bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiparametric Evaluation of Geriatric Patients Admitted to Intermediate Care: Impact on Geriatric Rehabilitation
Authors: Murdaca Giuseppe , Banchero Sara , Casciaro Marco , Paladin Francesca , Tafuro Michele , Monacelli Fiammetta , Nencioni Alessio , Bruschetta Roberta , Pioggia Giovanni , Tartarisco Gennaro , Gangemi Sebastiano
Published: September 2023

Optimizing the functional status of patients of any age is a major global public health goal. Rehabilitation is a process in which a person with disabilities is accompanied to achieve the best possible physical, functional, social, intellectual, and relational outcomes. The Intermediate Care Unit within the O.U. of Geriatrics and Gerontology of the San Martino Hospital in Genoa is focused on the treatment and motor reactivation of patients with geriatric pathologies. The objective of this study was to identify which factor, among the characteristics related to the patient and those identified by the geriatric evaluation, had the greatest impact on rehabilitation outcomes. Our findings revealed significant correlations between the Barthel Index delta, the 4AT Screening Test, and the number of drugs taken. This association highlights the potential benefits of medication management in enhancing the overall well-being and functional abilities of frail older adults, despite the literature suggesting that polypharmacotherapy is associated with a reduction in functional status and an increase in mortality. These findings underscore the significance of a multidimensional geriatric assessment. Refining and optimising these multidisciplinary approaches is the objective of a more effective geriatric rehabilitation strategy.
Pre-Existing Interstitial Lung Abnormalities in Patients with Head and Neck Squamous Cell Carcinoma and Their Follow Up after Therapy
Authors: Vuković Danica , Budimir Mršić Danijela , Jerković Kristian , Benzon Benjamin , Tadić Tade
Published: September 2023

Interstitial lung abnormalities (ILAs) are incidentally found nondependent parenchymal abnormalities affecting more than 5% of any lung zone and are potentially related to interstitial lung disease and worsening post-treatment outcomes in malignancies and infectious diseases. The aim of this study was to determine the prevalence and type of ILA changes in patients with head and neck squamous cell carcinoma (HNSCC) and their change in the follow-up period. This retrospective single-center study included 113 patients with newly diagnosed HNSCC who underwent lung MSCT prior to treatment. ILAs were reported in 13.3% of patients on pretreatment MSCT. Patients with ILAs were significantly older (median 75 vs. 67 years). ILAs were most prevalent in lower zones (73.3%) (p = 0.0045). The most reported ILA subtype was subpleural non-fibrotic (60%) (p = 0.0354). Reticulations were the most frequently described pattern (93.3%) (p < 0.0001). Progression of ILAs was reported in almost 30% of patients after receiving therapy. Patients with pre-existing ILAs were more likely to develop radiation-induced lung fibrosis after adjuvant radiotherapy (p = 0.0464). In conclusion, ILA’s incidence, distribution and presentation were similar to previous research conducted in other special cohorts. Our research suggests a possible association of more frequent radiation pneumonitis with ILA changes in patients with HNSCC, which should be further investigated.
IVDR: Analysis of the Social, Economic, and Practical Consequences of the Application of an Ordinance of the In Vitro Diagnostic Ordinance in Switzerland
Authors: Coste Alix , Egli Adrian , Schrenzel Jacques , Nickel Beatrice , Zbinden Andrea , Lienhard Reto , Dumoulin Alexis , Risch Martin , Greub Gilbert
Published: September 2023

IVDR regulation represents a major challenge for diagnostic microbiology laboratories. IVDR complicates a broad range of aspects and poses a risk given the high diversity of pathogens (including rare but highly virulent microbes) and the large variety of samples submitted for analysis. The regular emergence of new pathogens (including Echovirus E-11, Adenovirus 41, Monkeypox virus, Alongshan virus, and Enterovirus D68, as recent examples in Europe in the post SARS-CoV-2 era) is another factor that makes IVDR regulation risky, because its detrimental effect on production of in-house tests will negatively impact knowledge and expertise in the development of new diagnostic tests. Moreover, such regulations negatively impact the availability of diagnostic tests, especially for neglected pathogens, and has a detrimental effect on the overall costs of the tests. The increased regulatory burden of IVDR may thereby pose an important risk for public health. Taken together, it will have a negative impact on the financial balance of diagnostic microbiology laboratories (especially small ones). The already-high standards of quality management of all ISO-accredited and Swissmedic-authorized laboratories render IVDR law of little value, at least in Switzerland, while tremendously increasing the regulatory burden and associated costs. Eventually, patients will need to pay for diagnostic assays outside of the framework of their insurance in order to obtain a proper diagnostic assessment, which may result in social inequity. Thus, based on the risk assessment outlined above, the coordinated commission for clinical microbiology proposes adjusting the IvDO ordinance by (i) introducing an obligation to be ISO 15189 accredited and (ii) not implementing the IvDO 2028 milestone.
The Impact of Pulmonary Vein Anatomy on P-Wave Appearance during Sinus Rhythm: Cardiac Computed Tomography Study
Authors: Verhovceva Viktorija , Zvaigzne Ligita , Lācis Romans , Kalējs Oskars
Published: September 2023

Electrocardigraphy remains a first-line evaluation method for cardiac electrical activity, recorded from the body surface. Since atrial activation is seen on the ECG as a P-wave, several factors are known to impact the appearance of the P-wave, such as the direction of electric impulse, conduction abnormalities, and anatomical characteristics of the atria. This retrospective study aimed to find statistically significant associations between the anatomy of pulmonary veins (PVs) observed in cardiac computed tomography (CT) and P-wave appearance during sinus rhythm on resting ECG. For each patient, a resting 12-lead ECG was recorded, and the field of analysis was P-wave—its duration, morphology, and axis. The evaluation of the CT scan recordings was performed by creating 3D models of the left atrium and analyzing the anatomy of the PVs and left atrial appendages (LAA). Noteworthy correlations were found: anatomy of the left PVs showed an association with LAA volume, LAA morphology, and P-wave notching in lead II. The right PVs demonstrated a relation with the P-wave axis and amplitude. Although these correlations cannot be classified as strong, the results not only expand understanding about discussed variables but also suggest the presence of a subtle and complex relationship, that warrants further exploration.
Designing an Environmental Wind Tunnel Cooling System for High-Speed Trains with Air Compression Cooling and a Sensitivity Analysis of Design Parameters
Authors: Zhuang Junjun , Liu Meng , Wu Hao , Wang Jun
Published: September 2023

Environmental wind tunnels for high-speed trains play a significant role in their development. The cooling system of the wind tunnel poses a challenge as it requires lower temperatures and a higher cooling capacity during operation. The conventional approach to wind tunnel refrigeration uses evaporative cooling, which is less efficient at low temperatures and comes with environmental and safety risks. In this study, we propose an innovative air compression refrigeration method based on the Brayton cycle. This method converts high-pressure air into low-temperature air at atmospheric pressure for wind tunnel refrigeration. The new cooling system has reduced energy usage by 3.72 MW, leading to a 13.15% improvement. The return cooler of the system is modeled using the effective number of heat transfer units and the mean temperature difference design method. Additionally, the turbine within the system is analyzed using one-dimensional flow characteristic analysis and the principle of similarity. This method has been validated by comparing it to other published papers. Subsequently, we perform a thorough sensitivity analysis on the key design parameters of the system. We observe that with a sufficient heat transfer area of the recooler, the cooling efficiency of the system exhibits a gradual decline from 64% to 60% as the mass flow rate of the system rises. For a fixed turbine, the cooling efficiency of the system rises from 20% to 62% and subsequently declines to 37%, with an increase in the mass flow rate. As a result, we conclude that the design parameters of the turbine have a more significant influence on the cooling efficiency of the system than the recooler. Our study will establish a foundation for selecting parameters to optimize the refrigeration system in the future.
Joint Detection and Communication over Type-Sensitive Networks
Authors: Shaska Joni , Mitra Urbashi
Published: September 2023

Due to the difficulty of decentralized inference with conditional dependent observations, and motivated by large-scale heterogeneous networks, we formulate a framework for decentralized detection with coupled observations. Each agent has a state, and the empirical distribution of all agents’ states or the type of network dictates the individual agents’ behavior. In particular, agents’ observations depend on both the underlying hypothesis as well as the empirical distribution of the agents’ states. Hence, our framework captures a high degree of coupling, in that an individual agent’s behavior depends on both the underlying hypothesis and the behavior of all other agents in the network. Considering this framework, the method of types, and a series of equicontinuity arguments, we derive the error exponent for the case in which all agents are identical and show that this error exponent depends on only a single empirical distribution. The analysis is extended to the multi-class case, and numerical results with state-dependent agent signaling and state-dependent channels highlight the utility of the proposed framework for analysis of highly coupled environments.
Assisted Identification over Modulo-Additive Noise Channels
Authors: Lapidoth Amos , Ni Baohua
Published: September 2023

The gain in the identification capacity afforded by a rate-limited description of the noise sequence corrupting a modulo-additive noise channel is studied. Both the classical Ahlswede–Dueck version and the Ahlswede–Cai–Ning–Zhang version, which does not allow for missed identifications, are studied. Irrespective of whether the description is provided to the receiver, to the transmitter, or to both, the two capacities coincide and both equal the helper-assisted Shannon capacity.
Path Counting on Tree-like Graphs with a Single Entropic Trap: Critical Behavior and Finite Size Effects
Authors: Gulyaev Alexey V. , Tamm Mikhail V.
Published: September 2023

It is known that maximal entropy random walks and partition functions that count long paths on graphs tend to become localized near nodes with a high degree. Here, we revisit the simplest toy model of such a localization: a regular tree of degree p with one special node (“root”) that has a degree different from all the others. We present an in-depth study of the path-counting problem precisely at the localization transition. We study paths that start from the root in both infinite trees and finite, locally tree-like regular random graphs (RRGs). For the infinite tree, we prove that the probability distribution function of the endpoints of the path is a step function. The position of the step moves away from the root at a constant velocity v=(p−2)/p. We find the width and asymptotic shape of the distribution in the vicinity of the shock. For a finite RRG, we show that a critical slowdown takes place, and the trajectory length needed to reach the equilibrium distribution is on the order of N instead of logp−1N away from the transition. We calculate the exact values of the equilibrium distribution and relaxation length, as well as the shapes of slowly relaxing modes.
Delegated Proof of Stake Consensus Mechanism Based on Community Discovery and Credit Incentive
Authors: Li Wangchun , Deng Xiaohong , Liu Juan , Yu Zhiwei , Lou Xiaoping
Published: September 2023

Consensus algorithms are the core technology of a blockchain and directly affect the implementation and application of blockchain systems. Delegated proof of stake (DPoS) significantly reduces the time required for transaction verification by selecting representative nodes to generate blocks, and it has become a mainstream consensus algorithm. However, existing DPoS algorithms have issues such as “one ballot, one vote”, a low degree of decentralization, and nodes performing malicious actions. To address these problems, an improved DPoS algorithm based on community discovery is designed, called CD-DPoS. First, we introduce the PageRank algorithm to improve the voting mechanism, achieving “one ballot, multiple votes”, and we obtain the reputation value of each node. Second, we propose a node voting enthusiasm measurement method based on the GN algorithm. Finally, we design a comprehensive election mechanism combining node reputation values and voting enthusiasm to select secure and reliable accounting nodes. A node credit incentive mechanism is also designed to effectively motivate normal nodes and drive out malicious nodes. The experimental simulation results show that our proposed algorithm has better decentralization, malicious node eviction capabilities and higher throughput than similar methods.
Trends in the Second Law of Thermodynamics
Authors: Zivieri Roberto
Published: September 2023

The Second Law of Thermodynamics represents a milestone in the history of not only physics but also chemistry, engineering, and, more generally, life and natural sciences [...]
On the Emergence of the Deviation from a Poisson Law in Stochastic Mathematical Models for Radiation-Induced DNA Damage: A System Size Expansion
Authors: Cordoni Francesco Giuseppe
Published: September 2023

In this paper, we study the system size expansion of a stochastic model for radiation-induced DNA damage kinetics and repair. In particular, we characterize both the macroscopic deterministic limit and the fluctuation around it. We further show that such fluctuations are Gaussian-distributed. In deriving such results, we provide further insights into the relationship between stochastic and deterministic mathematical models for radiation-induced DNA damage repair. Specifically, we demonstrate how the governing deterministic equations commonly employed in the field arise naturally within the stochastic framework as a macroscopic limit. Additionally, by examining the fluctuations around this macroscopic limit, we uncover deviations from a Poissonian behavior driven by interactions and clustering among DNA damages. Although such behaviors have been empirically observed, our derived results represent the first rigorous derivation that incorporates these deviations from a Poissonian distribution within a mathematical model, eliminating the need for specific ad hoc corrections.
Multifractality of Complex Networks Is Also Due to Geometry: A Geometric Sandbox Algorithm
Authors: Rak Rafał , Rak Ewa
Published: September 2023

Over the past three decades, describing the reality surrounding us using the language of complex networks has become very useful and therefore popular. One of the most important features, especially of real networks, is their complexity, which often manifests itself in a fractal or even multifractal structure. As a generalization of fractal analysis, the multifractal analysis of complex networks is a useful tool for identifying and quantitatively describing the spatial hierarchy of both theoretical and numerical fractal patterns. Nowadays, there are many methods of multifractal analysis. However, all these methods take into account only the fact of connection between nodes (and eventually the weight of edges) and do not take into account the real positions (coordinates) of nodes in space. However, intuition suggests that the geometry of network nodes’ position should have a significant impact on the true fractal structure. Many networks identified in nature (e.g., air connection networks, energy networks, social networks, mountain ridge networks, networks of neurones in the brain, and street networks) have their own often unique and characteristic geometry, which is not taken into account in the identification process of multifractality in commonly used methods. In this paper, we propose a multifractal network analysis method that takes into account both connections between nodes and the location coordinates of nodes (network geometry). We show the results for different geometrical variants of the same network and reveal that this method, contrary to the commonly used method, is sensitive to changes in network geometry. We also carry out tests for synthetic as well as real-world networks.
First Report and Genetic Analysis of the Invasive Species A. fruticosa L. in Greece: A Combined Genetic and Regeneration Study
Authors: Avramidou Evangelia V. , Korakaki Evangelia , Malliarou Ermioni , Solomou Alexandra D. , Mantakas George , Karetsos George
Published: September 2023

A first assessment of genetic diversity and expansion of the invasive species Amorpha fruticosa L. was estimated in the 91E0* (Annex I Dir. 92/43/EEC) priority habitat of Nestos. A. fruticosa is a perennial deciduous shrub belonging to the family Fabaceae and is native to the south-western part of North America, which recently was introduced to Europe as an ornamental plant. Its expansion through the cross-border river of Nestos has significant negative effects on the 91E0* habitat (a habitat with Alnus glutinosa, Populus alba, and Ulmus minor). Herein, we studied the genetic diversity with amplified fragment length polymorphisms fragments of eastern and western populations in order to see if there is one genetic pool for the species. Furthermore, we estimated the regeneration index (IR) in order to see the potential expansion of this invasive species. Results showed that eastern and western populations have almost equal heterozygosity (He) and with a slightly higher value for the western population, which was in agreement with the results of the IR. This is an innovative study that discusses the implications of A. fruticosa of 91E0* habitat in Nestos and proposes management measures to eliminate its expansion and improve the resilience of the habitat.
Evaluating a Secondary Education Urban Ecology Project within the Framework of a Problem-Based Learning Methodology
Authors: Ayerbe-López Joaquin , Perales-Palacios Francisco Javier
Published: September 2023

As a reaction to the current situation of local and global environmental deterioration, recent decades have seen the emergence of multiple educational strategies and methodologies within Environmental Education (EE) with the aim of promoting pro-environmental attitudes among young people. One of these strategies is Problem-Based Learning (PBL), and it is necessary to analyze the didactic implications of this methodology in the field of EE in order to maximize its effectiveness. This study evaluates the implementation and didactic implications of an environmental project designed under the PBL methodology. The project was carried out over consecutive years of compulsory secondary education (Grades 9 and 10) in two secondary schools in the province of Granada, Spain. The data collection instruments used included the researcher’s diary, self-assessment and reflection questionnaires for the participating students, and questionnaires and rubrics completed by the audience during the presentation of the final product. The study employed a qualitative interpretative approach, using response categorization and SWOT analyses, the results of which revealed multiple conclusions, highlighting the high level of motivation, work and participation of the students, as well as the didactic benefits of the enriching socialization of the project. Difficulties, such as time management and cooperative group work, lack of practice in the methodology used and challenges related to the use of information and communication technologies (ICT), were also identified. Finally, recommendations are provided for the application of this methodology in other educational contexts.
Exploring User Engagement in Museum Scenario with EEG—A Case Study in MAV Craftsmanship Museum in Valle d’Aosta Region, Italy
Authors: Castiblanco Jimenez Ivonne Angelica , Nonis Francesca , Olivetti Elena Carlotta , Ulrich Luca , Moos Sandro , Monaci Maria Grazia , Marcolin Federica , Vezzetti Enrico
Published: September 2023

In the last decade, museums and exhibitions have benefited from the advances in Virtual Reality technologies to create complementary virtual elements to the traditional visit. The aim is to make the collections more engaging, interactive, comprehensible and accessible. Also, the studies regarding users’ and visitors’ engagement suggest that the real affective state cannot be fully assessed with self-assessment techniques and that other physiological techniques, such as EEG, should be adopted to gain a more unbiased and mature understanding of their feelings. With the aim of contributing to bridging this knowledge gap, this work proposes to adopt literature EEG-based indicators (valence, arousal, engagement) to analyze the affective state of 95 visitors interacting physically or virtually (in a VR environment) with five handicraft objects belonging to the permanent collection of the Museo dell’Artigianato Valdostano di Tradizione, which is a traditional craftsmanship museum in the Valle d’Aosta region. Extreme Gradient Boosting (XGBoost) was adopted to classify the obtained engagement measures, which were labeled according to questionnaire replies. EEG analysis played a fundamental role in understanding the cognitive and emotional processes underlying immersive experiences, highlighting the potential of VR technologies in enhancing participants’ cognitive engagement. The results indicate that EEG-based indicators have common trends with self-assessment, suggesting that their use as ‘the ground truth of emotion’ is a viable option.
Hierarchical Decentralized Federated Learning Framework with Adaptive Clustering: Bloom-Filter-Based Companions Choice for Learning Non-IID Data in IoV
Authors: Liu Siyuan , Liu Zhiqiang , Xu Zhiwei , Liu Wenjing , Tian Jie
Published: September 2023

The accelerating progress of the Internet of Vehicles (IoV) has put forward a higher demand for distributed model training and data sharing in vehicular networks. Traditional centralized approaches are no longer applicable in the face of drivers’ concerns about data privacy, while Decentralized Federated Learning (DFL) provides new possibilities to address this issue. However, DFL still faces challenges regarding the non-IID data of passing vehicles. To tackle this challenge, a novel DFL framework, Hierarchical Decentralized Federated Learning (H-DFL), is proposed to achieve qualified distributed training among vehicles by considering data complementarity. We include vehicles, base stations, and data center servers in this framework. Firstly, a novel vehicle-clustering paradigm is designed to group passing vehicles based on the Bloom-filter-based compact representation of data complementarity. In this way, vehicles train their models based on local data, exchange model parameters in each group, and achieve a qualified local model without the interference of imbalanced data. On a higher level, a local model trained by each group is submitted to the data center to obtain a model covering global features. Base stations maintain the local models of different groups and judge whether the local models need to be updated according to the global model. The experimental results based on real-world data demonstrate that H-DFL dose not only reduces communication latency with different participants but also addresses the challenges of non-IID data in vehicles.
Decoupling Methods in Planar Ultra-Wideband Multiple-Input-Multiple-Output Antennas: A Review of the Design, State-of-the-Art, and Research Challenges
Authors: Jayant Shailesh , Srivastava Garima , Kumar Sachin , Mostafa Hala , Goyal Bhawna , Choi Hyun Chul , Kim Kang Wook
Published: September 2023

Ultra-wideband (UWB) antennas cover a frequency range of 3.1 to 10.6 GHz and have sparked a lot of research interest as an essential part of wireless communication systems as they provide high data transmission speeds, are less expensive, and consume less power. UWB antennas are widely used in radar imaging, radio frequency identification, public security, and other high-accuracy positioning devices such as altimetry. Some smart applications of UWB antennas are vehicular radar systems, surveillance systems, software-defined radios, spectrum analysis, proximity fuses, etc. Multiple-input-multiple-output (MIMO) is a multiplexing technology that adopts multiple antennas both at the transmitter and receiver, which can enhance the channel capacity. MIMO technology is extensively used in several applications, such as in portable devices, wireless body area networks (WBANs), vehicular communication, and satellite–terrestrial networks. Generally, the MIMO antennas are used to obtain high reliability, high capacity, high throughput, and high security. The UWB MIMO antennas (UMAs) are considered the best choice for wireless communication systems as they offer reliability and wide transmission capacity, in contrast to unit antenna elements (AEs), without increasing system bandwidth or transmission power. The present-day and future communications systems need higher throughput to meet the demands of users. The signal transfer rate can be improved by improving channel bandwidth or increasing the number of receiving antennas. However, the main issue in designing UMAs is to provide high isolation between AEs because mutual coupling interactions between them affect the generated radiation patterns, leading to worse performance and failing to meet the operative constraints and requirements. When introducing decoupling techniques (DTs), researchers experience numerous challenges, including an increase in antenna size, design complexity, and cross-polarization. This article offers an organized review and simulated study of the various DTs in UMAs. The simulated study has been carried out through the implementation of various types of DTs on the same two-port UMA, which consists of two microstrip-fed circular-shaped AEs with open-end slotted partial ground. In contrast with previously reported review articles, this article provides a detailed study of various types of DTs reported so far and a better understanding for selecting appropriate DTs, which help in designing UMAs with better performance.
Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks
Authors: Alyami Mnassar , Alghamdi Abdulmajeed , Alkhowaiter Mohammed A. , Zou Cliff , Solihin Yan
Published: September 2023

Despite encryption, the packet size is still visible, enabling observers to infer private information in the Internet of Things (IoT) environment (e.g., IoT device identification). Packet padding obfuscates packet-length characteristics with a high data overhead because it relies on adding noise to the data. This paper proposes a more data-efficient approach that randomizes packet sizes without adding noise. We achieve this by splitting large TCP segments into random-sized chunks; hence, the packet length distribution is obfuscated without adding noise data. Our client–server implementation using TCP sockets demonstrates the feasibility of our approach at the application level. We realize our packet size control by adjusting two local socket-programming parameters. First, we enable the TCP_NODELAY option to send out each packet with our specified length. Second, we downsize the sending buffer to prevent the sender from pushing out more data than can be received, which could disable our control of the packet sizes. We simulate our defense on a network trace of four IoT devices and show a reduction in device classification accuracy from 98% to 63%, close to random guessing. Meanwhile, the real-world data transmission experiments show that the added latency is reasonable, less than 21%, while the added packet header overhead is only about 5%.
Pairwise Guided Multilayer Cross-Fusion Network for Bird Image Recognition
Authors: Lei Jingsheng , Jin Yao , Huang Liya , Ji Yuan , Yang Shengying
Published: September 2023

Bird identification is the first step in collecting data on bird diversity and abundance, which also helps research on bird distribution and population measurements. Most research has built end-to-end training models for bird detection task via CNNs or attentive models, but many perform unsatisfactorily in fine-grained bird recognition. Bird recognition tasks are highly influenced by factors, including the similar appearance of different subcategories, diverse bird postures, and other interference factors such as tree branches and leaves from the background. To tackle this challenge, we propose the Progressive Cross-Union Network (PC-Net) to capture more subtle parts with low-level attention maps. Based on cross-layer information exchange and pairwise learning, the proposed method uses two modules to improve feature representation and localization. First, it utilizes low- and high-level information for cross-layer feature fusion, which enables the network to extract more comprehensive and discriminative features. Second, the network incorporates deep semantic localization to identify and enhance the most relevant regions in the images. In addition, the network is designed with a semantic guidance loss to improve its generalization for variable bird poses. The PC-Net was evaluated on an extensively used birds dataset (CUB-200-2011), which contains 200 birds subcategories. The results demonstrate that the PC-Net achieved an impressive recognition accuracy of 89.2%, thereby outperforming maintained methods in bird subcategory identification. We also achieved competitive results on two other datasets with data on cars and airplanes. The results indicated that the PC-Net improves the accuracy of diverse bird recognition, as well as other fine-grained recognition scenarios.
AP-NURSE: A Modular Tool to Ease the Life of Patients Suffering from Cognitive Diseases
Authors: Čerba Štefan , Vrban Branislav , Lüley Jakub , Vojs Marian , Vrška Martin , Behúl Miroslav , Bendík Jozef , Cenký Matej , Janíček František , Majer Libor , Gyepes Gábor
Published: September 2023

This paper presents the development and testing of the AP-NURSE monitoring tool aimed to ease the everyday life of patients with Alzheimer’s and Parkinson’s diseases as well as those who take care of such patients. It summarises the activities carried out as part of the international Interreg CE niCE-life project. The team of STU designed, developed, constructed, and tested the AP-NURSE intelligent tool. The paper starts with demographic information on Slovakia. It highlights the fact that population aging is a significant issue, which requires measures to mitigate its consequences at local and European levels. The next part describes the final design of AP-NURSE Home and Care devices and their versions, including details on their hardware and software. A significant part of the paper deals with the testing of AP-NURSE in both laboratory and authentic environments. The laboratory testing was carried out directly at STU. The final testing in an authentic environment was conducted in two social and healthcare centres in Bratislava and in Warsaw. After the activities described in this paper, AP-NURSE reached TRL-6. The AP-NURSE Home platform developed based on the ESP microcontrollers demonstrated the best functionality and has the potential to be used in real practice.
Improved Resnet Model Based on Positive Traffic Flow for IoT Anomalous Traffic Detection
Authors: Li Qingfeng , Liu Yaqiu , Niu Tong , Wang Xiaoming
Published: September 2023

The Internet of Things (IoT) has been highly appreciated by several nations and societies as a worldwide strategic developing sector. However, IoT security is seriously threatened by anomalous traffic in the IoT. Therefore, creating a detection model that can recognize such aberrant traffic is essential to ensuring the overall security of the IoT. We outline the main approaches that are used today to detect anomalous network traffic and suggest a Resnet detection model based on fused one-dimensional convolution (Conv1D) for this purpose. Our method combines one-dimensional convolution and a Resnet network to create a new network model. This network model improves the residual block by including Conv1D and Conv2D layers for two-dimensional convolution. This change enhances the model’s ability to identify aberrant traffic by enabling the network to extract feature information from one-dimensional linearity and two-dimensional space. The CIC IoT Dataset from the Canadian Institute for Cybersecurity Research was used to assess the effectiveness of the proposed enhanced residual network technique. The outcomes demonstrate that the algorithm performs better at identifying aberrant traffic in the IoT than the original residual neural network. The accuracy achieved can be as high as 99.9%.
A Flexible Solid-State Marx Modulator Module Based on Discrete Magnetic Coupling Drivers
Authors: Chen Lei , Zhu Caihui , Zheng Jiaming , Qiu Jian , Zhao Hui , Liu Kefu
Published: September 2023

With the increasing and deepening application of high-voltage nanosecond solid-state pulse generators in biological, industrial, and environmental fields, the development of existing pulse generators faces many challenges, such as fixed pulse shapes, the usage of isolated driver power supplies, lower power density, and limited output electrical performance. Hence, a novel high-frequency multilevel nanosecond modular solid-state Marx modulator (SSMM) based on discrete magnetic coupling gate drivers is proposed. The gate voltage of the two MOSFETs can be rapidly synchronized at a high repetition frequency to achieve an amplitude-controlled gate voltage within 100 ns. The feasibility of the driver was verified by PSpice simulation and prototype testing. Moreover, a stackable SSMM module (S2M3) structure is proposed to solve the problem of common-mode interference conducted through the driver, which improves the reusability, scalability, and redundancy of modulators. The characteristic parameters of the developed 14-stage S2M3 are as follows: an output voltage amplitude of 5.45 kV with a 100 ns–50 ms width, a minimum rise time of approximately 18 ns, and a continuous repetition frequency of 100 kHz. S2M3 has the ability to change the pulse shape, and the pulse frequency can reach 2.8 MHz within the burst.
Factor Graph with Local Constraints: A Magnetic Field/Pedestrian Dead Reckoning Integrated Navigation Method Based on a Constrained Factor Graph
Authors: Li Zehua , Shang Junna , Shi Huli
Published: September 2023

The method of multi-sensor integrated navigation improves navigation accuracy by fusing various sensor data. However, when a sensor is disturbed or malfunctions, incorrect measurement information will seriously affect the estimation of the trajectory, which will lead to a decrease in accuracy. Existing factor graph models based on weights can neither fully resist the influence of disturbances nor guarantee the local rationality of estimated trajectories. In this paper, a factor graph with local constraints model that fuses the magnetic field and pedestrian dead reckoning data is proposed to navigate complex curved trajectories. First, adding local constraints to the pedestrian dead reckoning measurement converts the navigation solution problem into a hard-constrained nonlinear least squares problem. Then, a mapping model is constructed to reconstruct the variable space and the Adam gradient algorithm is used to realize a fast calculation. The navigation accuracy of this algorithm is better than that of the state-of-the-art method in real-world experiments, with an average accuracy of 0.83 m.
Single-Stage CMOS Operational Transconductance Amplifiers (OTAs): A Design Tutorial
Authors: Choi Jaesuk , Kweon Soon-Jae , Jeon Hyuntak
Published: September 2023

This paper presents a comprehensive design tutorial for four types of single-stage operational transconductance amplifiers (OTAs): (1) five-transistor OTAs, (2) telescopic cascode OTAs, (3) folded cascode OTAs, and (4) current mirror OTAs. These OTAs serve as fundamental building blocks in analog circuits. The operational principles of each OTA are reviewed, and the key performance metrics are derived through a hand analysis. These performance metrics encompass most crucial parameters, including small-signal parameters, frequency response, input and output swing ranges, rising and falling slew rates, nonidealities, and bias circuit simplicity. All of these metrics are verified and compared using the simulation. Furthermore, the practical applications of each OTA are summarized, and a case study demonstrates the enhancement of a neural recording amplifier’s performance through appropriate OTA selection. A thorough review of the essential building blocks will become a stepping stone to design high-performance analog amplifiers across diverse applications.
Facial Expression Recognition in the Wild for Low-Resolution Images Using Voting Residual Network
Authors: Gómez-Sirvent José L. , López de la Rosa Francisco , López María T. , Fernández-Caballero Antonio
Published: September 2023

Facial expression recognition (FER) in the wild has attracted much attention in recent years due to its wide range of applications. Most current approaches use deep learning models trained on relatively large images, which significantly reduces their accuracy when they have to infer low-resolution images. In this paper, a residual voting network is proposed for the classification of low-resolution facial expression images. Specifically, the network consists of a modified ResNet-18, which divides each sample into multiple overlapping crops, makes a prediction of the class to which each of the crops belongs, and by soft-voting the predictions of all the crops, the network determines the class of the sample. A novel aspect of this work is that the image splitting is not performed before entering the network, but at an intermediate point in the network, which significantly reduces the resource consumption. The proposed approach was evaluated on two popular benchmark datasets (AffectNet and RAF-DB) by scaling the images to a network input size of 48 × 48. The proposed model reported an accuracy of 63.06% on AffectNet and 85.69% on RAF-DB with seven classes in both cases, which are values comparable to those provided by other current approaches using much larger images.
Optimal Reconstruction of Single-Pixel Images through Feature Feedback Mechanism and Attention
Authors: Gao Zijun , Su Jingwen , Zhang Junjie , Song Zhankui , Li Bo , Wang Jue
Published: September 2023

The single-pixel imaging technique can reconstruct high-quality images using only a bucket detector with no spatial resolution, and the image quality is degraded in order to meet the demands of real-time applications. According to some studies of algorithm performance, the network model performs differently in simulated and real-world experiments. We propose an end-to-end neural network capable of reconstructing 2D images from experimentally obtained 1D signals optimally. In order to improve the image quality of real-time single-pixel imaging, we built a feedback module in the hidden layer of the recurrent neural network to implement feature feedback. The feedback module fuses high-level features of undersampled images with low-level features through dense jump connections and multi-scale balanced attention modules to gradually optimize the feature extraction process and reconstruct high-quality images. In addition, we introduce a learning strategy that combines mean loss with frequency domain loss to improve the network’s ability to reconstruct complex undersampled images. In this paper, the factors that lead to the degradation of single-pixel imaging are analyzed, and a network degradation model suitable for physical imaging systems is designed. The experiment results indicate that the reconstructed images utilizing the proposed method have better quality metrics and visual effects than the excellent methods in the field of single-pixel imaging.
Deep Reinforcement Learning-Based 2.5D Multi-Objective Path Planning for Ground Vehicles: Considering Distance and Energy Consumption
Authors: Wu Xiru , Huang Shuqiao , Huang Guoming
Published: September 2023

Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length in a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in three steps: (1) transform the high-resolution 2.5D map into a small-size map, (2) use a trained deep Q network (DQN) to find the desired path on the small-size map, and (3) build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward-shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. The simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency and quality. Also, simulations prove that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks.
Mutual Inductance and Load Identification of LCC-S IPT System Considering Equivalent Inductance of Rectifier Load
Authors: Shen Haomin , Wang Xiaona , Sun Pan , Wang Lei , Liang Yan
Published: September 2023

The variation of mutual inductance and load parameters will affect the transmission power and efficiency of the inductive power transfer (IPT) system. The identification of mutual inductance and load parameters is an essential part of establishing a stable and reliable IPT system. This paper presents a joint identification method of load and mutual inductance for the LCC-S IPT system, which does not require the establishment of primary and secondary communication and related control. Firstly, the resistance-inductance characteristics of the equivalent load of the rectifier are analyzed by simulation, and then the rectifier and system load are equivalent to the circuit model of resistance and inductance in series. Secondly, the characteristics of the reflected impedance are analyzed, and the functional relationship between the transmitter impedance and the rectifier impedance is established by using the ratio of the real part to the imaginary part of the reflected impedance, which realizes the decoupling of the load and the mutual inductance. Thirdly, the functional relationship between the equivalent impedance of the rectifier and the load resistance of the system is obtained by data fitting. Then, the equations of the above two functional relationships are combined. By measuring the voltage of the parallel compensation capacitor at the transmitting side, the current of the transmitting coil and the phase difference between the two, the battery load can be solved first, and then the mutual inductance can be calculated, so that the high-precision identification of the load and mutual inductance can be realized. Finally, an experimental platform of the LCC-S IPT system is built for experimental verification. The experimental results show that the maximum identification errors of mutual inductance and load are 5.20% and 5.53%, respectively, which proves that the proposed identification method can achieve high precision identification.
Energy Consumption and Human Well-Being: A Systematic Review
Authors: tho Pesch Gereon , Einarsdóttir Anna Kristín , Dillman Kevin Joseph , Heinonen Jukka
Published: September 2023

Understanding the relationship between energy use and well-being is crucial for designing holistic energy policy. The latter has to both effectively mitigate climate change driven by current fossil-based energy systems as well as promote human development, which requires energy. While a significant body of research investigates this relationship, study designs differ significantly, so findings cannot be easily generalized. This machine learning-aided review provides an overview of the current state of the literature examining this relationship. We highlight and discuss methodological differences between the studies, including their perspective (top-down or bottom-up), spatial scope, and the respective energy and well-being indicators used. The review reveals that most research takes a top-down perspective, analyzing country-level data across multiple countries. These studies typically find a positive relationship between energy use and well-being, and most confirm the existence of a saturation effect. We reveal that countries in the Global South are underrepresented in current studies. Bottom-up studies focus on specific countries or country groups using household-level data, yielding more nuanced findings that can be further disaggregated by consumption domain. We find that energy and well-being indicators differ substantially across studies, yet the implications of this choice are not always sufficiently discussed. The review shows and discusses the current shift from production- to consumption-based energy indicators.
Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility
Authors: Hesami Simin , Vafaeipour Majid , De Cauwer Cedric , Rombaut Evy , Vanhaverbeke Lieselot , Coosemans Thierry
Published: September 2023

As autonomous vehicle technology advances, the development of energy-efficient control methodologies emerges as a critical area in the literature. This includes the behavior control of vehicles near signalized intersections, which still needs comprehensive exploration. Through connectivity, the adoption of promising eco-driving approaches can manage a vehicle’s speed profile to improve energy consumption. This study focuses on controlling the speed of an autonomous electric vehicle (AEV) both up and downstream of a signalized intersection in the presence of preceding vehicles. In order to achieve this, a dynamic pro-active predictive cruise control eco-driving (eco-PPCC) framework is developed that, instead of merely reacting to the preceding vehicle’s speed changes, uses the preceding vehicle’s upcoming data to actively adjust and optimize the speed profile of the AEV. The proposed algorithm is compared to the conventional Gipps and eco-PCC models for benchmarking and performance analysis through numerous scenarios. Additionally, real-world measurements are performed and taken to consider practical use cases. The results demonstrate that when compared to the two baseline methods, the proposed framework can add significant value to reducing energy consumption, preventing unnecessary stops at intersections, and improving travel time.