Portfolio management is a multi-period multi-objective optimization problem subject to a wide range of constraints. However, in practice, portfolio management is treated as a single-period problem partly due to the computationally burdensome hyper-parameter search procedure needed to construct a multi-period Pareto frontier. This study presents the Pareto Driven Surrogate (ParDen-Sur) modelling framework to efficiently perform the required hyper-parameter search. ParDen-Sur extends previous surrogate frameworks by including a reservoir sampling-based look-ahead mechanism for offspring generation in Evolutionary Algorithms (EAs) alongside the traditional acceptance sampling scheme. We evaluate this framework against, and in conjunction with, several seminal Multi-Objective (MO) EAs on two datasets for both the single- and multi-period use cases. Our results show that ParDen-Sur can speed up the exploration for optimal hyper-parameters by almost 2x with a statistically significant improvement of the Pareto frontiers, across multiple EAs, for both datasets and use cases.

Non-Fungible Tokens (NFTs) do hold the promise of providing Web3 with the opportunity for self-sovereignty of users’ physical assets. However, existing NFT marketplaces lack a generic design that allows the value of assets to flow efficiently. In this paper, we propose a generic NFT architecture for Web3. The architecture supports the rapid development of the upper application environment and automated value mapping of the underlying physical asset environment. To connect these two environments, a generic connecter has been designed to provide flexible storage for mapping data management, and to support universal cross-chain transactions. With these features, the values of heterogeneous physical assets can coexist in a unified Web3 world, and rich value transfer services can be developed on demand. This paper discusses the background of the proposed architecture, the open problems and our initial solution, as well as our design principles and advantages, and finally validates this novel NFT architecture.

In this article, the first experimental demonstration of a non-iterative electronic dispersion compensation (EDC) solution implemented at the transmitter using a finite impulse response (FIR) filter optimized with the Gerchberg-Saxton (GS) algorithm, is presented, for intensity-modulation and direct- detection (IM/DD) systems. The theoretical framework for the GS FIR filter and preliminary simulations have been presented in Part I of this work. Here, the performance of the GS FIR filter is compared to that of the iterative GS algorithm in the transmissions of 56-Gb/s on-off keying (OOK) over 80 km of single mode fiber (SMF) with a post feed-forward equalizer (FFE) for combating residual inter-symbol interference (ISI). Furthermore, the influence of the pulse shape (raised cosine or rectangular) and modulation format (return-to-zero (RZ) or non- return-to-zero (NRZ)) on the measured bit error ratio (BER) is experimentally investigated, while changing the number of FIR taps, the number of post-FFE taps, and the number of post-FFE samples-per-symbol. It is shown that within the range of the target digital extinction ratios (DERs) for which the original iterative GS algorithm offers benefit, both analytical and numerical methods for calculating the optimum FIR taps, outlined in Part I of this work, produce similar BER performance as predicted. To that end, the former method is extended, here, through a non-recursive frequency response formula, which offers insight into the action of the GS filter with different pulse shaping and enables the derivation of the explicit GS impulse response. It is also shown that rectangular RZ pulse shaping exploits the full benefit of GS filtering through a uniform spectrum, enabling a BER below the 7% hard-decision forward error correction (HD-FEC) limit, with a 641-tap T/2-spaced pre-EDC FIR filter and a 3-tap adaptive T-spaced post-FFE.

The electrical grid undergoes a fundamental change with the introduction of the Smart Grid; in particular, power substations have been evolving towards automated systems. Power substation communication networks evolved from legacy infrastructures mostly formed by serial devices to Ethernet-based digital communications networks, thanks to the introduction of the IEC 61850 standard. However, this evolution inherited the shortcomings of the traditional decentralized network management. This paper explores the upcoming evolution of IEC 61850 when it meets the ground-breaking programmable network technologies: Software Defined Networks (SDN) and Programmable Data Planes (PDP). Here, we describe how recent proposals leverage SDN to improve network management tasks such as topology discovery, multicast traffic management, QoS provisioning, among others in IEC 61850 based systems. We also outline potential improvements to critical network management tasks in power substations using PDP features such as in-band network telemetry. Finally, we discuss different challenges in the management of the communication network of Smart Power Substations and how they can be addressed with the implementation of programmable networks

The nasal breathing parameters have shown promising applications in hyperventilation, Kussmaul breathing, sleep apnea, shortness of breath in COVID-19, bradypnea, sinus arrhythmia, and breathing pranayama. However, a wearable setup, which is affordable, aesthetic, and accurate in real-time during various physical activities, is not available currently. The present work develops a novel, low-cost and wearable instrumented Nasal Temperature Sensing (NTS) device for measuring various breathing parameters. The proposed device was tested during controlled breathing exercises in seated position at three ambient temperature settings (18°C, 28°C, and 38°C). The device was further tested for standing and walking at various speeds including slow (1.5 Km/hr), medium (3.5 Km/hr), and fast (5.5 Km/hr). The NTS device is compared against a commercially available respiration belt (gold standard). The mean absolute error (averaged across subjects and ambient temperature settings) was 0.09±0.05 breaths per minute (bpm), 0.17±0.10 bpm, and 0.13±0.13 bpm for controlled breathing at 6 bpm, 12 bpm, and 18 bpm, respectively. The Root-mean-square error between the NTS device and respiration belt across the subjects at the normal standing-position was 1.13±0.39 bpm. The results suggest that the proposed NTS device is accurate for real-time applications with the potential advantage of being robust enough to motion artifacts.

Omnidirectional Videos (or 360° videos) are widely used in Virtual Reality (VR) to facilitate immersive and interactive viewing experiences. However, the limited spatial resolution in 360° videos does not allow for each degree of view to be represented with adequate pixels, limiting the visual quality offered in the immersive experience. Deep learning Video Super-Resolution (VSR) techniques used for conventional videos could provide a promising software-based solution; however, these techniques do not tackle the distortion present in equirectangular projections of 360° video signals. An additional obstacle is the limited 360° video datasets to study. To address these issues, this paper creates a novel 360° Video Dataset (360VDS) with a study of the extensibility of conventional VSR models to 360° videos. This paper further proposes a novel deep learning model for 360° Video Super-Resolution (360° VSR), called Spherical Signal Super-resolution with a Proportioned Optimisation (S3PO). S3PO adopts recurrent modelling with an attention mechanism, unbound from conventional VSR techniques like alignment. With a purpose-built feature extractor and a novel loss function addressing spherical distortion, S3PO outperforms most state-of-the-art conventional VSR models and 360° specific super-resolution models on 360° video datasets. A step-wise ablation study is presented to understand and demonstrate the impact of the chosen architectural subcomponents, targeted training and optimisation.

With the increasing popularity of smart terminals, the Internet of Things (IoT) vastly expands the influence of information technology and creates great values to our society. The Internet of Medical Things (IoMT) is a typical application of the IoT specifically for medical and healthcare related purposes. In IoMT, personal health records (PHRs) generated by intelligent equipment are usually stored in a cloud sever managed by a semi-trusted cloud provider. However, there is still a possibility of the exposure of private information to unauthorized parties. In order to protect a patient’s private information, and ensure that doctors can search a patient’s PHRs without decrypting all ciphertexts, an efficient searchable encryption framework with weighted keywords is proposed in this paper. In this framework, we implement a weighted keyword model to raise the accuracy of search results, and reduce the computational and storage overhead. Besides, compared with the existing schemes, the framework is supportable of multi-search mechanism, which is adaptable to more application scenarios. Finally, the efficiency and security of this framework is proved through security and performance analysis in this paper.

The skin’s ability to sense its environment is vital to activities of daily living. Cutaneous sensory perception diagnostics allow for the early detection and symptom tracking of tactile dysfunction. However, lack of access to healthcare and the limited frequency of current screening tools can leave skin sensation impairments undiscovered or unmonitored. This work presents a smartphone application for Cutaneous Hand Assessment with a Smartphone Interface (CHASI) to establish Vibrational Perception Thresholds (VPT). CHASI’s vibrational output and force measurement abilities are also characterized. An 18-participant cross-sectional study, with both normative subjects and subjects with sensation impairment, compares the monofilament test with smartphone established VPT (SE- VPT). We find a high positive correlation between SE-VPT and monofilament scores (rs=0.83, p = 0.00014). We also investigate the sensitivity of our proposed SE-VPT method to the motions and forces applied to the touchscreen. We find that variations in force do not alter the practical significance of the monofilament correlation. These results further the smartphone as a potential diagnostic and monitoring tool for hand health.

Research in music perception and brain activity has led to the development of Music Brain-Computer Interface Systems. While previous studies have focused on aspects such as song identification, stimulus-response correlation, and inter-subject correlations, they have often overlooked the understanding of individual differences in subjective experiences. In this research, our objective is to identify listener-specific neural signatures by analyzing EEG data obtained from six naturalistic music datasets collected in the USA, Greece, and India, involving a total of 161 listeners. Our approach consists of a feature representation pipeline that decomposes the EEG signals into five primary brain waves and extracts 21 features to predict the listener’s identity. We utilize linear and non-linear features in training Random Forest classifiers to determine the most discriminating feature among listeners. The results demonstrate that neural oscillations in higher frequency bands are crucial in distinguishing subjective differences. Specifically, beta waves emerge as the most effective in predicting these differences, yielding an average accuracy of 91.03% across all datasets. Furthermore, our findings highlight the Hjorth Mobility feature as having the highest predictive ability. Additionally, we observe that the frontal region exhibits increased sensitivity in capturing unique features for person identification. Moreover, we discuss previous studies that align with our research direction, focusing on encoding individual-related information within high-frequency brain rhythms. These findings have significant implications for the field of bio-metrics in Music Brain-Computer Interface Technology, providing valuable insights into personalized user experiences and paving the way for future advancements in this exciting research area.

Further progress in industrial electrochemical generators' designs utilising SOFC considerably depends on problem of obtaining fuel cell materials with predefined properties [1,2]. Due to this fact, considerable efforts of designers are directed on creation of optimum composition «anode-electrolyte-cathode», and on microconstruction of SOFC materials (selection of phase and element composition, defect structure formation, etc.). In the second problem nanocomposite systems having unique properties are of greatest interest [3]. Utilising of such materials as solid electrolyte may allow significantly improve cell's parameters, in particular, increase ionic conductivity [4,5]. However mechanisms leading to this effect were not determined singly. In this article the influence of nanoparticles ensemble with work of exit that different from the rest of the matrix on the change of electron density in SOFC materials have been examined theoretically. The calculations of concentration distribution of free carriers around single particle and for microparticles ensemble were fulfilled. In conclusion possible directions of forming of nanostructures conformably to SOFC materials discussed.

Although the digital infrastructure that underpins biotechnology benefits the entire globe, it is also becoming a target for sophisticated information operations and information retrieval. The new geopolitics of inequality and insecurity spanning beyond cultures and nations is being caused by developing cyber incidents against AI and cybersecurity in the bioeconomy. To maintain both national security interests and global security, it is crucial to maintain the accuracy, legibility, and integrity of data on contemporary biotechnology research. The increasing incidence of malware attacks on biological sequences such as DNA, to change resultant products in development, the rising incidence of data theft from crucial biomanufacturing processes, and increasing vulnerabilities in smart, connected healthcare technology could lead to the creation of biological weapons are some key factors boosting demand for cyberbiosecurity solutions and are expected to drive market revenue growth until 2030. The markets' revenue growth will be influenced by increased cyberbiosecurity research, high investment to enhance the security of biotechnology infrastructure, and an increasing number of efforts to raise cyberbiosecurity awareness.

In industry deep learning application, we should fix the badcase by human evaluation after we achieve more than 95% accuracy at dev dataset. The badcase reason is from the wrong rule/knowledge of human labeling and will cause low accuracy under human evaluation. In this paper, we propose the pattern-based method to fix the badcase for industry application inference. We propose the pipeline to solve the problem and improve the accuracy of human evaluation. The experiment results verify our idea, which means label-edit/data-edit is the method to implement controllable deep learning application.

The powder metallurgy database is a data system for effective management of powder metallurgy information. With the development of information technology, its construction scale and quality will directly affect the convenient inquiry of powder metallurgy information, the selection of gear materials, the research and development and engineering application of micro-drive systems, and have engineering significance and market value. However, the groundwork in this field still lags behind other materials fields and needs to be developed systematically. Consider the small modulus powder metallurgy gear’s elastic modulus, Poisson’s ratio, fatigue life, metallographic structure and processing parameters as research objectives, the functional requirements of the database, system functions and data relationships are analyzed. The function module, database model, user authority and system architecture are designed in detail. Based on the Flask framework and MySQL, the database system on the basic properties of powder metallurgy materials is designed and developed, which realizes the systematic and standardization of mechanical property data of powder metallurgy. Users are provided with upload, query and retrieval services of chemical composition, heat treatment and molding properties, fracture mechanics, material suppliers and other information. In this paper, the necessity and urgency of building a powder metallurgy database to increase the efficiency of transmission system development and its application are demonstrated. The deficiencies of the existing powder metallurgy database are analyzed, and suggestions are provided for the selection, design, data coverage, and construction of big data applications for smart materials. The technical challenges of small modulus powder metallurgy gear selection and plan formulation are then ameliorated. Github repository: PMdatabase2 (github.com)

We present optimized modulation and coding for the recently introduced dual modulated QR (DMQR) codes that extend traditional QR codes to carry additional secondary data in the orientation of elliptical dots that replace black modules in the barcode images. By dynamically adjusting the dot size, we realize gains in embedding strength for both the intensity modulation and the orientation modulation that carry the primary and secondary data, respectively. Furthermore, we develop a model for the coding channel for the secondary data that enables soft-decoding via 5G NR (new radio) codes already supported by mobile devices. The performance gains for the proposed optimized designs are characterized via theoretical analysis, simulations, and actual experiments using smartphone devices. The theoretical analysis and simulations inform our design choices for the modulation and coding, and the experiments characterize the overall improvement in performance for the optimized design over the prior unoptimized designs. Importantly, the optimized designs significantly increase usability of DMQR codes with commonly used QR code beautification that cannibalizes a portion of the barcode image area for the insertion of a logo or image. In experiments with a capture distance of 15 inches, the optimized designs increase the decoding success rates between 10% and 32% for the secondary data while also providing gains for primary data decoding at larger capture distances. When used with beautification in typical settings, the secondary message is decoded with a high success rate for the proposed optimized designs, whereas it invariably fails for the prior unoptimized designs.

Human kinetics, specifically joint moments and ground reaction forces (GRFs) can provide important clinical information and can be used to control assistive devices. Traditionally, collection of kinetics is mostly limited to the lab environment because it relies on data that measured from a motion capture system and floor-embedded force plates to calculate the dynamics via musculoskeletal models. This spatially limited method makes it extremely challenging to measure kinetics outside the laboratory in a variety of walking conditions due to the expensive device setup and large space required. Recently, employing machine learning with IMU sensors are suggested as an alternative method for biomechanical analyses. Although these methods enable estimating human kinetic data outside the laboratory by linking IMU sensor data with kinetics dataset, they were limited to show inaccurate kinetic estimates even in highly repeatable single walking conditions due to the employment of generic deep learning algorithms. Thus, this paper proposes a novel deep learning model, KineticsFM-DLR-Ensemble-Net to estimate the hip, knee, and ankle joint moments in the sagittal plane and 3 dimensional ground reaction forces (GRFs) using three IMU sensors on the thigh, shank, and foot under several representative walking conditions in daily living, such as treadmill, level-ground, stair, and ramp with different walking speeds. This is the first study that implements both joint moments and GRFs in multiple walking conditions using IMU sensors via deep learning. Our deep learning model is versatile and accurate for identifying human kinetics across diverse subjects and walking conditions and our model outperforms state-of-the-art deep learning model for kinetics estimation by a large margin.

This paper studies a secure satellite-terrestrial communication system assisted by a hybrid intelligent reflecting surface (IRS). The hybrid IRS, which composes of active and passive reflecting elements, is deployed to enhance the secure communication from the satellite to multiple users against multiple eavesdroppers. A joint design optimization problem for the satellite beamforming and the hybrid IRS interaction is formulated to maximize the system worst-case secrecy rate under time-varying channel conditions. With high system dynamic and complexity, deep reinforcement learning (DRL) is employed to solve the non-convex optimization problem. We propose a fast DRL algorithm, namely deep PDS-DPG, to obtain the robust secure beamforming design for satellite and hybrid IRS. Numerical results show a better learning efficiency of the proposed algorithm as to the state-of-the-art deep deterministic policy gradient (DDPG) algorithm with comparable system secrecy performance.

By extending the context of the Game Theory to the understanding of P vs NP Problem, fundamental principles of observation can be explained to the very limit of existence.

The EU digital decade 2030 connectivity targets require parallel fixed Fiber and mobile 5G investments from Mobile Network Operators, however majority of data traffic generated and monetized by Big Techs (Hyperscalers), therefore operators require a fair share contribution. The aim of this paper to compare fair share contribution to virtual and cloud technology enabled cost efficient co-investment scenario between Operators and Big Techs in RAN and Core networks. The Research Question seeks to address the question of to what extant Operators and Big Techs public cloud co-investment meet the fair share financial contribution principle for 5G rollout. The Hypothesis tested is the fair share contribution principle requested by operators and already politicians to accelerate 5G deployment. The Research Methodology employed focusing on conclusive researches mainly with descriptive analysis based on secondary data. Qualitative analysis covers incumbent and rival MNOs’ financial incentives and conduct, as well as quantitative analysis applied on consultancies forecasted financial impact. The expected finding emerged from this study is the co-investment partnership with Big Techs into public cloud provides cost efficiency to Mobile Network Operators that is already a market driven first contribution to fair cost sharing of 5G investments. The originality of the paper to call attention for this connection, so fair share contribution has a prelude that parallel is widespread.