In this paper, a study of modeling PV cell (solar cell), PV module (solar modules) and PV array solar array) using Matlab/Simulink software is presented. This study is focused on the study to design photovoltaic cell, photovoltaic modules, photovoltaic panels and PV array (solar array) and to analyze the change in PV performance according to changes in different parameters such temperature, solar irradiation, reverse saturation current, series resistance and shunt resistance. This study shows the PV and IV characteristics curve for each model from which MPP (maximum power point) is determined. In this study, different PV and IV characteristic curves are gained for varying temperatures with specified solar irradiation conditions,s and then the MPP is tracked. The highest power output is also compared between the designed PV module and the selected PV module (MX Solar USA MX60-245)

Rapid reduction in the $/Wp prices of photovoltaic (solar PV) energy has been proceeded recently, resulting in near exponential deployments with an annual capacity of 200 GW expected by 2020. Achieving high efficiency is necessary for many solar manufacturers to break even. In addition, new innovative installation methods are emerging to complement the improvement of system performance. The floating PV (FPV) solar market space has emerged over the past decade as a method for utilizing the cool ambient environment of the FPV system near the water surface to boost the power output performance of the PV module and ultimately the yield of the PV system. PV module temperature, which is the most critical factor affecting efficiency, ultimately governs the effective performance of solar cells, module, and all semiconductor materials in general. We propose the first ever electrical efficiency equations (${\eta }_{c,FP{V}_1}$ and ${\eta }_{c,FP{V}_2}$) for an FPV module installed on water based on two new predictions of FPV temperature operation models (T_m1 and T_m2), whose coefficients are derived from FPV site data with MATLAB. The theoretical prediction of module temperature shows respective errors of 2% and 4% when compared to the FPVM measured data.

Due to an increase of the monocrystalline PV systems installations, for a drive towards the sustainable smart cities in the hot arid developing country such as Pakistan, pose challenges of the performance and degradation issues. Monocrystalline PV module efficiencies are declining and damaging under the continuous exposure to higher surface day-time temperatures in the different parts of the country. A MATLAB simulations were performed based on the validated mathematical approach. This paper investigates the hot arid surface temperature impacts on the performance of PV modules during the summer and winter seasons in Pakistan. The investigations are performed examining the power generating efficiency of the PV system. This paper also investigates the influence of installations of PV-system in the North, South, East and West regions of Pakistan. It was examined that the northern areas of Pakistan are more suitable for maintaining the long-term durability of the PV system. Investigations are performed for the peak summer and peak winter days. During summer months, cooling strategies have to be implemented to overcome the heating effects whilst reducing degradation effect on installed PV-system.

Power semiconductor devices are essential from the operation point of view, size, efficiency and cost, these components are used in a myriad of applications, providing features that make them an important part of the system in which they are operating. This document analyzes and compares the basic structure, properties, design aspects, as well as temperature performance, stability and switching losses, present in devices on silicon (Si), silicon carbide (SiC) and new generation devices fabricated in gallium nitride (GaN) applied in renewable energy systems. The main objective is determinate the viability of the new generation components, which present a superior performance in view of an increase in efficiency, conductivity, decreases in switching losses, lower resistances and parasitic capacitances as well as higher operating frequency range. Therefore demonstrating the GaN components are a strong and viable candidate to solve some of the problems present in renewable energy systems.

The outdoor field test of the 4-terminal on Si tandem photovoltaic module (specifically, InGaP/GaAs on Si) was investigated and performance model, considering spectrum change affected by fluctuation of atmospheric parameters, was developed and validated. The 4-terminal on Si tandem photovoltaic module had about 40 % advantage in seasonal performance loss compared with standard InGaP/GaAs/InGaAs 2-terminal tandem photovoltaic module. This advantage is expanded in (subarctic zone) < (temperate zone) < (subtropical zone). The developed and validated model used all-climate spectrum model and considered fluctuation of atmospheric parameters, and can be applied every type of on-Si tandem solar cells.

The present study aims at investigating the relationship between Initial Public Offerings (IPOs) variables mainly underpricing on one hand, and the brand value measures on the other. Our final data set is 104 international brand companies. We implement empirical approach using hierarchical OLS regression and descriptive statistics. We show that underpricing is positively related to brand value which emphasizes the marketing role of going public and underpricing in enhancing brand equity through the product market, which additionally confirms some information asymmetry models. We also find that on average brand companies had not been recognized as brands at the IPO time. Moreover, we show the positive role of private equity in enhancing brand value, additionally, the non-linear association between underpricing and brand value is not evident. Finally, we draw some policy implication and suggestions for future research.

This paper aims to present a smart high speed battery charger, powered by a photovoltaic module array, for a LiFePO4 battery as a solar energy storage device. With a battery charging strategy, the presented battery charger involves a Buck converter as the core equipped with a simple maximum power point (MPP) tracker. Considering complexity reduction and easy hardware implementation, a constant voltage MPP tracking approach is adopted such that the maximum amount of output power can be delivered to the load in response to an arbitrary change in the solar radiation. A smart two-stage charging strategy, with a constant current mode followed by a constant voltage mode, is employed in such a way that the battery charge process can be accelerated largely, while the damage caused by overcharging can be prevented. In the end, the performance of this proposal is validated experimentally.

In this review, we summarize recent progress in crystal growth and understanding the influence of crystal structure on the superconductivity in pure and Pb-doped Bi2Sr2CuOy (Bi-2201) materials belonging to the overdoped region of high temperature cuprate superconductors. The crystal growth of Bi-2201 superconductors faces challenges due to intricate materials chemistry and lack of knowledge of corresponding phase diagrams. Historically, a crucible-free floating zone method has emerged as the most promising growth approach for these materials, resulting in high-quality single crystals. This review outlines the described in the literature and the authors' synthesis endeavors, encompassing Pb-doped Bi-2201 crystals, detailed structural characterization of as-grown and post-growth annealed samples, and highlights optimal growth conditions that yield large-size, single-phase, and compositionally homogeneous Bi-2201 single crystals.

This paper proposes an innovative mechatronic piezo-actuated module to control vibrations in modern machine tools. Vibrations represent one of the main issues that compromise seriously the quality of the workpiece. The active vibration control (AVC) device is composed by a host part integrated with sensors and actuators synchronized by a regulator, able to make a self-assessment and adjust to the environmental alteration. This study presents the mechatronic model based on the kinematic and dynamic analysis of the AVC device. To ensure a real time performance, a H2-LQG controller has been developed and validated by simulations involving machine tool, PZT actuator and controller models. The Hardware-in-the-loop (HIL) architecture is adopted to control and attenuate the vibrations. A set of experimental tests has been performed to validate the AVC module on a commercial machine tool. The feasibility of the real time vibration damping is demonstrated and the simulation accuracy is evaluated.

Underwater visual detection technology plays a pivotal role in fields such as marine exploration. With the increasing demand for underwater monitoring, the quest for efficient and reliable methods for underwater target recognition has become particularly significant. To address this requirement, this study developed an innovative underwater object detection architecture based on YOLOv8, named YOLOv8-MU, aimed at significantly enhancing detection accuracy.By integrating the LarK module proposed in UniRepLKNet to optimize the backbone network, YOLOv8-MU aims to achieve a larger receptive field without increasing the model’s depth. Further, this research introduces C2fSTR, an innovative method that combines Swin Transformer with the C2f module. Additionally, we have incorporated the SPPFCSPC_EMA module, which combines Cross-Stage Partial Fast Spatial Pyramid Pooling (SPPFCSPC) with attention mechanisms, significantly improving the detection accuracy and robustness of various biological targets. Moreover, by introducing a fusion block based on DAMO-YOLO into the neck of the model, we further enhanced the model’s capability in multi-scale feature extraction. Finally, the adoption of the MPDIoU loss function, designed around vertex distance, effectively tackles the challenges of localization accuracy and boundary clarity in underwater organism detection. Experimental results on the URPC2019 dataset demonstrate that the YOLOv8-MU model achieved an [email protected] of 78.4%, marking improvements of 5.6%, 1.1%, and 4.0% over YOLOv5s, YOLOv7, and YOLOv8n respectively, indicating the leading performance (SOTA) of this method. On the other hand, further evaluation on the URPC2020 dataset confirmed the generalization capability of the YOLOv8-MU architecture, with its [email protected] reaching 80.4%, surpassing various models including YOLOv5x and YOLOv8n, showcasing the wide applicability of our proposed improved model architecture.

Modular steel buildings (MSBs) interlocking inter-module connections (IMCs) have piqued researchers’ interest. However, the existing literature has not yet studied the optimisation of its plate thicknesses. This paper, therefore, focuses on optimising interlocking (IMCs) plate thickness in (MSBs), leveraging previous experimental and numerical simulation methodologies. Various numerical models for four MSBs interlocking (IMCs) with plate thicknesses (4 mm, 6 mm, 10 mm, and 12 mm) were developed under compression loading conditions. The study’s novelty lay in its comprehensive parametric analysis, which evaluated the slip phenomenon in (MSBs) connections and introduced a slip prediction model validated against empirical data. Further innovative machine learning approach was utilised to predict slip values based on applied force through a random forest regression model trained using the 'Treebagger' function. Sensitivity analysis and comparisons with alternative methods were utilised to underscore the reliability and applicability of the findings. The results showed that 11.03 mm was the optimal plate thickness for interlocking (IMCs) in modular steel buildings, with up to 8.08% reduced material costs. Increasing plate thickness boosts its deformation resistance by up to 50.75%. ‘TreeBagger’ random forest for anomaly detection and machine learning improves slip prediction up to 7% at higher force levels.

In this study, we evaluated the antibacterial effect against Staphylococcus aureus and Salmonella Typhimurium in fried fish paste using floating electrode-dielectric barrier discharge (FE-DBD) plasma (1.1 kV, 43 kHz, N2 1.5 m/s, 1-60 min). In addition, quality evaluation (pH, VBN) of fried fish paste was conducted after FE-DBD plasma treatment. When FE-DBD plasma was treated for 1, 5, 10, 20, 30 and 60 min, S. aureus decreased by 0.16-1.13 log10 CFU/g, and S. Typhimurium decreased 0.25-1.13 log10 CFU/g. Both decreased &gt; log10 CFU/g at 60 min. The D-value was 58.92 and R2 was 0.97 for S. aureus using first-order kinetics, and the D-value was 43.60 and R2 was 0.97 for S. Typhimurium using the Weibull model. There was no significant difference in pH after FE-DBD plasma treatment (p &gt; 0.05). However, volatile basic nitrogen (VBN) significantly decreased as the treatment time increased (p &lt; 0.05), and it was the lowest at 60 min to 3.46. Therefore, FE-DBD plasma treatment could be considered as a technology for preserving the quality of processed foods.

In April 2019, a team of Keio University and Bucknell University students was assembled to participate in Ericsson Innovation Awards with a novel concept for generating renewable energy. This conceptual system consists of a vertical axis wind turbine, a crossflow marine hydrokinetic turbine, a floating platform integrated with a quadcopter system, and three to four temporary mooring lines with ship-type anchors. The proposed designed aims to offer solutions to two current problems of floating offshore wind energy: high construction cost of floating platforms and difficulties in maintenance of mooring lines. The combination of two vertical-axis turbines into a single floating platform would enable the system, namely ESwift, to extract energy from both wind and current resources. Additionally, due to the utilization of vertical axis turbines, the center of gravity of the proposed concept is significantly lower with respect to water level, compared to that of existing floating horizontal axis wind turbines, which would potentially reduce the floater's size and construction cost. Lastly, the integrated quadcopter mechanism would assist the floater in terms of stability and mobility, and enables an array of ESwifts to automatically rearrange for maximal energy generation. The authors hope that readers would find the idea described in this open access letter worth pursuing and would further develop and commercialize the ESwift concept.

In many developing countries of the world including Pakistan motorcycle steeling is one of the main issue that needs to be dealt with an iron hand as it leads to economic losses as well as crime increase in the society, according to the DOWN REPORT there were 28609 motorcycle stolen in 2019 and this number has been increased to 34908 in 2020. Now due to the increasing motorcycles stealing crime what measures can we take to reduce the number of motorcycle theft the rule of technology is enormous in this regards specially information technology (IT) , information technology can play an important role in the security of motorcycles from being theft by the thieves, Information technology provide us the GSM based control system which can be very help full to construct a security system for motorcycle in order to make it antitheft.

The difficult current environmental situation, caused by construction industry residues containing ceramic materials could be improved by using these materials as recycled aggregates in mortars, with their processing causing a reduction in their use in landfill, contributing to recycling and also minimizing the consumption of virgin materials. Although some research is currently being carried out into recycled mortars, little is known about their stress-strain (σ-ε); therefore this work will provide the experimental results obtained from recycled mortars with recycled ceramic aggregates (with contents of 0, 10, 20, 30, 50 and 100%), such as: the density, the compression strength, as well as the σ-ε curves representative of their behavior. The values obtained from the analysis process of the results are those of: σ (elastic ranges and failure maximum), ε (elastic ranges and failure maximum), and Resilience and Toughness; in order to finally obtain, through numerical analysis, the equations to predict their behavior (related to their recycled content). At the end of the investigation it is established that mortars with recycled ceramic aggregate contents of up to 20% could be assimilated just like mortars with the usual aggregates, and the prediction equations produced could be used in cases of similar applications.

Here we present a fast and highly scalable community structure preserving network module detection that recursively integrates graph sparsification and clustering. Our algorithm, called SparseClust, participated in the most recent DREAM community challenge on disease module identification, an open competition to comprehensively assess module identification methods across a wide range of biological networks.

Gastro-retentive floating microspheres were developed as a result of the recent advancements in floating delivery systems for drugs (FDDS), which included the uniform dispersion of multiparticulate dosage forms along the GIT. This could lead to more consistent drug absorption and a lower risk of local irritation. Microballoons (MB), a multi-unit extended release with a sphere-shaped cavity encased in a tough polymer shell, have been developed as a dosage form with exceptional buoyancy in the stomach. This preparation for constrained intestinal absorption is made to float on top of gastric acid, that has a relative density lower than 1.By using enteric acrylic polymers and the emulsion solvent diffusion method, microballoons are prepared and filled to drug in one‘s outer polymer casings. Enteric acrylic plastics are used to generate microballoons that are drug-loaded in one‘s external polymer casings and dissipate in a solution of dichloromethane and ethanol. Cavity development in microparticles seems to be particularly correlated with dichloromethane evaporation. Microballoons with a drug distributed or dispersed all through the particle-matrix have the potential for a controlled drug release and float continuously for more than 12 hours in vitro out over the surface of an acidified dissolution medium with surfactant. The drug is released slowly and at the desired rate as the microballoons glide over the components of the stomach, increasing gastro-retention time and lowering fluctuations in plasma concentration.

Floating Offshore Wind Turbine (FOWT) is an innovative technology with little industry guidance for its hull design. Various FOWT floaters with different hull shapes claim to support the same turbines. Structural integrity and material expense analyses of different pontoon shapes were conducted, and it was found that some configurations, such as those with every two columns connected by both pontoon and bracing, have advantages over others. However, it is important to note that the choice of pontoon shape should be based on the wave loading conditions the floater will be exposed to. While a T-shaped pontoon provides a cost-effective solution under certain wave loading scenarios, it may not be the best option for all conditions. Specifically, ring pontoon designs with full bracing were found to be necessary for withstanding certain wave loads. Therefore, it is important to consider different Dominant Load Parameters (DLP) and ensure that a FOWT floater can withstand all applicable DLPs. An uneven hexahedral column shape, which combines the best attributes of square and round shapes, is proposed as a better alternative to cylindrical columns. It offers ease of manufacture and reasonably low drag. Bracing is found to be necessary for withstanding the wind turbine’s incurred moment and forces. The conclusion is that platform design should prioritize manufacturing costs and strength over maximizing hydrodynamic performance.

While previous research has shown the use of attachable air-caps on windows to efficiently reduce a building’s energy consumption, the air-caps considered had to be attached to the entire window’s surface, thus limiting the occupants’ view and creating the inconvenience of needing to detach and attach the air-caps. In this study, a window-mounted air-cap roller module using Velcro tape that may be easily attached, detached, and rolled up or down was developed and performance tested in a full-scale test bed. It was found that as the area of the air-caps attached on a window increased, the required indoor lighting energy increased. However, the window insulation improved, thus reducing the cooling and heating energy needed. Attaching the air-caps to the entire window surface effectively reduced the building’s energy consumption, but views through the window may be disturbed. Thus, the developed window-mounted air-caps enable an occupant to reduce the building energy consumption and maintain their view according to their need. The findings of this study may contribute to a reduction in building energy consumption without sacrificing a pleasant indoor environment. Further studies may be needed to verify their efficacy under varying indoor and outdoor conditions.

The aim of this work is to introduce new ways to model the I-V characteristic of a PV cell or PV module using straight lines and Bézier curves. This is a complete novel approach, Bézier curves being previously used mainly for computer graphics. The I-V characteristic is divided in three sections, modeled with lines and a quadratic Bézier curve in the first case and with three cubic Bézier curves in the second case. The result proves to be accurate and relies on the fundamental points usually present in the PV cell datasheets: Voc (the open circuit voltage), Isc (the short circuit current), Vmp (the maximum power corresponding voltage) and Imp (the maximum power corresponding current) and the parasitic resistances Rsh0 (shunt resistance at Isc) and Rs0 (series resistance at Voc). The proposed algorithm completely defines all the implied control points and the error is analyzed. The temperature and irradiance influence is also analyzed. The model is also compared using the least squares fitting method. The final validation shows how to use Bézier cubic curves to accurately represent the I-V curves of an extensive range of PV cells and arrays.

The motion of a circular elastic plate floating on the surface is investigated in the time–domain. The solution is found from the single frequency solutions and the method to solve for the circular plate is given using the eigenfunction matching method. Simple plane incident waves with a Gaussian profile in wavenumber space are considered and a more complex focused wave group is considered. Results are given for a range of plate and incident wave parameters are investigated. Code is provided to show how to simulate the complex motion.

This paper presents a unified reconfigurable coordinate rotation digital computer (CORDIC) processor for floating-point arithmetic. It can be configured to operate in multi-mode to achieve a variety of operations and replaces multiple single-mode CORDIC processors. A reconfigurable pipeline-parallel mixed architecture is proposed to adapt different operations, which maximizes the sharing of common hardware circuit and achieves the area-delay-efficiency. Compared with previous unified floating-point CORDIC processors, the consumption of hardware resources is greatly reduced. As a proof of concept, we apply it to 1638416384 points target Synthetic Aperture Radar (SAR) imaging system, which is implemented on Xilinx XC7VX690T FPGA platform. The maximum relative error of each phase function between hardware and software computation and the corresponding SAR imaging result can meet the accuracy index requirements.

It is well known that working PV plants show several maintenance needs due to wiring and modules degradation, mismatches, dust and PV cells defects and faults. There are a wide range of studies that show the theoretical and some laboratory tests of how these circumstances may affect the PV production. Thus, it results mandatory to evaluate the whole PV plant performance and, then, it’s payback time, profitability and environmental impact or carbon footprint. However, very few studies include a systematic procedure to quantify and supervise the real degradation effects and faults impacts on the field. In this paper, the authors first conduct a brief review of the most frequent PV faults and degradation that can be found on real conditions operative PV Plants. Then, they propose and develop an innovative Geographic Information System application to locate and supervise them. The designed tool has been applied to either a large PV plant of 108 kWp and a small PV plant of just 9 kWp installed on a home rooftop. For the large PV plant, 24 strings of PV modules have been modelized and introduced into the GIS application and every module in the power plant has been studied including voltage, current, power, series and parallel resistance, fill factor, normalized PV curve to STC, thermography and visual analysis. For the small PV installation 3 strings of PV panels have been studied identically. It must be noticed that PV modules in this case include power optimizers. The precision of the study allows the researchers to locate and supervise up to a third part of every PV cell in the system, which are adequately georreferenciated. The developed tool allows both the researchers and the investors to increase control on the PV plant performance and conducts to a better planification of maintenance actuations and to evaluate several PV modules replacement strategies in a preventive maintenance programme. Found PV faults include hot spots, snail tracks, EVA discoloration, PV cells fractures, busbars discoloration, bubbles and Si discoloration.

In response to the problem of increasing climate change and energy security, investment in renewable energy sources has increased significantly both in Europe and globally. Wind and solar power plants are expected to be the largest contributors to global decarbonization, ranking first and second in projected capacity by 2050. Since all power plants have some impact on the environment, this also applies to photovoltaic power plants. Because of the large capacities projected for PV installed capacity, their environmental impacts should be evaluated. The environmental impacts of PV power plants change over time. Improving the manufacturing technology of PV system components, increasing the efficiency of solar cells, and using materials that are less harmful to the environment will reduce these impacts. Manufacturing PV system components is a highly energy-intensive process that involves greenhouse gas emissions. As new renewable energy capacity is built, the amount of "green" electricity on the grid increases, reducing CO2 emissions per kWh consumed. The objective of this paper is to analyze the current status of the environmental impact of PV power plants under these changing conditions in terms of CO2 emissions, land use, pollutant and noise emissions, and water consumption. The capacity installed to date will reach the end of its lifetime by 2050, which means that the amount of waste associated with it will increase over time. This can have a significant impact on the environment, which is why part of the work is dedicated to this problem. In addition to the available information from the literature, in the part related to land use, the authors also made their own estimates based on data on newly installed PV power plants and PV modules available on the market. The results of the analysis show that there is enough land both in Europe and worldwide to install the planned capacities of rooftop and ground-mounted PV power plants. CO2 emissions are at the same level as for concentrated solar power, with a decreasing trend. Pollutant emissions, noise and water consumption are not a major problem compared to other types of power plants. Overall, it can be concluded that the expansion of PV capacity has a very positive impact on the environment.

In the field of construction, materials referred to as sustainable are currently undergoing a process of technological development. This study aims to contribute to the understanding of the behavior of the fundamental properties of concretes prepared with recycled coarse aggregates that incorporate in their matrix a polyethylene terephthalate-based additive in an attempt to reduce their high porosity. Techniques to measure the gas adsorption, water porosity and x-ray diffraction (XRD) were used to evaluate the effect of the additive on the physical, mechanical and microstructural properties of these concretes. Porosity reductions of up to 30.60% are achieved with the addition of 1, 3, 4, 5, 7 and 9% of the additive, defining a new state in the behavioral model of the additive (the overdosage point) in the concrete matrix; in addition, the porous network of these concretes and their correlation whit other physical and mechanical properties are also explained.

The exponential growth of population and the consistent food demand has compelled humanity to seek alternatives to traditional farming and innovative technologies to increase production. Exploring offshore for natural resources and alleviating pressure on land has been an ongoing research field, especially in the energy and aquaculture sector. However, the idea of floating farming is still in its infancy and requires significant innovations. The work presented here shed further light on this area by proposing a comprehensive model of ‘Integrated, multicultural, Multileveled Floating Farm (MFF).’ Various aspects of planning, design, constructions and operations of such MFF are discussed. An integrated waste management system is proposed to improve sustainability. The conceptual design and associated financial analysis demonstrated that such integration of various modes of farming could be profitable and sustainable at the same time. The cost estimation and profit analysis are presented in the context of Singapore, and a conservative approach is followed for the calculation. However, the model can easily be extended for application in other countries.

This research, which focuses on wave energy generating technology, suggests a brand-new oscillating float-type wave energy conversion system that is appropriate for the offshore wave environment. To accurately model the device, a three-dimensional viscous numerical pool is created based on STAR-CCM+. The array-type power generation device is numerically simulated using theoretical analysis and numerical simulation, and the wave energy power generation device is arranged as double floats in the front and rear rows, representing triangular array type and rectangular array type, respectively. The triangular array type is a superior layout and can make greater use of marine resources, according to this paper's analysis of the hydrodynamic and power generation impacts of the three array types. The findings are meant to serve as a guide for engineering practice.

Construction of Pontoons is based on multiple elements, dimensions and weight. The study has addressed about how the industry of the floating reinforcement concrete precast (pontoons) installs in the factory with the combinations of utility, electricity services, and Internet service. The pontoon bridges are successfully installed in the road for transport or sea for shops. The installation process for pontoons is successfully attempted in a balanced situation above surface of the sea to the resistant of floating precast (pontoons) to any ambient effects such as weather conditions, the movement of the waves or any others effects. The findings elaborate that it is not just a military solution. Pontoon installation can significantly serve for civil purposes.

Star image registration is the most important step in the application of astronomical image differencing, stacking and mosaicking, which requires high robustness, accuracy and real--time of the algorithm, but there is no high--performance registration algorithm in this field. In this paper, we propose a star image registration algorithm that relies only on radial module features (RMF) and rotation angle features (RAF), which has excellent robustness, high accuracy, and good real--time performance. The test results on a large amount of simulated and real data show that the comprehensive performance of the proposed algorithm is significantly better than the four classical baseline algorithms in the presence of rotation, insufficient overlapping area, false stars, position deviation, magnitude deviation and complex sky background, which is a more ideal star image registration algorithm.

One of the key areas of the International Renewable Energy Agency’s (IRENA) programme of work is the analysis of renewable technology costs and performance and the dissemination of these results to as wide an audience as possible. In addition to analysis, IRENA seeks to engage a broad range of stakeholders in the context of this work through the Renewable Costing Alliance. The Costing Alliance brings together companies, industry association, governments and researchers to share, confidentially, data for real-world renewable energy project cost and performance, helping to build further on IRENA’s costing work to date. In this context the authors analyse the current cost differentials and cost reduction potentials for solar PV in Japan compared to best practice levels and identify the reasons for higher costs in Japan and how to reduce them. This study analyses the current installed cost differentials for utility-scale, commercial and residential rooftop solar PV systems. In addition to identifying the reasons for the cost differentials, potential policy recommendations to reduce the cost differential are identified.

The paper proposes a new implementation of the PID algorithm in digital hardware. The proposed circuit implements an advanced PID formula, containing a non ideal derivative component, and weighting coefficients, which enable reducing influence of setpoint changes in the proportional and derivative components. The implementation operates on standard single precision (32 bit) floating-point numbers. The proposed circuit structure is optimized for cost. It uses just one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequential manner. The circuit was implemented in a Cyclone V FPGA device from Intel, using the Quartus Prime software. Proper operation of the circuit was verified by simulation. The proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on standard single precision floating-point numbers, while being significantly cheaper. However, it outperforms by several orders of magnitude the speed of any software-based implementation, including solutions using PLCs, and CPU/MCUs. The proposed circuit structure, together with the overall regulator device concept, suit well the SoC (System on Chip), or SoPC (System on Programmable Chip) idea, i. e. a device, that contains a CPU core immersed in “FPGA fabric” - logic resources characteristic for FPGA devices.

Floating wind is becoming an essential part in terms of renewable energy. Therefore, highlighting the perspectives in developing the floating wind platforms is very important. In this paper, we focus on floating wind concepts and projects around the world. This will give a taste to the reader about what is going on in terms of the projects around the world. The main aim of this work is to further explain the collected data regarding the floating wind concepts and projects, and further classify them in terms of cost, power capacity, wind speed, water depth, and distance to shore.

Determining disability prevalence is an important area for population statistics, especially among young adolescents. The Washington Group on Disability Statistics is one source of reporting disabilities through functional difficulties. However, young adolescents self-reporting this measure is in its infancy. The purpose of this study was to carry out an intra-rater test-retest reliability study on a modified set of items for self-reporting functional difficulties. Young adolescents (n=74; boys=64%; age m=13.7, SD=1.8) completed a self-reported version of the child functioning module in a supervised classroom. The second administration took place two weeks later. Intraclass correlation coefficient (ICC) and Kappa (k) statistics were used to test reliability of the items, and interpretation through Landis &amp; Koch, and Cohen, respectively. The majority of items had substantial or moderate agreement, although there was only fair agreement for self-care (ICC=0.59), concentration (ICC=0.50), and routine (ICC=0.54). Kappa statistics of behaviour were interpreted to be large (k=0.65), and seeing (k=0.49), walking (k=0.49), and speaking (k=0.49) difficulties were moderate. The majority of the items in the self-reported version of the child functioning module can be used in a scale format, although some caution may be required on items of self-care and concentration when used as a dichotomous variable.

Solar energy is one of the most important renewable energy sources for many settings, such as households, solar-powered vehicles, power grid support, etc. However, the amount of solar energy produced is directly proportional to the surface area of the solar panel exposed to the sun rays, and so the challenges arise for the space required to install solar panels in order to get maximum area coverage of sun radiation energy absorption in constricted spaces such as vehicles powered by solar, small residential apartments, etc. The challenge with solar energy is that once a plate is installed, the space under it or above it cannot be used for another solar panel installation. This is due to the fact that the shadow cast by the above solar panel or structure prevents sun rays from reaching the below solar panel. The introduction of flexible solar panels that can take on any shape has given us the opportunity to explore the possibility of maximising solar plate surface area using surface manifold techniques. In this optimisation study, the constraint is that we have a space with fixed dimensions of length, width, and height, and the objective is to maximise the surface area of the panel within the given space dimensions to maximise solar energy absorption. Thus, this paper then proposes curving the panel surface and/or protruding the surfaces of the panel using the concept of a topology surface manifold to maximise the surface area: Protruding panel surface with shapes such as pyramidal, cone, hemisphere, sinusoidal wavy curve, etc. are analysed to check if the panel surface increases at all. The geometrical design result shows that the panel surface can increase by more than 20%. Although this paper only presents concept work and details of experimental design are yet to be performed, it should also be noted that curving surfaces have drawbacks, such as some protruding parts of solar panels when the plate is tilted, not facing the sun directly, which may cast shadows on parts of the solar panel, causing hindrance.

This research presents a maximum power point tracking of traditional single-ended primary converter (SEPIC) with the aid of fuzzy logic controller (FLC) for regulating the voltage and perturbation and observation(P&O) algorithms for regulating the reference voltage and reaching the maximum power point tracking. This is a promising technique for the conventional SEPIC converter to achieve maximum power point tracking with much less error. Moreover, the method is simple, reliable, and understandable for photovoltaic systems.

This paper investigates the hydroelastic responses of offshore floating solar photovoltaic farms (OFPVs) to minimize structural motion. The OFPV usually occupies a large sea space in the order of hectares and due to its huge structural length-to-thickness ratio, the structural deformation under wave action has to be taken into consideration. The flexible deformation of the structure under hydrodynamic loading is termed the hydroelastic response. Due to the relatively long structural length with respect to the encountering wavelength, the diffraction and radiation of waves have to be taken into account to accurately represent the hydrodynamic loadings on the floating platform. The numerical model is first validated by comparing the eigenvalues and eigenvectors of the OFPV obtained from the proposed numerical scheme with their counterparts obtained from an established finite element software. This is followed by the investigation of the hydroelastic response of various OFPVs designed in varying layout configurations. The various layout configurations are obtained by altering the floating modular units’ dimensions as well as the spacing of the OFPVs when deployed adjacent to each other. The optimal configuration that gives the best performance in terms of the overall smallest response, known as compliance, is then suggested. The results suggest that a longish OFPV layout has a lower hydroelastic response and the motion could be further reduced by increasing the global flexural stiffness via layout arrangement.

Aerodynamic performance of a floating offshore wind turbine (FOWT) is significantly influenced by platform surging motions. Accurate prediction of the unsteady aerodynamic loads is imperative for determining the fatigue life, ultimate loads on key components such as FOWT rotor blades, gearbox and power converter. The current study examines the predictions of numerical codes by comparing with unsteady experimental results of a scaled floating wind turbine rotor. The influence of platform surge amplitude together with the tip speed ratio on the unsteady aerodynamic loading has been simulated through unsteady CFD. It is shown that the unsteady aerodynamic loads of FOWT are highly sensitive to the changes in frequency and amplitude of the platform motion. Also, the surging motion significantly influences the windmill operating state due to strong flow interaction between the rotating blades and generated blade-tip vortices. Almost in all frequencies and amplitudes, CFD, LR-BEM and LR-uBEM predictions of mean thrust shows a good correlation with experimental results.

Thermal safety of lithium-ion traction batteries is a highly concerned issue in the field of electric transportation. The large amount of gas emission during the thermal runaway process of batteries has highly safety hazards such as fire and explosion. Quantitative analysis of emission is one of the important challenges for testing and evaluating battery safety. Focus on quantifying the gas emission through large-scale thermal propagation in battery modules and packs, based on the idea of cell result multiplication, this article conducts thermal runaway emission analysis on single cell and modules, and compares the behavior of thermal runaway and gas emission of the cell and module from the aspects of temperature, pressure, gas composition and battery morphology. The feasibility of the cell result multiplication method was verified from the perspective of experimental data.

High-penetration of Distributed Energy Resources (DER) in low voltage distribution grids, mainly photovoltaics (PV), might lead to overvoltage in the point of common coupling. Volt-VAr is one of the common control functions for DER power converters used to enhance the stability and the reliability of the voltage in the distribution system and, thus, fulfilling the network operator requirements. In this study, a centralized algorithm will provide local Volt-VAr control parameters to each PV inverter, based on the electrical grid characteristics where each equipment is installed. Since accurate information of grid characteristics is typically not available, the parametrization of the electrical grid is done using power meter data in DER location and a voltage sensitivity matrix. The algorithm has different optimization modes to both minimize voltage deviation and line current. In order to validate the effectiveness of the algorithm and its deployment in real infrastructure, it has been tested in an experimental setup with PV emulators in a set of 5-day tests. Volt-VAr control algorithm successfully adapted its parameters based on grid topology and PV inverter characteristics, achieving a voltage reduction up to 25% of the allowed voltage deviation.

This paper proposes a novel model for a PV cell with parameters variance dependency on temperature and irradiance included. The model relies on commercial available data, calculates the cell parameters for standard conditions and then extrapolates them for the whole operating range. An up-to-date review of the PV modeling is also included with series and parallel parasitic resistance values and dependencies discussed. The parameters variance is analyzed and included in the proposed PV model, where the self-heating phenomenon is also considered. Each parameter variance is compared to the results from different authors. The model includes only standard components and can be run on any SPICE-based simulator. Unlike other approaches that consider the internal temperature as a parameter, our proposal relies on air temperature as an input and computes the actual internal temperature accordingly. Finally, the model is validated via experiments and comparisons to similar approaches are provided.

Integration of battery energy storage in photovoltaic (PV) systems can reduce the electricity costs and provide desirable flexibility and reliability to these systems decreasing renewable energy fluctuations. This paper presents a review of the PV-battery application in Brazil, highlighting the challenges and prospects based on the state-of-art. A PV-battery systems description is presented in this work, as well as the most applied battery technology and its comparison. The paper also describes the set of applications such as voltage and frequency regulation, renewable energy integration, power quality, etc. In the Brazilian scenario, there are applications of PV-battery systems, most of them part of research and development projects (R&amp;D’s), and some real cases are shown, including its goals, applied equipment, operation modes, strategies, and perspectives. Additionally, this work evaluates the Brazilian scenario regarding the energy storage systems implementation challenges, such as regulatory barriers, business models, and opportunities for R&amp;D in the energy market. In conclusion, it is need develop proper regulatory models to expand PV-battery systems and make them visible to the agents in the electricity sector.

Due to the high solar irradiance or energy price, certain regions in the U.S. may reach 100% PV penetration and experience degradation of frequency response greater than the interconnection as a whole. Therefore, in this section, the 100% PV penetration region in each interconnection is simulated to study the local high PV penetration effects. The study was performed by quantifying RoCoF, frequency nadir, and settling frequency at different regional PV penetration levels. The impact of high regional PV penetration on the compliance of grid code on frequency response is also studied.

We report cyclic growth/retraction phenomena observed for saline droplets placed on the cured PDMS membrane with the thickness of 7.8±0.1 µm floating on pure water surface. Osmotic mass transport across the micro-scaled floating PDMS membrane provided the growth of the sessile saline droplets followed by evaporation of the saline droplets. The observed growth/retraction cycle was reversible. The model of the osmotic mass transfer across the cured PDMS membrane is suggested. The model explains semi-quantitatively the time evolution of a droplet.

Nizatidine is a gastroprotective drug with a short biological half-life and narrow absorption window. This study aimed at developing floating tablets of nizatidine using various HPMC viscosity grades, namely K4M, E4M, K15 and K200M. Directly compressed tablets revealed an excellent uniformity in hardness, thickness and weight and nizatidine was evenly distributed within the matrix floating tablets. Buoyancy study revealed floating lag time as low as 18–38 s, and tablets remain buoyant for upto 24 h. However, the later depended upon viscosity grade of HPMC and that the higher the viscosity, the less was the total floating time. In vitro dissolution indicated viscosity dependent nizatidine release from the floating tablets. HPMC K4M and E4M based floating tablets released almost 100% drug in 12 h, whilst higher viscosity polymers such as K15 and K200M only released 81.88% and 75.81% drug, respectively. The drug release followed non-Fickian diffusion from tablets formulated with K4M, K15 and K200M, whilst super case II transport was observed with E4M based tablets. More interestingly, K4M and E4M polymers have similar viscosity yet exhibited different drug release mechanism. This was attributed to the difference in degree of substitution of methoxyl- and hydroxypropoxyl- groups on polymer backbone.

Growing crystals of nickel niobate (NiNb2O6), we noticed that changing growth conditions allowed our material to enter different areas of the phase diagram. In particular, we found that excess material accumulated within and above the liquid zone. Analysis showed that this was an unincorporated constituent. Changing the ratio of the constituent oxides - an excess of ~4% of either NiO or Nb2O5 gave us the opportunity to investigate changes in zone stability, melting temperature and quality of the resulting crystal. We found that a small excess of nickel oxide decreases the melting temperature significantly, and created the best pseudo-rutile NiNb2O6 crystal studied, while higher amounts of niobium oxide allowed us to stabilize the NiNb2O6 columbite phase. This research reinforces the idea that self-flux as a travelling solvent can significantly impact crystal growth parameters and quality.

Recently, attention mechanisms have developed into an important tool for performance improvement of deep neural networks. In computer vision, attention mechanisms are generally divided into two main branches: spatial and channel attention. Both attention categories have their own advantages. The fusion of both attentions achieves higher performance, on the cost of the computational load. This paper introduces an innovative and lighter n-shifted sigmoid channel and spatial attention (CSA) module to reduce the computational cost and to improve the 3D scene relevant features selection. To validate the proposed attention module, 3D scene object detection in the deep Hough voting point sets is considered as the testing application. The proposed attention module with its piecewise n-shifted sigmoid activation function improves the network’s learning and generalization capacity which effectively predict bounding box parameters directly from 3D scenes and detect objects more accurately. This advantage is achieved by selectively attending to more relevant features of the input data. When used in the deep Hough voting point sets, the proposed attention module outperforms state-of-the-art 3D detection methods on the sizable SUNRGBD dataset. Experiments conducted showed an increase of 12.02 mean accuracy precision (mAP) when compared to the celebrated VoteNet (without attention). It also got 9.92 mAP higher compared to the MLVCNet, and 10.32 mAP higher than the Point Transformer. The proposed model not only decreases the sigmoid vanishing gradient problem but also brings out valuable features by fusing channel-wise and spatial information while improving accuracy results in 3D object detection.

This paper presents the design of a new neuro-generator of pseudorandom number type PRNG Pseudorandom Number Generator, which produces complex sequences with an adequate bit distri-bution. The circuit is connected to a neuronal module with six impulse neurons with different be-haviors: spike frequency adaptation, phasic spiking, mixed mode, phasic bursting, tonic bursting and tonic spiking. This module aims to generate a non-periodic signal that becomes the clock sig-nal for one of the LFSRs Linear Feedback Shift Register that the neuro-generator has. To verify its correct operation, the neuro-generator was subjected to a series of tests where the frequencies of the impulse neurons were modified. This modification allows generating a greater number of pulses at the output of the neuronal module, to obtain sequences with different characteristics that pass different NIST statistical tests National Institute of Standards and Technology of U.S. The results show that the new neuro-generator maintains pseudo-randomness in the sequences obtained with different frequencies and it can be implemented on reconfigurable FPGA Field Programmable Gate Array Virtex 7 xc7vx85t-2ffg1761 device. Therefore, it can be used for applications such as biologi-cal systems.

Human activities and natural events alter the earth’s surface and pose a constant threat to the environment. Thus, accurately monitoring and predicting these changes in a timely manner is crucial to provide solutions and mitigate the environmental consequences beforehand. This research introduces a novel framework, called HCD-Net, for change detection using bi-temporal hyperspectral images. The framework is based on double-stream deep feature extraction and an attention mechanism. The first stream focuses on deep feature extraction through 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks. The second stream focuses on deep feature extraction through 2D convolution and 2D SE blocks. The extracted deep features are then concatenated and fed to dense layers for decision-making. The efficiency of HCD-Net is compared to the state-of-the-art change detection methods. Additionally, the bi-temporal Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) hyperspectral dataset was used for further evaluation of the change detection results. The results show that HCD-Net has a higher accuracy and the lowest false alarm rate, where the overall classification accuracy is above 96%, and the kappa coefficient is over 0.9.

Dye sensitized solar cell technology is having an important role in renewable energy research due to its features and low cost manufacturing processes. Devices based on this technology appear very well suited for integration into glazing systems due to their characteristics of transparency, color tuning and manufacturing directly on glass substrates. Field data of thermal and electrical characteristics of dye sensitized solar modules (DSM) are important since they can be used as input of building simulation models for the evaluation of their energy saving potential when integrated into buildings. However still few works in the literature provide this information. The study here presented wants to contribute to fill this gap providing a thermal and electrical characterization of a DSM in real operating conditions using a method developed in house. This method uses experimental data coming from test boxes exposed outdoor and dynamic simulation to provide thermal transmittance and solar heat gain coefficient (SHGC) of a DSM prototype. The device exhibits an U-value of 3.6 W/m²K, confirmed by an additional measurement carried on in the lab using a heat flux meter, and a SHGC of 0.2, value compliant with literature results. Electrical characterization evidences an increase of module power with respect to temperature causing DSM suitable for integration in building facades.

The lactic acid bacteria (LAB) have great potential to produce homoexopolysaccharides (HoPS), have been the subject of extensive research efforts, given their health benefits and physicochemical properties. The HoPS functional properties are determined by structural characteristics of varied molecular weights, types of glycosidic linkages, degrees of branching and chemical composition. The dextransucrases (DSases) are responsible of the synthesis of a kind of HoPS (dextran polymers), which are among the first biopolymers produced at industrial scale with applications in medicine and biotechnology. The concept of glycodiversification opens additional applications for DSases. In that sense the design and characterization of new DSases is of prime importance. Previously, we described the isolation and characterization of a novel extracellular dextransucrase (DSR-F) encoding gene. In this study, from DSR-F, we design a novel chimeric dextransucrase DSR-F-∆SP-∆GBD-CBM2a, where DSR-F-∆SP-∆GBD is fused to the carbohydrate-binding module (CBM2a) of the β-1-4 exoglucanase/xylanase Cex (Xyn10A) of Cellulomonas fimi ATCC 484. This dextransucrase variant is active and without alteration in its specificity. The DSR-F-∆SP-∆GBD-CBM2a is purified by cellulose affinity chromatography for the very first time. Our results indicate that new hybrids and chimeric DSases with novel binding capacity to cellulose can be designed to obtain glyco-biocatalysts from renewable lignocellulosic materials.

The assessment of Photovoltaic (PV) systems, a key component of sustainable energy solutions, is of paramount importance in the pursuit of a greener future. This abstract provides an overview of environmental assessments associated with PV systems, considering their life cycle, energy production, and ecological impact. The study explores various methodologies and tools employed in the evaluation of PV systems’ environmental performance, addressing issues such as material sourcing, manufacturing, installation, and end-of-life disposal. Additionally, it delves into the trade-offs and challenges in achieving sustainability objectives in the solar energy sector. By critically examining the environmental implications of PV systems, this research contributes to informed decision-making and the sustainable integration of solar power into our energy landscape.

Bifacial PV modules have become the pervasive choice for utility-scale solar photovoltaic (PV) installations. Understanding bifacial PV performance for commercial deployments is, therefore, critically important. Although there have been studies on the modeling of Bifacial PV performance, to the best of our knowledge, the comprehensive integration of racking and mounting geometry for Bifacial PV performance simulation is yet to be reported. In this work, we modeled the racking and mounting geometry for a field-deployed operational PV array. We then applied optical ray-trace simulation to study the impact of those structures on array performance. The study reveals that the support structures have a considerable impact on the rear surface irradiance and overall bifacial PV performance, amounting to as much as 30% shading in certain situations. The work analyzes the impact of installation parameters such as tilt, height, and row spacing on the bifacial PV array’s performance. Thus, a novel holistic approach to studying the sensitivity of various installation parameters’ impact on bifacial PV array performance, with the racking and mounting structure’s geometry integrated, has been demonstrated through the work. This knowledge and its practice enables site-specific design optimization for utility-scale bifacial solar PV arrays and synergistic PV applications such as agrivoltaics and carports, where maximizing power generation is not necessarily the sole objective.

The purpose of this study is to analyze the self-regulated learning and the ability to write scientific papers of students after learning with the Project-Based Digital Science Module (DSM). This research is a mixed methods by analyzing quantitatively and qualitatively. The research instrument used to obtain data is using a product assessment sheet and a self-regulated learning questionnaire. Data analysis uses N-Gain to determine the increase in self-regulated learning scores and student scientific writing skills scores, and uses linear regression analysis to determine the effect of self-regulated learning on student scientific writing skills. The results obtained showed N-Gain score of learning independent is 0,65 and scientific writing ability is 0,94, then significant’s score from linear regression by using SPSS is 0,22 < 0,05. This result showed an increase in self-regulated learning and the ability to write scientific papers of students on each indicator after learning using the Project-Based Digital Science Module (DSM). In addition, based on the results of data analysis, it can be seen that there is an influence of self-regulated learning on the ability to write scientific papers of students after learning using the Project-Based Digital Science Module.

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. Single-, double-, and PV module models are investigated to test ICO's parameter estimation performance. Statistical analysis uses minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared to other state-of-the-art optimization algorithms, the ICO algorithm is proven to be highly reliable and accurate when identifying PV parameters.

Nowadays, there are implemented devices whose purpose is to perform massive computations by saving resources at the time they reduce the latency of arithmetic operations. These devices are usually GPUs, FPGAs and other specialised devices such as "Coral". Neural networks, digital filters and numerical simulators take advantage of the massively parallel operations of such devices. One way to reduce the amount of resources used is to limit the size of the registers that store data. This has led to the proliferation of numeric formats with a length of less than 32 bits, known as short floating point or SFP. We have developed several SFP’s for use in our neural network accelerator design, allowing for different levels of accuracy. We use a 16-bit format for data transfer and different formats can be used simultaneously for internal operations. The internal operations can be performed in 16-bit, 20-bit and 24-bit. The use of registers larger than 16-bit allows the preservation of fractional information while increasing precision. By leveraging some of the FPGA’s arithmetic resources, our design outperforms designs implemented from scratch and is competitive with specialized arithmetic circuits already implemented in the FPGA.

This study employed a TSMC 0.18-µm 50 V process to establish a high-voltage n-LDMOS structure. In the reference device, the number of floating-poly (poly-2) turns was 7, and the width and spacing of each turn was 1 µm. Although increasing the number of turns reduces the peak field, directly reducing or increasing the number of poly-2 turns is a common practice. The present experiment was realized in three steps, namely fixing the number of occupied area adjustment turns, adjusting the width of each turn, and adjusting the distance between turns. The first step was fixing the number of occupied area adjustment turns. The number of turns was increased from 7 to 9, and the number of turns was reduced to 5 and 3 in the reference group. A reduction in the number of turns led to a greater decrease in the peak field and increase in the breakdown voltage despite the area remaining unchanged. A comparison of the use of three turns with that of seven turns revealed that the maximum electric field decreased by 42%, from 4.17e+3 to 2.46e+3 V/cm, and that the breakdown voltage (VBK) increased from 30.2 to 35.84 V. The second step was adjusting the width of each turn from 1 to 1.4, 1.2, 0.8, and 0.6 µm in the reference group. This increase in width reduced the maximum electric field and led to a greater increase in VBK. When the width was increased to 1.4 µm, the maximum electric field decreased by 29% from 1.95e+3 to 1.4e+3 V/cm, and the VBK increased by 183% from 30.2 to 85.6 V. The third step was adjusting the spacing of each turn from 1 to 1.4, 1.2, 0.8, and 0.6 µm in the reference group. The results revealed that a reduction in the spacing led to a greater reduction in the maximum electric field and an in-crease in the VBK. When the pitch was reduced to 0.6 µm, the maximum electric field decreased by 33%, from 1.95e+3 to 1.32e+3 V/cm, and the VBK increased by 345%, from 30.2 to 134.4 V.

Greenwater (splashing of water on the deck) loading is a classical problem faced by the designer of ship-shaped vessels, which becomes even worst when the vessel operates at harsh weather conditions for an extended period. Installation of breakwaters on the deck can play a crucial role in minimizing this impact. However, research on the design and optimization of the breakwater is still at its infancy, and this study aims at shedding further light on this area by proposing and analyzing the effectiveness of three breakwater designs on a fixed box-shaped vessel. Commercial CFD software is used for this investigation. However, the design model (without breakwater) was validated at first against experimental results of green water splashing, before performing the actual simulations with proposed breakwater design. A vertical plate is used as the deck structure, and greenwater pressure at several locations on that plate is measured to compare the effectiveness of various breakwater designs. Overall, breakwaters with openings (perforations, grillages etc.) found to be more effective in minimizing the pressure generated by the greenwater. Nevertheless, there are significant rooms for improvement on breakwater designs, and some topics for further research are also suggested in this regard.

New technologies such as "connected" and "autonomous" vehicles are going to change the future of traffic signal control and management and possibly will introduce new traffic signal systems that will be based on floating car data (FCD). The use of floating car data to regulate, in real-time, traffic signal systems has the potential for an increased sustainability of transportation in terms of energy efficiency, traffic safety and environmental issues. However, research has never explored how not "connected" vehicles would benefit by the implementation of such systems. This paper explores the use of floating car data to regulate in real-time traffic signal systems in terms of cooperative-competitive paradigm between "connected" vehicles and conventional vehicles. In a dedicated laboratory, developed for testing regulation algorithms, results show that "invisible vehicles" for the system (which are not "connected") in most simulated cases also benefit when real time traffic signal settings based on floating car data are introduced. Moreover, the study estimates the energy and air quality impacts of signal regulation by evaluating fuel consumption and pollutant emissions. Specifically, the study demonstrates that significant improvements in air quality are possible with the introduction of FCD regulated traffic signals.

Building integrated photovoltaics (BIPV) consists of PV panels that are integrated into the building as part of the construction. This technology has advantages such as the production of electricity without necessary additional land area. This paper provides a literature review about the recent developments in urban building energy modelling, including tools and methods, and how they can be used to predict the effect of PV systems on building outdoor and indoor environments. It is also intended to provide a critical analysis on how PV systems affect the urban environment, both from an energy and a comfort point of view. The microclimate, namelly the urban heat island concept, is introduced and related with the existence of PV systems. It is concluded that urban building energy modelling (UBEM) can be effective to study the performance of PV systems in the urban environment. It allows to simultaneously predict the building energy performance and the microclimate effect. However, there is a need to develop new methodologies to overcome the challenges associated to UBEM, especially in what concerns non-geometric data, which leads to a major source of errors. and to find an effective method to predict the effect of PV systems in the urban environment.

Swarm electrification-driven communities face significant challenges, including implementing advanced distributed control in areas with limited ICT access and establishing trust among villagers hesitant to grant access to their assets. This paper proposes a distributed DC microgrid architecture for P2P energy exchange in these communities, ensuring stability and effective exchange operation. By implementing a Blockchain marketplace specifically designed to suit the rural context, the proposed architecture ensures tracing of exchange transactions to fairly settle participants. Validation experiments demonstrate its efficacy in achieving peak shaving. It provides 11% of the requester's total demand from the community even while maintaining the constraint of reducing discharge-charge cycles to one per day, thereby preserving battery life. Additionally, the solution reduces prosumer production losses by 16% of the total PV production.

Based on collected power generation data associated with solar irradiance, PV system conversion efficiency and cell temperature, an hourly solar PV power estimation model by a parallel artificial neural network (ANN) and particle swarm optimization (PSO) algorithm is proposed. Weight matrices related to different seasons and geographic areas for estimation power generations have been trained by real measured operation data. The parallel PSO algorithm with heuristic global optimization technique assists the ANN training process to get near optimal solutions precisely. The accuracy and reliability of estimation is audited by actual details of photovoltaic power stations in various regions and scales. The results of estimation model can not only assist the electricity dispatcher to explicitly monitor the trend of solar power generation in different areas, but also coordinate with traditional power plants to meet load demand more accurately. The presented model will bring benefits to power dispatching for larger scales of intermittent and unstable solar power generation.

In this paper we propose a module that uses features learned by a deep convolutional neural network to infer areas of corrosion and segment pixels to corrosion areas of inspection interest. Our segmentation module is based on eigen tree decomposition and information based decision criteria. To interrogate performance we use several state-of-the-art deep learning architectures and compare to our approach. The results indicate that our method produces better results in terms of accuracy and precision, whilst maintaining good (f-score) significance over the entire dataset.

The study examined the effectiveness of developed instructional module on the enhancement of learners’ academic achievements specifically this current situation of COVID-19 pandemic under distance learning delivery modalities. This instructional module in Physics was personally developed by the researcher and intended to used in teaching-learning process in Senior High School under the Department of Education. The participants of the study are the grade 12 SHS students at Aliaga National High School into two groups –the controlled and experimental. The parameters are the content knowledge, alignment to learning competencies, learning tasks, assessment method, design course, friendliness and timeliness. To examine the effectiveness, the experimental research designed was employed. The following findings were made: the mean pre-test scores of two groups had 5.40 and 5.33 which means almost the same while the post-test mean scores of controlled group had 14.40 and experimental group had 17.80 which means the mean scores of experimental group were higher than the mean scores of the controlled groups. The pretest of two groups of participants had value of 14.976 with significant value of .000 and found out “not significant” while the post-test had value of 14.976 with significant value of .000 and interpreted as “significant” therefore the developed instructional module in Physics found “effective” and had impact to students’ academic performance. On the assessment made on the effectiveness of developed instructional module in Physics the overall weighted mean got 3.77 with an interpretation of “Very Satisfactory”. Continuous development and enhance of instructional module is recommended.

Many remote sensing analytical data products are most useful when they are in an appropriate regional or national projection, rather than globally based projections like Universal Transverse Mercator (UTM) or geographic coordinates, i.e., latitude and longitude. Furthermore, leaving data in the global systems can create problems, either due to misprojection of imagery because of UTM zone boundaries, or because said projections are not optimised for local use. We developed the open-source Irish Earth Observation (IEO) Python module to maintain a local remote sensing data library for Ireland. This pure Python module, in conjunction with the IEOtools Python scripts, utilises the Geospatial Data Abstraction Library (GDAL) for its geoprocessing functionality. At present, the module supports only Landsat TM/ETM+/OLI/TIRS data that have been corrected to surface reflectance using the USGS/ESPA LEDAPS/ LaSRC Collection 1 architecture. This module and the IEOtools catalogue available Landsat data from the USGS/EROS archive, and includes functions for the importation of imagery into a defined local projection and calculation of cloud-free vegetation indices. While this module is distributed with default values and data for Ireland, it can be adapted for other regions with simple modifications to the configuration files and geospatial data sets.

With advances in next-generation sequencing(NGS) technologies, large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer mechanism is to identify the driver genes from the mutation genes by analyzing and integrating multi-types genomics data. Breast cancer is known as a heterogeneous disease. The identification of subtype-specific driver genes is critical to guide the diagnosis, assessment of prognosis and treatment of breast cancer. We developed an integrated frame based on gene expression profilings and copy number variation(CNV) data to identify breast cancer subtype-specific driver genes. In this frame, we employed statistical machine-learning method to select gene subsets and utilized an module-network analysis method to identify potential candidate driver genes. The final subtype-specific driver genes were acquired by paired-wise comparison in subtypes. To validate specificity of the driver genes, the gene expression data of these genes were applied to classify the patient samples with 10-fold cross validation and the enrichment analysis were also conducted on the identified driver genes. The experimental results show that the proposed integrative method can identify the potential driver genes and the classifier with these genes acquired better performance than with genes identified by other methods.

This paper studied the solar PV frequency control in the U.S. Eastern Interconnection (EI) and Texas Interconnection (ERCOT) systems. The studied frequency control approaches include droop frequency control, inertia control, and droop-inertia-combined frequency control. The control effects of different frequency controls of PV in the EI and ERCOT are studied using actual high PV penetration interconnection grid models to provide suggestions to the future revision of future PV frequency control standards.

In this paper, an extensive study on the operation, control and performance of a hybrid photovoltaic and wind turbine (PV-WT) system is presented. The study includes the operation of the hybrid systems under different weather conditions which are reflected on a change in the wind speed and irradiance. Various loading conditions are investigated such a change in active and reactive power demands for static and dynamic loads. The performance of the hybrid system is examined via MATLAB/SIMULINK simulations.

Efficient implementation of clean energy technologies, including pioneering solutions like mobile solar PV systems mounted on trailers (MSPT), is pivotal for addressing energy challenges, especially in regions with constrained power grid access. This endeavour is vital for meeting escalating elec-tricity demands and aligning with the UN Sustainable Development Goal (SDG) aimed at ensuring dependable and sustainable energy provision in developing countries. This paper investigates the aerodynamic behaviour of a designed MSPT using numerical and experimental approaches. Spe-cifically, the study focuses on the effects of wind velocity and tilt angels on the drag and lift forces, as well as drag and lift coefficients on the panel used in the MSPT system. Additionally, the overall wind force acting on the entire MSPT (including 9 large solar PV panels) was examined, considering the combined effects of wind flow and the system's geometry. The simulations were conducted using ANSYS-Fluent software and experimental testing was performed utilizing scaled-down models to validate the accuracy of the simulation. The insights and the knowledge from this study can be utilized to optimize the design, installation, and operation of such systems, ultimately enhancing their performance, reliability, and paving ways for further improvements in mobile solar PV technology.

In this paper a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM)and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.

One of the most crucial and economically beneficial tasks for energy customer is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may warry. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data driven forecasting method based on fuzzy logic for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, left and right from load peak, searching for BESS capacity equal. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) with respect to BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak shaving method, offline digital time-domain simulations have been performed on a real-life practical test micro grid system in MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical micro grid system with real-life data obtained from smart meter and also, compared with several previously-reported methods.

The horizontal flatbed electrophoresis system has been used for the electrophoresis of protein samples and is often preferred for a two-dimensional polyacrylamide gel electrophoresis. In currently available equipment, cathode and anode electrodes were placed on top of a gel at each end. An electric field entered from the top of a gel, and its strength was relatively weaker in the bottom part of the gel. Such configuration caused migrating protein bands to lie down forward in the migrating direction. New electrodes, i.e., the clamp-shaped and the double-deck electrodes, were developed to simultaneously apply an electric field from the top and bottom of a gel. Electrophoretic separation with these electrodes made protein bands more focused and improved protein band resolutions. When the FIGE technique was tested together with new electrodes, protein bands were aligned nearly vertically and showed the best electrophoretic resolutions.

Education on Innovation and Entrepreneurship (I&E) has increased in the last two decades, specially, through MOOCs. Lately, these reusable online alternatives have tended to be revalorized by HEIs into blended learning activities, posing new challenges for instructors, specially, on how to bridge prior knowledge with in-class activities. Adopting a discursive approach to knowledge, our proposal aims to meet this challenge by identifying student’s ‘representations’, i.e., patterned constructions on disciplinary knowledge. Representations can be found across different cohorts and thus further complemented by instructors. To test this assumption and build our proposal, we analysed student’s representations in two observations. We mapped students’ representations over key I&E definitions (e.g., ‘start-up’) and, to know how prior knowledge may be complemented by instructors, we identified students’ alignment with expert disciplinary knowledge. Firstly, we found that the two cohorts tended to express representations by turning attention to several dimensions, e.g., referring to different types of features or finalities associated with concepts. Secondly, the disciplinary alignment description revealed that students tended to focus on the same components present in experts’ definitions, but with a greater level of generality. Our results have been packaged into a proposal that aims to help instructors scale their blended activities.

Previous research has proven that one of the fundamental requirements for ensuring increased profitability and economic competitiveness of offshore-based projects is co-locating different technologies within the same marine space. This paper presents a number of techno-feasibility analyses for floating offshore technologies for the Maltese Islands, located in the Central Mediterranean Sea. The first part compares the feasibility of offshore-based solar photovoltaic prices with those onshore, taking into consideration Malta’s land prices. The second part considers the use of a novel floating breakwater design that integrates energy storage. The breakwater is used to create a sheltered water area in deep waters for a multi-use marine park. Different revenue streams for the floating break water are considered, including renting the sheltered marine space out to operators of floating solar farms, aquaculture cages and vessel berthing facilities, and the provision of energy storage services. It is found that the combined income from the multiple revenue streams is still insufficient to justify the investment and that financially support from Government is essential to render the floating breakwaters viable.

As global warming intensifies, the development of offshore wind farms is swiftly progressing, especially deep-water floating offshore wind turbines (FOWTs) capable of energy capture in deep-sea regions, which have emerged as a focal point of both academic and industrial interest. Although numerous researchers have conducted comprehensive and multifaceted studies on various components of wind turbines, less attention has been paid to the operational stage response of FOWTs to wind, waves, and currents, and the reliability of structural components. This study primarily employs theoretical analysis to establish mathematical models under a series of reasonable assumptions, examining the possibility of collisions between FOWT transport fleets and other vessels in the passage area during the towing process. Using the model, this paper takes the Wanning Offshore Floating Wind Farm (FOWF) project, which is scheduled to be deployed in the South China Sea, as the research object and calculates the probability of collisions between FOWTs and other vessels in three time periods from the total assembly pier in Hainan Wanning to the predetermined position 22 km away. The findings of the analysis suggest that the mathematical model has the capability to efficiently determine the frequency of collisions between vessels within the domain and the transport team during the transportation process. This holds substantial relevance for future risk assessment and disaster prevention and mitigation measures in the context of FOWT transportation.

The influence of a background vortex flow on clustering of floating tracer is addressed. The vortex flow considered is induced by an ellipsoidal vortex evolving in a deformation. The system exhibits various vortex motion regimes: (i) steady state, (ii) oscillation and (iii) rotation of the ellipsoidal vortex core. The latter two induce unsteady velocity field for the tracer thus leading to irregular (chaotic) tracer motion. Superimposing a stochastic divergent velocity field onto the deterministic vortex flow allows us to observe significantly different tracer evolution. We use the method of characteristics to integrate the floating tracer density evolution equation and the Euler-Ito scheme for obtaining the floating tracer trajectories with the random velocity field. The cluster area and cluster mass from the statistical topography were used as the quantitative diagnostics of floating-tracer’s clustering. For the case of the steady ellipsoidal vortex embedded into the deformation flow with a random velocity field component we found that clustering characteristics are weakened by the steady vortex. For the cases of an unsteady ellipsoidal vortex, we observed clustering in the floating-tracer density field if the contribution of the divergent component is higher or equal to that of the rotational (nondivergent) component. Even when the initial floating tracer patch was set on the boundary of oscillating ellipsoidal vortex, we observed formation of clusters. In the case of a rotating ellipsoidal vortex, we also observed pronounced clustering. Thus, we argue that unsteady ellipsoidal vortex regimes (oscillation and rotation), which induce chaotic motion of the nearby passive tracer’s trajectories, are still conducive to clustering of floating tracer observed in the density field despite the intense deformation introduced by strain and shear.

We investigate the performance of a novel flat photovoltaic-thermal (PV-T) module under high-vacuum through a 1D numerical model based on steady-state energy balance, with the aims of optimizing the simultaneous production of thermal and electrical energy. In the proposed design, the photovoltaic (PV) cell is positioned directly above the selective solar absorber (SSA), in a multilayer or fully integrated PV-SSA structure, which allows full exploitation of spectral solar radiation. In fact, in this configuration the losses related to non-absorption of low-energy photons and thermalization, typical of a classical single-junction PV cell, are reduced. The present study is conducted as the emittance and energy bandgap of the PV layer varied, thus admitting a wide variety of materials into the analysis. The dependence of the temperature coefficient, β(%/K), on the energy bandgap of the PV cell is also included. In the last part of the work, we discuss the performance of the proposed evacuated PV-T equipped with a SSA layer and thin film solar cells, namely those made of CdTe, CdS and GaAs. Overall, the paper highlights the great advantage of using high vacuum insulation, which suppresses conductive losses, and the versatility of the proposed system, which could be adapted to the user's needs simply by choosing the appropriate material for the photovoltaic layer.

Around 14% of the global population does not have access to electricity. About 95% of those are living in rural Sub-Saharan Africa. Often in these regions, diesel generators are the only source of electricity. The operating cost of these diesel generators is high. However, solar and wind energy are available in most of African countries. This study presents the analysis of designing an off-grid hybrid system with a wind turbine, PV, diesel generator, and battery to power a hospital, school, and 200 household village in four locations across Somalia. The research investigated the availability of wind-solar resources in selected locations. Designing of the system and economic-technical calculations were performed using HOMER. The selection of the optimum design was based on the Cost of Electricity and Net Present Cost. The results show that for Kabaal and Ceel Buur, a WT-PV-DG-Battery is the optimal system as the wind resource in these regions is high. For Saakov and Baki, a PV-DG-Battery system proves to be optimum as the wind resource is limited here. The study also evaluated the control strategy and proved that combined dispatch was the most cost-effective for these locations. The study concluded that hybrid systems are more economical than diesel systems.

This article proposes a new photovoltaic (PV) solar tracker design based on the advantage that installation latitude offers according to efficiency in function of orientation (EFO) of PVs. First, is described a methodology to let incorporate a low-precision, low-cost and high-availability solar tracking mechanism and control system. The design methodology considers the installation location (latitude and azimuth) as a starting point for establishing an adequate angular range of EFO, simultaneity the aspects of available technology and the knowledge accords to developer. Finally, the design technique is experimentally validated by the implementation of a solar tracker at latitude of 28° longitude of 109° and evaluates the efficiency on a specific day. According to result the feasibility of this type of solar tracker for latitudes close to or greater than 30° is highlighted, given that this tracking system costs 30% less than traditional commercial systems as slew drive with its incorporation of lower-resolution azimuth tracking mechanisms. It also increases collection efficiency by 26%, just as continuous or time-based dual-axis solar trackers do, without the more complex controls and mechanisms of these designs.

In this study, we propose a module-type triboelectric nanogenerator (TENG) capable of harvesting power from a variety of mechanical energy sources. The potential energy and kinetic energy of water are used for the rotational motion of the generator module, and electricity is generated by the contact/separation generation mode between the two triboelectric surfaces inside the rotating TENG. Through the parametric design of the internal friction surface structure and mass ball, we optimized the output of the proposed structure. To magnify the power, experiments were conducted to optimize the electrical output of the series of TENG units. The electrical signal generated by the module-type TENG can be used as a sensor to recognize the strength and direction of various physical quantities, such as wind or earthquake vibrations.

Photovoltaic(PV) power generation is highly nonlinear and stochastic. Accurate prediction of PV power generation plays a crucial role in grid connection as well as the operation and scheduling of power plants. To predict the PV power combination model, this paper suggests a method based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Auto-Regressive Neural Networks with Exogenous Input (NARXNN), Long Short Term Memory (LSTM) Neural Network, and Light Gradient Boosting Machine (LightGBM) algorithms. To attempt to reduce the non-smoothness of PV power, the weather variable features with the greatest effect on PV power are first identified by correlation analysis. Following this, the PV power modal decomposition is split and reorganized into a new feature matrix. Finally， a NARX is used to obtain preliminary PV power components and residual vector features， and the PV power is predicted by combining three models of LightGBM， LSTM, and NARX and then the final prediction results are obtained by combining the PV power prediction results using error inverse method weighted optimization. The prediction results demonstrate that the model put forth in this paper outperforms those of other models and validate the model's validity by utilizing real measurement data from Andre Agassi College in the United States.

The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). Simulations conducted in MATLAB/SIMULINK evaluated these algorithms’ performance under various shading scenarios. The results indicate that while traditional methods like P&O and IC are effective under uniform conditions, advanced techniques, particularly ANN-based MPPT, exhibit superior efficiency and faster convergence under PSCs. The study concludes that integrating AI and ML into MPPT algorithms significantly enhances the reliability and efficiency of PV systems, paving the way for broader adoption of solar energy technologies in diverse environmental conditions. These findings contribute to advancing renewable energy technology and support the green energy transition.

Abstract: Like other plants, every PV power plant will one day reach the end of its service life. Calculations show that 20,400 tons of PV waste will be generated worldwide by 2030 and 60.2 million tons by 2050, not including the mass of the support structure. Such large amounts of waste pose a potential threat to the environment and people if not properly managed. The paper analysed the options for dealing with PV waste, namely reuse, recycling and landfilling. For recycling as the best option in terms of environmental protection and circular economy, an overview of recycling technologies and the percentage of achievable recycling for the materials contained in each PV system component is given. In addition, the current situation of legislation and recycling of PV modules in Europe was examined with special reference to the Balkan countries. There are a small number of factories for recycling PV modules in Europe, but none in the Balkan countries. The main reason for this is the small amount of PV waste in these countries and consequently the economic unprofitability. For this reason, PV modules (after dismantling the aluminum frame and cables) are mostly disposed of as non-hazardous waste in landfills in these countries. Finally, the main barriers to faster implementation of PV module reuse and recycling are listed, along with guidelines for their removal. The cost of recycling Si modules is about ten times higher than the cost of disposal. To change this ratio in favor of recycling, cheaper recycling methods need to be developed and taxes on landfill disposal need to be increased.

Recently, more wind turbine systems are being installed in deep waters far from the coast. Several concepts of floating wind turbine systems (FWTSs) are developed, among which the semisubmersible platform, due to its applicability in different water depths, good hydrodynamic performance, and facility in the installation process, constitutes the most explored technology compared to the others. However, a significant obstacle to the industrialization of this technology is a design of a cost-effective FWTS, which can be achieved by optimizing the geometry, size and weight of the floating platform, along with the mooring system. This is only possible by selecting a method capable of accurately analyzing the FWTS coupled hydro-aero-structural dynamics at each design stage. Accordingly, this paper aims at providing a detailed overview of the most common coupled numerical and physical methods, including their basic assumptions, formulations, limitations, and costs, used for analyzing the dynamics of FWTSs, mainly those supported by a semisubmersible, to assist the choice of the most suitable method at each design phase of FWTSs. Finally, the article discusses possible future research to address challenges in modeling FWTSs dynamics that persist to date.

In this paper, a modular wire-actuated robotic arm was designed to improve the safety and adaptability of service robots during human-robot interaction. To raise the stiffness adjustment range of the robotic arm, a symmetric variable-stiffness unit was designed based on flexure, which had compact and simple structure, and lowly nonlinear stiffness-force relationship. In this paper, we focused on the research of the symmetric 1-DOF joint module. Based on the kinematics and stiffness analysis, the pose of the joint module could be adjusted by controlling the length of the wires, and the stiffness of the joint module could be adjusted by controlling the tension of the wires. Because of the actuation redundancy, the pose and stiffness of the joint module could be controlled synchronously. Furthermore, a directly method was proposed for the stiffness-oriented wire tension distribution problem of the 1-DOF joint module. A simulation was carried out to verify the proposed method.

Building energy consumption is the main urban energy consumption component, which mainly serves people-centered work and living energy demands. Based on the physical requirements of humans in urban buildings and to determine the comfortable body temperature in each season, this paper establishes a sizing optimization model of building-type integrated energy systems (IES), where the cooling and heating loads required to maintain indoor somatosensory body comfortable temperature are calculated. Depending on the external energy price, internal power balance and other constraints, the model develops an optimal sizing and capacity panning method of energy conversion and storage unit in a building-type IES with PV generation. The operating principle is described as follows: the PV generation is fully consumed, a gas engine satisfies the electric and thermal base load requirements, while the power system and a heat pump supply the remaining loads. The gas price, peak-valley electricity price gap and heat-topower ratio of gas engines are considered as important factors for the overall techno-economic analysis. The developed method is applied to optimize the economic performance of building-type IES and verified by practical examples. The results show that using the complementary characteristics of different energy conversion units is important to the overall IES cost.

Recent years have seen a rapid uptake in distributed energy resources (DER). Such technologies pose a number of challenges to network operators, which can ultimately limit the amount of rooftop solar photovoltaics (PV) systems that can be connected to a network. The objective of this industry-based research was to determine the potential network effects of forecast levels of customer owned rooftop solar PV on Energy Queensland’s distribution network and formulate functions that can be used to determine such effects without the requirement for detailed network modeling and analysis. In this research, many of Energy Queensland’s distribution feeders were modelled using DIgSILENT PowerFactory and analyzed with forecast levels of solar PV and customer load. Python scripts were used to automate this process and quasi dynamic simulation (QDSL) models were used to represent the dynamic volt-watt and volt-var response of inverters, as mandated by the Australian Standard AS/NZS 4777. In analyzing the results, linear regression was used to form trend equations that represent various network characteristics against the number of PV connections. The trend equations provide a way of approximating network characteristics for other feeders under various levels of customer owned rooftop solar PV without the need for detail modeling.

The battery energy storage systems (BESS) are most promising solution for increasing efficiency and flexibility of distribution networks (DNs) with significant penetration level of photovoltaic (PV) systems. There are various issues related to the optimal operation of DNs with integrated PV sys-tems and BESS that need to be addressed to maximize DN performance. This paper deals with the day-ahead optimal active-reactive power dispatching in unbalanced DNs with integrated sin-gle-phase PV generation and BESSs. The objectives are the minimization of cost for electricity, en-ergy losses in the DN and voltage unbalance at three-phase load buses by optimal managing of active and reactive power flows. To solve this highly constrained nonlinear optimization problem, a hybrid particle swarm optimization with sigmoid-based acceleration coefficients (PSOS) and cha-otic gravitational search algorithm (CGSA), called PSOS-CGSA algorithm, is proposed. A scenario based approach encompassing the Monte Carlo simulation (MCS) method with the simultaneous backward reduction algorithm is used for the probabilistic assessment of the uncertainty of PV generation and power of loads. The effectiveness of the proposed procedure is evaluated through the series test cases in a modified IEEE 13-bus feeder.

This work deals with the performance evaluation of novel flat photovoltaic-thermal (PV-T) modules under vacuum. Through a 1D (dimensional) steady-state-energy-balance numerical model developed in MATLAB, two different layouts are studied: the first consisting of a photovoltaic (PV) cell installed just below the glass encapsulating the flat panel, and the second where the PV cell is placed on the selective solar absorber (SSA). In both cases the thermal and electrical efficiencies have been evaluated at different SSA operating temperatures, in the range of 323 K to 423 K. The analysis has been conducted at different energy bandgap (E_bg) of the PV cell and assuming a variable transmittance or emittance of the PV cell, depending on the design. The two systems efficiency comparison has been carried out at the same operating temperature. Overall, this work highlights the importance of high vacuum insulation, which guarantees the reduction of convective thermal losses, and shows that the maximum energy is produced for PV cells with E_bg ≈1.5-1.7 eV, depending on layout and operating temperature, by including the thermal output in the PV-T optimization. The energy and exergy efficiencies obtainable using the proposed PV-T systems are considerably improved compared to the results previously reported in the literature.

In this paper, the seven traditional models of PV modules are reviewed comprehensively to find out the appropriate model to be reliable. All the models are validated using the Matlab code and make a graphical comparison. The accuracy and convergence of each model are evaluated using data of manufactured PV panels. Then, a novel model is proposed showing its consistent performance. The most three key parameters of single-diode model are self revised to adapt to various type of PV modules. This new method is verified in three types of PV panels' data measured by National Renewable Energy Laboratory (NREL), US. The validated data show promising results when the error RMSEs' range of the proposed model is under 0.36.

This research work deals with the modeling and control of hybrid photovoltaic (PV) - Wind micro-grid using Quasi Z-Source inverter. This inverter provides better buck/boost characteristics, able to regulate the phase angle output, less harmonic content, no requirement of the filter and has high power performance characteristics over conventional inverter as major benefits. A SEPIC converter as dc-dc switched power apparatus is employed for maximum power point tracking (MPPT) functions which provides high voltage gain throughout the process. Moreover, a modified power ratio variable step (MPRVS) based perturb & observe (P&O) method has been proposed in the PV MPPT action which forces the operating point close to maximum power point (MPP). Practical responses justify the performance of hybrid PV-Wind micro-grid with Quasi Z-Source inverter structure.

To conduct a precise impact assessment of marine diesel engines, a 200t floating impact platform was utilized to simulate realistic testing conditions. The testing generated the acceleration time curve and the impact response spectrum for the diesel engine. According to the applicable standards, the spectral velocity was chosen as the evaluation index, and an evaluation of the longitudinal, transverse, and vertical impact environment of the diesel engine was conducted. The shock factor interpolation method was corrected using the confidence interval based on normal distribution, and the interpolated confidence interval of the shock factor was determined. The findings reveal that: The 200t-class floating shock platform is capable of providing a reliable shock environment for diesel engine tests. The shock wave serves as the primary external force during the experiment, and the impact of bubble pulsation can be disregarded when assessing using the floating shock platform. To more accurately describe the experimental shock intensity when presented with dispersed data, the confidence interval can be utilized, and the resulting prediction formula provides a higher level of impact safety. The obtained conclusions can serve as a reference for predicting the shock environment of large shipboard equipment using the shock platform.

The issue of water and energy crisis have been turned into global matters which need to be tackled jointly. Accordingly, floating solar power plants, in which photovoltaic modules are used on the surface of water infrastructures, has recently been attracting much interest. This system provides some additional advantages over the ground-based system such as conserving the land and the water and increasing the efficiency of the module. This study first reviews the relevant literature comprehensively and then evaluates the potential of using floating solar photovoltaic (FSPV) on some of Iran’s water infrastructures which have experienced a large amount of evaporation every year due to high solar radiation. To this end, the five important dam reservoirs are selected as the representatives of the five important watersheds in Iran, and the advantages of the FSPV plant is analyzed in terms of energy generation, evaporation reduction, economic and environmental aspects considering different coverage percentages of reservoir’s surfaces. Considering Iran's vast potential for solar radiation, and, on the other hand, huge energy demand and critical water situation, results indicated that Iran can effectively harness solar energy through FSPV systems which help conserve the water in addition to support sustainable energy production.

This article analyzes and compares the integration of two different maximum power point tracking (MPPT) control methods, which are tested under partial shading and fast ramp conditions. These MPPT methods are designed by Improved Particle Swarm Optimization (IPSO) and a combination technique between Neural Network and Perturb & Observe method (NN_P&O). These two methods are implemented and simulated for photovoltaic systems (PV), where various system responses, such as: voltage and power are obtained. The MPPT techniques were simulated using Matlab/Simulink environment. A comparison of the performance of IPSO and NN_P&O algorithms is carried out to confirm the best accomplishment of the two methods in terms of speed, accuracy and simplicity.

There are ~200.000 households living in the detached houses and ger (yurt) with the small-coal stove which burns a raw coal in the Ulaanbaatar city. The proper heating system and improvement on the energy efficiency of residential dwellings is vital important for Ulaanbaatar city to reduce an air pollution as well as for the operation of current central energy system. This study shows the experimental results of two Gers with two different heating system and different thermal insulation. Technical availability of the system consisting of ETS heater with daytime charging schedule and areal PV system is also examined by using a simulation of MATLAB programming. As a result of the experiment, the indoor comfort level and energy efficiency of the Ger with added insulation and ETS heater with nighttime charging was enhanced to compare with the reference Ger. The Ger with added insulation and ETS heater consumed 3169 kWh for electric appliances and 5989 kWh for the heating season. The simulation was resulted that the ETS heater to be installed in the Ger 2 had prospected to increase the PV self-consumption rate by 76% because of the daytime charging schedule of ETS heater. The PV system supplied 31% of the total energy consumption with remained 69% of the main grid.

With a high proportion of distributed photovoltaic and lower fossil energy integrated into the distribution network, it is very difficult to ensure the reliability of power supply. The distributed photovoltaic planning model based on big data is proposed. According to the impact stochastic photovoltaics and loads on reliability planning, the static and dynamic capacity-load ratios are proposed. The big data analysis model of distributed photovoltaic planning is established. The big data multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source-load big data scenario matching model is proposed. Ac-cording to the source load big data scenario, the dynamic capacity-load ratio of the distribution network is obtained. The static capacity-load ratio calculation method in distribution network planning is studied to ensure the reliability of power supply. Finally, the IEEE 33-bus system is used as an example. The results show that distributed photovoltaic planning based on big data and multi-scenario methods can improve photovoltaic utilization and power supply reliability.

This paper focuses on designing and assessing Pumped Hydroelectric Energy Storage Systems (PHES), connected to the grid and PV system for self-consumption structured at Mutah university in an area of high solar potential. In focusing on PHES and PV literature was made to have data on the field, based on the grid code needed in Jordan. Next, a prospection to find the proper location for the installation was done. Afterward, a load profile was inserted to know the energy demand of the university. Then The productivity of the solar power plant of Mutah University was included. Finally, MATLAB software was used to realize the amount of energy to be stored, this data was used to implement the system which was chosen and sized. PHES layout was created to find the most accurate values for parameters to optimize the system performance, and to investigate the loss analysis. The system attains 9230.89 MWh/year. An annual load yields 4430 MWh/year, which covers the Mutah university demand with an estimated saving of 2039773 JD.

The solar heat pump system based on photovoltaic photothermal (PV/T) modules is a new technology that integrates photovoltaics and heat reutilization. It can improve the efficiency of photovoltaic conversion and recovery the remaining heat generated during the photoelectric conversion process, thereby improving the comprehensive efficiency of the whole system. Simulink software is a visual simulation tool, which has the advantages of strong applicability, clear simulation structure and calculation process. The design of its graphical programming greatly simplifies the user modeling process and allows for direct viewing of results, therefore improving the simulation efficiency. At present, there is little research on simulation of solar heat pump systems with PV/T modules based on Simulink software. In this paper, the mathematical models of each component contained in the PV/T solar heat pump system were established based on the system working principle, energy conservation equations and some empirical formulas, and then the operation of the system was simulated by the Simulink software. Due to the operation characteristic of PV/T modules is the key influencing factor for the whole system performance, the model and simulation process for PV/T modules were introduced in detail. The results showed that the surface temperature of PV/T modules under different operating conditions was significantly lower than that of ordinary photovoltaic panels, and the temperature change amplitude was lower. As results, the power generation and photovoltaic conversion efficiency were significantly improved.

This research emphasizes a meta-heuristic Modified Ant Lion Optimizer (MALO) optimization approach for simultaneous utilization of DSTATCOM devices and distributed Photovoltaic (PV) sources with network reconfiguration in a radial power distribution scheme. In a radial power distribution network with network reconfiguration majority of the research is based on Constant Power model analysis. However, it is noticed that load models have a substantial impact on the distributed PV sources and the D-STATCOM device's optimal size and position. The effect of the Constant Power (CP) and Polynomial (ZIP) with load growth load models for the simultaneous insertion of distributed PV sources and DSTATCOM devices with network reconfiguration examined in this research work for power system planning. The penetration levels of distributed PV sources considered for the investigation are 25%, 50%, 75%, and 100%. The principal objective of this research is to reduce network total power losses, enhance the voltage magnitude profile at all buses, and reduce the overall operating cost while adhering to equality and inequality constraints. The proposed algorithm verified on 118-node test systems. The investigation is carried out for planning network upgradation to a High Voltage Distribution System(HVDS) on 317 nodes in the rural Bangalore Electricity Supply Company Limited(BESCOM) radial distribution scheme. Simulated results obtained by this method are validated with the BAT algorithm and available techniques in the literature.

Nearly Zero Energy Buildings (NZEBs) play a key role in the world energy transition and this is motivating the scientific community to develop innovative electrical and thermal systems, characterized by very high efficiency to specifically address the energy needs of modern buildings. Naturally, the integration on buildings of latest generation PV-systems helps to satisfy this need and, having in mind this objective, an innovative and highly efficient BIPV-system is presented and discussed in this paper. First, the proposed PV-system is purpose-built to be fully integrated into a variety of buildings and, preferably, into their rooftop. It is based on a certain number of innovative rotating and bifacial PV-modules which are specifically made to be installed “under-glass”, within a likewise custom-made transparent casing. Each of the aforementioned PV-module has the form of single row of bifacial PV-cells and is equipped with two special terminations; the PV-modules are then installed inside their transparent casing, side by side and suitably spaced from each other. Thanks to the aforementioned properties, the PV-modules can rotate around their central longitudinal axis, by using a very low-power, reliable and efficient mono-axial solar tracking system. Finally, by the aforementioned way, the proposed PV-system assumes – de facto - the form of a transparent BIPV-generator (PV-skylight), equipped with an innovative “under-glass” single axis solar tracking system. Once the proposed PV-generator is fully integrated into a building, it generates electricity and, additionally, helps to improve both the whole energy performances and the aesthetic appearance of the building. The electricity generation and illuminance performances of the proposed BIPV-generator are experimentally tested by using a low-power home-made prototype driven by different solar tracking logics and under different operating conditions; the most relevant results are summarized and extensively discussed.

Accurately predicting the power of solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for the balanced operation and optimized dispatch of the power grid system, and reduces operating costs. Solar PV power generation depends on weather conditions, which are prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility and intermittency. Recently, the demand for further investigation and effective use on the uncertainty of short-term solar PV power generation prediction has been getting increasing attention in many application of renewable energy sources. In order to improve the predictive accuracy of output power of solar PV power generation and develop a precise predictive model, the authors worked predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is one of the important aspects for optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods in the reviewed literature are classified according to the input data source used for accurate predictive models, and the case studies and examples proposed are analyzed in detail. The contributions, advantages and disadvantages of the predictive probabilistic methods are compared. Finally, the future studies of short-term solar PV power forecasting is proposed.