Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement of antimicrobial and antimutagenic activities of Korean barberry (Berberis koreana Palib.) by the combined process of high-pressure extraction with probiotic fermentation.

BACKGROUND To evaluate the combined effects of high pressure extraction (HPE) and probiotic fermentation on the antimicrobial and antimutagenic activities, Berberis koreana was subjected to 500 MPa for 30 min and then fermented with Bifidobacterium longum B6 (HPE-BLF) and Lactobacillus paracasei (HPE-LPF) at 37 °C for 6 days. RESULTS The phenol content was significantly increased to 228 mg GAE g(-1) by the HPE compared to the conventional extraction (CE, 188 mg GAE g(-1)). The HPE-BLF and HPE-LPF showed the highest antimicrobial activity (MIC < 4 mg mL(-1)) against β-lactam antibiotic sensitive and resistant Staphylococcus aureus. No significant mutagenic effect was observed for CE, HPE, HPE-BLF, and HPE-LPF extracts. The highest antimutagenic activities against frame-shift mutant Salmonella typhimurium were observed at the HPE-LPF (82%), followed by the HPE-BLF (77%). CONCLUSION The combined HPE and fermentation process could be used as an alternative extraction method for improving the extraction efficacy of medicinal plants. The results will provide pharmaceutically useful information and potential direction for finding new drug sources from medicinal plants.
A Survey of the Usage of Sticky Notes

The near-ubiquitous usage of sticky notes is rarely the object of examination or research in itself. This paper details the initial findings of a structured survey and analysis of existing research literature mentioning sticky note usage. The main contributions of the paper are: An overview of our findings so far, which includes characterizations of seven categories of sticky note usage, reported in two related research communities - the Association for Computing Machinery (ACM) and the Design Research Society (DRS) - with accompanying highlighted examples; a discussion of the results alongside suggestions for questions worth pursuing in future research, based on our findings.
How Information Sharing about Care Recipients by Family Caregivers Impacts Family Communication

Previous research has shown that tracking technologies have the potential to help family caregivers optimize their coping strategies and improve their relationships with care recipients. In this paper, we explore how sharing the tracked data (i.e., caregiving journals and patient's conditions) with other family caregivers affects home care and family communication. Although previous works suggested that family caregivers may benefit from reading the records of others, sharing patients' private information might fuel negative feelings of surveillance and violation of trust for care recipients. To address this research question, we added a sharing feature to the previously developed tracking tool and deployed it for six weeks in the homes of 15 family caregivers who were caring for a depressed family member. Our findings show how the sharing feature attracted the attention of care recipients and helped the family caregivers discuss sensitive issues with care recipients.
Gesture Input for Users with Motor Impairments on Touchscreens: Empirical Results based on the Kinematic Theory

We capitalize in this work on the motor performance criteria of the Kinematic Theory to examine stroke gestures articulated on touchscreens by people with motor impairments. We report empirical results on 278 stroke gestures collected from 7 participants with spinal cord injury and cerebral palsy, for which we show that the Kinematic Theory can successfully model their strokes and reflect their motor skills entailed by gesture articulation. To encourage and support future work on stroke gesture UIs for users with motor impairments, we show that computer-generated gestures can be successfully synthesized with the same geometric and kinematic characteristics of the gestures actually produced by people with motor impairments.
A diagnostic procedure for transformative change based on transitions, resilience and institutional thinking

Urban water governance regimes around the world have traditionally planned large-scale, centralized infrastructure systems that aim to control variables and reduce uncertainties. There is growing sectoral awareness that a transition toward sustainable alternatives is necessary if systems are to meet society’s future water needs in the context of drivers such as climate change and variability, demographic changes, environmental degradation, and resource scarcity. However, there is minimal understanding of how the urban water sector should operationalize its strategic planning for such change to facilitate the transition to a sustainable water future. We have integrated concepts from transitions, resilience, and institutional theory to develop a diagnostic procedure for revealing insights into which types of strategic action are most likely to influence the direction and pace of change in the overall system toward a desired trajectory. The procedure used the multipattern approach, from transition theory, to identify the system conditions and type of changes necessary for enabling system transformation. It incorporated the adaptive cycle, from resilience theory, to identify the current phase of change for different parts of the system. Finally, it drew on the concepts of institutional pillars and institutional work to identify mechanisms that are likely to be most effective in influencing the transformative dynamics of the system toward a desired trajectory. We have demonstrated application of the proposed diagnostic procedure on a case study of recent transformative change in the urban water system of Melbourne, Australia. We have proposed that an operational diagnostic procedure provides a useful platform from which planners, policy analysts, and decision makers could follow a process of deduction that identifies which types of strategic action best fit the current system conditions.
Design and validation of a 3-wire demonstration device for programming industrial manipulator

Programming by Demonstration (PbD) is an intuitive way of teaching new skills to a robot either by the guidance of demonstration devices or by means of natural communications, such as gestures, voice commands, and kinesthetic methods. This paper proposes a novel low-cost demonstration device for an industrial manipulator that can considerably reduce time and effort required for programming. The demonstration device is designed to be portable and can be easily integrated to an existing industrial manipulator. In a repeatability test, the robustness of the device is validated to meet the industrial standards. The device is integrated to an ABB industrial manipulator and the functionality is verified for a pattern tracing task.
ElGamal Public-Key Cryptosystem Using Reducible Polynomials Over a Finite Field

The classical ElGamal encryption scheme is described in the setting of the multiplicative group Z¤p; the group of units of the ring of integers modulo a prime p, but it can be easily generalized to work in any . nite cyclic group G. Among the groups of most interest in cryptography are the multiplicative groups F ¤ q of the . nite . eld Fq: These require . nding irreducible polynomials h(x) over Zp; for some prime p; and constructing the quotient group Zp[x]= < h(x) >= Fq: Recently, El-Kassar et al. modi.ed the ElGamal public-key encryption scheme from the domain of natural integers, Z, to the domain of Gaussian integers, Z[i] by extending the arithmetic needed for the modi.cations in this domains. The ElGamal public-key cryptosystem is extended to quotient rings of polynomials over . nite . elds having cyclic group of units. The major . nding is that the quotient rings need not be . elds. In particular, when p is an odd prime, a second degree reducible polynomial over Zp is used to easily implement the extended ElGamal public-key cryptosystems and to avoid . nding irreducible polynomials.
Moving Beyond a "one-size fits all": Exploring Individual Differences in Privacy

As our lives become increasingly digitized, how people maintain and manage their networked privacy has become a formidable challenge for academics, practitioners, and policy-makers. A shift toward people-centered privacy initiatives has shown promise; yet many applications still adopt a "one-size fits all" approach, which fails to consider how individual differences in concerns, preferences, and behaviors shape how different people interact with and use technology. The main goal of this workshop is to highlight individual differences (e.g., age, culture, personal preference) that influence users' experiences and privacy-related outcomes. We will work towards best practices for research, design, and online privacy regulation policies that consider these differences.
Dynamic Range Reduction of SAR Image via Global Optimum Entropy Maximization With Reflectivity-Distortion Constraint

The visualization of synthetic aperture radar (SAR) images plays a critical role in remote sensing applications. To effectively obtain the image suitable for human observation, this paper introduces a new SAR image visualization algorithm to map the high dynamic range SAR amplitude values to low dynamic range displays via reflectivity distortion preserved entropy maximization. Its designed objective is to present the maximal amount of information content in the displayed image, and being optimal in an information theoretical sense, as well as restricting the upper bound of the reflection distortion caused by tone mapping. The resulting optimization problem can be graph theoretically modeled as a $K$ -edges maximum weight path problem in a directed acyclic graph, and it can be solved efficiently by dynamic programming in real time. Empirical evidences are provided to demonstrate the superior visual quality obtained by our new visualization technique.
Transcription factor AP-1 modulates the activity of the human foamy virus long terminal repeat

The human foamy virus (HFV) contains within the U3 region of its long terminal repeat (LTR) three perfect consensus sequences for the binding of the inducible transcription factor AP-1. Results of DNase I footprint protection and gel retardation assays demonstrated that proteins in extracts of HeLa and BHK-21 cells as well as bacterially expressed Jun and Fos proteins bind to these AP-1 sites. By conducting transient expression assays using chloramphenicol acetyltransferase plasmids carrying LTR sequences with point-mutated AP-1 sites, it was found that the three AP-1 sites contribute to the optimal activity of the HFV promoter. It is shown that induction of the HFV LTR by 12-O-tetradecanoylphorbol-13-acetate (TPA) and serum factors is mediated through the AP-1 sites.
Choosing to Help Monsters: A Mixed-Method Examination of Meaningful Choices in Narrative-Rich Games and Interactive Narratives

The potential of narrative-rich games to impact emotions, attitudes, and behavior brings with it exciting opportunities and implications within both entertainment and serious game contexts. However, effects are not always consistent, potentially due to game choices not always being perceived as meaningful by the players. To examine these perceptual variations, we used a mixed-method approach. A qualitative study first investigated meaningful game choices from the players' perspectives. Building on the themes developed in this first study, a quantitative study experimentally examined the effect of meaningful game choices on player experiences of appreciation, enjoyment, and narrative engagement. Results highlight the importance of moral, social, and consequential characteristics in creating meaningful game choices, which positively affected appreciation. Meaningfulness of game choices may therefore be crucial for narrative-rich games and interactive narratives to impact players.
Permuted Coordinate-Wise Optimizations Applied to Lp-Regularized Image Deconvolution

Image deconvolution is an ill-posed problem that usually requires prior knowledge for regularizing the feasible solutions. In the literature, iterative methods estimate an intrinsic image, minimizing a cost function regularized by specific prior information. However, it is difficult to directly minimize the constrained cost function, if a nondifferentiable regularization (e.g., the sparsity constraint) is employed. In this paper, we propose a nonderivative image deconvolution algorithm that solves the under-constrained problem (i.e., a non-blind image deconvolution) by successively solving the permuted subproblems. The subproblems, arranged in permuted sequences, directly minimize the nondifferentiable cost functions. Various <inline-formula> <tex-math notation="LaTeX">$L^{p}$ </tex-math></inline-formula>-regularized (<inline-formula> <tex-math notation="LaTeX">$0<p \leq 1$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$p=2$ </tex-math></inline-formula>) objective functions are utilized to demonstrate the pixel-wise optimization, in which the projection operator generates simplified, low-dimensional subproblems for estimating each pixel. The subproblems, after projection, are dealt within the corresponding hyperplanes containing the adjacent pixels of each image coordinate. Furthermore, successively solving the subproblems can accelerate the deconvolution process with a linear speedup, by parallelizing the subproblem sequences. The image deconvolution results with various regularization functionals are presented and the linear speedup is also demonstrated with a parallelized version of the proposed algorithm. Experimental results demonstrate that the proposed method outperforms the conventional methods in terms of the improved signal-to-noise ratio and structural similarity index measure
Cloud classification of satellite image based on convolutional neural networks

Cloud classification of satellite image is the basis of meteorological forecast. Traditional machine learning methods need to manually design and extract a large number of image features, while the utilization of satellite image features is not high. This paper constructs a convolution neural network for cloud classification, which can automatically learn features and obtain classification results. The experimental results on the FY-2C satellite image show that the features extracted by deep convolution neural network are more favorable to the classification of satellite cloud. The performance of cloud classification based on deep convolution neural network is better than that of traditional machine learning methods. The method has high precision and good robustness.
Fact-finding Survey of Nosocomial Infection Control in Hospitals in Kathmandu, Nepal—A Basis for Improvement

The purpose of this study was to investigate the actual conditions of nosocomial infection control in Kathmandu City, Nepal as a basis for the possible contribution to its improvement. The survey was conducted at 17 hospitals and the methods included a questionnaire, site visits and interviews. Nine hospitals had manuals on nosocomial infection control, and seven had an infection control committee (ICC). The number of hospitals that met the required amount of personal protective equipment preparation was as follows: gowns (13), gloves (13), surgical masks (12). Six hospitals had carried out in-service training over the past one year, but seven hospitals responded that no staff had been trained. Eight hospitals were conducting surveillance based on the results of bacteriological testing. The major problems included inadequate management of ICC, insufficient training opportunities for hospital staff, and lack of essential equipment. Moreover, increasing bacterial resistance to antibiotics was recognized as a growing issue. In comparison with the results conducted in 2003 targeting five governmental hospitals, a steady improvement was observed, but further improvements are needed in terms of the provision of high quality medical care. Particularly, dissemination of appropriate manuals, enhancement of basic techniques, and strengthening of the infection control system should be given priority.
Reproducing the internal and external anatomy of fossil bones: Two new automatic digital tools.

OBJECTIVES We present two new automatic tools, developed under the R environment, to reproduce the internal and external structures of bony elements. The first method, Computer-Aided Laser Scanner Emulator (CA-LSE), provides the reconstruction of the external portions of a 3D mesh by simulating the action of a laser scanner. The second method, Automatic Segmentation Tool for 3D objects (AST-3D), performs the digital reconstruction of anatomical cavities. MATERIALS AND METHODS We present the application of CA-LSE and AST-3D methods to different anatomical remains, highly variable in terms of shape, size and structure: a modern human skull, a malleus bone, and a Neanderthal deciduous tooth. Both methods are developed in the R environment and embedded in the packages "Arothron" and "Morpho," where both the codes and the data are fully available. RESULTS The application of CA-LSE and AST-3D allows the isolation and manipulation of the internal and external components of the 3D virtual representation of complex bony elements. In particular, we present the output of the four case studies: a complete modern human endocast and the right maxillary sinus, the dental pulp of the Neanderthal tooth and the inner network of blood vessels of the malleus. DISCUSSION Both methods demonstrated to be much faster, cheaper, and more accurate than other conventional approaches. The tools we presented are available as add-ons in existing software within the R platform. Because of ease of application, and unrestrained availability of the methods proposed, these tools can be widely used by paleoanthropologists, paleontologists and anatomists.
Light‐Driven Reversible Transformation between Self‐Organized Simple Cubic Lattice and Helical Superstructure Enabled by a Molecular Switch Functionalized Nanocage

Self‐organized stimuli‐responsive smart materials with adjustable attributes are highly desirable for a plethora of device applications. Simple cubic lattice is quite uncommon in soft condensed matter due to its lower packing factor. Achieving a stable simple cubic soft lattice and endowing such a lattice with dynamic reconstruction capability solely by a facile light irradiation are of paramount significance for both fundamental studies and engineering explorations. Herein, an elegant stable self‐organized simple cubic soft lattice, i.e., blue phase II, in a chiral liquid crystal (LC) system is disclosed, which is stable down to room temperature and exhibits both reversible lattice deformation and transformation to a helical superstructure, i.e., cholesteric LC, by light stimulation. Such an amazing trait is attained by doping a judiciously designed achiral photoresponsive molecular switch functionalized polyhedral oligomeric silsesquioxane nanocage into a chiral LC host. An unprecedented reversible collapse and reconstruction of such a high symmetric simple cubic blue phase II driven by light has been achieved. Furthermore, a well‐defined conglomerate micropattern composed of simple cubic soft lattice and helical superstructure, which is challenging to fabricate in organic and inorganic crystalline materials, is produced using photomasking technology. Moreover, the promising photonic application based on such a micropattern is demonstrated.
Double‐sigmoid model for fitting fatigue profiles in mouse fast‐ and slow‐twitch muscle

We present a curve‐fitting approach that permits quantitative comparisons of fatigue profiles obtained with different stimulation protocols in isolated slow‐twitch soleus and fast‐twitch extensor digitorum longus (EDL) muscles of mice. Profiles from our usual stimulation protocol (125 Hz for 500 ms, evoked once every second for 100–300 s) could be fitted by single‐term functions (sigmoids or exponentials) but not by a double exponential. A clearly superior fit, as confirmed by the Akaiki Information Criterion, was achieved using a double‐sigmoid function. Fitting accuracy was exceptional; mean square errors were typically <1% and r2 > 0.9995. The first sigmoid (early fatigue) involved ∼10% decline of isometric force to an intermediate plateau in both muscle types; the second sigmoid (late fatigue) involved a reduction of force to a final plateau, the decline being 83% of initial force in EDL and 63% of initial force in soleus. The maximal slope of each sigmoid was seven‐ to eightfold greater in EDL than in soleus. The general applicability of the model was tested by fitting profiles with a severe force loss arising from repeated tetanic stimulation evoked at different frequencies or rest periods, or with excitation via nerve terminals in soleus. Late fatigue, which was absent at 30 Hz, occurred earlier and to a greater extent at 125 than 50 Hz. The model captured small changes in rate of late fatigue for nerve terminal versus sarcolemmal stimulation. We conclude that a double‐sigmoid expression is a useful and accurate model to characterize fatigue in isolated muscle preparations.
EM based machine learning attack for XOR arbiter PUF

The physical unclonable functions (PUFs) have been attracted attention to prevent semiconductor counterfeits. However, the risk of machine learning attack for an arbiter PUF, which is one of the typical PUFs, has been reported. Therefore, an XOR arbiter PUF, which has a resistance against the machine learning attack, was proposed. However, in recent years, a new machine learning attack using power consumption during the operation of the PUF circuit was reported. Also, it is important that the detailed tamper resistance verification of the PUFs to consider the security of the PUFs in the future. Therefore, this study proposes a new machine learning attack using electromagnetic waveforms for the XOR arbiter PUF. Experiments by an actual device evaluate the validity of the proposed method and the security of the XOR arbiter PUF.
Capacities and Limitations of Wind Tunnel Physical Experiments on Motion and Dispersion of Different Density Gas Pollutants

Abstract The article focuses on the analysis of the possibilities to model motion and dispersion of plumes of different density gas pollutants in lowspeed wind tunnels based on the application of physical similarity criteria, in this case the Froude number. The analysis of the physical nature of the modeled process by the Froude number is focused on the influence of air flow velocity, gas pollutant density and model scale. This gives an idea of limitations for this type of physical experiments in relation to the modeled real phenomena. The resulting statements and logical links are exemplified by a CFD numerical simulation of a given task calculated in ANSYS Fluent software.
Iterative Frequency Domain Equalization for MIMO-GFDM Systems

This paper proposes a new iterative frequency domain equalization (FDE) algorithm for multiple-input multiple-output (MIMO)-frequency division multiplexing (GFDM) systems. This new FDE scheme is capable of enhancing the system fidelity by considering the complete frequency-domain second order description of the received signal. In addition, a new nulling filter design is also proposed for MIMO-GFDM systems to remove the residual interference, which further improves the system fidelity compared with the traditional scheme. Simulation results are presented to verify the effectiveness and efficiency of the proposed FDE algorithm.