Optimizing photovoltaic arrays: A tested dataset of newly manufactured PV modules for data-driven analysis and algorithm development.

This data article presents a comprehensive dataset comprising experimentally tested characteristics of newly manufactured photovoltaic (PV) modules, which have been collected by using a commercial PV testing system from a solar panel manufacturer company. The PV testing system includes an artificial sunlight simulator to generate input light for the PV and the outputs of the PV are tested by a professional IV tracer in a darkroom environment maintaining IEC60904-9 standard. The dataset encompasses modules with power ratings of 10 W, 85 W, and 247 W, each represented by 40 individual module records. The tested and collected characteristics of each module include open circuit voltage, short circuit current, maximum power point voltage, maximum power point current, maximum power point power, and fill factor. The motivation for this dataset lies in addressing the challenges posed by manufacturing defects and a ± 5 % manufacturing tolerance, which can lead to mismatch power losses in newly installed PV arrays. These losses result in lower current in series strings and lower voltage in parallel branches, ultimately decreasing the array's output power. The dataset serves as a valuable resource for academic research, particularly in the domain of PV array optimization. To facilitate optimization efforts, different algorithms have been explored in the literature. This dataset supports the exploration of these optimization algorithms to find solutions that enhance the position of each module within the array, consequently increasing the overall output power and efficiency of the PV system. The objective is to mitigate mismatch power losses, which, if unaddressed, can contribute to increased degradation rates and early aging of PV modules. This dataset lays the groundwork for addressing critical PV array performance and efficiency issues. In future research, this dataset can be reused to explore and implement optimization algorithms, to improve the overall output power and lifespan of newly installed PV arrays. The smart solution proposed in [1], utilizing a genetic algorithm-based module arrangement, demonstrates promising results for maximizing PV array output power using this dataset.

Electrical data of 10W, 40W, 80W, and 250W photovoltaic modules under the aging condition: Tested by a Solar Manufacturer Company in Bangladesh.

The health monitoring system of photovoltaic modules throughout their lifespan is an important research topic. The dataset of aged PV modules is required to investigate the performance of the aged PV array for simulation work. Different aging factors are responsible for decreasing the output power of aged PV modules and increasing the degradation rate. In addition, mismatch power losses are increases with the nonuniformity of aged PV modules due to different aging factors. In this work, four datasets of 10W, 40W, 80W, and 250W PV modules are collected under nonuniform aging conditions. Each dataset contains forty modules with a four-year aged average. The average deviation of each electrical parameter of the PV modules can be calculated from this data. Moreover, a correlation can be developed between the average deviation of electrical parameters and mismatch power loss in PV array modules under early aging conditions.

Electrical experimental data collection of polycrystalline and monocrystalline photovoltaic modules in an indoor environment using artificial sun simulator.

In the twenty-first century, energy sustainability and reliability are one of the major challenges in the world and prime factors of the national development plan. Recently, Solar PV is gaining popularity and making a significant effect as an alternative to fossil fuels due to reduction of cost and enhanced efficiency. However, the production performance of Solar PV over the period gets significantly impacted owing to a variety of problems such as dust, aging due to shading and soiling over the cell, hot spot, discoloration and corrosion for excessive atmospheric temperature, inadequate solar light, cell damage, and so on. In this research, a low-cost halogen-based artificial sun simulator is developed and deployed to examine the electrical properties of Solar PV in indoor conditions. Two monocrystalline and three polycrystalline PV panels under Standard Test Conditions, as well as a prototype 5 × 8 PV array, using this artificial light source, were evaluated rigorously for experimental purposes. With the help of a microcontroller-based I-V tracer and an actual data storage system, Open Circuit Voltage (Voc), Short Circuit Current (Isc), Maximum Power Voltage (Vmp), Maximum Power Current (Imp), and Maximum Power (Pmax) at three irradiance levels were measured and recorded. Utilizing Microsoft Excel software, the data logger's recorded data were analyzed and I-V and P-V curves were plotted. These data are extremely valuable for obtaining a good understanding of the validity of the Sun Simulator and the rate of deterioration of solar PV performance depending on irradiance. These data will aid the research community in future research regarding PV array performance monitoring, corresponding solution modeling, and developing cost-effective installation of large-scale PV arrays.

Greenotyper: Image-Based Plant Phenotyping Using Distributed Computing and Deep Learning.

Image-based phenotype data with high temporal resolution offers advantages over end-point measurements in plant quantitative genetics experiments, because growth dynamics can be assessed and analysed for genotype-phenotype association. Recently, network-based camera systems have been deployed as customizable, low-cost phenotyping solutions. Here, we implemented a large, automated image-capture system based on distributed computing using 180 networked Raspberry Pi units that could simultaneously monitor 1,800 white clover (Trifolium repens) plants. The camera system proved stable with an average uptime of 96% across all 180 cameras. For analysis of the captured images, we developed the Greenotyper image analysis pipeline. It detected the location of the plants with a bounding box accuracy of 97.98%, and the U-net-based plant segmentation had an intersection over union accuracy of 0.84 and a pixel accuracy of 0.95. We used Greenotyper to analyze a total of 355,027 images, which required 24-36 h. Automated phenotyping using a large number of static cameras and plants thus proved a cost-effective alternative to systems relying on conveyor belts or mobile cameras.

Adult Neural Stem Cells and Multiciliated Ependymal Cells Share a Common Lineage Regulated by the Geminin Family Members.

Adult neural stem cells and multiciliated ependymal cells are glial cells essential for neurological functions. Together, they make up the adult neurogenic niche. Using both high-throughput clonal analysis and single-cell resolution of progenitor division patterns and fate, we show that these two components of the neurogenic niche are lineally related: adult neural stem cells are sister cells to ependymal cells, whereas most ependymal cells arise from the terminal symmetric divisions of the lineage. Unexpectedly, we found that the antagonist regulators of DNA replication, GemC1 and Geminin, can tune the proportion of neural stem cells and ependymal cells. Our findings reveal the controlled dynamic of the neurogenic niche ontogeny and identify the Geminin family members as key regulators of the initial pool of adult neural stem cells.

Ependymal cilia beating induces an actin network to protect centrioles against shear stress.

Multiciliated ependymal cells line all brain cavities. The beating of their motile cilia contributes to the flow of cerebrospinal fluid, which is required for brain homoeostasis and functions. Motile cilia, nucleated from centrioles, persist once formed and withstand the forces produced by the external fluid flow and by their own cilia beating. Here, we show that a dense actin network around the centrioles is induced by cilia beating, as shown by the disorganisation of the actin network upon impairment of cilia motility. Moreover, disruption of the actin network, or specifically of the apical actin network, causes motile cilia and their centrioles to detach from the apical surface of ependymal cell. In conclusion, cilia beating controls the apical actin network around centrioles; the mechanical resistance of this actin network contributes, in turn, to centriole stability.

Calibrated mitotic oscillator drives motile ciliogenesis.

Cell division and differentiation depend on massive and rapid organelle remodeling. The mitotic oscillator, centered on the cyclin-dependent kinase 1-anaphase-promoting complex/cyclosome (CDK1-APC/C) axis, spatiotemporally coordinates this reorganization in dividing cells. Here we discovered that nondividing cells could also implement this mitotic clocklike regulatory circuit to orchestrate subcellular reorganization associated with differentiation. We probed centriole amplification in differentiating mouse-brain multiciliated cells. These postmitotic progenitors fine-tuned mitotic oscillator activity to drive the orderly progression of centriole production, maturation, and motile ciliation while avoiding the mitosis commitment threshold. Insufficient CDK1 activity hindered differentiation, whereas excessive activity accelerated differentiation yet drove postmitotic progenitors into mitosis. Thus, postmitotic cells can redeploy and calibrate the mitotic oscillator to uncouple cytoplasmic from nuclear dynamics for organelle remodeling associated with differentiation.

Smooth 2D manifold extraction from 3D image stack.

Three-dimensional fluorescence microscopy followed by image processing is routinely used to study biological objects at various scales such as cells and tissue. However, maximum intensity projection, the most broadly used rendering tool, extracts a discontinuous layer of voxels, obliviously creating important artifacts and possibly misleading interpretation. Here we propose smooth manifold extraction, an algorithm that produces a continuous focused 2D extraction from a 3D volume, hence preserving local spatial relationships. We demonstrate the usefulness of our approach by applying it to various biological applications using confocal and wide-field microscopy 3D image stacks. We provide a parameter-free ImageJ/Fiji plugin that allows 2D visualization and interpretation of 3D image stacks with maximum accuracy.