Miracle-dz/UniFAR-FTUs huggingface dataset:
Microstructure and Mechanical Properties of Sputter-Deposited Ti-Ni Alloy Thin Films

The shape memory effect, superelasticity and two-way shape memory effect of Ni-rich Ti-Ni sputter-deposited thin films were evaluated quantitatively. The results indicated that they are almost comparable to those of bulk specimens. On the other hand, Ti-rich Ti-Ni thin films were found to exhibit a good shape memory behavior owing to a peculiar fine microstructure unlike bulk specimens. Addition of Cu or Pd to a Ti-50at. %Ni thin film was also confirmed to improve the shape memory behavior. These results suggest that shape memory thin films are a promising material for microactuators.
Deep pedestrian attribute recognition based on LSTM

Automatically recognizing attributes such as gender, age, footwear and clothing style from pedestrian images at far distance is an important task in surveillance scenarios. However, the appearance diversity and ambiguity in these images make it a challenging task. This paper presents an end-to-end Neural Pedestrian Attribute Recognition (Neural PAR) model to address these challenges. Rather than taking it as a recognition problem like previous methods, Neural PAR formulates it as an end-to-end image to attribute description problem. To this end, the training images and their corresponding attributes are used as inputs. Specifically, the attributes are concatenated into different attribute descriptions to well contextualize the potential relationships among them. Then, a neural network model is trained based on CNN and LSTM to learn the complex relations between visual features and their corresponding attributes. Extensive experiments show that the proposed Neural PAR significantly outperforms the state-of-the-art methods on the benchmark PETA dataset.
Semantic segmentation with multi-path refinement and pyramid pooling dilated-resnet

Recently, fully convolutional network (FCN) and dilated convolution have shown significantly improvment in semantic segmentation task. Deep residual network (ResNet) has shown strong ability in object recognition. However, FCN-based methods utilize intermediate layers and spatial context information ineffectively. Repeated 2-step striding in ResNet is harmful for segmentation tasks. In this paper, we propose a new semantic segmentation method based on FCN and ResNet. Here, we combine the dilated convolution designed for semantic segmentation with residual unit to enlarge receptive field of ResNet. Meanwhile, “pre-activation” method is used in dilated residual unit. We propose a new segmentation architecture which intergrate multiple intermediate layers and global context information. We extract low-level features of intermediate layers with multi-path refinement which consists of relu-conv unit and chained residual pooling. Global context is gathered by a pyramid pooling method which is connected to the final output of ResNet. Outputs of these 2 modules are then fused to reach high-resolution prediction. In this way, global and local information from pyramid pooling can be enhanced by multi-path refinement. Fully-connected conditional random field is added as a “post-processing” after fusion to receive accurate boundary performance. Our proposed approach achieves 74.7% IoU on Cityscapes benchmark.
Developing Biodegradable Plastics from starch

The diversity and ubiquity of plastic products substantially testify to the versatility of the special class of engineering materials known as polymers. However, the non-biodegradability of these petrochemical-based materials has been a source of environmental concerns and hence, the driving force in the search for ‘green’ alternatives for which starch remains the frontliner. Starch is a natural biopolymer consisting predominantly of two polymer types of glucose namely amylose and amylopectin. The advantages of starch for plastic production include its renewability, good oxygen barrier in the dry state, abundance, low cost and biodegradability. The longstanding quest of developing starch-based biodegradable plastics has witnessed the use of different starches in many forms such as native granular starch, modified starch, plasticized starch and in blends with many synthetic polymers, both biodegradable and non-biodegradable, for the purpose of achieving cost effectiveness and biodegradation respectively. In this regard, starch has been used as fillers in starch-filled polymer blends, thermoplastic starch (TPS) (produced from the combination of starch, plasticizer and thermomechanical energy), in the production of foamed starch and biodegradable synthetic polymer like polylactic acid (PLA) with varying results. However, most starch-based composites exhibit poor material properties such as tensile strength, yield strength, stiffness and elongation at break, and also poor moisture stability. This therefore warranted scientific inquiries towards improving the properties of these promising starch-based biocomposites through starch modification, use of compatibilizers and reinforcements (both organic and inorganic), processing conditions, all in the hope of realizing renewable biodegradable substitutes for the conventional plastics.
I3MS: Intelligent Multi-Sensor Multi-Baseline Mapping System

Recently, stereovision has been used in robotics for obtaining information for real-time mapping. Stereovision makes use of two cameras to solve the problems of a lack of discriminative image features and repetitive patterns and to extend the field of view. Stereovision algorithms and techniques are working to create a map to know its surrounding environment. Despite advances in the field of autonomous robotics, several challenges still exist. The majority of these challenges exist owing to the dynamic nature of the navigational environment and the lack of complete knowledge of new, unknown, and complex environments. In this paper, we create a novel, intelligent, and dynamic mapping system for autonomous navigation using stereovision. The proposed system is called intelligent multi-sensor multi-baseline mapping system (I3MS). The main contribution of this paper is the dynamic tuning of the stereovision parameters to address the dynamics of the environment (various light conditions, movement obstacles, and various times of day). The I3MS works in unstructured, unknown, dynamic, and indoor environments. It is based on an in-depth study of the effects of light and distance from obstacles on the accuracy of the 3-D map. It uses a fuzzy-logic system to tune the stereovision parameters to handle dynamic environments. The obtained experimental results illustrate the robustness and accuracy of the proposed system.
A Simple Method for the Reinforcement of UV‐Cured Coatings via Sol‐Gel Photopolymerization

A difunctional methacrylate oligomer was mixed with a variable amount of a MAPTMS precursor in the presence of both a radical and a cationic photoinitiator. The simultaneous photolysis of both photosensitive molecules upon UV irradiation allowed the single-step generation of a type-II polymethacrylate/polysiloxane nanocomposite film. Methacrylate and methoxysilyl conversions during irradiation were efficiently monitored by FTIR spectroscopy. The inorganic structure of the resulting silica-based hybrid films was characterized using 29 Si solid-state NMR. Finally, the reinforcement ability of the resulting hybrid films was also assessed by using a unique range of characterization techniques: DMA, scratch test, and nanoindentation.
Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples

Abstract Over the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn answer set programs. We present a sound and complete algorithm which takes the input in a slightly different manner and performs an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), which to the best of our knowledge was not previously possible. The system is publicly available at https://goo.gl/KdWAcV.
Environment representation for mobile robot localisation

An adequate representation of the environment is an essential component of a mobile robot navigation system. This paper reviews the techniques reported in the literature for capturing the geometry of the space surrounding a mobile robot. In particular, the use of distance functions that combine some of the advantages of feature based and occupancy grid based representations for mobile robot localisation is described in detail. The effectiveness of various distance function based representations is demonstrated using a number of practical examples for localising ground and air vehicles.
Assessing the social criteria for human-robot collaborative navigation: A comparison of human-aware navigation planners

This paper focuses on requirements for effective human robot collaboration in interactive navigation scenarios. We designed several use-cases where humans and robot had to move in the same environment that resemble canonical path-crossing situations. These use-cases include open as well as constrained spaces. Three different state-of-the-art humanaware navigation planners were used for planning the robot paths during all selected use-cases. We compare results of simulation experiments with these human-aware planners in terms of quality of generated trajectories together with discussion on capabilities and limitations of the planners. The results show that the human-robot collaborative planner [1] performs better in everyday path-crossing configurations. This suggests that the criteria used by the human-robot collaborative planner (safety, time-to-collision, directional-costs) are possible good measures for designing acceptable human-aware navigation planners. Consequently, we analyze the effects of these social criteria and draw perspectives on future evolution of human-aware navigation planning methods.
Genetic background-dependent role of Egr1 for eyelid development

Significance Eyelid formation begins at approximately day E15.5 in mice. Over the next 24 h, the epidermis of both upper and lower eyelids rapidly grows and merges to cover the cornea. Here, we demonstrate that Egr1−/− mice on the C57BL/6 background have normal eyelid development, but back-crossing to BALB/c background for four or five generations resulted in defective eyelid development by embryonic day E15.5. This defective eyelid formation was then further associated with profound ocular anomalies evident by postnatal days 1-4. The BALB/c albino phenotype associated with the Tyrc tyrosinase mutation also appeared to contribute to the phenotype. Thus EGR1 in a genetic background-dependent manner plays a critical role in mammalian eyelid development, with subsequent impact on ocular integrity. EGR1 is an early growth response zinc finger transcription factor with broad actions, including in differentiation, mitogenesis, tumor suppression, and neuronal plasticity. Here we demonstrate that Egr1−/− mice on the C57BL/6 background have normal eyelid development, but back-crossing to BALB/c background for four or five generations resulted in defective eyelid development by day E15.5, at which time EGR1 was expressed in eyelids of WT mice. Defective eyelid formation correlated with profound ocular anomalies evident by postnatal days 1–4, including severe cryptophthalmos, microphthalmia or anophthalmia, retinal dysplasia, keratitis, corneal neovascularization, cataracts, and calcification. The BALB/c albino phenotype-associated Tyrc tyrosinase mutation appeared to contribute to the phenotype, because crossing the independent Tyrc-2J allele to Egr1−/− C57BL/6 mice also produced ocular abnormalities, albeit less severe than those in Egr1−/− BALB/c mice. Thus EGR1, in a genetic background-dependent manner, plays a critical role in mammalian eyelid development and closure, with subsequent impact on ocular integrity.
Characterisation of nutraceutical compounds from different parts of particular species of Citrus sinensis ‘Ovale Calabrese’ by UHPLC-UV-ESI-HRMS

Abstract Consumers are aware of diet causing health problems and therefore there is an increased demand for natural ingredients that are expected to be safe and health-promoting. Many of these compounds belong to the class of flavonoids and can be divided into these five groups: flavanones, flavones, flavonols, flavanols, isoflavones and anthocyanidins. Extracts from citrus fruits are usually used as functional ingredients for several products. The aim of this paper was to develop an UHPLC-UV-ESI-HRMS method to define the metabolite profile of different parts of citrus fruit, of a particular cultivar called ‘Ovale Calabrese’, and in its main by-products. The high resolution mass spectrometry analysis allowed the identification of 27 compounds belonging to the classes of flavonoids and terpenoids. The high contents of phytochemical compounds, reveal the potential use of the ‘Ovale Calabrese’ as a rich source of nutraceutical compounds.
A load identification algorithm based on SVM

Load identification technique can identify the different types of loads in a power system. This paper presents a load identification algorithm based on support vector machine (SVM), which adopts the one-against-one method which combines multiple SVMs to build a multi-classifier to deal with load identification because SVM is only a binary-classifier. In this paper, the SVM-based multi-classifier is trained by extracted characteristic quantities normalized, during which the genetic algorithm (GA) is used to optimize the SVM parameters for the highest recognition accuracy. The experimental results show the validity of the SVM-based multi-classifier.
To measure or not to measure direct oral anticoagulants before surgery or invasive procedures

Patients on direct oral anticoagulants (DOAC) may need interruption of treatment before surgery or invasive procedures. Owing to their favorable pharmacokinetics, DOAC could be interrupted for a fixed number of days before surgery or invasive procedures without laboratory testing. However, there are a number of issues that raise concerns about the safety of this strategy. In contrast, laboratory testing prior to surgery or invasive procedures would provide a direct assessment of the residual drug concentration and minimize the risk of bleeding. This forum is aimed at discussing the pros and cons of the two strategies and fostering discussion on this important issue. Overall, the laboratory strategy appears superior in terms of patient safety and should be considered in patients undergoing surgical or invasive procedures.
What is current practice for upper limb rehabilitation in the acute hospital setting following stroke? A systematic review.

OBJECTIVE To investigate the amount of time and types of interventions used during therapy sessions in the acute setting following stroke. METHODS A systematic search of relevant databases was conducted. Studies were eligible if they were observational studies of adults with a confirmed diagnosis and within 4 weeks post-stroke; receiving Physiotherapy (PT) and/or Occupational Therapy (OT); and the outcome included amount of therapy time devoted to UL and/or types of interventions. Two authors independently selected studies for inclusion, assessed methodological quality and extracted data. This review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. RESULTS From the 94 studies reviewed, seven studies involving 3236 participants met the inclusion criteria. Pooled results indicated that 7.9 min/day (21.4%) of a total 36.7 min/day combined PT and OT session was devoted to UL therapy. Two of the seven studies reported types of interventions, predominantly upper limb activity and control. CONCLUSIONS A small proportion of total PT and OT time is directed to the involved UL during acute rehabilitation. Given the evidence for early and intense rehabilitation, there is a need to explore the reasons for this low intensity of UL therapy in the acute stage post-stroke.
Feeding of phytosterols reduced testosterone production by modulating GnRH and GnIH expression in the brain and testes of male Japanese quail (Coturnix coturnix japonica)

Abstract Phytosterols (PS), or plant sterols used as cholesterol‐lowering agents, have been shown to act as endocrine‐disrupting chemicals in some laboratory animals. Moreover, dietary PS efficiently pass through the blood‐brain barrier and accumulate in brain cell membranes. We asked whether the accumulation of PS affects reproduction through the hypothalamic‐pituitary‐gonadal axis. Thirty male quail chicks were randomly divided into 3 groups (control, 80 mg/kg BW, and 800 mg/kg BW), and daily single doses of PS or vehicle were gavaged into the crop sac from 15 to 100 d of age. At the end of the entire period, half of each group was injected intramuscularly with either 10 &mgr;g of chicken gonadotropin‐releasing hormone 1 (cGnRH‐1) or phosphate‐buffered saline solution (PBS) as the vehicle. Blood was collected before and 30 min after cGnRH‐1 challenge by jugular venipuncture and decapitation, respectively. The results indicated that testosterone concentrations were low (P < 0.05) before (800 mg/kg BW) and after GnRH challenge in PS‐treated quails compared with controls (P < 0.001). However, luteinizing hormone (LH) levels were not different among the groups before cGnRH‐1 challenge. In addition, PS‐gavaged animals failed to manifest increased LH levels after cGnRH‐1 injection (P < 0.01). The same trends were observed in pituitary LH levels at 800 mg/kg BW PS after cGnRH‐1 injection (P < 0.05). Real‐time PCR results revealed that PS (800 mg/kg BW) feeding reduced expression of GnRH‐1 in the brain and testes compared to controls. However, gonadotropin‐inhibitory hormone (GnIH) expression was significantly elevated before and after GnRH‐1 challenges in the brain and testes. Collectively, these results suggest that brain‐mediated effects of PS on gonadal function occurs via the induction of GnIH gene expression, and these indirect effects are less potent than direct effects.
Defibrotide sodium for the treatment of hepatic veno-occlusive disease/sinusoidal obstruction syndrome

ABSTRACT Introduction: Hepatic veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS) is an unpredictable condition associated with endothelial-cell damage due to conditioning for hematopoietic stem-cell transplantation (HSCT) or chemotherapy without HSCT. Mortality in patients with VOD/SOS and multi-organ dysfunction (MOD) may be >80%. Areas covered: Defibrotide is the only approved drug for the treatment of severe hepatic VOD/SOS after HSCT in the European Union and hepatic VOD/SOS with renal or pulmonary dysfunction in the United States. Its efficacy in patients with VOD/SOS with MOD post-HSCT was demonstrated in a clinical-trial program that included a historically controlled treatment study, a phase 2 trial, and a large T-IND expanded-access program that also included patients without MOD and who received chemotherapy without HSCT. Expert commentary: Defibrotide appears to protect endothelial cells and restore the thrombolytic–fibrinolytic balance. It addresses a significant clinical need and has demonstrated favorable Day +100 survival and overall adverse-event rates that seem similar to control groups receiving supportive care alone. Currently, defibrotide is under investigation for the prevention of VOD/SOS in high-risk pediatric and adult patients.
Highly Sensitive and Specific Detection of Rare Variants in Mixed Viral Populations from Massively Parallel Sequence Data

Viruses diversify over time within hosts, often undercutting the effectiveness of host defenses and therapeutic interventions. To design successful vaccines and therapeutics, it is critical to better understand viral diversification, including comprehensively characterizing the genetic variants in viral intra-host populations and modeling changes from transmission through the course of infection. Massively parallel sequencing technologies can overcome the cost constraints of older sequencing methods and obtain the high sequence coverage needed to detect rare genetic variants (<1%) within an infected host, and to assay variants without prior knowledge. Critical to interpreting deep sequence data sets is the ability to distinguish biological variants from process errors with high sensitivity and specificity. To address this challenge, we describe V-Phaser, an algorithm able to recognize rare biological variants in mixed populations. V-Phaser uses covariation (i.e. phasing) between observed variants to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates base quality scores to increase specificity. Overall, V-Phaser achieved >97% sensitivity and >97% specificity on control read sets. On data derived from a patient after four years of HIV-1 infection, V-Phaser detected 2,015 variants across the ∼10 kb genome, including 603 rare variants (<1% frequency) detected only using phase information. V-Phaser identified variants at frequencies down to 0.2%, comparable to the detection threshold of allele-specific PCR, a method that requires prior knowledge of the variants. The high sensitivity and specificity of V-Phaser enables identifying and tracking changes in low frequency variants in mixed populations such as RNA viruses.
Actuator, sensor and MEMS devices based on regenerated cellulose

In our early work, cellulose has been discovered as a smart material that can be used as sensors, actuators and smart devices. This newly discovered material is described as electro-active paper (EAPap) and has many advantageous properties: lightweight, flexible, dryness, biodegradable, easy to chemically modify, cheapness and abundance. The actuation principle of cellulose EAPap is based on a combination of piezoelectric and ion-migration effect. This paper reports the fabrication of various micro-patterns and structures, such as rectifying antenna and inter-digital transducer on regenerated cellulose film by adopting a micro-transfer printing technique. Fabrication steps are briefly discussed herein and performance of the actuators was evaluated by means of a tip displacement test. Further, potential application of cellulose as humidity sensor was demonstrated by measuring the impedance change on the inter-digital transducer on cellulose film at different humidity levels.
h 1 h 2 h 3 Layer 1 : Patches Layer 2 : Dielectric Layer 3 : Metallic Coupling strips Layer 4 : Dielectric Layer 5 : Ground Plane Layer 6 : Dielectric Layer 7 : Feeding Network

Massive MIMO has attracted significant interests in both academia and industry. It has been considered as one of most promising technologies for 5G wireless systems. The largescale antenna array for base stations naturally becomes the key to deploy the Massive MIMO technologies. In this paper, we present a dual-polarized antenna array with 144 ports for Massive MIMO operating at 3.7GHz. The proposed array consists of 18 low profile sub-arrays. Each sub-array consists of 4 single units. Each single antenna unit consists of one vertically polarized port and one horizontally polarized port connected to power splitters, which serve as a feeding network. A stacked patch design is used to construct the single unit with the feeding network, which gives higher gain and lower mutual coupling within the size of a conversional dual-port patch antenna. Simulation results of the proposed single antenna unit, sub-array and Massive MIMO array are verified by measurement.