Miracle-dz/UniFAR-FTUs huggingface dataset:
A Computationally Efficient Formulation to Accurately Represent Start-Up Costs in the Medium-Term Unit Commitment Problem

Nowadays, the changing paradigm in power systems highlights the necessity of improving detail in energy models. The deployment of non-dispatchable renewable energy resources that regulators traditionally promoted, like the European Union case and some areas of the United States, is being accelerated due to the enhanced competitiveness of clean technologies. However, the delay in the widespread use of non-conventional energy-storage techniques, like batteries, is causing an increase in the variability of the thermal-generated demand for electricity. In that context, many combined cycle gas turbine generators have redressed their proceedings, increasing their start-up and shut-down frequency for operating fewer hours than historically have done. Their fast ramping capabilities position them as the best alternative to cover demand peaks in the near term. Consequently, there is a growing interest in representing these thermal units properly. This paper exposes a great-detailed analysis of the start-up cost modeling to accomplish future market trends. Likewise, a new mathematical formulation of the unit commitment problem is also presented. Finally, the formulation is compared to one of the most renowned methodologies. Its successful performance is described in several case studies, where authentic power-demand curves and technical details of a gas-fired generation portfolio are handled in medium-term horizons.
Long‐term survival trends in solid cancers in the Nordic countries marking timing of improvements

Survival studies are an important indicator of the success of cancer control. We analyzed the 5‐year relative survival in 23 solid cancers in Denmark, Finland, Norway and Sweden over a 50‐year period (1970‐2019) at the NORDCAN database accessed from the International Agency for Research on Cancer website. We plotted survival curves in 5‐year periods and showed 5‐year periodic survival. The survival results were summarized in four groups: (1) cancers with historically good survival (>50% in 1970‐1974) which include melanoma and breast, endometrial and thyroid cancers; (2) cancers which constantly improved survival at least 20% units over the 50 year period, including cancers of the stomach, colon, rectum, kidney, brain and ovary; (3) cancer with increase in survival >20% units with changes taking place in a narrow time window, including oral, oropharyngeal, testicular and prostate cancers; (4) the remaining cancers with <20% unit improvement in survival including lung, esophageal, liver, pancreatic, bladder, soft tissue, penile, cervical and vulvar cancers. For cancers in groups 1 and 2, the constant development implied multiple improvements in therapy, diagnosis and patient care. Cancers in group 3 included testicular cancers with known therapeutic improvements but for the others large incidence changes probably implied that cancer stage (prostate) or etiology (oropharynx) changed into a more tractable form. Group 4 cancers included those with dismal survival 50 years ago but a clear tendency upwards. In 17 cancers 5‐year survival reached between 50% and 100% while in only six cancers it remained at below 50%.
Thin-film thermoelectric energy harvesting for security and sensing applications

The past decade has seen significant advances in distributed sensors and sensor networks. Many of these advances have been driven by programs that support national intelligence and security interests. With these advances have come an increased interest in energy harvesting to provide continuous power sources to replace or augment existing power storage systems. The use of waste heat is an attractive source of energy for many applications where μW-mW power is required. The implementation of a thermoelectric power conversion system requires several basic elements in addition to an assumed heat source. These elements are: 1) a thermoelectric device, 2) a heat sink, 3) voltage regulation, 4) an energy storage device and 5) load management. The design and optimization of the system (and each element within the system) is highly dependent on the thermal boundary conditions and the power load. This presentation will review the key performance factors and considerations required to optimize each element of the system to achieve the required I-V characteristics for output power.
Real-Time Visual Inertial Odometry with a Resource-Efficient Harris Corner Detection Accelerator on FPGA Platform

Visual Inertial Odometry (VIO) is a widely studied localization technique in robotics. State-of-the-art VIO algorithms are composed of two parts: a frontend which performs visual perception and inertial measurement pre-processing, and a backend which fuses vision and inertial measurements to estimate the robot's pose. Both image processing in the frontend and sensor fusion in the backend are computationally expensive, making it very challenging to run the VIO algorithm, especially the optimization-based VIO algorithm in real time on embedded platforms with limited power budget. In this paper, a real-time optimization-based monocular VIO algorithm is proposed based on algorithm-and-hardware co-design and successfully implemented on an embedded platform with only 2.6W processor power consumption. In particular, the time-consuming Harris corner detection (HCD) is accelerated on Field Programmable Gate Array (FPGA), achieving an average 16 × processing time reduction compared with the ARM implementation. Compared with the state-of-the-art HCD accelerator provided by Xilinx, the hardware resource required of our accelerator is largely reduced without any compromise in speed, thanks to the proposed dedicated pruning and paral-lelization techniques. Finally, experiment on the public dataset demonstrates that the proposed real-time VIO algorithm on the FPGA-based platform has comparable accuracy with respect to the existing state-of-the-art VIO algorithm on the desktop, and 3 × faster frontend processing speed over the ARM-based implementation.
Work-in-Progress: Generalized Demand-Based Schedulability Test for Dual-Criticality Sporadic Task Model

In this paper, we consider the scheduling of dual-criticality sporadic task systems with arbitrary deadlines using demand bound functions. In dual-criticality systems, tasks are assigned either low-criticality or high-criticality based on assurance needs with associated worst-case execution times. Arbitrary deadlines are those that allow the deadline to be larger than the minimum separation between consecutive task instances. Demand bound functions have been used to successfully schedule dual-criticality task sets for constrained deadlines, i.e., deadlines that are always less than or equal to minimum inter-arrival separation time. We formulate a new demand bound function for a more generalized dual-criticality task system with both constrained and arbitrary deadlines on a preemptive uniprocessor.
Venous Stent Placement for Malignant Vena Cava Syndrome in a Patient with Liposarcoma

Patient: Female, 36-year-old Final Diagnosis: Malignant vena cava syndrome Symptoms: Lower leg edema Medication:— Clinical Procedure: Inferior vena cava stenting Specialty: Palliative Medicine • Radiology Objective: Rare disease Background: Reports of venous stenting for inferior vena cava (IVC) syndrome (IVCS) due to sarcoma are limited, and the treatment’s efficacy and safety are not clear. Case Report: A 36-year-old woman with myxoid liposarcoma was admitted to the Department of Respiratory Medicine for treatment of bilateral lower-leg edema and to be evaluated for acute liver dysfunction. She was 13 years old when she was diagnosed with myxoid liposarcoma. Over the next 18 years, she had 4 tumor resections and 1 round of radiation therapy. She had been on chemotherapy for 4 years and then pazopanib at the age of 35. The edema did not improve after admission despite treatment with diuretics. Computed tomography revealed a huge liposarcoma occupying the right thoracic cavity and a compressed IVC, which caused the edema. Although doxorubicin was administered as fifth-line treatment, there was no response. Since there was no additional chemotherapy regimen, her prognosis was considered to be less than 6 months. She could not be discharged to her home since she was unable to walk due to the edema; therefore, IVC stenting was performed to improve her dysmotility. After IVC stenting, the lower-leg edema improved without any adverse events, enabling her to walk and eventually return home. Conclusions: In patients with IVCS caused by rare malignancies such as myxoid liposarcoma, an IVC stent can be safely implanted and can help to alleviate symptoms. IVC stenting can improve symptoms and allow for home care, resulting in improved quality of life.
Optimal Order Polynomial Transformation for Calibrating Systematic Errors in Multisite Testing

Multisite (parallel) testing is becoming more widely used in analog and mixed-signal testing to increase throughput and meet high customer demand. However, site-to-site variations are inevitable due to the complexities involved in massive multisite test board design. Sites with pronounced systematic errors (issue sites) lead to measurements not reflecting the true performance of the device under test (DUT), causing potential yield loss and test escapes. Traditional mechanical repair of such sites is expensive, time-consuming, and labor-intensive. Polynomial transformation methods have been successfully explored to calibrate measurements at issue test sites. However, its rigid application could lead to overfitting or underfitting without foreknowledge of the nature and level of induced errors. This paper presents an optimal order polynomial transformation method that is flexible and self-adaptive. It uses a predefined error metric to find the optimal-order of the transformation polynomial and provides optimal calibration coefficients. Simulations and real test data are used to evaluate the effectiveness of the proposed method. Further validation is also provided by comparing the die measurements of issue sites after calibration against a more accurate reference.
Observations of Disks around Brown Dwarfs in the TW Hydra Association with the Spitzer Infrared Spectrograph

Using SpeX at the NASA Infrared Telescope Facility and the Spitzer Infrared Spectrograph, we have obtained infrared spectra from 0.7 to 40 μm for three young brown dwarfs in the TW Hydra association (τ ∼ 10 Myr), 2MASSW J1207334–393254, 2MASSW J1139511–315921, and SSSPM J1102–3431. The spectral energy distribution for 2MASSW J1139511–315921 is consistent with a stellar photosphere for the entire wavelength range of our data, whereas the other two objects exhibit significant excess emission at λ > 5μm. We are able to reproduce the excess emission from each brown dwarf using our models of irradiated accretion disks. According to our model fits, both disks have experienced a high degree of dust settling. We also find that silicate emission at 10 and 20 μm is absent from the spectra of these disks, indicating that grains in the upper disk layers have grown to sizes larger than ~5 μm. Both of these characteristics are consistent with previous observations of decreasing silicate emission with lower stellar masses and older ages. These trends suggest that either (1) the growth of dust grains, and perhaps planetesimal formation, occurs faster in disks around brown dwarfs than in disks around stars or (2) the radii of the mid-IR-emitting regions of disks are smaller for brown dwarfs than for stars, and grains grow faster at smaller disk radii. Finally, we note the possible detection of an unexplained emission feature near 14 μm in the spectra of both of the disk-bearing brown dwarfs.
An Impedance-Controlled Testbed for Simulating Variations in the Mechanical Fit of Wearable Devices

The fit of a wearable device, such as a prosthesis, can be quantitatively characterized by the mechanical coupling at the user-device interface. It is thought that the mechanical impedance, specifically the stiffness and damping, of wearable device interfaces can significantly impact human performance while using them. To test this theory, we develop a forearm-mounted testbed with a motorized, two degree of freedom (2-DOF) gimbal to simulate variations in the mechanical fit of an upper-extremity wearable device during pointing and target tracking tasks. The two gimbal motors are impedance-controlled to vary the mechanical stiffness and damping between the user and the device's laser pointer end-effector. In this paper, experiments are conducted to determine the torque constants of the motors before implementation in the testbed, and to validate the accuracy of the joint impedance controller. The completed impedance-controlled wearable interface testbed is validated further by comparing the gimbal joint displacements and torques, recorded during 2-DOF base excitation experiments, to MATLAB Simulink simulation data.
Deep Reinforcement Learning Based on Local GNN for Goal-Conditioned Deformable Object Rearranging

Object rearranging is one of the most common deformable manipulation tasks, where the robot needs to rearrange a deformable object into a goal configuration. Previous studies focus on designing an expert system for each specific task by model-based or data-driven approaches and the application scenarios are therefore limited. Some research has been attempting to design a general framework to obtain more advanced manipulation capabilities for deformable rearranging tasks, with lots of progress achieved in simulation. However, transferring from simulation to reality is difficult due to the limitation of the end-to-end CNN architecture. To address these challenges, we design a local GNN (Graph Neural Network) based learning method, which utilizes two representation graphs to encode keypoints detected from images. Self-attention is applied for graph updating and cross-attention is applied for generating manipulation actions. Extensive experiments have been conducted to demonstrate that our framework is effective in multiple 1-D (rope, rope ring) and 2-D (cloth) rearranging tasks in simulation and can be easily transferred to a real robot by fine-tuning a keypoint detector.
A modal‐based Kalman filtering framework for mode extraction and decomposition of damped structures

The mode shape is one of the important modal parameters that enables to visualize the intrinsic behavior of a structure as well as the quantity of interest by extracting or separating modal response from measurements. In this study, a new output‐only framework is proposed to extract modes using a modal‐based Kalman filter defined in the modal space and identify the mode shape by manipulating the correlation between the separated modes and the measured responses. It is also shown that the proposed framework can be extended to estimate the mode shapes of a non‐classically damped structure in state space when the state variable is constructed from the measured responses and applied to the modal‐based Kalman filter. The mode shape estimation framework proposed in this study was verified by numerical simulations and full‐scale measurements. From the verification examples and their results, it was noted that the proposed modal identification framework is not influenced by the presence of noise, and it can be applied to identify the state‐space mode shapes of non‐classically damped systems as well as systems with very closely distributed modes such as buildings equipped with tuned mass dampers.
Development of a cable-driven Growing Sling to assist patient transfer

As the aging of society continues to accelerate, the number of elderly patients is increasing, as is the demand for manpower to care for them. In particular, there is an urgent need for bedridden patient care. However, limitations in the supply of human resources have caused an increase in the burden for care. In particular, nursing personnel often experience inconvenience and difficulties owing to the great deal of effort required to transfer a patient from bed to wheelchair, or vice versa. The most difficult process during the patient transfer is inserting the sling under the patient. Aiming to solve this problem, a mechanical Growing Sling was devised. The proposed sling adapts a growing mechanism comprising a low-friction fabric and steel shafts, and the sling is inserted under the patient by towing the steel shafts with cables connected to a motor. For the comfort and safety of the sling insertion, the required towing force was analyzed to find the minimum diameter of the shaft. The results from experimental evaluations using the proposed sling verified that it can be inserted under the patient without moving the patient, and with an acceptable level of pressure being applied to the patient.
A Multi-level Approach to Evaluate the Impact of GPU Permanent Faults on CNN's Reliability

Graphics processing units (GPUs) are widely used to accelerate Artificial Intelligence applications, such as those based on Convolutional Neural Networks (CNNs). Since in some domains in which CNNs are heavily employed (e.g., automotive and robotics) the expected lifetime of GPUs is over ten years, it is of paramount importance to study the impact of permanent faults (e.g. due to aging). Crucially, while the impact of transient faults on GPUs running CNNs has been widely studied, an accurate evaluation of the impact of permanent faults is still lacking. Performing this evaluation is challenging due to the complexity of GPU devices and the software implementing a CNN. In this work, we propose a methodology that combines the accuracy of gate-level fault simulation with the speed and flexibility of software fault injection to evaluate the effects of permanent hardware faults affecting a GPU. First, we profile the executed low-level GPU instructions during the CNN inference. Then, using extensive gate-level fault injection campaigns, we provide an accurate analysis of the effects of permanent faults on the internal modules executing the targeted instructions. Finally, we propagate these effects using fast software-based fault injection. The method allows, for the first time, to estimate the percentage of permanent faults leading the CNN to produce wrong results (i.e., changing the result of its work). The method's feasibility, which allows for flexibly trade-off accuracy with the required computational effort, is shown using LeNet running on an Ampere Nvidia GPU as a case study. The method reduces the computational effort for the evaluation by several orders of magnitude with respect to plain gate- and RTL-level faults simulation.
Thermal stability of a nanostructured trilayer synthetic antiferromagnet

Thermal stability of a nanostructured trilayer synthetic antiferromagnet is investigated in the Worledge model. No or minimal shape anisotropy effect is predicted from the model due to the simplifying assumption that the self-demagnetizing field is equal to the dipole field. A slight relaxation of the assumption causes a large impact on the thermal stability. In the case that the dipole field is 90% of the demagnetizing field, the energy barrier to spin flop is doubled to 80kT in a typical ellipsoid cell (212×106nm2) for high density magnetic random access memory. The thermal stability of half-selected cells in the writing scheme based on the spin flop is also improved by relaxing the assumption over the applied field range relevant to magnetic random access memory applications.
Reflection of sound from a randomly layered ocean bottom

This paper investigates the sensitivity of the plane‐wave reflection coefficient to the small‐scale random layering in turbidite sediment. A Monte Carlo algorithm is used to generate many numerical realizations of a finely layered stochastic turbidite model. The complex reflection coefficient R is computed for each realization so that a distribution of R values is produced at each frequency and grazing angle. The scatter in the R values is a measure of the sensitivity of R to small‐scale turbidite layering. Below 100–200 Hz, R is relatively insensitive to the details of the layering; at higher frequencies the detailed layering is important. To a rough approximation, the sensitivity to layering is independent of the grazing angle. The lack of sensitivity to small‐scale layering for f<100–200 Hz indicates that long wavelengths ’’average out’’ the fine structure and suggests that R can be calculated at low frequencies from a ’’smooth’’ geoacoustic model (i.e., a model with no small‐scale layering). For f≳100.
Identification of enamel demineralization using high performance convolutional neural network

Here, we traces use segmentation and convolutional neural network (CNN) to trace, diagnose and quantify enamel demineralization for research. The preprocessing, histograms based methods are used to enhance the contrast and equalize the brightness through the scanning electron microscope images. Our result evidence that the deep learning based CNN model is highly efficient to process the dental image to achieve high accuracy of enamel demineralization and presents promising outcomes with optimal precision.
Impact of Innovation and R & D on Financial Performance of Telecommunication Sector in Sri Lanka

The intention of this research is to investigate and analyze how innovation affects organizational performance in the Sri Lankan telecommunication sector. Three variables were used to measure the innovation construct: product innovation, process innovation, and disruptive innovation. The moderating variable organizational culture is used to measure the moderating effect between the independent and dependent variables (organization performance). The findings of this study will enable enhancing the performance of the telecommunication sector. By examining the impact of innovation and R&D on the telecommunication sector in Sri Lanka, as well as organizational performance, this analysis will address the research gap in the field of telecommunication sector in Sri Lanka. There are six hypotheses available in research methodology that was tested. Three main telecommunication sector firms out of five telecommunication sector firms in Sri Lanka was selected to collect data. The selected three companies are Sri Lankan Telecom, Mobitel and Dialog Axiata PLC. The result of the analysis shows that organizational innovation has a positive relationship towards organizational performance. The moderating variable “organizational culture” does not show any moderating effect between independent and dependent variables. The telecommunication firms that are more into organizational innovation have scored high in organizational performance criteria. Organizational culture does not highlight a moderating impact on the independent and dependent variables. -