Abstract: Obstacle avoidance is the one key for the robot system in unknown environment. The robots should be able to know their position and safety region. This research starts on the path planning which are SLAM and AMCL in ROS system. In addition, the best parameters of the obstacle avoidance function are required. In situation on Hard Disk Drive Manufacturing, the distance between robots and obstacles are very serious due to the manufacturing constraint. The simulations are accomplished by the SLAM and AMCL with adaptive velocity and safety region calculation.
Abstract: Dynamic Voltage and Frequency Scaling (DVFS)
multicore platforms are promising execution platforms that enable
high computational performance, less energy consumption and
flexibility in scheduling the system processes. However, the
resulting interleaving and memory interference together with per-core
frequency tuning make real-time guarantees hard to be delivered.
Besides, energy consumption represents a strong constraint for the
deployment of such systems on energy-limited settings. Identifying
the system configurations that would achieve a high performance and
consume less energy while guaranteeing the system schedulability is
a complex task in the design of modern embedded systems. This work
studies the trade-off between energy consumption, cores utilization
and memory bottleneck and their impact on the schedulability of
DVFS multicore time-critical systems with a hierarchy of shared
memories. We build a model-based framework using Parametrized
Timed Automata of UPPAAL to analyze the mutual impact of
performance, energy consumption and schedulability of DVFS
multicore systems, and demonstrate the trade-off on an actual case
study.
Abstract: The generalization of relativistic theory of gravity based essentially on the principle of equivalence stipulates that for all bodies, the grave mass is equal to the inert mass which leads us to believe that gravitation is not a property of the bodies themselves, but of space, and the conclusion that the gravitational field must curved space-time what allows the abandonment of Minkowski space (because Minkowski space-time being nonetheless null curvature) to adopt Riemannian geometry as a mathematical framework in order to determine the curvature. Therefore the work presented in this paper begins with the evolution of the concept of gravity then tensor field which manifests by Riemannian geometry to formulate the general equation of the gravitational field.
Abstract: The control over delivered iron ore blend characteristics is one of the most important aspects of the mining business. The iron ore price is a function of its composition, which is the outcome of the beneficiation process. So, end-to-end integrated planning of mine operations can reduce risks of penalties on the iron ore price. In a standard iron mining company, the production chain is composed of mining, ore beneficiation, and client supply. When mine planning and client supply decisions are made uncoordinated, the beneficiation plant struggles to deliver the best blend possible. Technological improvements in several fields allowed bridging the gap between departments and boosting integrated decision-making processes. Clusterization and classification algorithms over historical production data generate reasonable previsions for quality and volume of iron ore produced for each pile of run-of-mine (ROM) processed. Mathematical modeling can use those deterministic relations to propose iron ore blends that better-fit specifications within a delivery schedule. Additionally, a model capable of representing the whole production chain can clearly compare the overall impact of different decisions in the process. This study shows how flexibilization combined with a planning optimization model between the mine and the ore beneficiation processes can reduce risks of out of specification deliveries. The model capabilities are illustrated on a hypothetical iron ore mine with magnetic separation process. Finally, this study shows ways of cost reduction or profit increase by optimizing process indicators across the production chain and integrating the different plannings with the sales decisions.
Abstract: In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.
Abstract: Today, a consistent segment of the world’s population lives in urban areas, and this proportion will vastly increase in the next decades. Therefore, understanding the key trends in urbanization, likely to unfold over the coming years, is crucial to the implementation of sustainable urban strategies. In parallel, the daily amount of digital data produced will be expanding at an exponential rate during the following years. The analysis of various types of data sets and its derived applications have incredible potential across different crucial sectors such as healthcare, housing, transportation, energy, and education. Nevertheless, in city development, architects and urban planners appear to rely mostly on traditional and analogical techniques of data collection. This paper investigates the prospective of the data science field, appearing to be a formidable resource to assist city managers in identifying strategies to enhance the social, economic, and environmental sustainability of our urban areas. The collection of different new layers of information would definitely enhance planners' capabilities to comprehend more in-depth urban phenomena such as gentrification, land use definition, mobility, or critical infrastructural issues. Specifically, the research results correlate economic, commercial, demographic, and housing data with the purpose of defining the youth economic discomfort index. The statistical composite index provides insights regarding the economic disadvantage of citizens aged between 18 years and 29 years, and results clearly display that central urban zones and more disadvantaged than peripheral ones. The experimental set up selected the city of Rome as the testing ground of the whole investigation. The methodology aims at applying statistical and spatial analysis to construct a composite index supporting informed data-driven decisions for urban planning.
Abstract: Nowadays, Corporate Social responsibility (CSR) is becoming a buzz word, and more and more academics are putting efforts on CSR studies. It is believed that CSR could influence Corporate Reputation (CR), and they hold a favourable view that CSR leads to a positive CR. To be specific, the CSR related activities in the reputational context have been regarded as ways that associate to excellent financial performance, value creation, etc. Also, it is argued that CSR and CR are two sides of one coin; hence, to some extent, doing CSR is equal to establishing a good reputation. Still, there is no consensus of the CSR-CR relationship in the literature; thus, a systematic literature review is highly in need. This research conducts a systematic literature review with both bibliometric and content analysis. Data are selected from English language sources, and academic journal articles only, then, keyword combinations are applied to identify relevant sources. Data from Scopus and WoS are gathered for bibliometric analysis. Scopus search results were saved in RIS and CSV formats, and Web of Science (WoS) data were saved in TXT format and CSV formats in order to process data in the Bibexcel software for further analysis which later will be visualised by the software VOSviewer. Also, content analysis was applied to analyse the data clusters and the key articles. In terms of the topic of CSR-CR, this literature review with bibliometric analysis has made four achievements. First, this paper has developed a systematic study which quantitatively depicts the knowledge structure of CSR and CR by identifying terms closely related to CSR-CR (such as ‘corporate governance’) and clustering subtopics emerged in co-citation analysis. Second, content analysis is performed to acquire insight on the findings of bibliometric analysis in the discussion section. And it highlights some insightful implications for the future research agenda, for example, a psychological link between CSR-CR is identified from the result; also, emerging economies and qualitative research methods are new elements emerged in the CSR-CR big picture. Third, a multidisciplinary perspective presents through the whole bibliometric analysis mapping and co-word and co-citation analysis; hence, this work builds a structure of interdisciplinary perspective which potentially leads to an integrated conceptual framework in the future. Finally, Scopus and WoS are compared and contrasted in this paper; as a result, Scopus which has more depth and comprehensive data is suggested as a tool for future bibliometric analysis studies. Overall, this paper has fulfilled its initial purposes and contributed to the literature. To the author’s best knowledge, this paper conducted the first literature review of CSR-CR researches that applied both bibliometric analysis and content analysis; therefore, this paper achieves its methodological originality. And this dual approach brings advantages of carrying out a comprehensive and semantic exploration in the area of CSR-CR in a scientific and realistic method. Admittedly, its work might exist subjective bias in terms of search terms selection and paper selection; hence triangulation could reduce the subjective bias to some degree.
Abstract: Noise estimation is essential in today wireless systems
for power control, adaptive modulation, interference suppression and
quality of service. Deep learning (DL) has already been applied in the
physical layer for modulation and signal classifications. Unacceptably
low accuracy of less than 50% is found to undermine traditional
application of DL classification for SNR prediction. In this paper,
we use divide-and-conquer algorithm and classifier fusion method
to simplify SNR classification and therefore enhances DL learning
and prediction. Specifically, multiple CNNs are used for classification
rather than a single CNN. Each CNN performs a binary classification
of a single SNR with two labels: less than, greater than or equal.
Together, multiple CNNs are combined to effectively classify over a
range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained
CNNs to predict SNR over a wide range of joint channel parameters
including multiple Doppler shifts (0, 60, 120 Hz), power-delay
profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The
approach achieves individual SNR prediction accuracy of 92%,
composite accuracy of 70% and prediction convergence one order
of magnitude faster than that of traditional estimation.
Abstract: Sustainability is no longer a choice for firms. To achieve sustainable supply chain, all three dimensions of sustainability should be considered. Unlike the economic and environmental aspects, social sustainability has been rarely given attention. The problem surrounding social sustainability and employees’ welfare in Egypt is complex and remains unsolved. The aim of this study is to qualitatively assess the current level of application of social sustainability in the retailing sector in Egypt through using the social sustainability indicators identified in the literature. The purpose of this investigation is to gain knowledge about the complexity of the system involved. A case study is conducted on one of the largest retailers in Egypt. Data were collected through semi-structured interviews with managers and employees to determine the level of application and identify the major obstacles affecting the social sustainability in the retailing context. The work developed gives insights about the details and complexities of the application of social sustainability in developing countries, from the retailing perspective. The outcomes of this study will help managers to understand the enablers of social sustainability and will direct them to methods of sound implementation.
Abstract: Public sector budgeting, all over the world, is underpinned by some universally accepted principles of sound budget management such as budget unity, universality, annuality, and a balanced budget. These traditional principles, though fundamental, had, in recent years, been augmented by the more modern principles of budgeting within fiscal objective, alignment with medium-term strategic plans as well as the observance of such related concepts as transparency, openness and accessibility. In this paper, we have endeavored to shed light, from literature and practice, on the meaning and purposes of such fundamental budgeting principles. We have also assessed the extent to which the Government of Ghana’s budget complies with the four traditional principles of budget unity, universality, annuality, and a balanced budget and the three out of the ten modern principles of budgetary governance of Organisation for Economic Co-operation and Development (OECD). We did so by using a qualitative method of review and analysis of existing documents and the performance assessment reports on Ghana’s Public Financial Management (PFM) measured using such frameworks as the Public Expenditure and Financial Accountability (PEFA), the Open Budget Survey (OBS) and its Index (OBI), the reports and action plans of Open Government Partnership (OGP) and the Global Initiative for Fiscal Transparency (GIFT). Other performance assessment reports that were relied on included, but not limited to, the Joint Evaluation Report of PFM in Ghana, 2001-2010, and the Joint Evaluation of Budget Support to Ghana, 2005-2015. We have, through this paper, brought to the fore the lessons that could be learned on how those budgetary principles undergird the Government of Ghana’s budget formulation, execution, accounting, control, and oversight. These lessons include, but are not limited to, the need for both scholars and practitioners in the PFM space to be aware of the impact of those principles on public sector budgeting.
Abstract: Fuzzy Logic, an advanced method to support decision-making, is used by various scientists in many disciplines. Fuzzy programming is a product of fuzzy logic, fuzzy rules, and implication. In marine science, fuzzy programming for ships is dramatically increasing together with autonomous ship studies. In this paper, a program to support the decision-making process for ship navigation has been designed. The program is produced in fuzzy logic and rules, by taking the marine accidents and expert opinions into account. After the program was designed, the program was tested by 46 ship accidents reported by the Transportation Safety Investigation Center of Turkey. Wind speed, sea condition, visibility, day/night ratio have been used as input data. They have been converted into a risk factor within the Fuzzy Logic Designer application and fuzzy rules set by marine experts. Finally, the expert's meteorological risk factor for each accident is compared with the program's risk factor, and the error rate was calculated. The main objective of this study is to improve the navigational safety of ships, by using the advance decision support model. According to the study result, fuzzy programming is a robust model that supports safe navigation.
Abstract: Understanding the concept of function has been important in mathematics education for many years. In this study, the models built by a group of five business administration and accounting undergraduate students when carrying out a population growth activity are analyzed. The theoretical framework is the Models and Modeling Perspective. The results show how the students included tables, graphics, and algebraic representations in their models. Using technology was useful to interpret, describe, and predict the situation. The first model, the students built to describe the situation, was linear. After that, they modified and refined their ways of thinking; finally, they created exponential growth. Modeling the activity was useful to deep on mathematical concepts such as covariation, rate of change, and exponential function also to differentiate between linear and exponential growth.
Abstract: The structure is made using different members and combining them with each other. These members are basically based on technical and engineering principles and are combined in different ways and have their own unique effects on the building. Trusses are one of the most common and important members of the structure, accounting for a large percentage of the power transmission structure in the building. Different types of trusses are based on structural needs and evaluating and making complete comparisons between them is one of the most important engineering analyses. In the present study, four types of trusses have been studied; 1) Hawe truss, 2) Pratt truss, 3) k truss, and 4) warren truss, under cyclic loading for 80 seconds. The trusses are modeled in 3d using st37 steel. The results showed that Hawe trusses had higher values than all other trusses (k, Pratt and Warren) in all the studied indicators. Indicators examined in the study include; 1) von Mises stresses, 2) displacement, 3) support force, 4) velocity, 5) acceleration, 6) capacity (hysteresis curve) and 7) energy diagram. Pratt truss in indicators; Mises stress, displacement, energy have the least amount compared to other trusses. K truss in indicators; support force, speed and acceleration are the lowest compared to other trusses.
Abstract: Natural language often conveys emotions of speakers.
Therefore, sentiment analysis on what people say is prevalent in the
field of natural language process and has great application value
in many practical problems. Thus, to help people understand its
application value, in this paper, we survey various applications of
sentiment analysis, including the ones in online business and offline
business as well as other types of its applications. In particular,
we give some application examples in intelligent customer service
systems in China. Besides, we compare the applications of sentiment
analysis on Twitter, Weibo, Taobao and Facebook, and discuss
some challenges. Finally, we point out the challenges faced in the
applications of sentiment analysis and the work that is worth being
studied in the future.
Abstract: Automated object recognition and identification systems
are widely used throughout the world, particularly in assembly lines,
where they perform quality control and automatic part selection tasks.
This article presents the design and implementation of an object
recognition system in an assembly line. The proposed shapes-color
recognition system is based on deep learning theory in a specially
designed convolutional network architecture. The used methodology
involve stages such as: image capturing, color filtering, location
of object mass centers, horizontal and vertical object boundaries,
and object clipping. Once the objects are cut out, they are sent to
a convolutional neural network, which automatically identifies the
type of figure. The identification system works in real-time. The
implementation was done on a Raspberry Pi 3 system and on a
Jetson-Nano device. The proposal is used in an assembly course
of bachelor’s degree in industrial engineering. The results presented
include studying the efficiency of the recognition and processing time.
Abstract: It is well known that cemented sand is one of the best approaches for soil stabilization. In some cases, a blend of sand, cement and other pozzolan materials such as zeolite, nano-particles and fiber can be widely (commercially) available and be effectively used in soil stabilization, especially in road construction. In this research, we investigate the effects of CaO which is based on the geotechnical characteristics of zeolite composition with sandy silt soil. Zeolites have low amount of CaO in their structures, that is, varying from 3% to 10%, and by removing the cement paste, we want to investigate the effect of zeolite pozzolan without any activator on soil samples strength. In this research, experiments are concentrated on various weight percentages of zeolite in the soil to examine the effect of the zeolite on drainage shear strength and California Bearing Ratio (CBR) both with and without curing. The study also investigates their liquid limit and plastic limit behavior and makes a comparative result by using Feng's and Wroth-Wood's methods in fall cone (cone penetrometer) device; in the final the SEM images have been presented. The results show that by increasing the percentage of zeolite in without-curing samples, the fine zeolite particles increase some soil's strength, but in the curing-state we can see a relatively higher strength toward without-curing state, since the zeolites have no plastic behavior, the pozzolanic property of zeolites plays a much higher role than cementing properties. Indeed, it is better to combine zeolite particle with activator material such as cement or lime to gain better results.
Abstract: Augmented Reality and Augmented Intelligence are radically changing information technology. The path that starts from the keyboard and then, passing through milestones such as Siri, Alexa and other vocal avatars, reaches a more fluid and natural communication with computers, thus converting the dichotomy between man and machine into a harmonious interaction, now heads unequivocally towards a new IT paradigm, where holographic computing will play a key role. The OLOS® platform contributes substantially to this trend in that it infuses computers with human features, by transferring the gestures and expressions of persons of flesh and bones to anthropomorphic holographic interfaces which in turn will use them to interact with real-life humans. In fact, we could say, boldly but with a solid technological background to back the statement, that OLOS® gives reality to an altogether new entity, placed at the exact boundary between nature and technology, namely the holographic human being. Holographic humans qualify as the perfect carriers for the virtual reincarnation of characters handed down from history and tradition. Thus, they provide for an innovative and highly immersive way of experiencing our cultural heritage as something alive and pulsating in the present.
Abstract: Introduction: Marginal gingivitis is a disease with considerable frequency among patients who present routinely for periodontal control and treatment. In fact, this disease may not have alarming symptoms in patients and may go unnoticed by themselves when personal hygiene conditions are optimal. The aim of this study was to collect retrograde data on the prevalence of marginal gingiva in the respective group of patients, evaluated according to specific periodontal diagnostic tools. Materials and methods: The study was conducted in two patient groups. The first group was with 34 patients, during December 2019-January 2020, and the second group was with 64 patients during 2010-2018 (each year in the mentioned monthly period). Bacterial plaque index, hemorrhage index, amount of gingival fluid, presence of xerostomia and candidiasis were recorded in patients. Results: Analysis of the collected data showed that susceptibility to marginal gingivitis shows higher values according to retrograde data, compared to cross-sectional ones. Susceptibility to candidiasis and the occurrence of xerostomia, even in the combination of both pathologies, as risk factors for the occurrence of marginal gingivitis, show higher values according to retrograde data. The female are presented with a reduced bacterial plaque index than the males, but more importantly, this index in the females is also associated with a reduced index of gingival hemorrhage, in contrast to the males. Conclusions: Cross-sectional data show that the prevalence of marginal gingivitis is more reduced, compared to retrograde data, based on the hemorrhage index and the bacterial plaque index together. Changes in production in the amount of gingival fluid show a higher prevalence of marginal gingivitis in cross-sectional data than in retrograde data; this is based on the sophistication of the way data are recorded, which evolves over time and also based on professional sensitivity to this phenomenon.
Abstract: Air pollution is a serious environmental threat on a global scale and can cause harm to human health, morbidity and premature mortality. Reliable monitoring and control systems are therefore necessary to develop coping skills against the hazards associated with this phenomenon. However, existing environmental monitoring means often do not provide a sufficient response due to practical and technical limitations. Commercial microwave links that form the infrastructure for transmitting data between cell phone towers can be harnessed to map rain at high tempo-spatial resolution. Rainfall causes a decrease in the signal strength received by these wireless communication links allowing it to be used as a built-in sensor network to map the phenomenon. In this study, we point to the potential that lies in this system to indirectly monitor areas where air pollution is reduced. The relationship between pollutant wash-off and rainfall provides an opportunity to acquire important spatial information about air quality using existing cell-phone tower signals. Since the density of microwave communication networks is high relative to any dedicated sensor arrays, it could be possible to rely on this available observation tool for studying precipitation scavenging on air pollutants, for model needs and more.
Abstract: Evaluating the levels of cyber-security risks within an enterprise is most important in protecting its information system, services and all its digital assets against security incidents (e.g. accidents, malicious acts, massive cyber-attacks). The existing risk assessment methodologies (e.g. eBIOS, OCTAVE, CRAMM, NIST-800) adopt a technical approach considering as attack factors only the capability, intention and target of the attacker, and not paying attention to the attacker’s psychological profile and personality traits. In this paper, a socio-technical approach is proposed in cyber risk assessment, in order to achieve more realistic risk estimates by considering the personality traits of the attackers. In particular, based upon principles from investigative psychology and behavioural science, a multi-dimensional, extended, quantifiable model for an attacker’s profile is developed, which becomes an additional factor in the cyber risk level calculation.