Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Single Versus Multifraction Radiotherapy in Palliation of Painful Bone Metastases

Background Bone is a common site of dissemination in advanced cancer accounting for one-third of all distant metastases. Various fractionation schedules of radiotherapy have been used for palliation of bone metastases. The aim of this study was to compare three schedules of external radiation therapy (8 Gy single session versus 20 Gy/5 fractions versus 30 Gy/10 fractions) for palliative management of bone metastases. Methods In the present study, 60 patients of bone metastases from any primary site were enrolled and randomly divided into three groups of 20 patients each by draw of lots. These patients received palliative external beam radiation therapy to the involved site. Patients were given 8 Gy single session, 20 Gy/5 fractions/1 week and 30 Gy/10 fractions/2 weeks in groups I, II and III, respectively. Results The percentage of patients with overall pain relief was 80% in group I, 75% in group II and 85% in group III (P = 0.7). The number of patients with complete pain relief was 4 (20%) in each group. Maximum patients got pain relief at 4 weeks post-radiotherapy. The number of patients with improved performance status was 4 (20%) in group II and 2 (10%) each in groups I and III (P = 0.5). Thirteen (65%) patients in each of the groups had decreased analgesic requirement at 2-month follow-up. Retreatment rate was more in the single fraction (20%) compared to only 5% in group II and none in the group III (P = 0.05). Conclusion From the present study we conclude that 8 Gy single fraction is as effective as multifraction radiotherapy for the palliation of painful bone metastases. However, for a center like ours being the only Government Tertiary Cancer Care Centre in the State, general consensus drawn after this study, recommends external radiation therapy 20 Gy/5 fraction regimen to be an appropriate means of palliation of painful bone metastases.
Design of a capacitive power transfer system for charging of electric vehicles

Electric Vehicles (EV) nowadays are an attractive means of transportation, as they do not emit harmful gasses. Charging of EVs can be achieved through either direct contact or contactless approaches. Contactless charging approach, as Inductive Power Transfer (IPT) system, provides several merits. Yet, the IPT system has several problems; the most severe one is that it causes health issues, since it is based on electromagnetic induction. The alternative method of transferring power wirelessly is the Capacitive Power Transfer (CPT). The basic concept of a CPT system is based on the power transferal via electric fields between the plates. In this paper, a 5kW CPT system is designed with SiC MOSFET-based inverter. A compensating network, connected to the coupling capacitor, is used. The system is then connected to a rectifier to feed a battery. CPT different aspects are studied in this paper. The system is simulated using MATLAB/Simulink and tested experimentally to examine the performance.
Flexible distributed control plane deployment

For large-scale programmable networks, flexible deployment of distributed control planes is essential for service availability and performance. However, existing approaches only focus on placing controllers whereas the consequent control traffic is often ignored. In this paper, we propose a black-box optimization framework offering the additional steps for quanti-fying the effect of the consequent control traffic when deploying a distributed control plane. Evaluating different implementations of the framework over real-world topologies shows that close to optimal solutions can be achieved. Moreover, experiments indicate that running a method for controller placement without considering the control traffic, cause excessive bandwidth usage (worst cases varying between 20.1%-50.1% more) and congestion, compared to our approach.
Successful and unsuccessful weight-loss maintainers: strategies to counteract metabolic compensation following weight loss

Adaptive thermogenesis and reduced fat oxidative capacity may accompany weight loss, continuing in weight maintenance. The present study aimed (1) to determine whether weight-reduced and weight-loss relapsed women are at greater metabolic risk for weight gain compared with BMI-matched controls with no weight-loss history, and (2) to identify protective strategies that might attenuate weight loss-associated adaptive thermogenesis and support successful weight-loss maintenance. Four groups of women were recruited: reduced-overweight/obese (RED, n 15), controls (low-weight stable weight; LSW, n 19) BMI <27 kg/m2; relapsed-overweight/obese (REL, n 11), controls (overweight/obese stable weight; OSW, n 11) BMI >27 kg/m2. Body composition (bioelectrical impedance), 75 g oral glucose tolerance test, fasting and postprandial metabolic rate (MR) and substrate utilisation (RER) and physical activity (accelerometer (7 d)) were measured. Sociobehavioural questionnaires and 3 × 24 h diet recalls were completed. Fasting and postprandial MR, RER and total daily energy intake (TDEI) were not different between RED and REL v. controls (P > 0·05). RED consumed less carbohydrate (44·8 (sd 10·3) v. 53·4 (sd 10·0) % TDEI, P = 0·020), more protein (19·2 (sd 6·0) v. 15·6 (sd 4·2) % TDEI, P = 0·049) and increased physical activity, but behaviourally reported greater dietary restraint (P = 0·002) compared with controls. TDEI, macronutrient intake and physical activity were similar between OSW and REL. REL reported higher subjective fasting and lower postprandial ratings of prospective food consumption compared with OSW. Weight-reduced women had similar RMR (adjusted for fat-free mass) compared with controls with no weight-loss history. Increased physical activity, higher protein intake and greater lean muscle mass may have counteracted weight loss-associated metabolic compensation and highlights their importance in weight-maintenance programmes.
Path loss measurements in wideband radio link designed for maritime environment

In the article, the research and analysis of path loss in designed and evaluated digital radio link for high-speed data transmission in maritime environment is presented. Collected samples of received signal were used to determine the mean path loss and its standard deviation. Measuring campaign was carried out in a real maritime conditions. The mean value of path loss reached 126 dB on a distance of 11.16 km.
Accelerometer-based gait recognition via deterministic learning

In this paper we propose a new accelerometer-based gait recognition method, which consists of two stages: a training stage and a recognition stage. In the training stage, gait features representing gait motion dynamics, including acceleration data measured in the y-axis and z-axis of the right side of pelvis and the left thigh of the human body, are derived from accelerometers. Gait dynamics underlying different gait patterns are locally accurately modeled and approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In the recognition stage, a bank of dynamical estimators is constructed for all the training patterns. Prior knowledge of gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern to be recognized, a set of recognition errors are generated. The average Li norms of the errors are taken as the recognition measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern according to the smallest error principle. Finally, experimental results on the publicly available ZJU-GaitAcc dataset of 175 subjects demonstrated that our algorithm outperformed existing methods. By using the 2-fold and leave-one-out cross-validation styles on two subsets of this dataset, the correct recognition rates are reported to be 90.9%, 86.9% and 96.2%, 92.2%, respectively.
Exploring the Design Space of Fair Scheduling Supports for Asymmetric Multicore Systems

Although traditional CPU scheduling efficiently utilizes multiple cores with equal computing capacity, the advent of multicores with diverse capabilities pose challenges to CPU scheduling. For such asymmetric multi-core systems, scheduling is essential to exploit the efficiency of core asymmetry, by matching each application with the best core type. However, in addition to the efficiency, an important aspect of CPU scheduling is fairness in CPU provisioning. Such uneven core capability is inherently unfair to threads and causes performance variance, as applications running on fast cores receive higher capability than applications on slow cores. Depending on co-running applications and scheduling decisions, the performance of an application may vary significantly. This study investigates the fairness problem in asymmetric multi-cores, and explores the design space of OS schedulers supporting multiple fairness constraints. In this paper, we consider two fairness-oriented constraints, minimum fairness for the minimum guaranteed performance and uniformity for performance variation reduction. This study proposes four scheduling policies which guarantee a minimum performance bound while improving the overall throughput and reducing performance variation too. The proposed fairness-oriented schedulers are implemented for the Linux kernel with an online application monitoring technique. Using an emulated asymmetric multi-core with frequency scaling and a real asymmetric multi-core with the big.LITTLE architecture, the paper shows that the proposed schedulers can effectively support the specified fairness while improving overall system throughput.
Classification of radar signal features in electronic warfare with convolutional long-short time memory

Radar signals are time series that have pulse repetition interval, pulse width and pulse amplitude as their features. After reception of them by electronic warfare systems, their features are classified and kept in a database. This procedure brings vision for the system user if the same signal is received again in the future. For this classification purpose, three algorithms were implemented. The first one is a combined network consisting of a Convolutional Neural Network (CNN) and a Long-Short Time Memory (LSTM), the second is a Hybrid Network including the first network and a CNN which enables parallel training having histogram data as its input. The last algorithm is Stacked Autoencoder. Performance analysis was made on real radar data. The best performer is Hybrid Network which reached 99.6% accuracy as it involves histogram data usage and an LSTM for the time series problem. Convolutional LSTM reached 98.3% while Stacked Autoencoder had 87% accuracy.
Beamforming Techniques for Nonorthogonal Multiple Access in 5G Cellular Networks

In this paper, we develop various beamforming techniques for downlink transmission for multiple-input single-output nonorthogonal multiple access (NOMA) systems. First, a beamforming approach with perfect channel state information is investigated to provide the required quality of service for all users. Taylor series approximation and semidefinite relaxation (SDR) techniques are employed to reformulate the original nonconvex power minimization problem to a tractable one. Furthermore, a fairness-based beamforming approach is proposed through a max–min formulation to maintain fairness between users. Next, we consider a robust scheme by incorporating channel uncertainties, where the transmit power is minimized while satisfying the outage probability requirement at each user. Through exploiting the SDR approach, the original nonconvex problem is reformulated in a linear matrix inequality form to obtain the optimal solution. Numerical results demonstrate that the robust scheme can achieve better performance compared to the nonrobust scheme in terms of the rate satisfaction ratio. Furthermore, simulation results confirm that NOMA consumes a little over half transmit power needed by orthogonal multiple access for the same data rate requirements. Hence, NOMA has the potential to significantly improve the system performance in terms of transmit power consumption in future 5G networks and beyond.
Ballistocardiogram-based Authentication using Convolutional Neural Networks

The goal of this work is to demonstrate the use of the ballistocardiogram (BCG) signal, derived using head-mounted wearable devices, as a viable biometric for authentication. The BCG signal is the measure of an person's body acceleration as a result of the heart's ejection of blood. It is a characterization of the cardiac cycle and can be derived non-invasively from the measurement of subtle movements of a person's extremities. In this paper, we use several versions of the BCG signal, derived from accelerometer and gyroscope sensors on a Smart Eyewear (SEW) device, for authentication. The derived BCG signals are used to train a convolutional neural network (CNN) as an authentication model, which is personalized for each subject. We evaluate our authentication models using data from 12 subjects and show that our approach has an equal error rate (EER) of 3.5% immediately after training and 13\% after about 2 months, in the worst case. We also explore the use of our authentication approach for people with motor disabilities. Our analysis using a separate dataset of 6 subjects with non-spastic cerebral palsy shows an EER of 11.2% immediately after training and 21.6% after about 2 months, in the worst-case.
GeneFuse: detection and visualization of target gene fusions from DNA sequencing data

In recent years, gene fusion detection for cancer treatment has become increasingly important since more therapeutic agents have been developed to suppress fusion kinases. Although a number of tools have been developed to detect gene fusions from DNA sequencing data, most of them are not sensitive enough for processing the data from the samples with low tumor DNA composition, like cell-free tumor DNA. In this paper, we will introduce GeneFuse, a tool to detect and visualize gene fusions with high sensitivity and specificity. GeneFuse focuses on the curated gene fusions, which are available in COSMIC (the Catalogue of Somatic Mutations in Cancer) database. For each detected fusion, GeneFuse reports its genome locus, inferred protein forms, and supporting sequencing reads. The fusion detection results are visualized in an HTML page for cloud-friendly validation. GeneFuse is an open source tool available at GitHub: https://github.com/OpenGene/GeneFuse
Bibliometric analysis of workflow scheduling in grids and clouds

Workflow scheduling is crucial for QoS requirements to be met in grids and clouds. Scheduling algorithms may consider one or more criteria and constraints, including makespan, deadline, cost, budget, reliability, energy consumption and security. Although the literature reports some surveys on workflow scheduling algorithms, no bibliometric review of such topic has been conducted. This paper addresses a bibliometric analysis of workflow scheduling algorithms for grids and clouds including the evolution of publications according to the criteria and restrictions met, authors and articles most cited, journals and conferences with most accepted papers, a geographic study and the collaboration network among authors. Among the articles indexed in three databases, those that met the inclusion and exclu­sion criteria established were selected. According to our review, time and cost are the most common criteria, whereas energy consumption, security, and reliability present fewer publications on workflow scheduling algorithms.
Who draw this? Caricaturist recognition using convolutional neural networks

Caricature is the art of expressing an event that will be described in many pages in writing, visually with a small drawing. The details of the drawing, the style, and the objects used in the drawing, which make up the visual structure, are very important in conveying the event or thought. Therefore, different caricaturists express similar events (eg. social or political messages) using different details and objects according to their own style and imagination worlds. Determining which caricature belongs to which caricaturist will help us to organize large collections, determine originality, and make caricature suggestions based on subject/style. In this study, a total of 4212 caricature from 10 different Turkish caricaturists were collected from the Internet and a new caricature data set was created. Using this dataset, both the existing Convolutional Neural Network models and the proposed new network, ComicNet, are trained and their performances are compared. Experiments show that ComicNet is the most successful model with an accuracy of 94.68%.
Low ecological representation in the protected area network of China

Abstract Protected areas are considered as an essential strategy to halt the decline of biodiversity. Ecological representation in protected areas is crucial for assessment on the progress toward conservation targets. Although China has established a large number of protected areas since the 1950s, ecological representation of protected areas is poorly understood. Here, we performed the complementarity analysis to evaluate ecological representation of protected areas in China. We used a database of the geographical distribution for 10,396 woody plant species, 2,305 fern species, 406 amphibian species, 460 reptile species, 1,364 bird species, and 590 mammal species from 2,376 counties across China. We identified complementary sets of counties for all species or threatened species of plant and vertebrate species using a complementarity algorithm. We evaluated ecological representation of 3,627 protected areas and discerned conservation gaps by comparing the distribution of protected areas with complementary sets. The results show that the spatially representative and complementary sites for biodiversity are poorly covered, and a fairly large proportion of protected areas is not designed to efficiently represent biodiversity at the national scale. Our methodology can serve as a generic framework for assessment on ecological representation of protected areas at the national scale.
Computational luminance constancy from naturalistic images

The human visual system supports stable percepts of object color even though the light that reflects from object surfaces varies significantly with the scene illumination. To understand the computations that support stable color perception, we study how estimating a target object’s luminous reflectance factor (LRF; a measure of the light reflected from the object under a standard illuminant) depends on variation in key properties of naturalistic scenes. Specifically, we study how variation in target object reflectance, illumination spectra, and the reflectance of back-ground objects in a scene impact estimation of a target object’s LRF. To do this, we applied supervised statistical learning methods to the simulated excitations of human cone photoreceptors, obtained from labeled naturalistic images. The naturalistic images were rendered with computer graphics. The illumination spectra of the light sources and the reflectance spectra of the surfaces in the scene were generated using statistical models of natural spectral variation. Optimally decoding target object LRF from the responses of a small learned set of task-specific linear receptive fields that operate on a contrast representation of the cone excitations yields estimates that are within 13% of the correct LRF. Our work provides a framework for evaluating how different sources of scene variability limit performance on luminance constancy.
Harvesting & regenerating energy from multiple low-cost energy generators for low energy consumers and towards circular economy

Harvesting energy from the environment is being considered as a viable option to replace the current power supplies for energy constrained embedded system. With the few limitations such as a low amount of power generated using the power harvesters, the researchers are working towards generating new methods. The current vibration induced energy harvesting research falls into two key areas. One is developing optimal energy harvesting structures and the other is designing electrical circuits that are efficient enough to store the generated charge. The projects presented in this paper are pioneering this field at the University of Montenegro. We investigate, model, and test new methods of generating electrical power from ambient fluid motion through aeroelastic flutter vibrations of the belt. Such a system, when extended to aeroelastic flutter vibrations of flexible piezoelectric, could be designed for applications ranging from distributed power generation in densely populated urban areas to creating self-power in-situ wireless sensors for aerospace applications. As part of that but also as an autonomous possible harvester we look at the shoe generator too.
Rectangular Patch Antenna Strain Sensor with Plastic Substrate for Curvature Measurements

A strain sensor based on a microstrip antenna has as principle of operation the relationship between an applied deformation and the resonance frequency shifts. This paper proposes a curvature sensor based on a rectangular microstrip patch antenna with plastic dielectric substrate and two operation bands around 2.6 and 6.1 GHz. Experimental test with different curvature radius were performed (from a curvature of 0 m-1 to a curvature of 40 m-1 ) The experimental results were compared with results obtained in numerical simulations, where a good match for small values of curvature was found. The proposed sensor obtained a sensitivity of 1.78 and 2.41, and the range of operation was 21 and 67 MHz. The sensitivity of the sensor makes itself useful in Structural Health Monitoring (SHM) systems, in addition, plastic dielectric substrate features allows it to memorize applied deformations when the external force is gone.
When more may not be better: Toward cost-efficient CDN selection

Selecting suitable content delivery networks (CDNs) is critical for any content provider (CP). Normally, a CP chooses the CDN that has a larger point of presence (PoP) footprint and thus is believed to have a better performance. In this paper, we investigate if smaller, cheaper CDNs could have a comparable performance to that of big ones. We develop a method for CDN selection and perform a case study using real-world CDN demand trace data over a synthetic PoP network. Our study would help the CP better understand the intricate relationship between the number of PoPs and the performance of CDN.