Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Child Maltreatment on Social Relationships in Emerging Adulthood

This retrospective study examined the relationship between childhood maltreatment and social relationships in an emerging adult sample of 159 individuals between 18 and 25 years of age. This research provides additional evidence on the effects of child maltreatment by investigating outcomes found in both friendship and romantic relationship quality in emerging adulthood. Protective factors likely to reduce the negative impact of child maltreatment on social relationships, including emotion regulation, coping, and optimism were examined as moderators. Emotion regulation was the only significant moderator that reduced the negative effect of child maltreatment on friendship and romantic relationship outcomes. Self-report of child maltreatment was associated with friendship quality and romantic relationship quality during emerging adulthood. Differential effects of maltreatment type were also assessed. The results indicated that physical abuse was the only significant indicator of child maltreatment that was associated with the outcomes of friendship experiences, satisfaction, and conflict. Sexual abuse was the strongest indicator of child maltreatment associated with romantic relationship satisfaction, conflict, and trust. Results contribute to the extant literature by revealing earlier life predictors of friendship and romantic relationship outcomes in emerging adults and the influence that emotion regulation has as a protective factor. Findings may inform additional interventions for children who have experienced child maltreatment by focusing on strengthening emotion regulation skills that increase the likelihood of building healthy social relationships in emerging adults. CHILD MALTREATMENT AND RELATIONSHIPS 3 Table of
Networks of Power : Using Social Network Analysis to understand who will rule and who is really in charge in the Chinese Communist Party

Patronage networks are said to help elites advance into a regime’s inner circle. But researchers have only systematically studied individual patron-client ties instead of taking advantage of the tools provided by social network analysis (SNA). This paper shows that in the case of the Chinese Communist elite 1982-2012, such networks can be inferred from publicly available information, by noting who has been promoted under whom. A hazard analysis demonstrates that direct connections to patrons double the chance of being appointed to the Politburo. But links to current and former subordinates unlike those to superiors among the other elite also have a significant positive effect. Finally, network centrality measures can identify current patrons and predict appointments to the inner circles five or ten years later even if the identity of the patrons is unknown. Future Politburo members are found in network positions that capture popularity as a coalition partner (closeness centrality), while patrons hold network positions from which they can prevent the formation of opposing coalitions (betweenness centrality). The paper thus shows the importance of analyzing informal elite networks instead of just the ties between one specific leader and his or her followers. It also proposes SNA as a new theoretical and empirical approach to the understudied informal institutions of authoritarian regimes, suggesting a more principled, but also more nuanced way of measuring one such institution: political patronage.1 I especially would like to thank Liu Mingxing for generously sharing data on the members of the 17th Central Committee and his insights into the data base in general, and Ma Xiao and Lu Fengming, as well
Crashworthy Design of Composite Structures by CAE Process Chain

The materials used in automotive industry should play a key role in overcoming the current challenging demands such as increased global competition, need for vehicles with highest efficiency, reduction in costs, stringent environmental and safety requirements. This eventual usage of lighter materials means lighter vehicles and low greenhouse gas emissions. Composites are gettingmore recognition and, hence, being used increasingly in the automotive industry due to their excellent weight specific characteristics such as strength and stiffness. Thus, the requirements for the simulations along the complete production process chain involving reinforced plastics have increased immensely. The main objective of this paper is to present a workflow for numerical modeling and simulation of carbon fiber reinforced plastic composite structures including computer-aided engineering process integration. In this regard, a computational constitutive model for anisotropic damage is developed to characterize the elastic-brittle behavior of fiberreinforced laminated composites. The presented work will introduce and discuss single steps along the process chain with in-house tools and the commercial finite element program LS-DYNA®.
Exploring Communication Between Skilled Musicians and Novices During Casual Jam Sessions

This paper explores the use of communication between skilled musicians and novices during casual jam sessions within a community of practice in order to gain insight into how design might be used to augment this communication and assist novice musicians during the process of playing collaboratively. After observation, color coded stickers were introduced as placemarkers, which aided in the communication of instruction. The learning that took place and the roles of skilled musicians and novices are considered, and design implications of these considerations are discussed.
Natural Program Synthesis from Examples in Haskell

We present a new programming-by-example technique that efficiently synthesizes natural, readable fitting functions that combine user-defined higher-order functions with standard and third-party library code. The search works by dismantling higher-order functions in order to deduce suitable refinement types. These refinement types are then used to prune the search space of possible higher-order functions for a given example set. Since refinement type under-approximate, we can apply Liquid Haskell to arbitrary syntax extensions while still preserving soundness. We evaluate an implementation of our tool against a large set of synthesis examples including lists, trees, maps, and specialized musical score data structures. This evaluation demonstrates the scalability and versatility of this approach.
Deep Learning with Spatial Constraint for Tunnel Crack Detection

Cracks are the most common defect on the surface of tunnels, which potentially brings threaten to the safety of the tunnel and the running vehicles. Timely repairing of the crack is of critical importance. In the past two decades, various vehicle platforms have been developed on the purpose of efficient crack detection and maintenance. With these platforms, images can be captured in a traffic speed, and automatic methods can be developed for fast crack localization. However, for image-based crack detection, traditional methods often meet difficulties in handling cracks with low contrast and poor continuity. In this paper, deep learning based techniques are exploited for feature learning and representation for crack detection. A novel deep neural network is presented for pixel-level crack recognition. Hierarchical features in different stages of the convolution are fused together to overcome the influence of noise and a spatial constraint placed on the target pixels is used to guarantee the crack continuity. In the experiment, a tunnel crack dataset is constructed for performance evaluation. Experimental results demonstrate the effectiveness of proposed method.
Half-Life α IIb β 3 Antagonist ANTP 266 Reduces Thrombus Formation

Integrin αIIbβ3 plays a pivotal role in platelet aggregation. Three αIIbβ3 antagonists have been approved by the Food and Drug Administration (FDA) for the treatment of cardiovascular diseases. Unfortunately, all of these three drugs can cause the side effect of severe bleeding. Therefore, developing a new αIIbβ3 antagonist with low bleeding was needed. In the present study, we screened compounds by using a fibrinogen/integrin αIIbβ3 enzyme-linked immunosorbent assay (ELISA), and a novel αIIbβ3 antagonist ANTP266 was attained. The antithrombotic effects of ANTP266 were estimated by using two animal models, the bleeding risk was estimated by using a mice tail cutting assay, and the plasma half-life time was tested by LC-MS/MS. The results showed that ANTP266 potently decreased thrombosis formation, while not prolonging bleeding time at its effective dosage. The bleeding of ANTP266 reduced rapidly as time went on from 5 to 60 min, but tirofiban produced high bleeding continuously. The plasma half-life of ANTP266 in rats was 10.8 min. Taken together, ANTP266 is an effective antithrombotic agent with a low bleeding risk. The shorter bleeding time benefits from its short plasma half-life. ANTP266 could be a candidate for developing the αIIbβ3 antagonist of rapid elimination for a patient undergoing percutaneous coronary intervention.
The Effects of Ranitidine in Chronic Heart Failure Patients

Objectives: To determine whether the blockade of histamine H 2 -receptors using ranitidine is beneficial for the pathophysiology of chronic heart failure (CHF). Background: Because the β 1 -adrenergic blocking agent is beneficial for the pathophysiology of CHF and because activated H 2 -receptors cause activation of Gs-protein the same way as activated β 1 -adrenergic receptors do, it appears reasonable that H 2 -receptor blocking agents may be beneficial for the treatment of CHF. Methods: 50 stable symptomatic CHF patients were randomly divided into 2 groups. One group received ranitidine 300 mg/day for 16 weeks, and the other group received placebo for the same period of time. Blood levels of NTproBNP and left ventricular function obtained by echocardiography were recorded both before and after treatment in both groups. Results: Ranitidine 300 mg/d increased plasma NTproBNP levels (from 2,131 ± 3,039 pg/ml to 2,811 ± 4,714 pg/ml) while placebo decreased plasma NTproBNP levels (from 1,710 ± 1,796 pg/ml to 1,485 ± 1,660 pg/ml) but without statistical significance (p = 0.262). Conclusions: Ranitidine did not decrease NTproBNP levels in CHF patients within a 16 weeks period.
Recovering Transitive Traceability Links among Various Software Artifacts for Developers

Traceability links between software artifacts can assist in several software development tasks. There are some automatic traceability recovery methods that help with managing the massive number of software artifacts and their relationships, but they do not work well for software artifacts whose descriptions are different in terms of language or abstraction level. To overcome these weakness, we propose the Connecting Links Method (CLM), which recovers transitive traceability links between two artifacts by intermediating a third artifact. In order to apply CLM for general use without limitation in terms of software artifact type, we have designed a standardized method to calculate the relation score of transitive traceability links using the scores of direct traceability links between three artifacts. Furthermore, we propose an improvement of CLM by considering software version. We evaluated CLM by applying it to three software products and found that it is more effective for software artifacts whose language type or vocabulary are different compared to previous methods using textual similarity. key words: traceability link recovery, transitive, connecting link, version
An Online Capacitance Modeling Tool for Conductors that may be Represented as Simply-Connected Polygonal Geometries in 2 . 5

We present an online software tool that quickly and accurately calculates the capacitance of conductors that may be represented as simply-connected polygonal geometries in 2.5D with Dirichlet boundary conditions. We achieve numerical accuracy of the tool by using SchwarzChristoffel mapping (SCM) which treats the fringing fields at vertices exactly. Our tool compares favorably in both accuracy and speed to the results found by 2.5D finite element analysis (FEA). For a test case, we model and simulate a MEMS cantilever model. We find that our tool computes capacitance about 1000 times faster than traditional FEA.
MINING EXPLOSION IDENTIFICATION AND DISCRIMINANT ASSESSMENT IN TWO UNIQUE REGIONS

With the contribution of data from high-quality seismic stations throughout the globe, the event detection threshold is reduced to smaller magnitudes. At magnitude 4 and below, delay-fired explosions are often commonly observed along with natural seismicity. The addition of this class within an event catalog leads to several questions which must be addressed: (1) can delay-fired mining explosions be discriminated from earthquakes; (2) can the understanding of mining discriminants illuminate the explosion discrimination process in regions where there are no known nuclear explosions; and (3) are mining discriminants regionally dependent, and are there corrections to account for this dependence? A broader question is in understanding the ability to separate source and propagation path effects from regional data. Addressing this question utilizing regional datasets has direct application to the problems noted above but more generally to our ability to interpret regional seismic records from all types of events including earthquakes. To address these types of questions, we have assembled a database focused on two regions, the Western United States (WUS) and the Altai-Sayan (AS) region of Russia, which are both areas of prolific mining activity. As part of an extensive collaboration with the largest coal mine in the WUS, we have detailed shot information for ~1000 mining events, classified into six distinct blast types; we have limited information for events in the AS.
Risk Factors for Community-Acquired Methicillin-Resistant Staphylococcus Aureus (CAMRSA) Infectors in Military Trainees: Review of an Outbreak in San Diego, California, 2002

Abstract : An outbreak of community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) skin infections was observed in a population of US military trainees in the summer of 2002. A questionnaire was developed and administered to 209 trainees, 22 of whom had MRSA infections. Factors associated with infection were described by multivariable logistic regression modeling, and included having a roommate in training with a prior skin infection (odds ratio [OR] = 3.44), or having a family member or friend who worked in a healthcare setting (OR = 2.79). Previous antibiotic use, hospitalization, or health problems were not associated with MRSA infection. This outbreak of MRSA skin infections in an otherwise healthy, well-defined, military population provided an opportunity to describe risk factors for CA-MRSA, which may help focus prevention efforts in this and other communities.
Efficient Planning for High-Speed MAV Flight in Unknown Environments Using Sparse Topological Graphs

Safe high-speed autonomous navigation for MAVs in unknown environments requires fast planning to enable the robot to adapt and react quickly to incoming information about obstacles within the world. Furthermore, when operating in environments not known a priori, the robot may make decisions that lead to dead ends, necessitating global replanning through a map of the environment. Many state-of-the-art approaches are limited to reduced velocities—precluding their usage in time-critical applications such as search and rescue—while those that do allow for high-speeds are myopic, restricting their success in large-scale, complex environments that require reasoning about the world outside of a local planning grid. This thesis proposes a computationally efficient planning architecture for safe high-speed operation in unknown environments that incorporates a notion of longerterm memory into the planner to enable the robot to accurately plan to locations no longer contained within the local map. A motion primitive based local receding horizon planner that uses a probabilistic collision avoidance methodology enables the robot to generate safe plans at fast replan rates. To provide global guidance, a sparse topological graph is created online from a time history of the robot’s path and a notion of visibility within the environment that may be searched to find alternate pathways towards the desired goal if a dead end is encountered. The safety and performance of the proposed planning system is evaluated at speeds up to 10m/s, and the approach is tested in a set of large-scale, complex simulation environments containing dead ends. These scenarios lead to failure cases for competing methods; however, the proposed approach enables the robot to safely reroute and reach the desired goal.
Multi-Criteria Agent Based Systems Generation of circulations through local decisions

This study explores to what extent Agent Based Systems (ABS) can handle multi-criteria optimization problems. The implementation of ABS in the field of optimization has limitations to address multiple criteria in a continuous generation process due to ABS usually merge the perceived information in a single response. To address this limitation, we increase the responsiveness of the systems through a multiple production approach. This approach breaks down the problem into two parts: the configuration through the interactions of the agents, and the overall performance through their local decisions. The method is tested in a case study of the network circulations of a park, optimizing the slope, views and sun. Performance and differentiation capabilities are evaluated in populations generated in two different scenarios. Data analysis methods verify the effectiveness of the algorithm and quantify the influence of each parameter on the final results.
Solutions of Large Problems of Computational Electromagnetics at Bilkent University †

We present our efforts to solve large numerical problems, which are obtained from mathematical formulations of real-life electromagnetic problems. For the iterative solutions of dense matrix equations resulting from the formulations by the surface integral equations, we utilize advances in both solution algorithms and computer hardware. We employ parallel multilevel fast multipole algorithm (MLFMA) on a cluster of relatively inexpensive processors connected via special fast networks. Since the parallelization of MLFMA is not trivial, we consider different strategies to obtain the solutions with high efficiency. Various preconditioning schemes are also investigated to increase convergence rates, especially for the solutions of ill-conditioned matrix equations that are obtained from the electric-field integral equation. 1. Surface Integral Equations There are three different surface integral equations to formulate the radiation and scattering problems [1]. These are the electric-field integral equation (EFIE), magnetic-field integral equation (MFIE), and combined-field integral equation (CFIE) defined as CFIE = αEFIE + (1− α)MFIE, (1) where α is a constant between 0 and 1. CFIE is usually preferred over EFIE and MFIE for closed surfaces mainly because it is free of the internal-resonance problem [2] and it generates better-conditioned matrix equations that are crucial for contemporary iterative solvers [3], such as multilevel fast multipole algorithm (MLFMA) [4]. For open surfaces, MFIE and thus CFIE are not applicable, leaving EFIE as the only choice. Unfortunately, EFIE usually produces ill-conditioned matrix equations that are difficult to solve iteratively. By the simultaneous discretization of integral equations and the geometry, we obtain N ×N matrix equations, i.e,
Canadian Tissue Residue Guidelines for the Protection of Wildlife Consumers of Aquatic Biota POLYCHLORINATED DIBENZO-P-DIOXINS AND POLYCHLORINATED DIBENZOFURANS ( PCDD / Fs )

Canadian Council of Ministers of the Environment, 2001 olychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs), commonly known as dioxins and furans, respectively, are planar tricyclic aromatic compounds (WHO 1989). Combined, there are 210 possible congeners, but the 17 congeners that have chlorine atoms substituted in at least the 2,3,7, and 8 lateral positions are believed to be the most toxic. PCDD/Fs are omnipresent in the air, soil, sediments, and biota even though they have never been intentionally produced and have no known use (WHO 1989; Fiedler et al. 1990). They are by-products formed as a result of anthropogenic activities, including waste incineration, chemical manufacturing, petroleum refining, wood burning, metallurgical processes, fuel combustion (automobiles), residential oil combustion, and electric power generation, among others. Today, municipal waste incineration is one of the most significant sources of PCDD/Fs to the Canadian environment (Environment Canada 1999). Historically, pulp and paper mill effluent released significant amounts of PCDD/Fs, but major reductions have occurred since the Pulp and Paper Regulations of 1992 (Halliburton and Simpson 1999). Natural sources include forest fires and volcanic activity (Hicks and McColl 1995).
Universal Lesion Detector : Deep Learning for Analysing Medical Scans

Accurate, automated lesion detection is an important yet challenging task due to the large variation of lesion types, sizes, locations and appearances. Most of the recent work only focuses on lesion detection in a constrained setting detecting lesions in a specific organ. We tackle the general problem of detecting lesions across the whole body and propose approaches which are not limited to a particular dataset. This is done by redesigning RetinaNet to be more applicable to medical imaging, using a general approach for optimising anchor configurations and by generating additional weak labels from the provided ground truth. We evaluate our approach on two different datasets a large public Computed Tomography (CT) dataset called DeepLesion, consisting of 32,735 lesions, and a much smaller whole-body Magnetic Resonance Imaging (MRI) dataset made up of only 213 scans. We show that our approach achieves state-of-the-art results, significantly outperforming the best reported methods by over 5% on the DeepLesion benchmark, while being generalisable enough to achieve excellent results on the much smaller whole-body MRI dataset.
Comparison of two summer crop mapping methods in the state of Paraná in 2008

The accuracy of crop mapping is still very dependent on the quality of processing remote sensing data, so the objective of this paper is to compare the accuracy of two different methodologies for mapping summer crops in the State of Paraná, Brazil. EVI (Enhanced Vegetation Index) images were used for this purpose, following these methodologies: classic mask generation by the difference in EVI this methodology were used in some studies such as (Schroeder et al. 1999; Labus et al. 2002; Jakubauskas et al, 2002;Fontana et al, 2007), and the RGB method, as used by Johann et al (2011). The quality of the methods was measured using higher spatial resolution images (LANDSAT and AWIFS / IRS) as a reference to calculate the Kappa Index (KI) (Congalton, 1991), Overall Accuracy(EG), omission (EO) and commission (EI) errors. With 400 random samples for each mask, an error matrix was drawn up. The results for the classic methodology presented values of: KI=0.78, EO=11% and EI=11%, while the RGB methodology presented a KI=0.895, EO=6.1% EI=5.9%. KI was 12.8% higher for the RGB methodology as EO and EI were 4.9% and 5.1% lower, respectively. The RGB method presented a greater accuracy potential than the conventional method.