Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-Virulence Properties of Coridothymus capitatus Essential Oil against Pseudomonas aeruginosa Clinical Isolates from Cystic Fibrosis Patients

Pseudomonas aeruginosa is an opportunistic pathogen responsible for nosocomial infections, and is often involved in airway infections of cystic fibrosis (CF) patients. P. aeruginosa virulence is related to its ability to form biofilm, trigger different types of motilities, and produce toxins (for example, bacterial pigments). In this scenario, essential oils (EOs) have gained notoriety for their role in phenotype modulation, including virulence modulation. Among different EOs previously analyzed, herein we investigated the activity of Coridothymus capitatus EO (CCEO) against specific virulence factors produced by P. aeruginosa isolated from CF patients. CCEO showed inhibition of new biofilm formation and reduction in mature biofilm in about half of the tested strains. On selected strains, SEM analysis provided interesting information regarding CCEO action in a pre-adhesion assay. CCEO treatment showed a dramatic modification of the extracellular matrix (ECM) structure. Our results clearly showed a drastic reduction in pyocyanin production (between 84% and 100%) for all tested strains in the presence of CCEO. Finally, CCEO was also able to strongly affect P. aeruginosa swarming and swimming motility for almost all tested strains. In consideration of the novel results obtained on clinical strains isolated from CF patients, CCEO may be a potential candidate to limit P. aeruginosa virulence.
Building renovation: A new industry?

Based on construction economics, building economics and assessments of building service life, the renovation need of buildings is notably higher than the actual amount of building renovation. As energy saving renovations become more common, the volume of building renovation is forecast to grow even further. Building renovation suffers from both a lack of suppliers and tailored solutions. This study has searched for a reason for this phenomenon. This is a theoretical study in economics, testing the rationalisation ability of three different economics theories, including the theory of construction economics, evolutionary economic theory and the theory of creating customer value. In accordance with construction economics theory, renovation construction competes in all owner sectors with other expenditures, and since renovations can be postponed, they often are. Demand is not activated by interesting supply or by supply corresponding to the needs. Renovation projects are implemented with a production-oriented new construction concept. In addition, a user-oriented approach does not produce a good outcome if it does not include catering for the special features of renovation construction.
EMISSIONS OF CRITERIA POLLUTANTS, TOXIC AIR POLLUTANTS AND GREENHOUSE GASES FROM THE USE OF ALTERNATIVE TRANSPORTATION MODES AND FUELS.

Policy makers in transportation often make investment decisions involving hundreds of millions of dollars. Typically they evaluate a wide range of alternatives -- from expanding highway capacity to managing existing demand to building a new rail line -- with respect to a broad array of seemingly incommensurable criteria. In theory, a policy maker can evaluate alternatives by cost-benefit analysis, in which one quantifies and monetizes all of the costs and benefits to society, and picks the alternative that yields the greatest net present-value of benefits. In this report, we quantify a key component of the social-cost part of cost-benefit analysis: emissions of air pollutants from different transportation modes.
Softwarized IP Multicast in the Cloud

In this article, we revisit the problem of how to provide “native” IP multicast support to enterprise distributed applications in today's clouds, without touching either the application implementation or the underlying network hardware. We propose SDM, Software-Defined IP Multicast, to explore the idea of performing Multicast-to-Unicast (M2U) and Unicast-to-Multicast (U2M) translations at the virtualized network edge. At the data plane, with novel architecture designs, SDM achieves efficient M2U and U2M translations by only using Open vSwitch (OVS), an OpenFlow-compatible open-source software switch widely deployed in modern clouds. At the control plane, SDM dynamically adopts multicast trees respecting the join and leave of receivers with flexible cost-based algorithms. SDM is very easy to deploy and use, as its required features are already supported by off-the-shelf OVS software (version 2 2.3) and the OpenFlow protocol (version 2 1.1). Performance evaluations of its control-plane algorithms also imply that SDM is flexible to construct through-put and latency/height optimized multicast trees respecting task requirements.
Recombinase Polymerase Amplification Assay with and without Nuclease-Dependent-Labeled Oligonucleotide Probe

The combination of recombinase polymerase amplification (RPA) and lateral flow test (LFT) is a strong diagnostic tool for rapid pathogen detection in resource-limited conditions. Here, we compared two methods generating labeled RPA amplicons following their detection by LFT: (1) the basic one with primers modified with different tags at the terminals and (2) the nuclease-dependent one with the primers and labeled oligonucleotide probe for nuclease digestion that was recommended for the high specificity of the assay. Using both methods, we developed an RPA-LFT assay for the detection of worldwide distributed phytopathogen—alfalfa mosaic virus (AMV). A forward primer modified with fluorescein and a reverse primer with biotin and fluorescein-labeled oligonucleotide probe were designed and verified by RPA. Both labeling approaches and their related assays were characterized using the in vitro-transcribed mRNA of AMV and reverse transcription reaction. The results demonstrated that the RPA-LFT assay based on primers-labeling detected 103 copies of RNA in reaction during 30 min and had a half-maximal binding concentration 22 times lower than probe-dependent RPA-LFT. The developed RPA-LFT was successfully applied for the detection of AMV-infected plants. The results can be the main reason for choosing simple labeling with primers for RPA-LFT for the detection of other pathogens.
Dynamics-Adapted Radiotherapy Dose (DARD) for Head and Neck Cancer Radiotherapy Dose Personalization

Standard of care radiotherapy (RT) doses have been developed as a one-size-fits all approach designed to maximize tumor control rates across a population. Although this has led to high control rates for head and neck cancer with 66–70 Gy, this is done without considering patient heterogeneity. We present a framework to estimate a personalized RT dose for individual patients, based on pre- and early on-treatment tumor volume dynamics—a dynamics-adapted radiotherapy dose (DDARD). We also present the results of an in silico trial of this dose personalization using retrospective data from a combined cohort of n = 39 head and neck cancer patients from the Moffitt and MD Anderson Cancer Centers that received 66–70 Gy RT in 2–2.12 Gy weekday fractions. This trial was repeated constraining DDARD between (54, 82) Gy to test more moderate dose adjustment. DDARD was estimated to range from 8 to 186 Gy, and our in silico trial estimated that 77% of patients treated with standard of care were overdosed by an average dose of 39 Gy, and 23% underdosed by an average dose of 32 Gy. The in silico trial with constrained dose adjustment estimated that locoregional control could be improved by >10%. We demonstrated the feasibility of using early treatment tumor volume dynamics to inform dose personalization and stratification for dose escalation and de-escalation. These results demonstrate the potential to both de-escalate most patients, while still improving population-level control rates.
An Intelligent Hierarchical Security Framework for VANETs

Vehicular Ad hoc Networks (VANETs) are an emerging type of network that increasingly encompass a larger number of vehicles. They are the basic support for Intelligent Transportation Systems (ITS) and for establishing frameworks which enable communication among road entities and foster the development of new applications and services aimed at enhancing driving experience and increasing road safety. However, VANETs’ demanding characteristics make it difficult to implement security mechanisms, creating vulnerabilities easily explored by attackers. The main goal of this work is to propose an Intelligent Hierarchical Security Framework for VANET making use of Machine Learning (ML) algorithms to enhance attack detection, and to define methods for secure communications among entities, assuring strong authentication, privacy, and anonymity. The ML algorithms used in this framework have been trained and tested using vehicle communications datasets, which have been made publicly available, thus providing easily reproducible and verifiable results. The obtained results show that the proposed Intrusion Detection System (IDS) framework is able to detect attacks accurately, with a low False Positive Rate (FPR). Furthermore, results show that the framework can benefit from using different types of algorithms at different hierarchical levels, selecting light and fast processing algorithms in the lower levels, at the cost of accuracy, and using more precise, accurate, and complex algorithms in nodes higher in the hierarchy.
A Survey of Cyberattack Countermeasures for Unmanned Aerial Vehicles

Unmanned aerial vehicle (UAV) has been increasingly used in a wide range of commercial and civilian applications. As an advanced cyber-physical system, UAVs are exposed to a wide range of cyberattacks. This paper first surveys existing literature for different cyberattacks. Then, we classify these attacks based on their attack entry points, which can be radio channels, messages or on-board systems. There are six classes of UAV cyberattacks, namely channel jamming, message interception, message deletion, message injection, message spoofing and on-board system attack. In the existing literature, there is no survey focusing on UAV cyberattack countermeasures. To close this gap, we survey existing countermeasures for the six attack classes. A comprehensive review of countermeasures is important because countermeasure may not be exclusive to an attack. Knowing a wide range of existing countermeasures can prepare us against existing and new cyberattacks. We classify countermeasure into three classes, namely prevention, detection and mitigation. Prevention countermeasures stop a cyberattack from starting. When prevention countermeasures fail, detection countermeasures alert UAV operator of an attack. After detecting an attack, mitigation countermeasures limits the damage. Following the survey, we further discuss the open challenges in developing countermeasures and propose some potential future research works.
Pitfalls and a feasible solution for using KGE as an informal likelihood function in MCMC methods: DREAM(ZS) as an example

Abstract. The Kling-Gupta Efficiency (KGE) is a widely used performance measure because of its advantages in orthogonally considering bias, correlation and variability. However, in most Markov chain Monte Carlo (MCMC) algorithms, error-based formal likelihood functions are commonly applied. Due to its statistically informal characteristics, using the original KGE in MCMC methods leads to problems in posterior density ratios due to negative KGE values and high proposal acceptance rates resulting in less identifiable parameters. In this study we propose adapting the original KGE using a gamma distribution to solve these problems and to apply KGE as an informal likelihood function in the DiffeRential Evolution Adaptive Metropolis DREAM(ZS), which is an advanced MCMC algorithm. We compare our results with the formal likelihood function to show whether our approach is robust and plausible to explore posterior distributions of model parameters and to reproduce the discharge behaviors. For that, we set three case studies that contain different uncertainties. Our results show that model parameters cannot be identified and the uncertainty of discharge simulations is large when directly using the original KGE. Our approach finds similar posterior distributions of model parameters compared to the formal likelihood function. Even though the acceptance rate of the adapted KGE is lower than the formal likelihood function for some systems, the convergence rate (efficiency) is similar between the two approaches for the calibration of real hydrological systems showing generally acceptable performances. We also show that both the adapted KGE and the formal likelihood function provide low performances for low flows, with the larger overestimations obtained from using the formal likelihood function. Furthermore, the adapted KGE approach behaves closely to the formal likelihood function in terms of the correlation between simulations and observations. Thus, our study provides a feasible way to use KGE as an informal likelihood in the MCMC algorithm and provides possibilities to combine multiple data for better and more realistic model calibrations.
Financial Decision under Uncertainty: Lessons from Indonesian Millennial

Financial decision under uncertain event is relatively tough not only for individual but also government and business owner. It impacts the individual welfare and other critical aspects. This paper attempts to examine millennial financial behavior in order to promote better understanding in facing uncertain event and conduct better risk mitigation. This study analyzed millennial financial decision by involving 2270 respondents. The result reveals that social influence, personality traits, financial literacy, and perceived risk provide strong relationship on millennial financial decision which present important implication for both academics and decision makers.
An Investigation of Statistical Terrorism in the Field of Academics and Research

This paper explores the term ‘statistical terrorism’ in the field of academics and research. Our approach is based on identification of the statistical components in the research studies where researchers and academicians used a number of statistical tools in one study which actually reduces the gist of the research. We developed Perception Based Model (PBM) for evaluating research studies in terms of statistical check. Evidence shows significant increase in the number of statistical techniques (more than four statistical techniques) in one research paper between 2005 and 2008. Almost 160 percent increasing trend has been observed between the said periods, which actually decrease the readership / audience of the journal, if other things remain the same.
Performance Evaluation of Semantic Segmentation using Efficient Neural Network (ENet) on Various Traffic Scene Conditions

— Object recognition, object detection, and semantic segmentation are fundamental components of the intelligent vehicle. Recently, there have been various methods proposed to create a reliable and accurate model to provide intelligent assistance to drivers. However, a reliable and accurate model in adverse conditions such as snow, rain, and fog remain a problem for advance driving assistance systems. The methods proposed only effectively solve the problem in a specific condition. Therefore, in this work, we focus on performing semantic segmentation in normal, rainy, foggy, and low light conditions using Efficient Neural Network (ENet) and ResNet18 and subsequently evaluating the trained model’s performance in these conditions. In the experiment, we used a daytime dataset from CamVid and synthetically transformed the daytime dataset into rainy, foggy, and low light conditions. To verify the accuracy of the proposed method, the Intersection over Union (IoU) is used, and the result is elaborated in the section result and discussion. This approach only performs accurately during daylight. From the experiments, both methods do suffer from various conditions, but the ENet method performs better in certain conditions compared to ResNet18.
Implementation of Intelligent Compaction Performance Based Specifications in Minnesota

This study documents relationships between intelligent compaction measurement values (IC-MVs) and various in-situ point measurement techniques for monitoring compaction of non-granular and granular materials. Factors affecting correlations are discussed (e.g., soil type, moisture contents, stress level, etc.). Measurements from earth pressure cells document the relationship between in-ground stresses for rollers and various in-situ test methods. Comparisons were made between test roller rut depth measurements and IC-MVs and various point measurements as a quality assurance (QA) check for the subgrade pavement foundation layer. It was concluded that IC-MVs and in-situ point measurements can serve as reliable alternatives to test rolling. Site specific target values were calculated for IC-MVs, dynamic cone penetrometer (DCP), light weight deflectometer (LWD), and shear strength. Measurement error and protocols for field testing were evaluated for LWDs. Laboratory compacted samples were used to assess an approach for determining LWD field target values. Future research is recommended to evaluate this approach for materials on a state-wide basis. Results from field studies were used to develop four IC specification options. Three specifications do not require on-site roller calibration. One specification option requires on-site calibration of IC-MVs and in-situ point measurements. This specification option has the advantages of quantifying risk, establishing a framework for a performance specification, providing information for incentive-based pay, and better linking as-built quality to long-term performance. An IC training/certification program, new IC field data analysis tools, and additional pilot projects will assist with greater implementation of these technologies.
A Multiplex PCR Assay Combined with Capillary Electrophoresis for the Simultaneous Identification of Atlantic Cod, Pacific Cod, Blue Whiting, Haddock, and Alaska Pollock

With an increased consumption of seafood products, food fraud with fish resources has been continuously reported. In particular, codfish has been exploited worldwide as a processed product in fresh, frozen, smoked, canned, or ready-to-eat dish forms. However, it is challenging to identify processed fish products after processing because of their similar morphological characteristics. Substitution and mislabeling of codfish among different species are also happening deliberately or unintentionally. Thus, it is necessary to distinguish cod species to prevent fish adulteration and food fraud. In this study, we developed a multiplex PCR for simultaneously identifying five cod species within Gadidae using capillary electrophoresis. Then, their species-specific primer sets were designed by targeting the mitochondrial cytochrome b gene. Subsequently, the amplicon sizes obtained were 237 bp, 204 bp, 164 bp, 138 bp, and 98 bp for Atlantic cod, Pacific cod, blue whiting, haddock, and Alaska pollock, respectively. The specificity of each primer was further tested using 19 fish species, and no cross-reactivity was observed. The limit of detection of this multiplex PCR assay was 1 pg. The developed multiplex PCR assay can be applied to 40 commercial food products successfully. This detection method will be efficient for managing seafood authentication by simultaneously analyzing multiple cod species.
Feeding Concept, Military Vs Civilian System

Abstract : Four Navy sites were compared to four equivalent civilian sites with regard to feeding concepts as assessed by average foodservice cost, menu adequacy, and customer reactions on questionnaires. Customers rated food items in their own dining hall, the same items as served in commercial restaurants, the environment in their dining hall, and the environment in commercial restaurants. The Navy's average daily food cost was $3.46, while the equivalent civilian institutions averaged $3.73, Menu analysis indicated the variety of food offered at Navy sites was more than adequate and that Military Recommended Dietary Allowances for all nutrients except folacin were exceeded. Sailors rated their dining hall food and environment significantly higher than the civilian rated theirs, though some of these differences could be traced to difference in sample composition. Sailors rated their food and foodservice environments lower, however, than that offered by commercial restaurants, indicating room for further improvement.
Hyperbolic tree visualization on mobile devices

The use of mobile devices such as smartphones is increasing exponentially as they become cheaper and more flexible to use. The communication technologies are now widespread throughout the developed countries. People don’t need to be seated in front of a desk to work anymore. The desktop paradigm is rapidly becoming a thing of the past. Nowadays with services like Flickr or Facebook people do not need a computer to store their pictures or talk with friends. The information is spread all over the internet. The reality is that people are now more connected in every possible meaning. The content available on the internet is reaching dimensions that wouldn’t be imagined a few years ago. Twenty years ago, during the analog era, people were used to take a couple of photos from their vacations. Today they and their friends take hundreds of photos. However, despite the increasing number of content the human capacity remains the same. People still have limits regarding the number of memories they can process and keep. The goal of the project is to focus on new ways of enhancing the browsing experience by filtering and revealing implicit relations between content. The working domain is media content which is available on the internet. The medium are mobile devices. The development of new visualization and interaction methods on this kind of small computers must be the next step. More often, people have a busy life which means they don’t want to waste time dealing with technology. They expect to find the information they want as fast as possible in the most simple way possible. The developed solution is a visualization software module that can be integrated within a mobile application as a browsing tool. It uses the hyperbolic tree visualization technique as the main starting point. It is aimed for hierarchical information such as graphs and is capable of giving both context and focus views. It employes a fish-eye distortion over a set of nodes, showing nodes in different sizes depending on their selected level of detail. This technique is known for an optimal ratio between content displayed and screen area usage. This is specially useful on mobile devices which have very limited screen sizes. The platform of choice for the prototype development was the iPhone. The visualization module was integrated with two applications for the iPhone. TinDI is used to upload photos and videos to a private storage service. KleinerExpresso is a pure visualization tool of services such as Flickr, Facebook, Picasa and Twitter. In both applications the goal is to prove that the developed visualization is useful and innovative in the mobile market. The prototypes showed are early examples of what might become a commercial product. The priority was the visualization and graphics development. They were not taken under exhaustive usability or performance tests. The overall feedback from empirical test