Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on the Fabrication of Hierarchical ZnO Nanostructures for Photocatalysis Application

Semiconductor photocatalysis provides potential solutions for many energy and environmental-related issues. Recently, various semiconductors with hierarchical nanostructures have been fabricated to achieve efficient photocatalysts owing to their multiple advantages, such as high surface area, porous structures, as well as enhanced light harvesting. ZnO has been widely investigated and considered as the most promising alternative photocatalyst to TiO2. Herein, we present a review on the fabrication methods, growth mechanisms and photocatalytic applications of hierarchical ZnO nanostructures. Various synthetic strategies and growth mechanisms, including multistep sequential growth routes, template-based synthesis, template-free self-organization and precursor or self-templating strategies, are highlighted. In addition, the fabrication of multicomponent ZnO-based nanocomposites with hierarchical structures is also included. Finally, the application of hierarchical ZnO nanostructures and nanocomposites in typical photocatalytic reactions, such as pollutant degradation and H2 evolution, is reviewed.
Synthesis and biological evaluation of potential acetylcholinesterase inhibitors based on a benzoxazine core

With the purpose of expanding the structural variety of chemical compounds available as pharmacological tools for the treatment of Alzheimer's disease, we synthesized and evaluated a novel series of indole‐benzoxazinones (Family I) and benzoxazine‐arylpiperazine derivatives (Family II) for potential human acetylcholinesterase (hAChE) inhibitory properties. The most active compounds 7a and 7d demonstrated effective inhibitory profiles with Ki values of 20.3 ± 0.9 μM and 20.2 ± 0.9 μM, respectively. Kinetic inhibition assays showed non‐competitive inhibition of AChE by the tested compounds. According to our docking studies, the most active compounds from both series (Families I and II) showed a binding mode similar to donepezil and interact with the same residues.
Road-illuminance level inference across road networks based on Bayesian analysis

This paper proposes a road-illuminance level inference method based on the naive Bayesian analysis. We investigate quantities and types of road lights and landmarks with a large set of roads in real environments and reorganize them into two safety classes, safe or unsafe, with seven road attributes. Then we carry out data learning using three types of datasets according to different groups of the road attributes. Experimental results demonstrate that the proposed method successfully classifies a set of roads with seven attributes into safe ones and unsafe ones with the accuracy of more than 85%, which is superior to other machine-learning based methods and a manual-based method.
The Design and Implementation of Scalable Deep Neural Network Accelerator Cores

Due to the recent advances in Deep Neural Network (DNN) technologies, recognition and inference applications are expected to run on mobile embeddedsystems. Developing high-performance and power-efficient DNN engines becomesone of the important challenges for embedded systems. Since DNN algorithms orstructures are frequently updated, flexibility and performance scalability todeal with various types of networks are crucial requirement of the DNNaccelerator design. In this paper, we describe the architecture and LSI designof a flexible and scalable CNN accelerator called SNACC (Scalable NeuroAccelerator Core with Cubic integration) which consists of several processingcores, on-chip memory modules, and ThruChip Interface (TCI). We evaluate thescalability of SNACC with detailed simulation varying the number of cores andoff-chip memory access bandwidth. The results show that the energy efficiency of the accelerator becomes the highest in eight cores configuration with500MB/s off-chip bandwidth.
Optimizing flow setup time in software defined network

To address the issues of scalability in Software Defined Network (SDN), researchers have proposed a distributed control plane, where controllers are physically distributed but logically centralized. Network traffic is dynamic with respect to time and space, however, the key limitation of state-of-art work is that they make the unrealistic assumption that the traffic matrix is static. In this work, we assume that the network traffic is dynamic and to optimize the load on the SDN controller, we propose adaptive mapping between switches and the controller. The objective is to minimize the flow setup time as well as reduce the cost of reassignment.
Initial Evaluations of LoC Prediction Algorithms Using the NASA Vertical Motion Simulator

Flying near the edge of the safe operating envelope is an inherently unsafe proposition. Edge of the envelope here implies that small changes or disturbances in system state or system dynamics can take the system out of the safe envelope in a short time and could result in loss-of-control events. This study evaluated approaches to predicting loss-of-control safety margins as the aircraft gets closer to the edge of the safe operating envelope. The goal of the approach is to provide the pilot aural, visual, and tactile cues focused on maintaining the pilot's control action within predicted loss-of-control boundaries. Our predictive architecture combines quantitative loss-of-control boundaries, an adaptive prediction method to estimate in real-time Markov model parameters and associated stability margins, and a real-time data-based predictive control margins estimation algorithm. The combined architecture is applied to a nonlinear transport class aircraft. Evaluations of various feedback cues using both test and commercial pilots in the NASA Ames Vertical Motion-base Simulator (VMS) were conducted in the summer of 2013. The paper presents results of this evaluation focused on effectiveness of these approaches and the cues in preventing the pilots from entering a loss-of-control event.
Separable covariance models for health care quality measures across years and topics

Public quality reports for Medicare Advantage health plans include 11 measures of patient experiences reported in the annual Consumer Assessment of Healthcare Providers and Systems surveys. Computing summaries at the health plan level (of multiple measures in multiple years) yields an array‐structured random variable. To summarize associations among measures and years, we model the variance‐covariance matrix governing the plan‐level vectors of yearly quality measures as a Kronecker product of an across‐measure matrix and an across‐year matrix, or a sum of such Kronecker products. This approach extends separable covariance structure to Fay‐Herriot models. In addition, we develop linear combinations of Kronecker products similar to principal components for array random variables. To each Kronecker‐product term, we apply post hoc analyses suited to the corresponding dimension of the cross‐classification: 1‐way factor analysis for the across‐measure factor and time‐series analysis to the across‐year factor. These methods draw out key patterns of variation in the quality measures over time and suggest new strategies for reporting quality information to consumers.
Specifying Functional Requirements and QoS Parameters for IoT Systems

The Internet of Things (IoT) has emerged as a new paradigm aimed at providing systems for monitoring and controlling of physical entities. As with other application domains, the ultimate goal of an IoT system is to address the requirements elicited from its various stakeholders. However, the specification of requirements for IoT systems is a non-trivial task. First, there is a reasonable potential for conflicts across stakeholders' requirements. Second, regarding the multi-layered architecture of IoT, the specification of requirements of lower layers may influence on the requirements specification regarding upper layers. Without support for conceiving a precise requirements model serving as the basis for the IoT system design, there is a considerable risk that the stakeholders' concerns will not be addressed properly. The objective of this study is to investigate how multiple stakeholders' requirements can be precisely specified in a requirements model, considering conflicts and influences. In this sense, we propose a modeling language, named as IoT-RML, which provide means for conceiving requirements models for IoT systems.
Optimum location of external markers using feature selection algorithms for real‐time tumor tracking in external‐beam radiotherapy: a virtual phantom study

In external‐beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation‐based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two “Genetic” and “Ranker” searching procedures. The performance of these algorithms has been evaluated using four‐dimensional extended cardiac‐torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro‐fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F‐test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation‐based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers. PACS numbers: 87.55. km, 87.56. Fc
MicroRNA-135a is involved in podocyte injury in a transient receptor potential channel 1-dependent manner

Transient receptor potential (TRP) cation channels are essential for normal cellular physiology, and their abnormal expression may lead to a number of disorders, including podocytopathy. Therefore, it is crucial to understand the mechanisms underlying the regulation of TRP channels. In the present study, microRNA (miR)-135a was found to be upregulated in patients with focal segmental glomerulosclerosis and mice treated with adriamycin (ADR). In cultured podocytes, transforming growth factor (TGF)-β and ADR were found to promote miR-135a expression. Conversely, TRP channel 1 (TRPC1) protein levels were markedly downregulated in podocytes from mice treated with ADR, as well as in cultured podocytes treated with ADR and TGF-β. Ectopic expression of miR-135a led to severe podocyte injury and disarray of the podocyte cytoskeleton, whereas podocyte-specific expression of TRPC1 was able to reverse the pathological effects of miR-135a in cultured podocytes. Moreover, using Luciferase reporter assays and western blot analysis, TRPC1 was identified as a target gene of miR-135a. To the best of our knowledge, this is the first study to demonstrate the role of TRPC1 in the development of podocyte injury and disorders of the podocyte cytoskeleton, which may contribute to the development of novel therapeutics for podocyte injury-associated kidney diseases.
Development and validation of an automated algorithm to evaluate the abundance of bubbles in small bowel capsule endoscopy

Abstract Background and study aims Bubbles can impair visualization of the small bowel (SB) mucosa during capsule endoscopy (CE). We aimed to develop and validate a computed algorithm that would allow evaluation of the abundance of bubbles in SB-CE still frames. Patients and methods Two sets of 200 SB-CE normal still frames were created. Two experienced SB-CE readers analyzed both sets of images twice, in a random order. Each still frame was categorized as presenting with < 10 % or ≥ 10 % of bubbles. Reproducibility (κ), sensitivity (Se), specificity (Sp), receiver operating characteristic curve, and calculation time were measured for different algorithms (Grey-level of co-occurrence matrix [GLCM], fractal dimension, Hough transform, and speeded-up robust features [SURF]) using the experts’ analysis as reference. Algorithms with highest reproducibility, Se and Sp were then selected for a validation step on the second set of frames. Criteria for validation were κ = 1, Se ≥ 90 %, Sp ≥ 85 %, and a calculation time < 1 second. Results Both SURF and GLCM algorithms had high operating points (Se and Sp over 90 %) and a perfect reproducibility (κ = 1). The validation step showed the GLCM detector strategy had the best diagnostic performances, with a Se of 95.79 %, a Sp of 95.19 %, and a calculation time of 0.037 seconds per frame. Conclusion A computed algorithm based on a GLCM detector strategy had high diagnostic performance allowing assessment of the abundance of bubbles in SB-CE still frames. This algorithm could be of interest for clinical use (quality reporting) and for research purposes (objective comparison tool of different preparations).
Once-weekly dosing of recombinant human erythropoietin alpha in patients with myelodysplastic syndromes unresponsive to conventional dosing.

BACKGROUND Once-weekly dosing of recombinant human erythropoietin (rhEPO) in patients with myelodysplastic syndromes (MDS) has not been investigated thoroughly. We performed a clinical trial to evaluate the effects of this new dosing regimen in patients with MDS who were unresponsive to the conventional three-times-weekly schedule. PATIENTS AND METHODS Forty-eight patients with low- or intermediate-risk MDS were enrolled in a 12-week study. rhEPO alpha (rhEPOalpha) was administered once-weekly by subcutaneous injection with a starting dose of 40,000 U fixed dose. The drug dosage was increased to 60,000 U fixed dose if after 6 weeks there was no or suboptimal erythroid response. RESULTS Clinically significant responses were seen in 13 (27%) patients, with 11 improving their response after dose escalation of rhEPOalpha. Only one patient (case 23) maintains a response after a follow-up period of 14 months. All other patients had responses lasting between 10 and 43 weeks, with a median time to relapse of 20 weeks. Treatment was well tolerated, with no relevant adverse events. Response to therapy was associated with significantly higher concentrations of circulating erythroid blast-forming units and a decrease of the bone marrow fraction of apoptic CD34+ cells. CONCLUSIONS Once-weekly rhEPOalpha therapy results in an improvement of erythropoiesis in a subset of MDS patients who are unresponsive to conventional dosing, and may act by inhibiting apoptosis of erythroid precursors. These results warrant further investigation of this dosing regimen either alone or in combination with other agents.
A Pilot Online Mindfulness Intervention to Decrease Caregiver Burden and Improve Psychological Well-Being

Interventions to reduce caregiver burden are of great interest as the number of informal family caregivers continues to grow. The purpose of this study was to test the feasibility of an online mindfulness meditation intervention for community-dwelling older adult caregivers and to evaluate its impact on quality of life, caregiver burden, and psychological well-being. A total of 40 caregivers were recruited from 2 community center support groups to participate in an 8-week online mindfulness intervention. Pre and post surveys were administered. Retention rates were high with 55% completing the post surveys and attending at least 5 out of 8 sessions. Matched pairs t test indicated that the intervention reduced caregiver burden, perceived stress, anxiety, and loneliness and improved mental well-being. Online interventions offer flexibility for caregivers regardless of their responsibilities. Future research should expand this opportunity and explore the scalability of online mindfulness interventions.
Analytical Model of Dual-Pumped Kerr Micro-Resonators for Variable Free Spectral Range and Composite Frequency Comb Generation

We develop an analytical model to understand the generation of tunable free spectral range frequency comb (FC) and composite FC using two competing phenomena, namely nondegenerate four-wave mixing (FWM) and intensity-dependent modulation instability (MI) in dual-pumped micro-resonator configurations. Our analytical approach is based on the popular four-wave model. The MI rate at the onset, corresponding to the most susceptible frequency component that conditionally accompanies threshold-less comb generation in case of dual pump, is derived. Based on the analytical model, the mutual interplay of nondegenerate FWM and threshold intensity-dependent MI is investigated in detail for the comb generation process, and their regions of operations are clearly demarcated. The proposed analytical model provides a deterministic and quick estimation of micro-resonator parameters for generating a variety of FCs while avoiding the computationally intensive route of solving the Lugiato–Lefever equation. A close agreement between the analytical and numerical simulation is found.
Dynamic range compression and contrast enhancement for digital images in the compressed domain

We develop a simple and efficient algorithm for dynamic range compression and contrast enhancement of digital images in the compressed domain. The basic idea of our approach is to separate illumination and reflectance components of an image in the compressed domain. We adjust the amount of contribution of the illumination component to effectively compress the dynamic range of the image. For contrast enhancement, we modify the reflectance component based on a new measure of the spectral contents of the image. The spectral content measure is computed from the energy distribution across different spectral bands in a discrete cosine transform (DCT) block. The advantages of the proposed algorithm are (1) high dynamic range scenes are effectively mapped to the smaller dynamic range of the image, (2) the details in very dark or bright areas become clearly visible, (3) the computational cost is low, and (4) the compressibility of the original image is not affected by the algorithm. We evaluate the performance of the proposed algorithm with well-known existing methods, such as histogram equalization and -rooting algorithm, using a few different enhancement quality metrics.
A Lattice-Based Public Encryption with Key Search Scheme for Wireless Sensor Networks

With the rapid progress in technology, wireless sensor networks (WSNs) have attracted people's interest in the research community. However, due to the limited node communication and computing power, the traditional public key encryption algorithm can't be effectively applied. And as step into post-quantum era, pairing related assumption is fragile. Lattice is an ideal choice for building secure encryption scheme against quantum attack. Based on this, we propose a public encryption with key search scheme from lattice for wireless sensor networks. Compared with the existing public searchable encryption algorithm, the proposed encryption algorithm requires less computation time and achieves more security level.
A novel daily QA system for proton therapy

We describe the design and use of a daily quality assurance (QA) system for proton therapy. The QA system is designed to check the overall readiness of proton therapy system consistently within certain reference tolerances by a home‐made QA device (the QA device). The QA device is comprised of a commercially available QA device, rf‐Daily QA 3, a home‐made acrylic phantom, a set of acrylic compensators with various thicknesses, and a mechanical indexing jig. The indexing jig indexes the rf‐Daily QA 3, as well as the acrylic phantom, onto the patient treatment couch. Embedded fiducial markers in the acrylic phantom are used to check X‐ray image quality and positioning alignment accuracy of the imaging system. The rf‐Daily QA 3 is used to check proton beam output, range and symmetry with one single beam delivery. We developed in‐house software to calculate beam range and symmetry, based on various ion chambers' readings inside the rf‐Daily QA 3. With a single setup and one beam irradiation, the QA system is employed to check couch movement, laser alignment, image registration, and reference proton beam characteristics. The simplicity and robustness of this QA system allows for a total QA time of less than 20 minutes per room. The system has been in use at three proton therapy centers since June 2009. PACS numbers: 87.55. Qr, 87.53. Bn
Design, synthesis, biological evaluations, molecular docking, and in vivo studies of novel phthalimide analogs

A series of novel phthalimide analogs containing an indole or brominated indole moiety were synthesized and their antimicrobial activity was evaluated. Compound 8 showed a broad spectrum activity, revealing 53–67% of erythromycin activity on the tested bacteria and 60–70% of miconazole activity on the tested fungi. Anticancer activity was evaluated on the cell lines HepG2, MCF‐7, A549, H1299, and Caco2. The results revealed that the new phthalimide analog 8 has broad‐spectrum anticancer activity toward all the tested cancer cell lines, followed by compound 11, which showed good activity toward all the tested cell lines except for MCF‐7. The ability of the promising analogs 5, 8, and 11 to bind to topoisomerase II DNA gyrase was investigated. Caspase‐3 activation and Bcl‐2 assay of the best active derivatives 8, 11 in addition to compound 5 were evaluated. The antifibrotic activity was studied in an in vivo model and the histopathological studies revealed that treatment with the new compound 8 improved the fibrotic liver tissues to normality.
StreamSpace: Pervasive Mixed Reality Telepresence for Remote Collaboration on Mobile Devices

We present a system that exploits mobile rotational tracking and photospherical imagery to allow users to share their environment with remotely connected peers “on the go. ” We surveyed related interfaces and developed a unique groupware application that shares a mixed reality space with spatially-oriented live video feeds. Users can collaborate through realtime audio, video, and drawings in a virtual space. The developed system was tested in a preliminary user study, which confirmed an increase in spatial and situational awareness among viewers as well as reduction in cognitive workload. Believing that our system provides a novel style of collaboration in mixed reality environments, we discuss future applications and extensions of our prototype.