Miracle-dz/UniFAR-FTUs huggingface dataset:
Value Propositions in the Internet of Things: A Taxonomy of B2B Smart Services

Connected and smart products are giving rise to smart services that leverage their advanced capabilities and promise profitable business models. However, many companies in the Internet of Things domain are still struggling to integrate smart services into their portfolios, and more research is needed to facilitate service innovation and adoption. We, therefore, identify common characteristics of the value propositions of B2B smart services and summarize them in a taxonomy. The taxonomy development follows established methods and is based on a systematic literature review of 31 scientific articles and the study of 100 empirical objects. To confirm the validity of our findings, we conduct two ex-post evaluations. Our research provides descriptive knowledge about B2B smart services that can serve as a foundation for further research on smart service innovation.
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting

This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past. The problem is typically solved by modeling complex spatio-temporal correlations in traffic data using spatio-temporal graph neural networks (GNNs). However, the performance of these methods is still far from satisfactory since GNNs usually have limited representation capacity when it comes to complex traffic networks. Graphs, by nature, fall short in capturing non-pairwise relations. Even worse, existing methods follow the paradigm of message passing that aggregates neighborhood information linearly, which fails to capture complicated spatio-temporal high-order interactions. To tackle these issues, in this paper, we propose a novel model named Dynamic Hypergraph Structure Learning (DyHSL) for traffic flow prediction. To learn non-pairwise relationships, our DyHSL extracts hypergraph structural information to model dynamics in the traffic networks, and updates each node representation by aggregating messages from its associated hyperedges. Additionally, to capture high-order spatio-temporal relations in the road network, we introduce an interactive graph convolution block, which further models the neighborhood interaction for each node. Finally, we integrate these two views into a holistic multi-scale correlation extraction module, which conducts temporal pooling with different scales to model different temporal patterns. Extensive experiments on four popular traffic benchmark datasets demonstrate the effectiveness of our proposed DyHSL compared with a broad range of competing baselines.
A Lightweight Framework for Fast Trajectory Simplification

The ubiquitous GPS sensors collect massive trajectory data from moving objects, which is useful in data mining applications. However, trajectory data is enormous in volume, and thus, directly storing and processing the raw data is expensive. Using trajectory simplification, a trajectory can be reduced to a set of continuous line segments with acceptable data loss, which is an efficient method. Although many algorithms are proposed, they still suffer from the following issues including (i) non-data driven capability as most studies rely on human-crafted rules or pre-defined parameters, (ii) bound with error measures that yield high computational cost, and (iii) focusing only on the local information preservation in trajectories, but failing in capturing the global mobility patterns for trajectory compression.To address the above issues, we propose a Seq2Seq2Seq framework, abbreviated S3, which consists of two chained Seq2Seq. With differentiable reconstruction learning, S3 enables self-supervised trajectory simplification in a lightweight manner. Besides, we deploy S3 over the graph neural architecture to capture the context-aware mobility patterns and enhance the representation paradigm of trajectories with geographical semantics, where a context-aware distance measure is designed for quality evaluation. An online extension of S3 is also developed to enable streaming trajectory simplifications. Finally, extensive experiments using two real-world datasets in both offline and online scenarios show that S3 achieves much higher efficiency (e.g., it achieves up to one order of magnitude speed-up gains) and comparable compression quality, compared with both non-learning and state-of-the-art learning-based methods.
Study of medical image traffic using MPLS technology

Multiprotocol Label Switching (MPLS)combines multiple advanced systems for embedding techniques like ATM and IP with Quality of Service (QoS). This paper investigates the implementation of a local recovery mechanism using Label Switched Path (LSP). This mechanism allows for an alternative path to be set up when an accidental drop in the topology affects an LSP that carries a privileged data flow. The new path has equivalent properties to its predecessor, thus privileged traffic can be redirected. The analysis was performed on the traffic of medical image data.
A Comparative Analysis of Two Deep Learning Neural Networks for Defect Detection in Steel Structures Using UAS

Steel is used in different infrastructural constructions. The durability and serviceability of steel made it more suitable than other construction materials. However, exposure to weather elements can cause defects in steel structures. Early detection and treatment of structural defects can prevent the structure from becoming more damaged and more expensive to repair. Corrosion resistance and fatigue strength in any steel structure can be influenced by defects such as patches, scratches, and coating erosion. Current methods to detect steel defects are based on manual visual inspection. Autonomous UAS imaging-based defect detection methods have shown promising results in terms of accuracy and time. This paper compares the performance of two deep learning models, InceptionResnetV2 and ResNet152V2, for detecting steel defects. These models were trained in transfer learning mode and tested on two different datasets, the Severstal dataset present on Kaggle and a dataset generated by the authors of this paper. The results show that ResNet152V2 outperforms InceptionResnetV2 with an average accuracy of 95% and a misdetection rate of 5%. Overall, both the models, ResNet152V2 and InceptionResNetV2, showed an improvement of 12.59% and 9.59%, respectively, compared to MobileNet used in a previous study, when all were trained and tested on the Severstal dataset.
Few-Shot Learning for Issue Report Classification

We describe our participation in the tool competition in the scope of the 2nd International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on SETFIT, a framework for few-shot learning and sentence-BERT (SBERT), a variant of BERT for effective sentence embedding. We experimented with different settings, achieving the best performance by training and testing the SETFIT-based model on a subset of data with manually verified labels (Fl-micro $=.8321$). For the sake of the challenge, we evaluate the SETFIT model on the challenge test set, achieving Fl-micro $=. 7767$.
From nanoscopic to macroscopic photo-driven motion in azobenzene-containing materials

Abstract The illumination of azobenzene molecules with UV/visible light efficiently converts the molecules between trans and cis isomerization states. Isomerization is accompanied by a large photo-induced molecular motion, which is able to significantly affect the physical and chemical properties of the materials in which they are incorporated. In some material systems, the nanoscopic structural movement of the isomerizing azobenzene molecules can be even propagated at macroscopic spatial scales. Reversible large-scale superficial photo-patterning and mechanical photo-actuation are efficiently achieved in azobenzene-containing glassy materials and liquid crystalline elastomers, respectively. This review covers several aspects related to the phenomenology and the applications of the light-driven macroscopic effects observed in these two classes of azomaterials, highlighting many of the possibilities they offer in different fields of science, like photonics, biology, surface engineering and robotics.
Modulation of tropospheric circulation over the North Pacific by stratospheric quasi-biennial oscillation and its implications for prediction

The stratospheric quasi-biennial oscillation (QBO) is the most prominent interannual variability of zonal wind in the tropical lower stratosphere. The associated QBO momentum anomalies tend to move poleward and downward, affecting surface climate in a major way. Particularly, it is found that the surface temperature over East Asia in late winter is anomalously warm during the easterly phase of QBO. This temperature anomaly is roughly comparable in amplitude to the one associated with El Nin�o Southern Oscillation (ENSO) in mid-winter. In order to explore how the QBO affects the North Pacific circulation, we examine the role of eddy-mean flow interactions and local Rossby wave sources over the North Pacific. It is found that the response to QBO is consistent with the fluctuation-dissipation theorem (FDT). When additional torques, associated with poleward propagation of QBO-induced zonal wind, are well projected onto annular mode pattern in the troposphere, the tropospheric circulation pattern is changed to annular-mode-like dipole patterns, as suggested by FDT. It is also found that the modulation of storm tracks along with jet shift induces convection anomalies, indicating that QBO-related tropospheric circulation changes may cause hydrological changes in the North Pacific.
A Variable Speed Fault Detection Approach for Electric Motors in EV Applications based on STFT and RegNet

Electric motors play a significant role in the power train system in Electric Vehicles (EV s). Therefore, their online condition monitoring is essential in ensuring the reliable operation of the entire powertrain. In EV applications, the motors work in a non-ideal environment and continuously varying operating conditions. Therefore, the fault diagnosis of the EV motors is challenging, and the fault diagnosis model must work in wide ranges of speeds and loads. In this paper, a short-time Fourier transform with varying window is proposed as the current signal processing of the motor along with a convolutional neural-based network for the detection of the interturn short circuit of permanent magnet synchronous motor. The proposed method is evaluated using a simulation dataset and a benchmark bearing fault dataset. by Matlab/Simulink® and the model is trained with the data in four speeds, and the model tests are carried out for a different operating speed.
Imperfect Bandwidth-Sharing Policies using Network Calculus

Bandwidth-sharing policies aim at enforcing fairness among several classes of traffic by reserving one share of the available bandwidth for each class. They recently attracted a lot of attention, in particular in the context of time-sensitive networking. One advantage of these policies is their simplicity of implementation, especially the Round-Robin policies. Moreover, if a traffic class does not use all its reserved bandwidth, the other classes can use the unused part. Recent works greatly improve the delay bounds by considering this phenomenon for the Deficit Round-Robin policy, when the knowledge about the incoming traffic is available. There was also an attempt to use this approach for Weighted Round-Robin policy. Unfortunately, this was incorrect, mainly due to the variability of the packet lengths and the share of the bandwidth is not perfectly known. In this paper, we propose a generalization of bandwidth-sharing policies, that we call imperfect bandwidth sharing. We compute per-class service guarantees that correct and improves the state of the art on Round-Robin policies, and assess through numerical experiments the algorithmic and performance gain of our solution.
Energy-Aware Spreading Factor Selection in LoRaWAN Using Delayed-Feedback Bandits

LoRaWAN networks can involve large numbers of wireless devices relying on batteries to sense the environment and send data to gateways. A critical trade-off for transmission performance (packet delivery ratio) versus energy conservation (and hence, the device lifespan) appears when deciding the transmission parameters, in particular, the Spreading Factor (SF) to be used by each node. In this paper, we use lightweight reinforcement learning techniques, namely multi-armed bandits, for each node to select an appropriate SF, based on preferences regarding that trade-off. Unlike previous works on that topic, we relax some assumptions to aim at a realistic implementation: our solution does not assume immediate rewards, or that each device communicates with only one gateway. Additionally, we build explicit MAC commands for the method to work in practice and implement it in the ns-3 simulator using a state-of-the-art LoRaWAN module. We share the source code of our implementation and our simulation results. Those simulations show that when energy conservation is critical for IoT nodes, such lightweight learning algorithms outperform LoRaWAN's legacy Adaptive Data Rate algorithm, both in single- and multi-gateway scenarios.
Methods for Evaluating DC Arc-Flash Incident Energy in Battery Energy Storage Systems

Renewable energy systems continue to be one of the fastest growing segments of the energy industry. This paper focuses on the understanding of how energy storage technology behaves under direct current (dc) arc conditions. Because of the fast proliferation of renewable energy systems and the lack of formal dc equivalent calculation guidelines such as IEEE 1584 for alternating current (ac) systems, it has been necessary to rely on different equations and models presented by various researchers when calculating arc-flash (AF) incident energy (IE) for dc systems. This paper discusses the behavior of energy storage systems under arcing conditions and presents the results of available methods to estimate the dc arc-flash incident energy. This paper provides a comparative analysis of a proposed arc-flash incident energy calculation method against available laboratory tests. Explanations are provided regarding the effect the various types of batteries may have under short-circuit (SC) and arcing conditions. Comparisons of the proposed calculation method simulation results and laboratory dc arc test measurements are included.
On the effect of capacity drops in highways with service stations

This paper studies the capacity drops phenomena on a macroscopic, first-order model for freeway traffic. In particular, we focus on the effect that a Service Station (ST) has on the mainstream traffic evolution. We propose a modified formulation of the Cell Transmission Model with service station (CTM-s) that considers this important phenomenon, and use a micro-simulation based on Aimsun Next to identify the model parameters via a structured identification procedure. Finally, we validate the ability of the CTM-s with capacity drops to better describe the traffic evolution with respect to the classical formulation.
Tactical Traffic Engineering with Segment Routing Midpoint Optimization

Tactical Traffic Engineering (TE) plays a crucial role in the operation of modern backbone networks as it enables operators to quickly react to failures or unforeseen traffic changes. In this paper, we propose a new Segment Routing (SR)-based optimization algorithm called MOLS. It is the first algorithm that applies the recent concept of Midpoint Optimization (MO) for SR to the use case of fast, tactical TE. In an extensive evaluation, based on various real-world topologies, we show that our algorithm performs virtually on par or better than comparable state-of-the-art tactical TE approaches that rely on conventional SR. However, especially for larger networks, the number of SR policies required by our algorithm is substantially lower. This not only reduces the introduced overhead but also allows for faster deployment of the computed configurations since less changes have to be applied to the network. Furthermore, we show that MOLS is able to remove congestion in sub-second fashion for multiple TE use cases. Lastly, MOLS also is the first algorithm in literature to fully utilize the capabilities of MO without any artificial limitations. This enables it to find similar or even better solutions than the only other MO-capable algorithm in literature in just a fraction of the time.
Analyzing the effects of confidence thresholds on opinion clustering in homogeneous Hegselmann–Krause models

Hegselmann–Krause (HK) models exhibit complex behaviors which are not easily tractable through mathematical analysis. In this paper, a characterization of the steadystate behaviors of homogeneous HK models and sensitivity to confidence thresholds is discussed by commenting on existing and new numerical results. The typical decreasing of number of clusters and convergence time by increasing the confidence thresholds are discussed and motivations for the behavior of some counterexamples are provided. A tighter upper bound for the dependence of the number of clusters with respect to the confidence thresholds is proposed. Differences and analogies between the opinions’ evolution for symmetric and asymmetric HK models are commented.
Standardizing the Definition of a “Pulse” Thunderstorm

AbstractIsolated, short-lived thunderstorms forming in weakly forced environments are referenced through a surplus of terminology. Further, the language used to describe the strongest, severe-weather-producing subset of these storms is applied inconsistently, posing a communication hurdle for the effective dissemination of hazardous weather risks. The term “pulse thunderstorm” was originally coined to describe an anomalously strong airmass thunderstorm often associated with a larger convective complex. However, recent applications of “pulse” have evolved to also describe nonsevere, single-cell storms, and both uses can currently be observed within research, operational, and educational texts. This paper reviews the history of the term “pulse,” performs a content analysis on nearly 1,500 pulse-referencing Storm Prediction Center (SPC) convective outlooks (CO) and mesoscale discussions (MD), and summarizes the deficiencies with the contemporary disorganized convection nomenclature. The larger CO sample (n =.
In Vitro Studies of Calcitonin Release in Man

The influence of various agents on calcitonin release from human thyroid was studied in vitro. Under the condition of this investigation, calcium, magnesium and phosphate did not stimulate calcitonin release from short-term incubated slices of human thyroid. However, pentagastrin and USP glucagon were potent stimulators of calcitonin release. Theophylline and dibutyryl cyclic AMP were also potent stimuli. A highly purified preparation of pancreatic glucagon was without an effect. Those agents which stimulated calcitonin release were associated with augmented cyclic AMP accumulation. Although maximal discharge of calcitonin required the presence of calcium, out in vitro experiments raise the question as to whether a gastrointestinal hormone, rather than calcium, might not be the principal agent affecting calcitonin release.
Temporal Changes in Metabolic Syndrome Indices and Factors of Metabolic Syndrome Development in Patients With Rheumatic Disease

Patients with rheumatic disease have a high prevalence of metabolic syndrome. The purpose of this study was to investigate temporal changes in metabolic syndrome indices and to identify factors influencing metabolic syndrome development. A prospective cohort study design was adopted. The study participants were 68 outpatients with a rheumatic disease at an outpatient clinic of a university hospital. Data on demographics, health-related characteristics, steroid use, serum C-reactive protein levels, and metabolic syndrome indices were collected between December 2017 and March 2021. Temporal changes in body mass indices, serum triglyceride, and cholesterol levels were significant. Body mass indices, diastolic blood pressure, serum triglyceride, high-density lipoprotein, and fasting blood glucose levels at time of diagnosis were found to influence metabolic syndrome development. Temporal changes in serum triglyceride, cholesterol, and fasting blood glucose levels were significantly influenced by inflammatory status. The findings demonstrate the importance of controlling inflammatory activities in the context of inhibiting the progression of metabolic syndrome and rheumatic diseases.
Gaps in Parental Understanding of Sleep Disturbances During Maintenance Therapy for Pediatric Acute Lymphoblastic Leukemia

Background: Acute lymphoblastic leukemia (ALL) is the most common cancer in childhood, with survival rates approaching 90%. Sleep disturbance is common among ALL patients, often developing during the initial stages of chemotherapy treatment. While there have been significant efforts to understand and intervene in this issue during survivorship, there is far less research on children who are actively receiving treatment. In the current study, we sought to better understand the parent's experience in the sleep domain during maintenance therapy, including their perceptions of how their child's medical team had managed sleep disturbances, and recommendations for how to improve sleep management. Method: Fifteen parents of pediatric ALL patients (aged 4–12 years) completed semistructured interviews. Interview content was analyzed using a multistage thematic analysis. Results: Parents consistently expressed feeling unprepared to manage the sleep disruptions that arose during treatment, often reporting that they did not recall being told this would be a side effect. They were enthusiastic about learning how to improve their child's sleep, though they did not want pharmacotherapeutic interventions or additional medical/psychosocial appointments to address this. Conclusion: Despite consistent provider communication on sleep, parents report limited knowledge of the issue. This provides an obvious intervention target to improve treatment-related sleep disturbances. Clear messaging may help direct parents’ attention and expectations regarding their child's treatment and potential for disturbed sleep, possibly in the form of a behavioral intervention that empowers parents with information about how to support their child's sleep health while they are undergoing treatment for ALL.
Barriers in Hypertension Management among Culturally Diverse Patients in an Urban Urgent Care Setting

BARRIERS IN HYPERTENSION MANAGEMENT AMONG CULTURALLY DIVERSE PATIENTS IN AN URBAN URGENT CARE SETTING Hypertension is the leading cause of deaths related to cardiovascular disease conditions. A vast number of patients are not taking their medications as prescribed by the physicians. This non-adherence to hypertensive treatment plans results in increased numbers of cases of unstable blood pressure. Medication adherence will help to manage the controllable cardiovascular diseases and will automatically decrease the incidences of emergent cases in hospitals and urgent care clinics. It will further decrease the costs of diagnostic evaluations used during the emergent visits. This decreased patient load in the emergency rooms will improve quality patient care. The study timeframe of data collection and result analysis was from September 14, 2018 to October 30, 2019. This pilot study identified a relationship between medication non-adherence rate and number of barriers. The greater the barrier, the higher the non-adherence rate. With the help of open-ended questions, the study identified multiple barriers which are consistent with medication non-adherence rates. Navpreet Kaur May 2019 BARRIERS IN HYPERTENSION MANAGEMENT AMONG CULTURALLY DIVERSE PATIENTS IN AN URBAN URGENT CARE SETTING