Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning for Cognitive Radar Spectrum Sharing: A Continuous Control Approach

The growing demand for RF spectrum has placed considerable strain on radar systems, which must share limited spectrum resources with an ever-increasing number of devices. It is necessary to design radar systems with coexistence in mind so that the radar avoids harmful mutual interference that compromises the quality of service for other users in the channel. This work presents a deep reinforcement learning (RL) approach to spectrum sharing that enables a pulse-agile radar to operate in heavily congested spectral environments. A cognitive agent dynamically adapts the radar waveform to trade off collision avoidance, bandwidth utilization, and distortion losses due to pulse-agile behavior. Unlike existing RL approaches, this method formulates waveform parameter selection as a continuous control task, significantly increasing the flexibility of the agent and making it possible to scale its behavior to wideband, high-resolution operation. The RL agent uses a recurrent attention-based neural network to select actions, making it suitable for parallelized, real-time implementation. The proposed algorithm makes minimal assumptions about the spectral environment or other users in the spectrum, and the performance of the approach is evaluated on over-the-air data collected from a USRP X310 software-defined radio (SDR) system. Through these experiments, it is shown that the RL approach provides a flexible method for solving multi-objective waveform design problems in dynamic, high-dimensional spectrum environments.
Exit as Governance : An Empirical Analysis

Recent theory posits a new corporate governance channel available to blockholders: the threat of exit. Threats, however, are distinct from actual exits and thus difficult to measure directly. This paper exploits financial crises as natural experiments that exogenously changed stock liquidity and thus the credibility of blockholder exit threats. During these crises, firms with larger blockholder ownership suffered larger declines in firm value, attesting to the governance role of exit threats. Additional tests distinguish this governance role from traditional blockholder monitoring through intervention. ∗We are especially grateful to the editors and the referee. We also thank Anat Admati, Lauren Cohen, Alex Edmans, Ron Masulis and workshop participants at Emory University, University of North Carolina, University of Michigan, University of Utah, and Washington University for helpful comments. All errors remain our own. Contact address: Sreedhar Bharath, Associate Professor of Finance, W.P. Carey School of Business, Arizona State University, Tempe, AZ 85287. Tel: (347) 256 8784. e-mail: sbharath@asu.edu. Sudarshan Jayaraman, Assistant Professor of Accounting, Olin Business School, Washington University in St. Louis, St. Louis, MO 63130. Tel: (314) 935 6116. e-mail: jayaraman@wustl.edu. Venky Nagar, Associate Professor of Accounting, Ross School of Business, University of Michigan, Ann Arbor, MI 48109. Tel: (734) 647 3292. e-mail: venky@umich.edu.
SMOOTH LOCAL SOLUTIONS TO SCHRÖDINGER FLOWS WITH DAMPING TERM FOR MAPS INTO SYMPLECTIC MANIFOLDS

We show the existence of short-time very regular solutions to the initial Neumann boundary value problem of Schrödinger flows with damping term (or Landau–Lifshitz–Gilbert flows) for maps from a 3-dimensional compact Riemannian manifold with smooth boundary into a compact symplectic manifold.
Patient-Speciﬁc Dosimetry for 131 I Thyroid Cancer Therapy Using 124 I PET and 3-Dimensional–Internal Dosimetry (3D–ID) Software

Compared with conventional, whole-organ, S-factor–based do-simetry, 3-dimensional (3D), patient-speciﬁc dosimetry better accounts for radionuclide distribution and anatomic patient variability. Its accuracy, however, is limited by the quality of the cumulated activity information that is provided as input. This input has typically been obtained from SPECT and planar imaging studies. The objective was to implement and evaluate PET-based, patient-speciﬁc, 3D dosimetry for thyroid cancer patients. Methods: Three to 4 PET imaging studies were obtained over a 7-d period in 15 patients with metastatic thyroid carcinoma after administration of 124 I-NaI. Subsequently, patients were treated with 131 I on the basis of established clinical parameters. Retrospective dosimetry was performed using registered 124 I PET images that were corrected for the half-life difference between 124 I and 131 I. A voxel-by-voxel integration, over time, of the resulting 131 I-equivalent PET-derived images was performed to provide a single 3D dataset representing the spatial distribution of cumulated activity values for each patient. Image manipulation and registration were performed using Multiple Image Analysis Utility (MIAU), a software package developed previously. The software package, 3D–Internal Dosimetry (3D–ID), was used to obtain absorbed dose maps from the cumulated activity image sets. Results: Spatial distributions of absorbed dose, isodose contours, dose–volume histograms (DVHs), and mean absorbed dose estimates were obtained for a total of 56 tumors. Mean absorbed dose values for individual tumors ranged from 1.2 to 540 Gy. The absorbed dose distribution within individual tumors was widely distributed ranging from a minimum of 0.3 to a maximum of 4,000 Gy. Conclusion: 124 I PET–based, patient-speciﬁc 3D dosimetry is feasible, and sequential PET can be used to obtain cumulated activity images for 3D dosimetry.
Simultaneous localization and mapping in a multi-robot system in a dynamic environment with unknown initial correspondence

A basic assumption in most approaches to simultaneous localization and mapping (SLAM) is the static nature of the environment. In recent years, some research has been devoted to the field of SLAM in dynamic environments. However, most of the studies conducted in this field have implemented SLAM by removing and filtering the moving landmarks. Moreover, the use of several robots in large, complex, and dynamic environments can significantly improve performance on the localization and mapping task, which has attracted many researchers to this problem more recently. In multi-robot SLAM, the robots can cooperate in a decentralized manner without the need for a central processing center to obtain their positions and a more precise map of the environment. In this article, a new decentralized approach is presented for multi-robot SLAM problems in dynamic environments with unknown initial correspondence. The proposed method applies a modified Fast-SLAM method, which implements SLAM in a decentralized manner by considering moving landmarks in the environment. Due to the unknown initial correspondence of the robots, a geographical approach is embedded in the proposed algorithm to align and merge their maps. Data association is also embedded in the algorithm; this is performed using the measurement predictions in the SLAM process of each robot. Finally, simulation results are provided to demonstrate the performance of the proposed method.
An Agent-based Ontology Evolution Strategy for Ontology-driven Knowledge Management Systems

Domain ontologies are used in ontology-driven knowledge management approaches with the aim of capturing knowledge, providing a compromised understanding of a domain, and allowing integration between different formats of information sources. Because the domain knowledge changes and grows over time, enabling such evolution by means of the extension of the domain ontology, it is necessary to maintain the whole semantics of organizational documents. In this paper, we propose an agent-based strategy to extend the domain ontology by adding new concepts during the annotated process of a document. Moreover, with the objective of trying out the proposed strategy, we developed a ﬂexible framework to experiment with ontology-driven knowledge management approaches and agent-based ontology evolution strategies. Using the developed framework, the proposed strategy and a document semantic annotation algorithm are implemented, tested and experimental results are evaluated.
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: Much of the existing literature on the social aspects of sustainability in the supply chain has focused on dyadic buyer-supplier relationships. However, supply chains are much more extensive, featuring multi-tiered systems consisting of many interconnected sequential and parallel dyadic relationships; therefore, a more expansive and holistic approach to exploring the management and integration of social sustainability standards across the extended supply chain is desirable. This research attempts to help ﬁll this void and considers the extent to which a series of sequential upstream and downstream supply chain partners, rather than only a focal organization’s immediate suppliers and buyers, inﬂuence the formulation process of the social aspects of a sustainability strategy and the deployment of associated practices across the extended supply chain. Findings in the literature indicate that, inter alia, sustainability e ﬀ orts in the supply chain are likely to be guided by stakeholders’ sustainability desires / requirements, the geographical location of buyers and suppliers and the associated sustainability enforcement regulations and cultural norms, and the volume of trade between the buyer and supplier. This paper uses the results gleaned from a review of the literature to propose a conceptual framework for selection of sustainability strategy across the multi-tiered supply chain. Finally, we introduce a conceptual approach to the process of implementing and deploying the social aspects of sustainability strategies and practices across the supply chain using an integrated social-sustainability information management system (ISIMS).
Data-driven surrogate model for wind turbine damage equivalent load

Aeroelastic simulations are used to assess wind turbines in accordance with IEC standards in the time domain. Doing so can calculate fatigue and extreme loads on the wind turbine’s components. These simulations are conducted for several reasons, such as reducing safety margins in wind turbine component design by covering a wide range of uncertainties in wind and wave conditions, and meeting the requirements of the digital twin, which needs a thorough set of simulations for calibration. Thus, it’s essential to develop computationally efficient yet accurate models that can replace costly aeroelastic 5 simulations and data processing. We suggest a data-driven approach to build surrogate models for the Damage Equivalent Load (DEL) based on aeroelastic simulation outputs to tackle this challenge. Our method provides a quick and efficient way to calculate DEL using wind input signals without the need of time-consuming aeroelastic simulations. Our study will focus on utilizing a sequential Machine Learning (ML) method to map wind speed time series to DEL. Furthermore, we demonstrate the versatility of the developed and trained surrogate models by testing them for a wind turbine in the wake and using transfer 10 learning to enhance their prediction.
Cucumber Leaf Disease Detection and Classification Using a Deep Convolutional Neural Network

Due to obstruction in photosynthesis, the leaves of the plants get affected by the disease. Powdery mildew is the main disease in cucumber plants which generally occurs in the middle and late stages. Cucumber plant leaves are affected by various diseases, such as powdery mildew, downy mildew and Alternaria leaf spot, which ultimately affect the photosynthesis process; that’s why it is necessary to detect diseases at the right time to prevent the loss of plants. This paper aims to identify and classify diseases of cucumber leaves at the right time using a deep convolutional neural network (DCNN). In this work, the Deep-CNN model based on disease classification is used to enhance the performance of the ResNet50 model. The proposed model generates the most accurate results for cucumber disease detection using data enhancement based on a different data set. The data augmentation method plays an important role in enhancing the characteristics of cucumber leaves. Due to the requirements of the large number of parameters and the expensive computations required to modify standard CNNs, the pytorch library was used in this work which provides a wide range of deep learning algorithms. To assess the model accuracy large quantity of four types of healthy and diseased leaves and specific parameters such as batch size and epochs were compared with various machine learning algorithms such as support vector machine method, self-organizing map, convolutional neural network and proposed method in which the proposed DCNN model gave better results.
Caribbean Oceans: Utilizing NASA Earth Observations to Detect, Monitor, and Respond to Unprecedented Levels of Sargassum in the Caribbean Sea

In 2011 and 2015, the nations of the Caribbean Sea were overwhelmed by the unprecedented quantity of Sargassum that washed ashore. This issue prompted international discussion to better understand the origins, distribution, and movement of Sargassum, a free-floating brown macro alga with ecological, environmental, and commercial importance. In the open ocean, Sargassum mats serve a vital ecological function. However, when large quantities appear onshore without warning, Sargassum threatens local tourist industries and nearshore ecosystems within the Caribbean. As part of the international response, this project investigated the proliferation of this macro alga within the Caribbean Sea from 2003-2015, and used NASA Earth observations to detect and model Sargassum growth across the region. The Caribbean Oceans team calculated the Floating Algal Index (FAI) using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data, and compared the FAI to various oceanic variables to determine the ideal pelagic environment for Sargassum growth. The project also examined the annual spread of Sargassum throughout the region by using Earth Trends Modeler (ETM) in Clark Labs' TerrSet software. As part of the international effort to better understand the life cycle of Sargassum in the Caribbean, the results of this project will help local economies promote sustainable management practices in the region.
Incidence of SARS-CoV-2 infection among healthcare workers before and after COVID-19 vaccination in a tertiary paediatric hospital in Warsaw: a retrospective cohort study

A retrospective observational study was conducted among healthcare workers (HCWs) in a tertiary paediatric hospital. The study covered the period before and after implementation of the vaccination programme and evaluated the incidence of new SARS-CoV-2 infections in both periods. Risk factors of the new SARS-CoV-2 infection and COVID-19 vaccine eﬀectiveness was also assessed in a real-world setting. The overall incidence of SARS-CoV-2 infections among HCWs in the study period was 19.4% with a high proportion of asymptomatic individuals (45.1%). The incidence before vaccination was 16.6% and nurses had a higher risk of infection, while physicians had a reduced risk (OR 1.80, 95% CI 1.29–2.52; and OR 0.45, 95% CI 0.30–0.68). Within two months of implementation, the programme achieved a high (88.9%) vaccination coverage in our cohort, although some disparities in vaccination rates were observed. In particular, older individuals, physicians, those working in clinical settings, and those previously uninfected were more likely to be vaccinated. The overall incidence of SARS-CoV-2 infection after vaccination deployment was 6.3% (40.0% in unvaccinated individuals and 3.2% in individuals vaccinated with at least one dose). The estimated vaccine eﬃcacy was high (95.0%) in fully vaccinated HCWs and similar to those observed previously in clinical trials and real-world settings.
Interregional Interaction and Dilmun Power in the Bronze Age: A Provenance Study of Ceramics from Bronze Age Sites in Kuwait and Bahrain Using Non-Destructive pXRF Analysis

Known as the most interactive period of trade and interregional interaction, Dilmun led and controlled the flow of commodities and the transshipment between Arabian Gulf political entities such as Mesopotamia and far distance ones such as the Indus Valley. This paper presents the first provenance study on 2nd millennium Dilmun pottery in the Arabian Gulf, specifically Kuwait and Bahrain. Our aims are to construct a chemical database of Bronze Age ceramics and to discuss standardization of Dilmun wares using trace elements Nb, Th, Sr, Y, Zr, Rb, and Ba obtained with a non-destructive portable X-ray fluorescence spectrometer. Multiple spots on artifact surfaces were tested to inspect the quantitative precision of the technique and the homogeneity of ceramics was analyzed non-destructively.
Health Supplier Quality and the Distribution of Child Health

There is emerging evidence to suggest that initial differentials between the health of poor and more affluent children in the UK do not widen over early childhood. One reason may be that through the universal public funded health care system all children have access to equally effective primary care providers. This paper examines this explanation. The analysis has two components. It first examines whether children from poorer families have access to general practitioners of a similar quality to children from richer families. It then examines whether the quality of primary care to which a child has access has an impact on their health at birth and on their health during early childhood. The results suggest that children from poor families do not have access to markedly worse quality primary care, and further, that the quality of primary care does not appear to have a large effect on differentials in child health in early childhood. JEL Code: I12
Using Distributed Simulation for Analyzing Enterprise Models

Nowadays the incremental use of distributed simulation presents new challenges to overcome. In the Enterprise model analysis, this simulation paradigm is valuable since it allows us to represent the model in a modular way. In this sense, this paradigm makes it easier to represent enterprises with different structures such as the virtual enterprise, the distributed enterprise and the networked enterprise. This paper presents both an environment for developing an enterprise model using a business-oriented language - called Coordinates - and its automatic transformation in a simulation model that is based on DEVS formalism. In order to execute the simulation model in a distributed environment we have used HLA specification as middleware.
Understanding the impact of data gaps on long-term offshore wind resource estimates

In the context of a wind farm project, the wind resource is assessed to predict the power output and the optimal positioning of the wind turbines. That requires taking wind measurements on the site of interest and extrapolating these to the long-term using so-called "measure, correlate, and predict" (MCP) methods. The failure of sensors, power supply, or software are common phenomena. These disruptions cause gaps in the measured data, which can be especially long in offshore measurement campaigns due to harsh weather conditions causing system failures and preventing servicing and redeployment. 5 The present study investigates the effect of measurement data gaps on long-term offshore wind estimates by analyzing the bias they introduce in the parameters commonly used for wind resource assessment. Furthermore, it aims to show how filling the gaps can mitigate their effect. To achieve this, we perform the investigations for three offshore sites in Europe with 2 years of concurrent measurements. We use reanalysis data and various MCP methods to fill gaps in the measured data and extrapolate this data to the long term. The results of the investigations show that the effects of gaps on long-term extrapolations are lower 10 than expected. For instance, gaps of 180 days cause an average deviation of the long-term mean wind speed of less than 0.04 ms -1 for all tested sites. Filling the gaps can slightly reduce their impact if the MCP method used for gap filling performs better for predicting known data than the MCP method used for long-term extrapolating.
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Background: Buprenorphine is a semi-synthetic opioid used for the treatment of opioid dependence. Opioid use, including buprenorphine, has been increasing in recent years, in the general population and in pregnant women. Consequently, there has been a rise in frequency of neonatal abstinence syndrome (NAS), associated with buprenorphine use during pregnancy. The purpose of this study was to investigate correlations between buprenorphine and buprenorphine-metabolite concentrations in cord blood and onset of NAS in buprenorphine exposed newborns. Methods: Nineteen (19) newborns who met inclusion criteria were followed after birth until discharge in a double-blind non-intervention study, after maternal consent. Cord blood and tissue samples were collected and analyzed by liquid chromatography–mass spectrometry (LC–MS) for buprenorphine and metabolites. Simple and multiple logistic regressions were used to examine relationships between buprenorphine and buprenorphine metabolite concentrations in cord blood and onset of NAS, need for morphine therapy, and length of stay. Results: Each increase in 5 ng/ml level of norbuprenorphine in cord blood increases odds of requiring treatment by morphine 2.5 times. Each increase in 5 ng/ml of buprenorphine-glucuronide decreases odds of receiving morphine by 57.7 %. Along with concentration of buprenorphine metabolites, birth weight and gestational age also play important roles, but not maternal buprenorphine dose. Conclusions: LC–MS analysis of cord blood concentrations of buprenorphine and metabolites is an effective way to examine drug and metabolite levels in the infant at birth. Cord blood concentrations of the active norbuprenorphine metabolite and the inactive buprenorphine-glucuronide metabolite show promise in predicting necessity of treat-ment of NAS. These finding have implications in improving patient care and reducing healthcare costs if confirmed in a larger sample.
Research on flow distribution optimization of flue gas cooler based on numerical simulation

Based on Fluent, the flow distribution in flue gas cooler and its inlet and outlet flue in a power plant was numerically simulated. The calculation results show that the flue gas velocity distribution at the import of flue gas cooler was imbalance. The no uniform distribution of flue gas velocity can easily lead to problems such as wear, ash deposition and uneven heat transfer. To address the problem of uneven distribution of flue gas velocity, a diversion structure is designed and installed in the inlet flue of flue gas cooler. The numerical calculation results show that compared with the original flow distribution structure, the diversion structure can make the flow distribution of the flue gas cooler and its inlet and outlet flue more uniform, which can effectively avoid the problems of wear, ash deposition and uneven heat transfer.