Miracle-dz/UniFAR-FTUs huggingface dataset:
Aeration of water with oxygen microbubbles and its purging effect

In this paper, we apply aeration with oxygen microbubbles to tap water; the intent is to quantitatively evaluate whether nitrogen gas originally dissolved in the water under the atmosphere is purged by the aeration with oxygen microbubbles. Oxygen microbubbles are continuously injected into the circulation system of tap water open to the atmosphere. While the concentration of dissolved oxygen (DO) can be detected by a commercial DO meter, that of dissolved nitrogen (DN) is unavailable. To detect the DN level, we observe the growth of millimetre-sized gas bubbles nucleated at glass surfaces in contact with the aerated water and compare it with the multi-species theory of Epstein and Plesset where the (unknown) DN concentration is treated as a fitting parameter. In the theory, we solve binary diffusion of each gas species (oxygen or nitrogen) in the water independently, under the assumption that the dissolved gases are sufficiently dilute. Comparisons between the experiment and the theory suggest that the DN in the water is effectively purged by the oxygen aeration. The supplemental experiment of aeration with nitrogen microbubbles is also documented to show that the DO can be effectively purged as well.
Adulterated pharmaceutical chemicals in botanical dietary supplements: novel screening approaches

Abstract The increased availability and use of botanical dietary supplements (BDS) has been accompanied by an increased frequency of adulteration of these products with pharmaceutical chemicals. These adulterated products are a worldwide problem, and their consumption poses health risks to consumers. The main focus of this paper is to highlight novel screening approaches utilized in the detection of adulterants in BDS marketed for different therapeutic purposes. We summarize spectroscopic methods involving near-infrared, infrared, Raman, and nuclear magnetic resonance spectroscopy as feasible and interesting in-field screening tools for the analysis of suspected products (adulterated or not) before being sent to a laboratory for deeper inspection. In addition, the new approaches based on chromatographic methods such as liquid chromatography-circular dichroism, liquid chromatography-mass spectrometry, thin layer chromatography-surface enhanced Raman spectroscopy, and thin layer chromatography-mass spectrometry are discussed and reviewed. Novel analysis strategies from targeted analysis to post-targeted and non-targeted analysis allowing simultaneous determination of the number of multiclass pharmaceuticals are discussed.
Design and rationale of dabigatran's stroke prevention in real life in Turkey (D-SPIRIT).

OBJECTIVE The D-SPIRIT registry is designed to investigate the safety and efficacy of dabigatran etexilate in patients with nonvalvular atrial fibrillation (NVAF) and to collect data on outcomes in clinical practice. METHODS The D-SPIRIT is a national, prospective, observational, post-marketing registry involving patients with NVAF who have been taking dabigatran etexilate therapy for stroke prevention for a minimum of 6 months prior to enrollment. The registry will collect and analyze data from routine care, enrolling up to 600 patients in 9 centers. Patients will be followed up for 2 years to evaluate effectiveness and safety. A sample size of 600 subjects is proposed based on the following assumptions; Two-sided significance level of 0.05 (1-sided significance level of 0.025), ischemic stroke incidence rate of 0.768%-1.111%, hemorrhagic stroke incidence rate of 0.109%-0.130%, transient ischemic attack incidence rate of 0.722%-0.623%, therapy discontinuation incidence rate of 40% at day 730, and duration of enrollment period of 12 months with non-uniformed enrollment rate. Ethics approval was given by Dokuz Eylül University Ethics Committee of Clinical Research (2014/54) and approved by the Turkish Ministry of Health. CONCLUSION Potential results of D-SPIRIT registry will add data from clinical practice to those from the RE-LY trial to expand knowledge of dabigatran etexilate treatment in patients with NVAF.
An O(n log n) algorithm for maximum st-flow in a directed planar graph

We give the first correct <i>O</i>(<i>n</i> log <i>n</i>) algorithm for finding a maximum <i>st</i>-flow in a directed planar graph. After a preprocessing step that consists in finding single-source shortest-path distances in the dual, the algorithm consists of repeatedly saturating the leftmost residual <i>s</i>-to-<i>t</i> path.
Role of reactive oxygen species in modulating carbohydrate metabolism in acute myeloid leukaemia

Reactive oxygen species (ROS) are known to play a significant role in cellular signalling pathways, associated with growth, differentiation and survival and in many cancers, elevated ROS levels have been linked with aberrant signalling. Previously, we showed that human haematopoietic progenitor cells expressing mutant RAS (a common abnormality in AML) had elevated NOX2 derived ROS which led to increased cell proliferation. However, it is currently unknown how ROS mediates these effects. Previous preliminary data suggests significant ROS attributable gene changes are associated with glycolysis, a feature also common in solid tumours. Using human haematopoietic progenitor cells, it is reported here for the first time that, elevated ROS leads to changes in extracellular lactate production and increases in glucose uptake. Furthermore, modulation of ROS levels in AML cell lines also generate results consistent with a ROS induced model of increased glycolysis. Gene expression profiling identified ROS related changes in specific glycolytic enzymes, whilst immunoblotting verified ROS dependent increases in protein expression of a key regulatory glycolytic enzyme, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3), in both human cells and AML cell lines. Metabolomic analysis using mass spectrometry, was performed on AML cell lines in which ROS levels were manipulated and AML patient samples were characterised according to ROS production. This revealed changes in concentrations of metabolites associated with glycolysis and metabolic pathways important in ROS regulation. Modulation of PFKFB3 expression generated changes in glucose uptake consistent with ROS mediated changes in this enzyme. In summary, this study establishes for the first time that increased ROS production in AML models leads to increased glycolysis and metabolic reprogramming as a consequence of modulation of PFKFB3 expression by ROS.
Cinchona-Based Primary Amine Catalyzed a Proximal Functionalization of Dienamines: Asymmetric α-Fluorination of α-Branched Enals

Fluorination of dienamines generated by α-branched enals and 6′-hydroxy-9-amino-9-deoxy-epi-quinidine (30 mol %) with NSFI show excellent α-regioselectivity to construct allylic fluorides containing a highly stereocontrolled quaternary fluorinated carbon (E/Z ≥ 20/1 and up to 93% enantiometric excess (ee)). By DFT calculation, the quinuclidine moiety of the catalyst was shown to function as a coordinating group to promote a reaction at the proximal α-position, and the nonclassical CH hydrogen bond plays an important role in the high enantioselectivity.
The polypropylene composition

A polyolefin composition comprising (percent by weight): A) from 65% to 75% of a propylene homopolymer or a propylene copolymer containing from 0.1% to 5.0% of ethylene derived units having a fraction soluble in Xylene at 25 DEG C lower than 3wt%; B) from 25 % to 35% of an ethylene copolymer containing from 3% to 12 % of propylene derived units; said composition having a melt flow rate (MFR) determined according to ISO method 133(230 DEG C and 2.16 kg)comprised between 11 g/10 min and 50 g/10 min; the xylene soluble fraction ranging from 3% to 15% and the intrinsic viscosity (IV) of the xylene soluble fraction ranging from 0.8 dl/g to 2 dl/g.
Adaptive neural network control for nonlinear cyber-physical systems subject to false data injection attacks with prescribed performance

Cyber-physical systems (CPSs), as emerging products of industry 4.0, play a key role in the development of intelligent manufacturing. This paper proposes an observer-based adaptive neural network (NN) control for nonlinear strict-feedback CPSs subject to false data injection attacks. Since there may be strict constraints on the state or output signals of nonlinear cyber-physical systems (NCPSs), we propose a time-varying asymmetric barrier Lyapunov function to realize the specific output constraints of NCPSs under cyber-attacks. Besides, since false data injection attacks will corrupt the transmitted state variables, an observer is designed to obtain observations of the exact states, and NN is used to approximate the unknown nonlinearity of NCPSs. With the proposed control strategy, the constraint control problem of NCPSs subject to false data injection attacks is settled. Finally, a numerical simulation example verifies the effectiveness of the proposed controller. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.
Neighbor Colonies Affect Level of Foraging in the Generalist Ant Pheidole oxyops (Hymenoptera: Formicidae)

Abstract Pheidole oxyops Forel (Hymenoptera: Formicidae) is a generalist ant that can display passive and active forage strategies. Moreover, this species can forage actively for a variety of food resources from plant debris to dead arthropods. In this study we tested whether abiotic factors (e.g., daytime, temperature, and residual soil accumulation), as well as neighboring colonies, could influence the patterns of foraging activity. Forage activity was observed in morning, afternoon, and evening sessions. Also, we recorded the frequency of individuals, direction, and ground surface temperature during foraging events. Our results showed that forage direction was not affected by residual soil accumulation and neighboring P. oxyops colonies. The rate of departures from nests decreased as ground surface temperature increased, but nest exits increased significantly with the occurrence of nearby P. oxyops colonies. We observed also that P. oxyops appeared to maximize forage activities during the evening period that would reduce their exposure to potential predators, competitors, and any adverse effects associated with direct solar radiation.
Occupational Health and Safety Practices in Cape Coast Metropolis

Abstract The rate of construction accidents in the Ghanaian construction industry is not encouraging. The study sought to identify the factors affecting the effective health and safety practices in the construction industry within the Cape Coast Metropolis. Purposive sampling technique was used to select the construction firms and ‘Yamane formula’ to arrive at the number of respondents (44) for the study. Structured questionnaires were distributed among site engineers, site supervisors, safety officers, quantity surveyors, foremen, chief masons and chief carpenters. Statistical Packages for the Social Sciences (SPSS) was used for the data analysis and results were presented in a form of descriptive and inferential statistics. Findings show that the level of health and safety practices within the Metropolis was high and the factors that affected the implementation of health and safety programmes were severe among the firms. Few of the company’s health and safety programmes were effective and this calls for the strengthenig of the construction sector policy by the ministries responsible for implementation.
Effect of Lambda-Cyhalothrin and Dimethoate on the Growth Response of Cowpea Plants and the Surrounding Soil

Abstract Insecticides are used widely to control insect pests affecting cowpea and their residues are often deposited in the soil. This may impact the physico-chemical characteristics of the soil and the plant health. This study was designed to investigate the growth response of cowpea treated with lambda-cyhalothrin and dimethoate and the insecticides influence on the physico-chemical characteristics of the soil. Three cowpea seeds were planted per polyethylene bag containing top soil, and later thinned to two plants. The insecticides were assayed singly or combined. The insecticides were applied at the pre-flowering (30th day), flowering (50th day) and at podding (70th day) stages of cowpea growth. The physico-chemical parameters of treated soils were assayed. Treated soils with lambdacyhalothrin and dimethoate and the control were slightly acidic with pH varying from 5.78 to 6.55. Soil organic matter and soil nutrients such as nitrogen, phosphorous, potassium and sodium had lowest values in untreated soil; they had highest values in soil treated with combined insecticides. Lambda-cyhalothrin and dimethoate treatments elicited diverse response from the cowpea plant. Generally, there were slight increase in the vegetative parameters, yield and pod dimensions of treated plants when compared with the control. These insecticides also increased the root nodules, root length and dry weight of cowpea at different concentrations. The combination of these insecticides had positive effect on cowpea without adverse influence on the yield and as well as the soil physicochemical properties.
Hardware-In-The-Loop Labs for SCADA Cybersecurity Awareness and Training

In this paper, we present a SCADA cybersecurity awareness and training program based on a Hands-On training using two twin cyber-ranges named WonderICS and G-ICS. These labs are built using a Hardware-In-the-Loop simulation system of the physical process developed by the two partners. The cyber-ranges allow replication of realistic Advanced Persistent Threat (APT) attacks and demonstration of known vulnerabilities, as they rely on real industrial control devices and softwares. In this work, we present both the demonstration scenarios used for awareness on WonderICS and the training programs developed for graduate students on G-ICS.
A Comparison of Natural Language Processing and Machine Learning Methods for Phishing Email Detection

Phishing is the most-used malicious attempt in which attackers, commonly via emails, impersonate trusted persons or entities to obtain private information from a victim. Even though phishing email attacks are a known cybercriminal strategy for decades, their usage has been expanded over last couple of years due to the COVID-19 pandemic, where attackers exploit people’s consternation to lure victims. Therefore, further research is needed in the phishing email detection field. Recent phishing email detection solutions that extract representational text-based features from the email’s body have proved to be an appropriate strategy to tackle these threats. This paper proposes a comparison approach for the combined usage of Natural Language Processing (TF-IDF, Word2Vec, and BERT) and Machine Learning (Random Forest, Decision Tree, Logistic Regression, Gradient Boosting Trees, and Naive Bayes) methods for phishing email detection. The evaluation was performed on two datasets, one balanced and one imbalanced, both of which were comprised of emails from the well-known Enron corpus and the most recent emails from the Nazario phishing corpus. The best combination in the balanced dataset proved to be the Word2Vec with the Random Forest algorithm, while in the imbalanced dataset the Word2Vec with the Logistic Regression algorithm.
Classifying SMEs for Approaching Cybersecurity Competence and Awareness

Cybersecurity is increasingly a concern for small and medium-sized enterprises (SMEs), and there exist many awareness training programs and tools for them. The literature mainly studies SMEs as a unitary type of company and provides one-size-fits-all recommendations and solutions. However, SMEs are not homogeneous. They are diverse with different vulnerabilities, cybersecurity needs, and competencies. Few studies considered such differences in standards and certificates for security tools adoption and cybersecurity tailoring for these SMEs. This study proposes a classification framework with an outline of cybersecurity improvement needs for each class. The framework suggests five SME types based on their characteristics and specific security needs: cybersecurity abandoned SME, unskilled SME, expert-connected SME, capable SME, and cybersecurity provider SME. In addition to describing the five classes, the study explains the framework's usage in sampled SMEs. The framework proposes solutions for each class to approach cybersecurity awareness and competence more consistent with SME needs.
MESH: A Memory-Efficient Safe Heap for C/C++

While memory corruption bugs stemming from the use of unsafe programming languages are an old and well-researched problem, the resulting vulnerabilities still dominate real-world exploitation today. Various mitigations have been proposed to alleviate the problem, mainly in the form of language dialects, static program analysis, and code or binary instrumentation. Solutions like AdressSanitizer (ASan) and Softbound/CETS have proven that the latter approach is very promising, being able to achieve memory safety without requiring manual source code adaptions, albeit suffering substantial performance and memory overheads. While performance overhead can be seen as a flexible constraint, extensive memory overheads can be prohibitive for the use of such solutions in memory-constrained environments. To address this problem, we propose MESH, a highly memory-efficient safe heap for C/C++. With its constant, very small memory overhead (configurable up to 2 MB on x86-64) and constant complexity for pointer access checking, MESH offers efficient, byte-precise spatial and temporal memory safety for memory-constrained scenarios. Without jeopardizing the security of safe heap objects, MESH is fully compatible with existing code and uninstrumented libraries, making it practical to use in heterogeneous environments. We show the feasibility of our approach with a full LLVM-based prototype supporting both major architectures, i.e., x86-64 and ARM64, in a Linux runtime environment. Our prototype evaluation shows that, compared to ASan and Softbound/CETS, MESH can achieve huge memory savings while preserving similar execution performance.
Distributed Approaches to Supply Chain Simulation

The field of Supply Chain Management (SCM) is experiencing rapid strides in the use of Industry 4.0 technologies and the conceptualization of new supply chain configurations for online retail, sustainable and green supply chains, and the Circular Economy. Thus, there is an increasing impetus to use simulation techniques such as discrete-event simulation, agent-based simulation, and hybrid simulation in the context of SCM. In conventional supply chain simulation, the underlying constituents of the system like manufacturing, distribution, retail, and logistics processes are often modelled and executed as a single model. Unlike this conventional approach, a distributed supply chain simulation (DSCS) enables the coordinated execution of simulation models using specialist software. To understand the current state-of-the-art of DSCS, this paper presents a methodological review and categorization of literature in DSCS using a framework-based approach. Through a study of over 130 articles, we report on the motivation for using DSCS, the modelling techniques, the underlying distributed computing technologies and middleware, its advantages and a future agenda, and also limitations and trade-offs that may be associated with this approach. The increasing adoption of technologies like Internet-of-Things and Cloud Computing will ensure the availability of both data and models for distributed decision-making, which is likely to enable data-driven DSCS of the future. This review aims to inform organizational stakeholders, simulation researchers and practitioners, distributed systems developers and software vendors, as to the current state-of-the art of DSCS, and which will inform the development of future DSCS using new applied computing approaches.
Linking CVE’s to MITRE ATT&CK Techniques

The MITRE Corporation is a non-profit organization that has made substantial efforts into creating and maintaining knowledge bases relevant to cybersecurity and has been widely adopted by the community. ATT&CK ”Adversarial Tactics, Techniques, and Common Knowledge” is a popular taxonomy by MITRE, which describes threat actor behaviors. Techniques are the foundation of the ATT&CK model, they are the actions that adversaries perform to accomplish goals, which translate into the model’s tactics. The aim of ATT&CK is to categorize adversary behavior to help improve the post-compromise detection of advanced intrusions. Software vulnerabilities (CVE) play an important role in cyber-intrusions, mostly classified into 4 ATT&CK techniques, which cover the exploitation phase of the attack chain. Identifying vulnerabilities that are actively exploited by the attackers, and understanding how a vulnerability can enable the attacker at each stage of the attack life cycle is absolutely critical for vulnerability assessments. Given the sparse classification of a CVE into ATT&CK taxonomy, lack of methods to extract labels from threat reports and, the volume of vulnerabilities disclosed defenders lack a concrete approach to prioritize CVE’s based on their role in the attack chain and in the context of controls in place. In this work, we propose a Multi-Head Joint Embedding Neural Network model to automatically map CVE’s to ATT&CK techniques. We address the problem of lack of labels for this task, by a novel unsupervised labeling technique. We enrich CVE’s with a curated knowledgebase 50 mitigation strategies, which help the model to learn both attacker and defender view of a given CVE. We evaluate our approach with the dataset containing CVE’s disclosed from the past 10 years and compare it with standard baseline models and ablation analysis. Using the proposed model, we mapped 62,000 CVE records to 37 different ATT&CK techniques and show that the proposed multi head design performs well in the absence of labels in the training dataset.
PGNet: Progressive Feature Guide Learning Network for Three-dimensional Shape Recognition

Three-dimensional (3D) shape recognition is a popular topic and has potential application value in the field of computer vision. With the recent proliferation of deep learning, various deep learning models have achieved state-of-the-art performance. Among them, multiview-based 3D shape representation has received increased attention in recent years, and related approaches have shown significant improvement in 3D shape recognition. However, these methods focus on feature learning based on the design of the network and ignore the correlation among views. In this article, we propose a novel progressive feature guide learning network (PGNet) that focuses on the correlation among multiple views and integrates multiple modalities for 3D shape recognition. In particular, we propose two information fusion schemes from visual and feature aspects. The visual fusion scheme focuses on the view level and employs the soft-attention model to define the weights of views for visual information fusion. The feature fusion scheme focuses on the feature dimension information and employs the quantified feature as the mask to further optimize the feature. These two schemes jointly construct a PGNet for 3D shape representation. The classic ModelNet40 and ShapeNetCore55 datasets are applied to demonstrate the performance of our approach. The corresponding experiment also demonstrates the superiority of our approach.