Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, round-trip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.
Automatic Lane-Changing Decision Based on Single-Step Dynamic Game with Incomplete Information and Collision-Free Path Planning

Traffic accidents are often caused by improper lane changes. Although the safety of lane-changing has attracted extensive attention in the vehicle and traffic fields, there are few studies considering the lateral comfort of vehicle users in lane-changing decision-making. Lane-changing decision-making by single-step dynamic game with incomplete information and path planning based on Bézier curve are proposed in this paper to coordinate vehicle lane-changing performance from safety payoff, velocity payoff, and comfort payoff. First, the lane-changing safety distance which is improved by collecting lane-changing data through simulated driving, and lane-changing time obtained by Bézier curve path planning are introduced into the game payoff, so that the selection of the lane-changing start time considers the vehicle safety, power performance and passenger comfort of the lane-changing process. Second, the lane-changing path without collision to the forward vehicle is obtained through the constrained Bézier curve, and the Bézier curve is further constrained to obtain a smoother lane-changing path. The path tracking sliding mode controller of front wheel angle compensation by radical basis function neural network is designed. Finally, the model in the loop simulation and the hardware in the loop experiment are carried out to verify the advantages of the proposed method. The results of three lane-changing conditions designed in the hardware in the loop experiment show that the vehicle safety, power performance, and passenger comfort of the vehicle controlled by the proposed method are better than that of human drivers in discretionary lane change and mandatory lane change scenarios.
Status of Leaky Mode Holography

It will soon be a decade since leaky mode waveguide devices were presented as a solution for holographic video displays. This paper seeks to provide a brief, topical review of advances made during that time. Specifically, we review the new methods and architectures that have been developed over this period. This work draws primarily from papers seeking to present dynamic holographic patterns using mode coupling from indiffused waveguides on lithium niobate. The primary participants during this time period have been groups from the Massachusetts Institute of Technology, Brigham Young University, and Draper. We also describe the challenges that remain. The body of work reviewed speaks to the need for further development, but it also reaffirms that leaky mode waveguides continue to hold a unique place within spatial light modulation for holographic video displays.
Core–shell nanocapsules containing essential oil for textile application

A novel polysiloxane-modified aromatic nanocapsule with hard core and soft shell has been prepared by two-stage emulsion polymerization method. The core of the nanocapsule is made up of styrene/methyl methacrylate copolymer, while the shell is poly(butyl acrylate) modified by octamethylcyclotetrasiloxane. Scanning electron microscopy and transmission electron microscope images reveal well-defined core–shell morphology of the particles, and the average particle size is about 100 nm. Ultraviolet–visible analysis shows that the essential oil can be well encapsulated by the nanocapsules and the encapsulation efficiency and loading capacity are 78.76% and 26.25%, respectively. The residual rate of essential oil is about 25.3% after being washed 15 times, which indicates that the nanocapsules have excellent washing durability. These core–shell aromatic nanocapsules show great potential application in fabrics treatment. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45695.
A SEI Modeling Approach Distinguishing between Capacity and Power Fade

In this paper we introduce a pseudo two-dimensional (P2D) model for a common lithium-nickel-cobalt-manganese-oxide versus graphite (NCM/graphite) cell with solid electrolyte interphase (SEI) growth as the dominating capacity fade mechanism on the anode and active material dissolution as the main aging mechanism on the cathode. The SEI implementation considers a growth due to non-ideal insulation properties during calendar as well as cyclic aging and a re-formation after cyclic cracking of the layer during graphite expansion. Additionally, our approach distinguishes between an electronic (σSEI) and an ionic (κSEI) conductivity of the SEI. This approach introduces the possibility to adapt the model to capacity as well as power fade. Simulation data show good agreement with an experimental aging study for NCM/graphite cells at different temperatures introduced in literature.
Multiple dimensions of bird beta diversity support that mountains are higher in the tropics

We examine latitudinal effects of breeding bird taxonomic, phylogenetic and functional β‐diversity (Tβ, Pβ and Fβ, respectively) along elevational gradients to test predictions derived from Janzen's (The American Naturalist 101:233–249, 1967) classic ideas that tropical mountains represent stronger dispersal barriers than temperate mountains.
Viscoelastic constitutive models and their fractional representatives to capture the time-dependent response of DEAs on sinus excitation

Dielectric elastomer actuators (DEAs) are subjected to creep especially in the first few excitation cycles. This paper deals with viscoelastic constitutive models and their fractional representatives to capture time-dependent deformation of DEA on sinus excitation. The sine voltage signal with three different frequencies and the same amplitude was used for deformation of the DEA. Basic constitutive models and their fractional representations of Kelvin–Voigt, Zener, solid linear model (SLM), Burger model and Kelvin–Voigt–Maxwell model were taken into consideration for finding the optimal model describing the behavior of the DEA. The simplicity of forming governing equations of motion derived with the help of Laplace transformation for basic and fractional constitutive models is shown in the work. Electrical force was calculated with the help of Maxwell equation. Responses of constitutive models were calculated with the help of Matlab. Constitutive parameters were optimized with the help of direct search and multiple starting point search method in Matlab. Fractional SLM was found to be the most optimal for describing time-dependent deformation on sinus excitation signal for the DEA. This work can be the basis on which control algorithms can be simulated for control of the DEA or any similar soft actuator.
Critical success factors and challenges for Lean Startup: a systematic literature review

PurposeThe term Lean Startup (LS) was coined by Eric Ries, and his 2011 book has popularized the concept with organizations, both startups and established organizations, implementing LS. However the empirical grounding is thin and for a long time this subject has been neglected by academia. The purpose of this paper is to present a systematic literature review (SLR) on LS, while highlighting core knowledge and identify gaps.Design/methodology/approachA SLR was carried out based on the Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) protocol of Scopus and Web of Science databases. In total, 45 articles published in journals and conferences over 10 years were collected which revealed a number of LS research gaps.FindingsThe SLR revealed the tools and methods associated with LS, most cited pros and cons, reasons that cause LS failure, the challenges that companies face in the implementation of LS, and critical success factors (CSFs) that can support these challenges and minimize the reasons for failure.Practical implicationsThe findings of the study can be beneficial to practitioners and senior managers in organizations who wish to delve into the journey of LS. The study also discloses challenges and barriers that can hinder the implementation of LS.Originality/valueAcademic publications regarding LS are sparse and this SLR is one of the first SLRs to explore both the critical failure factors (CFFs) and the CSFs based on peer-reviewed journal and conference proceedings.
Space and place in immigrant entrepreneurship literature in the Nordic countries: A systematic literature review

ABSTRACT In recent decades, immigrant entrepreneurship research has developed from exploring a niche economic activity to being a lens through which to explore wider social and economic processes. As the topic grows in importance, research on immigrant entrepreneurship in Nordic contexts is joining these trends, but the unique settings of the region might have been neglected. Space and place underpin many explorations of immigrant entrepreneurship in the Nordic countries, from absolute conceptions of space and place to sensitive portraits of social complexity. Thus, the question the authors address is how space and place are articulated, presented, and interpreted within immigrant entrepreneurship literature in and about the Nordic context. From a systematic literature review of academic articles covering Nordic countries and immigrant entrepreneurship between 2000 and 2019, they found that space and place play a role in immigrant entrepreneurship in Nordic research. However, opportunities remain to engage theoretically with these concepts in order to develop research aimed at understanding immigrant entrepreneurship in the Nordic countries and contribute to a growing need to understand space and place in entrepreneurial activities generally. The authors conclude that there are five potential strategies for incorporating space and place in future research.
The Role of HRCT chest in COVID-19 Pneumonia

COVID-19 pneumonia is a recently identified lung infection, the hazardous factor of this pneumonia is pandemic dissemination of infection by corona virus. Due to its high mortality and morbidity it has become a threat to the human life. As pneumonia is the commonest manifestation chest x-ray becomes the primary modality of investigation however many symptomatic patients do not have a positive chest X-ray findings hence, HRCT is used as primary modality for both screening as well as a diagnosing COVID-19 pneumonia. It has been observed that in many situations RT-PCR test are negative or inconclusive but the HRCT in such cases is useful and conclusive. Aim of the article is to highlight the role of HRCT in diagnosing COVID-19 pneumonia and providing proper guidance to the Clinicians for assessing the response and therapeutic purpose for covid19 pneumonia. recently, HRCT score have also been used to provide the clinicians an idea about the Prognosis of this disease progress.
Uncited papers are not useless

Abstract We study the citation dynamics of the papers published in three scientific disciplines (Physics, Economics, and Mathematics) and four broad scientific categories (Medical, Natural, Social Sciences, and Arts & Humanities). We measure the uncitedness ratio, namely, the fraction of uncited papers in these data sets and its dependence on the time following publication. These measurements are compared with a model of citation dynamics that considers acquiring citations as an inhomogeneous Poisson process. The model captures the fraction of uncited papers in our collections fairly well, suggesting that uncitedness is an inevitable consequence of the Poisson statistics.
A Theoretical Model for Global Optimization of Parallel Algorithms

With the quickly evolving hardware landscape of high-performance computing (HPC) and its increasing specialization, the implementation of efficient software applications becomes more challenging. This is especially prevalent for domain scientists and may hinder the advances in large-scale simulation software. One idea to overcome these challenges is through software abstraction. We present a parallel algorithm model that allows for global optimization of their synchronization and dataflow and optimal mapping to complex and heterogeneous architectures. The presented model strictly separates the structure of an algorithm from its executed functions. It utilizes a hierarchical decomposition of parallel design patterns as well-established building blocks for algorithmic structures and captures them in an abstract pattern tree (APT). A data-centric flow graph is constructed based on the APT, which acts as an intermediate representation for rich and automated structural transformations. We demonstrate the applicability of this model to three representative algorithms and show runtime speedups between 1.83 and 2.45 on a typical heterogeneous CPU/GPU architecture.
A 250–310 GHz Power Amplifier With 15-dB Peak Gain in 130-nm SiGe BiCMOS Process for Terahertz Wireless System

This article presents a broadband THz power amplifier (PA) operating close to the maximum oscillation frequency (<italic>f</italic><sub>max</sub>) with proposed power combining and stagger-tuned gain boosting techniques. A compact broadband power combiner based on the compensated three-conductor transmission line (T-line) balun is proposed to simultaneously achieve broadband matching and power combining. A 4-stage stagger-tuned gain boosting amplifier operating closely to <italic>f</italic><sub>max</sub> is investigated to achieve a flattened power gain. Based on the proposed techniques, a broadband 300-GHz PA is implemented in a 130-nm SiGe BiCMOS process with <italic>f</italic><sub>t</sub>/<italic>f</italic><sub>max</sub> = 350/450 GHz. It exhibits a peak gain of 15 dB and 3-dB bandwidth of 67 GHz (247–314 GHz) with 2.5 V supply voltage. At 290 GHz, a 5-dBm maximum output power <italic>P</italic><sub>OUT-MAX</sub> was measured with a 1-dB compressed output power <italic>OP</italic><sub>−1 dB</sub> of 3.3 dBm and a maximum PAE of 1.19%. The measured output power <italic>P</italic><sub>OUT-MAX</sub> and <italic>OP</italic><sub>-1dB</sub> of the PA are 2.2–5 dBm and 0.6–3.3 dBm over 250–300 GHz, respectively. To the best of our knowledge, this PA achieves the highest operational frequency and widest bandwidth compared with other works in SiGe/CMOS process operating closely to <italic>f</italic><sub>max</sub>.
RENT: A Python Package for Repeated Elastic Net Feature Selection

Due to modern data acquisition techniques, the number of generated features in measurement data keeps increasing. This increase can make the analysis with standard machine learning methods difficult because of underdetermined systems where the dimensionality of the feature space (number of features) exceeds the dimensionality of the object space (number of observations). A concrete example of such a situation is data acquisition in the healthcare domain, where the number of patients (observations) suffering from a specific condition may be relatively low, but a lot of measurements (number of features) are generated for each patient to acquire a good understanding of the patient’s health. A very common challenge is that not all features in a high dimensional space are equally important for predictive tasks — many might even be redundant. Feature selection deals with finding the most relevant features of a dataset. With help of appropriate methodology, feature selection can reduce (a) the complexity of and (b) noise in the dataset. More importantly, data interpretation of the model becomes easier with fewer features, which is of great importance within domains such as healthcare. Even though feature selection is a well-established research topic, relatively few approaches are focusing on the stability of the selection. The important question at hand is: can we trust that the selected features are really valid or is their selection very dependent on which observations are included in the data? Providing information on the stability of feature selection is vital, especially in wide data sets where the number of features can be many times higher than the number of observations. Here, the inclusion or exclusion of a few observations can have a high impact on which features may be selected.
Blockchain-based cross-user data shared auditing

In cloud storage, public auditing is a more popular data integrity verification technique since it allows users to delegate auditing tasks to a fully trusted third-party auditor (TPA). However, it is difficult to find such a TPA in practical application. Besides, the centralised auditing model makes TPA have to bear burdensome work pressure, which limits the practicability of existing schemes. In this paper, we firstly proposed a blockchain-based generalised shared auditing mechanism BCSA in the cross-user scenario, which aims at achieving available public auditing with a non-fully trusted TPA, and reducing the user's auditing fees and TPA's work pressure by allowing data users to share their auditing procedure with others. Furthermore, we initialise a concrete construction BCSAD with Diffie–Hellman protocol for the cross-user auditing scenario with different data. Likewise, we also propose a novel construction BCSAI for the cross-user auditing scenario with identical data, which utilises a password-authenticated key exchange (PAKE) protocol to achieve shared auditing and ciphertext deduplication, reducing data storage and auditing fees for data users and alleviating service pressure on the cloud server and TPA. Security and performance analysis evaluate the practicability of the proposed scheme.
Towards Enhanced System Efficiency while Mitigating Row Hammer

In recent years, DRAM-based main memories have become susceptible to the Row Hammer (RH) problem, which causes bits to flip in a row without accessing them directly. Frequent activation of a row, called an aggressor row, causes its adjacent rows’ (victim) bits to flip. The state-of-the-art solution is to refresh the victim rows explicitly to prevent bit flipping. There have been several proposals made to detect RH attacks. These include both probabilistic as well as deterministic counter-based methods. The technique of handling RH attacks, however, remains the same. In this work, we propose an efficient technique for handling the RH problem. We show that the mechanism is agnostic of the detection mechanism. Our RH handling technique omits the necessity of refreshing the victim rows. Instead, we use a small non-volatile Spin-Transfer Torque Magnetic Random Access Memory (STTRAM) that ensures no unnecessary refreshes of the victim rows on the DRAM device and thus allowing more time for normal applications in the same DRAM device. Our model relies on the migration of the aggressor rows. This accounts for removing blocking of the DRAM operations due to the refreshing of victim rows incurred in the previous solution. After extensive evaluation, we found that, compared to the conventional RH mitigation techniques, our model minimizes the blocking time of the memory that is imposed due to explicit refreshing by an average of 80.72% in the worst-case scenario and provides energy savings of about 15.82% on average, across different types of RH-based workloads. A lookup table is necessary to pinpoint the location of a particular row, which, when combined with the STTMRAM, limits the storage overhead to 0.39% of a 2 GB DRAM. Our proposed model prevents repeated refreshing of the same victim rows in different refreshing windows on the DRAM device and leads to an efficient RH handling technique.
Urban Flow Pattern Mining Based on Multi-Source Heterogeneous Data Fusion and Knowledge Graph Embedding

Urban flow analysis is an essential research for smart city construction, in which urban flow pattern analysis focuses on the continuous state of urban flow. How to mine, store and reuse traffic patterns from urban multi-source heterogeneous big data is challenging. Therefore, this paper proposes a knowledge mining network for regional flow pattern to mine and store the urban flow pattern. The proposed model consists of two modules. In the first module, the features of the region and its flow pattern are extracted as the entity and relation, respectively. In the second module, POI features are modeled to enhance the embedding representation of relation and entity. Based on the translation distance method, the knowledge triplets of regional flow patterns are mined. Finally, the proposed model is compared with some benchmark methods using Chengdu Didi order and POI datasets. Experimental results show that the proposed model is effective. In addition, the knowledge triplets are visualized and some application examples are introduced.
Reduction in Insect Attachment Caused by Different Nanomaterials Used as Particle Films (Kaolin, Zeolite, Calcium Carbonate)

In the present investigation, we compared the reduction in attachment ability of the southern green stinkbug Nezara viridula (Hemiptera: Pentatomidae) to glass induced by three different nanoparticle (kaolin, zeolite, and calcium carbonate) films. Using traction force experiments, behavioral experiments, and scanning electron microscopy observations, we analyzed the insect attachment ability and linear speed on untreated and treated glass with the three particle films. The three nanomaterials strongly reduced insect attachment ability mainly owing to contamination of attachment pads. The ability to reduce insect attachment was different for the three tested particle films: kaolin and zeolite induced a significantly higher reduction in N. viridula safety factor than calcium carbonate. The coating of the surface was more uniform and compact in kaolin and zeolite compared to calcium carbonate particle film. Moreover, kaolin and zeolite particles can more readily adhere to N. viridula attachment devices, whereas calcium carbonate particles appeared less adherent to the cuticular surface compared to the two aluminosilicate (kaolin and zeolite) particles. Only the application of kaolin reduced insect linear speed during locomotion. Nanoparticle films have a great potential to reduce insect attachment ability and represent a good alternative to the use of insecticides for the control of pentatomid bugs and other pest insects.