Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Event-Triggered Distributed Secondary Control for a Microgrid Considering Time-Delay and Disturbances

The paper presents the design of a distributed dynamic event-triggered secondary controller (DETC) that considers time delay, external disturbances, and cyber disturbances in a microgrid (MG). We propose this new distributed event-triggered control scheme to address the communication issues in an MG. A dynamic event-triggered technique is proposed considering a positive pre-specified lower bound of the inter-event time that remains invariant despite external disturbances. The stability of the proposed dynamic event-triggered controller is proved using Lyapunov. It considers both the maximum allowable time delay and minimum inter-event time. The performance of the proposed dynamic event-triggered controller is validated using both MATLAB simulations and real-time experimentation using OPAL-RT. The comparison results with a static event-triggered controller show that the proposed dynamic event-triggered controller provides improved power-sharing and accurate voltage/frequency tracking among the DGs.
Developing Sleep Apnea Screening Models Compatible with Low-Resolution SpO2 Data

Sleep apnea is a common sleep disorder which has severe health consequence in the long term. Existing sleep apnea screening models rely on high-resolution physiological signals that are attainable with medical devices. Those models are not compatible with consumer devices as the latter only allow the retrieval of low-resolution data. In this paper, we develop machine learning models for automatic sleep apnea screening with low-resolution SpO2 signals. Using a large sleep dataset collected from 5741 subjects, we derived in total 67 time-domain, frequency-domain, nonlinear and demographic features from cleaned SpO2 signals. Features were normalized and transformed using independent component analysis (ICA). Support vector machine with radial basis function kernel was applied to train the models. Evaluation results showed that the best performance was achieved when the resolution of the SpO2 data was 30s. The best AUC, sensitivity, specificity was 82.7%, 79.5%, and 86.0%, respectively. ICA slightly improved model performance when data resolution was lower than 30s. Overall our method was resilient to the decrease in data resolution and thus is promising to work for consumer sleep technologies.
Effects of SEM Preparation Techniques on the Appearance of Bacteria and Biofilms in the Carter Sandstone

Abstract When biofilms (aggregations of bacteria and extracellular polymer secretions) in samples from the Carter Sandstone of Alabama were prepared for scanning electron microscopy (SEM) using different dehydration techniques, the organic material had visibly different textures and distributions. In order to assess whether the variation was attributable to sample preparation or to inherent biofilm heterogeneity, each of five techniques were tested 3 to 10 times on small (1 cm) pieces of the Carter Sandstone containing either a strain of bacteria cultured from and reintroduced into the rock, or an in situ biofilm grown by injection of nutrients through core samples. The techniques tested were (1) air drying alone; (2) fixation in 10% glutaraldehyde with air drying; (3) ethanol dehydration with hexamethyldisilazane (HMDS) drying [2.5% glutaraldehyde, ethanol dehydration, and HMDS]; (4) ethanol dehydration with critical-point drying; and (5) ethanol and acetone dehydration with critical-point drying. Unpreserved control samples were either imaged wet in an environmental scanning electron microscope (ESEM) or vacuum-dried for SEM. Observations were based on SEM microscopy of over 60 samples and study of over 150 photomicrographs. In both experiments, the original morphology of individual bacteria was best preserved by ethanol dehydration with HMDS drying, ethanol dehydration with critical-point drying, or ethanol-acetone dehydration with critical-point drying. Critical-point drying preserved bacteria but stripped away mucilaginous material, revealing filamentous structures within the biofilm. These filaments, along with masses of microspheres (nannobacteria? ) and the smooth mucilaginous outer layer, also occur in wet samples studied by ESEM, and are, therefore, not dehydration artifacts. However, different sample preparation techniques accentuated different components of the heterogeneous biofilm, thus resulting in vastly different textures. The cultured bacteria produced a biofilm that had a different surface texture and was more susceptible to sample preparation artifacts than the in situ biofilm. Use of more than one sample preparation technique is recommended in order to avoid bias.
A Stability Enhancement Method of Synchronous Voltage Source Based on Reshaped Voltage Controller

The strength of a renewable-dominated power system fluctuates over a wide range. The synchronous voltage source (SVS) has the proven ability to actively support weak power grids and has been put into practical application. However, when the SVS with voltage and current closed-loop control is connected to a strong grid, it faces oscillation problems. This paper proposes a method to reshape the voltage controller, realizes the impedance-reshaping of the converter, and enhances the stability of the inverter when it is connected to a strong grid. The simulation results verify the effectiveness of the method.
A new method for the transplanting of the seagrass Posidonia oceanica

The fast decline of coastal habitats happening worldwide is pushing for active restoration measures to mitigate the effects of anthropogenic impacts. In the Mediterranean Sea, meadows of the endemic seagrass Posidonia oceanica are declining mostly due to human activities carried out along the coast. Given the high conservation value of this habitat, several transplanting efforts have been attempted in the last decades to slow down this general decline and enhance the recovery of stressed meadows. In this study, we applied a new technique to transplant P. oceanica based on 100% natural materials. The study was carried out at the Tremiti Islands Marine Protected Area (Adriatic Sea), where 100 m2 of meadow were transplanted. We used drifting cuttings, already uprooted by tourists’ anchors, as donor material. These were transplanted on organic mats made of coconut fibers, subsequently stabilized with local boulders and gravels. The preliminary success was assessed with a two-month monitoring based on visual techniques. The photographic monitoring protocol was set up in order to be accessible for divers and citizen scientists, as well as to avoid any direct impact on the cuttings. Our preliminary results showed promising transplanting success in terms of mats’ integrity and cutting coverage (%). Further monitoring will be carried out to evaluate the success over a longer time span and to assess the feasibility of this restoration method at a larger scale.
Breakdown Voltage Stability Testing of Gas Surge Arrester Littelfuse CG2300 Under Different Environmental Conditions

This paper presents a comprehensive investigation of voltage stability and breakdown characteristics of gas surge arrester under the influence of ionizing radiation. The study involves testing 20 identical gas components. The gas surge arresters were subjected to different voltage increase rates. Experimental results revealed three distinct breakdown voltage groups. This study provides insights into the effects of radiation on gas surge arrester behavior, offering valuable information for applications in diverse fields.
Robust Dynamic Average Consensus with Bounded Reference Signals Under Directed Networks

Our work considers the dynamic average consensus (DAC) problem for bounded time-varying reference signals under weight-unbalanced directed networks, which is the most general and challenging claim from the perspective of topological structure. Firstly, the conventional surplus-based approach is embedded in the nonsmooth signum function to develop a nonlinear DAC algorithm under unbalanced directed networks. Then, we design a Lyapunov function with perturbation term and use the tools from the nonsmooth analysis as well as the Matrix Perturbation theory to establish convergence properties of the proposed algorithm. Finally, simulation examples and theoretical analysis show that the precise and robust DAC is guaranteed as long as the signals' states and their derivatives are all bounded, which possess the potential in a wider spectrum of application.
Anomaly Detection for In-Vehicle Communication Using Transformers

With the advancements of modern vehicle infrastructures, vehicles are increasingly relying on the signals received from a vast number of sensors and electronic components. Wireless technologies enable communication between vehicles and infrastructure, but it also increase the vulnerability surface. Malicious actors can remotely disrupt the vehicle's normal behavior, causing vehicle damage or worse, putting human lives in danger. To address these challenges, this paper proposes a transformer neural network-based intrusion detection system (CAN-Former IDS) that predicts anomalous behavior within the CAN protocol communication. Previous work typically addresses the prediction over the sequence of the CAN IDs. In this paper, we will simultaneously analyze both the sequence of IDs and the message payload values. The advantages of our approach are: 1) fully self-supervised training, which does not require labeled data, 2) self learning interactions between input tokens without relying on hand-crafted features. The transformer neural network is trained to predict the next communication sequence and anomalous communication is identified by comparing the real sequence to the predicted expected sequence. We evaluated our approach using a publicly available data set known as survival analysis data set, containing CAN communication from three different cars.
A Low-cost Raspberry Pi and Deep Learning System for Customer Analysis

Knowing what customer’s interest when they visit a store may help them increasing its efficiency and turnover. Using current technology advancements, this project intends to create a system that recognizes the presence of consumers visiting a store by constructing designated bounding boxes to compute how long the client stays in each retail area. Our system was developed utilizing the COCO dataset using SSD MobileNet V2 system based on Raspberry Pi. The Raspberry Pi is linked to a camera, which records customer activity in each indicated location and calculates the customer's time spent. A low-cost specification allows the system to be easily implemented in the real environment. From the simulations with two bounding boxes, in region 1 there are 7 people with an average time spent of 12 seconds and in region 2 there are 13 people with an average time spent reaching 20 seconds with an accuracy of 87.5 percent and 70 percent, respectively.
Intelligent Framework for Early Prediction of Type – II Diabetes by Accelerating Palm Print Images using Graph Data Science

Worldwide, Type II Diabetes Mellitus (T2DM) is a major condition whose incidence is rising. It is a public health issue as well as the silent killer of humanity. Its diverse aetiology, which includes genetic predisposition and environmental influences, may offer a distinctive trait in the population that is useful for early diagnosis. As a result, researchers are seeking novel approaches to its early diagnosis and treatment. Now, manual methods are used to measure and compare the axial triradius (ATD) angles of both palms using a Goniometer for the early prediction of T2DM. The creation of datasets containing individuals' left and right palm prints is the primary objective of this project, which aims to develop a model that would assist in the early prediction of T2DM. The model would have the option to track down the distinction in the ATD points in the two palms and foresee assuming that the singular will be determined to have T2DM in the future. A total of 390 instances were collected out of which 130 are diabetic and 260 were non-diabetic patients. It consists of various image pre-processing techniques and delta-point detection methods. The model would be able to predict the results if the user simply uploaded images of both palms. The Image Thresholding and Segmentation Method was used to evaluate the best features and the crossing number method was used to automate the model, indicating that the model achieved an accuracy of 91.02% in predicting the ATD angle. Using biomarkers, this project can be implemented in the health sector for early detection of Type II Diabetes Mellitus.
Sea Level Assimilation Experiments in the Tropical Pacific

Abstract Idealized twin experiments with the HOPE ocean model have been used to study the ability of sea level data assimilation to correct for errors in a model simulation of the tropical Pacific, using the Cooper and Haines method to project the surface height increments below the surface. This work should be seen in the context of the development of the comprehensive real-time ocean analysis system used at ECMWF for seasonal forecasting, which currently assimilates only thermal data. Errors in the model simulation from two sources are studied: those present in the initial state and those generated by errors in the surface forcing during the simulation. In the former, the assimilation of sea level data improves the convergence of the model toward its twin. Without assimilation convergence occurs more slowly on the equator, compared to an experiment using only correct surface forcing. With forcing errors present the sea level assimilation still significantly reduces the errors almost everywhere. An excep.
The relationship between people’s financial anxiety and negative attitudes toward robots

This study examined the relation between people’s financial anxiety and negative attitudes toward robots (NARS) through a survey of Japanese residents. This study indicated a significant positive association between participants’ financial anxiety and negative attitudes toward robots. Higher financial anxiety of the participants elicited stronger negative attitudes toward robots. We observed that the S1 (negative attitude toward situations of interaction with robots) and S2 (negative attitude toward the social influence of robots) were influenced by changes in financial anxiety. Financial anxiety was higher in younger adults than in older adults. No gender differences or age differences related to NARS were found in this study. Our results indicate the importance of paying attention to users’ financial anxiety profiles in human-robot interaction research and have important implications for the development of economic and cultural policies related to robotics.
Fast and Accurate Calculation of Mutual Inductance in the Presence of Conductive and Magnetic Media

This paper presents a complex image approach for fast and accurate calculation of the mutual inductance between two coils in the presence of conductive and magnetic layered media. The complex images are obtained by application of Prony’s method to the spectral domain form of the vector magnetic potential. The accuracy of the method is demonstrated by comparison of the mutual inductance between two coaxial coils separated by a conductive medium with the electromagnetic fullwave simulator HFSS as well as with published measurement data. The technique is general, versatile, and fast and has wide applicability, including in wireless power transfer and shielding.
Application of Some Zooplankton Community Characteristics as Indicators for Assessment of the Black Sea Coastal Environment

: The aim of the study is to test some zooplankton metrics (abundance, biomass, taxonomic composition - key groups, Shannon diversity index) as indicators for assessment of the ecological status and potential of the coastal environment along the Bulgarian Black Sea coast. The case study is performed in the system of Beloslav Lake-Varna Lake and Varna Bay, area under various anthropogenic impacts. Lakes were identified as heavily modified water bodies on the base of the WFD principles. According to the discussed zooplankton characteristics the ecosystem of Beloslav and Varna lakes could be assess as “problematic area” while Varna Bay as “potential problematic area”. Applied zooplankton metrics could be used as complementary plankton indicators of the eutrophication effects, respectively for a water quality assessment.
Design and Simulation of a Novel MEMS Based Microfluidic Lab-on-a-Chip Device for Dengue Virus Detection

Micro-electromechanical Systems (MEMS) based Lab on a Chip (LoC) devices are becoming increasingly popular as they have the capacity to perform complicated chemical and biological analyses with small sample quantities. The paper proposes a MEMS based microfluidic LoC device that incorporates a novel process with three preliminary stages; insertion, mixing, and dielectrophoretic (DEP) separation. The novel approach proposes an in vitro-based method to detect the existence of Dengue Virus (DENV) in a serum sample through electroosmotic flow generation followed by micromixing, DEP separation, and through fluorescence detection. The proposed LoC is 8 mm x 4 mm x 2 mm in size which will take minimally 5 minutes to generate the results using a 1.5 μL blood sample with an adjustment volume up to 15 μL. Simulations from COMSOL Multiphysics justify the overall operation, flow parameters maintained within the micro-channels, overlapping regions of sample concentration, and ensuring the 100% separations of viral particles from the substance. Moreover, L-Edit software was used to design the required masks and an appropriate fabrication process is proposed for the LoC device.
Towards At-the-Edge ECG Signal Processing with Accuracy-tunable Approximate Adders

Edge devices used in healthcare require high energy efficiency due to battery operation and replacement challenges. Approximate computing is effective in reducing energy consumption for applications tolerant to errors. Specifically, we investigate the application of approximate addition in the Pan-Tompkins algorithm for ECG processing. Our initial findings suggest that certain processes within the algorithm, such as high-pass filtering and derivative, are well-suited for approximation. We also observed that low-pass filtering is not as amenable to approximation. One noteworthy observation was that the handling of negative numbers through approximate addition did not introduce any adverse effects, reinforcing the viability of using this approach in the context of ECG processing for edge devices in healthcare applications. Our preliminary experiments demonstrate that a 16.5% reduction in power during additions is achieved.
An Occlusion-Resilient mmWave Imaging Radar-Based Object Recognition System Using Synthetic Training Data Generation Technique

An occlusion-resilient mmWave imaging radar-based object recognition system for advanced driver-assistance systems (ADAS) of construction machinery application is developed. As ADAS for construction sites, millimeter wave application is required in poor visibility environments such as nighttime, bad weather, and muddy conditions where object recognition by RGB cameras and LiDAR is difficult. A remaining technical challenge for ADAS is occlusion. Two techniques are proposed to improve the accuracy in occlusion scenes. First is a technique which generates simulated training data for occlusion environment to improve accuracy while reducing the cost for the training data preparation. The second is a parallel inference DNN architecture which enables object recognition with high accuracy in both normal and occlusion scenes by running two DNNs optimized respectively for normal and occlusion scenes in parallel. The object recognition accuracy of mAP50 in occlusion scenes improves by 15 points compared to the conventional technique. The decrease in recognition accuracy in non-occlusion scenes is only 4 points.
Multi-Requirement Satisfaction Oriented Decision-Making Under Uncertainty for Intelligent Manufacturing Systems

Intelligent manufacturing systems are often designed with decision-making ability and higher capabilities than required, referred to as capability redundancy. Considering the effects of degradation and wear, during operation system capability redundancy decreases gradually under uncertainty. This uncertainty needs to be considered to decide if the requirements are still satisfied after some years of operation. The proposed approach includes this uncertainty in the system model using SysML as modeling language and is represented through a metamodal. The metamodel encapsulates capability redundancy and multi-requirement satisfaction oriented decision-making under uncertainty. The metamodel can be used as knowledge base supporting intelligent decision-making during runtime as demonstrated in earlier work and thereby increasing the overall equipment effectiveness. A conveyor system is introduced as a case study.
Silicon pools in human impacted soils of temperate zones

Besides well‐known effects of climate and parent material on silicate weathering the role of land use change as a driver in the global silicon cycle is not well known. Changes in vegetation cover have altered reservoirs of silicon and carbon in plants and soils. This has potential consequences for plant‐Si availability, agricultural yields, and coastal eutrophication, as Si is a beneficial element for many crop plants and an essential nutrient for diatom growth. We here examined the role of sustained and intensive land use and human disturbance on silicon (Si) pool distribution in soils with similar climatological and bulk mineralogical characteristics. We show that land use impacts both biogenic and nonbiogenic Si pools. While biogenic Si strongly decreases along the land use change gradient (from forest to croplands), pedogenic silica fractions (e.g. pedogenic clays) increase in topsoils with a long duration of cultivation and soil disturbance. Our results suggest that nonbiogenic Si pools might compensate for the loss of reactive biogenic silicon in temperate zones.