Herodotos Herodotou

We determine the fine-tuning of the Yukawa couplings of supersymmetric QCD, discretized on a lattice. We use perturbation theory at one-loop level. The modified minimal subtraction scheme (MS) is employed; by its definition, this scheme requires perturbative calculations, in the continuum and/or on the lattice. On the lattice, we utilize the Wilson formulation for gluon, quark, and gluino fields; for squark fields we use naive discretization. The sheer difficulties of this study lie in the fact that different components of squark fields mix among themselves at the quantum level and the action’s symmetries, such as parity and charge conjugation, allow an additional Yukawa coupling. Consequently, for an appropriate fine-tuning of the Yukawa terms, these mixings must be taken into account in the renormalization conditions. All Green’s functions and renormalization factors are analytic expressions depending on the number of …

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes metaheuristic optimisation …

SMDB 2023 Message and Committees | Ktisis Cyprus University of Technology Skip navigation Ktisis Tepak Home Collections Research Outputs Researchers Faculty & Departments Theses Patents Projects Journals Conferences Explore by Research Outputs Researchers Faculty & Departments Theses Patents Projects Journals Conferences Language Ελληνικά English Sign on to: Login Receive email updates Edit Account details 1.Ktisis Cyprus University of Technology 2.Cyprus University of Technology (Research Output) 3.Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30928 Title: SMDB 2023 Message and Committees Authors: Herodotou, Herodotos Wu, Yingjun Major Field of Science: Engineering and Technology Field Category: ENGINEERING AND TECHNOLOGY Issue Date: 3-Apr-2023 Source…

The Internet of things (IoT) ecosystem provides a platform for the connectivity of interrelated smart devices to automate manual processes and reduce labor costs. IoT has brought significant benefits to all industries, including maritime, as various objects (e.g., ports, ships, agents, etc.) are connected to gather and share information within the maritime ecosystem. The innovative technological aspects of IoT are promoting the effective collaboration between the research community and the maritime industry, for enhancing the performance of maritime transportation systems. Therefore, this study discusses recent advances delivered by the IoT and other emerging technologies, like machine learning (ML) and computer vision (CV), for smart maritime transportation systems (SMTSs). In particular, this paper presents two specific use cases of SMTSs, namely, predictive maintenance and container damage/seal inspection …

The stability control of nominal frequency and terminal voltage in an interconnected power system (IPS) is always a challenging task for researchers. The load variation or any disturbance affects the active and reactive power demands, which badly influence the normal working of IPS. In order to maintain frequency and terminal voltage at rated values, controllers are installed at generating stations to keep these parameters within the prescribed limits by varying the active and reactive power demands. This is accomplished by load frequency control (LFC) and automatic voltage regulator (AVR) loops, which are coupled to each other. Due to the complexity of the combined AVR-LFC model, the simultaneous control of frequency and terminal voltage in an IPS requires an intelligent control strategy. The performance of IPS solely depends upon the working of the controllers. This work presents the exploration of control methodology based on a proportional integral–proportional derivative (PI-PD) controller with combined LFC-AVR in a multi-area IPS. The PI-PD controller was tuned with recently developed nature-inspired computation algorithms including the Archimedes optimization algorithm (AOA), learner performance-based behavior optimization (LPBO), and modified particle swarm optimization (MPSO). In the earlier part of this work, the proposed methodology was applied to a two-area IPS, and the output responses of LPBO-PI-PD, AOA-PI-PD, and MPSO-PI-PD control schemes were compared with an existing nonlinear threshold-accepting algorithm-based PID (NLTA-PID) controller. After achieving satisfactory results in the two-area IPS, the …

The growing need to identify patterns in data and automate decisions based on them in near-real time, has stimulated the development of new machine learning (ML) applications processing continuous data streams. However, the deployment of ML applications over distributed stream processing engines (DSPEs) such as Apache Spark Streaming is a complex procedure that requires extensive tuning along two dimensions. First, DSPEs have a plethora of system configuration parameters, like degree of parallelism, memory buffer sizes, etc., that have a direct impact on application throughput and/or latency, and need to be optimized. Second, ML models have their own set of hyperparameters that require tuning as they can affect the overall prediction accuracy of the trained model significantly. These two forms of tuning have been studied extensively in the literature but only in isolation from each other. This …

Electricity load and price data pose formidable challenges for forecasting due to their intricate characteristics, marked by high volatility and non-linearity. Machine learning (ML) and deep learning (DL) models have emerged as valuable tools for effectively predicting data exhibiting high volatility, frequent fluctuations, mean-reversion tendencies, and non-stationary behavior. Therefore, this review article is dedicated to providing a comprehensive exploration of the application of machine learning and deep learning techniques in the context of electricity load and price prediction. In contrast to existing literature, our study distinguishes itself in several key ways. We systematically examine ML and DL approaches employed for the prediction of electricity load and price, offering a meticulous analysis of their methodologies and performance. Furthermore, we furnish readers with a detailed compendium of the datasets …

Eutrophication is a major environmental issue with many negative consequences, such as hypoxia and harmful cyanotoxin production. Monitoring coastal eutrophication is crucial, especially for island countries like the Republic of Cyprus, which are economically dependent on the tourist sector. Additionally, the open-sea aquaculture industry in Cyprus has been exhibiting an increase in recent decades and environmental monitoring to identify possible signs of eutrophication is mandatory according to the legislation. Therefore, in this modeling study, two different types of artificial neural networks (ANNs) are developed based on in situ data collected from stations located in the coastal waters of Cyprus. These ANNs aim to model the eutrophication phenomenon based on two different data-driven modeling procedures. Firstly, the self-organizing map (SOM) ANN examines several water quality parameters’(specifically water temperature, salinity, nitrogen species, ortho-phosphates, dissolved oxygen, and electrical conductivity) interactions with the Chlorophyll-a (Chl-a) parameter. The SOM model enables us to visualize the monitored parameters’ relationships and to comprehend complex biological mechanisms related to Chl-a production. A second feed-forward ANN model is also developed for predicting the Chl-a levels. The feed-forward ANN managed to predict the Chl-a levels with great accuracy (MAE= 0.0124; R= 0.97). The sensitivity analysis results revealed that salinity and water temperature are the most influential parameters on Chl-a production. Moreover, the sensitivity analysis results of the feed-forward ANN captured the winter …

Modern data storage systems optimize data access by distributing data across multiple storage tiers and caches, based on numerous tiering and caching policies. The policies' decisions, and in particular the ones related to data prefetching, can severely impact the performance of the entire storage system. In recent years, various machine learning algorithms have been employed to model access patterns in complex data storage workloads. Even though data storage systems handle a constantly changing stream of file requests, current approaches continue to train their models offline in a batch-based approach. In this paper, we investigate the use of streaming machine learning to support data prefetching decisions in data storage systems as it introduces various advantages such as high training efficiency, high prediction accuracy, and high adaptability to changing workload patterns. After extracting a …

The use of storage tiering is becoming popular in data-intensive compute clusters due to the recent advancements in storage technologies. The Hadoop Distributed File System, for example, now supports storing data in memory, SSDs, and HDDs, while OctopusFS and hatS offer fine-grained storage tiering solutions. However, current big data platforms (such as Hadoop and Spark) are not exploiting the presence of storage tiers and the opportunities they present for performance optimizations. Specifically, schedulers and prefetchers will make decisions only based on data locality information and completely ignore the fact that local data are now stored on a variety of storage media with different performance characteristics. This article presents Trident, a scheduling and prefetching framework that is designed to make task assignment, resource scheduling, and prefetching decisions based on both locality and storage …

A surfeit of Dissolved Inorganic Nitrogen (DIN), which is defined as the total amount of nitrite, nitrate, and ammonium levels in water, may cause negative effects to the marine environment. For example, elevated levels of DIN may promote surplus production of algae and possible depletion of oxygen in the water column. The DIN in the marine water column is monitored as part of the Water Framework Directive (WFD), the Nitrates Directive and the EU Marine Strategy Framework Directive (MSFD). Data-driven models have been proved to be an excellent management tool for environmental issues related to coastal water quality protection and management. Based on data-drive models, and specifically the Artificial Neural Networks (ANNs), the DIN levels from coastal stations in Cyprus were predicted. To do so, three different ANNs models were created, each of them calculating nitrite, nitrate, and ammonium levels …

Maritime container terminals (MCTs) play a fundamental role in international maritime trade, handling inbound, outbound, and transshipped containers. The increasing number of ships and containers creates several challenges to MCTs, such as congestion, long waiting times before ships dock, delayed departures, and high service costs. The berth allocation problem (BAP) concerns allocating berthing positions to arriving ships to reduce total service cost, waiting times, and delays in vessels’ departures. In this work, we extend the study of continuous BAP, which considers a single quay (straight line) for berthing ships, to multiple quays, as found in many ports around the globe. Multi-Quay BAP (MQ-BAP) adds the additional dimension of assigning a preferred quay to each arriving ship, rather than just specifying the berthing position and time. In this study, we address MQ-BAP with the objective of minimizing the total service cost, which includes minimizing the waiting times and delays in the departure of ships. MQ-BAP is first formulated as a mixed-integer linear problem and then solved using the cuckoo search algorithm (CSA), a computational intelligence (CI)-based approach. In addition, the exact mixed-integer linear programming (MILP) method, two other state-of-the-art metaheuristic approaches, namely the genetic algorithm (GA) and particle swarm optimization (PSO), as well as a first come first serve (FCFS) approach, are also implemented for comparison purposes. Several experiments are conducted using both randomly generated and real data from the Port of Limassol, Cyprus, which has five quays serving commercial vessel traffic. The …

Recently, microgrids have become a fundamental element within the framework of a smart grid. They bring together distributed renewable energy sources (RESs), prediction of RESs, energy storage units, and load control to enhance the reliability of the power system, promote sustainable growth, and decrease carbon emissions. Simultaneously, the swift progress in sensor and metering technologies, wireless and network communication, IoT-based technologies, as well as cloud and fog computing, is resulting in the gathering and storage of substantial volumes of data, such as device status information, energy generation statistics, and consumption data. Furthermore, IoT devices are found in various parts of the smart grid, such as smart appliances, smart meters, and substations. These IoT devices generate petabytes of data, which are known to be one of the most scalable properties of a smart grid. Without smart grid analytics, it is difficult to make efficient use of data and to make sustainable decisions related to smart grid operations. With the energy system of the developing world heading towards smart grids, there needs to be a forum for analytics that can collect and interpret data from multiple endpoints. Data analytics platforms can analyze data to produce invaluable results that lead to many advantages, such as operational efficiency and cost savings. In addition, proper forecasting of energy generation from RESs and energy theft detection help a lot while maintaining smart and sustainable energy systems. This reprint comprises a variety of noteworthy and original research contributions that pertain to smart grid analytics for sustainability …

Revisit intention is a key indicator of business performance, studied in many fields including hospitality. This work employs big data analytics to investigate revisit intention patterns from tourists’ electronic word of mouth (eWOM) using text classification, negation detection, and topic modelling. The method is applied on publicly available hotel reviews that are labelled automatically based on consumers’ intention to revisit a hotel or not. Topics discussed in revisit-annotated reviews are automatically extracted and used as features during the training of two Extreme Gradient Boosting models (XGBoost), one for each of two hotel categories (2/3 and 4/5 stars). The emerging patterns from the trained XGBoost models are identified using an explainable machine learning technique, namely SHAP (SHapley Additive exPlanations). Results show how topics discussed by tourists in reviews relate with revisit/non revisit intention …

Eutrophication is a major environmental issue with many negative consequences, such as hypoxia and harmful cyanotoxins production. Monitoring coastal eutrophication is a crucial, especially for island countries like the Republic of Cyprus, which are economically dependent on the touristic sector. Additionally, the open-sea aquaculture industry in Cyprus has been exhibiting an increase in the last decades and environmental monitoring to identify possible signs of eutrophication is mandatory according to the legislation. Therefore, in this modelling study, two different types of Artificial Neural Networks (ANNs) are developed based on in situ-data collected from stations located in the coastal waters of Cyprus. Theses ANNs aim to model the eutrophication phenomenon based on two different data-driven modelling procedures. Firstly, the self-organizing map (SOM) ANN examines several water quality parameters (specifically water temperature, salinity, nitrogen species, ortho-phosphates, dissolved oxygen and electrical conductivity) interactions with the Chlorophyll-a parameter. The SOM model enables us to visualize the monitored parameters relationships and to comprehend complex biological mechanisms related to Chlorophyll-a production. A second feed-forward ANN model is also developed for predicting the Chlorophyll-a levels. Based on this ANN model, several scenarios associated to the eutrophication-related water quality parameters can be extracted. The combination of these two ANNs models is considered a holistic modelling approximation for the identification of eutrophication scenarios, since it enables not only the prediction of …

The Automatic Identification System (AIS) provides information for tracking and monitoring vessel activity in real time. The vessel traffic data from AIS includes position coordinates in latitude and longitude, speed and course over ground, the vessel's unique identification number, and many more. In this work, we investigate the use of AIS data for estimating sea surface current velocities in the Eastern Mediterranean sea. Specifically, we apply the dead reckoning technique to compute the difference between the projected position and the true position of a vessel over time, which is mainly attributed on the force of sea surface currents. The estimated sea surface current velocities and directions are compared with the ones provided by the Copernicus Marine ocean product system. The analysis reveals that the dead reckoning technique can be used reliably for estimating sea currents at a very fine granularity, especially …

Recommendation systems are popular tools assisting consumers with the over-choice problem; however, they have been criticized of insufficient performance in highly complex domains. This work focuses on the analysis of consumers’ personalities, due to its recent popularity in recommender systems, within topics discussed by users in electronic word of mouth (e-WOM) to improve the recommendation of restaurants to tourists. The proposed method utilizes structured and unstructured data from online reviews to predict the probability of a user enjoying a restaurant he/she had not visited before and based on that make recommendations to different users. A personality classification model that analyses the textual information of reviews and predicts the personality of the author is employed. Topic modelling is used to identify additional features that characterize users’ preferences and restaurants features. Structured …

Restaurant recommender systems are designed to support restaurant selection by assisting consumers with the information overload problem. However, despite their promises, they have been criticized of insufficient performance. Recent research in recommender systems has acknowledged the importance of personality in improving recommendation; however, limited work exploited this aspect in the restaurant domain. Similarly, the importance of user preferences in food has been known to improve recommendation but most systems explicitly ask the users for this information. In this paper, we explore the influence of personality and user preference by utilizing text in consumers’ electronic word of mouth (eWOM) to predict the probability of a user enjoying a restaurant he/she had not visited before. Food preferences are extracted though a trained named-entity recognizer learned from a labelled dataset of foods, generated using a rule-based approach. The prediction of user personality is achieved through a bi-directional transformer approach with a feed-forward classification layer, due to its improved performance in similar problems over other machine learning models. The personality classification model utilizes the textual information of reviews and predicts the personality of the author. Topic modelling is used to identify additional features that characterize users’ preferences and restaurants properties. All aforementioned features are used collectively to train an extreme gradient boosting tree model, which outputs the predicted user rating of restaurants. The trained model is compared against popular recommendation techniques such as …

The port call process encapsulates a visitation cycle of a ship to a port and can generate a wealth of data. The real time analysis of port call data can be used to find bottlenecks in the port call process, establish targets based on key performance indicators (KPIs), and to understand how shipping traffic impacts a port's efficiency. This demonstration will showcase a new Power BI interactive report powered by a multidimensional OLAP cube for very fast performance, which is built on top of a data warehouse collecting information from various sources in real time. The report currently visualizes several KPIs and other types of information that can be filtered per port, time-period, vessel type, origin or destination ports, and various other categories to help manage arrivals, departures, and port operations.

Proper utilization of port resources and efficient berth planning play a crucial role in minimizing port congestion and overall handling costs. Therefore, this study focuses on efficient berth planning in maritime container terminals composed of multiple quays. In particular, this study addresses the Multi-Quay Berth Allocation Problem (MQ-BAP), where a continuous berthing layout is considered along with dynamic ship arrivals and practical constraints such as safety time windows and safety distances between ships. Since MQ-BAP is an NP-hard problem, this study proposes a metaheuristic-based approach, the Cuckoo Search Algorithm (CSA) for solving the problem. A comparative study is also performed using real data instances collected from the Port of Limassol, Cyprus, against a genetic algorithm solution proposed in the recent literature, as well as the optimal exact solution implemented using MILP. The results of the experiments show the effectiveness of our proposed CSA approach in handling real-world berth allocation in ports with multiple quays while also considering practical constraints.