Miracle-dz/UniFAR-FTUs huggingface dataset:
Measure the Errors of Treatment Set-Ups of Prostate Cancer Patient Using Electronic Portal Imaging Device (EPID)

The purpose of this work was the determination of the set-up errors for Prostate cancer patients by using electronic portal imaging device (EPID) [14], as tool to verify the patient treatment positioning. This could be done by the means of identifying and correcting the field displacements in patient’s setups which requires accurate patient positioning with reference to the initial three-dimension conformal radiotherapy (3DCRT). Patient setup is controlled by comparing the Digitally reconstructed radiographs (DRR) with portal images acquired immediately before patient treatment. It is generally accepted that two classes of set-up uncertainties identified by systematic and random errors. A study on 15th prostate cancer patients using Varian Linear accelerator model DMX to treat and portal image device Si500 shall be used to evaluate the selected portal images taken for each patient. A comparison has been made between previous publish work and new approach of this study. The calculation of PTV margins according to three formulas used in the study, the margins which are calculated according to ICRU Report 62 is suggested as an optimum margin for target volume coverage which is <5 mm in all three directions. This study using an electronic portal imaging device (EPID) to measure the set-up deviations μ for Prostate cancer patients and calculate the set-up errors Σ set-up and σ set-up and, for my Department of Medical Physics in my hospital to find the optimal correction strategy to decrease μ.
Three-dimensional-subwavelength field localization, time reversal of sources, and infinitely asymptotic degeneracy in spherical structures

High-resolution field localization in three dimensions is one of the main challenges in optics and has immense importance in fields such as chemistry, biology, and medicine. In order to generate the time reversed signal of a monochromatic source \emph{discrete} sources that are modulated according to the wave amplitude on a spherical envelope are required, rendering it applicable only in acoustics. Here we approach these challenges by introducing a spherical layer with a resonant permittivity, which naturally generates the spatially \emph{continuous} time-reversed signal of an atomic and molecular multipole transition at the origin. We start by utilizing a spherical layer with a resonant TM $l=1$ permittivity situated in a uniform medium to generate a free-space-subwavelength focal spot at the origin. We remove the degeneracy of the eigenfunctions of the composite medium by situating a point current source (or polarization) directed parallel to the spherical layer, which generates a focal spot at the origin \emph{independently} of its location. The free-space focal spot has a full width at half maximum of $0.4\lambda$ in the lateral axes and $0.58\lambda$ in the axial axis, which is tighter by a factor of $\sqrt{2}$ in each dimension in excitation-collection mode, overcoming the $\lambda/2$ far-field resolution limit in three dimensions. We then explore two directions to localize electric field with deep-subwavelength resolution in three dimensions using this setup. In addition, we show that spherical structures exhibit a new type of degeneracy in which an infinite number of eigenvalues asymptotically coalesce. This high degeneracy results in a variety of optical phenomena such as strong scattering and enhancement of absorption and emission from an atom or molecule by orders of magnitude compared with a standard resonance.
Exploring the chemical space without bias: data-free molecule generation with DQN and SELFIES

We propose a method for de novo molecule design that is free of humanand databiases. We used a deep q-network [1] to train an agent to learn how to sequentially generate a molecule with SELFIES representation [2]. Using SELFIES instead of SMILES [3] frees the agent from having to learn a specific grammar, as even completely random SELFIES produce valid molecule. The network is rewarded according to the output molecule’s value of the partition coefficient, logP, which is considered as the Drosophila of computational chemistry. After less than an hour of training on a single GPU, the agent was successfully producing molecules with the targeted property value. Bias-free design could lead to unrestricted exploration of the entire chemical space, and exploring regions that humans (and human-generated data) might have entirely overlooked so far. This property makes our algorithm directly applicable to other design questions in the physical sciences.
Ti3+ Defective SnS2/TiO2 Heterojunction Photocatalyst for Visible-Light Driven Reduction of CO2 to CO with High Selectivity

In recent years, defective TiO2-based composite nanomaterials have received much attention in the field of photocatalysis. In this work, TiB2 was used as a precursor to successfully prepare Ti3+ defective TiO2 (TiO2-B) with a truncated bipyramidal structure by a one-step method. Then, the SnS2 nanosheets were assembled onto the as-prepared TiO2-B through simple hydrothermal reaction. TiO2-B exhibits strong visible light absorption properties, but the recombination rate of the photo-generated electron-hole pair was high and does not exhibit ideal photocatalytic performance. Upon introducing SnS2, the heterojunction catalyst SnS2-Ti3+ defective TiO2 (SnS2/TiO2-B) not only possesses the strong light absorption from UV to visible light region, the lowest photo-generated charge recombination rate but also achieves a more negative conduction band potential than the reduction potential of CO2 to CO, and thereby, exhibits the significantly enhanced selectivity and yield of CO in photocatalytic CO2 reduction. Notably, SnS2/TiO2-B produces CO at a rate of 58 µmol·h−1·g−1 with CO selectivity of 96.3% under visible light irradiation, which is 2 and 19 times greater than those of alone TiO2-B and SnS2, respectively. Finally, a plausible photocatalytic mechanism on SnS2/TiO2-B was proposed that the electron transfer between TiO2 and SnS2 follows the Z-scheme mode. Our results present an effective way to gain highly efficient TiO2 based photocatalysts for CO2 reduction by combining different modification methods of TiO2 and make full use of the synergistic effects.
FLUCTUATION EFFECT OF EQUILIBRIUM MOISTURE CONTENT OF LOW SUBGRADE UNDER HIGH GROUNDWATER LEVEL IN HOT AND HUMID CLIMATIC REGIONS

In order to reveal the fluctuation effect of equilibrium moisture content of low subgrade in hot and humid climatic regions, the effect of temperature on the fluctuation of the equilibrium moisture content of subgrade was analysed. Taking the typical climate and the subgrade soil in Fujian province as an example, three technological methods theoretical analysis, numerical simulation and indoor simulation experiment were adopted in the investigation of the fluctuation effect of equilibrium moisture content of subgrade. The results show that, computing results from the formula of the equilibrium moisture content of subgrade, the numerical simulation results are closer to each other in consideration of the temperature effect. The test results can not reflect the relationship between the equilibrium moisture content and the height of embankment. The maximum fluctuation range of the equilibrium moisture content of the cement concrete pavement is less than 2 percent in Fujian area, and this phenomenon presents the effect of the moist-hot climate on the equilibrium moisture content. Equilibrium moisture content presents a declining trend with the increment of the temperature and the compactness. So, if matric potential considering temperature indirectly reflects the influence of thermal potential, then the equilibrium moisture content of low subgrade under high groundwater level can be estimated approximately. The fluctuation range of equilibrium moisture content in different layers of subgrade can be reduced effectively with the increment of the roadbed compaction degree.
Cumulative Environmental Effects of Hydropower Stations Based on the Water Footprint Method—Yalong River Basin, China

The construction of hydropower stations is not without controversy as they have a certain degree of impact on the ecological environment. Moreover, the water footprint and its cumulative effects on the environment (The relationship between the degree of hydropower development and utilization in the basin and the environment) of the development and utilization of cascade hydropower stations are incompletely understood. In this paper, we calculate the evaporated water footprint (EWF, water evaporated from reservoirs) and the product water footprint of hydropower stations (PWF, water consumption per unit of electricity production), and the blue water scarcity (BWS, the ratio of the total blue water footprint to blue water availability) based on data from 19 selected hydropower stations in the Yalong River Basin, China. Results show that: (a) the EWFs in established, ongoing, proposed, and planning phases of 19 hydropower stations are 243, 123, 59, and 42 Mm3, respectively; (b) the PWF of 19 hydropower stations varies between 0.01 and 4.49 m3GJ−1, and the average PWF is 1.20 m3GJ−1. These values are quite small when compared with hydropower stations in other basins in the world, and the difference in PWF among different hydropower stations is mainly derived from energy efficiency factor; (c) all the BWS in the Yalong River Basin are below 100% (low blue water scarcity), in which the total blue water footprint is less than 20% of the natural flow, and environmental flow requirements are met. From the perspective of the water footprint method, the cumulative environmental effects of hydropower development and utilization in the Yalong River Basin will not affect the local environmental flow requirements.
SPRAY DRYING OF UNFERMENTED COCONUT SAP OR SWEET TODDY INTO AN AMORPHOUS POWDER

This study was carried out to convert unfermented coconut sap or the sweet toddy into powder form through spray drying using maltodextrin (DE 10) as the drying aid. Three different formulations of sweet toddy: maltodextrin (70%: 30%, 80%: 20% and 90%:10%) were spray dried at inlet and outlet temperatures of 165 C and 65 C, respectively. Powder recovery in a pilot scale spray dryer was used as a measure of the ease of spray drying for a given formulation. The spray dried sweet toddy powder was characterized for moisture content, water activity, crystalline-amorphous nature, particle morphology, sugar profile and the keeping quality. The best powder recovery (56.35±2.90%) was obtained for the formulation of sweet toddy: maltodextrin in the ratio of 80%:20%. The moisture content and water activity value of the spray dried coconut sweet toddy powder were 1.24±0.01% and 0.29±0.00, respectively. It was interesting to note that both the moisture content and the water activity of sweet toddy powder did not vary significantly (p>0.05) on storage of one year. X-ray diffraction studies revealed that the spray dried sweet toddy powder was amorphous. The scanning electron micrograph of spray dried coconut sweet toddy powder showed that the particles were spherical in shape. The sugar profile of this sweet toddy powder showed that it had a total sugar content of 66.1±0.04% out of which 32.7±0.02% was sucrose, 22.4±0.01% was glucose and 11.0±0.01% was fructose.
MACA-I: A Malware Detection Technique using Memory Management API Call Mining

Present day malicious software, often encountered in an Internet-enabled electronic system, turn to be extremely difficult to be detected by the existing malware detection solutions. In this paper, we introduce a novel malware detection strategy, Memory management with API CAll mIning (MACA-I), that efficiently detects malware based on the dynamic analysis. MACA-I analyzes API calls that are responsible for accessing the system memory and, thereby, generates required features by observing the transitions of memory management APIs as well as the allocated memory block space during the runtime. We find that MACA-I is approximately 95.45% accurate while detecting malware programs on the basis of system memory-related runtime behavior.
Adaptive RED for FreeBSD: Design, Implementation and Challenges

Bufferbloat problem arises due to buffering of large amounts of data in queues, owing to the large size of these queues. Bufferbloat being a relatively new phenomenon meant that earlier queue management algorithms did not specifically address this problem. Despite this issue, there is merit in analysing and evaluating old queue management algorithms which have helped alleviate the undesirable performance issues that arose due to persistently full buffers. One of the earliest and most significantly studied Active Queue Management (AQM) algorithms is Random Early Drop (RED). RED helps to keep the average size of the queues low and allow occasional bursts of packets through the queue. Once the number of packets queued crosses a minimum threshold, incoming packets are dropped with a random probability. However, the resulting average queue length is quite sensitive to the level of congestion and the RED parameter settings. Adaptive RED (ARED) solves most of the issues faced by RED with minimal changes and leaves its basic idea intact. The ARED algorithm regularly adapts the value of the maximum dropping probability and ensures that the queue length stays within the targeted range. Despite its ability to resolve the inherent problems in RED, ARED went largely unnoticed for several years, until the issue of Bufferbloat arose. Although ARED predates Bufferbloat, its fundamental design makes it an effective solution to handle Bufferbloat. This discovery led to the implementation of ARED in Linux and in network simulators like ns-3. Besides Linux, FreeBSD is one of the most popular open source operating systems. Although RED is supported in FreeBSD, ARED is not. Since ARED is one of the viable solutions to tackle Bufferbloat, this paper discusses the design and implementation of ARED in FreeBSD. We also detail the challenges faced during the implementation, and validate through real testbed experiments that our implementation in FreeBSD exhibits ARED's key characteristics.
Gaussian Process Auto Regression for vehicle center coordinates Trajectory Prediction

With the increase in autonomous car technology, and advance driver assistance systems (ADAS), the demand for vehicle trajectory prediction is increasing. These systems mostly use many sensors such as lidar, radar, stereo cameras which are big and expensive to detect the location of the ego vehicles with respect to other vehicles. Such sensors are not readily available to Vulnerable Road Users (VRU) such as motorcyclist, cyclist as they mostly only rely on their phones or a small device for navigation and safety warnings. Trajectory prediction is important to VRU as it is able to predict if the trajectory of other vehicles is a threat or not. Most of the current Trajectory prediction for vehicles involves plotting a relative position of other vehicles with respect to the ego vehicle and this is not possible with just a mobile device. Hence, this paper proposes a method to predict trajectory of other vehicles based on detected vehicles center coordinates. This is achieved using Gaussian Process Auto-Regression based on past data of vehicle center coordinates to predict future coordinates on an x-y pixel plane using only a camera sensor and You Only Look Once (YOLO) vehicle detection.
Addressing Flooding Attacks in IPv6-based Low Power and Lossy Networks

In the RPL routing protocol, DODAG Information Solicitation (DIS) control messages are sent by nodes to join the network. In turn, the receiver node replies with DODAG Information Object (DIO) control message after resetting its trickle timer. A malicious node can utilize this RPL protocol behavior to perform the DIS flooding attack by sending illegitimate DIS frequently which forces normal nodes to reset their trickle timers and flood the network with DIO messages. In this study, we show that such attacks can severely degrade the performance of Low Power and Lossy Networks (LLNs) because of the increase in control packet overhead and power consumption. To address DIS flooding attacks, we propose a lightweight mitigation scheme that detects and mitigate such attacks in order to improve LLNs performance.
Connectivity Influences on Nonlinear Dynamics in Weakly-Synchronized Networks: Insights From Rössler Systems, Electronic Chaotic Oscillators, Model and Biological Neurons

Natural and engineered networks, such as interconnected neurons, ecological and social networks, coupled oscillators, wireless terminals and power loads, are characterized by an appreciable heterogeneity in the local connectivity around each node. For instance, in both elementary structures such as stars and complex graphs having scale-free topology, a minority of elements are linked to the rest of the network disproportionately strongly. While the effect of the arrangement of structural connections on the emergent synchronization pattern has been studied extensively, considerably less is known about its influence on the temporal dynamics unfolding within each node. Here, we present a comprehensive investigation across diverse simulated and experimental systems, encompassing star and complex networks of Rössler systems, coupled hysteresis-based electronic oscillators, microcircuits of leaky integrate-and-fire model neurons, and finally recordings from in-vitro cultures of spontaneously-growing neuronal networks. We systematically consider a range of dynamical measures, including the correlation dimension, nonlinear prediction error, permutation entropy, and other information-theoretical indices. The empirical evidence gathered reveals that under situations of weak synchronization, wherein rather than a collective behavior one observes significantly differentiated dynamics, denser connectivity tends to locally promote the emergence of stronger signatures of nonlinear dynamics. In deterministic systems, transition to chaos and generation of higher-dimensional signals were observed; however, when the coupling is stronger, this relationship may be lost or even inverted. In systems with a strong stochastic component, the generation of more temporally-organized activity could be induced. These observations have many potential implications across diverse fields of basic and applied science, for example, in the design of distributed sensing systems based on wireless coupled oscillators, in network identification and control, as well as in the interpretation of neuroscientific and other dynamical data.
Analysis of Electromagnetic Actuation System for Different Coil Topologies

Electromagnetic actuation systems are used to generate magnetic forces for manipulation of magnetic nanoparticles. In this paper, we present a comparative performance analysis of actuation systems, consisting of coils having the following five different topologies: single coil, Helmholtz coil, Maxwell coil, Helmholtz-Maxwell pair and differential current coil (DCC). The experiments are performed using PASCO EX-5540A setup while the numerical analyses are done using COMSOL Multiphysics software. It is observed that the experimental results conform to analytical results as well as simulation results. Furthermore, the results highlight that both Helmholtz-Maxwell pair and DCC topologies produce strong gradient magnetic flux density, thus leading to better manipulation of magnetic nanoparticles as compared to that of other topologies. Additionally, the DCC approach with two coils achieves better gradient flux density while having lesser geometrical complexity as compared to that of Helmholtz-Maxwell pair consisting of four coils.
Online Parameter identification of PMSG Wind turbine for Output Power control

This paper proposed the wind sensorless identification method for wind turbine using online parameter identi cation. In the proposed On-line parameter identification is used to identify the parameter $k_{opt}$ and improve the characteristics in optimal torque control that does not require wind speed information. The parameter identification is performed using the Recursive least squares method in the wind turbine using PMSG. Further, the identied optimal parameter $k_{opt}$ contributes to the detection of all parameter error of the wind turbine and characteristic change with aged deteriorations. The effectiveness of the proposed identi cation method was veried by simulations with using MATLAB / SIMULINK.
An Analysis of Threads with No Responses in Online Asynchronous Discussions

This study examines the causes of “no-response threads” in thread-based online discussion support systems. We investigated the factors contributing to the occurrence of no-response threads by using a logistic regression analysis of discussions on the topic of Nagoya City’s attractions in the online discussion system COLLAGREE. Results showed that the content quality of a post did not significantly affect the occurrence of no-response threads in the divergence phase, where non-response was found to be more dependent on environmental factors such as the displayed time on the first page and the number of entries and unique viewers after the thread was posted. On the other hand, in the convergence phase, content quality greatly affected whether a new post would receive a response.
Automatic License Plate Recognition System for Bangla License Plates using Convolutional Neural Network

We present an automatic license plate recognition system that can detect and recognize Bangla license plates from an image of vehicles. It is mandatory for vehicles in Bangladesh to have Bangladesh Road Transport Authority (BRTA) standard license plates attached in front and back of the vehicle. We build a database containing vehicles of these type. From this dataset, detecting the license plates in an input image is the first step of our system. We use a convolutional neural network (CNN) model for this. From that, we take the detected license plate image as a new input for the second and similar CNN model to segment and recognize license plate numbers. We tested our model on 200 images and achieved an accuracy of 99.5%. For speed, we tested our model on Google Colaboratory's Nvidia Tesla K80 GPU and attained a speed of 9 frames per second while detecting and recognizing the license plate numbers in a video.
Ternary Composite MnO 2 @MoS 2 /Polypyrrole from In-situ Synthesis for Binder-free and Flexible Supercapacitor

Abstract MnO2@MoS2/Polypyrrole ternary composite is prepared through hydrothermal methods and a simple oxidation process by using MnO2@MoS2 sheet as the substrate and polypyrrole. The ternary composite serves as an electrode for pseudocapacitor which has more superior electrochemical properties compared with the binary complex. The supercapacitor electrode consists of two dimensional MoS2 layers as load skeleton, MnO2 providing electrochemical performance and polypyrrole improving high electric conductivity. These three components form a compact structure and synergistic effect leads to enhancing sufficient oxidation reduction for supercapacitor performance. Hence, MnO2@MoS2/ Polypyrrole structure possesses higher specific capacitance of 490 F/g at a current density of 1 A/g and excellent cycling stability of 90% after 1000 cycles at 1 A/g. Here, Polypyrrole is also used as the bender material, exhibiting mechanical flexibility for electrode. The results of this study provides a simple method to produce an effective material for flexible pseudocapacitor electrodes for higher energy storage devices.