Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissimilar friction-stir welding of 430 stainless steel and 6061 aluminum alloy: Microstructure and mechanical properties of the joints

The effect of friction-stir welding parameters on the microstructure and the mechanical properties of the dissimilar 430 stainless steel and 6061 aluminum alloy joints were investigated. Optical and scanning electron microscopes in conjunction with energy dispersive X-ray analysis were employed to study the microstructure of the joints. Tensile and microhardness tests were used to evaluate the mechanical properties. The results showed that the best appearance quality was achieved at a rotational speed of 900 r/min, a traverse speed of 120 mm/min, and a tool offset of zero. The tool offset was the most effective parameter affecting the weld quality. The stir zone of the joints had a composite structure in which the dispatched steel particles were distributed in aluminum. The best interface quality belonged to the joints welded at an offset of zero, which had a serrated nature with mechanical locking of the dissimilar parts. However, at negative and positive values of offsets, formation of voids and microcracks reduced the tensile properties of the joints. The tensile fracture of the joints occurred in the heat affected zone of the aluminum part, which had the lowest hardness amount between the microstructural zones. The fracture surfaces of the tensile specimens showed bimodal behavior.
Hierarchical least squares identification methods for multivariable systems

For multivariable discrete-time systems described by transfer matrices, we develop a hierarchical least squares iterative (HLSI) algorithm and a hierarchical least squares (HLS) algorithm based on a hierarchical identification principle. We show that the parameter estimation error given by the HLSI algorithm converges to zero for the deterministic cases, and that the parameter estimates by the HLS algorithm consistently converge to the true parameters for the stochastic cases. The algorithms proposed have significant computational advantage over existing identification algorithms. Finally, we test the proposed algorithms on an example and show their effectiveness.
Mental health research in HIV/AIDS and aging: problems and prospects.

The National Institute of Mental Health workshop , convened in April 2002 (‘Mental Health Research Issues in HIV/AIDS and Aging’), formed the basis of this supplement. The workshop revolved around two strategic issues to help identify the specific areas of opportunity described in detail below: (i) Do older adults infected with HIV differ from older uninfected adults or younger HIV-infected adults on a variety of factors related to HIV disease, such as natural course, risk factors, HIV-related comorbidities, neurocognitive, neuropsychiatric and substance abuse complications, neuropathogenesis and response to treatment? (ii) What is the nature of the relationships and the interactions of HIV infection with the aging process or with ageassociated diseases of the central nervous system (CNS) on the HIV-related variables of interest?
Implementation of the Arabic Numerals and their Syntax in GF

The numeral system of Arabic is rich in its morphosyntactic variety yet suffers from the lack of a good computational resource that describes it in a reusable way. This implies that applications that require the use of rules of the Arabic numeral system have to either reimplement them each time, which implies wasted resources, or use simplified, imprecise rules that result in low quality applications. A solution has been devised with Grammatical Framework (GF) to use language constructs and grammars as libraries that can be written once and reused in various applications. In this paper, we describe our implementation of the Arabic numeral system, as an example of a bigger implementation of a grammar library for Arabic. We show that users can reuse our system by accessing a simple language-independent API rule.
Inhibition of metalloproteinase and proteasome activities in colon cancer cells by citrus peel extracts

Abstract Background: Citrus peels are consumed in the form of infusions, candy or wine, based on their well-documented nutritional and medicinal properties. This study sought to investigate the effect of some citrus peels’ [grapefruit (Citrus paradisii), orange (Citrus sinensis) and shaddock (Citrus maxima)] extracts on matrix metalloproteinase (MMP) and proteasome activities in primary human colonic tumor (Caco-2) and the metastatic cell lines (LoVo and LoVo/ADR) in a bid to explain the possible mechanism by which the peels could manage/prevent colon cancer. Methods: The inhibition of MMP and proteasome activities in the cells by the peel extracts, as well as the identification of phenolic compounds using high-performance liquid chromatography with diode-array detection (HPLC-DAD), was determined. Results: Orange peel extracts had the strongest inhibition of MMP in Caco-2 and LoVo cells, while shaddock had the least. Shaddock peel extracts also had the least MMP inhibition in LoVo/ADR lysates. Grapefruit had the least proteasome inhibition in Caco-2 and LoVo lysates, while there was no significant (p>0.05) difference in the proteasome inhibition of the peel extracts in LoVo/ADR lysates. The extracts inhibited proteasome activity in extract-treated cells, and HPLC fingerprinting of the extracts revealed the presence of some phenolic compounds such as quercetin, caffeic acid, kaempferol, catechin and naringin. Conclusions: The inhibition of MMP and proteasome activities in colon cancer cell lines suggests the potential use of citrus peels as functional food in the management and/or prevention of colon cancer.
Cholesteryl Ester Transfer Protein Inhibition With Anacetrapib Decreases Fractional Clearance Rates of High-Density Lipoprotein Apolipoprotein A-I and Plasma Cholesteryl Ester Transfer Protein

Objective—Anacetrapib (ANA), an inhibitor of cholesteryl ester transfer protein (CETP) activity, increases plasma concentrations of high-density lipoprotein cholesterol (HDL-C), apolipoprotein A-I (apoA)-I, apoA-II, and CETP. The mechanisms responsible for these treatment-related increases in apolipoproteins and plasma CETP are unknown. We performed a randomized, placebo (PBO)-controlled, double-blind, fixed-sequence study to examine the effects of ANA on the metabolism of HDL apoA-I and apoA-II and plasma CETP. Approach and Results—Twenty-nine participants received atorvastatin (ATV) 20 mg/d plus PBO for 4 weeks, followed by ATV plus ANA 100 mg/d for 8 weeks (ATV-ANA). Ten participants received double PBO for 4 weeks followed by PBO plus ANA for 8 weeks (PBO-ANA). At the end of each treatment, we examined the kinetics of HDL apoA-I, HDL apoA-II, and plasma CETP after D3-leucine administration as well as 2D gel analysis of HDL subspecies. In the combined ATV-ANA and PBO-ANA groups, ANA treatment increased plasma HDL-C (63.0%; P<0.001) and apoA-I levels (29.5%; P<0.001). These increases were associated with reductions in HDL apoA-I fractional clearance rate (18.2%; P=0.002) without changes in production rate. Although the apoA-II levels increased by 12.6% (P<0.001), we could not discern significant changes in either apoA-II fractional clearance rate or production rate. CETP levels increased 102% (P<0.001) on ANA because of a significant reduction in the fractional clearance rate of CETP (57.6%, P<0.001) with no change in CETP production rate. Conclusions—ANA treatment increases HDL apoA-I and CETP levels by decreasing the fractional clearance rate of each protein.
Multi-perspective enterprise modeling (MEMO) conceptual framework and modeling languages

For many companies, the strategic as well as the organizational fit of their information systems is a pivotal factor for staying competitive. At the same time, there is an increasing demand for integrating business processes and informations systems with those of customers and suppliers. The resulting need for organizational changes and the introduction of corresponding information systems is a challenging task. The complexity of the task requires a separation of concerns. At the same time it causes language barriers between various stakeholders, especially between business people and information technology professionals. Enterprise models provide various abstractions that help with the design of corporate information systems which are in line with a company's organization and its long term strategy. They also promise to provide a common conceptual foundation to foster the communication between people with different professional backgrounds. In this paper we introduce a model for enterprise modelling that is based on an extendable set of special purpose modeling languages, e.g. for describing corporate strategies, business processes, resources or information. The visual languages provide intuitive abstractions for various observers. The languages are defined in metamodels which in turn are specified through a common meta-metamodel. Similar to a specialized technical language, they provide concepts that help with structuring and analyzing a domain according to specific objectives. Since the languages are specified in a semi formal way, the models allow for the generation of software prototypes. The languages share common concepts which allow for a tight integration of the various parts of an enterprise model. In addition to offering specialized modeling languages, the modeling method also includes examples, case studies and reference models - to promote the re-use of concepts and artefacts. The use of the method is illustrated by an example, where two different partial models are being integrated.
Energy Management for Real-Time Embedded Systems with Reliability Requirements

With the continued scaling of CMOS technologies and reduced design margins, the reliability concerns induced by transient faults have become prominent. Moreover, the popular energy management technique dynamic voltage and frequency scaling (DVFS) has been shown to have direct and negative effects on reliability. In this work, for a set of real-time tasks, we focus on the slack allocation problem to minimize their energy consumption while preserving the overall system reliability. Building on our previous findings for a single real-time application where a recovery task was used to preserve reliability, we identify the problem of reliability-aware energy management for multiple tasks as NP-hard and propose two polynomial-time heuristic schemes. We also investigate the effects of on-chip/off-chip workload decomposition on energy management, by considering a generalized power model. Simulation results show that ordinary energy management schemes could lead to drastically decreased system reliability, while the proposed reliability-aware heuristic schemes are able to preserve the system reliability and obtain significant energy savings at the same time
Towards Automatic Structured Web Data Extraction System

Automatic extraction of structured data from web pages is one of the key challenges for the Web search engines to advance into the more expressive semantic level. Here we propose a novel data extraction method, called ClustVX. It exploits visual as well as structural features of web page elements to group them into semantically similar clusters. Resulting clusters reflect the page structure and are used to derive data extraction rules. The preliminary evaluation results of ClustVX system on three public benchmark datasets demonstrate a high efficiency and indicate a need for a much bigger up-to-date benchmark data set that reflects contemporary WEB 2.0 web pages.
Nanocellulose as Novel Supportive Functional Material for Growth and Development of Cells

The exploration of biological polymer is a new interest due to its favorable properties used in biomedical and clinical applications. Within the used biopolymer, cellulose emerged as a most exploitable functional material at nanoscale. Working with nanosize cellulose, provides some additional advantages compare to other manmade functional polymers. The fabrication of cellulosic scaffold as a platform for growth and development of cells is a thrust area of tissue engineering but this fabricated scaffold needs to have some fundamental prerequisite before implantation in donor. Thus, this short report discusses about the NC based scaffolds for the growth and development of cells and tissue. The main focus is on derived wood and microbial NCs. Thereafter; some interesting examples will be discussed to understand the necessity of cellulose-based supports in tissue engineering field.
White Matter Hyperintensities and Medication Adherence

White matter hyperintensities (WMH) are associated with hypertension, age, and cognitive function, but the association between WMH and medication adherence has not been examined. The intent of this investigation was to consider the potential implications of hypertension-related brain morphological changes on medication adherence and thereby improve understanding of the self-management consequences of hypertension. The associations between WMH, blood pressure, age, cognitive function (specifically assessments of prefrontal function), and medication adherence were examined in 16 middle-aged and older adults self-managing at least one prescribed antihypertensive agent. Magnetic resonance imaging using an axial fluid attenuated inversion recovery (FLAIR) sequence was used to assess the presence of WMH. Cognitive assessments included measures of executive function, working memory, attention, and immediate recall. Adherence was monitored for 8 weeks using electronic medication monitoring. More WMH were associated with poorer adherence (rs = —.25) and with higher systolic blood pressure (rs = .46), although these relationships were not statistically significant. WMH were associated with cognitive assessments in the expected direction including Digit Span Backward (rs = —.53, p < .05). Adherence was associated with immediate memory (rs = .54, p < .05) and inversely associated with failure to maintain set on the Wisconsin Card Sorting Test (WCST; r s = —.61, p < .05). These findings provide preliminary evidence for the association between WMH, assessments of prefrontal function, and medication adherence.
A Modular Cloning System for Standardized Assembly of Multigene Constructs

The field of synthetic biology promises to revolutionize biotechnology through the design of organisms with novel phenotypes useful for medicine, agriculture and industry. However, a limiting factor is the ability of current methods to assemble complex DNA molecules encoding multiple genetic elements in various predefined arrangements. We present here a hierarchical modular cloning system that allows the creation at will and with high efficiency of any eukaryotic multigene construct, starting from libraries of defined and validated basic modules containing regulatory and coding sequences. This system is based on the ability of type IIS restriction enzymes to assemble multiple DNA fragments in a defined linear order. We constructed a 33 kb DNA molecule containing 11 transcription units made from 44 individual basic modules in only three successive cloning steps. This modular cloning (MoClo) system can be readily automated and will be extremely useful for applications such as gene stacking and metabolic engineering.
Calibration of Material Model for Trabecular Bone Verified by Time-Lapse X-Ray Microtomography

The paper deals with investigation of the trabecular bone behaviour during compression test. Paper describes loading test of trabecular bone specimen using real-time Computer Tomography scanning and its simulation by Finite Element Method. Incremental loading was applied during the experiment. The tomographic images were acquired for each load step and the applied force was recorded. Model for the Finite Element (FE) simulation was obtained using the reconstruction from tomographic projections of the zero deformation state. Deformation states were reconstructed to assess the three-dimensional strain field in the microstructure and compared with results from FE simulations. For the Finite Element analysis (FEA) a proper material model is needed. Assessment of the material model was based on nanoindentation experiment. The nanoindentation test of the trabecular bone sample was performed and the material model was identified from FE simulation of the nanoindentation test. Material model with von Mises yield criterion and bilinear isotropic hardening was chosen. Required material constants were obtained from fitting the load-displacement curve using the least squares method. Remaining constants were directly identified from the experiment. The deformation behaviour of the FE model during the compression test was compared to the experimentally obtained response to verify the FE model.
Saliency Map Generation by the Convolutional Neural Network for Real-Time Traffic Light Detection Using Template Matching

A critical issue in autonomous vehicle navigation and advanced driver assistance systems (ADAS) is the accurate real-time detection of traffic lights. Typically, vision-based sensors are used to detect the traffic light. However, the detection of traffic lights using computer vision, image processing, and learning algorithms is not trivial. The challenges include appearance variations, illumination variations, and reduced appearance information in low illumination conditions. To address these challenges, we present a visual camera-based real-time traffic light detection algorithm, where we identify the spatially constrained region-of-interest in the image containing the traffic light. Given, the identified region-of-interest, we achieve high traffic light detection accuracy with few false positives, even in adverse environments. To perform robust traffic light detection in varying conditions with few false positives, the proposed algorithm consists of two steps, an offline saliency map generation and a real-time traffic light detection. In the offline step, a convolutional neural network, i.e., a deep learning framework, detects and recognizes the traffic lights in the image using region-of-interest information provided by an onboard GPS sensor. The detected traffic light information is then used to generate the saliency maps with a modified multidimensional density-based spatial clustering of applications with noise (M-DBSCAN) algorithm. The generated saliency maps are indexed using the vehicle GPS information. In the real-time step, traffic lights are detected by retrieving relevant saliency maps and performing template matching by using colour information. The proposed algorithm is validated with the datasets acquired in varying conditions and different countries, e.g., USA, Japan, and France. The experimental results report a high detection accuracy with negligible false positives under varied illumination conditions. More importantly, an average computational time of 10 ms/frame is achieved. A detailed parameter analysis is conducted and the observations are summarized and reported in this paper.
Computation using mismatch: Neuromorphic extreme learning machines

In this paper, we describe a low power neuromorphic machine learner that utilizes device mismatch prevalent in today's VLSI processes to perform a significant part of the computation while a digital back end enables precision in the final output. The particular machine learning algorithm we use is extreme learning machine (ELM). Mismatch in silicon spiking neurons and synapses are used to perform the vector-matrix multiplication (VMM) that forms the first stage of this classifier and is the most computationally intensive. System simulations are presented to evaluate the dependence of performance (in a classification and a regression task) on analog and digital parameters like weight resolution, maximum spike frequency etc. SPICE simulations show that the proposed implementation is ≈ 92X more energy efficient as opposed to custom digital implementations for a classification task with 100 dimensional inputs. Measurement results for a regression task from a field programmable analog array (FPAA) fabricated in 0. 35μm CMOS are presented as a proof of concept.
Atomistic modeling of fracture

Atomistic modeling of fracture is intended to illuminate the complex response of atoms in the very high stressed region just ahead of a sharp crack. Accurate modeling of the atomic scale fracture is crucial for describing the intrinsic nature of a material (intrinsic ductility/brittleness), chemical effects in the crack-tip vicinity, the crack interaction with different defects in solids such as grain boundaries, solutes, precipitates, dislocations, voids, etc. Here, different methods for atomistic modeling of fracture are compared in their ability to obtain quantitatively useful results that are in agreement with the basic principles of linear elastic fracture mechanics (LEFM). We demonstrate that the complicated atomic crack-tip behavior is precisely described in simulations of semi-infinite cracks, where the loading is uniquely controlled by the applied stress intensity factor K. Such ‘K-test’ simulations are shown to be equally applicable in crystalline and amorphous materials, and to be suitable for quantitative evaluation of various critical stress intensity factors, the overall material fracture toughness, and quantitative comparison with theories. We further demonstrate that the simulation of a nanoscale center-crack tension (CCT) specimen often leads to the results that do not satisfy the conditions for application of LEFM. The simulated intrinsic fracture toughness, one of the basic material properties, using CCT test geometry is shown to be dependent on the crack size and far-field loading. In general, this study resolves quantitative differences between several methods for atomistic modeling of fracture and recommends that application of simulations based on nanoscale finite size cracks not be pursued.