Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-term Inter-protocol kV CBCT image quality assessment for a ring-gantry linac via automated QA approach

We develop a fully automated QA process to compare the image quality of all kV CBCT protocols on a Halcyon linac with ring gantry design, and evaluate image quality stability over a 10-month period. A total of 19 imaging scan and reconstruction protocols were characterized with measurement on a newly released QUART phantom. A set of image analysis algorithms were developed and integrated into an automated analysis suite to derive key image quality metrics, including HU value accuracy on density inserts, HU uniformity using the background plate, high contrast resolution with the modulation transfer function (MTF) from the edge profiles, low contrast resolution using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), slice thickness with the air gap modules, and geometric accuracy with the diameter of the phantom. Image quality data over 10 months was tracked and analyzed to evaluate the stability of the Halcyon kV imaging system. The HU accuracy over all 19 protocols is within tolerance (±50HU). The maximum uniformity deviation is 12.2 HU. The SNR and CNR, depending on the protocol selected, range from 18.5–911.9 and 1.9–102.8, respectively. A much-improved SNR and CNR were observed for iterative reconstruction (iCBCT) modes and protocols designed for large subjects over low dose and fast scanning modes. The Head and Image Gently protocols have the greatest high contrast resolution with MTF10% over 1 lp/mm and MTF50% over 0.6 lp/mm. The iCBCT mode slightly improved the MTF10% and MTF50% compared to the Feldkamp-Davis-Kress approach. The slice thickness (maximum error of 0.31 mm) and geometry metrics (maximum error of 0.7 mm) are all within tolerance (±0.5 mm for slice thickness and ±1 mm for geometry metrics). The long-term study over 10-month showed no significant drift for all key image quality metrics, which indicated the kV CBCT image quality is stable over time.
Procedural Behavior Model using Behavior Tree in Virtual Reality Applications

This paper introduces a study for procedurally generating the behavior of objects in a virtual environment at runtime. This study was initiated to enable the behavioral model of objects in virtual reality applications to evolve in response to user behavior at runtime. Our approach is to describe the behavior of an object as a behavior tree, and to make a node of the behavior tree change to another type if a certain condition is satisfied. We defined four types of node changes: "parameterized", "probabilistic", "alternate", and "variant". We experimented with a virtual environment that includes a variety of simple procedural elements to explore the possibilities of our approach. As a result of the implementation, if an optimization algorithm that can select and apply the optimized procedural elements in response to the user's behavior is complemented, it is confirmed that more intelligent objects and agents can be implemented in virtual reality applications.
RSU-Assisted Adaptive Scheduling for Vehicle-to-Vehicle Data Sharing in Bidirectional Road Scenarios

This study investigates the synergy between centralized and decentralized (i.e., ad hoc) data scheduling in vehicular ad hoc networks (VANETs) for offloading and balancing the workloads of roadside units (RSU) in bidirectional road scenarios. In the centralized scheduling, an RSU schedules data dissemination using a hybrid of infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications. Specifically, RSUs cooperate with each other by transferring unserved requests, and each RSU schedules the data services based on its locally received requests and the transferred requests. In the decentralized scheduling, vehicles driving in opposite directions share the cached data items via V2V communication when out of the coverage of RSU. The ad hoc scheduling can be benefited from the cooperation among RSUs since the chance for V2V data sharing could be enhanced when considering transferred requests into scheduling at each RSU. We formulate a hybrid of centralized and ad hoc data scheduling (HCA) problem, aiming at best exploiting the synergistic effects of I2V and V2V communication based on centralized data broadcast and ad hoc data sharing. On this basis, we propose an RSU Cooperation-based Adaptive Scheduling (RCAS) algorithm that consists of three mechanisms, including a centralized scheduling mechanism at each RSU, an ad hoc scheduling mechanism for vehicles, and a cluster management mechanism. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed solution under a variety of circumstances.
A STUDY OF REAL TIME SEARCH IN FLOOD SCENES FROM UAV VIDEOS USING DEEP LEARNING TECHNIQUES

Following a natural disaster, one of the most important facet that influence a persons chances of survival/being found out is the time with which they are rescued. Traditional means of search operations involving dogs, ground robots, humanitarian intervention; are time intensive and can be a major bottleneck in search operations. The main aim of these operations is to rescue victims without critical delay in the shortest time possible which can be realized in real-time by using UAVs. With advancements in computational devices and the ability to learn from complex data, deep learning can be leveraged in real time environment for purpose of search and rescue operations. This research aims to solve the traditional means of search operation using the concept of deep learning for real time object detection and Photogrammetry for precise geo-location mapping of the objects(person,car) in real time. In order to do so, various pre-trained algorithms like Mask-RCNN, SSD300, YOLOv3 and trained algorithms like YOLOv3 have been deployed with their results compared with means of addressing the search operation inreal time.
Time-lapse ground penetrating radar difference reflection imaging of saline tracer flow in fractured rock

The characterization of flow and transport processes in fractured rock is challenging because they cannot be observed directly and hydrologic tests can only provide sparse and local data. Time-lapse ground penetrating radar (GPR) can be a valuable tool to monitor such processes in the subsurface, but it requires highly reproducible data. As part of a tracer injection experiment at the Grimsel Test Site (GTS) in Switzerland, borehole reflection GPR data were acquired in a time-lapse survey to monitor saline tracer flow through a fracture network in crystalline rock. Because the reflections from the tracer in the sub-mm fractures appear extremely weak, a differencing approach has been necessary to identify the tracer signal. Furthermore, several processing steps and corrections had to be applied to meet the reproducibility requirements. These steps include (1) single-trace preprocessing, (2) temporal trace alignment, (3) correction of sampling rate fluctuations, (4) spatial trace alignment, (5) spike removal, and (6) postprocessing procedures applied to the difference images. This allowed successful tracer propagation monitoring with a clear signal that revealed two separate tracer flow paths. The GPR results are confirmed by conductivity meters that were placed in boreholes in the GTS. If sufficient data processing is applied, GPR is shown to be capable of resolving tracer flow through sub-mm aperture fractures by difference reflection imaging even in challenging surroundings where many reflectors are present.
Bayesian calibration at the urban scale: a case study on a large residential heating demand application in Amsterdam

ABSTRACT A bottom-up building energy modelling at the urban scale based on Geographic Information System and semantic 3D city models can provide quantitative insights to tackle critical urban energy challenges. Nevertheless, incomplete information is a common obstacle to produce reliable modelling results. The residential building heating demand simulation performance gap caused by input uncertainties is discussed in this study. We present a data-driven urban scale energy modelling framework from open-source data harmonization, sensitivity analysis, heating demand simulation at the postcode level to Bayesian calibration with six years of training data and two years of validation data. Comparing the baseline and the calibrated simulation results, the averaged absolute percentage errors of energy use intensity in the study area have significantly improved from 25.0% to 8.3% and from 19.9% to 7.7% in two validation years, while and . The overall methodology is extendable to other urban contexts.
Bagging deep convolutional autoencoders trained with a mixture of real data and GAN-generated data

While deep neural networks have achieved remarkable performance in representation learning, a huge amount of labeled training data are usually required by supervised deep models such as convolutional neural networks. In this paper, we propose a new representation learning method, namely g enerative a dversarial n etworks (GAN) based b agging d eep c onvolutional a uto e ncoders (GAN-BDCAE), which can map data to diverse hierarchical representations in an unsupervised fashion. To boost the size of training data, to train deep model and to aggregate diverse learning machines are the three principal avenues towards increasing the capabilities of representation learning of neural networks. We focus on combining those three techniques. To this aim, we adopt GAN for realistic unlabeled sample generation and bagging deep convolutional autoencoders (BDCAE) for robust feature learning. The proposed method improves the discriminative ability of learned feature embedding for solving subsequent pattern recognition problems. We evaluate our approach on three standard benchmarks and demonstrate the superiority of the proposed method compared to traditional unsupervised learning methods.
Effects of Zingiber Officinale and Ambrosia Maritima on some Physiological Parameters and Kidney Structure in Rats

The present work was carried out to investigate the effect of ZOR Roscoe (ZOR), AML L (AML) and their mixture on some physiological parameters and kidney structure of male albino rats. This study was undertoken in the animal house belong to Animal Production Department, Faculty of Agriculture, Al-Azhar University. Twenty-four male albino rats with an average body weight 100-130 g. The animals were divided into 4 similar groups group1 (Control untreated group), group2, ( rats fed with diet contain 5% ZOR Roscoe), group3 (rats fed on diet contain 5% AML L) and group4 (rats fed on diet containg 2.5% ZOR Roscoe +2.5% AML L.). During the whole experimental period blood samples were collected and serum was analyzed for concentration of glucose, cholesterol, triglyceride , urea and criatinin. At the end of the experiment, rats were scarificed to obtain the kidneys. Results indicated that treatment of rats with zingiber officinal and AML for 8 Weeks showed mild ischemic charges of some glomeruli and tubules in the cortex of the kidney, Meanwhile mix with zingiber officinal and AML showed minimal ischemic changes of some glomeruli and tubules in the cortex of the kidney. All medicinal plants treatment had hypochlosterolemic and hypolepidimic effects being the highest for the comparing treatment. Treatment of rats with medicinal plants for 8 weeks significantly increased serum urea and creatinine concentration.
Leaning in or Not Leaning Out? Opt-Out Choice Framing Attenuates Gender Differences in the Decision to Compete

In most organizations, promotions often require self-nomination and competition among applicants. However, research on gender differences in preferences for competition suggests that this process might result in fewer women choosing to participate. We study whether changing promotion schemes from a default where applicants must opt in (i.e., self-nominate) to a default where applicants must opt out (i.e., they are automatically considered for promotion, but can choose not to be considered) attenuates gender differences. In our first experiment, although women are less likely than men to choose competitive environments under the traditional opt-in framing, in the opt-out system both women and men have the same participation rate as men in the opt-in system. The increase in participation of women into competition is not associated with negative consequences on performance or well-being. In our second experiment, we show that opt-out framing does not entail penalties from evaluators making decisions about whom to hire. These results support the promise of choice architecture to reduce disparities in organizations. More generally, our findings suggest that gender differences in attitudes toward completion may be context-dependent.
Inclusion-based boundary element method for design of building envelopes

an novel numerical method to simulate the transient conduction through a concrete wall containing PCM using the Green’s The on simulation of PCM and heat equation applied finite element method (FEM), finite difference method (FDM). FDM and taking derivatives, and it equations. the solutions of FDM For inhomogeneity Eshelby proposed equivalent inclusion filling with the same with the and fictious heat source, eigen gradient of temperature. The inclusion-based boundary element method (IBEM) combines boundary element method, requires to mesh convex surface. Upon experimental validation, the iBEM model is used for efficient building and savings
Dividend Determinants of Indonesian Sharia Stocks List (Case Study for The Period of 2009-2017)

The study tried to help the Muslim investors and prospective Muslim investors deciding their investment plan in sharia firms, by seeking for the pattern of dividend policy which is the income sources of the Muslim investors. The data used are 33 companies listed in Sharia Stock List continuously for 9 periods, also consistent to distribute the dividend. As for the factors that probably can affect the dividend are cash position, debt to equity ratio, return on investment, and the institutional ownership. The study also used firm size as a control variable, and suspecting return on investment and institutional ownership as moderating variables for cash position and debt ratio in affecting the dividend policy. The result showed only institutional ownership can affect dividend policy directly, while cash position will be able to influence the policy by being moderated by the return on investment and institutional ownership. Therefore, the Muslim investor or prospective Muslim investors can consider more to the ownership structure of the firms before deciding their investment allocation. The study found out that the management along with the institutional shareholders as one of the major shareholders, has considering the shareholders’ rights as well. The policy taken is intended not only to keep the shareholders’ need of the return of their investment, but also to keep the Muslims’ shares of the firms still safe and stand on the good price.
A Dynamic Robust Restoration Framework for Unbalanced Power Distribution Networks

The increasing penetration of photovoltaic (PV) generators has led to a reduction in the effectiveness of existing strategies for restoring the power distribution network. This article proposes a dynamic robust restoration (DRR) framework for the recovery of outage power considering uncertain PV outputs and demands. This framework is presented in two subsequent steps. In the first step, optimal decisions regarding the network configurations are generated. The second step then computes the modified dynamic Distflow equations and constraints under consideration of the worst operating conditions over the associated uncertainty sets with the aim of maximizing the recovery of outage power. The DRR model is formulated as a bilevel mixed-integer linear programming problem. A decomposition algorithm in a master–sub structure is used to solve the resulting system. The results of case studies show that the proposed DRR model yields obvious advantages over the existing deterministic dynamic restoration model in terms of robustness against system uncertainties.
Digitalization and the future of Management Learning: New technology as an enabler of historical, practice-oriented, and critical perspectives in management research and learning

How are historical, practice-oriented, and critical research perspectives in management affected by digitalization? In this article, we describe and discuss how two digital research approaches can be applied and how they may influence the future directions of management scholarship and education: Social Media Analytics and digital archives. Our empirical illustrations suggest that digitalization generates productivity improvements for scholars, making it possible to undertake research that was previously too laborious. It also enables researchers to pay closer attention to detail while still being able to abstract and generalize. We therefore argue that digitalization contributes to a historical turn in management, that practice-oriented research can be conducted with less effort and improved quality and that micro-level data in the form of digital archives and online contents make it easier to adopt critical perspectives.
Physicists and outreach : implications of schools physics outreach programmes from the perspective of the participating physicists

This thesis examines physics outreach activities from the perspective of the participating physicists. It explores the nuances of physics outreach to school audiences in the context of wider outreach and public engagement agendas for higher education. The study employs mixed methods, comprising an electronic survey and case studies. The case studies follow audiences through nine physics outreach activities that are observed, complemented by semi-structured interviews with the physicists that took part in the activity after the event. Physicists’ perceptions and understandings of their audience, and the ways in which the audience are recruited, are shown to be crucial factors in delivering successful outreach activities. Parameters for successful outreach from this perspective are determined, and recommendations made for how such indicators might be developed further by outreach programme managers and influencers such as the Institute of Physics. Obtaining the data needed for this study was only made possible through links to the growing networks of outreach professionals in this field, and the positive impact of such roles was made clear by the participating physicists. The findings of this study will be used to influence physics schools outreach practice, and have relevance to other subjects and audiences.
ANALYSIS OF THE FACTORS AFFECTING THE ABILITY OF WORKING WITH OTHERS AS AN ELEMENT OF STUDENTS’ BURNOUT

This paper explores the factors that affect students' ability in working with others. Students’ ability to co-operate with their tutors and their fellow students emerged as a crucial element of the burnout syndrome; the approach was based on an exploratory factor analysis of a structured questionnaire answered by 582 students of the University of Patras. In particular, the student's ability to collaborate with others was analysed via a hierarchical regression tree, the factors of which were in turn decomposed into their dominant independent variables creating thus, nested hierarchical regression trees of direct and indirect influences. The results showed that the main set of factors influencing students' ability to cooperate with each other and with their tutors are in principle the physical - psychoemotional exhaustion, appearing a strong direct correlation. Further set of factors towards the ability of student collaboration emerged to be student's lack of emotional self-determination as well as the lack of interest in their studies. In addition, this research indicated that the students' workload pressure causes physical and psychoemotional exhaustion and ultimately degrades their ability in working with others.
Quality improvement in hospitals' surgery-related processes: A systematic review

Background: There is a growing global movement toward quality and safety in healthcare and quality improvement (QI) in general surgery. The fundamentals of QI begin with an understanding of the underlying theoretical framework. This study aims to provide an overview of the existing QI models and frameworks for general surgery. Methods: In this systematic review, published literature from January 2007 until September 2018 were retrieved from PubMed, Scopus, Web of Science and Embase databases, and Google Scholar using the MeSH terms related to QI and surgery. In total, 25 fulltext articles were finally included, and data extraction was based on research objectives. Results: Nine models were identified for QI in general surgery. These models were categorized into two main groups: (i) conceptual models or frameworks designed for QI in industry and applied in surgery, and (ii) those designed specifically for QI in surgery. Identified QI models were more used for improving postoperative processes and pre-hospital trauma care, identifying causes of prolonged periods of stay and lowering LOS index, improving surgical antimicrobial prophylaxis and antibiotics administrating during surgery process, reducing and controlling infections, reducing complications, reducing mortality and morbidity, reducing waiting times and start time delays, reducing variability and improving surgical clinic experience, reducing costs, improving operating room efficiency by removing processes that add no value, and lowering per-capita costs. Conclusion: According to the findings of this study, there are different models and frameworks with different aspects and dimensions for QI in surgery, which is recommended to use either of these models alone or with each other for specific circumstances. The use of these models in surgery is increasing, and it is recommended that these models could be used according to their functions in cases such as reducing the unnecessary use of resources, increasing the satisfaction of patients and their families with health care and improving the efficiency, safety and quality of healthcare in the surgical departments.