Miracle-dz/UniFAR-FTUs huggingface dataset:
Trust trumps concern: findings from a seven-country study on consumer consent to ‘digital native’ vs. ‘digital immigrant’ service providers

ABSTRACT Many service providers (e.g. mobile network operators) want to turn their business into fully digitalised regimes and offer personalised services. To do so they need access to the consumers’ personal data and must explicitly ask consumers for consent. In this cross-national study, including representative samples from seven countries covering Scandinavia, Central-Eastern Europe and South-Eastern Asia, we investigate the effect of trust vs. privacy concerns and the moderating impact of culture when consents are requested. The study shows that trust in service provider has stronger impact on consent giving than privacy concerns, and indicates that ‘digital native’ more than ‘digital immigrant’ providers should focus on trust building activities when they enter countries with an individualistic culture. The analyses were made within a PLS-SEM framework.
Routing Architecture of Software Defined Energy Internet

Energy Internet is a new and constantly evolving technology, of which the most critical part is how the energy flows. The concept of software customization used in the field of information will decouple the control plane from the data plane. It adopts a logical centralized control model. The control part of the device is implemented by a centralized programmable controller. Referring to the basic concept of computer network, the energy internet can be divided into energy resource subnet and energy transmission subnet. Energy micro-network, wide area energy internet and global energy internet are introduced. The concept of software-defined is applied to the field of smart grid which is the foundation and critical part of energy internet. This paper describes a software defined wide area energy internet routing architecture and implementation approach, which not only takes into account the compatibility of power devices with smart grid standards such as IEC61850 but also provides flexible software defined innovative application deployment.
Theory of spike-train power spectra for multidimensional integrate-and-fire neurons

Multidimensional stochastic integrate-and-ﬁre (IF) models are a standard spike-generator model in studies of ﬁring variability, neural information transmission, and neural network dynamics. Most popular is a version with Gaussian noise and adaptation currents that can be described via Markovian embedding by a set of d + 1 stochastic differential equations corresponding to a Fokker-Planck equation (FPE) for one voltage and d auxiliary variables. For the speciﬁc case d = 1, we ﬁnd a set of partial differential equations that govern the stationary probability density, the stationary ﬁring rate, and, central to our study, the spike-train power spectrum. We numerically solve the corresponding equations for various examples by a ﬁnite-difference method and compare the resulting spike-train power spectra to those obtained by numerical simulations of the IF models. Examples include leaky IF models driven by either high-pass-ﬁltered (green) or low-pass-ﬁltered (red) noise (surprisingly, already in this case, the Markovian embedding is not unique), white-noise-driven IF models with spike-frequency adaptation (deterministic or stochastic) and models with a bursting mechanism. We extend the framework to general d and study as an example an IF neuron driven by a narrow-band noise (leading to a three-dimensional FPE). The many examples illustrate the validity of our theory but also clearly demonstrate that different forms of colored noise or adaptation entail a rich repertoire of spectral shapes. The framework developed so far provides the theory of the spike statistics of neurons with known sources of noise and adaptation. In the ﬁnal part, we use our results to develop a theory of spike-train correlations when noise sources are not known but emerge from the nonlinear interactions among neurons in sparse recurrent networks such as found in cortex. In this theory, network input to a single cell is described by a multidimensional Ornstein-Uhlenbeck process with coefﬁcients that are related to the output spike-train power spectrum. This leads to a system of equations which determine the self-consistent spike-train and noise statistics. For a simple example, we ﬁnd a low-dimensional numerical solution of these equations and verify our framework by simulation results of a large sparse recurrent network of integrate-and-ﬁre neurons.
Dynamic multi-concept user profile modelling in research paper recommender systems

The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts (DNTC) model. Our system improves existing vector and tree of concepts models to be adaptable with a complex ontology and a large number of papers. The proposed system uses the 2012 version of the ACM Computing Classification System (CCS) ontology. This ontology has a much deeper structure than previous versions, which makes it challenging for previous ontology-based approaches to recommender systems. We performed offline evaluations using papers provided by ACM digital library for classifier training, and papers provided by CiteSeerX digital library for measuring the performance of the proposed DNTC model. Our evaluation results show that the novel DNTC model significantly outperforms the other two models: non-normalized tree of concepts and the vector of concepts models. Further, our DNTC model provides high average precision and reliable results when used in a context which the user has multiple interests and reads a large quantity of papers over time. Keywords— normalized tree of concepts; recommander system; personalization; user profile; 2012 ACM CCS ontology.
A copy-at-neighbouring-node retransmission strategy for improved wireless sensor network lifetime and reliability

Abstract A Wireless Sensor Network (WSN) is composed of tiny autonomous sensors with limited battery power. WSNs are employed to observe specific fields of interest. In this paper, we study the energy efficient and reliable network design problem using a mathematical programming framework. Energy efficiency is vital since battery replenishment is not always viable and the network lifetime is measured as the time until the first sensor exhausts its energy. Moreover, reliability is important since sensors are mostly deployed unattended and transmission of data fully and correctly is obviously critical. We develop a retransmission strategy originated from the Pareto principle and the scale-free property of complex networks. In our modified hop-by-hop reliability definition, sensors forwarding data directly to the central node must perform retransmission. Central node is the sensor with the highest data transmission load and our motivation is to secure the transmission of data passing through the central node against malicious attacks or technical failures. To this end, we present a mixed 0–1 integer programming model and an efficient heuristic. Our test results show an improvement of 80.5% in network lifetime and of 86.3% in redundant data overhead when compared with the classical conservative data redundancy approaches. We provide extensive test results, which reveal the contribution of our strategy in several other strategic design dimensions.
Mathematical programming heuristics for nonstationary stochastic inventory control

This work focuses on the computation of near-optimal inventory policies for a wide range of problems in the field of nonstationary stochastic inventory control. These problems are modelled and solved by leveraging novel mathematical programming models built upon the application of stochastic programming bounding techniques: Jensen’s lower bound and Edmundson-Madanski upper bound. The single-item single-stock location inventory problem under the classical assumption of independent demand is a long-standing problem in the literature of stochastic inventory control. The first contribution hereby presented is the development of the first mathematical programming based model for computing near-optimal inventory policy parameters for this problem; the model is then paired with a binary search procedure to tackle large-scale problems. The second contribution is to relax the independence assumption and investigate the case in which demand in different periods is correlated. More specifically, this work introduces the first stochastic programming model that captures Bookbinder and Tan’s static-dynamic uncertainty control policy under nonstationary correlated demand; in addition, it discusses a mathematical programming heuristic that computes near-optimal policy parameters under normally distributed demand featuring correlation, as well as under a collection of time-series-based demand process. Finally, the third contribution is to consider a multi-item stochastic inventory system subject to joint replenishment costs. This work presents the first mathematical programming heuristic for determining near-optimal inventory policy parameters for this system. This model comes with the advantage of tackling nonstationary demand, a variant which has not been previously explored in the literature. Unlike other existing approaches in the literature, these mathematical programming models can be easily implemented and solved by using off-the-shelf mathematical programming packages, such as IBM ILOG optimisation studio and XPRESS Optimizer; and do not require tedious computer coding.
Influence of ignition timing on performance and emission characteristics of an SI engine fueled with equi-volume blend of methanol and gasoline

ABSTRACT In the present investigation, experiments were conducted in wide open throttle condition (WOT) for different speed ranging from 1400 rpm to 1800 rpm at an interval of 200 on a single-cylinder four-stroke port-injected; spark-ignition engine. The engine fueled with equi-volume blend of methanol/gasoline was tested for different ignition timing and its effects on engine characteristics. The experiment results shown, retardation of ignition timing to 14⁰ BTDC exhibits excellent results compared to 24⁰ BTDC ignition timing. The results obtained show a good agreement of improvisation observed with M50 fuel in terms of BTE and BSEC at a speed of 1600 rpm when compared to gasoline fuel. The optimal ignition timing attributes to good combustion efficiency with increasing cylinder pressure and heat release rate. However, low carbon–hydrogen ratio and oxygen content in methanol aids to reduced NOx, HC, and CO emissions by 50%, 35%, and 40%, respectively. The small increase of 10% in CO2 emission is observed; this is due to retardation of ignition time, which allows the M50 fuel to absorb sufficient energy and achieve complete combustion.
Collective Perception in Vehicular Ad-hoc Networks (Kollektive Perzeption in fahrzeugbasierten Ad-hoc Netzwerken)

In combination with the current developments in the area of automatically driving vehicles, the introduction of inter-vehicle communication plays a crucial role for realising the long-term objective of what is known as cooperative driving. A cornerstone for the expansion of automated vehicles is their thorough understanding of the current driving environment. For this purpose, each vehicle generates an environment model containing information about other perceived traffic participants and objects. Local perception sensors are important data providers for this model, as they contribute implicit knowledge about the environment. In combination with a direct communication link between traffic participants, explicit knowledge can be added to the environment model as well. The key concept developed within this thesis is called Collective Perception: it focuses on sharing data gathered by local perception sensors of one vehicle with other traffic participants by means of inter-vehicle communication. As a result of this concept, future applications relying on a comprehensive understanding of the current driving environment are made feasible. The analyses presented in this thesis employ a vehicular ad-hoc network (VANET) based on the standardised framework of the European IEEE 802.11p-based ITS G5 protocol stack for inter-vehicle communication. The effectiveness of the technology relies on an existing communication link between a sufficient number of communication partners - the critical mass. The expansion of inter-vehicle communication, however, can be supported by capacitating indirect effects. Collective Perception is one representative of these effects, as the information density within the network between the vehicles is increased, even at low market penetration rates. At the core of Collective Perception stands the introduction of a message format which serves as a vehicle for the exchange of sensor data within a VANET. The development of the message is influenced by two perspectives: First, the vehicle perspective affects the relevant contents of the message required by data-fusion processes and application algorithms. Second, from the network perspective, constraints resulting from the network stack and effects caused by congestion control mechanisms have to be considered. This thesis addresses both perspectives to develop a holistic concept for exchanging sensor data within a VANET.
Synthesis and Structural-chemical Studied of Adduct of Coordination Supramolecular Porous Polymer Hexaaquatribenzene-1,2,4,5- Teacarbonato-tetrairon(iii) with Dipyridyl

An α, α′-dipyridyl adduct of a complex compound hexaaquatribenzene-1,2,4,5-tetracarbonatotetra iron (III) with porous structure was synthesized for the first time. According to the results of elemental, X-ray, IR-spectroscopic and differential-thermal analyses the individuality, chemical formula, thermal destruction, and form of coordination of acid-ic anion and dipyridyl were established. During interaction of a complex compound with dipyridyl, it completely loses all crystallization molecule of water resulting in a compound with a chemical formula of Fe4(C6H2(COO)4)3(dpy)2 (dipyridyl). Using the identification of diffraction pattern the parameters of lattice cell of the complex compound were determined.
Heavy Gas Cloud Boundary Estimation and Tracking using Mobile Sensors

—This paper addresses issues concerned with de-sign and managing of monitoring systems comprised of mobile wireless sensing devices (MANETs). The authors focus on self-organizing, cooperative and coherent networks that maintain a continuous communication with a central operator and adopt to changes in an unknown environment to achieve a given goal. The attention is focused on the development of MANET for heavy gas clouds detection and its boundary estimating and tracking. Two strategies for constructing the MANET are described, in which sensors explore the region of interest to detect the gas cloud, create temporary network topology and ﬁnally, cover the cloud boundary, and track the moving cloud. The utility and eﬃciency of the proposed strategies has been justiﬁed through simulation experiments
A Tap-Based Back-of-Device Mobile Users Authentication System

Author(s): Kulshreshtha, Satvik | Advisor(s): Arif, Ahmed Sabbir | Abstract: We propose a novel tap-based mobile user authentication system that enables users to authenticatethemselves by performing tap patterns on the back of the device. With this approach, the user firstselects a pattern as her password, then performs it to authenticate herself. On each authenticationattempt, the system compares the data from the built-in microphone and accelerometer with thepassword, then authenticates the user when they are similar. Since the proposed approach requiresperforming the tap patterns on the back of the device, it increases security by reducing the risks ofshoulder surfing, smudge, and video attack. In a user study, the approach yielded 70% accuracyrate with just three samples, and was secure (17% successful attack rate) even in an ideal shouldersurfing threat model. Further, most participants found it easy-to-use, felt secure while using it, andwanted to keep using it on their devices.
Detection of Campylobacter in Chicken Parts by Conventional Methods and Polymerase Chain Reaction with Identification of Antibiotic Resistance Profile

Background & Objective(s): Campylobacteriosis is a zoonotic, food-borne bacterial disease caused by Campylobacter spp. The most common pathogenic species are Campylobacter jejuni (C. jejuni) and C. coli. Multiple reservoirs harbor Campylobacter but chicken are considered the most common. Different chicken parts can harbour Campylobacter, particularly the intestine while chicken breasts usually have minimal counts. Antibiotics are used as feed as well as for therapeutic purposes in animals, and thus antimicrobial resistance of some Campylobacter isolates to common antibiotics is an issue of public health importance. The aim of this study was to detect C. jejuni and C. coli in chicken using conventional methods (culture followed by biochemical tests) and PCR, with identification of antimicrobial resistance of isolates. Methods: In the present study, Campylobacter was isolated from 100 different chicken parts (thigh, neck, intestine and wings) collected from 40 different chickens. Culture on charcoal cefoperazone deoxycholate agar (CCDA) was followed by biochemical confirmation of Campylobacter spp then by matrix-associated laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS). Simultaneously, DNA of Campylobacter was detected from chicken broth by multiplex polymerase chain reaction (PCR). Both conventional and PCR methods were compared. Campylobacter colony count was determined for different chicken parts, and the antimicrobial resistance of isolates was identified. Results: Out of the 100 examined chicken samples, 79 were presumptively positive on CCDA while only 15 isolates were MALDI-TOF confirmed (18.98%). All samples had Campylobacter counts exceeding 104 cfu/g. Colony counts ≥105 cfu/gm were encountered in 77.7% of PCR positive samples. Multiplex PCR had low sensitivity (60%) for detection of Campylobacter in chicken broth compared to confirmed cultures. Despite this drawback, PCR was advantageous over culture in detecting samples with mixed Campylobacter species. The intestine had the highest frequency (27.5%) of Campylobacter, with 72.7% of its samples yielding ≥105 cfu/g. C. jejuni responded better to erythromycin, ciprofloxacin and chloramphenicol (susceptibility= 100%, 80% and 80% respectively) while C. coli had a poorer susceptibility profile. Tetracycline and nalidixic acid had a poor antibacterial effect on both C. jejuni and C. coli. Conclusion: The distribution of Campylobacter species varied according to chicken part, with the intestine having the highest counts. All chicken samples had Campylobacter counts more than 10 4 cfu/g. PCR had 60% sensitivity compared to culture, but was more superior in detecting mixed cultures. C. jejuni was more sensitive to erythromycin, ciprofloxacin and chloramphenicol antibiotics
Dynamics of Parallel Robots: From Rigid Bodies to Flexible Elements

This book starts with a short recapitulation on basic concepts, common to any types of robots (serial, tree structure, parallel, etc.), that are also necessary for computation of the dynamic models of parallel robots. Then, as dynamics requires the use of geometry and kinematics, the general equations of geometric and kinematic models of parallel robots are given. After, it is explained that parallel robot dynamic models can be obtained by decomposing the real robot into two virtual systems: a tree-structure robot (equivalent to the robot legs for which all joints would be actuated) plus a free body corresponding to the platform. Thus, the dynamics of rigid tree-structure robots is analyzed and algorithms to obtain their dynamic models in the most compact form are given. The dynamic model of the real rigid parallel robot is obtained by closing the loops through the use of the Lagrange multipliers. The problem of the dynamic model degeneracy near singularities is treated and optimal trajectory planning for crossing singularities is proposed. Lastly, the approach is extended to flexible parallel robots and the algorithms for computing their symbolic model in the most compact form are given. All theoretical developments are validated through experiments
Monsoonal deposition of sea salt and air pollutants over Bombay

Representative sampling of rainwater has been carried out in Greater Bombay and its neighbourhood during the SW monsoon period of 1974. It is estimated that on the average ? 200 kg ha ? 1 sea salt is deposited here, each monsoon. A high ‘excess’ of Ca is observed throughout Bombay, including relatively unpolluted places. The source of this excess is seen to be non-marine. The mean pH, ionic balance and salinity of rainwater has been used to characterise different areas of Bombay in varying degrees of pollution. DOI: 10.1111/j.2153-3490.1976. tb00676.x
Feature subset selection in dynamic stability assessment power system using artificial neural networks

This paper presents method of feature subset selection in dynamic stability assessment (DSA) power system using artificial neural networks (ANN). In the application of ANN on DSA power system, feature subset selection aims to reduce the number of training features, cost and memory computer. However, the major challenge is to reduce the number of features but classification rate gets a high accuracy. This paper proposes applying Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS) and Feature Ranking (FR) algorithm to feature subset selection. The effectiveness of the algorithms was tested on the GSO-37bus power system. With the same number of features, the calculation results show that SFS algorithm yielded higher classification rate than FR, SBS algorithm. SFS algorithm yielded the same classification rate as SFFS algorithm.
Graphitic Carbon Nitride Materials for Photocatalytic Hydrogen Production via Water Splitting: A Short Review

The generation of photocatalytic hydrogen via water splitting under light irradiation is attracting much attention as an alternative to solve such problems as global warming and to increase interest in clean energy. However, due to the low efficiency and selectivity of photocatalytic hydrogen production under solar energy, a major challenge persists to improve the performance of photocatalytic hydrogen production through water splitting. In recent years, graphitic carbon nitride (g-C3N4), a non-metal photocatalyst, has emerged as an attractive material for photocatalytic hydrogen production. However, the fast recombination of photoexcited electron–hole pairs limits the rate of hydrogen evolution and various methods such as modification, heterojunctions with semiconductors, and metal and non-metal doping have been applied to solve this problem. In this review, we cover the rational design of g-C3N4-based photocatalysts achieved using methods such as modification, metal and non-metal doping, and heterojunctions, and we summarize recent achievements in their application as hydrogen production photocatalysts. In addition, future research and prospects of hydrogen-producing photocatalysts are also reviewed.