Miracle-dz/UniFAR-FTUs huggingface dataset:
Electromagnetic Transient Modeling Method of Photovoltaic Power Station Based on FPGA

The rapid development of renewable energy needs the effective support of simulation technology. Aiming at the shortage of both modeling scale and simulation accuracy of the current large-scale photovoltaic power station, an electromagnetic transient modeling and real-time simulation method suitable for large-scale photovoltaic power stations are proposed. The microsecond accurate real-time simulation of 24 photovoltaic power units is implemented on a single FPGA. Through an equivalent modeling method, the electromagnetic transient model of a photovoltaic power unit including a PV array, DC boost circuit, grid-connected inverter, filter, and grid-side transformer is established. Then, based on the FPGA parallel computing and pipeline time division multiplexing technology, the updating framework of the station collecting network and the power unit is constructed, and the real-time simulation model of the station including 24 photovoltaic power units in 4 sub-stations is built. Finally, the microsecond real-time simulation of the photovoltaic power station is realized through RTRES, a self-developed real-time simulator in China’s Southern Power Grid. The comparison with the detailed PSCAD model shows that the simulation error is within ± 2%, which verifies the correctness of the proposed method and model.
Harmonising European Public Sector Accounting Standards (EPSAS): Issues and Perspectives for Europe’s Economy and Society

Abstract Accounting systems play a hidden but fundamental role as mode and instrument of representation, coordination and organisation for the public sector and its specific public action. Therefore, financial and accounting reforms transform, implement and reshape public policies as well as the working and very existence of public administration. Last March 2013, the European Commission started a relevant project with the intention to create harmonised “European Public Sector Accounting Standards” (EPSAS) and implement them in the Member States. Between 1995 and 2002, a similar project was already achieved for private sector accounting standards-setting, leading to adoption and implementation of International Financial Reporting Standards (IFRS) issued by International Accounting Standards Board (IASB). The EPSAS project should decide if public sector accounting standards-setting shall follow a similar pattern to converge towards the International Public Sector Accounting Standards (IPSAS) that transplant the IFRS in the public sector. This choice may have fundamental implications for the European (Monetary) Union, since public sector accounting and public finances are fundamental elements of its institutional framework. This thematic issue aims to provide analyses and perspectives on this ongoing public sector accounting harmonisation process in Europe, addressing its governance and contents, as well as its consequences and implications for Europe’s economy and society.
Wrong Skewness and Finite Sample Correction in Parametric Stochastic Frontier Models

In parametric stochastic frontier models, the composed error is speci…ed as the sum of a twosided noise component and a one-sided ine¢ ciency component, which is usually assumed to be half-normal, implying that the error distribution is skewed in one direction. In practice, however, estimation residuals may display skewness in the wrong direction. Model re-speci…cation or pulling a new sample is often prescribed. Since wrong skewness is considered a …nite sample problem, this paper proposes a …nite sample adjustment to existing estimators to obtain the desired direction of residual skewness. This provides another empirical approach to dealing with the so-called wrong skewness problem.
Epstein–Barr virus and multiple sclerosis

Purpose of reviewRecent studies have revived interest in the long-scrutinized association between Epstein–Barr virus (EBV) and multiple sclerosis (MS). We review this evidence and discuss it in relation to MS pathological and clinical features and patients' response to immunosuppressive therapies. Recent findingsSerological evidence of previous exposure to EBV in children with MS supports a role for EBV infection early in MS pathogenesis, as already indicated by prospective studies in adults. Higher antibody titers and T-cell responses to EBV in patients compared to healthy EBV carriers indicate possible continuous viral reactivation, whereas there is some evidence that EBV could break immune tolerance to myelin antigens through molecular mimicry. Detection of EBV-infected B-cells in patients' brain raises the possibility that intrathecal B-cell abnormalities and T-cell-mediated immunopathology in MS are the consequence of a persistently dysregulated EBV infection. Accordingly, targeting T-cells and/or B-cells with monoclonal antibody therapies ameliorates MS. Whether EBV has a causative or pathogenic role in MS can now be addressed in relation to genetic, hormonal and other environmental influences that may affect EBV–host interactions. SummaryBy shedding light on the involvement of EBV in MS, these findings will pave the way to disease prevention and increase the therapeutic index of future treatments.
River basin planning and management

This note is one in a series explaining the attributes and practical application of integrated river basin management. This note discusses: 1) Recent approaches to water resources planning; 2) How to integrate top down and bottom up approaches; 3) How to incorporate the participation of all basin partners and the basin community. The note stresses that integrated river basin management (IRBM), as it is known today, tries to achieve an acceptable and agreed balance or trade-off among economic, environmental, and social issues, with the input of all the governments or relevant government agencies operating in the basin and with the effective contribution and input of the basin community.
An Error Analysis of Discontinuous Finite Element Methods for the Optimal Control Problems Governed by Stokes Equation

Abstract In this article, an abstract framework for the error analysis of discontinuous finite element method is developed for the distributed and Neumann boundary control problems governed by the stationary Stokes equation with control constraints. A priori error estimates of optimal order are derived for velocity and pressure in the energy norm and the L2-norm, respectively. Moreover, a reliable and efficient a posteriori error estimator is derived. The results are applicable to a variety of problems just under the minimal regularity possessed by the well-posedness of the problem. In particular, we consider the abstract results with suitable stable pairs of velocity and pressure spaces like as the lowest-order Crouzeix–Raviart finite element and piecewise constant spaces, piecewise linear and constant finite element spaces. The theoretical results are illustrated by the numerical experiments.
Impact of Climate Change on Himalayan Glaciers and Glacial Lakes: Case Studies on GLOF and Associated Hazards in Nepal and Bhutan

The Himalayas have the largest concentration of glaciers outside the polar region. These glaciers are a freshwater reserve; they provide the headwaters for nine major river systems in Asia – a lifeline for almost one-third of humanity. There is clear evidence that Himalayan glaciers have been melting at an unprecedented rate in recent decades; this trend causes major changes in freshwater flow regimes and is likely to have a dramatic impact on drinking water supplies, biodiversity, hydropower, industry, agriculture and others, with far-reaching implications for the people of the region and the earth’s environment. One result of glacial retreat has been an increase in the number and size of glacial lakes forming at the new terminal ends behind the exposed end moraines. These in turn give rise to an increase in the potential threat of glacial lake outburst floods occurring. Such disasters often cross boundaries; the water from a lake in one country threatens the lives and properties of people in another. Regional cooperation is needed to formulate a coordinated strategy to deal effectively both with the risk of outburst floods and with water management issues.
Buddhist Murals of Northeast Thailand: Reflections of the Isan Heartland

Books on mural painting in Thailand have tended to focus on works commissioned by royalty or other elites from the centers of power. This volume is the first to examine a vibrant sub-school of painting from the rural heartland of the Northeastern Region, also known as Isan. It is a multifaceted and empathetic study of these lovely and lively paintings, and will appeal to anyone interested in the Mekong Region as well as to scholars of art history, Buddhism, and anthropology in Southeast Asia.
TBI+rATG为基础的联合预处理方案单倍体造血干细胞移植治疗化疗耐药进展期外周T细胞淋巴瘤11例

目的 评估以全身放射治疗（TBI）+兔抗人胸腺细胞免疫球蛋白（rATG）为基础预处理方案的单倍体造血干细胞移植（haplo-HSCT）治疗化疗耐药进展期外周T细胞淋巴瘤（PTCL）的疗效与安全性。 方法 纳入2019年9月至2022年12月在上海力泉医院和上海闸新中西医结合医院血液科接受TBI+rATG为基础预处理方案haplo-HSCT的11例化疗耐药进展期PTCL患者，对其临床资料进行回顾性分析。 结果 ①11例患者中男6例，女5例，中位年龄40（22～58）岁；外周T细胞淋巴瘤（非特指型）6例，血管免疫母细胞性T细胞淋巴瘤3例，蕈样真菌病（大细胞转化）1例，T大颗粒淋巴细胞白血病1例；Lugano分期均为Ⅲ、Ⅳ期；8例患者有B症状；移植前中位化疗线程数为4（2～10）线，移植前疾病状态均为疾病进展（PD）。 诊断至移植的中位时间为17（6～36）个月。 ②预处理方案：TBI总量10 Gy；rATG 2.5 mg·kg−1·d−1、−5 d～−2 d；依托泊苷15 mg·kg−1·d−1，−5 d、−4 d，环磷酰胺50 mg·kg−1·d−1，−3 d、−2 d。 原发病中枢神经系统侵犯的患者，将依托泊苷和环磷酰胺替换为塞替派（5 mg·kg−1·d−1，−5 d、−4 d）并加用阿糖胞苷（2.0 g/m2每日2次，−3 d、−2 d）。 ③所有患者均成功植入，中位中性粒细胞植入时间为14.5（11～16）d，中位血小板植入时间为13（8～18）d。 1例患者发生Ⅰ～Ⅱ度急性移植物抗宿主病（GVHD），1例患者发生Ⅲ/Ⅳ度急性GVHD，8例存活患者中4例发生慢性GVHD。 ④移植后有9例患者获得完全缓解（CR）。 ⑤所有患者预处理后发生造血抑制，3例出现腹泻，4例发生黏膜炎，3例转氨酶/胆红素升高，7例发生感染，经过治疗均得到缓解。 ⑥移植后1年非复发死亡率（NRM）为（22.5±14.0）％，累积复发率（CIR）为（20.2±12.7）％，总生存（OS）率为（72.7±13.4）％，无病生存（DFS）率为（63.6±14.5）％。 结论 TBI+rATG为基础预处理haplo-HSCT是化疗耐药进展期PTCL有效且安全的治疗方法。
X-crossing pneumatic artificial muscles

Artificial muscles are promising in soft exoskeletons, locomotion robots, and operation machines. However, their performance in contraction ratio, output force, and dynamic response is often imbalanced and limited by materials, structures, or actuation principles. We present lightweight, high–contraction ratio, high–output force, and positive pressure–driven X-crossing pneumatic artificial muscles (X-PAMs). Unlike PAMs, our X-PAMs harness the X-crossing mechanism to directly convert linear motion along the actuator axis, achieving an unprecedented 92.9% contraction ratio and an output force of 207.9 Newtons per kilogram per kilopascal with excellent dynamic properties, such as strain rate (1603.0% per second), specific power (5.7 kilowatts per kilogram), and work density (842.9 kilojoules per meter cubed). These properties can overcome the slow actuation of conventional PAMs, providing robotic elbow, jumping robot, and lightweight gripper with fast, powerful performance. The robust design of X-PAMs withstands extreme environments, including high-temperature, underwater, and long-duration actuation, while being scalable to parallel, asymmetric, and ring-shaped configurations for potential applications.
The role of amphibole in Merapi arc magma petrogenesis: insights from petrology and geochemistry of lava hosted xenoliths and xenocrysts

(1) Dept. of Petrology, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands, (2) Dept. of Earth Science, Uppsala University, Villavagen 16, SE-752 36 Uppsala, (3) Institut fur Mineralogie, Liebnitz Universitat Hannover, Hannover-Germany, (4) CNR, Centro di Studio per il Quaternario e l’Evoluzione Ambientale, Pisa, Italy, (5) Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy, (6) GeoDocCon, Unterpferdt 8, 95176 Konradsreuth, Germany, (7) Dept. of Mineralogy, Swedish Museum of Natural History, Box 50007, SE-10405, Stockholm, Sweden
Dendrosomal nanocurcumin prevents EBV-associated cell transformation by targeting the lytic cycle genes of the Epstein-Barr virus in the generation of lymphoblastoid cell line

Objective(s): Targeting the lytic cycle of the Epstein-Barr virus (EBV) has been considered a new treatment strategy for malignancies caused by this virus. This study aimed to investigate the effect of Dendrosomal NanoCurcumin (DNC) to prevent cell transformation and inhibit the expression of viral lytic gene expression in the generation of lymphoblastoid cell line (LCL). Materials and Methods: Cell viability of LCLs and PBMCs was performed by MTT assay, and flow cytometry (Annexin/PI) was used for evaluation of apoptosis. CD markers on the surface of generated LCL (CD19) cells were examined for cell validation. The effect of DNC on transformation was evaluated by examining cell morphology and determining the expression level of lytic genes BZLF1, Zta, BHRF1, and BRLF1 of EBV using Real-time PCR. Student’s t-test was used for statistical analysis. Results: The MTT assay showed that DNC can inhibit the proliferation of LCL in a dose-dependent manner. The 50% cytotoxic concentration (CC50) of DNC and curcumin for LCL was determined 38.8 µg/ml and 75 µg/ml, respectively after 72 hr. Also, Real-time PCR data analysis showed that DNC in 30 µg/ml concentration significantly inhibited cell transformation in the LCL and significantly reduced viral lytic genes such as BZLF1, Zta, BHRF1, and BRLF1expression compared to control. Conclusion: Overall, these findings show that DNC reduces the expression of the viral lytic cycle genes and also the induction of cell apoptosis and finally prevents the generation of LCL.
Magnitude and predisposing factors of intestinal parasitosis and tuberculosis coinfection at five health institutions in Southern Ethiopia: A cross‐sectional study

Intestinal parasites affect the tuberculosis disease outcome by shifting the cell‐mediated to humoral immune response and host immune system suppression. However, Mycobacterium tuberculosis (MTB) infection favors the immune escape of parasites. Hence, exploring the rate of intestinal parasitic coinfection with pulmonary tuberculosis (PTB) and its predisposing factors to take better preventive, control, and management measures.
Crowdotic: A Privacy-Preserving Hospital Waiting Room Crowd Density Estimation with Non-speech Audio

Privacy-preserving crowd density analysis finds application across a wide range of scenarios, substantially enhancing smart building operation and management while upholding privacy expectations in various spaces. We propose a non-speech audio-based approach for crowd analytics, leveraging a transformer-based model. Our results demonstrate that non-speech audio alone can be used to conduct such analysis with remarkable accuracy. To the best of our knowledge, this is the first time when non-speech audio signals are proposed for predicting occupancy. As far as we know, there has been no other similar approach of its kind prior to this. To accomplish this, we deployed our sensor-based platform in the waiting room of a large hospital with IRB approval over a period of several months to capture non-speech audio and thermal images for the training and evaluation of our models. The proposed non-speech-based approach outperformed the thermal camera-based model and all other baselines. In addition to demonstrating superior performance without utilizing speech audio, we conduct further analysis using differential privacy techniques to provide additional privacy guarantees. Overall, our work demonstrates the viability of employing non-speech audio data for accurate occupancy estimation, while also ensuring the exclusion of speech-related content and providing robust privacy protections through differential privacy guarantees.
Soft Merging: A Flexible and Robust Soft Model Merging Approach for Enhanced Neural Network Performance

Stochastic Gradient Descent (SGD), a widely used optimization algorithm in deep learning, is often limited to converging to local optima due to the non-convex nature of the problem. Leveraging these local optima to improve model performance remains a challenging task. Given the inherent complexity of neural networks, the simple arithmetic averaging of the obtained local optima models in undesirable results. This paper proposes a {\em soft merging} method that facilitates rapid merging of multiple models, simplifies the merging of specific parts of neural networks, and enhances robustness against malicious models with extreme values. This is achieved by learning gate parameters through a surrogate of the $l_0$ norm using hard concrete distribution without modifying the model weights of the given local optima models. This merging process not only enhances the model performance by converging to a better local optimum, but also minimizes computational costs, offering an efficient and explicit learning process integrated with stochastic gradient descent. Thorough experiments underscore the effectiveness and superior performance of the merged neural networks.
Interpolation Constraints for Computing Worst-Case Bounds in Performance Estimation Problems

The Performance Estimation Problem (PEP) approach consists in computing worst-case performance bounds on optimization algorithms by solving an optimization problem: one maximizes an error criterion over all initial conditions allowed and all functions in a given class of interest. The maximal value is then a worst-case bound, and the maximizer provides an example reaching that worst case. This approach was introduced for optimization algorithms but could in principle be applied to many other contexts involving worst-case bounds. The key challenge is the representation of infinite-dimensional objects involved in these optimization problems such as functions, and complex or non-convex objects as linear operators and their powers, networks in decentralized optimization etc. This challenge can be resolved by interpolation constraints, which allow representing the effect of these objects on vectors of interest, rather than the whole object, leading to tractable finite dimensional problems. We review several recent interpolation results and their implications in obtaining of worst-case bounds via PEP.
RadYOLOLet: Radar Detection and Parameter Estimation Using YOLO and WaveLet

Detection of radar signals without assistance from the radar transmitter is a crucial requirement for emerging and future shared-spectrum wireless networks like Citizens Broadband Radio Service (CBRS). In this paper, we propose a supervised deep learning-based spectrum sensing approach called RadYOLOLet that can detect low-power radar signals in the presence of interference and estimate the radar signal parameters. The core of RadYOLOLet is two different convolutional neural networks (CNN), RadYOLO and Wavelet-CNN, that are trained independently. RadYOLO operates on spectrograms and provides most of the capabilities of RadYOLOLet. However, it suffers from low radar detection accuracy in the low signal-to-noise ratio (SNR) regime. We develop Wavelet-CNN specifically to deal with this limitation of RadYOLO. Wavelet-CNN operates on continuous Wavelet transform of the captured signals, and we use it only when RadYOLO fails to detect any radar signal. We thoroughly evaluate RadYOLOLet using different experiments corresponding to different types of interference signals. Based on our evaluations, we find that RadYOLOLet can achieve 100% radar detection accuracy for our considered radar types up to 16 dB SNR, which cannot be guaranteed by other comparable methods. RadYOLOLet can also function accurately under interference up to 16 dB SINR.
Semi-supervised News Discourse Profiling with Contrastive Learning

News Discourse Profiling seeks to scrutinize the event-related role of each sentence in a news article and has been proven useful across various downstream applications. Specifically, within the context of a given news discourse, each sentence is assigned to a pre-defined category contingent upon its depiction of the news event structure. However, existing approaches suffer from an inadequacy of available human-annotated data, due to the laborious and time-intensive nature of generating discourse-level annotations. In this paper, we present a novel approach, denoted as Intra-document Contrastive Learning with Distillation (ICLD), for addressing the news discourse profiling task, capitalizing on its unique structural characteristics. Notably, we are the first to apply a semi-supervised methodology within this task paradigm, and evaluation demonstrates the effectiveness of the presented approach.
Cuprate universal electronic spin response and the pseudogap from NMR

High-temperature superconductivity, in particular in the cuprates, is central to condensed matter physics, and telltale experimental laws for guiding theory are desirable. Here we report on such a universal property from the linear response of the electronic matter to a homogeneous static magnetic field. From it, two different types of carriers are identified. The universal behavior concerns the carriers from hybridized copper and oxygen orbitals that span the defining element, the CuO$_2$ plane, of the superconducting cuprates. Their spin response is similar to that of a material independent metallic density of states which carries a temperature independent, but doping dependent pseudogap that closes beyond optimal doping. The second electronic spin component has a strong family and doping dependent density of states, and it involves only Cu (isotropic orbitals, except for \lsco). The condensation of both types of carriers is interconnected and sets the critical temperature of superconductivity ($T_\mathrm{c}$). The inter-planar component can condense at the same or lower temperatures compared to that of the planar component, and a certain match in density of states seems to be required for the highest $T_\mathrm{c}$. The second component reminds one of the proposed involvement of another Cu axial orbital that relates to the distance or presence of the apical oxygen \cite{Ohta1991,Pavarini2001,Mahony2022} and the charge distribution in the CuO$_2$ plane \cite{Kowalski2021,Jurkutat2023}, which correlates with the maximum $T_\mathrm{c}$, as well.
SignBank+: Multilingual Sign Language Translation Dataset

This work advances the field of sign language machine translation by focusing on dataset quality and simplification of the translation system. We introduce SignBank+, a clean version of the SignBank dataset, optimized for machine translation. Contrary to previous works that employ complex factorization techniques for translation, we advocate for a simplified text-to-text translation approach. Our evaluation shows that models trained on SignBank+ surpass those on the original dataset, establishing a new benchmark and providing an open resource for future research