Search results for: quantum algorithms

Authors: Balamurugan L.

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Artificial intelligence (AI) and its applications in Intellectual Property Rights (IPR) has been significantly growing in recent years. In last couple of years, AI tools for Patent Research and Patent Analytics have been well-stabilized in terms of accuracy of references and representation of identified patent insights. However, AI tools for Patent Prosecution and Patent Litigation are still in the nascent stage and there may be a significant potential if such market is explored further. Our research is primarily focused on identifying potential whitespaces and schematic algorithms to automate the Patent Prosecution and Patent Litigation Process of the Intellectual Property. The schematic algorithms may assist leading AI tool developers, to explore such opportunities in the field of Intellectual Property. Our research is also focused on identification of pitfalls of the AI. For example, Information Security and its impact in IPR, and Potential remediations to sustain the IPR in the digital environment.

Keywords: artificial intelligence, patent analytics, patent drafting, patent litigation, patent prosecution, patent research

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Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

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Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM

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Authors: Navdeep Singh Randhawa, Shally Sharma

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Wireless Sensor Network is an autonomous technology emanating in the current scenario at a fast pace. This technology faces a number of defiance’s and energy management is one of them, which has a huge impact on the network lifetime. To sustain energy the different types of routing protocols have been flourished. The classical routing protocols are no more compatible to perform in complicated environments. Hence, in the field of routing the intelligent algorithms based on nature systems is a turning point in Wireless Sensor Network. These nature-based algorithms are quite efficient to handle the challenges of the WSN as they are capable of achieving local and global best optimization solutions for the complex environments. So, the main attention of this paper is to develop a routing algorithm based on some swarm intelligent technique to enhance the performance of Wireless Sensor Network.

Keywords: wireless sensor network, routing, swarm intelligence, MPRSO

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Authors: Radhia Toujani, Jalel Akaichi

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Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm.

Keywords: agglomerative hierarchical clustering, community structure, divisive hierarchical clustering, hybrid hierarchical clustering, opinion mining, social network, social network analysis

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Authors: Tushar Bhardwaj

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Internet of Things (IoT) possesses a dynamic network where the network nodes (mobile devices) are added and removed constantly and randomly, hence the traffic distribution in the network is quite variable and irregular. The basic but very important part in any network is route searching. We have many conventional route searching algorithms like link-state, and distance vector algorithms but they are restricted to the static point to point network topology. In this paper we propose a model that uses the Ant Colony Algorithm for route searching. It is dynamic in nature and has positive feedback mechanism that conforms to the route searching. We have also embedded the concept of Non-Deterministic Finite Automata [NDFA] minimization to reduce the network to increase the performance. Results show that Ant Colony Algorithm gives the shortest path from the source to destination node and NDFA minimization reduces the broadcasting storm effectively.

Keywords: routing, ant colony algorithm, NDFA, IoT

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Authors: H. Elmsellem, A. Aouniti, S. Radi, A. Chetouani, B. Hammouti

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The synthesis of new organic molecules offers various molecular structures containing heteroatoms and substituents for corrosion protection in acid pickling of metals. The most synthesized compounds are the nitrogen heterocyclic compounds, which are known to be excellent complex or chelate forming substances with metals. The choice of the inhibitor is based on two considerations: first it could be synthesized conveniently from relatively cheap raw materials, secondly, it contains the electron cloud on the aromatic ring or, the electro negative atoms such as nitrogen and oxygen in the relatively long chain compounds. In the present study, (NE)‐2‐methyl‐N‐(thiophen‐2‐ylmethylidene) aniline(T) was synthesized and its inhibiting action on the corrosion of mild steel in 1 M hydrochloric acid was examined by different corrosion methods, such as weight loss, potentiodynamic polarization and electrochemical impedance spectroscopy (EIS). The experimental results suggest that this compound is an efficient corrosion inhibitor and the inhibition efficiency increases with the increase in inhibitor concentration. Adsorption of this compound on mild steel surface obeys Langmuir’s isotherm. Correlation between quantum chemical calculations and inhibition efficiency of the investigated compound is discussed using the Density Functional Theory method (DFT).

Keywords: mild steel, Schiff base, inhibition, corrosion, HCl, quantum chemical

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Authors: Hany M. Abd El-Lateef

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Two novel Schiff bases, 5-bromo-2-[(E)-(pyridin-3-ylimino) methyl] phenol (HBSAP) and 5-bromo-2-[(E)-(quinolin-8-ylimino) methyl] phenol (HBSAQ) have been synthesized. They have been characterized by elemental analysis and spectroscopic techniques (UV–Vis, IR and NMR). Moreover, the molecular structure of HBSAP and HBSAQ compounds are determined by single crystal X-ray diffraction technique. The inhibition activity of HBSAP and HBSAQ for carbon steel in 3.5 %NaCl+0.1 M HCl for both short and long immersion time, at different temperatures (20-50 ºC), was investigated using electrochemistry and surface characterization. The potentiodynamic polarization shows that the inhibitors molecule is more adsorbed on the cathodic sites. Its efficiency increases with increasing inhibitor concentrations (92.8 % at the optimal concentration of 10-3 M for HBSAQ). Adsorption of the inhibitors on the carbon steel surface was found to obey Langmuir’s adsorption isotherm with physical/chemical nature of the adsorption, as it is shown also by scanning electron microscopy. Further, the electronic structural calculations using quantum chemical methods were found to be in a good agreement with the results of the experimental studies.

Keywords: carbon steel, Schiff bases, corrosion inhibition, SEM, electrochemical techniques

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Authors: Linh Nguyen Tung, Anh Truong Viet, Nghien Nguyen Ba, Chuong Trinh Trong

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This paper proposes a reconfiguration methodology based on a continuous genetic algorithm (CGA) and particle swarm optimization (PSO) for minimizing active power loss and minimizing voltage deviation. Both algorithms are adapted using graph theory to generate feasible individuals, and the modified crossover is used for continuous variable of CGA. To demonstrate the performance and effectiveness of the proposed methods, a comparative analysis of CGA with PSO for network reconfiguration, on 33-node and 119-bus radial distribution system is presented. The simulation results have shown that both CGA and PSO can be used in the distribution network reconfiguration and CGA outperformed PSO with significant success rate in finding optimal distribution network configuration.

Keywords: distribution network reconfiguration, particle swarm optimization, continuous genetic algorithm, power loss reduction, voltage deviation

Procedia PDF Downloads 187

Authors: Y. Mehdipour, S. Ebrahimi, A. Jahanpour, F. Seyedzaei, B. Sabayan, A. Karimi, H. Amirifard

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Ischemic stroke is one of the common reasons for disability and mortality. The fourth leading cause of death in the world and the third in some other sources. Only 1/3 of the patients with ischemic stroke fully recover, 1/3 of them end in permanent disability and 1/3 face death. Thus, the use of predictive models to predict stroke has a vital role in reducing the complications and costs related to this disease. Thus, the aim of this study was to specify the effective factors and predict ischemic stroke with the help of DM methods. The present study was a descriptive-analytic study. The population was 213 cases from among patients referring to Ali ibn Abi Talib (AS) Hospital in Zahedan. Data collection tool was a checklist with the validity and reliability confirmed. This study used DM algorithms of decision tree for modeling. Data analysis was performed using SPSS-19 and SPSS Modeler 14.2. The results of the comparison of algorithms showed that CHAID algorithm with 95.7% accuracy has the best performance. Moreover, based on the model created, factors such as anemia, diabetes mellitus, hyperlipidemia, transient ischemic attacks, coronary artery disease, and atherosclerosis are the most effective factors in stroke. Decision tree algorithms, especially CHAID algorithm, have acceptable precision and predictive ability to determine the factors affecting ischemic stroke. Thus, by creating predictive models through this algorithm, will play a significant role in decreasing the mortality and disability caused by ischemic stroke.

Keywords: data mining, ischemic stroke, decision tree, Bayesian network

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Authors: S. C. Sharma, Ankit Gambhir, Rajeev Arya

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In today’s era data security is an important concern and most demanding issues because it is essential for people using online banking, e-shopping, reservations etc. The two major techniques that are used for secure communication are Cryptography and Steganography. Cryptographic algorithms scramble the data so that intruder will not able to retrieve it; however steganography covers that data in some cover file so that presence of communication is hidden. This paper presents the implementation of Ron Rivest, Adi Shamir, and Leonard Adleman (RSA) Algorithm with Image and Audio Steganography and Data Encryption Standard (DES) Algorithm with Image and Audio Steganography. The coding for both the algorithms have been done using MATLAB and its observed that these techniques performed better than individual techniques. The risk of unauthorized access is alleviated up to a certain extent by using these techniques. These techniques could be used in Banks, RAW agencies etc, where highly confidential data is transferred. Finally, the comparisons of such two techniques are also given in tabular forms.

Keywords: audio steganography, data security, DES, image steganography, intruder, RSA, steganography

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Authors: Muhammad Saqlain, Nazar Abbas Saqib, Muazzam A. Khan

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Heart Failure (HF) has become the major health problem in our society. The prevalence of HF has increased as the patient’s ages and it is the major cause of the high mortality rate in adults. A successful identification and progression of HF can be helpful to reduce the individual and social burden from this syndrome. In this study, we use a real data set of cardiac patients to propose a classification model for the identification and progression of HF. The data set has divided into three age groups, namely young, adult, and old and then each age group have further classified into four classes according to patient’s current physical condition. Contemporary Data Mining classification algorithms have been applied to each individual class of every age group to identify the HF. Decision Tree (DT) gives the highest accuracy of 90% and outperform all other algorithms. Our model accurately diagnoses different stages of HF for each age group and it can be very useful for the early prediction of HF.

Keywords: decision tree, heart failure, data mining, classification model

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Authors: Lucja Rodzik, Joanna Lewandowska-Lancucka, Michal Szuwarzynski, Krzysztof Szczubialka, Maria Nowakowska

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The analysis of nucleobases is of great importance for bioscience since their abnormal concentration in body fluids suggests the deficiency and mutation of the immune system, and it is considered to be an important parameter for diagnosis of various diseases. The presented conjugate meets the need for development of the effective, selective and highly sensitive sensor for nucleobase/nucleoside detection. The novel, highly fluorescent cadmium telluride quantum dots (CdTe QDs) functionalized with thymine and stabilized with thioglycolic acid (TGA) conjugates has been developed and thoroughly characterized. Successful formation of the material was confirmed by elemental analysis, and UV–Vis fluorescence and FTIR spectroscopies. The crystalline structure of the obtained product was characterized with X-ray diffraction (XRD) method. The composition of CdTe QDs and their thymine conjugate was also examined using X-ray photoelectron spectroscopy (XPS). The size of the CdTe-thymine was 3-6 nm as demonstrated using atomic force microscopy (AFM) and high resolution transmission electron microscopy (HRTEM) imaging. The plasmon resonance fluorescence band at 540 nm on excitation at 351 nm was observed for these nanoparticles. The intensity of this band increased with the increase in the amount of conjugated thymine with no shift in its position. Based on the fluorescence measurements, it was found that the CdTe-thymine conjugate interacted efficiently and selectively not only with adenine, a nucleobase complementary to thymine, but also with nucleosides and adenine-containing modified nucleosides, i.e., 5′-deoxy-5′-(methylthio)adenosine (MTA) and 2’-O-methyladenosine, the urinary tumor markers which allow monitoring of the disease progression. The applicability of the CdTe-thymine sensor for the real sample analysis was also investigated in simulated urine conditions. High sensitivity and selectivity of CdTe-thymine fluorescence towards adenine, adenosine and modified adenosine suggest that obtained conjugate can be potentially useful for development of the biosensor for complementary nucleobase/nucleoside detection.

Keywords: CdTe quantum dots, conjugate, sensor, thymine

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Authors: Umar D. M., Aminu A. M., Izaddeen K. Y.

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Deployments of mini macrocell base stations also referred to as femtocells, improve the quality of service of indoor and outdoor users. Nevertheless, mobility management remains a key issue with regards to their deployment. This paper is leaned towards this issue, with an in-depth focus on the most important aspect of mobility management -handover. In handover management, making a handover decision in the LTE two-tier macrocell femtocell network is a crucial research area. Decision algorithms in this research are classified and comparatively analyzed according to received signal strength, user equipment speed, cost function, and interference. However, it was observed that most of the discussed decision algorithms fail to consider cell selection with hybrid access policy in a single macrocell multiple femtocell scenario, another observation was a majority of these algorithms lack the incorporation of user equipment residence parameter. Not including this parameter boosts the number of unnecessary handover occurrence. To deal with these issues, a sophisticated handover decision algorithm is proposed. The proposed algorithm considers the user’s velocity, received signal strength, residence time, as well as the femtocell base station’s access policy. Simulation results have shown that the proposed algorithm reduces the number of unnecessary handovers when compared to conventional received signal strength-based handover decision algorithm.

Keywords: user-equipment, radio signal service, long term evolution, mobility management, handoff

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Authors: Federico Garrido, Mostafa El Hayani, Ahmed Shams

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The exploration of the potential of artificial intelligence (AI) in architecture is still embryonic, however, its latent capacity to change design disciplines is significant. 'Synthetic Classism' is a research project that questions the underlying aspects of classically organized architecture not just in aesthetic terms but also from a geometrical and morphological point of view, intending to generate new architectural information using historical examples as source material. The main aim of this paper is to explore the uses of artificial intelligence and machine learning algorithms in architectural design while creating a coherent narrative to be contained within a design process. The purpose is twofold: on one hand, to develop and train machine learning algorithms to produce architectural information of small pavilions and on the other, to synthesize new information from previous architectural drawings. These algorithms intend to 'interpret' graphical information from each pavilion and then generate new information from it. The procedure, once these algorithms are trained, is the following: parting from a line profile, a synthetic 'front view' of a pavilion is generated, then using it as a source material, an isometric view is created from it, and finally, a top view is produced. Thanks to GAN algorithms, it is also possible to generate Front and Isometric views without any graphical input as well. The final intention of the research is to produce isometric views out of historical information, such as the pavilions from Sebastiano Serlio, James Gibbs, or John Soane. The idea is to create and interpret new information not just in terms of historical reconstruction but also to explore AI as a novel tool in the narrative of a creative design process. This research also challenges the idea of the role of algorithmic design associated with efficiency or fitness while embracing the possibility of a creative collaboration between artificial intelligence and a human designer. Hence the double feature of this research, both analytical and creative, first by synthesizing images based on a given dataset and then by generating new architectural information from historical references. We find that the possibility of creatively understand and manipulate historic (and synthetic) information will be a key feature in future innovative design processes. Finally, the main question that we propose is whether an AI could be used not just to create an original and innovative group of simple buildings but also to explore the possibility of fostering a novel architectural sensibility grounded on the specificities on the architectural dataset, either historic, human-made or synthetic.

Keywords: architecture, central pavilions, classicism, machine learning

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Authors: S. Adhirai, R. P. Mahapatra, Paramjit Singh

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Optimization is an important tool in making decisions and in analysing physical systems. In mathematical terms, an optimization problem is the problem of finding the best solution from among the set of all feasible solutions. The paper discusses the Whale Optimization Algorithm (WOA), and its applications in different fields. The algorithm is tested using MATLAB because of its unique and powerful features. The benchmark functions used in WOA algorithm are grouped as: unimodal (F1-F7), multimodal (F8-F13), and fixed-dimension multimodal (F14-F23). Out of these benchmark functions, we show the experimental results for F7, F11, and F19 for different number of iterations. The search space and objective space for the selected function are drawn, and finally, the best solution as well as the best optimal value of the objective function found by WOA is presented. The algorithmic results demonstrate that the WOA performs better than the state-of-the-art meta-heuristic and conventional algorithms.

Keywords: optimization, optimal value, objective function, optimization problems, meta-heuristic optimization algorithms, Whale Optimization Algorithm, implementation, MATLAB

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Authors: David C. Ni

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The current efforts for Grand Unification Theory (GUT) can be classified into General Relativity, Quantum Mechanics, String Theory and the related formalisms. In the geometric approaches for extending General Relativity, the efforts are establishing global and local invariance embedded into metric formalisms, thereby additional dimensions are constructed for unifying canonical formulations, such as Hamiltonian and Lagrangian formulations. The approaches of extending Quantum Mechanics adopt symmetry principle to formulate algebra-group theories, which evolved from Maxwell formulation to Yang-Mills non-abelian gauge formulation, and thereafter manifested the Standard model. This thread of efforts has been constructing super-symmetry for mapping fermion and boson as well as gluon and graviton. The efforts of String theory currently have been evolving to so-called gauge/gravity correspondence, particularly the equivalence between type IIB string theory compactified on AdS5 × S5 and N = 4 supersymmetric Yang-Mills theory. Other efforts are also adopting cross-breeding approaches of above three formalisms as well as competing formalisms, nevertheless, the related symmetries, dualities, and correspondences are outlined as principles and techniques even these terminologies are defined diversely and often generally coined as duality. In this paper, we firstly classify these dualities from the perspective of physics. Then examine the hierarchical structure of classes from mathematical perspective referring to Coleman-Mandula theorem, Hidden Local Symmetry, Groupoid-Categorization and others. Based on Fundamental Theorems of Algebra, we argue that rather imposing effective constraints on different algebras and the related extensions, which are mainly constructed by self-breeding or self-mapping methodologies for sustaining invariance, we propose a new addition, momentum-angular momentum duality at the level of electromagnetic duality, for rationalizing the duality algebras, and then characterize this duality numerically with attempt for addressing some unsolved problems in physics and astrophysics.

Keywords: general relativity, quantum mechanics, string theory, duality, symmetry, correspondence, algebra, momentum-angular-momentum

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Authors: Jorge Rodriguez-Guerra, Carlos Calleja, Aron Pujana, Iker Elorza, Ana Maria Macarulla

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Society demands more reliable manufacturing processes capable of producing high quality products in shorter production cycles. New control algorithms have been studied to satisfy this paradigm, in which Fault-Tolerant Control (FTC) plays a significant role. It is suitable to detect, isolate and adapt a system when a harmful or faulty situation appears. In this paper, a general overview about FTC characteristics are exposed; highlighting the properties a system must ensure to be considered faultless. In addition, a research to identify which are the main FTC techniques and a classification based on their characteristics is presented in two main groups: Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant Controllers (PFTCs). AFTC encompasses the techniques capable of re-configuring the process control algorithm after the fault has been detected, while PFTC comprehends the algorithms robust enough to bypass the fault without further modifications. The mentioned re-configuration requires two stages, one focused on detection, isolation and identification of the fault source and the other one in charge of re-designing the control algorithm by two approaches: fault accommodation and control re-design. From the algorithms studied, one has been selected and applied to a case study based on an industrial hydraulic-press. The developed model has been embedded under a real-time validation platform, which allows testing the FTC algorithms and analyse how the system will respond when a fault arises in similar conditions as a machine will have on factory. One AFTC approach has been picked up as the methodology the system will follow in the fault recovery process. In a first instance, the fault will be detected, isolated and identified by means of a neural network. In a second instance, the control algorithm will be re-configured to overcome the fault and continue working without human interaction.

Keywords: fault-tolerant control, electro-hydraulic actuator, fault detection and isolation, control re-design, real-time

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Authors: Shoubhik Mandal, Debarghya Mallick, Abhishek Banerjee, R. Ganesan, P. S. Anil Kumar

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We report magnetotransport measurements in Bi₁Sb₁Te₁.₅Se₁.₅/RuCl₃ heterostructure nanodevices. Bi₁Sb₁Te₁.₅Se₁.₅ (BSTS) is a strong three-dimensional topological insulator (3D-TI) that hosts conducting topological surface states (TSS) enclosing an insulating bulk. α-RuCl₃ (namely, RuCl₃) is an anti-ferromagnet that is predicted to behave as a Kitaev-like quantum spin liquid carrying Majorana excitations. Temperature (T)-dependent resistivity measurements show the interplay between parallel bulk and surface transport channels. At T < 150 K, surface state transport dominates over bulk transport. Multi-channel weak anti-localization (WAL) is observed, as a sharp cusp in the magnetoconductivity, indicating strong spin-orbit coupling. The presence of top and bottom topological surface states (TSS), including a pair of electrically coupled Rashba surface states (RSS), are indicated. Non-linear Hall effect, explained by a two-band model, further supports this interpretation. Finally, a low-T logarithmic resistance upturn is analyzed using the Lu-Shen model, supporting the presence of gapless surface states with a π Berry phase.

Keywords: topological materials, electrical transport, Lu-Shen model, quantum spin liquid

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Authors: Aydin Teymourifar, Gurkan Ozturk

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In this paper, a distributed hybrid algorithm is proposed for solving the job shop scheduling problem. The suggested method executes different artificial neural networks, heuristics and meta-heuristics simultaneously on more than one machine. The neural networks are used to control the constraints of the problem while the meta-heuristics search the global space and the heuristics are used to prevent the premature convergence. To attain an efficient distributed intelligent method for solving big and distributed job shop scheduling problems, Apache Spark and Hadoop frameworks are used. In the algorithm implementation and design steps, new approaches are applied. Comparison between the proposed algorithm and other efficient algorithms from the literature shows its efficiency, which is able to solve large size problems in short time.

Keywords: distributed algorithms, Apache Spark, Hadoop, job shop scheduling, neural network

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Authors: Eman I. Raslan, Haitham S. Hamza, Reda A. El-Khoribi

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Fiber-Wireless (FiWi) networks are a promising candidate for future broadband access networks. These networks combine the optical network as the back end where different passive optical network (PON) technologies are realized and the wireless network as the front end where different wireless technologies are adopted, e.g. LTE, WiMAX, Wi-Fi, and Wireless Mesh Networks (WMNs). The convergence of both optical and wireless technologies requires designing architectures with robust efficient and effective bandwidth allocation schemes. Different bandwidth allocation algorithms have been proposed in FiWi networks aiming to enhance the different segments of FiWi networks including wireless and optical subnetworks. In this survey, we focus on the differentiating between the different bandwidth allocation algorithms according to their enhancement segment of FiWi networks. We classify these techniques into wireless, optical and Hybrid bandwidth allocation techniques.

Keywords: fiber-wireless (FiWi), dynamic bandwidth allocation (DBA), passive optical networks (PON), media access control (MAC)

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Authors: Alexey Mustafin

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This paper explores the complex interplay between materialism, spiritualism, dualism, and non-dualism in the context of both Western and Eastern philosophical traditions. The research question is centered around understanding the implications of these perspectives on our comprehension of reality. The study employs a comparative analysis of worldviews, scientific perspectives, and case studies to achieve its objectives. The theoretical framework examines the critiques of materialism, spiritualism, dualism, and non-dualism, synthesizing different perspectives. A comparative analysis of Western (Greek, Christian, and Enlightenment) and Eastern (Hinduism, Buddhism, and Taoism) philosophical traditions provides a holistic understanding of diverse worldviews. The study further investigates scientific perspectives, including classical physics, quantum physics, biology, neuroscience, and their implications on the understanding of reality. Case studies on near-death experiences, meditation, healing, and parapsychology serve as practical examples of the interplay between these perspectives. The synthesis of findings offers insights into the implications for our understanding of reality and highlights future directions for research in this interdisciplinary field.

Keywords: biocentrism, quantum physics, neurosciense and consciousness, meditation and brain, religion experiences and scientific research, buddhism and science

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Authors: Jonathan E. Oghenekohwo

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The return to scale constitutes a significant investment index in the determination of the quantum of resources that is deployed in investment decision on worker’s continuing professional development. Such investment decision is always predicted on the expected outcomes to the individual, institution and the society in context. Several investments in the development of human capacity on the job have been made, but the return to the scale of such seems not to have been correlated positively with the quantum of resources invested in terms of productivity and performance among workers in many universities. This paper thus found out that, despite the commitment and policy instrument to avail workers the right of continuing professional development, the multiplier effects are not evident in diligence, commitment, honesty, dedication, productivity and improved performance on the job among most administrative staff in Nigerian Universities This author, therefore concludes that, given the policy on the right of workers to get trained on-the job, the outcomes of such training must reflect on the overall performance indices, otherwise, institutions should carry out a forensic analysis of the types of continuing professional development programmes that workers participate in, whether or not, they are consistent with the vision and mission of the institutions in terms of economies of scale of workers professional development to the individual, institution and the nation in context.

Keywords: continuing, professional development, economies of scale, worker’s education, administrative staff

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Authors: Ho-Wa Li, Sai-Wing Tsang

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The excitonic effect in organic semiconductors plays a key role in determining the electronic devices performance. Strong exciton binding energy has been regarded as the detrimental factor limiting the further improvement in organic photovoltaic cells. To the best of our knowledge, only limited reported can be found in measuring the exciton binding energy in organic photovoltaic materials. Conventional sophisticated approach using photoemission spectroscopy (UPS and IPES) would limit the wide access of the investigation. Here, we demonstrate a facile approach to study the electrical and optical quantum efficiencies of a series of conjugated photovoltaic polymer, fullerene and non-fullerene materials. Quantitative values of the exciton binding energy in those prototypical materials were obtained with concise photovoltaic device structure. And the extracted binding energies have excellent agreement with those determined by the conventional photoemission technique. More importantly, our findings can provide valuable information on the excitonic dissociation in the first excited state. Particularly, we find that the high binding energy of some non-fullerene acceptors limits the combination of polymer acceptors for efficiency exciton dissociation. The results bring insight into the engineering of excitonic effect for the development of efficient organic photovoltaic cells.

Keywords: organic photovoltaics, quantum efficiency, exciton binding energy, device physics

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Authors: Md. Shah Alam

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Mushfiq Ahmad has defined a Mixed Number, which is the sum of a scalar and a Cartesian vector. He has also defined the elementary group operations of Mixed numbers i.e. the norm of Mixed numbers, the product of two Mixed numbers, the identity element and the inverse. It has been observed that Mixed Number is consistent with Pauli matrix algebra and a handy tool to work with Dirac electron theory. Its use as a mathematical method in Physics has been studied. (1) We have applied Mixed number in Quantum Mechanics: Mixed Number version of Displacement operator, Vector differential operator, and Angular momentum operator has been developed. Mixed Number method has also been applied to Klein-Gordon equation. (2) We have applied Mixed number in Electrodynamics: Mixed Number version of Maxwell’s equation, the Electric and Magnetic field quantities and Lorentz Force has been found. (3) An associative transformation of Mixed Number numbers fulfilling Lorentz invariance requirement is developed. (4) We have applied Mixed number algebra as an extension of Complex number. Mixed numbers and the Quaternions have isomorphic correspondence, but they are different in algebraic details. The multiplication of unit Mixed number and the multiplication of unit Quaternions are different. Since Mixed Number has properties similar to those of Pauli matrix algebra, Mixed Number algebra is a more convenient tool to deal with Dirac equation.

Keywords: mixed number, special relativity, quantum mechanics, electrodynamics, pauli matrix

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Authors: Hussain Syed Asad, Richard Kwok Kit Yuen, Gongsheng Huang

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Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance.

Keywords: energy performance, hybrid adaptive modeling, hybrid genetic algorithms, real-time optimization, heating, ventilation, and air-conditioning

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Authors: Yan Torres, Fernanda Simoes, Francisco Petrucci-Fonseca, Freddie-Jeanne Richard

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The non-invasive samples are an alternative of collecting genetic samples directly. Non-invasive samples are collected without the manipulation of the animal (e.g., scats, feathers and hairs). Nevertheless, the use of non-invasive samples has some limitations. The main issue is degraded DNA, leading to poorer extraction efficiency and genotyping. Those errors delayed for some years a widespread use of non-invasive genetic information. Possibilities to limit genotyping errors can be done using analysis methods that can assimilate the errors and singularities of non-invasive samples. Genotype matching and population estimation algorithms can be highlighted as important analysis tools that have been adapted to deal with those errors. Although, this recent development of analysis methods there is still a lack of empirical performance comparison of them. A comparison of methods with dataset different in size and structure can be useful for future studies since non-invasive samples are a powerful tool for getting information specially for endangered and rare populations. To compare the analysis methods, four different datasets used were obtained from the Dryad digital repository were used. Three different matching algorithms (Cervus, Colony and Error Tolerant Likelihood Matching - ETLM) are used for matching genotypes and two different ones for population estimation (Capwire and BayesN). The three matching algorithms showed different patterns of results. The ETLM produced less number of unique individuals and recaptures. A similarity in the matched genotypes between Colony and Cervus was observed. That is not a surprise since the similarity between those methods on the likelihood pairwise and clustering algorithms. The matching of ETLM showed almost no similarity with the genotypes that were matched with the other methods. The different cluster algorithm system and error model of ETLM seems to lead to a more criterious selection, although the processing time and interface friendly of ETLM were the worst between the compared methods. The population estimators performed differently regarding the datasets. There was a consensus between the different estimators only for the one dataset. The BayesN showed higher and lower estimations when compared with Capwire. The BayesN does not consider the total number of recaptures like Capwire only the recapture events. So, this makes the estimator sensitive to data heterogeneity. Heterogeneity in the sense means different capture rates between individuals. In those examples, the tolerance for homogeneity seems to be crucial for BayesN work properly. Both methods are user-friendly and have reasonable processing time. An amplified analysis with simulated genotype data can clarify the sensibility of the algorithms. The present comparison of the matching methods indicates that Colony seems to be more appropriated for general use considering a time/interface/robustness balance. The heterogeneity of the recaptures affected strongly the BayesN estimations, leading to over and underestimations population numbers. Capwire is then advisable to general use since it performs better in a wide range of situations.

Keywords: algorithms, genetics, matching, population

Procedia PDF Downloads 143

Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

Abstract:

Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

Procedia PDF Downloads 295

Authors: Elias Akoury

Abstract:

Nuclear Magnetic Resonance (NMR) spectroscopy is an indispensable technique used in structure determination of small and macromolecules to study their physical properties, elucidation of characteristic interactions, dynamics and thermodynamic processes. Quantum mechanics defines the theoretical description of NMR spectroscopy and treatment of the dynamics of nuclear spin systems. The phenomenon of residual dipolar coupling (RDCs) has become a routine tool for accurate structure determination by providing global orientation information of magnetic dipole-dipole interaction vectors within a common reference frame. This offers accessibility of distance-independent angular information and insights to local relaxation. The measurement of RDCs requires an anisotropic orientation medium for the molecules to partially align along the magnetic field. This can be achieved by introduction of liquid crystals or attaching a paramagnetic center. Although anisotropic paramagnetic tags continue to mark achievements in the biomolecular NMR of large proteins, its application in small organic molecules remains unspread. Here, we propose a strategy for the synthesis of a lanthanide tag and the measurement of RDCs in organic molecules using paramagnetic lanthanide complexes.

Keywords: lanthanide tags, NMR spectroscopy, residual dipolar coupling, quantum mechanics of spin dynamics

Procedia PDF Downloads 188

Authors: Hong Kai, Zhong Jun Jie, Chen Lin Qi, Wang Chen Guang

Abstract:

There are inefficient global scheduling parallelism and local scheduling parallelism prone to processor starvation in current scheduling algorithms. Regarding this issue, this paper proposed a load-aware queue partitioning scheduling strategy by first allocating the queues according to the number of processor cores, calculating the load factor to specify the load queue capacity, and it assigned the awaiting nodes to the appropriate perceptual queues through the precursor nodes and the communication computation overhead. At the same time, real-time computation of the load factor could effectively prevent the processor from being starved for a long time. Experimental comparison with two classical algorithms shows that there is a certain improvement in both performance metrics of scheduling length and task speedup ratio.

Keywords: load-aware, scheduling algorithm, perceptual queue, heterogeneous multi-core

Procedia PDF Downloads 145

Authors: Elham Kazemi

Abstract:

Quadratic Assignment Problem (QAP) is one the combinatorial optimization problems about which research has been done in many companies for allocating some facilities to some locations. The issue of particular importance in this process is the costs of this allocation and the attempt in this problem is to minimize this group of costs. Since the QAP’s are from NP-hard problem, they cannot be solved by exact solution methods. Cuckoo Optimization Algorithm is a Meta-heuristicmethod which has higher capability to find the global optimal points. It is an algorithm which is basically raised to search a continuous space. The Quadratic Assignment Problem is the issue which can be solved in the discrete space, thus the standard arithmetic operators of Cuckoo Optimization Algorithm need to be redefined on the discrete space in order to apply the Cuckoo Optimization Algorithm on the discrete searching space. This paper represents the way of discretizing the Cuckoo optimization algorithm for solving the quadratic assignment problem.

Keywords: Quadratic Assignment Problem (QAP), Discrete Cuckoo Optimization Algorithm (DCOA), meta-heuristic algorithms, optimization algorithms

Procedia PDF Downloads 517