Miracle-dz/UniFAR-FTUs huggingface dataset:
Feasibility of simultaneous integrated boost for high-dose treatment of high-risk prostate cancer

Background. Radiation therapy for high-risk prostate cancer presents a challenge for cancer radiotherapists. The improvement of treatment outcomes is associated with radiation dose escalation and prophylactic irradiation of lymph nodes, therefore, the development of the new treatment schemes is needed. Simultaneous integrated boost technique based on the volumetric modulated arc therapy is the most efficient treatment option. Material and Methods. The anatomical data of 10 patients with high-risk prostate cancer was used for dosimetry-based treatment planning. Both simultaneous integrated boost and sequential boost technique were considered. The treatment planning goal was to deliver the equivalent dose of 96 Gy at 2 Gy per fraction (EQD2=96 Gy) (α/β=1.5 Gy) to the prostate, EQD2=62.5 Gy to the seminal vesicles and EQD2=50 Gy to lymph nodes avoiding damaging the organs at risk, mainly the bladder and rectum. The irradiation was based on volumetric modulated arc therapy with two partially coplanar arcs and two rotations at each arc. The obtained dose distributions were compared with respect to dose-volume histograms and equivalent uniform doses (EUD). Results. In the case of sequential boost, the minimal dose delivered to the prostate was equal to 95.9 ± 2.1 Gy, EUD=104.9 ± 1.7 Gy. The dose delivered to 2 cm3 (D2cc) bladder was 97.4 ± 2.0 Gy. Normal tissue complication probability (NTCP) was 1.64 %. The dose delivered to 2 cm3 (D2cc) rectum was 103.4 ± 9.2 Gy and NTCP was 27.4 %. In the case of simultaneous integrated boost, the minimal dose delivered to the prostate was equal to 90.4 ± 2.3 Gy, EUD=103.9 ± 1.3 Gy. The bladder dose was as high as D2cc=96.1 ± 5.2 Gy, NTCP=0.176 ± 0.132 %, the rectum dose - D2cc=81.1 ± 6.0 Gy, NTCP=2.34 ± 1.92 %. Conclusion. Volumetric modulated arc therapy along with simultaneous integrated boost have shown the feasibility of simultaneous irradiation of the prostate, seminal vesicles and lymph nodes up to the prescribed dose values without significant over irradiation of the organs at risk (OARs). Dose values in the tumor as high as EUD=103.9 ± 1.3 Gy along with prophylactic irradiation of lymph nodes may result in higher tumor control probability value and should be considered for clinical trials.
Purinergic Activation of Store-Operated Calcium Entry (SOCE) Regulates Cell Migration in Metastatic Ovarian Cancer Cells

Store-operated calcium entry (SOCE) is an important process in calcium signaling. Its role in physiological and pathological events is well recognized. However, in cancerous systems, the importance of SOCE in relation to the degree of cancer aggressiveness, as well as its regulation by ligands such as purinergic molecules, are not well documented. This study aimed to characterize a differential effect of the P2Y2 receptor (promoted by UTP of 10 µM and inhibited by ARC118925XX of 1 µM) on intracellular calcium response between metastatic (SKOV-3) and non-metastatic (CAOV-3) ovarian cell lines in conditions of normal (1.5 mM) and zero extracellular calcium concentration. The sustained calcium influx observed exclusively in SKOV-3 cells was associated with the presence of SOCE (promoted by thapsigargin (74.81 ± 0.94 ΔF) and sensitive to 2-APB (20.60 ± 0.85 ΔF)), whereas its absence in CAOV-3 cells (26.2 ± 6.1 ΔF) was correlated with a low expression of ORAI1. The relevance of SOCE in metastatic SKOV-3 cells was further corroborated when 2-APB significantly inhibited (40.4 ± 2.8% of covered area) UTP-induced cell migration (54.6 ± 3.7% of covered area). In conclusion, our data suggest that SOCE activation elicited by the P2Y2 receptor is involved in the aggressiveness of ovarian cancer cells.
Are There Benefits of Bilingualism in the Socio-Affective Domain?/Postoji li korist od dvojezičnosti u socioafektivnoj domeni?

AbstractThe present paper deals with bilingualism from a „hot” point of view: it seeks the answer to the question whether Hungarian-Serbian bilinguals and monolinguals show differences and variations in empathic skills and emotional intelligence. For measuring these variables we have used a modified language history questionnaire, the Hungarian versions of the Davis Interpersonal Reactivity Index (IRI-HU) and the Wong and Law Emotional Intelligence Scale (WLEIS-HU). The data of 208 individuals from Serbia were analyzed. Comparing individuals who are monolinguals and bilinguals, at present use and know languages at various levels, our results have shown that individuals with higher levels of current language knowledge and frequency of language use rate their skills of evaluating the emotions of others and perspective taking as better.Keywords: benefit; Hungarian-Serbian bilingualism; emotional intelligence; empathy; Serbia---SažetakU ovom radu govori se dvojezičnosti iz „vruće“ perspektive, točnije pokušava se naći odgovor na pitanje pokazuju li mađarsko-srpski dvojezični govornici i jednojezični govornici razlike i varijacije u vještinama empatije i emocionalne inteligencije. Za mjerenje spomenutih varijabli, koristili smo prilagođeni upitnik o jezičnoj povijesti, mađarsku verziju Davisova Indeksa interpersonalne reaktivnosti (IRI-HU), te Wong i Law Skalu emocionalne inteligencije (WLEIS-HU). Analizirani su podatci dobiveni od 208 ispitanika u Republici Srbiji. Uspoređujući pojedince koji su jednojezični i dvojezični, koji trenutačno koriste i znaju svoje jezike na različitim razinama, rezultati su pokazali da osobe s višim razinama znanja jezika i većom učestalošću trenutačnoga korištenja jezika procjenjuju kao bolje svoje vještine procjene emocija drugih osoba te vještinu promjene perspektive. Ključne riječi: emocionalna inteligencija, empatija, korist, mađarsko-srpska dvojezičnost; Srbija
Prescribed performance tracking control for adjacent virtual coupling high‐speed trains with input saturation

In the present work, targeting at the perfect coupled tasks of two adjacent high‐speed trains under the virtual coupling tracking processes, a novel prescribed performance tracking control method is developed, where both the system uncertainties and the actuator constraints are taken into account. Notice that by utilizing a novel error conversion method, the singularity problem may occur in the prescribed performance control could be avoided, while the input constraints are also satisfied by using a smooth function to approximate the non‐smooth input saturation function. The system uncertainties are estimated by adopting the radial basis function neural network. Different with existing works, the initial speeds of the adjacent trains can be nonidentical. Furthermore, the stability of the proposed controller is rigorously analyzed under the framework of Lyapunov theory. To demonstrate the effectiveness of the proposed control approach, three different uncoupled cases are considered, where the corresponding tracking processes from coupling to completely coupled are described in details.
Iterative methods for solving coefficient inverse problems of wave tomography in models with attenuation

We develop efficient iterative methods for solving inverse problems of wave tomography in models incorporating both diffraction effects and attenuation. In the inverse problem the aim is to reconstruct the velocity structure and the function that characterizes the distribution of attenuation properties in the object studied. We prove mathematically and rigorously the differentiability of the residual functional in normed spaces, and derive the corresponding formula for the Fréchet derivative. The computation of the Fréchet derivative includes solving both the direct problem with the Neumann boundary condition and the reversed-time conjugate problem. We develop efficient methods for numerical computations where the approximate solution is found using the detector measurements of the wave field and its normal derivative. The wave field derivative values at detector locations are found by solving the exterior boundary value problem with the Dirichlet boundary conditions. We illustrate the efficiency of this approach by applying it to model problems. The algorithms developed are highly parallelizable and designed to be run on supercomputers. Among the most promising medical applications of our results is the development of ultrasonic tomographs for differential diagnosis of breast cancer.
Securely Compiling F ★ Programs With IO and Then Linking Them Against Weakly-Typed Interfaces

We propose a secure compilation chain for statically verified partial programs with IO. The source language is a subset of F ★ in which one can write and statically verify a partial IO program that interacts with its context via a strongly-typed higher-order interface, which includes refinement types as well as pre-and post-conditions that can talk about past IO events. The target language is a subset of the source in which the compiled program can be securely linked with a context via a weakly-typed interface, without refinement types or pre-and post-conditions. Compilation converts the logical assumptions the program makes about the context to runtime checks, while linking instruments the context by adding a reference monitor to soundly enforce a global safety property. In addition to soundness, we proved in F ★ that our secure compilation chain satisfies by construction Robust Relational Hyperproperty Preservation, which is the strongest secure compilation criterion of Abate et al. (CSF’19). In the proof-oriented language F ★ [13
The MoveIt Benchmark Suite for Whole-Stack Planner Evaluation

—Whereas the performance of motion planning algorithms can be analyzed theoretically, moving toward applications based on such algorithms requires computational evaluation on likely scenarios to choose variants that perform sufficiently well on the concrete tasks and tune relevant hyperparameters. Additionally, integrated planning systems rely on various implementations for sub-features, such as inverse kinematics solving or collision checking, which have a significant impact on the overall system performance. As a result of this complexity, seemingly benign changes to the code base can entail tremendous changes in overall performance. The MoveIt Benchmark Suite (MBS) constitutes a unified benchmarking framework to evaluate the performance of the whole system stack on unit as well as integration level to compare configurations and isolate performance changes over time.
International Journal of Electrical and Computer Engineering (IJECE)

Received Oct 10, 2022 Revised Jan 2, 2023 Accepted Feb 4, 2023 U-net convolutional neural network (CNN) is a famous architecture developed to deal with medical images. Fine-tuning CNNs is a common technique used to enhance their performance by selecting the building blocks which can provide the ultimate results. This paper introduces a method for tuning U-net architecture to improve its performance in medical image segmentation. The experiment is conducted using an x-ray image segmentation approach. The performance of U-net CNN in lung x-ray image segmentation is studied with different activation functions, optimizers, and pooling-bottleneck-layers. The analysis focuses on creating a method that can be applied for tuning U-net, like CNNs. It also provides the best activation function, optimizer, and pooling layer to enhance U-net CNN’s performance on x-ray image segmentation. The findings of this research showed that a U-net architecture worked supremely when we used the LeakyReLU activation function and average pooling layer as well as RMSProb optimizer. The U-net model accuracy is raised from 89.59 to 93.81% when trained and tested with lung x-ray images and uses the LeakyReLU activation function, average pooling layer, and RMSProb optimizer. The fine-tuned model also enhanced accuracy results with three other datasets.
Biomolecular electrostatics with continuum models: a boundary integral implementation and applications to biosensors

The implicit-solvent model uses continuum electrostatic theory to represent the salt solution around dissolved biomolecules, leading to a coupled system of the PoissonBoltzmann and Poisson equations. This thesis uses the implicit-solvent model to study solvation, binding and adsorption of proteins. We developed an implicit-solvent model solver that uses the boundary element method (bem), called PyGBe. bem numerically solves integral equations along the biomolecule-solvent interface only, therefore, it does not need to discretize the entire domain. PyGBe accelerates the bem with a treecode algorithm and runs on graphic processing units. We performed extensive verification and validation of the code, comparing it with experimental observations, analytical solutions, and other numerical tools. Our results suggest that a bem approach is more appropriate than volumetric-
The role of discrimination and vaccine distrust in vaccine uptake among Latines in the Midwest

Presents a study which aims to examine the role of discrimination and vaccine distrust in vaccine uptake among Latines in the Midwest. Participants were 137 adult Latine residents of Nebraska. The majority were cisgender women. Participants were recruited via bilingual flyers and referral from local community centers, public health agencies, medical clinics, cultural organizations, faith organizations, and educational systems in rural, urban, and ex-urban areas of Nebraska. At baseline, 53 participants reported having received the COVID-19 vaccine and 37 had registered to received it, meaning a majority had already taken steps to receive the vaccine. At follow-up, approximately two-thirds reported having already received the vaccine. Of the 19 who stated that they had not, 6 ad registered to received it. Thus, only 13 participants reported not having received the vaccine and not having registered to receive it. When asked about what would prevent them from receiving the vaccine, 35 reported not trusting the COVID-19 vaccine at baseline and 10 at follow-up. Structural barriers to vaccination were also identified by respondents, 25 at baseline and 6 at follow-up perceived cost as a barrier, 37 at baseline and 5 for follow-up reporting lack of insurance or not having insurance. While the current study provided only limited support for the Health Belief Model as applied to COVID-19 vaccine uptake among Latines, results still offer important insights into vaccine uptake and service utilization overall. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
Impact-aware Maneuver Decision with Enhanced Perception for Autonomous Vehicle

Autonomous driving is an emerging technology that has developed rapidly over the last decade. There have been numerous interdisciplinary challenges imposed on the current transportation system by autonomous vehicles. In this paper, we conduct an algorithmic study on the autonomous vehicle decision-making process, which is a fundamental problem in the vehicle automation field and the root cause of most traffic congestion. We propose a perception-and-decision framework, called HEAD, which consists of an enHanced pErception module and a mAneuver Decision module. HEAD aims to enable the autonomous vehicle to perform safe, efficient, and comfortable maneuvers with minimal impact on other vehicles. In the enhanced perception module, a graph-based state prediction model with a strategy of phantom vehicle construction is proposed to predict the one-step future states for multiple surrounding vehicles in parallel, which deals with sensor limitations such as limited detection range and poor detection accuracy under occlusions. Then in the maneuver decision module, a deep reinforcement learning-based model is designed to learn a policy for the autonomous vehicle to perform maneuvers in continuous action space w.r. t. a parameterized action Markov decision process. A hybrid reward function takes into account aspects of safety, efficiency, comfort, and impact to guide the autonomous vehicle to make optimal maneuver decisions. Extensive experiments offer evidence that HEAD can advance the state of the art in terms of both macroscopic and microscopic effectiveness.
Demosaicking Multispectral Images by Sphere Packing Filter Design

: We propose an optimal distribution of spectral ﬁlters in a multispectral ﬁlter array based on packing congruent spheres in a 3D-euclidean space, promoting uniformity in the sensing of the 3D-datacube while leading to improved reconstructions. © 2022 The Author(s) OCIS codes: 110.0110, 100.3020, 110.4234
X OSS IG : Leveraging OS Diversity to Automatically Extract Malware Code Signatures

—We present an automated approach to extract code signatures that serve as the forensic fingerprint of a given malware program. Our high-level idea is to compare the memory contents of a sandbox before and after infection by a malware. To pinpoint the actual memory changes caused by the malware, and ignore all others, we use a novel concept called Cross OS Execution . That is, we execute a malware program on multiple different but compatible operating systems (OSes) to identify its memory commonalities, while neglecting OS-specific noise. The commonalities of the dumps therefore contain patterns whose presence is the consequence of executing the malware, i.e., the forensic fingerprint of the malware. We show that we can use two different versions Windows to accurately extract fingerprints of all 17 popular Windows malware families in our test set. These signatures serve to re-identify malware infections in memory dumps with a TPR of 93% and an FPR of 0.15%.
One-Parameter-At-A-Time Combinatorial Testing Strategy Based on Harmony Search Algorithm Supporting Mixed Covering Array Mathematical Notation (OPATHS)

⸺ Software testing is a step by step process of employing a product in order to make sure it entertains user’s specification requirements. Testing is compulsory, to reassure that software works perfectively and to confirm that a software was successfully tested, the software should be tested exhaustively just to make sure that the software cannot be demolished by some random accidents, but the exhaustive testing is unattainable. There are many existing strategies that minimized the test suite size in a software system, but most of these strategies based on optimization algorithm are using one-test-at-a-time approach and none of them have been adopt one-parameter-at-a-time approach that is based on harmony search algorithm. Therefore, this paper will describe a new strategy called OPATHS. OPATHS is the first strategy based on Harmony Search Algorithm that adopt one-parameter-at-a-time approach. OPATHS was designed only to support Mixed Covering Array notations with a uniform interaction strength and from the result obtained in the experiments gives a comparable result and always appears to be best.
Subwavelength imaging and control of ultrafast optical near-field under resonant- and off-resonant excitation of bowtie nanostructures

We demonstrate subwavelength imaging and control of localized near-field distribution under resonant and off-resonant excitation of identical gold bowtie nanostructures through photoemission electron microscopy. Control of the near-field distribution was realized by polarization rotation of single femtosecond laser pulse and variation of the phase delay of two orthogonally polarized femtosecond laser pulses. We show that the localized optical near-field distribution can be well controlled either among the corners of the nano-prisms in the bowtie for resonant excitation or the edges for off-resonant excitation. A better visualization of the PEEM image is achieved for resonant excitation than in the case of off-resonant excitation. The experimental results of the optical near-field distribution control are well reproduced by finite-difference time-domain simulations and understood by linear combination of electric charge distribution of the bowtie by s- and p- polarized light illumination. In addition, a shift of the near-field excitation position with inverted or unchanged phase, alternatively an un-shift of the excitation position but only with inverted phase of the near-field, can be realized by rotating the polarization angle of a single pulse and coherent control of two orthogonally polarized fs laser pulses.
Harnessing AI in Quantitative Finance: Predicting GDP using Gradient Boosting, Random Forest, and Linear Regression Models

Predicting key macroeconomic indicators such as Gross Domestic Product (GDP) is a critical task in quantitative finance and economics. Precise forecasts of GDP can help in policy-making, investment decisions, and understanding the overall economic health of a country [8]. Machine learning has emerged as a powerful tool in this domain, offering sophisticated techniques for modeling complex systems and making predictions. This project presents a comparative analysis of three machine learning models – Gradient Boosting Regressor, Random Forest Regressor, and Linear Regression – for predicting GDP. Our aim is to assess their performance and identify the model that provides the most accurate forecasts.
Finite-time boundedness analysis for switched linear systems with large delay period

In this paper, finite-time stability (FTS) and finite-time boundedness (FTB) are investigated for a class of switched linear systems with large delay period and input disturbances. The limitation of the frequent and length of large delay period (LDP) are used to guarantee the properties of the FTS and FTB. By constructing a piecewise Lyapunov functional with large delay integral terms, the sufficient conditions satisfying the finite-time convergence are developed in the form of matrix inequalities. A numercial example is provided to demonstrate the effectiveness of the proposed results.
Improved bond graph model for fault diagnosis under uncertain conditions

The effectiveness of model-based diagnosis strongly depends on the model’s authenticity and is impacted by various uncertainties. Measurement uncertainty is governed by the probability method, and parameter uncertainty can be handled by the linear fractional transformation, whereas structure uncertainty and errors are rarely considered. In this paper, an improved bond graph (BG) model is proposed, which adopts subsystems to substitute modelling errors, namely parameter uncertainty and structure uncertainty. A multi-dimensional Fibonacci optimization algorithm is developed to identify the parameters of subsystems to obtain the subsystem-based diagnostic hybrid BG (SDHBG) model. Fault diagnosis is realized by comparing the residuals and thresholds derived from the SDHBG. Experiments are conducted to validate the key concepts of the proposed methods. Subsequently, the results suggest its effectiveness.
Doppler Broadening and Line-of-sight Effects in Core-collapse Supernovae and Young Remnants

The dynamics and spectral characteristics of supernova ejecta reveal details of the supernova energetics, explosive nucleosynthesis, and evolution of the progenitor. However, in practice, this important diagnostic information is only derived from CCD-resolution X-ray spectra of shock-heated material. If the spectra were to be observed at higher resolution, then important clues to the explosion energetics would be obvious through measurements of bulk Doppler motions and turbulence in the ejecta. Likewise, the unshocked ejecta in supernovae and young remnants are responsible for obscuring the emission from ejecta on the back side of the remnant. In light of these important effects, we present line-of-sight spectral maps of core-collapse supernova remnant models. We explore the bulk Doppler broadening of spectral lines, including line-of-sight effects. We also explore the time-dependent absorption from both shocked and unshocked ejecta. Finally, we discuss how future X-ray missions such as XRISM and Athena will be able to resolve these effects in nearby and extragalactic supernovae and their remnants.