bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of FOXP3 on Tumor Metastasis and Its Interaction with Traditional Chinese Medicine
Authors: Ma Benxu , Miao Wenjun , Xiao Jieqiong , Chen Xinyi , Xu Jing , Li Yinan
Published: October 2022

Forkhead box protein 3 (FOXP3) is an important transcription factor for regulatory T cells (Tregs) and plays an important role in their immunosuppressive function. In recent years, studies have found that FOXP3 is expressed in many kinds of tumors and plays different roles in tumors’ biological behaviors, including tumor proliferation, metastasis, drug resistance, and prognosis. However, the effects of FOXP3 on tumor metastasis and its interaction with traditional Chinese medicine (TCM) remain unclear. Therefore, in this review, we focus on the effects of FOXP3 on tumor metastasis and its relationship with TCM, which can provide evidence for further research and therapy in clinical settings.
Novel Unsymmetric 3,5-Bis(benzylidene)-4-piperidones That Display Tumor-Selective Toxicity
Authors: Chhikara Aruna , Roayapalley Praveen K. , Sakagami Hiroshi , Amano Shigeru , Satoh Keitaro , Uesawa Yoshihiro , Das Umashankar , Das Swagatika , Borrego Edgar A. , Guerena Cristina D. , Hernandez Clare R. , Aguilera Renato J. , Dimmock Jonathan R.
Published: October 2022

Two series of novel unsymmetrical 3,5-bis(benzylidene)-4 piperidones 2a–f and 3a–e were designed as candidate antineoplastic agents. These compounds display potent cytotoxicity towards two colon cancers, as well as several oral squamous cell carcinomas. These compounds are less toxic to various non-malignant cells giving rise to large selectivity index (SI) figures. Many of the compounds are also cytotoxic towards CEM lymphoma and HL-60 leukemia cells. Representative compounds induced apoptotic cell death characterized by caspase-3 activation and subG1 accumulation in some OSCC cells, as well as the depolarization of the mitochondrial membrane potential in CEM cells. A further line of inquiry was directed to finding if the SI values are correlated with the atomic charges on the olefinic carbon atoms. The potential of these compounds as antineoplastic agents was enhanced by an ADME (absorption, distribution, metabolism, and excretion) evaluation of five lead molecules, which revealed no violations.
Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions
Authors: Gallivanone Francesca , Bertoli Gloria , Porro Danilo
Published: October 2022

Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with –omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).
Quality of Experience Experimentation Prediction Framework through Programmable Network Management
Authors: Al-Mashhadani Ahmed Osama Basil , Mu Mu , Al-Sharbaz Ali
Published: October 2022

Quality of experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the Internet. End-to-end metrics are formed because QoE is dependent on both the users’ perception and the service used. Traditionally, network optimization has focused on improving network properties such as the quality of service (QoS). In this paper we examine adaptive streaming over a software-defined network environment. We aimed to evaluate and study the media streams, aspects affecting the stream, and the network. This was undertaken to eventually reach a stage of analysing the network’s features and their direct relationship with the perceived QoE. We then use machine learning to build a prediction model based on subjective user experiments. This will help to eliminate future physical experiments and automate the process of predicting QoE.
Reproductive Disorders in Domestic Ruminants: A One Health Concern
Authors: Ebani Valentina Virginia
Published: October 2022

Farm ruminants (cattle, sheep and goats) are an important economic and food source for humans, both in developing countries and in areas with advanced animal husbandry [...]
New Concepts of Budo Internalised as a Philosophy of Life
Authors: Cynarski Wojciech J.
Published: October 2022

Traditional martial arts continue to be interesting and inspiring to many people around the globe. Some of their contemporary adaptations attract enthusiasts for whom they are especially important. In this article, the author bases his observations on his own long-term participation. The analysis takes into account the influence of the perspectives of Jigoro Kano and several other creators of modern varieties of Japanese budo. It can be concluded that regular, even daily, practice—cultivating martial arts and internalizing its values—co-creates the lifestyle of instructors and advanced students.
Key Experiment and Quantum Reasoning
Authors: Waitzmann Moritz , Weber Kim-Alessandro , Wessnigk Susanne , Scholz Ruediger
Published: October 2022

For around five decades, physicists have been experimenting with single quanta such as single photons. Insofar as the practised ensemble reasoning has become obsolete for the interpretation of these experiments, the non-classical intrinsic probabilistic nature of quantum theory has gained increased importance. One of the most important exclusive features of quantum physics is the undeniable existence of the superposition of states, even for single quantum objects. One known example of this effect is entanglement. In this paper, two classically contradictory phenomena are combined to one single experiment. This experiment incontestably shows that a single photon incident on an optical beam splitter can either be reflected or transmitted. The almost complete absence of coincident clicks of two photodetectors demonstrates that these two output states are incompatible. However, when combining these states using two mirrors, we can observe interference patterns in the counting rate of the single photon detector. The only explanation for this is that the two incompatible output states are prepared and kept simultaneously—a typical consequence of a quantum superposition of states. (Semi-)classical physical concepts fail here, and a full quantum concept is predestined to explain the complementary experimental outcomes for the quantum optical “non-waves” called single photons. In this paper, we intend to demonstrate that a true quantum physical key experiment (“true” in the sense that it cannot be explained by any classical physical concept), when combined with full quantum reasoning (probability, superposition and interference), influences students’ readiness to use quantum elements for interpretation.
Education toward Rooted Cosmopolitanism Viewed from a Religious Prism: The Case of Rabbi Ashlag’s Social Altruistic Doctrine
Authors: Vinokur Eli
Published: October 2022

Cosmopolitanism, which has recently become a matter of great scholarly interest, is an ethical and political theory that envisions all human beings as citizens in a single community. This paper presents a “rooted” approach to cosmopolitanism, claiming that local attachments can be essential to our ability to nurture a cosmopolitan orientation. More specifically, this paper explores the potential that lies in the connection between the ideas of 20th century Kabbalist, Rabbi Yehuda Ashlag (1884–1954), which stem from the mystical interpretation of reality and Jewish scriptures, and cosmopolitanism, viewed from an educational prism. To exemplify how a cosmopolitan-oriented religious education might look, the author presents a hermeneutic account of the social doctrine of Ashlag, whose writings, mediated by both orthodox and secular successors, have influenced millions. Relying on Ashlag’s approach to otherness—one of the main themes in cosmopolitanism—the author shows how religious affiliations can serve as a strong impetus for the cultivation of cosmopolitan values.
COVID-19 Outcomes in a US Cohort of Persons Living with HIV (PLWH)
Authors: Spence Amanda Blair , Desale Sameer , Lee Jennifer , Kumar Princy , Huang Xu , Cooper Stanley Evan , Fernandez Stephen , Kassaye Seble G.
Published: October 2022

Reported coronavirus disease 2019 (COVID-19) outcomes in persons living with HIV (PLWH) vary across cohorts. We examined clinical characteristics and outcomes of PLWH with COVID-19 compared with a matched HIV-seronegative cohort in a mid-Atlantic US healthcare system. Multivariate logistic regression was used to explore factors associated with hospitalization and death/mechanical ventilation among PLWH. Among 281 PLWH with COVID-19, the mean age was 51.5 (SD 12.74) years, 63% were male, 86% were Black, and 87% had a HIV viral load <200 copies/mL. Overall, 47% of PLWH versus 24% (p < 0.001) of matched HIV-seronegative individuals were hospitalized. Rates of COVID-19 associated cardiovascular and thrombotic events, AKI, and infections were similar between PLWH and HIV-seronegative individuals. Overall mortality was 6% (n = 18/281) in PLWH versus 3% (n = 33/1124) HIV-seronegative, p < 0.0001. Among admitted patients, mortality was 14% (n = 18/132) for PLWH and 13% (n = 33/269) for HIV-seronegative, p = 0.75. Among PLWH, hospitalization associated with older age aOR 1.04 (95% CI 1.01, 1.06), Medicaid insurance aOR 2.61 (95% CI 1.39, 4.97) and multimorbidity aOR 2.98 (95% CI 1.72, 5.23). Death/mechanical ventilation associated with older age aOR 1.06 (95% CI 1.01, 1.11), Medicaid insurance aOR 3.6 (95% CI 1.36, 9.74), and multimorbidity aOR 4.4 (95% CI 1.55, 15.9) in adjusted analyses. PLWH were hospitalized more frequently than the HIV-seronegative group and had a higher overall mortality rate, but once hospitalized had similar mortality rates. Older age, multimorbidity and insurance status associated with more severe outcomes among PLWH suggesting the importance of targeted interventions to mitigate the effects of modifiable inequities.
Application of Continuous Wavelet Transform and Artificial Naural Network for Automatic Radar Signal Recognition
Authors: Walenczykowska Marta , Kawalec Adam
Published: October 2022

This article aims to propose an algorithm for the automatic recognition of selected radar signals. The algorithm can find application in areas such as Electronic Warfare (EW), where automatic recognition of the type of intra-pulse modulation or the type of emitter operation mode can aid the decision-making process. The simulations carried out included the analysis of the classification possibilities of linear frequency modulated pulsed waveform (LFMPW), stepped frequency modulated pulsed waveform (SFMPW), phase coded pulsed waveform (PCPW), rectangular pulsed waveforms (RPW), frequency modulated continuous wave (FMCW), continuous wave (CW), Stepped Frequency Continuous Wave SFCW) and Phase Coded Continuous Waveform (PCCW). The algorithm proposed in this paper is based on the use of continuous wavelet transform (CWT) coefficients and higher-order statistics (HOS) in the feature determination of selected signals. The Principal Component Analysis (PCA) method was used for dimensionality reduction. An artificial neural network was then used as a classifier. Simulation studies took into account the presence of noise interference with signal-to-noise ratio (SNR) in the range from −5 to 10 dB. Finally, the obtained classification efficiency is presented in the form of a confusion matrix. The simulation results show a high recognition test accuracy, above 99% with a signal-to-noise ratio greater than 0 dB. The article also deals with the selection of the type and parameters of the wavelet. The authors also point to the problems encountered during the research and examples of how to solve them.
A Coupled Visual and Inertial Measurement Units Method for Locating and Mapping in Coal Mine Tunnel
Authors: Zhu Daixian , Ji Kangkang , Wu Dong , Liu Shulin
Published: October 2022

Mobile robots moving fast or in scenes with poor lighting conditions often cause the loss of visual feature tracking. In coal mine tunnels, the ground is often bumpy and the lighting is uneven. During the movement of the mobile robot in this scene, there will be violent bumps. The localization technology through visual features is greatly affected by the illumination and the speed of the camera movement. To solve the localization and mapping problem in an environment similar to underground coal mine tunnels, we improve a localization and mapping algorithm based on a monocular camera and an Inertial Measurement Unit (IMU). A feature-matching method that combines point and line features is designed to improve the robustness of the algorithm in the presence of degraded scene structure and insufficient illumination. The tightly coupled method is used to establish visual feature constraints and IMU pre-integration constraints. A keyframe nonlinear optimization algorithm based on sliding windows is used to accomplish state estimation. Extensive simulations and practical environment verification show that the improved simultaneous localization and mapping (SLAM) system with a monocular camera and IMU fusion can achieve accurate autonomous localization and map construction in scenes with insufficient light such as coal mine tunnels.
High-Order Harmonic Film Bulk Acoustic Resonator Based on a Polymer Reflector
Authors: Hu Yuxuan , Dong Bo , Lei Liang , Wang Zhizhong , Ruan Shuangchen
Published: October 2022

A film bulk acoustic resonator (FBAR), based on a polymer air cavity, is presented. The polymer reflective layer on the polymer air cavity can serve both as the reflective layer and the function layer for inducing the high-order mode resonance. With the aluminum nitride as the piezoelectric layer, the resonance frequency of the FBAR can reach 6.360 GHz, based on the finite element method. The product of the corresponding frequency and the quality factor, f × Q is more than 2 × 1013. This design model provides a good solution for the high-frequency filters and high-sensitivity sensor designs.
A Parallel Classification Model for Marine Mammal Sounds Based on Multi-Dimensional Feature Extraction and Data Augmentation
Authors: Cai Wenyu , Zhu Jifeng , Zhang Meiyan , Yang Yong
Published: October 2022

Due to the poor visibility of the deep-sea environment, acoustic signals are often collected and analyzed to explore the behavior of marine species. With the progress of underwater signal-acquisition technology, the amount of acoustic data obtained from the ocean has exceeded the limit that human can process manually, so designing efficient marine-mammal classification algorithms has become a research hotspot. In this paper, we design a classification model based on a multi-channel parallel structure, which can process multi-dimensional acoustic features extracted from audio samples, and fuse the prediction results of different channels through a trainable full connection layer. It uses transfer learning to obtain faster convergence speed, and introduces data augmentation to improve the classification accuracy. The k-fold cross-validation method was used to segment the data set to comprehensively evaluate the prediction accuracy and robustness of the model. The evaluation results showed that the model can achieve a mean accuracy of 95.21% while maintaining a standard deviation of 0.65%. There was excellent consistency in performance over multiple tests.
Vibration-Based Approach to Measure Rail Stress: Modeling and First Field Test
Authors: Belding Matthew , Enshaeian Alireza , Rizzo Piervincenzo
Published: October 2022

This paper describes a non-invasive inspection technique for the estimation of longitudinal stress in continuous welded rails (CWR) to infer the rail neutral temperature (RNT), i.e., the temperature at which the net longitudinal force in the rail is zero. The technique is based on the use of finite element method (FEM), vibration measurements, and machine learning (ML). FEM is used to model the relationship between the boundary conditions and the longitudinal stress of any given CWR to the vibration characteristics (mode shapes and frequencies) of the rail. The results of the numerical analysis are used to train a ML algorithm that is then tested using field data obtained by an array of accelerometers polled on the track of interest. In the study presented in this article, the proposed technique was proven in the field during an experimental campaign conducted in Colorado. A commercial FEM software was used to model the rail track as a short rail segment repeated indefinitely and under varying boundary conditions and stress. Three datasets were prepared and fed to ML models developed using hyperparameter search optimization techniques and k-fold cross validation to infer the stress or the RNT. The frequencies of vibration were extracted from the time waveforms obtained from two accelerometers temporarily attached to the rail. The results of the experiments demonstrated that the success of the technique is dependent on the accuracy of the model and the ability to properly identify the modeshapes. The results also proved that the ML was also able to predict successfully the neutral temperature of the tested rail by using only a limited number of experimental data for the training.
Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes
Authors: Fei Liu , Shahzad Muhammad , Abbas Fazal , Muqeet Hafiz Abdul , Hussain Muhammad Majid , Bin Li
Published: October 2022

In the energy system, various sources are used to fulfill the energy demand of large buildings. The energy management of large-scale buildings is very important. The proposed system comprises solar PVs, energy storage systems, and electric vehicles. Demand response (DR) schemes are considered in various studies, but the analysis of the impact of dynamic DR on operational cost has been ignored. So, in this paper, renewable energy resources and storages are integrated considering the demand response strategies such as real-time pricing (RTP), critical peak pricing (CPP), and time of use (ToU). The proposed system is mapped in a linear model and simulated in MATLAB using linear programming (LP). Different case studies are investigated considering the dynamic demand response schemes. Among different schemes, results based on real-time pricing (58% saving) show more saving as compared to the CPP and ToU. The obtained results reduced the operational cost and greenhouse gas (GHG) emissions, which shows the efficacy of the model.
A Preliminary Contribution towards a Risk-Based Model for Flood Management Planning Using BIM: A Case Study of Lisbon
Authors: Del Duca Graziella , Rocha Gustavo , Orszt Marta , Mateus Luis
Published: October 2022

Preparing a city for the impact of global warming is becoming of major importance. Adopting climate-proof policies and strategies in response to climate change has become a fundamental element for city planning. To this end, this research considers a multidisciplinary approach, at the local scale, able to connect urban planning and architecture, as a vital base for considering a coastal cities’ ability to control the consequences of climate change, specifically floods. So far, there is a scarcity of research connecting sea ground and land surveys, and this study could become a foundational reference for coastline settlement management using BIM. We found in BIM (Building Information Modeling) a possible tool for managing coastal risk, since it can combine crowdsourced data for geometric and information modeling of the city. The proposed BIM model includes a topography used for 3D thematic maps, a riverbed model, and a waterway model. This model aims to facilitate coordination across separate actors and interests since the urban area model is always updatable and improvable. Focusing on a case study of Lisbon, we developed risk-based 3D maps of the area close to the shoreline of the Tagus River.
Using Smartphones to Locate Trapped Victims in Disasters
Authors: Tai Yenpo , Yu Teng-To
Published: October 2022

Large and unaccounted numbers of victims in disasters, events, or fires are often trapped in buildings or debris, and must be located and rescued as soon as possible. This study transforms smartphones into indoor locating tools without extra modification or complicated program installation, considering smartphones are likely to be carried when disasters strike. The study creates a system that converts smartphones into a lifesaving tool for trapped victims and rescuers. This study employs the Bluetooth beacon in smartphones to send signals using its low power consumption feature. The signal could continue transmitting for rescuers to locate trapped victims for longer. Rescuers could use the Bluetooth function on a regular notebook computer to search such signals without any hardware implementation or modification, allowing them to locate and determine the position of many trapped victims simultaneously. Implementing this system will decrease the search and rescue team’s need to enter unsafe areas and increase their rescue speed, a critical factor for the survival of trapped victims. Furthermore, when disasters strike, the smartphone calling function might not work, and the trapped victim might be too weak to call for help. Thus, autoreply messages from victims’ smartphones could help them be located within a 2-m error, even if covered by fallen debris such as wood piles or tiles. This effort will increase the chance of finding trapped victims within the golden rescue hours and reduce the exposure time of search and rescue teams in unsafe environments.
The Impedance Analysis of a Viscoelastic Petalous Structured Stearic Acid Functional Layer Deposited on a QCM
Authors: Masruroh  , Santjojo Dionysius J. D. H.
Published: October 2022

A functional layer is crucial in a QCM sensor, to immobilize target molecules. The microstructure of the layer determines the sensitivity of the sensor. On the other hand, the microstructure also affects the loading of the sensor. In this study, impedance analysis was used to investigate the relationship between the microstructure and the viscoelastic properties of a petalous stearic acid (SA) functional layer. The SA layer was deposited using a vacuum thermal evaporation technique. Different petalous pillar structures in the elastic layer were generated by varying the deposition time. Analysis showed that the growth of the embedded pillar structures dramatically reduced the conductance and increased the bandwidth. The energy dissipation during the vibration could be related to the interaction between the pillars and the elastic matrix.
Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
Authors: Mahmud Imtiaz , Lubna Tabassum , Cho You-Ze
Published: October 2022

The 5G cellular network comes with a promise to provide a very high data rate at low latency, which is becoming critical for advancing technologies. Mobile operators are currently deploying the 5G cellular network worldwide. However, because of limited coverage and high susceptibility of the 5G network to obstacles, handoffs from 5G to 4G and vice versa frequently occur, especially when the user equipment (UE) is moving. These handoffs often cause significant delays in data transmission due to packet losses and retransmissions. A promising solution can be to use both 4G and 5G networks simultaneously, which can solve this problem and yield a better throughput. Multipath transmission control protocol (TCP) is an effective solution for this problem, but it requires significant performance evaluation before practical deployment. In this study, we implement an MPTCP testbed based on NS3-DCE that enables to test the performance of MPTCP schedulers and congestion control algorithms (CCAs) in both 3GPP and non-3GPP networks. Through extensive simulation experiments in a scenario where a UE simultaneously utilizes both 4G and 5G networks, we found that blocking estimation (BLEST) scheduler implemented with balanced linked adaptation (BALIA) CCA can produce the highest throughput and lowest delay. Finally, we showed how received signal to interference and noise ratio (SINR), congestion window, throughput, and packet losses are interconnected.
Analysis of the Possibility of Using Cenospheres in the Production of Cement Mortars for Use in an Elevated Temperature Environment
Authors: Rutkowska Gabriela , Ogrodnik Paweł , Żółtowski Mariusz , Powęzka Aleksandra , Kaszewska Karolina
Published: October 2022

The topic of research included in this article was the evaluation of the influence of cenospheres on selected parameters of mortar cement. Samples were designed as CEM I 42.5 R Portland cement with the application of different additive amounts. In the experimental work, the consistency, compressive strength, and bending strength were tested after 28 and 56 days of maturation, and after heating temperatures of 20, 300, 500, and 700 °C. The compressive strength was tested on half beams (40 × 40 × 160 mm). Using the obtained results, the properties of the mortars were compared. The research confirmed the possibility of producing cenosphere-modified cement mortars. Cenospheres used in the preparation of cement mortar negatively affected the bending and compressive strength with increasing temperature (20, 300, 500, 700 °C) and increasing content of this additive (10, 20, 30%).
Anti-Blooming Clocking for Time-Delay Integration CCDs
Authors: Piechaczek Denis Szymon , Schrey Olaf , Ligges Manuel , Hosticka Bedrich , Kokozinski Rainer
Published: October 2022

This paper presents an investigation of the responsivity of a time-delay integration (TDI) charge-coupled device that employs anti-blooming clocking and uses a varying number of TDI stages. The influence of charge blooming caused by unused TDI stages in a TDI deployed selection scheme is shown experimentally, and an anti-blooming clocking mechanism is analyzed. The impact of blooming on sensor characteristics, such as the responsivity, the conversion gain, and the signal-to-noise ratio, is investigated. A comparison of the measurements with and without this anti-blooming clocking mechanism is presented and discussed in detail.
Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning
Authors: Jia Zhicheng , Sun Ming , Ou Chengming , Sun Shoujiang , Mao Chunli , Hong Liu , Wang Juan , Li Manli , Jia Shangang , Mao Peisheng
Published: October 2022

Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study, we developed a stacking ensemble learning (SEL) model for successfully identifying a single seed of sickle alfalfa (Medicago falcata), hybrid alfalfa (M. varia), and alfalfa (M. sativa). SEL adopted a three-layer structure, i.e., level 0 with principal component analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as models of dimensionality reduction and feature extraction (DRFE); level 1 with support vector machine (SVM), multiple logistic regression (MLR), generalized linear models with elastic net regularization (GLMNET), and eXtreme Gradient Boosting (XGBoost) as basic learners; and level 3 with XGBoost as meta-learner. We confirmed that the values of overall accuracy, kappa, precision, sensitivity, specificity, and sensitivity in the SEL model were all significantly higher than those in basic models alone, based on both spectral features and a combination of morphological and spectral features. Furthermore, we also developed a feature filtering process and successfully selected 5 optimal features out of 33 ones, which corresponded to the contents of chlorophyll, anthocyanin, fat, and moisture in seeds. Our SEL model in MSI data analysis provided a new way for seed identification, and the feature filter process potentially could be used widely for development of a low-cost and narrow-channel sensor.
Verifiable Delay Function and Its Blockchain-Related Application: A Survey
Authors: Wu Qiang , Xi Liang , Wang Shiren , Ji Shan , Wang Shenqing , Ren Yongjun
Published: October 2022

The concept of verifiable delay functions has received attention from researchers since it was first proposed in 2018. The applications of verifiable delay are also widespread in blockchain research, such as: computational timestamping, public random beacons, resource-efficient blockchains, and proofs of data replication. This paper introduces the concept of verifiable delay functions and systematically summarizes the types of verifiable delay functions. Firstly, the description and characteristics of verifiable delay functions are given, and weak verifiable delay functions, incremental verifiable delay functions, decodable verifiable delay functions, and trapdoor verifiable delay functions are introduced respectively. The construction of verifiable delay functions generally relies on two security assumptions: algebraic assumption or structural assumption. Then, the security assumptions of two different verifiable delay functions are described based on cryptography theory. Secondly, a post-quantum verifiable delay function based on super-singular isogeny is introduced. Finally, the paper summarizes the blockchain-related applications of verifiable delay functions.
Design and Development of an IoT Smart Meter with Load Control for Home Energy Management Systems
Authors: Munoz Omar , Ruelas Adolfo , Rosales Pedro , Acuña Alexis , Suastegui Alejandro , Lara Fernando
Published: October 2022

Electricity consumption is rising due to population growth, climate change, urbanization, and the increasing use of electronic devices. The trend of the Internet of Things has contributed to the creation of devices that promote the thrift and efficient use of electrical energy. Currently, most projects relating to this issue focus solely on monitoring energy consumption without providing relevant parameters or switching on/off electronic devices. Therefore, this paper presents in detail the design, construction, and validation of a smart meter with load control aimed at being part of a home energy management system. With its own electronic design, the proposal differs from others in many aspects. For example, it was developed using a simple IoT architecture with in-built WiFi technology to enable direct connection to the internet, while at the same time being big enough to be part of standardized electrical enclosures. Unlike other smart meters with load control, this one not only provides the amount of energy consumption, but rms current and voltage, active, reactive, and apparent power, reactive energy, and power factor—parameters that could be useful for future studies. In addition, this work presents evidence based on experimentation that the prototype in all its readings achieves an absolute percentage error of less than 1%. A real-life application of the device was also demonstrated in this document by measuring different appliances and switching them on/off manually and automatically using a web-deployed application.
E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures
Authors: Zlatintsi Athanasia , Filntisis Panagiotis P. , Garoufis Christos , Efthymiou Niki , Maragos Petros , Menychtas Andreas , Maglogiannis Ilias , Tsanakas Panayiotis , Sounapoglou Thomas , Kalisperakis Emmanouil , Karantinos Thomas , Lazaridi Marina , Garyfalli Vasiliki , Mantas Asimakis , Mantonakis Leonidas , Smyrnis Nikolaos
Published: October 2022

Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses.
A Fast North-Finding Algorithm on the Moving Pedestal Based on the Technology of Extended State Observer (ESO)
Authors: Bai Yunchao , Li Bing , Zhang Haosu , Wang Sheng , Yan Debao , Gao Ziheng , Pan Wenchao
Published: October 2022

We propose a kind of fast and high-precision alignment algorithm based on the ESO technology. Firstly, in order to solve the problems of rapid, high-accuracy, and anti-interference alignment on the moving pedestal in the north-seeker, the ESO technology in control theory is introduced to improve the traditional Kalman fine-alignment model. This method includes two stages: the coarse alignment in the inertial frame and fine alignment based on the ESO technology. By utilizing the ESO technology, the convergence speed of the heading angle can be greatly accelerated. The advantages of this method are high-accuracy, fast-convergence, strong ability of anti-interference, and short time-cost (no need of KF recursive calculation). Then, the algorithm model, calculation process, and the setting initial-values of the filter are shown. Finally, taking the shipborne north-finder based on the FOG (fiber-optic gyroscope) as the investigated subject, the test on the moving ship is carried out. The results of first off-line simulation show that the misalignment angle of the heading angle of the proposed (traditional) method is ≤2.1′ (1.8′) after 5.5 (10) minutes of alignment. The results of second off-line simulation indicate that the misalignment angle of the heading angle of the proposed (traditional) method is ≤4.8′ (14.2′) after 5.5 (10) minutes of alignment. The simulations are based on the ship-running experimental data. The measurement precisions of Doppler velocity log (DVL) are different in these two experiments.
Coverage Path Planning and Point-of-Interest Detection Using Autonomous Drone Swarms
Authors: Bezas Konstantinos , Tsoumanis Georgios , Angelis Constantinos T. , Oikonomou Konstantinos
Published: October 2022

Unmanned Aerial Vehicles (UAVs) or drones presently are enhanced with miniature sensors that can provide information relative to their environment. As such, they can detect changes in temperature, orientation, altitude, geographical location, electromagnetic fluctuations, lighting conditions, and more. Combining this information properly can help produce advanced environmental awareness; thus, the drone can navigate its environment autonomously. Wireless communications can also aid in the creation of drone swarms that, combined with the proper algorithm, can be coordinated towards area coverage for various missions, such as search and rescue. Coverage Path Planning (CPP) is the field that studies how drones, independently or in swarms, can cover an area of interest efficiently. In the current work, a CPP algorithm is proposed for a swarm of drones to detect points of interest and collect information from them. The algorithm’s effectiveness is evaluated under simulation results. A set of characteristics is defined to describe the coverage radius of each drone, the speed of the swarm, and the coverage path followed by it. The results show that, for larger swarm sizes, the missions require less time while more points of interest can be detected within the area. Two coverage paths are examined here—parallel lines and spiral coverage. The results depict that the parallel lines coverage is more time-efficient since the spiral increases the required time by an average of 5% in all cases for the same number of detected points of interest.
Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal
Authors: Theodorou Charalambos , Velisavljevic Vladan , Dyo Vladimir
Published: October 2022

In dynamic indoor environments and for a Visual Simultaneous Localization and Mapping (vSLAM) system to operate, moving objects should be considered because they could affect the system’s visual odometer stability and its position estimation accuracy. vSLAM can use feature points or a sequence of images, as it is the only source of input that can perform localization while simultaneously creating a map of the environment. A vSLAM system based on ORB-SLAM3 and on YOLOR was proposed in this paper. The newly proposed system in combination with an object detection model (YOLOX) applied on extracted feature points is capable of achieving 2–4% better accuracy compared to VPS-SLAM and DS-SLAM. Static feature points such as signs and benches were used to calculate the camera position, and dynamic moving objects were eliminated by using the tracking thread. A specific custom personal dataset that includes indoor and outdoor RGB-D pictures of train stations, including dynamic objects and high density of people, ground truth data, sequence data, and video recordings of the train stations and X, Y, Z data was used to validate and evaluate the proposed method. The results show that ORB-SLAM3 with YOLOR as object detection achieves 89.54% of accuracy in dynamic indoor environments compared to previous systems such as VPS-SLAM.
Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces
Authors: Haider Arsalan , Pigniczki Marcell , Köhler Michael H. , Fink Maximilian , Schardt Michael , Cichy Yannik , Zeh Thomas , Haas Lukas , Poguntke Tim , Jakobi Martin , Koch Alexander W.
Published: October 2022

This work introduces a process to develop a tool-independent, high-fidelity, ray tracing-based light detection and ranging (LiDAR) model. This virtual LiDAR sensor includes accurate modeling of the scan pattern and a complete signal processing toolchain of a LiDAR sensor. It is developed as a functional mock-up unit (FMU) by using the standardized open simulation interface (OSI) 3.0.2, and functional mock-up interface (FMI) 2.0. Subsequently, it was integrated into two commercial software virtual environment frameworks to demonstrate its exchangeability. Furthermore, the accuracy of the LiDAR sensor model is validated by comparing the simulation and real measurement data on the time domain and on the point cloud level. The validation results show that the mean absolute percentage error (MAPE) of simulated and measured time domain signal amplitude is 1.7%. In addition, the MAPE of the number of points Npoints and mean intensity Imean values received from the virtual and real targets are 8.5% and 9.3%, respectively. To the author’s knowledge, these are the smallest errors reported for the number of received points Npoints and mean intensity Imean values up until now. Moreover, the distance error derror is below the range accuracy of the actual LiDAR sensor, which is 2 cm for this use case. In addition, the proving ground measurement results are compared with the state-of-the-art LiDAR model provided by commercial software and the proposed LiDAR model to measure the presented model fidelity. The results show that the complete signal processing steps and imperfections of real LiDAR sensors need to be considered in the virtual LiDAR to obtain simulation results close to the actual sensor. Such considerable imperfections are optical losses, inherent detector effects, effects generated by the electrical amplification, and noise produced by the sunlight.
A Comparison of Machine Learning Algorithms and Feature Sets for Automatic Vocal Emotion Recognition in Speech
Authors: Doğdu Cem , Kessler Thomas , Schneider Dana , Shadaydeh Maha , Schweinberger Stefan R.
Published: October 2022

Vocal emotion recognition (VER) in natural speech, often referred to as speech emotion recognition (SER), remains challenging for both humans and computers. Applied fields including clinical diagnosis and intervention, social interaction research or Human Computer Interaction (HCI) increasingly benefit from efficient VER algorithms. Several feature sets were used with machine-learning (ML) algorithms for discrete emotion classification. However, there is no consensus for which low-level-descriptors and classifiers are optimal. Therefore, we aimed to compare the performance of machine-learning algorithms with several different feature sets. Concretely, seven ML algorithms were compared on the Berlin Database of Emotional Speech: Multilayer Perceptron Neural Network (MLP), J48 Decision Tree (DT), Support Vector Machine with Sequential Minimal Optimization (SMO), Random Forest (RF), k-Nearest Neighbor (KNN), Simple Logistic Regression (LOG) and Multinomial Logistic Regression (MLR) with 10-fold cross validation using four openSMILE feature sets (i.e., IS-09, emobase, GeMAPS and eGeMAPS). Results indicated that SMO, MLP and LOG show better performance (reaching to 87.85%, 84.00% and 83.74% accuracies, respectively) compared to RF, DT, MLR and KNN (with minimum 73.46%, 53.08%, 70.65% and 58.69% accuracies, respectively). Overall, the emobase feature set performed best. We discuss the implications of these findings for applications in diagnosis, intervention or HCI.
Transformer Based Binocular Disparity Prediction with Occlusion Predict and Novel Full Connection Layers
Authors: Liu Yi , Xu Xintao , Xiang Bajian , Chen Gang , Gong Guoliang , Lu Huaxiang
Published: October 2022

The depth estimation algorithm based on the convolutional neural network has many limitations and defects by constructing matching cost volume to calculate the disparity: using a limited disparity range, the authentic disparity beyond the predetermined range can not be acquired; Besides, the matching process lacks constraints on occlusion and matching uniqueness; Also, as a local feature extractor, a convolutional neural network lacks the ability of global context information perception. Aiming at the problems in the matching method of constructing matching cost volume, we propose a disparity prediction algorithm based on Transformer, which specifically comprises the Swin-SPP module for feature extraction based on Swin Transformer, Transformer disparity matching network based on self-attention and cross-attention mechanism, and occlusion prediction sub-network. In addition, we propose a double skip connection fully connected layer to solve the problems of gradient vanishing and explosion during the training process for the Transformer model, thus further enhancing inference accuracy. The proposed model in this paper achieved an EPE (Absolute error) of 0.57 and 0.61, and a 3PE (Percentage error greater than 3 px) of 1.74% and 1.56% on KITTI 2012 and KITTI 2015 datasets, respectively, with an inference time of 0.46 s and parameters as low as only 2.6 M, showing great advantages compared with other algorithms in various evaluation metrics.
Deep Metric Learning Using Negative Sampling Probability Annealing
Authors: Kertész Gábor
Published: October 2022

Multiple studies have concluded that the selection of input samples is key for deep metric learning. For triplet networks, the selection of the anchor, positive, and negative pairs is referred to as triplet mining. The selection of the negatives is considered the be the most complicated task, due to a large number of possibilities. The goal is to select a negative that results in a positive triplet loss; however, there are multiple approaches for this—semi-hard negative mining or hardest mining are well-known in addition to random selection. Since its introduction, semi-hard mining was proven to outperform other negative mining techniques; however, in recent years, the selection of the so-called hardest negative has shown promising results in different experiments. This paper introduces a novel negative sampling solution based on dynamic policy switching, referred to as negative sampling probability annealing, which aims to exploit the positives of all approaches. Results are validated on an experimental synthetic dataset using cluster-analysis methods; finally, the discriminative abilities of trained models are measured on real-life data.
Improvement and Performance Evaluation of a Dual-Probe Heat Pulse Distributed Temperature Sensing Method Used for Soil Moisture Estimation
Authors: Yao Jun-Cheng , Shi Bin , Liu Jie , Sun Meng-Ya , Fang Ke , Yao Jian , Gu Kai , Zhang Wei , Zhang Ji-Wen
Published: October 2022

Large-scale measurements of soil moisture play a critical role in many fields, such as agriculture, hydrology, and engineering. The distributed temperature sensing (DTS) technology, based on a dual-probe heat pulse (DPHP), is a novel approach to realizing large-scale soil moisture estimation. However, the application of the method is limited by the complex optical cable layout, calculation algorithm, and lack of standardized heating strategy. In this paper, an improved DPHP-DTS method considering the soil bulk density was proposed. The measurement accuracy of the DPHP-DTS method under different heating strategies was studied in laboratory experiments, and its long-term stability in regard to the monitoring of soil moisture during natural evaporation in different soils was tested. The results show that the improved DPHP-DTS method can accurately measure the soil moisture, and the fitting algorithm can reduce the error caused by the accuracy of the DTS temperature measurement under the low-power heating strategy. Its measurement accuracy increases with the increase in the heating strength and duration. In addition, the improved DPHP-DTS method can describe soil evaporation in both sand and loess with good reliability and stability.