Multivariate Analysis of Spectroscopic Data for Agriculture Applications

In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.

Spatial Variability of Brahmaputra River Flow Characteristics

Brahmaputra River is known according to the Hindu mythology the son of the Lord Brahma. According to this name, the river Brahmaputra creates mass destruction during the monsoon season in Assam, India. It is a state situated in North-East part of India. This is one of the essential states out of the seven countries of eastern India, where almost all entire Brahmaputra flow carried out. The other states carry their tributaries. In the present case study, the spatial analysis performed in this specific case the number of MODIS data are acquired. In the method of detecting the change, the spray content was found during heavy rainfall and in the flooded monsoon season. By this method, particularly the analysis over the Brahmaputra outflow determines the flooded season. The charged particle-associated in aerosol content genuinely verifies the heavy water content below the ground surface, which is validated by trend analysis through rainfall spectrum data. This is confirmed by in-situ sampled view data from a different position of Brahmaputra River. Further, a Hyperion Hyperspectral 30 m resolution data were used to scan the sediment deposits, which is also confirmed by in-situ sampled view data from a different position.

The Impact of the General Data Protection Regulation on Human Resources Management in Schools

The General Data Protection Regulation (GDPR), concerning the protection of natural persons within the European Union with regard to the processing of personal data and on the free movement of such data, became applicable in the European Union (EU) on 25 May 2018 and transformed the way personal data were being treated under the Data Protection Directive (DPD) regime, generating sweeping organizational changes to both public sector and business. A social practice that is considerably influenced in the way of its day-to-day operations is Human Resource (HR) management, for which the importance of GDPR cannot be underestimated. That is because HR processes personal data coming in all shapes and sizes from many different systems and sources. The significance of the proper functioning of an HR department, specifically in human-centered, service-oriented environments such as the education field, is decisive due to the fact that HR operations in schools, conducted effectively, determine the quality of the provided services and consequently have a considerable impact on the success of the educational system. The purpose of this paper is to analyze the decisive role that GDPR plays in HR departments that operate in schools and in order to practically evaluate the aftermath of the Regulation during the first months of its applicability; a comparative use cases analysis in five highly dynamic schools, across three EU Member States, was attempted.

Adaptive Envelope Protection Control for the below and above Rated Regions of Wind Turbines

This paper presents a wind turbine envelope protection control algorithm that protects Variable Speed Variable Pitch (VSVP) wind turbines from damage during operation throughout their below and above rated regions, i.e. from cut-in to cut-out wind speed. The proposed approach uses a neural network that can adapt to turbines and their operating points. An algorithm monitors instantaneous wind and turbine states, predicts a wind speed that would push the turbine to a pre-defined envelope limit and, when necessary, realizes an avoidance action. Simulations are realized using the MS Bladed Wind Turbine Simulation Model for the NREL 5 MW wind turbine equipped with baseline controllers. In all simulations, through the proposed algorithm, it is observed that the turbine operates safely within the allowable limit throughout the below and above rated regions. Two example cases, adaptations to turbine operating points for the below and above rated regions and protections are investigated in simulations to show the capability of the proposed envelope protection system (EPS) algorithm, which reduces excessive wind turbine loads and expectedly increases the turbine service life.

Biomechanical Prediction of Veins and Soft Tissues beneath Compression Stockings Using Fluid-Solid Interaction Model

Elastic compression stockings (ECSs) have been widely applied in prophylaxis and treatment of chronic venous insufficiency of lower extremities. The medical function of ECS is to improve venous return and increase muscular pumping action to facilitate blood circulation, which is largely determined by the complex interaction between the ECS and lower limb tissues. Understanding the mechanical transmission of ECS along the skin surface, deeper tissues, and vascular system is essential to assess the effectiveness of the ECSs. In this study, a three-dimensional (3D) finite element (FE) model of the leg-ECS system integrated with a 3D fluid-solid interaction (FSI) model of the leg-vein system was constructed to analyze the biomechanical properties of veins and soft tissues under different ECS compression. The Magnetic Resonance Imaging (MRI) of the human leg was divided into three regions, including soft tissues, bones (tibia and fibula) and veins (peroneal vein, great saphenous vein, and small saphenous vein). The ECSs with pressure ranges from 15 to 26 mmHg (Classes I and II) were adopted in the developed FE-FSI model. The soft tissue was assumed as a Neo-Hookean hyperelastic model with the fixed bones, and the ECSs were regarded as an orthotropic elastic shell. The interfacial pressure and stress transmission were simulated by the FE model, and venous hemodynamics properties were simulated by the FSI model. The experimental validation indicated that the simulated interfacial pressure distributions were in accordance with the pressure measurement results. The developed model can be used to predict interfacial pressure, stress transmission, and venous hemodynamics exerted by ECSs and optimize the structure and materials properties of ECSs design, thus improving the efficiency of compression therapy.

The Influence of Strengthening on the Fundamental Frequency and Stiffness of a Confined Masonry Wall with an Opening for а Door

This paper presents the observations from a series of shaking-table tests done on a 1:1 scaled confined masonry wall model, with opening for a door – specimens CMDuS (confined masonry wall with opening for a door before strengthening) and CMDS (confined masonry wall with opening for a door after strengthening). Frequency and stiffness changes before and after GFRP (Glass Fiber Reinforced Plastic) wall strengthening are analyzed. Definition of dynamic properties of the models was the first step of the experimental testing, which enabled acquiring important information about the achieved stiffness (natural frequencies) of the model. The natural frequency was defined in the Y direction of the model by applying resonant frequency search tests. It is important to mention that both specimens CMDuS and CMDS are subjected to the same effects. The tests are realized in the laboratory of the Institute of Earthquake Engineering and Engineering Seismology (IZIIS), Skopje. The specimens were examined separately on the shaking table, with uniaxial, in-plane excitation. After testing, samples were strengthened with GFRP and re-tested. The initial frequency of the undamaged model CMDuS is 13.55 Hz, while at the end of the testing, the frequency decreased to 6.38 Hz. This emphasizes the reduction of the initial stiffness of the model due to damage, especially in the masonry and tie-beam to tie-column connection. After strengthening of the damaged wall, the natural frequency increases to 10.89 Hz. This highlights the beneficial effect of the strengthening. After completion of dynamic testing at CMDS, the natural frequency is reduced to 6.66 Hz.

Simulation and Analysis of Passive Parameters of Building in eQuest: A Case Study in Istanbul, Turkey

With rapid development of urbanization and improvement of living standards in the world, energy consumption and carbon emissions of the building sector are expected to increase in the near future; because of that, energy-saving issues have become more important among the engineers. Besides, the building sector is a major contributor to energy consumption and carbon emissions. The concept of efficient building appeared as a response to the need for reducing energy demand in this sector which has the main purpose of shifting from standard buildings to low-energy buildings. Although energy-saving should happen in all steps of a building during the life cycle (material production, construction, demolition), the main concept of efficient energy building is saving energy during the life expectancy of a building by using passive and active systems, and should not sacrifice comfort and quality to reach these goals. The main aim of this study is to investigate passive strategies (do not need energy consumption or use renewable energy) to achieve energy-efficient buildings. Energy retrofit measures were explored by eQuest software using a case study as a base model. The study investigates predictive accuracy for the major factors like thermal transmittance (U-value) of the material, windows, shading devices, thermal insulation, rate of the exposed envelope, window/wall ration, lighting system in the energy consumption of the building. The base model was located in Istanbul, Turkey. The impact of eight passive parameters on energy consumption had been indicated. After analyzing the base model by eQuest, a final scenario was suggested which had a good energy performance. The results showed a decrease in the U-values of materials, the rate of exposing buildings, and windows had a significant effect on energy consumption. Finally, savings in electric consumption of about 10.5%, and gas consumption by about 8.37% in the suggested model were achieved annually.

Supervisor Controller-Based Colored Petri Nets for Deadlock Control and Machine Failures in Automated Manufacturing Systems

This paper develops a robust deadlock control technique for shared and unreliable resources in automated manufacturing systems (AMSs) based on structural analysis and colored Petri nets, which consists of three steps. The first step involves using strict minimal siphon control to create a live (deadlock-free) system that does not consider resource failure. The second step uses an approach based on colored Petri net, in which all monitors designed in the first step are merged into a single monitor. The third step addresses the deadlock control problems caused by resource failures. For all resource failures in the Petri net model a common recovery subnet based on colored petri net is proposed. The common recovery subnet is added to the obtained system at the second step to make the system reliable. The proposed approach is evaluated using an AMS from the literature. The results show that the proposed approach can be applied to an unreliable complex Petri net model, has a simpler structure and less computational complexity, and can obtain one common recovery subnet to model all resource failures.

Influence of Dynamic Loads in the Structural Integrity of Underground Rooms

Among many factors affecting the stability of mining excavations, rock-bursts and tremors play a special role. These dynamic loads occur practically always and have different sources of generation. The most important of them is the commonly used mining technique, which disintegrates a certain area of the rock mass not only in the area of the planned mining, but also creates waves that significantly exceed this area affecting the structural elements. In this work it is analysed the consequences of dynamic loads over the structural elements in an underground room and pillar mine to avoid roof instabilities. With this end, dynamic loads were evaluated through in situ and laboratory tests and simulated with numerical modelling. Initially, the geotechnical characterization of all materials was carried out by mean of large-scale tests. Then, drill holes were done on the roof of the mine and were monitored to determine possible discontinuities in it. Three seismic stations and a triaxial accelerometer were employed to measure the vibrations from blasting tests, establish the dynamic behaviour of roof and pillars and develop the transmission laws. At last, computer simulations by FLAC3D software were done to check the effect of vibrations on the stability of the roofs. The study shows that in-situ tests have a greater reliability than laboratory samples because of eliminating the effect of heterogeneities, that the pillars work decreasing the amplitude of the vibration around them, and that the tensile strength of a beam and depending on its span is overcome with waves in phase and delayed. The obtained transmission law allows designing a blasting which guarantees safety and prevents the risk of future failures.

Banking Risk Management between the Prudential and the Operational Approaches

Since the nineties, all Moroccan banking institutions have to respect an arsenal of prudential ratios. The respect of these prudential measures aims to ensure the financial system stability. In order to do so, regulatory authorities tried to reduce the financial and operational risks incurred by the banking entities. Meanwhile, regulatory authorities demanded a balance sheet management work from banks. They also asked them to establish a management control system to manage operational risk, as well as an effort in terms of incurred risk-based commitments. Therefore, the prudential approach has a macroeconomic nature and it is presented as a determinant of the operational, microeconomic approach. This operational approach takes the form of a strategy that each banking entity must develop to manage the different banking risks. This study seeks to analyze the problem of risk management between the prudential and the operational approaches. It was processed through a literature review followed by an analysis of the Moroccan banking sector’s performance. At first, we will reconcile the inductive logic and then, the analytical one. The first approach consists of analyzing the phenomenon from a normative and conceptual perspective, while the second one will consist of considering the Moroccan banking system and analyzing the behavior of Moroccan banking entities in terms of risk management and performance. The results identified a favorable growth in terms of performance, despite the huge provisioning effort made to meet the international standards and the harmonization of the regulations.

A Constructivist Approach and Tool for Autonomous Agent Bottom-up Sequential Learning

During the initial phase of cognitive development, infants exhibit amazing abilities to generate novel behaviors in unfamiliar situations, and explore actively to learn the best while lacking extrinsic rewards from the environment. These abilities set them apart from even the most advanced autonomous robots. This work seeks to contribute to understand and replicate some of these abilities. We propose the Bottom-up hiErarchical sequential Learning algorithm with Constructivist pAradigm (BEL-CA) to design agents capable of learning autonomously and continuously through interactions. The algorithm implements no assumption about the semantics of input and output data. It does not rely upon a model of the world given a priori in the form of a set of states and transitions as well. Besides, we propose a toolkit to analyze the learning process at run time called GAIT (Generating and Analyzing Interaction Traces). We use GAIT to report and explain the detailed learning process and the structured behaviors that the agent has learned on each decision making. We report an experiment in which the agent learned to successfully interact with its environment and to avoid unfavorable interactions using regularities discovered through interaction.

Using Lagrange Equations to Study the Relative Motion of a Mechanism

The relative motion of a robotic arm formed by homogeneous bars of different lengths and masses, hinged to each other is investigated. The first bar of the mechanism is articulated on a platform, considered initially fixed on the surface of the Earth, while for the second case the platform is considered to be in rotation with respect to the Earth. For both analyzed cases the motion equations are determined using the Lagrangian formalism, applied in its traditional form, valid with respect to an inertial reference system, conventionally considered as fixed. However, in the second case, a generalized form of the formalism valid with respect to a non-inertial reference frame will also be applied. The numerical calculations were performed using a MATLAB program.

Intelligent Transport System: Classification of Traffic Signs Using Deep Neural Networks in Real Time

Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Inflammatory Markers in the Blood and Chronic Periodontitis

Background: Plasma levels of inflammatory markers are the expression of the infectious wastes of existing periodontitis, as well as of existing inflammation everywhere in the body. Materials and Methods: The study consists of the clinical part of the measurement of inflammatory markers of 23 patients diagnosed with chronic periodontitis and the recording of parental periodontal parameters of patient periodontal status: hemorrhage index and probe values, before and 7-10 days after non-surgical periodontal treatment. Results: The level of fibrinogen drops according to the categorization of disease progression, active and passive, with the biggest % (18%-30%) at the fluctuation 10-20 mg/d. Fluctuations in fibrinogen level according to the age of patients in the range 0-10 mg/dL under 40 years and over 40 years was 13%-26%, in the range 10-20 mg/dL was 26%-22%, in the 20-40 mg/dL was 9%-4%. Conclusions: Non-surgical periodontal treatment significantly reduces the level of non-inflammatory markers in the blood. Oral health significantly reduces the potential source for periodontal bacteria, with the potential of promoting thromboembolism, through interaction between thrombocytes.

Influence of Silica Surface Hydrophilicity on Adsorbed Water and Isopropanol Studied by in-situ NMR

Surface wettability is a crucial factor in oil recovery. In oil industry, the rock wettability involves the interplay between water, oil, and solid surface. Therefore, studying the interplay between adsorptions of water and hydrocarbon molecules on solid surface would be very informative for understanding rock wettability. Here we use the in-situ Nuclear Magnetic Resonance (NMR) gas isotherm technique to study competitive adsorptions of water and isopropanol, an intermediate step from hydrocarbons. This in-situ NMR technique obtains information on thermodynamic properties such as the isotherm, molecular dynamics via spin relaxation measurements, and adsorption kinetics such as how fast the system can reach thermal equilibrium after changes of vapor pressures. Using surfaces of silica glass beads, which can be modified from hydrophilic to hydrophobic, we obtained information on the influence of surface hydrophilicity on the state of surface water via obtained thermodynamic and dynamic properties.

The Mechanism Underlying Empathy-Related Helping Behavior: An Investigation of Empathy-Attitude- Action Model

Empathy has been an important issue in psychology, education, as well as cognitive neuroscience. Empathy has two major components: cognitive and emotional. Cognitive component refers to the ability to understand others’ perspectives, thoughts, and actions, whereas emotional component refers to understand how others feel. Empathy can be induced, attitude can then be changed, and with enough attitude change, helping behavior can occur. This finding leads us to two questions: is attitude change really necessary for prosocial behavior? And, what roles cognitive and affective empathy play? For the second question, participants with different psychopathic personality (PP) traits are critical because high PP people were found to suffer only affective empathy deficit. Their cognitive empathy shows no significant difference from the control group. 132 college students voluntarily participated in the current three-stage study. Stage 1 was to collect basic information including Interpersonal Reactivity Index (IRI), Psychopathic Personality Inventory-Revised (PPI-R), Attitude Scale, Visual Analogue Scale (VAS), and demographic data. Stage two was for empathy induction with three controversial scenarios, namely domestic violence, depression with a suicide attempt, and an ex-offender. Participants read all three stories and then rewrite the stories by one of two perspectives (empathetic vs. objective). They would then complete the VAS and Attitude Scale one more time for their post-attitude and emotional status. Three IVs were introduced for data analysis: PP (High vs. Low), Responsibility (whether or not the character is responsible for what happened), and Perspective-taking (Empathic vs. Objective). Stage 3 was for the action. Participants were instructed to freely use the 17 tokens they received as donations. They were debriefed and interviewed at the end of the experiment. The major findings were people with higher empathy tend to take more action in helping. Attitude change is not necessary for prosocial behavior. The controversy of the scenarios and how familiar participants are towards target groups play very important roles. Finally, people with high PP tend to show more public prosocial behavior due to their affective empathy deficit. Pre-existing value and belief as well as recent dramatic social events seem to have a big impact and possibly reduce the effect of the independent variables (IV) in our paradigm.

Analyzing Microblogs: Exploring the Psychology of Political Leanings

Microblogging has become increasingly popular for commenting on current events, spreading gossip, and encouraging individualism--which favors its low-context communication channel. These social media (SM) platforms allow users to express opinions while interacting with a wide range of populations. Hashtags allow immediate identification of like-minded individuals worldwide on a vast array of topics. The output of the analytic tool, Linguistic Inquiry and Word Count (LIWC)--a program that associates psychological meaning with the frequency of use of specific words--may suggest the nature of individuals’ internal states and general sentiments. When applied to groupings of SM posts unified by a hashtag, such information can be helpful to community leaders during periods in which the forming of public opinion happens in parallel with the unfolding of political, economic, or social events. This is especially true when outcomes stand to impact the well-being of the group. Here, we applied the online tools, Google Translate and the University of Texas’s LIWC, to a 90-posting sample from a corpus of Colombian Spanish microblogs. On translated disjoint sets, identified by hashtag as being authored by advocates of voting “No,” advocates voting “Yes,” and entities refraining from hashtag use, we observed the value of LIWC’s Tone feature as distinguishing among the categories and the word “peace,” as carrying particular significance, due to its frequency of use in the data.

Deployment of a Biocompatible International Space Station into Geostationary Orbit

This study explores the possibility of a space station that will occupy a geostationary equatorial orbit (GEO) and create artificial gravity using centripetal acceleration. The concept of the station is to create a habitable, safe environment that can increase the possibility of space tourism by reducing the wide variation of hazards associated with space exploration. The ability to control the intensity of artificial gravity through Hall-effect thrusters will allow experiments to be carried out at different levels of artificial gravity. A feasible prototype model was built to convey the concept and to enable cost estimation. The SpaceX Falcon Heavy rocket with a 26,700 kg payload to GEO was selected to take the 675 tonne spacecraft into orbit; space station construction will require up to 30 launches, this would be reduced to 5 launches when the SpaceX BFR becomes available. The estimated total cost of implementing the Sussex Biocompatible International Space Station (BISS) is approximately $47.039 billion, which is very attractive when compared to the cost of the International Space Station, which cost $150 billion.

Estimation of Crustal Thickness within the Sokoto Basin North-Western Nigeria Using Bouguer Gravity Anomaly Data

This research proposes an interpretation of the Bouguer’ gravity anomaly data of some parts of Sokoto basin for the estimation of crustal thickness. The study area is bounded between latitudes 1100′0″N and 1300′0″N, and longitudes 400′0″E and 600′0″E that covered Koko, Jega, B/Kebbi, Argungu, Lema, Bodinga, Tamgaza, Gunmi,Daki Takwas, Dange, Sokoto, Ilella, T/Mafara, Anka, Maru, Gusau, K/Namoda, and Sabon Birni within Sokoto, Kebbi and Zamfara state respectively. The established map of the study area was digitized in X, Y and Z format using excel software package and the digitized data were processed using Surfer version 13 software. The Moho and Conrad depths based on a relationship between Bouguer’ gravity anomaly determined crustal thickness were estimated as 35 to 37 km and 19 to 21 km, respectively. The crustal region has been categorized into: Crustal thinning zone that is the region with high gravity anomaly value due to its greater geothermal energy and also Crustal thickening zone which the region with low anomaly values due to its lower geothermal energy. Birnin kebbi, Jega, Sokoto were identified as the region of hydrocarbon potential with an estimate of 35 km thickness within the crustal region which is referred to as crustal thickening as a result of its low but sufficient geothermal energy to decompose organic matter within the region to form hydrocarbons.

Mapping the Digital Landscape: An Analysis of Party Differences between Conventional and Digital Policy Positions

Although digitization is a buzzword in almost every election campaign, the political parties leave voters largely in the dark about their specific positions on digital issues. In the run-up to the 2019 elections in Switzerland, the ‘Digitization Monitor’ project (DMP) was launched in order to change this situation. Within the framework of the DMP, all 4,736 candidates were surveyed about their digital policy positions and values. The DMP is designed as a digital policy supplement to the existing ‘smartvote’ voting advice application. This enabled a direct comparison of the digital policy attitudes according to the DMP with the topics of the ‘smartvote’ questionnaire which are comprehensive in content but mainly related to conventional policy areas. This paper’s main research goal is to analyze and visualize possible differences between conventional and digital policy areas in terms of response patterns between and within political parties. The analysis is based on dimensionality reduction methods (multidimensional scaling and principal component analysis) for the visualization of inter-party differences, and on standard deviation as a measure of variation for the evaluation of intra-party unity. The results reveal that digital issues show a lower degree of inter-party polarization compared to conventional policy areas. Thus, the parties have more common ground in issues on digitization than in conventional policy areas. In contrast, the study reveals a mixed picture regarding intra-party unity. Homogeneous parties show a lower degree of unity in digitization issues whereas parties with heterogeneous positions in conventional areas have more united positions in digital areas. All things considered, the findings are encouraging as less polarized conditions apply to the debate on digital development compared to conventional politics. For the future, it would be desirable if in further countries similar projects to the DMP could emerge to broaden the basis for conclusions.