Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Technology Project Management Research: A Review of Works by Influential Pioneers

Information technology project management practices effectively help organizations achieve IT value. We employed a semistructured review with the practice of jizhuanti by tracing the development of the research intersection of IT and projects through the works of seven influential authors. From the analysis of the review, we build representative models of the intersection and suggest open lines of research. The results unveil stakeholder integration, knowledge management, and risk controls as principal themes for the sampling subset. Future scholars can continue to delve into these areas over more complex systems, consider other categories, or open new directions revealed by practice.
A new small thecate dinoflagellate Azadinium anteroporum sp. nov. (Amphidomataceae, Dinophyceae) isolated from the Asian Pacific

ABSTRACT The marine thecate dinoflagellate Azadinium includes species known to produce the diarrhetic shellfish toxins known as azaspiracids (AZAs). In this study, the morphology of a thecate dinoflagellate isolated from Mutsu Bay, Japan, was examined by LM and SEM, and its phylogenetic position was inferred from ITS and LSU rDNA sequences. The production of AZAs was examined by liquid chromatography-triple quadrupole mass spectrometer (LC-MS/MS). Cells were elliptical, 15.9–22.4 µm long and 11.1–17.1 µm wide. A chloroplast was positioned peripherally, with a pyrenoid near the cingulum, and the nucleus was placed in the hyposome. The hyposome was smaller than the episome, with an antapical spine on plate 2''''. Thecal tabulation was Po, cp, X, 4', 3a, 6'', 6C, 5S, 6''', 2''''. The apical pore complex consisted of a symmetrical apical pore plate (Po), a cover plate (cp) and a small canal plate (X), and was surrounded by a prominent collar formed by the apical plates. A ventral pore was located on the mid-anterior edge of plate 1' and contacted with the X-plate and/or the ventral left of plate Po. Plate 1'' was in contact with plate 1a. AZA was not detected in the cultures. Molecular phylogeny showed that the species was related to Azadinium species that possess the ventral pore on the left side of plate 1', i.e. A. cuneatum, A. dalianense, A. obesum, A. poporum, A. spinosum and A. trinitatum, but that it was not included in these species. The new species Azadinium anteroporum sp. nov. differs in the ventral pore position (mid-anterior edge of plate 1') from these related species (which have the ventral pore on the left side of plate 1'), and from A. polongum (with ventral pore on the posterior left side of 1') and all other Azadinium species (with ventral pore on the right side of 1').
Natural Cyclophilin A Inhibitors Suppress the Growth of Cancer Stem Cells in Non-Small Cell Lung Cancer by Disrupting Crosstalk between CypA/CD147 and EGFR

Non-small cell lung cancer (NSCLC) is a fatal malignant tumor with a high mortality rate. Cancer stem cells (CSCs) play pivotal roles in tumor initiation and progression, treatment resistance, and NSCLC recurrence. Therefore, the development of novel therapeutic targets and anticancer drugs that effectively block CSC growth may improve treatment outcomes in patients with NSCLC. In this study, we evaluated, for the first time, the effects of natural cyclophilin A (CypA) inhibitors, including 23-demethyl 8,13-deoxynargenicin (C9) and cyclosporin A (CsA), on the growth of NSCLC CSCs. C9 and CsA more sensitively inhibited the proliferation of epidermal growth factor receptor (EGFR)-mutant NSCLC CSCs than EGFR wild-type NSCLC CSCs. Both compounds suppressed the self-renewal ability of NSCLC CSCs and NSCLC-CSC-derived tumor growth in vivo. Furthermore, C9 and CsA inhibited NSCLC CSC growth by activating the intrinsic apoptotic pathway. Notably, C9 and CsA reduced the expression levels of major CSC markers, including integrin α6, CD133, CD44, ALDH1A1, Nanog, Oct4, and Sox2, through dual downregulation of the CypA/CD147 axis and EGFR activity in NSCLC CSCs. Our results also show that the EGFR tyrosine kinase inhibitor afatinib inactivated EGFR and decreased the expression levels of CypA and CD147 in NSCLC CSCs, suggesting close crosstalk between the CypA/CD147 and EGFR pathways in regulating NSCLC CSC growth. In addition, combined treatment with afatinib and C9 or CsA more potently inhibited the growth of EGFR-mutant NSCLC CSCs than single-compound treatments. These findings suggest that the natural CypA inhibitors C9 and CsA are potential anticancer agents that suppress the growth of EGFR-mutant NSCLC CSCs, either as monotherapy or in combination with afatinib, by interfering with the crosstalk between CypA/CD147 and EGFR.
Symbiotic Zooxanthellae Drive the δ13C Changes of Tridacna gigas Shell in the Southern South China Sea

The carbon isotope (δ13C) in marine biogenic carbonate is a valuable tool for comprehending the marine carbon cycle. However, the absence of monthly resolved δ13C records before the instrumental era (since 1850) hinders us to fully understand the global carbon cycle. In this study, we present a δ13C record from a modern Tridacna gigas shell (δ13Cshell) collected from the southern South China Sea (SCS) with an average resolution of ∼10 days. The δ13C record illustrates seasonal and interannual cycles which are superimposed by long‐term decreasing trend. We investigate the environmental controls influencing the δ13Cshell on different time scales. We suggest that more positive δ13Cshell values observed during the juvenile stage (first 8–10 years), which correspond to a high growth rate, can be attributed to 13C enrichment of the internal dissolved inorganic carbon pool. This resulted from the exponential increase of symbiotic zooxanthellae, and preferential 12C sequestration by the accelerated buildup of productive tissues. On the interannual time scale, changes in δ13Cshell were likely affected by both internal (vital effect) and external processes (sea water δ13C), as well as being disturbed by the super El Niño‐La Niña event. Seasonal changes of δ13Cshell were mainly dominated by the intensity of photosynthesis and respiration of symbiotic zooxanthellae, which depend on the seasonal solar irradiance variations in association with precipitation and the South Asian summer monsoon. Our study sheds light on the controlling factors of δ13Cshell variations on seasonal timescale and the long‐term trend, providing insight into the carbon cycle over geological history.
High Expression of Triggering Receptor Expressed on Myeloid Cells 1 Predicts Poor Prognosis in Glioblastoma

Background Glioblastoma (GBM) is a highly malignant tumor with poor prognosis, and new treatment strategies are urgently needed. Currently, the role of triggering receptor expressed on myeloid cells 1 (TREM-1) in tumors has been studied, but the role of TREM-1 in GBM remains unclear. Methods Immunohistochemical staining for TREM-1 was performed in 91 patients diagnosed with GBM. Clinicopathological characteristics and survival times were recorded. TREM-1 expression and its effect on prognosis were analyzed using online Gene Expression Profiling Interactive Analysis (GEPIA), The Cancer Genome Atlas (TCGA), and Chinese Glioma Genome Atlas (CGGA) databases. The expression profile of TCGA-GBM cohort was used to perform functional enrichment analysis. The CIBERSORT method and Tumor Immune Estimation Resource (TIMER) database were used to estimate the tumor-infiltrating immune cells (TIICs). The ESTIMATE algorithm was used to estimate the immune-stromal scores. Finally, the relationships of TREM-1 with TIICs, immune-stromal score, and immune checkpoint genes (ICGs) were analyzed. Results The expression of TREM-1 was upregulated in GBM, and high TREM-1 expression predicted a poor prognosis. TREM-1, surgical resection, postoperative radiotherapy, and temozolomide (TMZ) chemotherapy were associated with the survival time of patients with GBM, but only surgical resection and TREM-1 expression were independent prognostic factors. GBM with high TREM-1 expression exhibited increased neutrophil and macrophage infiltration. TREM-1 was positively associated with the immune-stromal score and multiple ICGs, and most of which were involved in immunosuppressive responses. Conclusion The present study revealed that high expression of TREM-1 in GBM is an independent poor prognosis factor and that TREM-1 is associated with the immunosuppressive microenvironment. Thus, blocking TREM-1 may be a strategy for enhancing the GBM immune response.
The Recursive Partitioning Algorithm (RPA): A Nonparametric Classification System

Leo Breiman (Breiman et al., 1984, 1998) was a statistician who was fond of practical applications, and this led him to develop several original studies. Based on the work begun by Friedman (1977), he developed a very accurate classification system, without the need for statistical assumptions, since it is a nonparametric methodology. The aim of this study is to present the work of Breiman known as the Recursive Partitioning Algorithm. The RPA will be introduced as a nonparametric approach to credit analysis, allowing for the incorporation of the costs of misclassifications. Several studies, such as Novak and LaDue (1999) and Marais, Patais and Wolfson (1984), have shown its applicability in the analysis and granting of credit. A long road has been traveled from the early work of Friedman (1977) to the CART model developed by Steinberg and Golovnya (2006). This paper – apart from presenting the fundamentals and possibilities for use of the RPA – seeks to show the effectiveness of the results attained through a comparison with a parametric model, the Discriminant Analysis, considered the most traditional and classical method of analysis. The results show the RPA to be a superior technique, as well as a technique of easy intuition by analysts. The conclusion of the paper confirms that the RPA system – little known and discussed by academics and market professionals – is a powerful classificatory tool, with the advantage of being nonparametric.
Using locally estimated geodesic distances to improve Hessian local linear embedding

To deal with highly curved data manifolds,the Hessian locally linear embedding(HLLE) algorithm was modified based on a locally estimated geodesic distance.It used the general conceptual framework of HLLE to guarantee correct setting of local isometry to an open connected subset.It employs the locally estimated geodesic distance instead of the Euclidean distance to determine the neighborhood of any point,so that it reduces the distorting influence of curvature of the data manifold on determining the neighborhood.This approach can be regarded as the integration of a local method and a global method,so that it has better performance and stability than HLLE,with only a slight increase in computational time.Experiments conducted on benchmark data sets verified etficioncy of the proposed approach.
First record of two Copepoda species parasitizing Colomesus tocantinensis (Tetraodontiformes: Tetraodontidae) in the Tocantins-Araguaia basin, Brazil

Abstract Considering the lack of studies on freshwater fishes of the genus Colomesus, we conducted a survey the parasite fauna of Colomesus tocantinensis collected from the Tocantins River, Brazil. We first recorded the presence of the ectoparasites Ergasilus colomesus and Lernaea sp. where 96.77% of the fish were parasitized.
The new approach for assessing the dependability of mobile applications

With the growing reliance on mobile applications, ensuring their dependability has become a crucial aspect of software development. This article introduces a new approach to evaluating the dependability of mobile applications based on the Corcoran Model, a comprehensive framework that considers various aspects of dependability, including performance, reliability, availability, scalability, security, usability, maintainability, and testability. The Corcoran Model provides a systematic way to assess mobile applications by analyzing key metrics and indicators associated with each of these aspects. By utilizing this model, developers and organizations can gain a holistic understanding of an application's dependability, leading to better decision-making and targeted improvements. Furthermore, this approach promotes increased end-user satisfaction and trust in mobile applications, ultimately contributing to their widespread adoption and success.
Information Entropy Estimation Using Indirect Conditional Information

Conditional entropy measures information flow from one discrete random to another. Conventional entropy is calculated from conditional or joint probabilities of two variables. When the joint probability is not known, the conditional entropy cannot be calculated. This paper presents an approach to estimate the conditional entropy using indirect conditional information. The result of the estimation is overestimated by less than ten percent of the actual value for normal cases.
Multi-View Stereo Network With Gaussian Distribution Iteration

Multi-view stereo estimates the depth maps of multiple perspective images in a scene and then fuses them to generate a 3D point cloud of the scene, which is an essential technology of 3D reconstruction. In this paper, we propose a deep learning method GDINet, applying probabilistic methods to the pyramid framework, which can significantly improve reconstruction quality. In detail, we first establish a Gaussian distribution for each image’s pixel and iterate it in the pyramid framework. The mean value is the estimated depth, and the variance represents the depth estimation error. In addition, we design a novel loss function with excellent convergence to train our network. Finally, we present an initialization module to generate the coarse Gaussian distribution, controlling the parameters in a reasonable range. Our results rank $2nd$ on both DTU and Tanks & Temples datasets, showing that our network has high accuracy, completeness, and robustness. We also make a visualization comparison on the BlendedMVS dataset (containing many aerial scene images) to demonstrate the generalization ability of our model.
Benchmark Evaluation of Hybrid Fixed-Flapping Wing Aerial Robot With Autopilot Architecture for Autonomous Outdoor Flight Operations

This letter is focused on the benchmark evaluation and comparison of the flapping and fixed wing flight modes on an hybrid platform developed for the realization of autonomous inspection operations outdoors. The platform combines the high range and endurance of fixed-wing UAVs (unmanned aerial vehicles), with the higher maneuverability and intrinsic safety of flapping wing in the interaction with humans during the hand launch and capture. A unified model of the hybrid platform is derived for both configurations following the Lagrange formulation to express the multi-body dynamics and aerodynamic forces of the flapping wing and the propellers. The proposed control scheme exploits the similarities of both flight modes in the tail actuation and in the generation of thrust either with the flapping wings or the propellers, in such a way that it can be implemented on conventional autopilots, facilitating in this way the adoption of this type of aerial platforms. To evaluate and compare the performance of both modes, a set of benchmark tests and metrics are defined, including the energy efficiency in forward flight, trajectory tracking, hand launch and capture, and accuracy in visual inspection. Experimental results in outdoors validate the developed prototype, identifying the fixed/flapping transitions, and evidencing the higher energy efficiency of the flapping wing mode compared to the fixed wing.
Structure learning in markov logic networks

Markov logic networks (MLNs) [86, 24] are a powerful representation combining first-order logic and probability. An MLN attaches weights to first-order formulas and views these as templates for features of Markov networks. Learning MLN structure consists of learning both formulas and their weights. This is a challenging problem because of its super-exponential search space of formulas, and the need to repeatedly learn the weights of formulas in order to evaluate them, a process that requires computationally expensive statistical inference. This thesis presents a series of algorithms that efficiently and accurately learn MLN structure. We begin by combining ideas from inductive logic programming (ILP) and feature induction in Markov networks in our MSL system. Previous approaches learn MLN structure in a disjoint manner by first learning formulas using off-the-shelf ILP systems and then learning formula weights that optimize some measure of the data's likelihood. We present an integrated approach that learns both formulas and weights to jointly optimize likelihood. Next we present the MRC system that learns latent MLN structure by discovering unary predicates in the form of clusters. MRC forms multiple clusterings of constants and relations, with each cluster corresponding to an invented predicate. We empirically show that by creating multiple clusterings, MRC outperforms previous systems. Then we apply a variant of MRC to the long-standing AI problem of extracting knowledge from text. Our system extracts simple semantic networks in an unsupervised, domain-independent manner from Web text, and introduces several techniques to scale up to the Web. After that, we incorporate the discovery of latent unary predicates into the learning of MLN clauses in the LHL system. LHL first compresses the data into a compact form by clustering the constants into high-level concepts, and then searches for clauses in the compact representation. We empirically show that LHL is more efficient and finds better formulas than previous systems. Finally, we present the LSM system that makes use of random walks to find repeated patterns in data. By restricting its search to within such patterns, LSM is able to accurately and efficiently find good formulas, improving efficiency by 2-5 orders of magnitude compared to previous systems.
Efficiency Improvement and Ripple Suppression Method Based on Partial Power Processing for Wireless Power Transfer System

For a conventional wireless power transfer (WPT), two-stage structure is one of the most common topologies, of which the efficiency will be dramatically decreased by a cascaded dc–dc converter. In this article, a partial power processing structure for WPT system is proposed to improve the system efficiency and reduce output ripple. The receiver is divided into the main receiver and the auxiliary receiver. Most of the power is transferred to the load through the main receiver, whereas the auxiliary receiver is used to regulate the output voltage. As a result, the switching loss and device stress of the dc–dc cascaded at receiver can be significantly reduced. Besides, the optimal switching phase can be obtained by analyzing the output waveforms of two receivers to minimize the output ripple. A 500-W prototype is built to validate the feasibility of the proposed system. With the same output capacitor, controlling the switching phase can reduce the output ripple by 36% compared with that without the control. The processing power of the proposed system is from 4% to 29% when the input voltage changes from 190 to 250 V. The efficiency is improved by 2.4% compared with the two-stage structure under the same input and output conditions.
On stress relaxation timescales for dense binary particulate systems

Abstract We study contact stress relaxation timescales, especially the temporal correlation involved in dense binary particulate systems, which offers insight into the intriguing relationship between the contact stresses and the contact time of particle interactions under non-equilibrium state. The contact time (also referred to as contact age) of a pair of particles is defined by the duration between current time and the instant when the contact was formed. The interspecies inter-particles contact stresses are derived from Liouville's theorem. We apply particle dynamics methods (e.g. molecular dynamics, discrete element method) to simulate 3D dense binary particulate systems with periodic boundary conditions. External perturbation is exerted on the system to balance the dissipation of energy due to the viscoelastic collisions. The contact stresses, Reynolds stresses, and the probability density function of the contact time of particles are predicted at different volume fraction of particles. The obtained stress-strain rate data are used to examine the constitutive relation of macroscopic materials. The study targets the impact of the short-term and the long-term contact/collision on the contact stress relaxation. The simulation results reveal distinct effects of the short-term and the long-term contact/collision on the contact stresses, which have been treated by only an averaged expression of particle interactions in discrete element methods before.
Plasma Assisted Turbulent Flow Separation control over a Backward Facing Step

This paper describes the use of Dielectric Barrier Discharge (DBD) plasma actuators to control the flow separation over a backward facing step. The two geometries of plasma actuator tested were linear and serpentine. The control was conducted for inflow velocity of 13.4 m/s with Re = 1.7×10 based on step height and freestream velocity. The actuators were placed in a co-flow arrangement. We found that the linear actuators do not show any reduction of reattachment point when run on continuous mode (1 kHz, 24 kVpp). However, for an amplitude modulated signal with a waveform 1 kHz, 28 kVpp and amplitude modulated at 125 Hz, the linear actuators showed a 14% reduction in reattachment point. For the AM mode serpentine actuator gave an impressive 31% reduction in reattachment location. These data along with the floor pressure data after the step clearly depict the benefits of using serpentine DBD actuator over linear actuators as an active flow control device.
A Method to Improve SIFT's Correct Rate Based on Constraints of Epipolar Geometry

A new enhanced algorithm based on epipolar gemometry is presented to reduce mismatches in SIFT,which extracts local feature of image and can't express image information fully.This method describes the epipolar geometric relationship between two input images,generated by the SIFT algorithm using the matched points.And then the correct matches are estimated using the relationship.The experimental results show that the correct rate is improved due to the raising of the error precision and the above method has good noise robustness.