Miracle-dz/UniFAR-FTUs huggingface dataset:
Bibliometric‐enhanced legal information retrieval: Combining usage and citations as flavors of impact relevance

Bibliometric‐enhanced information retrieval uses bibliometrics (e.g., citations) to improve ranking algorithms. Using a data‐driven approach, this article describes the development of a bibliometric‐enhanced ranking algorithm for legal information retrieval, and the evaluation thereof. We statistically analyze the correlation between usage of documents and citations over time, using data from a commercial legal search engine. We then propose a bibliometric boost function that combines usage of documents with citation counts. The core of this function is an impact variable based on usage and citations that increases in influence as citations and usage counts become more reliable over time. We evaluate our ranking function by comparing search sessions before and after the introduction of the new ranking in the search engine. Using a cost model applied to 129,571 sessions before and 143,864 sessions after the intervention, we show that our bibliometric‐enhanced ranking algorithm reduces the time of a search session of legal professionals by 2 to 3% on average for use cases other than known‐item retrieval or updating behavior. Given the high hourly tariff of legal professionals and the limited time they can spend on research, this is expected to lead to increased efficiency, especially for users with extremely long search sessions.
Estimating mixtures of exponential distributions using maximum likelihood and the EM algorithm to improve simulation of telecommunications networks

This thesis explores the topic of mixture-distributions as they relate to modeling call demand on a telecommunications network. Modeling call duration demand in particular proves difficult for a number of reasons. Historically, this has been modeled using a simple exponential distribution with a single parameter. This work extends that modeling technique to using multi-component exponential distributions. Development of these models is shown to be possible using non-linear programming as well as an application of the E M algorithm. These independent approaches yield remarkably similar results. Also relevant are the treatment of statistical significance testing for large data set samples, since these notoriously pose difficulty by magnifying statistical significance. This problem is treated through a more robust comparison of data to the theoretical distribution using a bootstrapping technique of sampling against the large data set. Finally, the results of the demand modeling are also validated using a more intuitive comparison to the simulation model output.
Effect of age, season, and gender on bronchoalveolar lavage fluid cytology in camels

Background: Bronchoalveolar lavage fluid (BALF) samples are valued mirrors of different parts of the airway and can be used with other approaches to the diagnosis of the lower respiratory tract. Several previous studies on various animal species showed the effect of the season, gender, and age on the percentage of cells in the BALF samples. Aim: The main aim of this study was to determine the impact of gender, age, and season on the cytological analysis in BALF of dromedary camels. Methods: Thirteen healthy camels were involved in this study. Camels were selected based on general respiratory clinical scoring. BALF was done using a special BALF catheter. BALF samples were analyzed from dromedary camels by microscopic examination of prepared smears. Results: The results of the BALF cytology percentage revealed that there was no variation between winter and summer in most cell types. Only the mean value of neutrophil cell percentage in BALF in winter increased significantly (10.75 ± 1.31) compared to summer (4.60 ± 0.81). The range of eosinophils was in summer (0–13) wider than in winter (0–2). A significant difference was recorded in lymphocytes, eosinophils, and epithelial cells percentage among adult and young camels. There was a high mean value of epithelial cells percentage in adult camels (10.17 ± 1.64) compared to young animals (3.0 ± 0.58). The results of the BALF cytology among males and camels showed no significant difference. Conclusion: The present study revealed significant differences in the BALF cytology regarding age and season, but no impact on gender.
Pengaruh Reputasi Auditor, Audit Complexity, Financial Expertise CEO, dan Bankruptcy Prediction terhadap Audit Delay

Ketepatan waktu dalam menyajikan laporan keuangan merupakan hal penting bagi perusahaan dalam peningkatan kualitasnya. Semakin lama waktu yang diperlukan dalam pengauditan dan penyajian laporan keuangan maka perusahaan dianggap tidak mampu mengelolanya dengan baik. Perusahaan harus melaporkan laporan keuangan yang telah diaudit 120 hari setelah tahun buku berakhir sesuai dengan peraturan yang telah ditetapkan. Penelitian ini bertujuan untuk menguji dan mengetahui pengaruh reputasi auditor, audit complexity, financial expertise CEO, dan bankruptcy prediction terhadap audit delay secara parsial dan simultan. Penelitian ini menggunakan jenis data sekunder bersifat kuantitatif. Populasi penelitian berfokus pada sektor basic materials yang terdaftar di Bursa Efek Indonesia periode 2017-2020. Pengambilan sampel menggunakan teknik purposive sampling sehingga terdapat 127 sampel yang digunakan. Hasil penelitian menunjukkan bahwa reputasi auditor, financial expertise CEO, dan bankruptcy prediction berpengaruh terhadap audit delay. Sedangkan hasil yang berbeda pada audit complexity yang tidak berpengaruh terhadap audit delay.
Clinical Heterogeneity and Different Phenotypes in Patients with SETD2 Variants: 18 New Patients and Review of the Literature

SETD2 belongs to the family of histone methyltransferase proteins and has been associated with three nosologically distinct entities with different clinical and molecular features: Luscan–Lumish syndrome (LLS), intellectual developmental disorder, autosomal dominant 70 (MRD70), and Rabin–Pappas syndrome (RAPAS). LLS [MIM #616831] is an overgrowth disorder with multisystem involvement including intellectual disability, speech delay, autism spectrum disorder (ASD), macrocephaly, tall stature, and motor delay. RAPAS [MIM #6201551] is a recently reported multisystemic disorder characterized by severely impaired global and intellectual development, hypotonia, feeding difficulties with failure to thrive, microcephaly, and dysmorphic facial features. Other neurologic findings may include seizures, hearing loss, ophthalmologic defects, and brain imaging abnormalities. There is variable involvement of other organ systems, including skeletal, genitourinary, cardiac, and potentially endocrine. Three patients who carried the missense variant p.Arg1740Gln in SETD2 were reported with a moderately impaired intellectual disability, speech difficulties, and behavioral abnormalities. More variable findings included hypotonia and dysmorphic features. Due to the differences with the two previous phenotypes, this association was then named intellectual developmental disorder, autosomal dominant 70 [MIM 620157]. These three disorders seem to be allelic and are caused either by loss-of-function, gain-of-function, or missense variants in the SETD2 gene. Here we describe 18 new patients with variants in SETD2, most of them with the LLS phenotype, and reviewed 33 additional patients with variants in SETD2 that have been previously reported in the scientific literature. This article offers an expansion of the number of reported individuals with LLS and highlights the clinical features and the similarities and differences among the three phenotypes associated with SETD2.
The Diversity of Astrocyte Activation during Multiple Sclerosis: Potential Cellular Targets for Novel Disease Modifying Therapeutics

Neuroglial cells, and especially astrocytes, constitute the most varied group of central nervous system (CNS) cells, displaying substantial diversity and plasticity during development and in disease states. The morphological changes exhibited by astrocytes during the acute and chronic stages following CNS injury can be characterized more precisely as a dynamic continuum of astrocytic reactivity. Different subpopulations of reactive astrocytes may be ascribed to stages of degenerative progression through their direct pathogenic influence upon neurons, neuroglia, the blood-brain barrier, and infiltrating immune cells. Multiple sclerosis (MS) constitutes an autoimmune demyelinating disease of the CNS. Despite the previously held notion that reactive astrocytes purely form the structured glial scar in MS plaques, their continued multifaceted participation in neuroinflammatory outcomes and oligodendrocyte and neuronal function during chronicity, suggest that they may be an integral cell type that can govern the pathophysiology of MS. From a therapeutic-oriented perspective, astrocytes could serve as key players to limit MS progression, once the integral astrocyte–MS relationship is accurately identified. This review aims toward delineating the current knowledge, which is mainly focused on immunomodulatory therapies of the relapsing–remitting form, while shedding light on uncharted approaches of astrocyte-specific therapies that could constitute novel, innovative applications once the role of specific subgroups in disease pathogenesis is clarified.
Aspect sentiment mining of short bullet screen comments from online TV series

Bullet screen comments (BSCs) are user‐generated short comments that appear as real‐time overlays on many video platforms, expressing the audience opinions and emotions about different aspects of the ongoing video. Unlike traditional long comments after a show, BSCs are often incomplete, ambiguous in context, and correlated over time. Current studies in sentiment analysis of BSCs rarely address these challenges, motivating us to develop an aspect‐level sentiment analysis framework. Our framework, BSCNET, is a pre‐trained language encoder‐based deep neural classifier designed to enhance semantic understanding. A novel neighbor context construction method is proposed to uncover latent contextual correlation among BSCs over time, and we also incorporate semi‐supervised learning to reduce labeling costs. The framework increases F1 (Macro) and accuracy by up to 10% and 10.2%, respectively. Additionally, we have developed two novel downstream tasks. The first is noisy BSCs identification, which reached F1 (Macro) and accuracy of 90.1% and 98.3%, respectively, through fine‐tuning the BSCNET. The second is the prediction of future episode popularity, where the MAPE is reduced by 11%–19.0% when incorporating sentiment features. Overall, this study provides a methodology reference for aspect‐level sentiment analysis of BSCs and highlights its potential for viewing experience or forthcoming content optimization.
PROGRESSIVE COLLAPSE ANALYSIS OF REINFORCED CONCRETE FRAMED STRUCTURE

The progressive collapse of reinforced concrete str uctures is initiated when one or more vertical load carrying members are removed due to man-made or natural hazards. The building’s weight transfers to neighboring columns in the stru cture, leads to the failure of adjoining members and finally to the failure of par ti l or whole structure system. In which the collap sing system continually seeks alternative load paths in order to survive. In the present study the demand capacity ratio (DCR) of re inforced concrete twelve storey framed structure are evaluated as per U.S. General Services Administration (GSA) guidelines. The Linear static analysis is carr ied out using software, ETABS V9.7. The structural behavior of the building for progressive collapse, a finite element model i s considered using the preprocessing function of structural anal ysis program. Further loading are assigned to model according to IS codes. Analysis is carried out for member forces and reinf orcement details. The obtained DCR values show that columns are safe and beams to be reinforced additionally.
Interface state density of SiO2/p-type 4H-SiC (0001), (112¯0), (11¯00) metal-oxide-semiconductor structures characterized by low-temperature subthreshold slopes

Interface properties of heavily Al-doped 4H-SiC ( 0001) (Si-face), ( 112¯0) (a-face), and ( 11¯00) (m-face) metal-oxide-semiconductor (MOS) structures were characterized from the low-temperature gate characteristics of metal-oxide-semiconductor field-effect transistors (MOSFETs). From low-temperature subthreshold slopes, interface state density (Dit) at very shallow energy levels (ET) near the conduction band edge (Ec) was evaluated. We discovered that the Dit near Ec (Ec − 0.01 eV < ET < Ec) increases in MOS structures with higher Al doping density for every crystal face (Si-, a-, and m-face). Linear correlation is observed between the channel mobility and Dit near Ec, and we concluded that the mobility drop observed in heavily doped MOSFETs is mainly caused by the increase of Dit near Ec.
A new discrete-time epidemic model describing information spread and its impact on the agree-disagree model

In this study, we propose a new modiﬁed discrete-time epidemic model that characterizes the diffusion of information and its impact on the agreement/disagreement model. So the goal is to increase the amount of information to inﬂuence people’s opinions. To do this, we proposed a control strategy based on the increase in the number of posts that inﬂuence people to agree with the subject studied (election, vaccine against COVID 19). The Pontryagin maximum principle is used to describe optimal control. Finally, numerical simulations are performed to verify the theoretical analysis using MATLAB
Use of Reactive Particles for Solid Rocket Combustion Instability Suppression

Research towards predicting and quantifying undesirable transient axial combustion instability symptoms in solid-propellant rocket motors necessitates a comprehensive numerical model for internal ballistic simulation under dynamic flow and combustion conditions. In the present investigation, a numerical evaluation of the usage of reactive aluminum particles for the suppression of axial shock wave development is brought forward, for a motor whose instability symptom driving mechanisms are based on transient combustion response and structural-vibration-induced normal acceleration. A primary focus is placed on evaluating the qualitative trends associated with the use of aluminum particles that in general diminish in size as they move downstream in the central internal flow. The particle size regression is stipulated to occur at a nonuniform rate, through an evaporation law that is governed by the particle’s current diameter. Individual transient internal ballistic simulation runs for a reference composite-propellant cylindrical-grain motor show the evolution of the axial pressure wave for a given initiating pressure disturbance, particle loading, and initial particle size. The limit pressure wave magnitudes at a later reference time in a given firing simulation run are collected for a series of runs, in order to assist in the evaluation of identifiable trends. The numerical results demonstrate that the ability of the particles to suppress axial wave development can be effective, but in general, not nearly as effective when comparing to the constant-diameter inert particle case, for the same particle loading. There may be some advantage in using a larger starting reactive particle size relative to the reference inert case, for improved overall symptom suppression, to a certain extent. However, increasing the reactive particle loading may ultimately be the only feasible means for reaching a desired symptom suppression level.
Quantum Simulation of Tunable Neuron Activation

The activation of the neuron is simulated by a quantum circuit. When the circuit is deep enough, the output qubit is deterministically in the state |1⟩$|1\rangle$ if the probability of the input qubit in the designated state is larger than the threshold value. Conversely, the output qubit is deterministically in the state |0⟩$|0\rangle$ if the probability is smaller than the threshold value. The threshold value is adjustable. When the depth of the circuit is limited, the nonlinear relations between the input and the output can also be realized.
Paid to lobby but up for debate: role conceptions and client selection of public affairs consultants

PurposeThis article addresses recent calls in the literature for advancing our understanding of public affairs consultants and their role conceptions. By testing and further exploring self-perceptions of public affairs consultants the study aims to offer new insight into how consultants define and view their occupational role.Design/methodology/approachThe study draws on a nationwide survey with public affairs consultants in Sweden.FindingsFour main role conceptions were identified (advocate, do-gooder, expert and intermediary). Further, the study tests how personal and professional characteristics correlate with different role conceptions, by viewing professional experience and consultants' selection of clients. Data also suggest that consultants' background in politics does not promote any specific role perception. Finally, the findings also show that how consultants choose clients is a divider in the industry, where some act as passive intermediaries while other take a more active role in their choice of clients.Originality/valueThe findings enhance our understanding of public affairs as a field, and specifically about the modelling of professional roles amongst consultants. The empirical results in this study show how contemporary role typologies needs to be extended to better capture the specificities of consultants' roles in public affairs. By addressing the issue of how consultants choose clients the study engages with the complex debate of whether consultants ought to act as objective or subjective agents and hence join the conversation on ethics in public affairs.
Research and Reality: A Survey of Educators’ Perceptions about Evidence-Based Practices in Inclusive Settings for Students with Intellectual Disabilities

As the world focuses on the acceptance and inclusion of diverse individuals often referring to race, ethnicity, gender, and sexual orientation, this study focuses on the culture and identity of individuals with intellectual disabilities who are educated in an inclusive setting. Although there are currently more students with disabilities who are included in the general education classroom as compared to 20 years ago, the number of students with more severe disabilities, including intellectual disabilities (ID), lag behind those with mild to moderate disabilities. This survey study examined both why and how to best educate students with ID in the American inclusive classroom. This study, including Likert-style and open-ended questions, was administered amongst practicing educators in order to determine their perceptions of the most prominent evidence-based practices (EBPs) related to supporting students with ID. Participants were asked to rate these EBPs in terms of their usefulness and effectiveness in the classroom for students with ID. The results of this study indicate that educators are regularly implementing EBPs, such as visual supports and behavior modifications. Overall, educators feel more confident in their ability to include students with intellectual disabilities in the general education classroom, and share their perspectives about this experience.
Long‐term fertilization and plastic film mulching modify temporal incorporation of 13C/15N‐labelled particulate organic matter

Plastic film mulching (PFM) is critical for agricultural planting and maximizing production in semiarid and arid areas. Particulate organic matter (POM) is assumed to be a sensitive indicator for evaluating the effects of different agricultural practices on soil fertility and the soil organic carbon (SOC) pool. Soil aggregates have the function of ‘wrap’ and protect the POM stored in them. However, there is limited information regarding how PFM and fertilization jointly influence the dynamic changes of newly added stalk‐derived POM in brown earth. Consequently, an in‐depth study of the fate of carbon (C) and nitrogen (N) derived from maize stalk residues within the particulate organic carbon (POC) and particulate organic nitrogen (PON) fractions in soil aggregates was undertaken. Its outcome would contribute to better predictions on the active organic matter components sequestered in the soil. The dynamics and accumulation of newly added maize stalk C and N as POC and PON in different soil aggregates (using the dry sieving method divided into >2, 1–2, 0.25–1 and <0.25 mm) were analysed by an in situ 13C/15N‐tracing technique under PFM and different fertilization treatments. Over 360 days of cultivation, the POC and PON contents were significantly (p < 0.05) larger in the nitrogen (N) and organic manure (M) treatments than in the MN (manure combined with nitrogen) and Control treatments. The PFM treatment accelerated the decomposition of maize stalk C in the N fertilizer treatment, with an increase of 64% in stalk‐derived POC after the 1‐day cultivation period. Stalk‐derived POM tended to accumulate in <0.25 mm microaggregates in the early cultivation period and then decreased rapidly with the extension of the cultivation period affected by PFM coupled with fertilization. However, stalk‐derived POM accumulation in macroaggregates (>0.25 mm) fluctuated over the 360‐day cultivation period. Accordingly, PFM application and fertilization practices had important effects on the accumulation of newly added stalk‐derived POM in soil aggregates. We conclude that the accumulation of maize residue POM was primarily affected by soil fertilization type, rather than by the presence or absence of PFM. These results provide new insights into agricultural management strategies for improving soil carbon sequestration capacity.
Test Cost Reduction for 5G and Beyond using Machine Learning

Software testing is essential, but expensive, especially for significant issues, feature-rich systems such as telecommunication systems evolving toward 5G and beyond. There is a need in this domain for effective testing techniques to ensure that a minimal number of test cases assess the most important combinations of system functions with respect to domain-specific criteria.Our approach aims to address this challenge by first automatically mapping existing test cases to the combinations of system capabilities they exercise and visualizing the mappings using decision tree learners. Then, the approach uses a combination of the engineers’ feedback (domain-specific criteria), mapping data, and test execution logs to propose new test cases covering newly-added capabilities or better exercising/verifying existing ones while ensuring the efficacy at fault detection, code coverage, equipment cost, test execution time, redundancy avoidance, among other things.
Distributed model predictive control with saturated inputs by a saturation‐dependent Lyapunov function

In this paper, distributed model predictive control (MPC) problems are considered for input‐saturated polytopic uncertain systems by a saturation‐dependent Lyapunov function approach. The actuator saturation is processed by the transformation into the linear convex combination form. By the decomposition of the control input, distributed MPC controllers are designed in parallel for each subsystems. The Lyapunov Function we select is saturation dependent, which is less conservative than the general Lyapunov Function approach. An invariant set condition is provided and min–max distributed MPC is proposed based on the invariant set. The robust distributed MPC controllers are determined by solving a linear matrix inequality (LMI) optimization problem. To reduce the conservatism, we present a robust distributed MPC algorithm, which is not only saturation dependent but also parameter dependent. A Jacobi iterative algorithm is developed to coordinate the distributed MPC controllers. A simulation example with multi‐subsystem is carried out to demonstrate the effectiveness of the proposed distributed MPC algorithms. Copyright © 2016 John Wiley & Sons, Ltd.