Miracle-dz/UniFAR-FTUs huggingface dataset:
Microbiome of Seven Full-Scale Anaerobic Digestion Plants in South Korea: Effect of Feedstock and Operational Parameters

In this study, the microbiomes linked with the operational parameters in seven mesophilic full-scale AD plants mainly treating food waste (four plants) and sewage sludge (three plants) were analyzed. The results obtained indicated lower diversity and evenness of the microbial population in sludge digestion (SD) plants compared to food digestion (FD) plants. Candidatus Accumulibacter dominated (up to 42.1%) in SD plants due to microbial immigration from fed secondary sludge (up to 89%). Its potential activity in SD plants was correlated to H2 production, which was related to the dominance of hydrogenotrophic methanogens (Methanococcus). In FD plants, a balance between the hydrogenotrophic and methylotrophic pathways was found, while Flavobacterium and Levilinea played an important role during acidogenesis. Levilinea also expressed sensitivity to ammonia in FD plants. The substantial differences in hydraulic retention time (HRT), organic loading rate (OLR), and total ammonium nitrogen (TAN) among the studied FD plants did not influence the archaeal methane production pathway. In addition, the bacterial genera responsible for acetate production through syntrophy and homoacetogenesis (Smithella, Treponema) were present in all the plants studied.
Polyphenols: A Promising Avenue in Therapeutic Solutions for Wound Care

In chronic wounds, the regeneration process is compromised, which brings complexity to the therapeutic approaches that need to be adopted, while representing an enormous loss in the patients’ quality of life with consequent economical costs. Chronic wounds are highly prone to infection, which can ultimately lead to septicemia and morbidity. Classic therapies are increasing antibiotic resistance, which is becoming a critical problem beyond complex wounds. Therefore, it is essential to study new antimicrobial polymeric systems and compounds that can be effective alternatives to reduce infection, even at lower concentrations. The biological potential of polyphenols allows them to be an efficient alternative to commercial antibiotics, responding to the need to find new options for chronic wound care. Nonetheless, phenolic compounds may have some drawbacks when targeting wound applications, such as low stability and consequent decreased biological performance at the wound site. To overcome these limitations, polymeric-based systems have been developed as carriers of polyphenols for wound healing, improving its stability, controlling the release kinetics, and therefore increasing the performance and effectiveness. This review aims to highlight possible smart and bio-based wound dressings, providing an overview of the biological potential of polyphenolic agents as natural antimicrobial agents and strategies to stabilize and deliver them in the treatment of complex wounds. Polymer-based particulate systems are highlighted here due to their impact as carriers to increase polyphenols bioavailability at the wound site in different types of formulations.
Dynamic Monitoring of Ongoing Clinical Trials

Abstract In late 2019, the U.S. FDA updated a new Guidance for Industry on Adaptive Designs for Clinical Trials of Drugs and Biologics. The guideline encourages developing and implementing innovative trial designs for future clinical trials. Group sequential, sample size re-estimation, and adaptive group sequential procedures have been very useful for improving the efficiency of conducting trials for decades. However, they face some challenges in operational aspects which limited their potentials. In this article, we introduce a procedure that addresses these issues. Our proposed procedure has the capability of providing anytime accessibility of the whole up-to-date data history. The procedure is largely built on the proven adaptive group sequential design theories and combined with advanced eClinical technologies in trial management to elevate the trial design and monitoring to a more dynamic level. The construct of a new trial monitoring screen is illustrated with real trial examples. The advantages of anytime accessibility of data and the monitoring a trial with the whole data history and trend are delineated. Data monitoring committees, sponsors and regulatory authorities are important stakeholders in the discussion of how future clinical trials should be monitored with respect to timing and frequency.
Insights into Biaxial Ordering of Bent-Core Nematics: X-Ray Diffraction Evidence

Recently we have reported the first X-ray diffraction evidence of biaxial order in the fluid cybotactic nematic phase of a pair of trimethylated bent-core mesogens. This evidence is based on the splitting of the wide-angle diffuse scattering in the plane normal to the nematic director. The additional experimental data presented here provide further insights into this unprecedented behavior observed over the entire nematic phase. Although we could not determine the spatial extent of biaxial order—intracluster or intercluster—our observations reveal an enhanced biaxial orientational correlation in the transverse molecular packing, possibly reflecting stronger anisotropic interactions between nearest-neighbor mesogens.
Evaluating Industry 4.0 Implementation Challenges Using Interpretive Structural Modeling and Fuzzy Analytic Hierarchy Process

Abstract The fourth industrial revolution known as Industry 4.0 is reshaping and evolving the way industries produce products and individuals live and work therefore, gaining massive attraction from academia, business, and politics. The manufacturing industries are optimistic regarding the opportunities that Industry 4.0 may offer such as improved efficiency, productivity and customization. The present research contributes to the Industry 4.0 literature by identifying, modeling, analyzing, and prioritizing the challenges in implementing Industry 4.0 in manufacturing industries. In doing so, the article first introduces the interpretive structural modeling (ISM) to develop the hierarchical relationships among the challenges and analyzes their mutual interactions. Further, “Matrice d’Impacts Croises Multiplication Appliquee aun Classement” (MICMAC) analysis is used to categorize the challenges into four categories, namely autonomous, driver, dependent, and linkage based on their driving power and dependence power. Moreover, fuzzy analytic hierarchy process (F-AHP) methodology is used to prioritize the challenges based on three criteria: driving power, dependence power, and change management. The hierarchical model developed through ISM methodology shows that “lack of vision and leadership from top management (C12), lack of skills training program and education (C2), and uncertainty of return on investment (C9)” are the major challenges in implementing Industry 4.0 in manufacturing industries. The findings of F-AHP analysis suggest that “lack of vision and leadership from top management (C12), lack of skilled workforce (C3), lack of skills training program and education (C2), and uncertainty of return on investment (C9)” are some of the major challenges of implementing Industry 4.0. Finally, the obtained results show how challenges affect other so that to uncover the root cause triggering the other challenges. The industrial practitioners and managers can then take advantage of these analyses to know which challenge acts as the main barrier in implementing Industry 4.0 and to be focused first in order to reach a solution.
Temporal evaluation of a woodrat (genus Neotoma) hybrid zone based on genotypic and georeferenced data

Mauldin et al. (2014) examined 103 woodrats collected in 1988 from a putative contact zone located in Major County, Oklahoma, and provided evidence for a substantial level of hybridization between Neotoma floridana and N. micropus. This site was resampled in 2010, with 42 woodrats collected, and again in 2011, when an additional 88 woodrats were collected from 11 localities along a transect extending northwest to southeast of the center of the hybrid zone. These specimens were examined to: 1) serve as independent data sets, separated by 22 years, for a temporal comparison of hybrid zone characteristics and 2) determine the geographic extent of genetic introgression between the two species. All individuals were genotyped at 11 genetic markers (eight microsatellite loci, one mitochondrial gene—Cytochrome b, and two nuclear introns—intron 2 of the vertebrate alcohol dehydrogenase gene and intron 7 of the beta-fibrinogen gene) that were shown to be informative in the original study. Levels of allelic introgression were assessed at 12 localities (11 new localities, as well as the previously sampled putative contact zone) to determine size and continuity of the hybrid zone. Expanded geographic sampling revealed evidence of genetic introgression at 11 of 12 localities, although only two localities were determined to be active areas of hybridization in 2011. The temporal comparison revealed that characteristics of the hybrid zone (i.e., frequency of hybridization, directionality, location of the zone, and ratios of hybrid classes) remained similar between sampling events. These findings suggest hybridization between these species is ongoing but is intermittent and potentially ephemeral in this region.
Computing the 4-Edge-Connected Components of a Graph in Linear Time

We present the first linear-time algorithm that computes the 4-edge-connected components of an undirected graph. Hence, we also obtain the first linear-time algorithm for testing 4-edge connectivity. Our results are based on a linear-time algorithm that computes the 3-edge cuts of a 3-edge-connected graph G, and a linear-time procedure that, given the collection of all 3-edge cuts, partitions the vertices of G into the 4-edge-connected components.
argXtract: Deriving IoT Security Configurations via Automated Static Analysis of Stripped ARM Binaries

Recent high-profile attacks on the Internet of Things (IoT) have brought to the forefront the vulnerability of"smart"devices, and have resulted in numerous IoT-focused security analyses. Many of the attacks had weak device configuration as the root cause. One potential source of rich and definitive information about the configuration of an IoT device is the device's firmware. However, firmware analysis is complex and automated firmware analyses have thus far been confined to devices with more traditional operating systems such as Linux or VxWorks. Most IoT peripherals, due to lacking traditional operating systems and implementing a wide variety of communication technologies, have only been the subject of smaller-scale analyses. Peripheral firmware analysis is further complicated by the fact that such firmware files are predominantly available as stripped binaries, without the ELF headers and symbol tables that would simplify reverse engineering. In this paper, we present argXtract, an open-source automated static analysis tool, which extracts security-relevant configuration information from stripped IoT peripheral firmware. Specifically, we focus on binaries that target the ARM Cortex-M architecture, due to its growing popularity among IoT peripherals. argXtract overcomes the challenges associated with stripped Cortex-M analysis and is able to retrieve arguments to security-relevant supervisor and function calls, enabling automated bulk analysis of firmware files. We demonstrate this via three real-world case studies. The largest case study covers a dataset of 243 Bluetooth Low Energy binaries targeting Nordic Semiconductor chipsets, while the other two focus on Nordic ANT and STMicroelectronics BlueNRG binaries. The results reveal widespread lack of security and privacy controls in IoT, such as minimal or no protection for data, fixed passkeys and trackable device addresses.
Short Communication: Serratia rubidaea as contaminant in laboratory environment

Abstract. Virgianti DP. 2020. Short Communication: Serratia rubidaea as contaminant in laboratory environment. Nusantara Bioscience 13: 47-51. There have been many cases of bacterial contamination in the laboratory. The bacterial genera identified as contaminants are Bacillus, Staphylococcus, Micrococcus, Pseudomonas, Shigella and Serratia. These bacteria are classified as non-pathogenic and pathogenic bacteria that can interfere with the test and potentially develop false-positive results. The present research has shown that red-colored contaminant bacteria develop in unused sterile media in our laboratory. Based on related information, Serratia marcescens is a red bacterial species that have been reported as a contaminant in the laboratory. The purpose of this study was to identifiy contaminant bacteria at the molecular level. Based on the phylogenetic characterization using the 16S rDNA gene region, this red contaminant bacterium was identified as Serratia rubidaea.
Improving thermodynamic profile retrievals from microwave 
radiometers by including Radio Acoustic Sounding System (RASS) 
observations

Abstract. Thermodynamic profiles are often retrieved from the multi-wavelength brightness temperature observations made by microwave radiometers (MWRs) using regression methods (linear, quadratic approaches), artificial intelligence (neural networks), or physical-iterative methods. Regression and neural network methods are tuned to mean conditions derived from a climatological dataset of thermodynamic profiles collected nearby. In contrast, physical-iterative retrievals use a radiative transfer model starting from a climatologically reasonable value of temperature and water vapor, with the model run iteratively until the derived brightness temperatures match those observed by the MWR within a specified uncertainty. In this study, a physical-iterative approach is used to retrieve temperature and humidity profiles from data collected during XPIA (eXperimental Planetary boundary layer Instrument Assessment), a field campaign held from March to May 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. During the campaign, several passive and active remote sensing instruments as well as in-situ platforms were deployed and evaluated to determine their suitability for the verification and validation of meteorological processes. Among the deployed remote sensing instruments was a multi-channel MWR, as well as two radio acoustic sounding systems (RASS), associated with 915-MHz and 449-MHz wind profiling radars. Having the possibility to combine the information provided by the MWR and RASS systems, in this study the physical-iterative approach is tested with different observational inputs: first using data from surface sensors and the MWR in different configurations, and then including data from the RASSs. These temperature retrievals are also compared to those derived by a neural network method, assessing their relative accuracy against 58 co-located radiosonde profiles. Results show that the combination of the MWR and RASS observations in the physical-iterative approach allows for a more accurate characterization of low-level temperature inversions, and that these retrieved temperature profiles match the radiosonde observations better than all other approaches, including the neural network, in the atmospheric layer between the surface and 5 km AGL. Specifically, in this layer of the atmosphere, both root mean square errors and standard deviations of the difference between radiosonde and retrievals that combine MWR and RASS are improved by ~0.5 °C compared to the other methods. Pearson correlation coefficients are also improved.
WiFi-RITA Positioning: Enhanced Crowdsourcing Positioning Based on Massive Noisy User Traces

Traditional WiFi positioning relies on a predefined radio map, which is labor-intensive and time-consuming for professionals. Recently, crowdsourcing has emerged as a promising solution for facilitating WiFi positioning. To crowdsense a radio map, traces collected from normal users are merged to recover the original walking paths. In this work, we design a robust iterative trace merging algorithm called WiFi-RITA based on WiFi access points as signal-marks. The algorithm formulates the trace merging problem as an optimization problem in which each trace is translated and rotated to minimize the limitation of distances among traces defined by WiFi access points. WiFi-RITA is further enhanced by removing outliers. WiFi-RITA is robust to the rotation errors of traces and efficient for a large number of short traces. According to the crowdsensed radio map, a sensor fusion approach based on particle filter by fusing inertial sensors and a multivariate Gaussian fingerprinting is proposed to enhance the accuracy of crowdsourcing indoor positioning. The experiment results in two large-scale environments demonstrate that WiFi-RITA positioning with zero-effort calibration achieves high positioning accuracy, which outperforms Pedestrian Dead Reckoning (PDR) and fingerprinting with K Nearest Neighbor.
Technology-dependent pedagogical process redesign: leveraging lean methods

PurposeThis research compared the efficacy of process outcomes leveraging lean methods versus traditional pedagogy applied to dental education dependent on emerging technology. The pedagogical objective was to improve system efficiency without compromising traditional outcomes of effectiveness (quality). Design/methodology/approachThe research team tested the efficacy of a lean A3 framework to identify, remove waste and redesign a technology-dependent simulation laboratory course (CAD/CAM/IR Restorative Dentistry). Students were also sensitized to time-in-chair to introduce a stronger patient focus. Baseline data collected from a control group were statistically compared to the research group's data after the course redesign. In addition, course time allocations were measured and then compared.FindingsThe results showed the interventions significantly reduced procedure cycle times without compromising quality. Additionally, the course was more efficiently conducted as measured by course time allocations.Practical implicationsThis research demonstrated that the use of the A3 framework enhanced learning through process documentation, reengineering and systems optimization resolving issues of inefficiency associated with the CAD/CAM/IR pedagogy. This work is significant because it demonstrates the practice of using lean interventions to redesign and improve a technology-based healthcare course to maximize benefits.Originality/valueThis research is the first to examine how to leverage lean methods in a healthcare simulation laboratory, dependent on innovative technology, to educate and train future practitioners. This research applied statistical rigor in a controlled experiment to maximize its applicability and generalizability.
Fate of volatile organic compounds in municipal activated sludge plants

The fate of volatile organic compounds (VOCs) in municipal activated sludge plants was investigated at pilot and full scale. More than 80% of the mass flow of nonchlorinated compounds was observed to be biodegraded while less than 20% was removed by stripping. Conversely, 46% of the mass flow of chlorinated compounds was biodegraded and 47% was removed by stripping. Adsorption of VOCs onto waste sludge was not a significant removal mechanism. The pilot plant employed was found to effectively emulate the liquid-gas phase partitioning and overall compound removals observed in a full-scale plant. Little difference in liquid-gas phase partitioning was observed for coarse and fine pore diffusers in spite of differing mass-transfer characteristics as demonstrated by oxygen-transfer measurements
Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey

With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution for solving complex computational tasks with quick response requirements. However, the complexity of a mobile crowdsourcing task makes it hard to pursue an optimal resolution with limited computing resources, as well as various task constraints. In this situation, deep learning has provided a promising way to pursue such an optimal resolution by training a set of optimal parameters. In the past decades, many researchers have devoted themselves to this hot topic and brought various cutting-edge resolutions. In view of this, we review the current research status of deep learning for mobile crowdsourcing from the perspectives of techniques, methods, and challenges. Finally, we list a group of remaining challenges that call for an intensive study in future research.
Time-kill profiles and cell-surface morphological effects of crude Polycephalomyces nipponicus Cod-MK1201 mycelial extract against antibiotic-sensitive and -resistant Staphylococcus aureus

Purpose: To examine the effect of crude Polycephalomyces nipponicus Cod-MK1201 mycelial extract on the viability and cell surface morphology of methicillin-susceptible Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA). Methods : Time-kill assays were conducted by incubating test bacteria with the extract and sampling at selected time points within a 24 h period. The effects of the extract on MSSA and MRSA ultrastructure were determined using a scanning electron microscope (SEM). Results : Time-kill assay data indicate a bactericidal effect against both strains of staphylococci. The extracts were rapidly bactericidal at concentrations of 1 x MIC and 2 x MIC, achieving complete elimination of the test bacterial strains within 2 h. SEM micrographs of S. aureus taken after treatment with various concentrations of the extract revealed extensive morphological alterations to the cell surface of both MSSA and MRSA. Conclusion : The results confirm the antibacterial activity of P. nipponicus Cod-MK1201 mycelial extract. Further research may allow this to be developed as an alternative therapy to alleviate S. aureus infection. Keywords : Antibacterial activity, Polycephalomyces nipponicus, Staphylococcus aureus, Time-kill assay, Cell surface morphology
Interactive learning in the classroom: A mobile augmented reality assistance application for learning

The rapid evolution of augmented reality (AR) technology has presented new opportunities in the domain of education. Acting as a bridge between the virtual and real worlds, AR technology overcomes the physical limitations of our classrooms at a low cost and provides an interactive learning experience. In this study, we present a smartphone AR application, named the AR‐E‐Helper, which assists the learning of students in higher education lectures. Our goal is to provide an AR‐enhanced learning experience for students. To validate the effectiveness of the AR‐E‐Helper, we conducted an experiment that compares three classes: AR‐enhanced, smartphone‐enhanced, and nontechnology‐enhanced classes. Through the experiment, we observed that our application was helpful in maintaining student's focus in class, promoting their interest, and increasing their satisfaction. Furthermore, we also found how to improve our application based on the observations that the application brought some downsides to the learning activities. We expect that this study will be helpful to design AR learning tools in the future.