Construction Port Requirements for Floating Offshore Wind Turbines

s the floating offshore wind turbine industry continues to develop and grow, the capabilities of established port facilities need to be assessed as to their ability to support the expanding construction and installation requirements. This paper assesses current infrastructure requirements and projected changes to port facilities that may be required to support the floating offshore wind industry. Understanding the infrastructure needs of the floating offshore renewable industry will help to identify the port-related requirements. Floating offshore wind turbines can be installed further out to sea and in deeper waters than traditional fixed offshore wind arrays, meaning it can take advantage of stronger winds. Separate ports are required for substructure construction, fit-out of the turbines, moorings, subsea cables and maintenance. Large areas are required for the laydown of mooring equipment, inter array cables, turbine blades and nacelles. The capabilities of established port facilities to support floating wind farms are assessed by evaluation of size of substructures, height of wind turbine with regards to the cranes for fitting of blades, distance to offshore site and offshore installation vessel characteristics. The paper will discuss the advantages and disadvantages of using large land based cranes, inshore floating crane vessels or offshore crane vessels at the fit-out port for the installation of the turbine. Water depths requirements for import of materials and export of the completed structures will be considered. There are additional costs associated with any emerging technology. However, part of the popularity of Floating Offshore Wind Turbines stems from the cost savings against permanent structures like fixed wind turbines. Floating Offshore Wind Turbine developers can benefit from lighter, more cost effective equipment which can be assembled in port and towed to site rather than relying on large, expensive installation vessels to transport and erect fixed bottom turbines. The ability to assemble Floating Offshore Wind Turbines equipment on shore means minimising highly weather dependent operations like offshore heavy lifts and assembly, saving time and costs and reducing safety risks for offshore workers. Maintenance might take place in safer onshore conditions for barges and semi submersibles. Offshore renewables, such as floating wind, can take advantage of this wealth of experience, while oil and gas operators can deploy this experience at the same time as entering the renewables space. The floating offshore wind industry is in the early stages of development and port facilities are required for substructure fabrication, turbine manufacture, turbine construction and maintenance support. The paper discusses the potential floating wind substructures as this provides a snapshot of the requirements at the present time, and potential technological developments required for commercial development. Scaling effects of demonstration-scale projects will be addressed; however the primary focus will be on commercial-scale (30+ units) device floating wind energy farms.

Predictions of Dynamic Behaviors for Gas Foil Bearings Operating at Steady-State Based on Multi-Physics Coupling Computer Aided Engineering Simulations

A simulation scheme of rotational motions for predictions of bump-type gas foil bearings operating at steady-state is proposed. The scheme is based on multi-physics coupling computer aided engineering packages modularized with computational fluid dynamic model and structure elasticity model to numerically solve the dynamic equation of motions of a hydrodynamic loaded shaft supported by an elastic bump foil. The bump foil is assumed to be modelled as infinite number of Hookean springs mounted on stiff wall. Hence, the top foil stiffness is constant on the periphery of the bearing housing. The hydrodynamic pressure generated by the air film lubrication transfers to the top foil and induces elastic deformation needed to be solved by a finite element method program, whereas the pressure profile applied on the top foil must be solved by a finite element method program based on Reynolds Equation in lubrication theory. As a result, the equation of motions for the bearing shaft are iteratively solved via coupling of the two finite element method programs simultaneously. In conclusion, the two-dimensional center trajectory of the shaft plus the deformation map on top foil at constant rotational speed are calculated for comparisons with the experimental results.

Properties of Bacterial Nanocellulose for Scenic Arts

Kombucha (a symbiotic culture of bacteria and yeast) produces material capable of acquiring multiple shapes and textures that change significantly under different environment or temperature variations (e.g., when it is exposed to wet conditions), properties that may be explored in the scenic industry. This paper presents an analysis of its specific characteristics, exploring them as a non-conventional material for arts and performance. Costume Design uses surfaces as a powerful way of expression to represent concepts and stories; it may apply the unique features of nano bacterial cellulose (NBC) as assets in this artistic context. A mix of qualitative and quantitative (interventionist) methodology approaches were used such as review of relevant literature to deepen knowledge on the research topic (crossing bibliography from different fields of studies: biology, art, costume design, etc.); as well as descriptive methods: laboratorial experiments, document quantities, observation to identify material properties and possibilities used to express a multiple narrative ideas, concepts and feelings. The results confirmed that NBC is an interactive and versatile material viable to be used in an alternative scenic context; its unique aesthetic and performative qualities, which change in contact to moisture, are resources that can be used to show a visual and poetic impact on stage.

Accelerated Ageing of Unidirectional Flax Fibers Reinforced Recycled Polypropylene Composites

Over the last decades, worldwide environmental awareness has grown due to the depletion of raw material resources and global warming. This awareness has prompted the development of new products more environmentally friendly. Among these products are biocomposite materials reinforced with natural fibers. The main challenge in developing the use of biocomposites in exterior applications is the lack of knowledge about their durability and the evolution of their mechanical and physicochemical properties in the long term. The aim of this work is to study the photooxidation of unidirectional (UD) composites based on recycled matrix. For this purpose, UD flax fiber composites based on recycled polypropylene were prepared by thermocompression. An accelerated aging test was carried out using a xenon arc WeatherOmeter. The consequences of UV exposure on the chemical composition and morphology of the surface of composites as well as on their tensile mechanical properties have been reported. The results showed that accelerated aging had a significant effect on the surface of these composites while it had little impact on their mechanical properties.

Multi-Robotic Partial Disassembly Line Balancing with Robotic Efficiency Difference via HNSGA-II

To accelerate the remanufacturing process of electronic waste products, this study designs a partial disassembly line with the multi-robotic station to effectively dispose of excessive wastes. The multi-robotic partial disassembly line is a technical upgrade to the existing manual disassembly line. Balancing optimization can make the disassembly line smoother and more efficient. For partial disassembly line balancing with the multi-robotic station (PDLBMRS), a mixed-integer programming model (MIPM) considering the robotic efficiency differences is established to minimize cycle time, energy consumption and hazard index and to calculate their optimal global values. Besides, an enhanced NSGA-II algorithm (HNSGA-II) is proposed to optimize PDLBMRS efficiently. Finally, MIPM and HNSGA-II are applied to an actual mixed disassembly case of two types of computers, the comparison of the results solved by GUROBI and HNSGA-II verifies the correctness of the model and excellent performance of the algorithm, and the obtained Pareto solution set provides multiple options for decision-makers.

Topic Modeling Using Latent Dirichlet Allocation and Latent Semantic Indexing on South African Telco Twitter Data

Twitter is one of the most popular social media platforms where users share their opinions on different subjects. Twitter can be considered a great source for mining text due to the high volumes of data generated through the platform daily. Many industries such as telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model in this experiment. A higher topic coherence score indicates better performance of the model.

Slow Pyrolysis of Biowastes: Environmental, Exergetic, and Energetic Assessment

Slow pyrolysis of a pellet of pistachio waste was studied using a lab-scale stainless-steel reactor. Experiments were conducted at different heating rates (5, 10, and 15 K/min). A 3-E (environmental, exergetic, and energetic) analysis for the processing of 20 kg/h of biowaste was carried out. Experimental results showed that biochar and gas yields decreased with an increase in the heating rate (43% to 36% and 28% to 24%, respectively), while the bio-oil yield increased (29% to 40%). Finally, from the 3-E analysis and the experimental results, it can be suggested that an increase in the heating rate resulted in a higher pyrolysis exergetic efficiency (70%), due to an increase of the bio-oil yield with high-energy content.

A Multi-Feature Deep Learning Algorithm for Urban Traffic Classification with Limited Labeled Data

Acoustic sensors, if embedded in smart street lights, can help in capturing the activities (car honking, sirens, events, traffic, etc.) in cities. Needless to say, the acoustic data from such scenarios are complex due to multiple audio streams originating from different events, and when decomposed to independent signals, the amount of retrieved data volume is small in quantity which is inadequate to train deep neural networks. So, in this paper, we address the two challenges: a) separating the mixed signals, and b) developing an efficient acoustic classifier under data paucity. So, to address these challenges, we propose an architecture with supervised deep learning, where the initial captured mixed acoustics data are analyzed with Fast Fourier Transformation (FFT), followed by filtering the noise from the signal, and then decomposed to independent signals by fast independent component analysis (Fast ICA). To address the challenge of data paucity, we propose a multi feature-based deep neural network with high performance that is reflected in our experiments when compared to the conventional convolutional neural network (CNN) and multi-layer perceptron (MLP).

Forecasting 24-Hour Ahead Electricity Load Using Time Series Models

Forecasting electricity load is important for various purposes like planning, operation and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria namely, the Mean Absolute Error and Root Mean Square Error. The National Renewable Energy Laboratory (NREL) residential energy consumption data are used to train the models. The results of this study show that SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts we can improve the robustness of the models for 24 hour ahead electricity load forecasting.

Self-Organizing Control Systems for Unstable and Deterministic Chaotic Processes

The paper proposes a method for constructing a self-organizing control system for unstable and deterministic chaotic processes in the class of catastrophe “hyperbolic umbilic” for objects with m-inputs and n-outputs. The self-organizing control system is investigated by the universal gradient-velocity method of Lyapunov vector-functions. The conditions for self-organization of the control system in the class of catastrophes “hyperbolic umbilic” are shown in the form of a system of algebraic inequalities that characterize the aperiodic robust stability in the stationary states of the system.

Evolution of Autonomous Vehicles and Advanced Automated Car Parking Development

The trend of autonomous vehicles is the future solution to road networks congestion in terms of their advanced ability to drive closer together and at higher speeds than humans can do safely. Infrastructure sector can drive the economic prosperity and provide a balance and inclusive growth of sustainable economy development. In this paper, the road infrastructure and the future development of electric car, self-driving of autonomous vehicles and the increasing demand of automated car parking system are critically revised and this paper aims to provide the insight and achieve better sustainable infrastructure and community in smart city.

Application of Molecular Materials in the Manufacture of Flexible and Organic Devices for Photovoltaic Applications

Many sustainable approaches to generate electric energy have emerged in the last few decades; one of them is through solar cells. Yet, this also has the disadvantage of highly polluting inorganic semiconductor manufacturing processes. Therefore, the use of molecular semiconductors must be considered. In this work, allene compounds C24H26O4 and C24H26O5 were used as dopants to manufacture semiconductor films based on PbPc by high-vacuum evaporation technique. IR spectroscopy was carried out to determine the phase and any significant chemical changes which may occur during the thermal evaporation. According to UV-visible spectroscopy and Tauc’s model, the deposition process generated thin films with an activation energy range of 1.47 eV to 1.55 eV for direct transitions and 1.29 eV to 1.33 eV for indirect transitions. These values place the manufactured films within the range of low bandgap semiconductors. The flexible devices were manufactured: polyethylene terephthalate (PET), Indium tin oxide (ITO)/organic semiconductor/Cubic Close Packed (CCP). The characterization of the devices was carried out by evaluating electrical conductivity using the four-probe collinear method. I-V curves were obtained under different lighting conditions at room temperature. OS1 (PbPc/C24H26O4) showed an Ohmic behavior, while OS2 (PbPc/C24H26O5) reached higher current values at lower voltages. The results obtained show that the semiconductor devices doped with allene compounds can be used in the manufacture of optoelectronic devices.

Public-Private Partnership Transportation Projects: An Exploratory Study

When public transportation projects were delivered through design-bid-build and later design-build, governments found a serious issue: inadequate funding. With population growth, governments began to develop new arrangements in which the private sectors were involved to cut the financial burden. This arrangement, Public-Private Partnership (PPP), has its own risks; however, performance outputs can motivate or discourage its use. On top of such output are time and budget, which can be affected by the type of project delivery methods. Project completion within or ahead of schedule as well as within or under budget is among any owner’s objectives. With a higher application of PPP in the highway industry in the US and insufficient research, the current study addresses the schedule and cost performance of PPP highway projects and determines which one outperforms the other. To meet this objective, after collecting performance data of all PPP projects, schedule growth and cost growth are calculated, and finally, statistical analysis is conducted to evaluate the PPP performance. The results show that PPP highway projects on average have saved time and cost; however, the main benefit is a faster delivery rather than an under-budget completion. This study can provide better insights to understand PPP highways’ performance and assist practitioners in applying PPP for transportation projects with the opportunity to save time and cost.

Small and Medium-Sized Enterprises, Flash Flooding and Organisational Resilience Capacity: Qualitative Findings on Implications of the Catastrophic 2017 Flash Flood Event in Mandra, Greece

On November 15th, 2017, a catastrophic flash flood devastated the city of Mandra in Central Greece, resulting in 24 fatalities and extensive damages to the built environment and infrastructure. It was Greece’s deadliest and most destructive flood event for the past 40 years. In this paper, we examine the consequences of this event to small and medium-sized enterprises (SMEs) operating in Mandra during the flood event, which were affected by the floodwaters to varying extents. In this context, we conducted semi-structured interviews with business owners-managers of 45 SMEs located in flood inundated areas and are still active nowadays, based on an interview guide that spanned 27 topics. The topics pertained to the disaster experience of the business and business owners-managers, knowledge and attitudes towards climate change and extreme weather, aspects of disaster preparedness and related assistance needs. Our findings reveal that the vast majority of the affected businesses experienced heavy damages in equipment and infrastructure or total destruction, which resulted in business interruption from several weeks up to several months. Assistance from relatives or friends helped for the damage repairs and business recovery, while state compensations were deemed insufficient compared to the extent of the damages. Most interviewees pinpoint flooding as one of the most critical risks, and many connect it with the climate crisis. However, they are either not willing or unable to apply property-level prevention measures in their businesses due to cost considerations or complex and cumbersome bureaucratic processes. In all cases, the business owners are fully aware of the flood hazard implications, and since the recovery from the event, they have engaged in basic mitigation measures and contingency plans in case of future flood events. Such plans include insurance contracts whenever possible (as the vast majority of the affected SMEs were uninsured at the time of the 2017 event) as well as simple relocations of critical equipment within their property. The study offers fruitful insights on latent drivers and barriers of SMEs’ resilience capacity to flash flooding. In this respect, findings such as ours, highlighting tensions that underpin behavioural responses and experiences, can feed into: a) bottom-up approaches for devising actionable and practical guidelines, manuals and/or standards on business preparedness to flooding, and, ultimately, b) policy-making for an enabling environment towards a flood-resilient SME sector.

The Use of Symbolic Signs in Modern Ukrainian Monumental Church Painting: Classification and Hidden Semantics

Monumental church paintings are often perceived either as the interior decoration of the temple or as the "Gospel for the illiterate," as the temple painting often contains scenes from Holy Scripture. In science the painting of the Orthodox Church is mainly the subject of study of art critics, but from the point of view of culturology and semiotics, it is insufficiently studied. The symbolism of monumental church painting is insufficiently revealed. The aim of this paper is to give a description of symbolic signs, to classify them, to give examples for each type of sign from the paintings of modern temples of Eastern Ukraine, on the basis of semiotic analysis of iconographic plots used in monumental church painting. We offer own classification of symbols of monumental church painting, using examples from the murals of modern Orthodox churches in Eastern Ukraine, mainly from the Donetsk region. When analyzing the semantics of symbolic signs, the following methods of the culturological approach were used: semiotic, iconological, iconographic, hermeneutic, culturological, descriptive, comparative-historical, visual-analytical. When interpreting the meanings of symbolic signs, scientific, cultural and theological literature were used. Photos taken by the author have been added to the article.

Face Recognition Using Principal Component Analysis, K-Means Clustering, and Convolutional Neural Network

Face recognition is the problem of identifying or recognizing individuals in an image. This paper investigates a possible method to bring a solution to this problem. The method proposes an amalgamation of Principal Component Analysis (PCA), K-Means clustering, and Convolutional Neural Network (CNN) for a face recognition system. It is trained and evaluated using the ORL dataset. This dataset consists of 400 different faces with 40 classes of 10 face images per class. Firstly, PCA enabled the usage of a smaller network. This reduces the training time of the CNN. Thus, we get rid of the redundancy and preserve the variance with a smaller number of coefficients. Secondly, the K-Means clustering model is trained using the compressed PCA obtained data which select the K-Means clustering centers with better characteristics. Lastly, the K-Means characteristics or features are an initial value of the CNN and act as input data. The accuracy and the performance of the proposed method were tested in comparison to other Face Recognition (FR) techniques namely PCA, Support Vector Machine (SVM), as well as K-Nearest Neighbour (kNN). During experimentation, the accuracy and the performance of our suggested method after 90 epochs achieved the highest performance: 99% accuracy F1-Score, 99% precision, and 99% recall in 463.934 seconds. It outperformed the PCA that obtained 97% and KNN with 84% during the conducted experiments. Therefore, this method proved to be efficient in identifying faces in the images.

Time Organization for Urban Mobility Decongestion: A Methodology for People’s Profile Identification

Quality of life, environmental impact, congestion of mobility means, and infrastructures remain significant challenges for urban mobility. Solutions like car sharing, spatial redesign, eCommerce, and autonomous vehicles will likely increase the unit veh-km and the density of cars in urban traffic, thus reducing congestion. However, the impact of such solutions is not clear for researchers. Congestion arises from growing populations that must travel greater distances to arrive at similar locations (e.g., workplaces, schools) during the same time frame (e.g., rush hours). This paper first reviews the research and application cases of urban congestion methods through recent years. Rethinking the question of time, it then investigates people’s willingness and flexibility to adapt their arrival and departure times from workplaces. We use neural networks and methods of supervised learning to apply a methodology for predicting peoples’ intentions from their responses in a questionnaire. We created and distributed a questionnaire to more than 50 companies in the Paris suburb. Obtained results illustrate that our methodology can predict peoples’ intentions to reschedule their activities (work, study, commerce, etc.).

The Reintegration of the Past as Self-Realisation: Zhao Tao in Jia Zhangke’s Films

This article examines the figure Zhao Tao in Jia Zhangke’s films in light of Carl Jung’s psychoanalytical theory. Zhao is a recurring aesthetic trope in Jia’s films, and the characters she plays often have an intimate relationship with the past. Nevertheless, this relationship has not been systematically investigated, especially its symbolism of the typical relationship between the past and the self in post-social China. To fill this research gap, the article will explore how Zhao’s characters discover, preserve, and adapt the past in I Wish I knew (2010), Mountains May Depart (2015), and Ash Is Purest White (2018). Through a Jungian lens, these three levels of engagement with the past will be demonstrated as corresponding with Jung’s psychoanalytical theory of self-realisation, which entails the confrontation with the shadow, the embodiment of the archetype, and individuation. Thus, by articulating a film-philosophy dialogue between Jia and Jung, this article will develop a philosophy of self-realisation based on the symbolism of Zhao. Through the reintegration of the past, the individuals can overcome the fragmentation of temporality and selfhood in the postmodern world and achieve self-realisation.

Relationship between Iron-Related Parameters and Soluble Tumor Necrosis Factor-Like Weak Inducer of Apoptosis in Obese Children

Iron is physiologically essential. However, it also participates in the catalysis of free radical formation reactions. Its deficiency is associated with amplified health risks. This trace element establishes some links with another physiological process related to cell death, apoptosis. Both iron deficiency and iron overload are closely associated with apoptosis. Soluble tumor necrosis factor-like weak inducer of apoptosis (sTWEAK) has the ability to trigger apoptosis and plays a dual role in the physiological versus pathological inflammatory responses of tissues. The aim of this study was to investigate the status of these parameters as well as the associations among them in children with obesity, a low-grade inflammatory state. The study was performed on groups of children with normal body mass index (N-BMI) and obesity. 43 children were included in each group. Based upon age- and sex-adjusted BMI percentile tables prepared by the World Health Organization, children whose values varied between 85 and 15 were included in N-BMI group. Children, whose BMI percentile values were between 99 and 95, comprised obese (OB) group. Institutional ethical committee approval and informed consent forms were taken prior to the study. Anthropometric measurements (weight, height, waist circumference, hip circumference, head circumference, neck circumference) and blood pressure values (systolic blood pressure and diastolic blood pressure) were recorded. Routine biochemical analyses, including serum iron, total iron binding capacity (TIBC), transferrin saturation percent (Tf Sat %) and ferritin, were performed. sTWEAK levels were determined by enzyme-linked immunosorbent assay. study data were evaluated using appropriate statistical tests performed by the statistical program SPSS. Serum iron levels were 91 ± 34 mcrg/dl and 75 ± 31 mcrg/dl in N-BMI and OB children, respectively. The corresponding values for TIBC, Tf Sat %, ferritin were 265 mcrg/dl vs. 299 mcrg/dl, 37.2 ± 19.1% vs. 26.7 ± 14.6%, and 41 ± 25 ng/ml vs 44 ± 26 ng/ml. In N-BMI and OB groups, sTWEAK concentrations were measured as 351 ng/L and 325 ng/L, respectively (p > 0.05). Correlation analysis revealed significant associations between sTWEAK levels and iron related parameters (p < 0.05) except ferritin. In conclusion, iron contributes to apoptosis. Children with iron deficiency have decreased apoptosis rate in comparison with that of healthy children. sTWEAK is an inducer of apoptosis. OB children had lower levels of both iron and sTWEAK. Low levels of sTWEAK are associated with several types of cancers and poor survival. Although iron deficiency state was not observed in this study, the correlations detected between decreased sTWEAK and decreased iron as well as Tf Sat % values were valuable findings, which point out decreased apoptosis. This may induce a proinflammatory state, potentially leading to malignancies in the future lives of OB children.

Director Compensation, CEO Duality, State Ownership, and Firm Performance in China: Proof from Panel Data of Publicly Listed Enterprises from 1999 to 2020

This paper offered the primary methodical proof on how director remuneration related to enterprise earnings in listed firms in China in light of most evidence focusing on cross-sectional data or data in a short span of time. Using full economic and business panel data on China’s publicly listed enterprise from 1999 to 2020 over two decades in the China Stock Market & Accounting Research database, we found statistically significant positive associations between director pay and firm performance in privately owned firms over this period, supporting the agency theory. In contrast, among the state-owned enterprises, there was a reverse relation between director compensation and firm financial performance, contributing to the existing literature. But the results also revealed that state-owned enterprises financially performed as well as private enterprises. Such findings suggested that state ownership might line up officials’ career incentives with party prime concern rather than pecuniary incentives. Also, CEO duality enhanced firm performance. As such, allegiance to the party and possible advancement to an upper-level political position would motivate company directors in state-owned enterprises. On the other hand, directors in privately owned enterprises might be motivated by monetary incentives. In addition, a statistical regression model was proposed and tested to get the results of the performance of state-owned enterprises. Finally, some suggestions were made about how to improve the institutional management of government-owned corporations in China.