Miracle-dz/UniFAR-FTUs huggingface dataset:
Gaussian Fluid Queue with Autocorrelated Input

This paper develops a generalization of Brownian motion with stationary, autocorrelated increments as a tractable model for problems in business and finance. We show that any real continuous Gaussian Markov process with stationary increments and smooth covariance function is characterized by three parameters quantifying drift, volatility, and autocorrelations. We model a queue as a functional of a process defined by those characteristics and derive its transient distribution conditional on its history.
Decoding Algorithms: Exploring End-Users’ Mental Models of the Inner Workings of Algorithmic News Recommenders

Abstract Algorithmic recommenders are omnipresent in our daily lives. While a multitude of studies focus on how people use algorithmic recommenders, far too little attention has been devoted to how they perceive and understand these complex systems. In this study we focus on Algorithmic News Recommenders (ANR). Drawing on 26 semi-structured interviews, we investigated how laypeople decode Google News and Facebook News. In our method we employ the scroll-back method, make use of visualizations and a double interview design. Our results differentiate between those with a high and low level of understanding. Those with a high level of understanding acknowledged the role of companies and developers in the workings of ANR. Others, who were less cognizant had a more instrumental view and mostly focused on the relation between their individual data disclosed and the ANR. More importantly, in both groups, their feelings (ranging from admiration to frustration) about and everyday interactions (both dominant and deviating) with ANR shape their general understanding. In the discussion we argue how it’s necessary for future research endeavors and algorithmic literacy initiatives to be mindful of the interconnection between knowledge, feelings, and interactions to understand layman’s perspectives.
Recurrence relations for higher moments of order statistics from doubly truncated Burr distribution

In this paper, we have obtained recurrence relations for higher moments of order statistics from doubly truncated Burr distribution, which enable one to obtain all the single, double (product) and higher moments of any order of all order statistics for any sample size from doubly truncated Burr distribution in a simple recursive manner, thus generalizing the earlier work done by Khan and Khan (1987) and also by Pushkarna, Saran and Tiwari (2012).
On the coherence and structure of discourse

Chomsky, Reflections on Language, p. 13. Between sentence (1) and sentence (2) there is a temporal relation, indicated by “then”, linking two topics Chomsky intends to discuss. Clause (1a) states the first topic, and clauses (1b) and (1c) elaborate on that by breaking it into two subtopics that will be discussed in sequence. This structure may be represented as in Figure 1. One could of course argue about details of this analysis; in fact, one of my aims in this paper is to develop a way of arguing about the details. Numerous other researchers have pointed out that such relations exist. Robert Longacre has a chapter in Anatomy of Speech Notions (1976) on “combinations of predications”, among which he includes conjunction, contrast, comparison, alternation, temporal overlap and succession, implication,
Experimental Investigation of the Fatigue Life of a Bridge Crane Girder Using S-N Method

Experimental measurement methods used in operational mode provide valuable information about the behavior of mechanical parts of equipment that cannot be determined in advance by analytical calculations or numerical modeling. The strain gauge method, which is often used to investigate the stresses in the load-bearing members of steel structures under operational conditions, was used. The advantage is the fast and accurate acquisition of stress values at critical locations selected based on analytical computations or numerical modeling. In the present paper, the residual operating life of two main girders of a bridge crane was assessed by an analytical-experimental approach. The input parameters for the assessment were obtained from the evaluated stress time records and using the Rainflow Counting method. Experimental measurements identified an almost 50% decrease in the residual life of one of the girders. It was caused by non-compliance with the technological procedures for the regular replacement of the rails, where the rail was welded to the top flange on one of the girders. Considering realistic operating conditions, predicting the effect of welded rail on fatigue damage accumulation, performed by other than experimental procedures, is almost impossible for such complex structures. This paper not only documents the importance of experimental measurements but also highlights the significance of selecting measurement locations with consideration of the current technical state of the structure.
Electroencephalogram-Based Motor Imagery Brain–Computer Interface Using Multivariate Iterative Filtering and Spatial Filtering

In motor imagery (MI)-based brain–computer interface (BCI), common spatial pattern (CSP) is most popularly used for discriminant feature extraction. However, the performance of CSP depends on the operational frequency bands, which are selected manually or set to a broad frequency range in most of the previously developed applications. Due to subject to subject or even trial to trial variability of frequency band affected by MI task, these methods suffer from the poor performance. We have proposed a novel approach, using combination of multivariate iterative filtering (MIF) and CSP (MIFCSP), to automatically select optimal frequency bands based on MIF which can be further used for discriminant feature extraction. MIF decomposes the signal into several multivariate intrinsic mode functions, from which features are extracted using CSP. We select the minimum number of most significant features for which highest classification accuracy is achieved. Subsequently, linear discriminant analysis (LDA) classifier is used to classify different MI tasks. Experimental results for BCI competition IV data set 2a and BCI competition III-IIIa are presented. For left-hand versus right-hand MI classification, proposed MIFCSP method provides 83.18% and 84.44% average accuracy, respectively. Superior classification performance confirms that MIFCSP is a promising candidate for MI BCI application.
Python for gene expression

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.
The influence of noise sources on cross-correlation amplitudes

SUMMARY We use analytical examples and asymptotic forms to examine the mathematical structure and physical meaning of the seismic cross-correlation measurement. We show that in general, cross correlations are not Green’s functions of medium, and may be very different depending on the source distribution. The modelling of noise sources using spatial distributions as opposed to discrete collections of sources is emphasized. When stations are illuminated by spatially complex source distributions, cross correlations show arrivals at a variety of time lags, from zero to the maximum surface-wave arrival time. Here, we demonstrate the possibility of inverting for the source distribution using the energy of the full cross-correlation waveform. The interplay between the source distribution and wave attenuation in determining the functional dependence of cross-correlation energies on station-pair distance is quantified. Without question, energies contain information about wave attenuation. However, the accurate interpretation of such measurements is tightly connected to the knowledge of the source distribution.
Human Capital as A Determinant of Service Quality in Accredited Universities in Kenya

The purpose of this study is to determine the effect of human capital on the service quality of accredited universities in Kenya. The study was guided by the positivist domain which is a major doctrine or theory in social sciences largely used in survey types of research. The study employed both a crosssectional research design and an explanatory research design. The target population comprised 74 public and private universities in Kenya. The sample size was 222 respondents. The main data collection tool was a questionnaire. Both descriptive and inferential statistics were used to analyze the data. Findings established that human capital had a significantly positive relationship with service quality. Human capital explained about 38% of the variation in service quality of accredited universities in Kenya. The practical implications for the study are that the human capital such as strategic leaders in universities should be aligned and increase their adoption of strategic leadership practices in order to inspire good managerial practices in universities. Additionally, the finding that human capital affects service quality is consistent with the Upper Echelons theory. This theory offers a framework for viewing leaders as wise, experienced, and educated change agents who serve as critical assets capable of enhancing service quality in their institutions.
Risk Management Practices and Enterprise Resilience - The Mediating Role of Alliance Management Capabilities

Enterprises face several obstacles in an interconnected world with dynamic and uncertain business climates. Volatility has become one of the greatest threats to the resiliency of companies but might also offer an opportunity. They must exert effort to be resilient by implementing proactive risk management. This study investigates the influence of Risk Management Practices (RMP) on Enterprise Resilience (ER) across state-owned companies (SOEs) in Indonesia, as well as the moderating role of alliance management capabilities (AMCs) on the relationship between RMP and ER. The Indonesian SOEs would be the units of analysis of the study due to their importance to the Indonesian economy and the scarcity of research on resilience and risk management in SOEs in emerging economies. The research utilized online surveys of SOE board members. Three hundred twenty-two valid responses are analyzed using PLS-SEM. The study indicated that RMP has a significant effect on ER. Leadership Capabilities (LC) mediates the link between RMP and ER. This research contributes to limited literature that integrates AMCs, RMPs, and ER through the perspective of dynamic capability theory and is done using a unique sample of SOEs from emerging countries. The results offer policymakers and SOE management recommendations for enhancing SOEs' sustainability.
Improved 3D full-wave multilevel Green's function interpolation method

A subset of infinitely smooth radial basis functions (RBFs), Bessel (BSL) RBF is proposed to improve the computational efficiency of the multilevel Green's function interpolation method for the solution of 3D complex full-wave electromagnetic problems. Numerical results reveal that the interpolation accuracy and efficiency, and computer memory storage requirement are greatly enhanced using the proposed BSL RBF when compared to previously used RBFs.
The Optimization and Evaluation of Flibanserin Fast-Dissolving Oral Films

Flibanserin (FLB) is a drug used for female hypotensive sexual desire disorder approved by the FDA in August 2015. FLB exhibits extensive hepatic first-pass metabolism and low aqueous solubility, hence poor oral bioavailability. In this study, beta hydroxypropyl cyclodextrin-FLB inclusion complexes were incorporated into orally fast dissolving films. This dosage form was expected to improve FLB aqueous solubility, which would give fast onset of action and decrease presystemic metabolism, hence improving oral bioavailability. The inclusion complex at a ratio of 1:1 was prepared by the kneading method. Differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), and powder X-ray diffractometry (XRD) were used to confirm complex formation. The Box–Behnken design (15 different formulae of FLB fast-dissolving oral films (FLBFDOFs) were utilized for the optimization of the prepared films. The Expert Design 11 program was utilized to examine the effects of three selected factors, polymer concentration (X1), plasticizer concentration (X2), and disintegrant concentration (X3) on four responses: disintegration time (DT), initial dissolution rate (IDR), dissolution efficiency (DE), and film quality (QF). Numerical optimization was performed by minimizing disintegration time (Y1), while maximizing the initial drug dissolution rate (Y2), dissolution efficiency (Y3), and the quality factor (Y4). The statistical analysis showed that X1 has a significant positive effect on the disintegration time and a significant negative effect on IDR. While X2 and X3 produced a nonsignificant negative effect on IDR. Dissolution efficiency was maximized at the middle concentration of both X2 and X3. The best film quality was observed at the middle concentration of both X1 and X2. In addition, increasing X3 leads to an improvement in film quality. The optimized film cast from an aqueous solution contains hydroxypropyl cellulose (2%) as a hydrophilic film-forming agent and propylene glycol (0.8%) as a plasticizer and cross povidone (0.2%) as a disintegrant. The prepared film released 98% of FLB after 10 min and showed good physical and mechanical properties. The optimized formula showed a disintegration time of 30 s, IDR of 16.6% per minute, DE15 of 77.7%, and QF of 90%. This dosage form is expected to partially avoid the pre-systemic metabolism with a fast onset of action, hence improving its bioavailability that favors an advantage over conventional dosage forms.
Magnetized galactic haloes and velocity lags

We present an analytic model of a magnetized galactic halo surrounding a Mestel gravitating disc. The magnetic field is taken to be in energy equipartition with the pressure dominant rotating halo gas ({\it not} with the cosmic rays), and the whole system is in a steady state. A more flexible `anisotropic equipartition' model is also explored. A definite pressure law is required to maintain the equilibrium, but the halo density is constant. The velocity/magnetic system is scale-free. The objective is to find the rotational velocity lag in such a halo. The magnetic field is not force-free so that angular momentum may be transported from the halo to the intergalactic medium. We find that the `X'-shaped structure observed for halo magnetic fields can be obtained together with a simple analytic formula for the rate of decline of the velocity with height $z$. The formula also predicts the change in lag with radius, $r$.
Malaysian Second Language Learners’ Linguistic Accuracy in Writing

Second language production, and proficiency are measured based on the Complexity, Accuracy, and Fluency dimensions. During their learning process, learners master the multicomponent dimensions also known as the CAF construct. Second language learners often master one component at a time and prior research on the present demography has been minimal. Hence, the present research looks at language accuracy among Malaysian English as a second language (ESL) learners’ writ ten language. The population chosen for this study is tertiary level students who have undergone formal English language classes in schools for 11 years. In the present setting, they are undergoing prerequisite English classes in the local university that they attend. Following six weeks of classes where learners revise the grammatical and writing aspects of the lesson, they then complete a writing task which is then analyzed for errors and language accuracy. Six major areas are identified as inaccuracy in learners’ language production. 20 writing tasks are analyzed through error analysis, and this process reveals that learners’ accuracies are apparent in their vocabulary, first language/mother tongue interference, sentence structure, subject-verb agreement, tenses and word form. Ten out the total 20 documents contained severe accuracy errors that affect coherence, comprehension and maturity in the written product. Learners who struggle with accuracy often have unsuitable, long-winded and/or incorrect products be it in listening, speaking, reading, or in the present study’s scenario, writing. Further research in the CAF construct is encouraged especially among the same demography to address the issue of poor language mastery and proficiency.
Cluster science from ROSAT to eROSITA

Galaxy clusters are one of the important cosmological probes to test the consistency of the observable structure and evolution of our Universe with the predictions of specific cosmological models. We use results from our analysis of the X-ray flux-limited REFLEX cluster sample from the ROSAT All-Sky Survey to illustrate the constraints on cosmological parameters that can be achieved with this approach. The upcoming eROSITA project of the Spektrum-Roentgen-Gamma mission will increase these capabilities by two orders of magnitude and importantly also increase the redshift range of such studies. We use the projected instrument performance to make predictions on the scope of the eROSITA survey and the potential of its exploitation. (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
A Comparison of RNA Interference via Injection and Feeding in Honey Bees

Simple Summary RNA interference is an important way to analyze gene function. It is also widely used in honey bees. It was thought that RNA interference with honey bee brain genes could only be achieved by injection. However, this method of injection is very complicated and can easily cause damage to bees. Recent studies have shown that specially treated siRNA can knockdown brain genes in animals by feeding them. Therefore, we used different approaches to deliver modified and unmodified siRNAs to investigate what methods can successfully interfere with honey bee brain genes. The results of this study are helpful to improve the application of RNA interference techniques in honey bees and other insects. Abstract RNA interference (RNAi) has been used successfully to reduce target gene expression and induce specific phenotypes in several species. It has proved useful as a tool to investigate gene function and has the potential to manage pest populations and reduce disease pathogens. However, it is not known whether different administration methods are equally effective at interfering with genes in bees. Therefore, we compared the effects of feeding and injection of small interfering RNA (siRNA) on the messenger RNA (mRNA) levels of alpha-aminoadipic semialdehyde dehydrogenase (ALDH7A1), 4-coumarate-CoA ligase (4CL), and heat shock protein 70 (HSP70). Both feeding and injection of siRNA successfully knocked down the gene but feeding required more siRNA than the injection. Our results suggest that both feeding and injection of siRNA effectively interfere with brain genes in bees. The appropriateness of each method would depend on the situation.
Robust control design with real parameter uncertainty using absolute stability theory. Ph.D. Thesis

The purpose of this thesis is to investigate an extension of/z theory for robust control design by considering systems with linear and nonlinear real parameter uncertainties. In the process, explicit connections are made between mixed/¢ and absolute stability theory. In particular, it is shown that the upper bounds for mixed/z are a generalization of results from absolute stability theory. Both state space and frequency domain criteria are developed for several nonlinearities and stability multipliers using the wealth of literature on absolute stability theory and the concepts of supply rates and storage functions. The state space conditions are expressed in terms of Riccati equations and parameter-dependent Lyapunov functions. For controller synthesis, these stability conditions are used to form an overbound of the 7-/2performance objective. A geometric interpretation of the equivalent frequency domain criteria in terms of off-axis circles clarifies the important role of the multiplier and shows that both the magnitude and phase of the uncertainty are considered. A numerical algorithm is developed to design robust controllers that minimize the bound on an 7-12cost functional and satisfy an analysis test based on the Popov stability multiplier. The controller and multiplier coefficients are optimized simultaneously, which avoids the iteration and curve-fitting procedures required by the D-K procedure of/_ synthesis. Several benchmark problems and experiments on the Middeck Active Control Experiment at M.I. T. demonstrate that these controllers achieve good robust performance and guaranteed stability bounds. PR_t_=D'tf'i_PAGEBLAN_ [NKTFILMED 3 _,. INTEi_TiONALI_
DGFlow-SLAM: A Novel Dynamic Environment RGB-D SLAM without Prior Semantic Knowledge Based on Grid Segmentation of Scene Flow

Currently, using semantic segmentation networks to distinguish dynamic and static key points has become a mainstream designing method for semantic SLAM systems. However, the semantic SLAM systems must have prior semantic knowledge of relevant dynamic objects, and their processing speed is inversely proportional to the recognition accuracy. To simultaneously enhance the speed and accuracy for recognizing dynamic objects in different environments, a novel SLAM system without prior semantics called DGFlow-SLAM is proposed in this paper. A novel grid segmentation method is used in the system to segment the scene flow, and then an adaptive threshold method is used to roughly detect the dynamic objects. Based on this, a deep mean clustering segmentation method is applied to find potential dynamic targets. Finally, the results of grid segmentation and depth mean clustering segmentation are jointly used to find moving objects accurately, and all the feature points of the moving objects are removed on the premise of retaining the static part of the moving object. The experimental results show that on the dynamic sequence dataset of TUM RGB-D, compared with the DynaSLAM system with the highest accuracy for detecting moderate and violent motion and the DS-SLAM with the highest accuracy for detecting slight motion, DGflow-SLAM obtains similar accuracy results and improves the accuracy by 7.5%. In addition, DGflow-SLAM is 10 times and 1.27 times faster than DynaSLAM and DS-SLAM, respectively.