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Most equations and models for photovoltaics are based on the assumption that photovoltaic (PV) devices are flat. Therefore, the actual performance of nonplanar PV devices should be investigated and developed. In this study, two algorithms were developed and defined using vector computations to describe a curved surface based on differential geometry and the interaction with non-uniform solar irradiance (i.e., non-uniform shading distribution in the sky). To validate the computational model, the power output from a commercial curved solar panel for the Toyota Prius 40 series was monitored at four orientation angles and in various climates. Then, these were compared with the calculation results obtained using the developed algorithm. The conventional calculation used for flat PV devices showed an overestimated performance due to ignorance of inherent errors due to curved surfaces. However, the new algorithms matched the measured trends, particularly on clear-sky days. The validated computation method for curved PV devices is advantageous for vehicle-integrated photovoltaic devices and PVs including building-integrated photovoltaics (BIPVs), drones, and agriphotovoltaics.

Propaganda in the digital era is often associated with online news. In this study, we focused on the use of large language models and their detection of propaganda techniques in the electronic press to investigate whether it is a noteworthy replacement for human annotators. We prepared prompts for generative pre-trained transformer models to find spans in news articles where propaganda techniques appear and name them. Our study was divided into three experiments on different datasets—two based on an annotated SemEval2020 Task 11 corpora and one on an unannotated subset of the Polish Online News Corpus, which we claim to be an even bigger challenge as an example of an under-resourced language. Reproduction of the results of the first experiment resulted in a higher recall of 64.53% than the original run, and the highest precision of 81.82% was achieved for gpt-4-1106-preview CoT. None of our attempts outperformed the baseline F1 score. One of the attempts with gpt-4-0125-preview on original SemEval2020 Task 11 achieved an almost 20% F1 score, but it was below the baseline, which oscillated around 50%. Part of our work that was dedicated to Polish articles showed that gpt-4-0125-preview had a 74% accuracy in the binary detection of propaganda techniques and 69% in propaganda technique classification. The results for SemEval2020 show that the outputs of generative models tend to be unpredictable and are hardly reproducible for propaganda detection. For the time being, these are unreliable methods for this task, but we believe they can help to generate more training data.

Abstract Objective The detection and monitoring of DNA methylation status in circulating tumor cell DNA (ctDNA) provides critical insights into cancer diagnosis and progression. The methylation status of the Dickkopf-related protein 3 (DKK3) promoter region is correlated with the metastasis and recurrence of multiple cancers. Thus, detecting the methylation status via non-invasive methods is essential for the diagnosis and prognosis of cancers. Using a droplet digital polymerase chain reaction approach, we have developed a highly sensitive and quantitative measurement of methylated and unmethylated DKK3 derived from circulating cell-free DNA (ccfDNA). Results We confirmed the specificity of droplet digital methylation specific polymerase chain reaction (ddMSP). We selected the optimal bisulfite conversion method using commercially available kits. We validated the ddMSP analysis system by analyzing the methylation status of genomic DNA extracted from cultured mesothelioma cells and mesothelial cells. Our system quantified approximately 30 copies of cell-free DNA per 4 mL, which is sufficient for detecting ctDNA. Finally, we quantified methylated and unmethylated DKK3 copies in ccfDNA from 21 patients with malignant mesothelioma.

Fires in photovoltaic modules are caused by hot spots, which are typically monitored by thermal images. This method helps visualize the hot spot, but it is affected by the environment (solar irradiance, wind, ambient temperature) and is not reproducible. Assessing the heat dissipation of the hot cell can be used for alternative assessment of the fire risk. This method was validated by comparing the value measured by the surface potential meter and the module potential measured directly by adding a bypass measurement circuit. The substantial reverse-bias voltage caused by mismatching or partial shading (depending on the operating conditions) leads to local heat consumption of the partially shaded solar cells and potentially causes fire. The fire risk can be assessed in the worst-case conditions (ex. 1380 W/m2 solar irradiance) by non-contact measurement of the reverse-bias voltage and calculating the heat dissipation and temperature rise. This work suggested that −13 V is the criterion and was close to the known value of reverse voltage for Si cells. The current technology inspects solar cells before assembly to the module, and there is no way of inspecting in the product test or detecting after degradation that can be covered by the proposed method in this work.

Climate change and increasing food production due to population growth are global challenges that need immediate attention. The introduction of renewable energy to mitigate climate change and the requirement of adequate land to increase food production are generally mutually exclusive. However, an agrivoltaic system generates renewable electricity and produces agricultural products from a common piece of land, thus increasing the land productivity. In addition, this system contributes to local production, thus reducing the CO2 emissions from logistics. Photovoltaic arrays in previous studies were designed by calculating the irradiance in W/m2, even in recent studies. A careful design of the farmland's illumination must be developed for effective agriculture. The simulations must be scaled based on photosynthetic photon flux density rather than irradiance commonly applied in photovoltaic technology simulations.This study focused on the photosynthetic photon flux density and employed an all-climate solar spectrum model to calculate the photosynthetic photon flux density accurately on farmland partially shaded by solar panels and supporting tubes. This study described an algorithm for estimating the photosynthetic photon flux density values under solar panels. The calculated data were validated using the photosynthetic photon flux density sensors. To calculate the photosynthetic photon flux density under the solar panels, it is essential to weigh the direct and diffused components shaded by the solar panels separately because they have different spectrums. A method to quantify the shading was explored here by solar panels and their supporting tubes for the direct and diffused component as the sun moves. The calculation formula was established by defining the sun's moves and the positions of solar panels and their supporting tubes in terms of elevation and azimuth angles from the observation point.It was found that the waveform based on the calculation formula for the photosynthetic photon flux density under the solar panels reproduced the same tendency as the measured photosynthetic photon flux density. To evaluate this trend numerically, the measured and calculated photosynthetic photon flux densities were compared using the standard residuals. Generally, the similarity of the two values is confirmed by a standard residual value between −3 and 3. The result of this study showed that the standard residual values were negative in more frequencies except for the zero photosynthetic photon flux density at night. This indicates that the calculated photosynthetic photon flux density tends to be higher than the measured photosynthetic photon flux density. The peak frequency of the standard residuals was between −6 and −3. This difference probably occurred because the established calculation formula targets the shading provided by the solar panels and supporting tubes but does not cover the shading provided by the other system structures. The calculation formula enables farmers to evaluate the economic efficiency of the system before introducing it using measured solar irradiation data at the target farmlands by introducing published neighborhood solar irradiation data and considering, in advance, measures to avoid the effects of shading on agricultural production. The next study will be to improve the accuracy of the calculation formula by increasing the number of days and develop a method that leads to the best practices of agricultural production and solar power generation by introducing the system.

Building-integrated photovoltaics (BIPVs) and vehicle-integrated photovoltaics (VIPVs) receive solar irradiance through non-uniform shading objects. Standard scalar calculations cannot accurately determine the solar irradiance of BIPV and VIPV systems. This study proposes a matrix model using an aperture matrix to accurately calculate the horizontal and vertical planes affected by non-uniform shading objects. This can be extended to the solar irradiance on a VIPV by applying a local coordinate system. The 3D model is validated by a simultaneous measurement of five orientations (roof and four sides, front, left, tail, and right) of solar irradiance on a car body. An accumulated logistic function can approximate the shading probability. Furthermore, the combined use of the 3D solar irradiance model is effective in assessing the energy performance of solar electric vehicles in various zones, including buildings, residential areas, and open spaces. Unlike standard solar energy systems, the energy yield of a VIPV is affected by the shading environment. This, in turn, is affected mainly by the location of vehicle travel or parking in the city rather than by the climate zones of the city.

Electric vehicles (EVs) have the advantage of being resilient to natural disasters. However, users hesitate to donate electricity when they lose the chance to recharge at the utility. Solar electric vehicles (SEVs) save energy through vehicle-integrated photovoltaics (VIPV) and make it possible to voluntarily donate excess energy, thus maintaining facility resilience. Given that the supply of solar energy to VIPV systems is not continuous and is difficult to forecast, the contribution of VIPV to the resilience of the larger energy system has been called into question. This is the first study in which the potential of VIPV to maintain utility resilience is investigated in the context of physical factors, such as irradiance, and social factors. The actual energy yield of a VIPV car was determined using an advanced 3D solar irradiation model under a nonuniform shading distribution, with validation from actual measures of solar irradiance on five orthogonal sides of the car body. The Monte Carlo method was used to model the complex factors in VIPV energy storage and energy donations under different scenarios. Depending on the climate, population density, and shading environment, the voluntary contribution of stored electricity in SEV is sufficient to provide disaster relief support.

The popularity of social media services has led to an increase of personality-relevant data in online spaces. While the majority of people who use these services tend to express their personality through measures offered by the Myers–Briggs Type Indicator (MBTI), another personality model known as the Big Five has been a dominant paradigm in academic works that deal with personality research. In this paper, we seek to bridge the gap between the MBTI, Big Five and another personality model known as the Enneagram of Personality, with the goal of increasing the amount of resources for the Big Five model. We further explore the relationship that was previously reported between the MBTI types and certain Big Five traits as well as test for the presence of a similar relationship between Enneagram and Big Five measures. We propose a new method relying on psycholingusitc features selected based on their relationship with the MBTI model. This approach showed the best performance through our experiments and led to an increase of up to 3% in automatic personality recognition for Big Five traits on the per-trait level. Our detailed experimentation offers further insight into the nature of personality and into how well it translates between different personality models.

Climate change and increasing food demand are global issues that require immediate attention. The agrivoltaic system, which involves installing solar panels above farmland, can simultaneously solve climate and food issues. However, current systems tend to reduce agricultural production and delay the harvest period due to shading by the solar panels. A delayed harvest period impacts the income of farmers who wish to sell produce at specific times. Incorporating a model that calculates the amount of electricity generated by solar irradiation, this study establishes a model to estimate the correct start date of cultivation for solar panel covered crops to ensure the correct harvest date and determines the expected income of farmers by calculating agricultural production and power generation. Using taro cultivation in Miyazaki Prefecture as a case study, the model estimated that the start date of cultivation should be brought forward by 23 days to ensure the ideal harvest period and agricultural production. This would prevent an opportunity loss of USD 16,000 per year for a farm area of 10,000 m2. Furthermore, an additional income of USD 142,000 per year can be expected by adjusting shading rates for the cultivation and non-cultivation periods.

The use of photovoltaic devices for energy harvesting in real-world applications requires that they are conformable to non-flat surfaces. Here, a micro-scale concentrator module shows 15.4% outdoor conversion efficiency and can stretch over curved 3D surfaces.

When people debate, they want to familiarize themselves with a whole range of arguments about a given topic in order to deepen their knowledge and inspire new claims. However, the amount of differently phrased arguments is humongous, making the process of processing them time-consuming. In spite of many works on using arguments (e.g. counter-argument generation), there is only a few studies on argument aggregation. To address this problem, we propose a new task in argument mining – Argument Extraction, which gathers similar arguments into key points, usually single sentences describing a set of arguments for a given debate topic. Such a short summary of related arguments has been manually created in previous research, while in our research key point generation becomes fully automatic, saving time and cost. As the first step of key point generation we explore existing similarity calculation methods, i.e. Sentence-BERT and MoverScore to investigate their performance. Next, we propose a combination of argument similarity and Maximal Marginal Relevance (MMR) for extracting key phrases to be utilized in our novel task of Argument Extraction. Experimental results show that MoverScore-based MMR outperforms strong baselines covering 72.5% of arguments when eleven or more arguments are extracted. This percentage is almost identical with the cover rate of human-made key points.

By mounting photovoltaics (PV) on the roof of an electric vehicle (EV), solar energy can be used to supply a considerable portion of the energy demand of the EV. The roof is the best place for PV installation on the vehicle body because doors and engine hood have less yearly-average sunlight available and more stringent mechanical requirements. The roof of a modern passenger vehicle such as a sedan is not flat, and parts with high curvature or a significant slope do not need to be covered. However, there are no design rules available for mounting PV on a vehicle roof, such as determining the coverage ratio. In this study, the distributions of roof shapes and sizes were obtained from trace drawings of various commercially available passenger vehicles. This was then used to calculate the distributions of the mechanical (i.e., local curvature) and optical (i.e., local solar utilization rate) properties. Based on the results, general guidelines were developed, including a potential coverage ratio of 96% for a hemispherical roof with a radius of curvature of 1 m. These guidelines have already been successfully demonstrated and prototyped with a small module.

Equivalent circuit network simulation is widely used in modeling solar cells in three dimensions. However, the computational time and numerical instability increases dramatically when the number of circuit element increases. This problem is exacerbated by increasing the number of junctions in the solar cells. We propose a downsampling algorithm to reduce the time complexity but retain reasonable accuracy within the appropriate parameter space of multi-junction solar cells. We also publish a full-featured software that implements this algorithm and the full circuit network simulation along with this paper.

Recent years have brought an unprecedented and rapid development in the field of Natural Language Processing. To a large degree this is due to the emergence of modern language models like GPT-3 (Generative Pre-trained Transformer 3), XLNet, and BERT (Bidirectional Encoder Representations from Transformers), which are pre-trained on a large amount of unlabeled data. These powerful models can be further used in the tasks that have traditionally been suffering from a lack of material that could be used for training. Metaphor identification task, which is aimed at automatic recognition of figurative language, is one of such tasks. The metaphorical use of words can be detected by comparing their contextual and basic meanings. In this work, we deliver the evidence that fully automatically collected dictionary definitions can be used as the optimal medium for retrieving the non-figurative word senses, which consequently may help improve the performance of the algorithms used in metaphor detection task. As the source of the lexical information, we use the openly available Wiktionary. Our method can be applied without changes to any other dataset designed for token-level metaphor detection given it is binary labeled. In the set of experiments, our proposed method (MIss RoBERTa WiLDe) outperforms or performs similarly well as the competing models on several datasets commonly chosen in the research on metaphor processing.

The outdoor field test of the 4-terminal on Si tandem photovoltaic module (specifically, InGaP/GaAs on Si) was investigated and a 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 increases (subarctic zone < temperate zone < subtropical zone). The developed and validated model used an all-climate spectrum model and considered fluctuation of atmospheric parameters. It can be applied every type of on-Si tandem solar cells.

Because semiconductors absorb wavelengths dependent on the light absorption coefficient, photovoltaic (PV) energy output is affected by the solar spectrum. Therefore, it is necessary to consider the solar spectrum for highly accurate PV output estimation. Bird’s model has been used as a general spectral model. However, atmospheric parameters such as aerosol optical depth and precipitable water have a constant value in the model that only applies to clear days. In this study, atmospheric parameters were extracted using the Bird’s spectrum model from the measured global spectrum and the seasonal fluctuation of atmospheric parameters was examined. We propose an overcast spectrum model and calculate the all-weather solar spectrum from clear to overcast sky through linear combination. Three types of PV modules (fixed Si, two-axis tracking Si, and fixed InGaP/GaAs/InGaAs triple-junction solar cells) were installed at the University of Miyazaki. The estimated performance ratio (PR), which takes into account incident angle and spectral variations, was consistent with the measured PR. Finally, the energy yield of various PVs installed across Japan was successfully estimated.

The energy yield of vehicle-integrated photovoltaics (VIPV) differs from that of standard photovoltaics (PV). It is mainly by the difference of the solar irradiance onto the car roof and car bodies as well as its curved shape. Both meaningful and practical modeling and measurement of solar irradiance for VIPV need to be established, rather than the extension of the current technologies. The solar irradiance is modeled by a random distribution of shading objects and car orientation with the correction of the curved surface of the PV modules. The measurement of the solar irradiance onto the car roof and car body is done using five pyranometers in five local axes on the car for one year. The measured dynamic solar irradiance onto the car body and car roof is used for validation of the solar irradiance model in the car.

We present ML-Ask – the first Open Source Affect Analysis system for textual input in Japanese. ML-Ask analyses the contents of an input (e.g., a sentence) and annotates it with information regarding the contained general emotive expressions, specific emotional words, valence-activation dimensions of overall expressed affect, and particular emotion types expressed with their respective expressions. ML-Ask also incorporates the Contextual Valence Shifters model for handling negation in sentences to deal with grammatically expressible shifts in the conveyed valence. The system, designed to work mainly under Linux and MacOS, can be used for research on, or applying the techniques of Affect Analysis within the framework Japanese language. It can also be used as an experimental baseline for specific research in Affect Analysis, and as a practical tool for written contents annotation. Funding statement: This research has been supported by: a Research Grant from the Nissan Science Foundation (years 2009–2010), The GCOE Program founded by Japan’s Ministry of Education, Culture, Sports, Science and Technology (years 2009–2010), (JSPS) KAKENHI Grant-in-Aid for JSPS Fellows (Project Number: 22-00358) (years 2010–2012), (JSPS) KAKENHI Grant-in-Aid for Scientific Research (Project Number: 24600001) (years 2012–2015), (JSPS) KAKENHI Grant-in-Aid for Research Activity Start-up (Project Number: 25880003) (years 2013–2015), and (JSPS) KAKENHI Grant-in-Aid for Encouragement of Young Scientists (B) (Project Number: 15K16044) (years 2015-present, project estimated to end in March 2018).

The highest-efficiency solar cell in the efficiency race does not always give the best annual energy yield in real world solar conditions because the spectrum is always changing. The study of radiative coupling of concentrator solar cells implies that efficiency could increase by recycling the radiative recombination generated by the surplus current in the upper junction. Such a configuration is called a super-multi-junction cell. We expand the model in the concentrator solar cell to a non-concentrating installation. It is shown that this super-multi-junction cell configuration is robust and can keep maximum potential efficiency (50% in realistic spectrum fluctuation) for up to 10 junctions. The super-multi-junction cell is also robust in the bandgap engineering of each junction. Therefore, a future multi-junction may not be required for tuning the bandgap to match the standard solar spectrum, as well as relying upon artificial technologies such as epitaxial lift-off (ELO), wafer-bonding, mechanical-stacking, and reverse-growth, but merely uses upright and lattice-matching growth technologies. We present two challenging techniques; one is the optical cap layer that may be the directional photon coupling layer in the application of the photonics technologies, and another is the high-quality epitaxial growth with almost 100% radiative efficiency.

As most of us subconsciously feel, it is a great difficulty to create a program which could imitate human