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The synthesis of hypochlorous acid from low concentrated chloride-containing electrolytes has been studied on various oxide materials at the anode current density of 50 mA cm–2. Boron doped diamond, platinized titanium, metallic titanium doped with platinum and palladium and materials based on lead (IV) oxide modified with fluorine and surfactants turned out to be promising for the synthesis of hypochlorous acid by electrolysis. Whereas, given the stability of oxidant synthesis during cumulative electrolysis, titanium modified with platinum and palladium as well as pre-treated lead (IV) oxide containing surfactants (sodium laureth sulfate) was the best. One should additionally take into account the possibility of combined use of electrocatalysts for the synthesis of strong oxidants in the reverse current mode in flow systems, when the implementation of the gas cathode leads to the formation of hydrogen peroxide and hypochlorous acid is formed at the anode. In fact, only a metal electrocatalyst, such as titanium modified with platinum and palladium, can be a suitable material. The kinetics of hypochlorite conversion is primarily determined by the pH value of freshly prepared solutions, temperature and storage conditions. The presence of different organic and inorganic micro-impurities in the solution also affects the kinetics of the hypochlorite salt decomposition. The following micro-impurities show the most negative impact on the stability of sodium hypochlorite solutions: Co(II), Cu(II), Mg(II), Al(III), and K3[Fe(CN)6]; Ni(ІІ), Fe(III), and K4[Fe(CN)6] influence the stability to a lesser extent. The effect of chlorate on the inhibition of sodium hypochlorite activity as a disinfectant has been investigated. The presence of chlorate in the disinfectant solution involved results in the absence of bactericidal activity against S. aureus and P. aeuruginosa. The growth of pseudomonas colonies becomes more abundant with increasing chlorate content in the disinfectant.

FDM 3D printed honeycombs are investigated. A honeycomb is composed of regular hexagonal cells. A honeycomb is 3D printed so that the fused filament runs along the walls of its cells. We emphasize that the thickness of these walls is one or two times the thickness of the fused filament. When calculating the mechanical properties of a honeycomb, its walls are considered as a Euler-Bernoulli beam bending in one plane. To describe honeycombs, a homogenization procedure is used, which reduces a honeycomb to a homogeneous orthotropic medium. An adequate analytical calculation of the mechanical properties of this medium is the subject of our research. Analytical formulae for calculating the mechanical properties of honeycombs are presented. To assess the adequacy of the calculation results, the analytical data are compared with the results of simulation in the commercial ANSYS package. For this, the mechanical properties of the honeycombs made of the ULTEM 9085 material are determined numerically. To assess these properties, from a large number of analytical formulae are selected those that predict them adequately. As a result of calculations, an analytical prediction of all mechanical properties is obtained, with the exception of the in-plane shear modulus of a honeycomb. This is due to the fact that to simulate such a shear modulus one has to use a three-dimensional theory that does not have an adequate analytical description. A thin aluminum honeycomb was considered. In the future, three-layer structures with such a honeycomb will be investigated. Analytical results for ULTEM 9085 and aluminum honeycombs are similar.

In integrated launch vehicles, one of the systems responsible for successful launch preparation and support is a ground thermal conditioning system supplying low-pressure thermostatic air to the “dry” compartments and head blocks of a launch vehicle. To connect the thermal conditioning system to the launch vehicle, a special interface is used. The proper functioning of the interface is critical to the reliability of the ground equipment of the system, the launch vehicle, and the space complex as a whole. This article describes key requirements to the interfaces of the thermal conditioning system and the drawbacks of their existing designs. The article proposes a new concept of interface design, according to which the pipeline of the ground thermal conditioning system is connected to the inlet tube of the launch vehicle via a corrugated rubber hose composed of three basic parts. The hose is attached to the inlet tube of the launch vehicle with the help of a metal lock/unlock device. The proposed solution provides good air tightness, ease of operation, easy multiple connections to the launch vehicle at different angles, and an automatic disconnection at launch or a manual disconnection in the case of a cancelled launch. Using rubber, which is a high-elasticity structural material, in the manufacturing of hoses makes it possible to minimise the effort required to disconnect the interface from the launch vehicle. In a high elasticity state, rubber can absorb and dissipate mechanical energy over a wide range of temperatures, which precludes the vibration caused by the engine operation from being transmitted to the ground thermal conditioning system. The article presents the key properties of rubber used as a structural material and its features to be considered in the design of similar devices. In contrast to metal, which shows two types of deformation (elastic and plastic), rubber can exhibit three types (elastic, superelastic, and plastic). During the design of interfaces, two types of deformation were taken into account: elastic and superelastic. Experimental tests of the interface presented in the article showed its full compliance with the requirements specification.

Additive manufacturing is very promising for aerospace engineering and aircraft construction. Using these technologies, light structures with preset strength properties can be made. For lack of tables of the mechanical properties of materials made by additive technologies, any calculation must be accompanied by the experimental determination of their mechanical properties. This paper presents an experimental approach to the determination of the mechanical characteristics of parts printed by FDM technologies. Parts manufactured from polymers by FDM technologies are shown to be orthotropic. Therefore, their elastic properties are described by nine constants: three Young’s moduli, three shear moduli, and three Poisson ratios. A cube is printed for the experimental determination of these constants. Six specimens are cut out from the cube. Three specimens are cut parallel to the cube edges, and the other three are cut at an angle of 45° to them. Each such specimen is manufactured in five pieces. This makes it possible to average the tensile stress–strain diagrams obtained for all the components of the stress tensor. The mechanical properties of the material are determined from these diagrams. The three Young’s moduli and the three Poisson ratios are determined from the three specimen types parallel to the cube edges. The three shear moduli are determined from the specimens cut at an angle of 45° to the cube edges. To determine these constants, tensile stress–strain diagrams are obtained experimentally. A technology is presented for manufacturing specimens on a Stratasys FORTUS 900 MC 3D printer. The mechanical properties of two polymer materials (ULTEM 9085 and PLA) are determined and compared. PLA has higher Young’s moduli and shear moduli and lower Poisson ratios than ULTEM 9085.

Shell structures provide a compromise between strength and mass, which motivates their use in rocket and space hardware (RSH). High and long-term loads cause plastic and creep deformations in structural elements. RSH structures feature inhomogeneity: design inhomogeneity (polythickness, the presence of reinforcements, openings, etc.) and technological inhomogeneity (defects produced in manufacturing, operation, storage. and transportation, defects produced by unforeseen thermomechanical effects, etc.). These factors, which characterize structural inhomogeneity, are stress and strain concentrators and may be responsible for an early failure of structural elements and inadmissible shape imperfections. In inhomogeneous structures, different parts thereof are deformed by a program of their own and exhibit a different stress and strain level. In accounting for a physical nonlinearity, which is governed by plastic and creep deformations, the following approach to the determination of the stress and strain field is efficient: the calculation is divided into stages, and at each stage parameters that characterize the plastic and creep deformations developed are introduced: additional loads in the equilibrium equations or boundary conditions, additional deformations, or variable elasticity parameters (the modulus of elasticity and Poisson’s ratio). Successive approximation schemes are constructed: at each stage, an elasticity problem is solved with the introduction of the above parameters. Special consideration is given to the determination of the launch vehicle and launch complex life. This is due to damages caused by alternate high-intensity thermomechanical loads. The basic approach relies on the theory of low- and high-cycle fatigue. The plasticity and the creep of a material are the basic factors in the consideration of the above problems. This paper considers various aspects of the solution of RSH strength and stability problems with account for the effect of plastic and creep deformations.

We considered the problem of weight optimization of parameters of multi-layer composite shell produced by the method of continuous cross-winding under axisymmetric loading. Shell layers are placed symmetrically relative to the middle surface. The angles of the reinforcing material winding variable along the meridian and the thickness of layers are taken as the variation parameters. We propose an algorithm of the automated determination of the elastic constants of a composite material variable along the shell meridian anisotropy. The connection of the composite structure with the technological process of shell manufacturing by winding with a reinforcing tape under different angles to the axis of rotation is taken into account. The values of four elastic constants obtained as a result of experimental testing of witness specimens of the composite material along and orthogonal to the reinforcement are used as output. The equations of state of the moment theory of shells of the variable along the meridian orthotropy and wall thickness are obtained as a boundary value problem for a system of ordinary differential equations with variable coefficients. The use of the necessary optimality conditions in the form of the principle maximum of Pontryagin in the presence of arbitrary phrasal restraints made it possible to reduce the emerging multiparameter problem to a sequence of extreme problems of a significantly smaller dimension. This approach greatly simplifies taking into account the conditions of strength reliability, and technological and structural requirements of real design, and the process of finding an optimal project as a whole. The results of the optimization of a two-layer fiberglass shell of rotation are presented in the form of a change in the distribution of layers’ thickness and the glass fiber winding angle. Materials of research can be used to reduce the material consumption of structural elements in rocket and space technology and other branches.

In this article, circumstances and factors which can lead to a degraded labour protection and occurrence of emergency in a district are reviewed and analyzed on the example of an accident occurred at sudden coal-and-gas outburst in the panel face № 42-1146 m of the Tovstyi-Zakhid seam m3 in the Central Mine of the Toretsvukhillia Mining Company. In this article, state of equipment and its location in the district in accordance with the requirements of the safety rules, records of devices which monitor gas-dynamic state of the coal seam and operation of the shield aggregate are considered. Mining and geological conditions of the seam bedding and technological conditions of the district location within the working horizon; methane content in the air of the roadways in the district; change of coal physical and mechanical properties in the area of possible geological disturbance; results of control of the coal seam gas-dynamic state analyzed by acoustic emission parameters; and other risk factors that led to emergencies were studied. The decisions made for controlling possible area with geological disturbance impact which was detected in the horizons of 916 m and 1026 m on the basis of mining-graphic materials and data of geological study in the mining roadways adjacent to the panel No. 14 in the district 42-1146 m were analyzed. The order and sequence of the works established for eliminating consequences of the coal and gas sudden outburst and detecting zone with disturbed coal and outburst cavity were considered. On the basis of the results obtained, conclusions were made and actions were elaborated on preventing accidents at mining steep prone-to-outburst coal seams by the shield aggregates. It is recommended to continue the scientific research for the purpose of detecting zone with possible impact of discontinuous geological disturbance and plicate disturbed zones in the coal seams by the changed hardness of coal in the adjacent panel, and developing certain local measures on preventing gas-dynamic phenomena at mining steep prone-to-outburst seams by the shield aggregates. The objective of this work was to analyze state of the working area and labor protection and to study the conditions and factors which had led to the accident with serious consequences and emergency condition of the panel.

Knowing the engineering properties of geomaterials is imperative to make the right decision while designing and executing any geotechnical project. For the economical and safe geotechnical design, quick characterization of the compressibility properties of the cohesive soil is often desirable; these properties are indeed tedious to determine through actual tests. Therefore, correlating the consolidation parameters of the soils with its index properties has a great significance in the geotechnical engineering field. Several attempts have been made in the past to develop correlations between the consolidation parameters and index properties of the cohesive soils, within certain limitations. However, there is still a need to develop such correlations based on the extensive database, composing of unified plasticity range of soils, i.e., low to high plasticity. In the current study, 148 undisturbed soil specimens were obtained from different areas of Pakistan. Out of which 120 samples were utilized to develop correlations, and 28 samples were used to check the validity of the developed correlations. In order to enhance the index properties database, 30 more bentonite mixed soil samples were prepared and tested accordingly. Correlations to envisage different consolidation parameters such as compression index, compression ratio and coefficient of volume compressibility were developed using 150 cohesive soil samples of low to high plasticity. In addition, the performance of these developed correlations was verified on a set of 40 soil samples and compared with the performance of different correlations available in the literature. The percentage deviation in the prediction of compressibility characteristics through developed correlations in the present study was found to be very less, which endorsed the excellent reliability of the developed correlations.

This paper presents the implementation and evaluation of a 50,000-pose-sample-per-second, 6-degree-of-freedom optical head tracking instrument with motion-to-pose latency of $28\mu\mathrm{s}$ and dynamic precision of 1–2 arcminutes. The instrument uses high-intensity infrared emitters and two duo-lateral photodiode-based optical sensors to triangulate pose. This instrument serves two purposes: it is the first step towards the requisite head tracking component in sub- $100\mu\mathrm{s}$ motion-to-photon latency optical see-through augmented reality (OST AR) head-mounted display (HMD) systems; and it enables new avenues of research into human visual perception – including measuring the thresholds for perceptible real-virtual displacement during head rotation and other human research requiring high-sample-rate motion tracking. The instrument's tracking volume is limited to about $120\times 120\times 250$ but allows for the full range of natural head rotation and is sufficient for research involving seated users. We discuss how the instrument's tracking volume is scalable in multiple ways and some of the trade-offs involved therein. Finally, we introduce a novel laser-pointer-based measurement technique for assessing the instrument's tracking latency and repeatability. We show that the instrument's motion-to-pose latency is $28\mu\mathrm{s}$ and that it is repeatable within 1–2 arcminutes at mean rotational velocities (yaw) in excess of 500°/sec.

Smart contracts are Turing-complete programs that are executed across a blockchain. Unlike traditional programs, once deployed, they cannot be modified. As smart contracts carry more value, they become more of an exciting target for attackers. Over the last years, they suffered from exploits costing millions of dollars due to simple programming mistakes. As a result, a variety of tools for detecting bugs have been proposed. Most of these tools rely on symbolic execution, which may yield false positives due to over-approximation. Recently, many fuzzers have been proposed to detect bugs in smart contracts. However, these tend to be more effective in finding shallow bugs and less effective in finding bugs that lie deep in the execution, therefore achieving low code coverage and many false negatives. An alternative that has proven to achieve good results in traditional programs is hybrid fuzzing, a combination of symbolic execution and fuzzing. In this work, we study hybrid fuzzing on smart contracts and present ConFuzzius, the first hybrid fuzzer for smart contracts. ConFuzzius uses evolutionary fuzzing to exercise shallow parts of a smart contract and constraint solving to generate inputs that satisfy complex conditions that prevent evolutionary fuzzing from exploring deeper parts. Moreover, ConFuzzius leverages dynamic data dependency analysis to efficiently generate sequences of transactions that are more likely to result in contract states in which bugs may be hidden. We evaluate the effectiveness of ConFuzzius by comparing it with state-of-the-art symbolic execution tools and fuzzers for smart contracts. Our evaluation on a curated dataset of 128 contracts and a dataset of 21K real-world contracts shows that our hybrid approach detects more bugs than state-of-the-art tools (up to 23%) and that it outperforms existing tools in terms of code coverage (up to 69%). We also demonstrate that data dependency analysis can boost bug detection up to 18%.

We envision a convenient telepresence system available to users anywhere, anytime. Such a system requires displays and sensors embedded in commonly worn items such as eyeglasses, wristwatches, and shoes. To that end, we present a standalone real-time system for the dynamic 3D capture of a person, relying only on cameras embedded into a head-worn device, and on Inertial Measurement Units (IMUs) worn on the wrists and ankles. Our prototype system egocentrically reconstructs the wearer's motion via learning-based pose estimation, which fuses inputs from visual and inertial sensors that complement each other, overcoming challenges such as inconsistent limb visibility in head-worn views, as well as pose ambiguity from sparse IMUs. The estimated pose is continuously re-targeted to a prescanned surface model, resulting in a high-fidelity 3D reconstruction. We demonstrate our system by reconstructing various human body movements and show that our visual-inertial learning-based method, which runs in real time, outperforms both visual-only and inertial-only approaches. We captured an egocentric visual-inertial 3D human pose dataset publicly available at https://sites.google.com/site/youngwooncha/egovip for training and evaluating similar methods.

International audience; Blockchain has emerged as a novel solution addressing a plethora of industrial issues in domains spanning from financial to educational. However, several challenges restrict the widespread adoption of the technology and data privacy, with throughput and scalability issues, ranks amongst the foremost. In this paper, we introduce a novel privacy management plane which integrates differential privacy to query existing relational databases through the blockchain as well as spearheads the use of blockchain for local differential privacy. The distinguishing feature in the latter is that the privacy management plane gives the data owners the right to perturb their data with the desired privacy budget, while in the former it gives the right to the data curator to change the privacy budget dynamically while answering queries through the blockchain. The paper also includes experimental evaluation of the developed privacy management plane and integrates management operations in it through another smart contract. The paper addresses the issue of GDPR and it's implications in the context of blockchain data, while highlighting the compliance of the proposed implementation.

Holography is perhaps the only method demonstrated so far that can achieve a wide field of view (FOV) and a compact eyeglass-style form factor for augmented reality (AR) near-eye displays (NEDs). Unfortunately, the eyebox of such NEDs is impractically small ($\sim \lt$ 1 mm). In this paper, we introduce and demonstrate a design for holographic NEDs with a practical, wide eyebox of $\sim$ 10 mm and without any moving parts, based on holographic lenslets. In our design, a holographic optical element (HOE) based on a lenslet array was fabricated as the image combiner with expanded eyebox. A phase spatial light modulator (SLM) alters the phase of the incident laser light projected onto the HOE combiner such that the virtual image can be perceived at different focus distances, which can reduce the vergence-accommodation conflict (VAC). We have successfully implemented a bench-top prototype following the proposed design. The experimental results show effective eyebox expansion to a size of $\sim$ 10 mm. With further work, we hope that these design concepts can be incorporated into eyeglass-size NEDs.

International audience; Virtual Asset Service Providers (VASPs) have recently been faced with increased compliance costs as both national and international bodies bring their due diligence compliance requirements inline with those of traditional financial entities. The 'travel rule', as defined by the Financial Action Task Force (FATF) is prominent amongst these and has created a need for efficient compliance solutions for VASPs. In this paper we improve upon current travel rule compliance solutions by utilising a mixed centralised and decentralised approach to provide trust and reduce the compliance burden on VASPs. Moreover, we provide a generic system capable of ensuring compliance with FATF recommendations as well other sanctions and embargoes that impact VASP trading relationships.

In this paper, we leverage the newly integrated extended Berkely Packet Filters (eBPF) and eXpress Data Path (XDP) to build ROS-FM, a high-performance inline network-monitoring framework for ROS. We extend the framework with a security policy enforcement tool and distributed data visualization tool for ROS1 and ROS2 systems. We compare the overhead of this framework against the generic ROS monitoring tools, and we test the policy enforcement against existing ROS penetration testing tools to evaluate their effectiveness. We find that the network monitoring framework and the associated visualization tools outperform the existing ROS monitoring tools for all robots with more than 10 running processes and that the monitoring tool uses only 4% of the overhead of the generic tools for robots with 80 processes. We further demonstrate the effectiveness of the security tool against common attacks in both ROS1 and ROS2.

Sodium valproate (SV) is an antiepileptic drug that is widely used in the treatment of different seizure disorders. The topical SV has a hair regenerative potential through activating the Wnt/&beta, catenin pathway and anagen phase induction. The aim of the current investigation was to fabricate nanospanlastics of SV for improving its dermal delivery by providing prolonged drug effect and increasing its permeability for treatment of androgenic alopecia (AGA). SV-loaded nanospanlastics were formulated according to 23 factorial design by ethanol injection method using a non-ionic surfactant (Span 60) and edge activators (EAs), such as Tween 80 and Cremophor RH 40, to explore the influence of different independent variables on entrapment efficiency (EE%) and percentage drug released after 12 h (Q12h) in order to choose the optimized formula using Design-Expert software. The optimized formula (F8) appeared as spherical deformable vesicles with EE% of 90.32 &plusmn, 2.18% and Q12h of 90.27 &plusmn, 1.98%. F8 exhibited significant improvement of ex vivo permeation than free SV. The clinical study exhibited no comparable difference between F8 and marketed minoxidil lotion. However, F8 demonstrates less adverse effects than minoxidil lotion. Nanospanlastics could be a safe and effective method for improving the topical delivery of SV in the management of AGA.

The Domain Name System (DNS), a fundamental protocol that controls how users interact with the Internet, inadequately provides protection for user privacy. Recently, there have been advancements in the field of DNS privacy and security in the form of the DNS over TLS (DoT) and DNS over HTTPS (DoH) protocols. The advent of these protocols and recent advancements in large-scale data processing have drastically altered the threat model for DNS privacy. Users can no longer rely on traditional methods, and must instead take active steps to ensure their privacy. In this paper, we demonstrate how the extended Berkeley Packet Filter (eBPF) can assist users in maintaining their privacy by leveraging eBPF to provide privacy across standard DNS, DoH, and DoT communications. Further, we develop a method that allows users to enforce application-specific DNS servers. Our method provides users with control over their DNS network traffic and privacy without requiring changes to their applications while adding low overhead.

Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self-driving cars industry. However, this framework is far from being totally secure, and the existing security breaches do not have robust solutions. In this paper we focus on the camera vulnerabilities, as it is often the most important source for the environment discovery and the decision-making process. We propose an unsupervised anomaly detection tool for detecting suspicious frames incoming from camera flows. Our solution is based on spatio-temporal autoencoders used to truthfully reconstruct the camera frames and detect abnormal ones by measuring the difference with the input. We test our approach on a real-word dataset, i.e. flows coming from embedded cameras of self-driving cars. Our solution outperforms the existing works on different scenarios.

In this paper, we explain that container engines are strengthening their isolation mechanisms. Therefore, non-intrusive monitoring becomes a must-have for the performance analysis of containerized user-space application in production environments. After a literature review and background of Linux subsystems and container isolation concepts, we present our lessons learned of using the extended Berkeley packet filter to monitor and profile performance. We carry out the profiling and tracing of several Interledger connectors using two full-fledged implementations of the Interledger protocol specifications.

The Brazilian transmission system is facing challenging problems with the distance between its generation areas and consumer centers that will be partly solved by the use of HVDC point-to-point systems. In the near future, the southeast subsystem will have a large amount of power injected through HVDC Systems in multiple points with relatively close electrical proximity. Therefore, the effects of a multi-infeed system are expected to influence the performance and operation of the network. Extensive studies and simulations will play an important role in determining the extension of the interactions among HVDC converters and determine if such interactions cause multiple commutation failures, thus disturbing the dynamic stability of the system. The use of CCC (Capacitor Commutated Converter) HVDC systems will also be assessed and is expected to diminish the need for a strong AC network (high short circuit level) and, therefore, mitigate multi-infeed interactions. The southeast subsystem of the Brazilian Power System currently has four LCC inverters, two of them belong to the Madeira power plant and the other two are from the Itaipu power plant. By the year 2024, four other HVDC systems will be arriving in the same region. This paper discusses the HVDC multi-infeed phenomena regarding the relevance of using synchronous machine models to represent important power plants and the application of mitigation methods regarding the 2020 network model, where six HVDC links will be present.

The authors express gratitude to everyone who favoured (directly or indirectly) the origination of the methodological approach being essentially innovative technological model to support competitiveness of mining enterprises. In particular, cooperation of the authors with the Department of Open-Pit Mining of the Dnipro University of Technology especially with Professor B.Yu. Sobko turned out to be determinative. The authors express kindly their thanks to Professor O.A. Temchenko from Kryvy Rih Economic Institute for his effective help rendered at the stage when scientific background of the general approach was being developed.

The bimetallic nanoparticles, used as catalysts due to their activity and selectivity, were prepared using one of the most versatile and convenient synthesis route: the alkaline polyol method. Mono and bimetallic nanoparticles, with both core-shell and alloy structure were obtained. The nanoparticles were characterized by TEM and XRD. The fractal dimension of each micrograph was computed using the “box-counting” method and the “information” method. Computing the box-counting fractal dimension at different grey-level thresholds one can obtain a specific curve. The paper will discuss the relation between the fractal dimension versus the grey-level threshold curve, the nanoparticles morphology and the preparation variables. The novelty of the work is that the shape of the fractal dimension versus the grey-level threshold curve is an indicator if the nanoparticles have alloy or core-shell structure. Also, the paper will provide a method to compute both fractal dimensions: of the nanoparticles' cores and of the nanoparticles' shells.

The authors are gratefully acknowledging the Badji Mokhtar University and Larbi Tebessi for provided the assistance to carry out this scientific study.

In this article we describe the discussion and conclusions of the "Roundtable on Real-Time Communications Research: 5G and Real-Time Communications --- Topics for Research" held at the Illinois Institute of Technology's Real-Time Communications Conference and Expo, co-located with the IPT-Comm Conference, October 5--8, 2015.

A simple, sensitive and selective fluorimetric method for the determination of trace amounts of nitrite in different water samples (e.g., underground water, geothermal waste water, domestic waste water and drinking water) was proposed. The fluorimetric determination is based on the diazotization reaction of nitrite with a fluorescent dye, tetraamino zinc (II) phthalocyanine in an acidic medium. Under the optimum experimental conditions, nitrite can be determined in the range 10− 6–10− 4 mol·L− 1 with the detection limit 2.04 × 10− 8 mol·L− 1. The proposed method has been successfully applied to the determination of nitrite in various water samples. The results compared favorably with the results from a standard isocertified method.

Wastewater and Sludge samples polluted with petroleum oil from Cairo Oil Refining Company (CORC), Mostorod, El-Qalyubiah, Cairo, Egypt. were used for isolation of indigenous bacterial communities. The isolation of bacterial strains followed four steps of adaptation and enrichment technique for selection of the most Naphthalene tolerant bacterial strains. Screening on four Naphthalene concentrations to determine the most potent strains having the abilities to use Naphthalene as a sole carbon and energy source have been conducted. The most potent bacterial isolates were MAM-P9, MAM-P14, MAM-P22, MAM-P25 and MAM-P26. The abilities of the five most potent bacterial isolates to grow on BSM amended with 4,5,6 and 7 mM Naphthalene have been determined by recording their growth (O.D) and secretion of extracellular protein after zero time (initial), 1,2,3,4,5,6,7,14 and 21 days spectrophotometrically. The degradation of Naph. have been determined quantitatively by High Performance Liquid Chromatography (HPLC) and qualitatively by GC/MS to determine the Intermediates resulted from degradation. The results revealed that isolate MAM-P22 was the best Naphthalene degrader. It degraded 95% of the highest concentration 7 mM. The best Naphthalene degrader bacterial Isolate MAM-22 was identified by 16S-rRNA showed a similarity of 98% to Bordetella avium strain with accession No. 041769.1. The results of GC/MS analysis revealed that Bordetella avium MAM-P22 degraded Naphthalene to give six intermediate compounds, These compounds were 1,2-Benzene dicarboxylic acid, Butyl-2,4-dimethyl-2-nitro-4-pentenoate, 1-Nonen-3-ol, Eicosane, Nonacosane.

Tryptophan is an important amino acid for humans with a significant role in cell metabolism. Depletion of tryptophan in the human body may contribute to diseases and development of disorders among ...

Well-defined Rh and Rh-Cu nanoparticles (NP's) of 1.6 nm and 1.3 nm, respectively, were synthesized by alkaline polyol method and then dispersed on insulating (Al 2 O 3 ) and semiconducting (TiO 2 ) supports. Both colloidal NP's and supported NP's were characterized using various experimental methods (TEM, XPS, XRD, etc.) to gather information about their specific morphology, structure and chemical state. The effects of size and support on the catalytic behavior of NP's for nitrate hydrogenation reaction were analyzed. Oxide supports, especially TiO 2 , were found to have a strong positive effect on the catalytic activity of metallic NP's. The non-supported, colloidal, Rh and Rh-Cu NP's are either inactive or posses very low hydrogenation activity. For supported materials, the intimate contact between two metals ( i.e. Rh-Cu) is required to attain good hydrogenation activity. The strong metal-support interaction, induced by hydrogen spillover, is a key point in determining hydrogenation activity. The Rh-Cu NP's dispersed on TiO 2 are extremely active for NO 3 − and NO 2 − (intermediate) deep hydrogenation, with high selectivity for NH 4 + . The hydrogenation activity of Rh-Cu NP's supported on Al 2 O 3 is hindered considerably, the main products of NO 3 − hydrogenation being NO 2 − intermediate.

Due to their promise of delivering real-time network insights, today's streaming analytics platforms are increasingly being used in the communications networks where the impact of the insights go beyond sentiment and trend analysis to include real-time detection of security attacks and prediction of network state (i.e., is the network transitioning towards an outage). Current streaming analytics platforms operate under the assumption that arriving traffic is to the order of kilobytes produced at very high frequencies. However, communications networks, especially the telecommunication networks, challenge this assumption because some of the arriving traffic in these networks is to the order of gigabytes, but produced at medium to low velocities. Furthermore, these large datasets may need to be ingested in their entirety to render network insights in real-time. Our interest is to subject today's streaming analytics platforms --- constructed from state-of-the art software components (Kafka, Spark, HDFS, ElasticSearch) --- to traffic densities observed in such communications networks. We find that filtering on such large datasets is best done in a common upstream point instead of being pushed to, and repeated, in downstream components. To demonstrate the advantages of such an approach, we modify Apache Kafka to perform limited native data transformation and filtering, relieving the downstream Spark application from doing this. Our approach outperforms four prevalent analytics pipeline architectures with negligible overhead compared to standard Kafka. (Our modifications to Apache Kafka are publicly available at https://github.com/Esquive/queryable-kafka.git)

In this paper, the CO oxidation on Pd/TiO 2 and Pd/SnO 2 /TiO 2 catalysts was studied using a non-conventional tool: AC electrical measurements in operando conditions. The structural and textural properties of the obtained powders have been characterized using BET, XRD, SEM, and CO chemisorption. Their redox properties were tested using the TPR technique. The surface dynamics was studied by electrical conductivity measurements under similar conditions with those encountered in the practical use in catalysis and correlated with their performances in CO oxidation.

Know Your Customer (KYC) processes performed by banks on their customers are redundant, cumbersome and costly. Therefore, a system is proposed to automate menial tasks and allow sharing of data related to KYC. A blockchain dictates the collaboration between different participants and several services are built around it to support the functionality of the system as a whole. An access control system is used to share datalegitimately.

Pretty Good Privacy (PGP) is one of the most prominent cryptographic standards offering end-to-end encryption for email messages and other sensitive information exchange. PGP allows to verify the identity of the correspondent in information exchange as well as the information integrity. PGP implements asymmetric encryption with certificates shared through a network of PGP key servers. In this paper, we propose a new PGP management framework with the key servers infrastructure implemented using blockchain technology. Our approach offers fast propagation of certificate revocation among PGP key servers and elimination of man-in-the-middle risks. It also grants users the required access control to update their own PGP certificates, which is not the case with the current PGP key servers. A prototype has been implemented using Ethereum blockchain and an open source key server, named Hockeypuck. Finally, we evaluated the prototype with extensive experiments. Our results show that our solution is practical and it could be integrated with the existing public PGP key servers infrastructure.

Cyber-attacks become more sophisticated and complex especially when adversaries steal user credentials to traverse the network of an organization. Detecting a breach is extremely difficult and this is confirmed by the findings of studies related to cyber-attacks on organizations. A study conducted last year by IBM found that it takes 206 days on average to US companies to detect a data breach. As a consequence, the effectiveness of existing defensive tools is in question. In this work we deal with the detection of malicious authentication events, which are responsible for effective execution of the stealthy attack, called lateral movement. Authentication event logs produce a pure categorical feature space which creates methodological challenges for developing outlier detection algorithms. We propose an auto semi-supervised outlier ensemble detector that does not leverage the ground truth to learn the normal behavior. The automatic nature of our methodology is supported by established unsupervised outlier ensemble theory. We test the performance of our detector on a real-world cyber security dataset provided publicly by the Los Alamos National Lab. Overall, our experiments show that our proposed detector outperforms existing algorithms and produces a 0 False Negative Rate without missing any malicious login event and a False Positive Rate which improves the state-of-the-art. In addition, by detecting malicious authentication events, compared to the majority of the existing works which focus solely on detecting malicious users or computers, we are able to provide insights regarding when and at which systems malicious login events happened. Beyond the application on a public dataset we are working with our industry partner, POST Luxembourg, to employ the proposed detector on their network.

A large body of research has been accomplished on prevention and detection of malicious events, attacks, threats, or botnets. However, there is a lack of automatic and sophisticated methods for investigating malicious events/users, understanding the root cause of attacks, and discovering what is really hap- pening before an attack. In this paper, we propose an attack model discovery approach for investigating and mining malicious authentication events across user accounts. The approach is based on process mining techniques on event logs reaching attacks in order to extract the behavior of malicious users. The evaluation is performed on a publicly large dataset, where we extract models of the behavior of malicious users via authentication events. The results are useful for security experts in order to improve defense tools by making them robust and develop attack simulations.

Global Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds., Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347

Managed security service providers increasingly rely on machine-learning methods to exceed traditional, signature- based threat detection and classification methods. As machine- learning often improves with more data available, smaller orga- nizations and clients find themselves at a disadvantage: Without the ability to share their data and others willing to collaborate, their machine-learned threat detection will perform worse than the same model in a larger organization. We show that Feder- ated Learning, i.e. collaborative learning without data sharing, successfully helps to overcome this problem. Our experiments focus on a common task in cyber security, the detection of unwanted URLs in network traffic seen by security-as-a-service providers. Our experiments show that i) Smaller participants benefit from larger participants ii) Participants seeing different types of malicious traffic can generalize better to unseen types of attacks, increasing performance by 8% to 15% on average, and up to 27% in the extreme case. iii) Participating in Federated training never harms the performance of the locally trained model. In our experiment modeling a security-as-a service setting, Federated Learning increased detection up to 30% for some participants in the scheme. This clearly shows that Federated Learning is a viable approach to address issues of data sharing in common cyber security settings.

Miners in Ethereum need to make a choice when they receive a block: they can fully validate the block by executing every transaction in order to validate the new state, but this consumes precious time that could be used on mining the next block. Alternatively, miners could skip some of the verification stages and proceed with the mining, taking the risk of building on top of a potentially invalid block. This is referred to as the verifier’s dilemma.