Miracle-dz/UniFAR-FTUs huggingface dataset:
PEM-Identification of a block-oriented nonlinear stochastic model with application to room temperature modeling

This contribution proposes a block-oriented nonlinear stochastic model, that can be identified by prediction error minimization (PEM) method. The model consists of a linear time invariant (LTI) stochastic block and a static nonlinear output feedback. Using measured data of a conference and an office room, it is demonstrated that this approach can be used for the identification of models suitable for short term room-temperature prediction.
Nuka: A Generic Engine with Millisecond Initialization for Serverless Computing

Serverless computing is becoming one of the mainstream trends in cloud computing due to its advantages of simplified programming and cost saving. However, existing serverless platforms still adopt Docker container as its execution engine, which has the cold start problem and causes high common-case invocation latency. In this work, we analyze the lifecycle of common-case serverless invocation on existing serverless platforms and find that current container startup and pulling remote images are the two main reasons causing cold start so slow. Based on the study, we implement Nuka, a generic engine with millisecond initialization for serverless computing. Nuka is fully compatible with Docker interface and can smoothly re-place Docker as the execution engine of existing serverless plat-forms. Through the isolation pool that reuses Linux’s isolation configurations, Nuka avoids the high cost of container startup’s scalability bottleneck, which reduces container’s startup time with high concurrency scale. Nuka also avoids pulling remote images through dynamically resolving and importing required software packages from local package caching. A self-adaptive container reuse strategy dynamically controls container's pause time and replica numbers, which effectively reduces the frequency of cold start. Compared with Docker, Nuka can get a millisecond initialization with high concurrency and significantly reduces average time cost of cold startup by 6× on existing serverless platforms.
Reducing Service Creation Time Leveraging on Network Function Virtualization

Fifth-generation (5G) networks are envisioned to simultaneously support several services with different connectivity requirements. In this respect, service creation time is a key performance indicator (KPI) for service providers when planning the migration to 5G. For example, the European 5G infrastructure public private partnership (5G-PPP) suggests to reduce this time from 90 hours to 90 minutes, in the different phases of the service creation time KPI identified by this organization. This reduction can be achieved by leveraging on 5G state-of-the-art technologies: network function virtualization, network slicing, software-defined networking, and cloud computing, among others. Although some authors and projects have already studied the service creation time KPI in 5G, there is no literature that comprehensively analyzes and presents results related to each phase of this KPI. In this article, we explore the potential of network function virtualization technologies to reduce service creation time. To this end, we investigate the various phases of the service creation time KPI by designing and implementing, a realistic as well as complex network service that leverages on network function virtualization and related technologies. For our use case, we chose a content delivery network service specifically designed to distribute video. This decision was based on an analysis where we considered several parameters, like the complexity in the phases of design, fulfillment, and service assurance. We dissected all phases of the service creation time KPI required to turn our service blueprint into a deployment by utilizing network function virtualization tools. Henceforth, we defined and conducted several experiments, which were oriented to analyzing the different phases of the service creation time KPI. After analyzing the obtained results, we can conclude that using these new tools permits a substantial reduction in the time taken by each phase of the service creation time KPI.
Building Health Monitoring Using Computational Auditory Scene Analysis

This paper presents a method to identify sound sources for structural monitoring, known as building health monitoring. This method allows to evaluate deterioration and damage of buildings by analyzing environmental sounds. The proposed method determines the location and features of sounds generated within a building, with its main characteristics being: (1) planar direction and height estimation; (2) visualization of sound features according to loudness, continuity, and pitch. The capabilities of the proposed building health monitoring method are verified using environmental sound data acquired at a building in Gunkanjima, which is a world heritage site from Japan.
Performance Metrics of Data Dissemination Protocols in VANET

Vehicle-specific ad-hoc network (VANET) is an emerging technology that has gained prominence over the years. Due to the high dynamic topology, often disconnected networks, unlimited power backup, and many other features of vehicle ad hoc networks, it becomes very difficult to create routing protocols for data transfer. This paper compares some data dissemination protocols based on their performance metrics. This paper also proposes two performance metrics based packet information, which contain all packet information and provides the basis for packet-based performance evaluation of the protocol.
State Feedback Control for Intelligent Traffic Light Systems

Traffic is an important factor affecting human life quality in crowded cities. Increasing population and increasing individual vehicle ownership lead to an increase in traffic density. This leads to an increase in the time lost for travelers and pollution. Intersections are areas that cause more traffic density because of design making hurdles. Traffic jam can be reduced by adapting the traffic light control that can be adapted to the traffic density change at the junctions. In this study, the State Feedback Controller is proposed for the traffic light system control at the four-leg intersection. Simulation of this control system is made and handled using Simulation of Urban Mobility (SUMO). Results are compared for the proposed types of Traffic Light Control Systems. It was observed that the State Feedback traffic light controller, which is the recommended method from the simulation results, gives better results than both the Fuzzy Logic Control (FLC) and the Fixed Time traffic light controller.
A Cumulative Risk Perspective for Occupational Health and Safety (OHS) Professionals

Cumulative risk assessment (CRA) addresses the combined risk associated with chemical and non-chemical exposures. Although CRA approaches are utilized in environmental and ecological contexts, they are rarely applied in workplaces. In this perspectives article, we strive to raise awareness among occupational health and safety (OHS) professionals and foster the greater adoption of a CRA perspective in practice. Specifically, we provide an overview of CRA literature as well as preliminary guidance on when to consider a CRA approach in occupational settings and how to establish reasonable boundaries. Examples of possible workplace co-exposures and voluntary risk management actions are discussed. We also highlight important implications for workplace CRA research and practice. In particular, future needs include simple tools for identifying combinations of chemical and non-chemical exposures, uniform risk management guidelines, and risk communication materials. Further development of practical CRA methods and tools are essential to meet the needs of complex and changing work environments.
Mining Technological Innovation Talents Based on Patent Index using t-SNE Algorithms*: Take the Field of Intelligent Robot as an Example

The purpose of this paper is to effectively evaluate the innovation ability and classification of technical talents in the intelligent robot field, and to be able to carry out adaptive learning and mining technical innovation talents according to the real-time change data corresponding to different indicators. Taking inventor's patent information retrieved and cleaned from DI database as research object, it constructs the evaluation index system of technological innovation talents. It reduces the dimension of the index and cluster automatically, shows the visual effect, and mines the similar technical innovation talents through t-SNE algorithm. For a large number of patent information data, machine learning algorithm improves the traditional recognition method. According to inventor similarity, automatic classification is realized. Combined with DWPI manual code mining, the corresponding innovators and members of the technical team in the intelligent robot technology field were found. According to the results of visual dimensional reduction, the specific inventors can be traced. Machine learning algorithm t-SNE can reduce dimension and analysis clustering. It breaks the limitations of artificial statistics, deals with the larger order of magnitude data, and analyzes data timely, accurate and intuitive.
A review of data-driven and probabilistic algorithms for detection purposes in local power systems

Power grid operators use data to guide their asset management decisions. However, as the complexity of collected data increases with time and amount of sensors, it becomes more difficult to extract relevant information. Therefore, methods that perform detection tasks need to be developed, especially in distribution systems, which are impacted by distributed generation and smart appliances. Until now, methods employed in local power systems for detection purposes using data with low sampling rate, have not been reviewed. This paper provides a literature review focused on anomaly detection, fault location, and load disaggregation. We analyze the methods in terms of their type, data requirements and ways they are implemented. Many belong to the machine learning field. We find that some methods are typically combined with others and perform specific tasks, while other methods are more ubiquitous and often used alone. Continued research is needed to identify how to guide the choice of methods, and to investigate combinations of methods that have not been studied yet.
Sulfur and Methane-Oxidizing Microbial Community in a Terrestrial Mud Volcano Revealed by Metagenomics

Mud volcanoes are prominent geological structures where fluids and gases from the deep subsurface are discharged along a fracture network in tectonically active regions. Microbial communities responsible for sulfur and methane cycling and organic transformation in terrestrial mud volcanoes remain poorly characterized. Using a metagenomics approach, we analyzed the microbial community of bubbling fluids retrieved from an active mud volcano in eastern Crimea. The microbial community was dominated by chemolithoautotrophic Campylobacterota and Gammaproteobacteria, which are capable of sulfur oxidation coupled to aerobic and anaerobic respiration. Methane oxidation could be enabled by aerobic Methylococcales bacteria and anaerobic methanotrophic archaea (ANME), while methanogens were nearly absent. The ANME community was dominated by a novel species of Ca. Methanoperedenaceae that lacked nitrate reductase and probably couple methane oxidation to the reduction of metal oxides. Analysis of two Ca. Bathyarchaeota genomes revealed the lack of mcr genes and predicted that they could grow on fatty acids, sugars, and proteinaceous substrates performing fermentation. Thermophilic sulfate reducers indigenous to the deep subsurface, Thermodesulfovibrionales (Nitrospirae) and Ca. Desulforudis (Firmicutes), were found in minor amounts. Overall, the results obtained suggest that reduced compounds delivered from the deep subsurface support the development of autotrophic microorganisms using various electron acceptors for respiration.
Dual-Branch Structured De-Striping Convolution Network Using Parametric Noise Model

The stripe fixed pattern noise (FPN) of infrared images significantly corrupts image quality, so that most infrared imaging systems suffer from the degradation of visibility and detectability during operation. Therefore, the FPN de-striping method, which eliminates stripe patterns without substantial loss of image information, remains a core technology in the field of infrared image processing. In this article, we propose the dual-branch structure based FPN de-striping deep convolutional neural network (DBS-DCN) to effectively extract structural features of FPN and preserve the image details in a single infrared image. In addition, we have established the parametric FPN model through the diagnostic experiments of infrared images based on the physical principle of an infrared detector and its signal response. We have optimized each parameter of the FPN model using measured data, which acquired on a wide range of detector temperatures. Further, we generate the training data using our FPN model to ensure stable learning performance against various stripe patterns. We performed comparative experiments with state-of-the-art methods using artificially corrupted infrared images and real corrupted infrared data, and our proposed method achieved outstanding de-striping results in both qualitative and quantitative evaluation compared to existing methods.
Triple Band Notched UWB Antenna

An Ultra Wideband (UWB) monopole antenna having small surface area consisting of a modified polygon shape with three notched bands is designed. Impedance bandwidth of almost 16.9 GHz (3.1-20 GHz) is obtained by introducing a rectangular slot in partial ground plane. The interfering Wi-MAX (from 3.3 to 3.8 GHz), WLAN (from 5 to 6 GHz) and satellite uplink (X band) communication (from 7.9 to 8.4 GHz) frequencies are eliminated by introducing T-, U- and spiral slots in the microstrip line-fed monopole radiator. The total chip size of proposed UWB antenna is 17×24 mm2 and its peak gain is 5.9 dB at 10.3 GHz.
Radiographic analysis of fatty infiltration of the extraocular muscles in thyroid eye disease

ABSTRACT Purpose Fatty infiltration of the extraocular muscles has been described radiographically in patients with thyroid eye disease (TED), yet it has not been studied on a large scale nor quantified. Our purpose was to define and characterize this entity in patients with TED. Methods An IRB-approved cross-sectional retrospective review of medical records identified patients with a clinical diagnosis of TED and at least one CT of the orbits. A 2:1 age and sex-matched control population consisted of patients without a history nor radiographic evidence of orbital disease or systemic thyroid abnormality. The presence of fatty infiltration in each extraocular rectus muscle was defined using Hounsfield units (HU). Laterality, muscles involved, and pattern of fatty infiltration were also evaluated. Student’s t-tests, Chi-square, and Fisher’s exact tests were used to compare TED and control groups. Results The study population consisted of 252 patients with TED and 504 age and sex-matched controls. Fatty infiltration was significantly more prevalent in TED patients (36/252, 14.3%) compared to controls (11/504, 2.2%) (p < .001). The mean density of fat infiltration was significantly lower in TED patients (−40.4 HU) than controls (−34.8 HU) (p = .048). In TED patients, the frequency of muscle involvement was inferior rectus (61.8%), lateral rectus (19.7%), superior rectus (11.8%) and medial rectus (6.6%), which was not significantly different than controls (p > .05). Most muscles (88.2%) in the TED group exhibited a heterogeneous pattern of infiltration, which did not differ from controls (p = .34). Conclusions This study characterizes fatty infiltration of the extraocular muscles in patients with TED.
Characterization of a High-Aspect Ratio Detector With Lateral Sides Readout for Compton PET

Although positron emission tomography is probably the most specific molecular imaging modality, it still lacks a high detection sensitivity. One way of improving this is by implementing larger axial scanners and, therefore, increasing the solid angle coverage. Alternatively, it is possible to increase the sensitivity gain by improving the timing capabilities of the detectors. However, from the most fundamental nature of particle interactions with matter, the 511-keV gamma rays suffer, in most of the cases, from scatter collisions either in the patient or within the detector block, before a photoelectric event eventually occurs. Recovering all scattered (Compton) events would improve scanner sensitivity. In this work, we show the performance of a detector block geometry suitable for the development of PET scanners based on several detector layers. A geometry using multiple layers favor the process of scattered events, at the time that allows one for their proper identification. The detector block consists of a LYSO crystal with $51.5\times 51.5$ mm2 surface and 3 mm thickness, resulting in a very high aspect ratio above 17. Four custom-made SiPM arrays of $1\times 16$ elements with $3\times 3$ mm2 area each are coupled to the lateral sides of the crystal. Four different methods to estimate the gamma-ray interaction position using the information collected by the four SiPM arrays have been implemented and compared in order to assess the most suitable one for this detector configuration and aspect ratio. A novel calibration method based on Voronoi diagrams has been successfully implemented, allowing us to recover data for the entire detector block. We have reached an intrinsic spatial resolution for the whole block of less than 1.6 mm FWHM, combined with an energy resolution of 12.1%. We have also compared the performance results with detector blocks using the same crystal but employing the standard backreading approach. Similar results were obtained but making use of four times less SiPM active area in the case of the lateral reading compared to the backreading method, and with the possibility to minimize the undesirable scatter in the photosensor layers.
Performance Evaluation of Ad-Hoc Networks in Static & Mobile Environment

The Internet Protocol version 6 (IPv6) based routing protocol for low power and lossy networks (RPL) is used for routing in the static networks such as wireless sensor networks (WSNs) and internet of things (IoT). The research is going on to implement the RPL for mobile networks like vehicular ad-hoc network (VANET) and mobile ad-hoc network (MANET). These networks are infrastructure less and self-configuration is required to connect these mobile devices. There are some limitations of the RPL when the network is dense and mobility is introduced in it. For the solution of this problem, this paper presents the performance evaluation of RPL in the mobile network and comparison is performed with the static network. The implementation is done on the Contiki environment and results are analyzed on Cooja (network simulator). It is observed that the RPL performance depends strongly on number of sender nodes, sink nodes, and also on the inclusion of mobility to the nodes. The metric values increase with more packet loss with increasing the number of nodes in the network. Further the increase in number of sink nodes provides lesser packet loss, retransmission, and the energy consumption. The introduced mobility increases the network energy consumption.
Molecular design of layer-by-layer functionalized liposomes for oral drug delivery.

Liposomes are small spherical vesicles composed mainly of phospholipids and cholesterol. Over the years, a number of liposomal formulations have shown clinical promise, but the use of liposomes in oral drug delivery is still limited. This is partly due to the vulnerability of conventional liposomes to the detrimental effect of gastrointestinal destabilizing factors and also to the poor efficiency in intestinal absorption of liposomes. Some of these issues can be ameliorated using the layer-by-layer (LbL) assembly technology, which has been widely applied in surface modification of various nanoparticulate systems. Discussions on LbL functionalization of liposomes as oral drug carriers, however, are scant in the literature. To fill this gap, this article presents an overview of the roles of LbL functionalization in the development of liposomes, followed by a discussion of major principles of molecular design and engineering of LbL-functionalized liposomes for oral drug delivery. Regarding the versatility offered by LbL assembly, it is anticipated that LbL-functionalized liposomes may emerge as one of the important carriers for oral drug administration in the future.
Celecoxib, indomethacin, and ibuprofen prevent 6-hydroxydopamine-induced PC12 cell death through the inhibition of NFκB and SAPK/JNK pathways

Objective(s): The possible action of nonsteroidal anti-inflammatory drugs (NSAIDs) in the reduction of reactive oxygen species (ROS) and also as anti-apoptotic agents may suggest them as putative agents for the treatment of neurodegenerative diseases. This study was designed to explore some pathways alterations induced by NSAIDs following 6-hydroxydopamine (6-OHDA)-induced cell death in PC12 cells as an in vitro model of Parkinson's disease (PD) and to compare the effects of celecoxib, indomethacin and ibuprofen. Materials and Methods: The cell viability, ROS content, glutathione (GSH) level, and apoptosis were measured using resazurin, dichlorofluorescein diacetate (DCFH-DA), 5,5′-dithiobis-2-nitrobenzoic acid (DTNB), propidium iodide (PI) and flowcytometry, real-time PCR and western blot. Results: Based on the results, pretreatment with celecoxib, indomethacin and ibuprofen for 24 hr significantly induced concentration and time-dependent protection against 6-OHDA-induced PC12 cell death. Cell viability (P<0.001), GSH level (P<0.01) and cytoplasmic content of nuclear factor kappa B (NFκB) (P<0.01) were increased, also ROS content (P<0.001) and apoptosis biomarkers such as the cleaved caspase-3 (P<0.001), Bax (P<0.01), phospho- stress-activated protein kinases / c-Jun N-terminal kinases (P-SAPK/JNK) (P<0.01) and cleaved poly ADP ribose polymerase (PARP) (P<0.001) protein levels were all decreased after pretreatment of cells with NSAIDs in 6-OHDA-induced PC12 cells. Conclusion: It is suggested that NFκB and SAPK/JNK pathways have an important role in 6-OHDA-induced cell injury. Overall, it seems that pretreatment with NSAIDs protect dopaminergic cells and may have the potential to slow the progression of PD.
Image Recognition of Supermarket Shopping Robot Based on CNN

With the development of society, more and more goods are sold intelligently and without people. The purpose of supermarket shopping robots is to help shop assistants replenish goods and free shop assistants from tedious mechanical work. Based on the “Innovation Robot Design and Production Competition–Supermarket Robot Challenge Competition”, the competition rules are taken as the design criteria. On the basis of CNN (convolution neural network), we design a CNN image recognition algorithm used in supermarket shopping robot. This algorithm overcomes the problems of low recognition accuracy and slow recognition speed in image recognition, and enables the robot to complete the competition more efficiently and quickly in the process of operation. The experimental results also verify that the image recognition algorithm can be applied to supermarket shopping robots and meet the needs of the competition.