Miracle-dz/UniFAR-FTUs huggingface dataset:
A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines (“MRI-QAMPER”, current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER’s functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test–retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.
220-to-320-GHz Fundamental Mixer in 60-nm InP HEMT Technology Achieving 240-Gbps Dual-Band Data Transmission

We designed and fabricated a 300-GHz-band fundamental mixer in indium phosphide (InP) high electron mobility transistor (HEMT) technology for 6G wireless communications. We devised a widely split frequency matching network to widen the bandwidth of a resistive mixer. The mixer IC achieved a conversion gain of −15 and −6-dB RF bandwidth of 100 GHz (220–320 GHz), the widest ever reported. We implemented the mixer IC as a WR3.4 waveguide mixer module and performed a back-to-back data transmission experiment in three different frequency bands. Data rates of 120, 152, and 168 Gbps were achieved in each band. We also developed a dual-band data transmission system with a local oscillator (LO) leakage canceling method. It was capable of transmitting data at 240 Gbps, the highest rate reported to date, and suppressing LO leakage by 60 dB.
A Nursing Care Model for Surge Capacity Management in Intensive Care Units During the COVID-19 Pandemic: Experience From Qatif Central Hospital, Saudi Arabia

Introduction The COVID-19 pandemic resulted in a surge of critically ill patients requiring intensive care. This posed challenges for healthcare systems in managing increased ICU bed demands with limited resources. Methods A retrospective qualitative review of institutional documents and plans was conducted. Key strategies related to ICU bed expansion, nursing staff classification and training, clinical supervision, and performance evaluation were analyzed. Results Qatif Central Hospital increased ICU beds from 20 to 50 by converting other clinical areas. Nursing staff were categorized based on critical care experience, and additional training was provided to non-ICU nurses. A preceptor-led nursing care model with staff responsibilities was implemented. Periodic evaluations ensured continued competence. Conclusion The nursing care model at Qatif Central Hospital effectively facilitated ICU surge capacity while maintaining care quality. The model offers a viable framework for other healthcare institutions facing similar challenges. However, the study is limited by its retrospective design and focus on a single institution.
Convection naturelle thermosolutale dans une cavité poreuse anisotrope : formulation de Darcy-Brinkman

Nous presentons une etude numerique et analytique concernant le transfert combine de chaleur et de masse dans un milieux poreux. Ce milieu est globalement homogene et presente une anisotropie thermique. L'equation qui gouverne l'ecoulement est celle de Darcy-Brinkman. Le systeme d'equations couplees est resolu par la methode classique des volumes finis. Dans le cas d'ecoulements d'origine thermique, l'analyse d'echelle est appliquee pour predire analytiquement l'evolution du transfert de chaleur et de masse (.)
Petrogenesis of Late Cretaceous Adakitic Magmatism in the İstanbul Zone (Çavuşbaşı Granodiorite, NW Turkey)

The Cavusbasi granodiorite intrudes Ordovician sedimentary rocks in the western part of the Istanbul Zone (NW Turkey). The intrusion is made up mainly of granodiorite, and subordinate tonalite and quartz diorite, and has a granular texture and some special mixing textures such as antirapakivi, blade-shaped biotite, acicular apatite, spongy cellular dissolution/melting plagioclase textures. The main mafic minerals are hornblende and biotite. U-Pb in-situ dating of zircons from two samples via SHRIMP yielded weighted age values of ~68 Ma, suggesting emplacement during the Late Cretaceous. Geochemically the Cavusbasi granodiorite resembles adakites with high Sr/Y and La/Yb ratios, low Y and HREE contents and no Eu anomaly. It contains 63.4 (wt%) >SiO2, and is I-type, metaluminous, middle-K calc-alkaline. These adakitic rocks have high values of MgO (0.77-2.56 wt%), Mg# (45.3-59.3) and LILE (e.g., Rb, K, Ba, Sr). Initial eNd and 87Sr/86Sr values are 3.2-3.7 and 0.7035-0.7036, respectively. Based on the continuing subduction along the Izmir-Ankara and Intra-Pontide Neo-Tethyan oceanic domains and depleted Sr-Nd isotopic signatures, we suggest that the adakitic magmas may be derived from the partial melting of an oceanic slab under amphibole-eclogite facies conditions.
Put Your Voice on Stage: Personalized Headline Generation for News Articles

In this article, we study the problem of personalized news headline generation, which aims to produce not only concise and fact-consistent titles for news articles but also decorate these titles as personalized irresistible reading invitations by incorporating readers’ preferences. We propose an approach named PNG (Personalized News headline Generator) by utilizing distant supervision in readers’ past click behaviors to resolve. First, user preference representations are learned through a knowledge-aware user encoder that comprehensively captures the genuine, sequential, and flash interests of users reflected in their historical clicked news. Then, a user-perturbed pointer-generator network is devised to accomplish the headline generation in which the learned user representations implicitly affect the word prediction. The proposed model is optimized by reinforcement learning solvers where indicators on factual, personalized, and linguistic aspects of the generated headline are regarded as rewards. Extensive experiments are conducted on the real-world dataset PENS,1 which is a large-scale benchmark collected from Microsoft News. Both the quantitative and qualitative results validate the effectiveness of our approach.
Long non‑coding RNA PEG13 regulates endothelial cell senescence through the microRNA‑195/IRS1 axis

Atherosclerosis is a chronic inflammatory disease characterized by endothelial dysfunction and plaque formation. The present study aimed to elucidate the pathological role of the long non-coding RNA (lncRNA) paternally expressed 13 (PEG13) in the onset and progression of atherosclerosis. Specifically, its effects on human umbilical vein endothelial cell (HUVEC) proliferation, angiogenesis, senescence and senescence-associated secretory phenotype (SASP)-related factors were investigated using cell proliferation, cellular angiogenesis, β-galactosidase staining, reverse transcription-quantitative PCR and enzyme-linked immunosorbent assays. The results showed that oxidized low-density lipoprotein (ox-LDL) inhibited lncRNA PEG13 expression and HUVEC viability in a dose-dependent manner and PEG13 overexpression partially reversed these effects. Additionally, PEG13 overexpression ameliorated the ox-LDL-induced impairment of angiogenesis, cellular senescence and SASP. Furthermore, lncRNA PEG13 directly targeted microRNA (miR/miRNA)-195-5p, suppressing the ox-LDL-induced upregulation of the miRNA. The gene coding for insulin receptor substrate 1 (IRS1), an activator of the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway, was confirmed as a direct target of miR-195. PEG13 overexpression attenuated the ox-LDL-induced inhibition of IRS1 expression and PI3K/AKT signaling and its protective effects on HUVEC viability, angiogenesis and senescence were partially reversed by small interfering RNAs targeting IRS1. The present study demonstrated that lncRNA PEG13 attenuates ox-LDL-induced senescence in HUVECs by modulating the miR-195/IRS1/PI3K/AKT signaling pathway, suggesting a potential therapeutic target for the treatment of atherosclerosis.
Active Fault-tolerant Control for Surface Permanent Magnet Synchronous Motor Under Demagnetization Fault

This paper introduces a novel method for controlling a surface permanent magnet synchronous motor (SPMSM) during demagnetization fault conditions. The proposed fault-tolerant control (FTC) system incorporates a combination of a fuzzy extended state observer (FESO) based on an interval type 2 fuzzy logic controller (IT2FLC) and second-order sliding mode control (SOSMC) utilizing the super-twisting algorithm. The FESO aims to identify and eliminate demagnetization faults through reconstruction control. The FTC system enhances the dynamic performance and disturbance rejection of the SPMSM, providing a robust solution in the event of a demagnetization fault.
Isolation and proteomic study of fish liver lipid droplets

Lipid droplets (LDs) are a neutral lipid storage organelle that is conserved in almost all species. Excessive storage of neutral lipids in LDs is directly associated with many metabolic syndromes. Zebrafish is a better model animal for the study of LD biology due to its transparent embryonic stage compared to other organisms. However, the study of LDs in fish has been difficult due to the lack of specific LD marker proteins and the limitation of purification technology. In this paper, the purification and proteomic analysis of liver LDs of fish including zebrafish and Carassius auratus were performed for the first time. 259 and 267 proteins were identified respectively. Besides most of the identified proteins were reported in previous LD proteomes of mammals, indicating the similarity between mammal and fish LDs. We also identified many unique proteins of liver LDs in fish that are involved in the regulation of LD dynamics. Through morphological and biochemical analysis, we found that the marker protein Plin2 of zebrafish LD was located on LDs in Huh7 cells. These results will facilitate further study of LDs in fish and liver metabolic diseases using fish as a model animal.
Digital Maturity and Digital Transformation Strategy among Greek Small and Medium Enterprises

The proposed study aims to investigate the digital transformation of Greek small and medium enterprises (SMEs), with a particular focus on their digital maturity and the strategic and organizational factors contributing to digital transformation. The research issue of digital transformation has been attracting considerable interest among academics and business practicians since COVID-19 accelerated the procedure of implementing Industry 4.0 principles all over global economies. Quantitative research on 147 Greek SMEs revealed the most important issues on how these businesses implement digital transformation, factors accelerating or decelerating the process, barriers and expected outcomes. The results indicate that digital maturity, digital transformation strategy and digital business models are strongly interconnected, while the main barriers of successfully implementing them is a lack of funding, cultural issues and the management’s engagement with the whole procedure. Greek SMEs are regarding digital transformation as a part of their customers’ satisfaction rather than as a holistic procedure of reshaping their operation. In almost 20% of Greek SMEs, there is no person who is responsible for the digital transformation procedure, while key drivers are suppliers and customers rather than competitors and the government. The results can be valuable for stakeholders who are enabled to the digital transformation process from both the business and academic points of view, while there exist aspects that can contribute to policy makers/motivation developers on the state’s level as well.
A Stochastic Charging Station Deployment Model for Electrified Taxi Fleets in Coupled Urban Transportation and Power Distribution Networks

Metropolitansworldwide are increasingly adopting electric taxis (ET) to address concerns about transportation-related emissions. However, the widespread deployment of electric taxis presents challenges in terms of increased electricity demand and changing demand profiles. This transition impacts both the urban transportation network (TN) and the electricity power distribution network (PDN), highlighting the interdependence between these two systems. In this paper, we propose a two-stage stochastic programming planning model that aims to optimize both the TN and PDN, enabling efficient deployment of charging stations and grid upgrades. Our model seeks to strike a balance between meeting ET drivers' charging preferences, minimizing the costs associated with infrastructure deployment and grid expansion, and harmonizing the coordination between the TN and PDN. Additionally, we explore the potential benefits of utilizing an autonomous ET fleet to enhance overall system performance.
Teaching Reform and Practice of Statistics Course Based on OBE-CDIO Concept in the Context of Big Data

Statistics is an important core course for big data majors, and it is one of the important abilities that big data majors must master, and one of the most important abilities that employers pay attention to. Based on the OBE-CDIO concept in engineering education mode, the teaching reform and practice of statistics course should focus on the specific needs of related enterprises for big data analysis talents, and study the training mode of innovative practical ability of big data analysis majors. The recruitment information released by the website reflects the current demand for talents in the society. 4 representative recruitment websites are selected as the research samples according to the market share and professionalism of the recruitment industry, and the methods of statistics and text mining are used to deeply analyze the demand of enterprises for big data analytics job seekers and the professional skills requirements of each position. We also use questionnaires to understand students' demands and set up courses from the point of view of needs rather than just theoretical completeness of knowledge, and the specific path of teaching reform of statistics courses is explored to provide reference for the teaching reform of statistics course in universities.
Travel times as a basic part of the LISB guidance strategy

The Berlin LISB Guidance and Information system is an individual, dynamic guidance system for motorised vehicles. LISB works in accordance with the principle of central route searching. Within a cycle of a few minutes, a computer in the traffic control centre of the road transport authorities calculates and offers the quickest routes for any given O-D relation in the entire urban area. The vehicle transmits to the beacon the travel and queueing times which it has gained from the network since it passed the last beacon; it then receives information about the quickest route to all surrounding beacons as well as the allocation of these routes to the destination coordinates. Thus, the navigation device can select the links by which the vehicle finds its destination. Over the day, travel times vary with the traffic volumes on the links of the network. Therefore a recommendation of quickest routes must not be static. The authors consider the role of travel time prediction and the methods for measuring times for the guidance system.
JiuJITsu: Removing Gadgets with Safe Register Allocation for JIT Code Generation

Code-reuse attacks have the capability to craft malicious instructions from small code fragments, commonly referred to as ”gadgets. ” These gadgets are generated by JIT (Just-In-Time) engines as integral components of native instructions, with the flexibility to be embedded in various fields, including Displacement. In this paper, we introduce a novel approach for potential gadget insertion, achieved through the manipulation of ModR/M and SIB bytes via JavaScript code. This manipulation influences a JIT engine’s register allocation and code generation algorithms. These newly generated gadgets do not rely on constants and thus evade existing constant blinding schemes. Furthermore, they can be combined with 1-byte constants, a combination that proves to be challenging to defend against using conventional constant blinding techniques. To showcase the feasibility of our approach, we provide proof-of-concept (POC) code for three distinct types of gadgets. Our research underscores the potential for attackers to exploit ModR/M and SIB bytes within JIT-generated native instructions. In response, we propose a practical defense mechanism to mitigate such attacks. We introduce JiuJITsu, a security-enhanced register allocation scheme designed to prevent harmful register assignments during the JIT code generation phase, thereby thwarting the generation of these malicious gadgets. We conduct a comprehensive analysis of JiuJITsu’s effectiveness in defending against code-reuse attacks. Our findings demonstrate that it incurs a runtime overhead of under 1% when evaluated using JetStream2 benchmarks and real-world websites.
Performance assessment of wood strand erosion control materials among varying slopes, soil textures, and cover amounts

Two blends of manufactured wood strands with different lengths were tested for effectiveness in controlling erosion. Wood strand blends were tested on two soils, two slopes, and at three coverage amounts. Laboratory rainfall simulations were conducted to evaluate runoff and sediment loss. Wood strands were effective in delaying runoff, reducing runoff volume, and reducing sediment loss. There was no statistically significant difference between the two wood strand blends with respect to runoff or sediment loss. In comparison to bare soil with no cover, sediment loss was reduced by at least 70 percent for all cover amounts tested and among each soil type, slope and flow event. In comparison to estimates of sediment loss reductions due to agricultural straw (Burroughs and King, 1989), wood strand materials were equally effective on coarse grained soils and superior to straw on fine grained soils. In contrast to agricultural straw, manufactured wood strands are inherently weed and pesticide free. Consequently, wood-based materials may be a more appropriate material for erosion mitigation, especially in areas where introduction of non-native species is of great concern.
Bended Differential Line Routing Adaptive Designed by Periodic Nonuniform Structure With Ultrawideband Mode Conversion Noise Suppression

In this article, a periodic nonuniform transmission line adaptively optimized by whale optimization algorithm (WOA) is proposed to suppress the differential-to-common mode conversion noise of the differential pair. The phase difference between the inner and outer traces can be compensated by periodic nonuniform structure with slow wave effect, which can reduce the electric length and achieve wideband impedance matching. The proposed nonuniform structure is parameterized by cosine exponential function, while the function coefficients can be optimized by WOA with properties of swarm-based and meta-heuristic. Compared with conventional mode conversion methods, wideband, and steep noise suppression level can be observed with the presented design, Time domain analysis is also conducted to verify the transmission performances. A compensated differential transmission line is fabricated with a suppression level |Scd21| of −25 dB from dc to 15 GHz.
Explanatory and predictive analytics for movie production efficiency by online word-of-mouth

It has become increasingly important to consider the efficiency of movies in creating box revenue while using fewer movie resources. Further, there is a lack of eWOM (online-word-of-mouth) studies regarding using the production efficiency of movies as a dependent outcome measure replacing box revenue. This study shows that production efficiency can be suggested by comparing movie resources powers, i.e., powers of actors, directors, distributors, and production companies, which are input for movie production, and the box office. For testing the validity of the measure of production efficiency, this study examines the effect of eWOM attributes, i.e., review depth, volume, rating, review sentiment, and helpfulness on production efficiency. Data envelopment analysis is adopted to produce the efficiency of movies. This study provides insights into a current movie study on eWOM by showing the effect of interaction between eWOM (review rating) and helpfulness on production efficiency. Further, this study purports to test the prediction power in predicting production efficiency using decision trees, neural networks, and logistic regression. These results show that k nearest neighbor and automated neural networks outperform the other machine learning methods in classifying efficient movies.