNet-5 and LeNet-300-100. Subsequently, the performance of these pruned neural networks has been assessed using an IoT hardware platform. The experimental results have outperformed other contemporary state-of-the-art pruning techniques, in terms of power efficiency and processing speed for a similar percentage of weight reduction, all while maintaining minimal impact on overall accuracy. In addition, a custom software tool has been developed to generate a C code designed to optimize the inference of these pruned networks on IoT edge devices. These findings hold important implications for advancing the development of efficient and scalable deep learning models that are specifically tailored to meet the demands of edge computing applications.

A document by Yajun ZHAO. Click on the document to view its contents.

We are designing and implementing a solar inverter system that generates green power from solar energy and reduces air pollution and other environmental impacts. Our system uses a pure sine wave inverter that produces a sine wave virtually identical to the utility grid. The IoT-based MPPT solar charge controller ensures that the maximum amount of power is transferred from the solar panels to the battery bank and monitors the system in real-time. We also use a solar tracker with a single-axis rotation that orients the panels toward the sun in two directions. Our solar inverter system can handle a maximum load of 300 watts.

In order to effectively manage agriculture and promote sustainable land use in Africa, accurate mapping of irrigated croplands is imperative. This paper pioneers a novel Hybrid Vegetation Index (HVI) and integrated an approach harnessing the synergies between Radar imagery from Sentinel-1 and optical data from Sentinel-2, key vegetation indices (HVI, MSAVI), and Principal Component Analysis (PCA) to significantly enhance mapping accuracy across diverse African landscapes. Employing Convolutional Neural Network (CNN), Random Forest (RF), and Support Vector Machine (SVM) classifiers, the combined potential of these satellite datasets is thoroughly Assessed. The integrated utilization of both Sentinel-1 and Sentinel-2 demonstrates a substantial enhancement in overall accuracy, elevating classification results by approximately 5-6% when compared to individual sensor applications. Specifically, in Kenya, Egypt, and Nigeria, the amalgamation of Sentinel-1 and Sentinel-2 enables CNN to achieve remarkable accuracies of 97.5%, 98.01%, and 98%, respectively. These findings underscore the superior performance of the fused Sentinel data and highlighted the pivotal role of their integration in advancing precise agricultural mapping. This research emphasizes the promising impact of HVI and Sentinel satellite missions in empowering informed decision-making and promoting sustainable land management practices across vital agricultural regions in Africa.

In this study, we present an analytical model for predicting the magnetic properties and optimization of thermomagnetic devices using mathematical models. The 3D analytical magnetic model is firstly validated by the dipole model and confirmed through experimentation, enabling to accurately estimate the magnetization of the used magnet. The stray field induced by the permanent magnet over the lateral surface of the rotor is computed. Then, the resultant force and torque are derived allowing to estimate the exact number of ferromagnetic active material required and their angular gap.

Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive tool for monitoring brain activity. The classification of fNIRS data in relation to conscious activity holds significance for advancing our understanding of the brain and facilitating the development of brain-computer interfaces (BCI). Many researchers have turned to deep learning to tackle the classification challenges inherent in fNIRS data due to its strong generalization and robustness. In the application of fNIRS, reliability is really important, and one mathematical formulation of the reliability of confidence is calibration. However, many researchers overlook the important issue of calibration. To address this gap, we propose integrating calibration into fNIRS field and assess the reliability of existing models. Surprisingly, our results indicate poor calibration performance in many proposed models. To advance calibration development in the fNIRS field, we summarize three practical tips. Through this letter, we hope to emphasize the critical role of calibration in fNIRS research and argue for enhancing the reliability of deep learning-based predictions in fNIRS classification tasks. All data from our experimental process are openly available on GitHub.

We present SCMI30-IITRPR, a dataset for smartphone camera model identification (CMI) performance assessment comprising 9937 diverse scene images collected using 30 different camera models. Importantly, to allow assessment of CMI performance under different application settings where either similar or random content images may be available across the camera models, SCMI30-IITRPR provides images grouped in two sets: one set with similar image content and another with random image content. SCMI30-IITRPR therefore overcomes a key limitation of prior datasets that provided either images with random or similar content but not both. Additionally, SCMI30-IITRPR also allows researchers to test the robustness of CMI techniques under test conditions mismatched with the training and to explore alternative data selection approaches for more robust training. We present benchmarks of five CMI methods on the SCMI30-IITRPR dataset highlighting the facts that significant performance variations can be encountered under a mismatch between training and testing scenarios and that training datasets that merge images with similar and random content offer the most robustness.

The energy sector is shifting towards decentralization and digitalization in view of climate change mitigation and decarbonization. In this context, the Web of Cells (WoC) approach is emerging. It aims at providing seamless coordination of flexibilities across network operators with the potential to incentivise flexibility usage more approriately, aligning it with system needs. Within this context, this paper makes use of a large-scale multi-energy system model to delve into the role of decentralized, small-scale flexibilities, with an emphasis on heat pump systems in residential buildings. Using a computationally tractable approximation of demand-side flexibilities, we explore the economic ramifications of integrating these flexibilities into the future energy system. A case study, envisioning decarbonization in Germany by 2050, sheds light on the implications for a decarbonized and decentralized energy sector. Key findings reveal that while these demand-side flexibilities can be economically advantageous for consumers, they also exert influence on system prices and the profitability of storage, as well as cross-sectoral units. The potential surge in market value for renewables and other technologies within a system following the WoC approach is highlighted. To foster collaborative exploration, the model's open-source code and the data set, vital for a 2050 scenario, are made available, supporting a deeper, collective assessment of the future's energy system.

The development of Internet of things has spawned the vehicular ad hoc network (VANET), which facilitates the safe and comfortable driving. The communications in VANET should be protected to deter against message leakage and modifications. To solve the security issues in VANET communications, we present a dynamic and efficient authenticated group key agreement (AGKA) protocol SC-AGKA with conditional privacy employing the self-certified cryptosystem, and prove its security based on the computational Diffie-Hellman problem. Our SC-AGKA protocol establishes a group key among multiple group users and achieves conditional privacy for the group users. Based on our SC-AGKA protocol, we propose an authentication and group key agreement protocol applying the design in VANET. Additionally, we also notify that through performance comparisons, our protocol has higher security and efficiency in computation and communication compared to other AGKA designs.

In this study, novel Lorentz-like fractional-order dynamical systems are proposed, offering potential applications across various engineering domains. Based on a threedimensional system of the Lorentz-like type of integer order, new nonlinear dynamic systems of fractional order are constructed for four, five, and six state variables. These systems can describe real convective processes in fractal media characterized by a memory effect. For these systems, equilibrium points and stability conditions are determined using the theorem on local asymptotic stability of fractional order systems. Utilizing the frequency domain approximation method, Matlab-Simulink models were developed for novel chaos generators characterized by a fractional order index of 0.95. Through the utilization of Multisim software, we designed electronic circuits to validate the physical feasibility of our proposed systems. The simulation results obtained from both Matlab and Multisim exhibit excellent agreement, reinforcing the reliability of our proposed models. To demonstrate the synchronization of two unidirectionally coupled 3.8d chaotic systems, Matlab-Simulink models were created in two versions. The first version assumes an identical fractional index for both the master and slave systems, while the second version involves different fractional indices for the two systems. These systems were further employed for the chaotic masking of a harmonic signal. An electronic circuit implementing the chaotic masking process in Multisim is also presented. The results obtained from this proposed scheme demonstrate the success of the approach in accomplishing the encryption and decryption procedures effectively.

Today modern web site accelerated by scripts, but the foundation, web page its self is still a static structure. Document Object Model (DOM) represents the structure of web page. Here we show a new approach: It is possible to put timetree and DOM together to shape a new structure named Time Object Model. TOM represents not only a static page but also a dynamic stream. We believe the best way for using TOM is to embed it into a HTML page in real time without changing the existence, it is the only way works now.

In this paper, comprehensive double-directional channel measurements at 300 GHz in various usage scenarios in corridor environments, such as Access, Device-to-device (D2D), and Backhaul over 40 different receiver (Rx) positions using an in-house developed channel sounder, are presented. The measurement results are analyzed and validated by ray tracing (RT) simulation. The quasi-optical propagation properties at 300 GHz make an accurate estimation of relatively simple propagation in a corridor environment possible by using ray optics theory. However, even though non-trivial quadruple-bounce specular reflection paths can be identified in both scenarios, propagation phenomena other than reflection exist irrespective of the Rx positions. Thus, to model the propagation mechanism appropriately, a quasi-deterministic (QD) channel model comprising deterministic and random components is also proposed. The results generated using the proposed model are found to agree well with our prior observations and measurement results. Finally, the paper concludes by characterizing and comparing the channel for all the investigated scenarios in terms of path loss (PL) and large-scale parameters (LSP). On analyzing the measurement results using synthesized power spectra, proposed QD model, and evaluated PL and LSP it is observed that the Access and D2D scenarios share almost similar propagation mechanisms. Furthermore, in the Access and Backhaul scenario the LoS is observed to be affected by the unresolvable ceiling-reflected components. This study, across three different scenarios, can aid the design of next-generation communication systems operating in the THz spectrum.

Controlling the charging and discharging procedures of Lithium-Ion Batteries is of paramount importance as violating safety constraints, such as current deviations, can lead to significant damage to the battery or circuit or interruption in service. Thus, it is crucial to employ a robust controller capable of handling uncertainties and unexpected scenarios. PI controllers have become prevalent in recent years for managing battery dynamics, but they exhibit limited robustness in unpredictable situations. In this paper, we propose a Reinforcement Learning (RL) driven control method as a substitute for the PI controller. The agent is trained using a co-simulation approach with simultaneous employment of Python and Matlab, ensuring an accurate estimation of the environment and, consequently, enhanced performance. A prototype of the proposed controller is developed using dSPACE rapid control prototyper. The performance is compared with the benchmark controller (PI) across different fault scenarios, considering three criteria: overshoot, undershoot, and stabilization time. The comparative analysis reveals that, in most scenarios, the RL agent outperforms the PI controller, exhibiting a remarkable 50% reduction in both overshoot and undershoot compared to the benchmark controller. This research contributes to advancing battery control systems by introducing an RL-based controller that proves to be a more robust alternative, delivering improved performance in the face of uncertainties and fault scenarios.

Enabling artificial intelligence native end-to-end systems in ultra-wideband sub-terahertz spectrum faces several challenges. The particularly complex channel variations and nonlinear behavior of analog components of the transceivers are major obstacles to the over-the-air adaptation of these systems. In this paper, we investigate an edge-based bidirectional long-shortterm memory neural network capable of predicting the channel gain variations in Non-Line-of-Sight conditions. We aim to enable end-to-end autoencoders with a predictive model for scheduling the training phase when the power is above the receiver sensitivity and there are no large fading variations. Otherwise, the training of the end-to-end system will likely fail. With only 16 BiLSTM cells our model is capable of inferring the channel gain variations with a worst-case root mean squared error lower than 0.0547 (i.e., 1.1% compared to the normalized channel gain range). Also, with lower computational complexity, our model decreased the propagation of the error compared to traditional recurrent neural networks and deep-learning-based forecasting models.

This work proposes a novel combination of behavioural-tracking sensors and immersive virtual reality in a gamified proof-of-concept prototype, which demonstrates affective treatment concepts for hypervigilance symptoms. A number of limitations have been identified in current approaches, prompting more advanced techniques that efficiently target hypervigilance at an individual patient level. In response, we developed a virtual reality first-person shooter that responds to inertial user behaviour in a way that aims to combat detrimental symptoms, proposed as an exploratory investigation into innovative technology and its potential to maximise cognitive behavioural therapy outcomes for hypervigilance treatment. The prototype is evaluated through interactive user studies with 22 participants, gathering a large volume of qualitative data regarding participant experiences and opinions after use. Rigorous thematic analysis finds that participants can independently identify the cognitive behavioural therapy purpose of the intervention without prior knowledge of such intentions, and relate efficacious approaches from the literature to their own experiences. Despite prospective apprehension, themes also demonstrate widespread adherence and acceptance of such approaches to hypervigilance treatment, alongside perceived effectiveness both of experienced outcomes and future potential. These results support the validity of combining such technologies in the context of cognitive behavioural therapy interventions, such that the standard of future interventions may be improved.

A document by Ali. Click on the document to view its contents.

In the context of 6G architecture development, the concept of a softwarized (orchestration) continuum is a key pillar. Nevertheless, achieving complete softwarization of network functionalities, tasks, and operations presents inherent challenges, leading to critical trade-offs and limitations. This article explores a novel approach to address these issues by integrating quantum technologies and the Physical Layer Service Integration (PLSI) paradigm. Specifically, we propose the formulation and analysis of network synchronization as a quantum PLSI problem. Our study evaluates synchronization time offset in both conventional Precision Time Protocol (PTP) and quantum-based approaches within the network. We investigate the impact of various network conditions on the precision of PTP synchronization, ranging from nanoseconds under ideal circumstances to microseconds when utilizing virtual network devices. Further, we perform a simulation to generate frequency-entangled photon pairs to access nonlocal temporal correlations and calculate the time offsets. Our findings reveal that entanglement-based PLSI for network synchronization achieves precision at the picosecond level. These results emphasises the high precision achievable by interpreting the network synchronisation problem in the perspective of PLSI and not as a service of the softwarized continuum.