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Photovoltaic panels are exposed to various external factors that can cause damage, with the formation of cracks in the photovoltaic cells being one of the most recurrent issues affecting their production capacity. Electroluminescence (EL) tests are employed to detect these cracks. In this study, a methodology developed according to the IEC TS 60904-13 standard is presented, allowing for the calculation of the percentage of type C cracks in a PV panel and subsequently estimating the associated power loss. To validate the methodology, it was applied to a polycrystalline silicon module subjected to incremental damage through multiple impacts on its rear surface. After each impact, electroluminescence images and I-V curves were obtained and used to verify power loss estimates. More accurate estimates were achieved by assessing cracks at the PV cell level rather than by substring or considering the entire module. In this context, cell-level analysis becomes indispensable, as the most damaged cell significantly influences the performance of the photovoltaic model. Subsequently, the developed methodology was applied to evaluate the conditions of four photovoltaic panels that had been in operation, exemplifying its application in maintenance tasks. The results assisted in decision making regarding whether to replace or continue using the panels. [ABSTRACT FROM AUTHOR]

Photovoltaic panels are exposed to various external factors that can cause damage, with the formation of cracks in the photovoltaic cells being one of the most recurrent issues affecting their production capacity. Electroluminescence (EL) tests are employed to detect these cracks. In this study, a methodology developed according to the IEC TS 60904-13 standard is presented, allowing for the calculation of the percentage of type C cracks in a PV panel and subsequently estimating the associated power loss. To validate the methodology, it was applied to a polycrystalline silicon module subjected to incremental damage through multiple impacts on its rear surface. After each impact, electroluminescence images and I-V curves were obtained and used to verify power loss estimates. More accurate estimates were achieved by assessing cracks at the PV cell level rather than by substring or considering the entire module. In this context, cell-level analysis becomes indispensable, as the most damaged cell significantly influences the performance of the photovoltaic model. Subsequently, the developed methodology was applied to evaluate the conditions of four photovoltaic panels that had been in operation, exemplifying its application in maintenance tasks. The results assisted in decision making regarding whether to replace or continue using the panels.

At present, the reliability of solar modules is affected by potential induced degradation (PID). In this paper, the performance deterioration of p-type single crystalline silicon solar modules affected by potential induced degradation in photovoltaic power plant was investigated in detail. Based on the measurements in the field and the flash solar simulator-determined powers under standard test conditions for solar modules dismounted from the power plant in one string, the deterioration of open circuit voltage and power for substrings were given out for the first time. The uneven power degradation of three substrings in one solar module was proved by the electroluminescence images and the thermography images. This phenomenon was explained by the distribution of electric field calculated by using ANSYS finite element software for a PID-affected solar module. The results obtained in this paper give some hints for further uncovering mystery of PID. [ABSTRACT FROM AUTHOR]

In many sequence data mining applications, the goal is to find frequent substrings. Some of these applications like extracting motifs in protein and DNA sequences are looking for frequently occurring approximate contiguous substrings called simple motifs. By approximate we mean that some mismatches are allowed during similarity test between substrings, and it helps to discover unknown patterns. Structured motifs in DNA sequences are frequent structured contiguous substrings which contains two or more simple motifs. There are some works that have been done to find simple motifs but these works have problems such as low scalability, high execution time, no guarantee to find all patterns, and low flexibility in adaptation to other application. The Flame is the only algorithm that can find all unknown structured patterns in a dataset and has solved most of these problems but its scalability for very large sequences is still weak. In this research a new approach named Next-Symbol-Array based Motif Discovery (NSAMD) is represented to improve scalability in extracting all unknown simple and structured patterns. To reach this goal a new data structure has been presented called Next-Symbol-Array. This data structure makes change in how to find patterns by NSAMD in comparison with Flame and helps to find structured motif faster. Proposed algorithm is as accurate as Flame and extracts all existing patterns in dataset. Performance comparisons show that NSAMD outperforms Flame in extracting structured motifs in both execution time (51% faster) and memory usage (more than 99%). Proposed algorithm is slower in extracting simple motifs but considerable improvement in memory usage (more than 99%) makes NSAMD more scalable than Flame. This advantage of NSAMD is very important in biological applications in which very large sequences are applied. [ABSTRACT FROM AUTHOR]

We investigate inference of variable-length codes in other domains of computer science, such as noisy information transmission or information retrieval-storage: in such topics, traditionally mostly constant-length codewords act. The study is relied upon the two concepts of independent and closed sets: given an alphabet A and a binary relation τ ⊆ A ⁎ × A ⁎ , a set X ⊆ A ⁎ is τ - independent if τ (X) ∩ X = ∅ ; X is τ - closed if τ (X) ⊆ X. We focus to those word relations whose images are computed by applying some peculiar combinations of deletion, insertion, or substitution. In particular, characterizations of variable-length codes that are maximal in the families of τ -independent or τ -closed codes are provided. [ABSTRACT FROM AUTHOR]

We consider the problem of substring search in a set of strings. The problem is the following: given a set of strings and an arbitrary substring, list all strings from the set that contain this substring.We describe search algorithms and obtain lower bounds for the running time and for the memory volume required by the fastest algorithms [ABSTRACT FROM AUTHOR]

Background: Large DNA sequence data sets require special bioinformatics tools to search and compare them. Such tools should be easy to use so that the data can be easily accessed by a wide array of researchers. In the past, the use of suffix trees for searching DNA sequences has been limited by a practical need to keep the trees in RAM. Newer algorithms solve this problem by using disk-based approaches. However, none of the fastest suffix tree algorithms have been implemented with a graphical user interface, preventing their incorporation into a feasible laboratory workflow. Results: Suffix Tree Searcher (STS) is designed as an easy-to-use tool to index, search, and analyze very large DNA sequence datasets. The program accommodates very large numbers of very large sequences, with aggregate size reaching tens of billions of nucleotides. The program makes use of pre-sorted persistent “building blocks” to reduce the time required to construct new trees. STS is comprised of a graphical user interface written in Java, and four C modules. All components are automatically downloaded when a web link is clicked. The underlying suffix tree data structure permits extremely fast searching for specific nucleotide strings, with wild cards or mismatches allowed. Complete tree traversals for detecting common substrings are also very fast. The graphical user interface allows the user to transition seamlessly between building, traversing, and searching the dataset. Conclusions: Thus, STS provides a new resource for the detection of substrings common to multiple DNA sequences or within a single sequence, for truly huge data sets. The re-searching of sequence hits, allowing wild card positions or mismatched nucleotides, together with the ability to rapidly retrieve large numbers of sequence hits from the DNA sequence files, provides the user with an efficient method of evaluating the similarity between nucleotide sequences by multiple alignment or use of Logos. The ability to re-use existing suffix tree pieces considerably shortens index generation time. The graphical user interface enables quick mastery of the analysis functions, easy access to the generated data, and seamless workflow integration. [ABSTRACT FROM AUTHOR]

It is proved that the set of scattered substrings of a language recognized by an n-state DFA requires a DFA with at least 2^{n/2-2} states (the known upper bound is 2^n), with witness languages given over an exponentially growing alphabet. For a 3-letter alphabet, scattered substrings are shown to require at least 2^{sqrt{2n+30}-6} states. A similar state complexity function for scattered superstrings is determined to be exactly 2^{n-2} + 1 for an alphabet of at least n - 2 letters, and strictly less for any smaller alphabet. For a 3-letter alphabet, the state complexity of scattered superstrings is at least 1/5 4sqrt{n/2}n-3/4. [ABSTRACT FROM AUTHOR]

In this paper, a complexity measure of symbolic sequences is proposed that generalizes the Lempel-Ziv complexity by taking into account a specific kind of the inexact copy in the text, and based on which, a new sequence distance measure for the similarity analysis is introduced. The utility of our approach is illustrated by an examination of the relationships among β-globin proteins of 13 species. [ABSTRACT FROM AUTHOR]

A lot has been done on the randomness of the decimal expansion of Pi with extensive tests of randomness that are used to distinguish good from not-so-good random number generators when applied to the decimal digits of Pi. Pi seems to pass these tests as well as some of the best random number generator (RNG) and could well serve as an RNG provided that the digits of Pi could be easily and quickly produced in the computer [Mar06]. We make an interesting study in the same context in which random substring of arbitrary length are extracted from arbitrary positions a large number of times and each sample is tested for randomness. Our results confirm the randomness of Pi and a recent claim that "Pi is less random than we thought" [TF05] stands refuted. George Marsaglia [Mar06] has also independently refuted the claim but in Marsaglia's work, the randomness is established on the whole for the first 960 million digits of pi. Our study confirms the randomness for arbitrary subsequences also. Finally, the investigation of some functions of pi-rather than pi itself-is proposed. [ABSTRACT FROM AUTHOR]