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Perovskite solar cells with an indium tin oxide (ITO)/SnO2/CH3NH3PbI3/Spiro-OMeTAD/2,2,2-trifluoroethanol (TFE) doped single-walled carbon nanotube (SWCNT) structure were developed by dropping TFE onto SWCNTs, which replaced the metal back electrode, and a conversion efficiency of 14.1% was achieved. Traditionally, acidic doping of the back electrode, SWCNT, has been challenging due to the potential damage it may cause to the perovskite layer. However, TFE has facilitated easy doping of SWCNT as the back electrode. The sheet resistance of the SWCNTs decreased and their ionization potential shifted to deeper levels, resulting in improved hole transport properties with a lower barrier to carrier transport. Furthermore, the Seebeck coefficient (S) increased from 34.5 μV/K to 73.1 μV/K when TFE was dropped instead of EtOH, indicating an enhancement in the behavior of p-type charge carriers. It was observed that hydrophilic substances adhered less to the SWCNT surface, and the formation of PbI2 was suppressed. These effects resulted in higher conversion efficiency and improved solar cell performance. Furthermore, the decrease in conversion efficiency after 260 days was suppressed, showing improved durability. The study suggests that combining SWCNTs and TFEs improves solar cell performance and stability.

Evaporable indano[60]fullerene ketone (FIDO) was converted to indano[60]fullerene thioketone (FIDS) in high yield by using Lawesson's reagent. Three compounds with different substituents in para position were successfully converted to the corresponding thioketones, showing that the reaction tolerates compounds with electron-donating and electron-withdrawing substituents. Computational studies with density functional theory revealed the unique vibrations of the thioketone group in FIDS. The molecular structure of FIDS was confirmed by single-crystal X-ray analysis. Bulk heterojunction organic solar cells using three evaporable fullerene derivatives (FIDO, FIDS, C60) as electron-acceptors were compared, and the open-circuit voltage with FIDS was 0.16 V higher than that with C60.

BackgroundImagination represents a pivotal capability of human intelligence. To develop human-like artificial intelligence, uncovering the computational architecture pertinent to imaginative capabilities through reverse engineering the brain's computational functions is essential. The existing Structure-Constrained Interface Decomposition (SCID) method, leverages the anatomical structure of the brain to extract computational architecture. However, its efficacy is limited to narrow brain regions, making it unsuitable for realizing the function of imagination, which involves diverse brain areas such as the neocortex, basal ganglia, thalamus, and hippocampus.ObjectiveIn this study, we proposed the Function-Oriented SCID method, an advancement over the existing SCID method, comprising four steps designed for reverse engineering broader brain areas. This method was applied to the brain's imaginative capabilities to design a hypothetical computational architecture. The implementation began with defining the human imaginative ability that we aspire to simulate. Subsequently, six critical requirements necessary for actualizing the defined imagination were identified. Constraints were established considering the unique representational capacity and the singularity of the neocortex's modes, a distributed memory structure responsible for executing imaginative functions. In line with these constraints, we developed five distinct functions to fulfill the requirements. We allocated specific components for each function, followed by an architectural proposal aligning each component with a corresponding brain organ.ResultsIn the proposed architecture, the distributed memory component, associated with the neocortex, realizes the representation and execution function; the imaginary zone maker component, associated with the claustrum, accomplishes the dynamic-zone partitioning function; the routing conductor component, linked with the complex of thalamus and basal ganglia, performs the manipulation function; the mode memory component, related to the specific agranular neocortical area executes the mode maintenance function; and the recorder component, affiliated with the hippocampal formation, handles the history management function. Thus, we have provided a fundamental cognitive architecture of the brain that comprehensively covers the brain's imaginative capacities.

The complex linguistic characteristics and limited resources present sentiment analysis in Roman Urdu as a unique challenge, necessitating the development of accurate NLP models. In this study, we investigate the performance of prominent ensemble methods on two diverse datasets of UCL and IMDB movie reviews with Roman Urdu and English dialects, respectively. We perform a comparative examination to assess the effectiveness of ensemble techniques including stacking, bagging, random subspace, and boosting, optimized through grid search. The ensemble techniques employ four base learners (Support Vector Machine, Random Forest, Logistic Regression, and Naive Bayes) for sentiment classification. The experiment analysis focuses on different N-gram feature sets (unigrams, bigrams, and trigrams), Chi-square feature selection, and text representation schemes (Bag of Words and TF-IDF). Our empirical findings underscore the superiority of stacking across both datasets, achieving high accuracies and F1-scores: 80.30% and 81.76% on the UCL dataset, and 90.92% and 91.12% on the IMDB datasets, respectively. The proposed approach has significant performance compared to baseline approaches on the relevant tasks and improves the accuracy up to 7% on the UCL dataset.

Perovskite solar cell (PSC) technology holds great promise with continuously improving power conversion efficiency; however, the use of metal electrodes hinders its commercialization and the development of tandem designs. Although single-walled carbon nanotubes (SWCNTs), as one-dimensional materials, have the potential to replace metal electrodes in PSCs, their poor conductivity still limits their application. In this study, the near-infrared (NIR)-absorbing anionic heptamethine cyanine dye-doped SWCNTs functioned in a dual role as an efficient charge-selective layer and electrode in PSCs. Benefiting from the improvement in conductivities and matched energy level of doped-SWCNT, the dual-role SWCNT electrodes applied to PSCs achieved a better performance than the undoped PSCs with a higher short circuit current (JSC) and fill factor (FF).

Abstract In mechanochromic material research, a serious problem is that mechanical treatment cannot be applied to the materials because of their responsiveness to stimuli. Inkjet printing is a useful solution deposition method for electronics, but materials must be processed to be suitable for an inkjet printer. Fluorenylidene-acridane (FA) exhibits ground-state mechanochromism with visual color changes and responds not only to mechanical pressure but also to alcohol. Alcohol inhibits the color change induced by mechanical stimulation because the mechanochromism of FA is based on a conformational change in its molecular structure. This phenomenon suggests that the mechanochromism of FA can be controlled using alcohol. For use in inkjet printing, minute particles of FA obtained by bead milling in ethanol were investigated for uniformity and size by scanning electron microscopy and gas adsorption measurement. Also, ink containing FA particles was prepared and examined for physical properties such as viscosity and surface tension. It was confirmed that the inkjet-printed pattern demonstrated visual color changes between yellow and green in response to mechanical pressure and alcohol. This report describing the control of mechanochromism and its specific application is expected to contribute to broadening the mechanochromic materials research field.

Fullerenes are intrinsically electron deficient species which can facilitate challenging transformations in organic chemistry. Here fullerene-fused alkoxy ethers are shown to undergo copper-promoted oxidation by single electron transfer to the corresponding ketones via a fullerene radical cation intermediate.

Abstract Recently, Gaiotto and Rapcak proposed a generalization of W N algebra by considering the symmetry at the corner of the brane intersection (corner vertex operator algebra). The algebra, denoted as Y L,M,N , is characterized by three non-negative integers L, M, N. It has a manifest triality automorphism which interchanges L, M, N, and can be obtained as a reduction of W 1+∞ algebra with a “pit” in the plane partition representation. Later, Prochazka and Rapcak proposed a representation of Y L,M,N in terms of L + M + N free bosons by a generalization of Miura transformation, where they use the fractional power differential operators. In this paper, we derive a q-deformation of the Miura transformation. It gives a free field representation for q-deformed Y L,M,N , which is obtained as a reduction of the quantum toroidal algebra. We find that the q-deformed version has a “simpler” structure than the original one because of the Miki duality in the quantum toroidal algebra. For instance, one can find a direct correspondence between the operators obtained by the Miura transformation and those of the quantum toroidal algebra. Furthermore, we can show that the both algebras share the same screening operators.

Abstract In the matrix model approaches of string/M theories, one starts from a generic symmetry gl(∞) to reproduce the space-time manifold. In this paper, we consider the generalization in which the space-time manifold emerges from a gauge symmetry algebra which is not necessarily gl(∞). We focus on the second nontrivial example after the toroidal compactification, the coset space G/H, and propose a specific infinite-dimensional symmetry which realizes the geometry. It consists of the gauge-algebra valued functions on the coset and Lorentzian generator pairs associated with the isometry. We show that the 0-dimensional gauge theory with the mass and Chern-Simons terms gives the gauge theory on the coset with scalar fields associated with H.

Abstract MoO3 doping of carbon‐nanotube top electrodes in perovskite solar cells is multi‐functional and facilitates p‐doping, favorable energy‐level alignment, and enhanced hole transport. The optimal layer thickness of MoO3 (8 nm) is determined for decreasing the sheet resistance of carbon‐nanotube electrodes without damaging the perovskite film. The sheet resistance decreases by approximately one‐third from its original value, which is a substantially better result than that previously reported for acid doping of carbon‐nanotube top electrodes. MoO3 deposition lowers the Fermi level of the carbon‐nanotube electrode, improving its energy‐level alignment and hole‐transfer performance. When coated with 2,2′,7,7′‐tetrakis[N,N‐di(4‐methoxyphenyl)amino]‐9,9′‐spirobifluorene (spiro‐MeOTAD), MoO3 crystallizes on the carbon nanotubes and further enhances hole collection. Semi‐transparent perovskite solar cells with MoO3‐doped carbon‐nanotube electrodes have a power conversion efficiency of 17.3% with a transmittance of approximately 60% (at a wavelength of 1000 nm). Because of their favorable transparency in the infrared region, these perovskite solar cells are evaluated for use in a tandem structure with silicon solar cells via computational simulations. The predicted device efficiency (23.7%) exceeds that of conventional indium‐tin‐oxide‐based tandem solar cells (23.0%).

Abstract The charge interaction and corresponding doping effect between single‐walled carbon nanotubes (SWNTs) and various fullerene derivatives, namely, C60, phenyl‐C61‐butyric acid methyl ester (PC61BM), methano‐indenefullerene (MIF), 1′,1″,4′,4″‐tetrahydrodi[1,4]methanonaphthaleno[5,6]fullerene (ICBA), 1,4‐bis(dimethylphenylsilylmethyl)[60]fullerene (SIMEF‐1), and dimethyl(orthoanisyl) silylmethyl(dimethylphenylsilylmethyl)[60]fullerene (SIMEF‐2), are investigated. A variety of analytical techniques, including field‐effect transistors (FETs) made of horizontally aligned arrays of SWNTs, is used as a means of investigation. Data from different measurements have to be used to obtain a concrete evaluation for the fullerene‐applied SWNTs. The data collectively points toward the conclusion that fullerenes with high molecular orbital energy levels, namely, MIF, SIMEF‐1, SIMEF‐2, and PC61BM, induce p‐type doping, while fullerenes with low molecular orbital energy levels, namely, ICBA and C60, induce n‐type doping on the carbon nanotubes. Nevertheless, the SWNTs retained p‐type characteristics because n‐doping induced by the fullerenes are weak compared to the p‐doping of the water and oxygen on carbon nanotubes. This means that fullerene derivatives have the ability to fine‐tune the energy levels of carbon nanotubes, which can play a crucial role in carbon nanotube‐based electronics, such as solar cells, light‐emitting devices, and FETs.

Human action recognition and detection from unmanned aerial vehicles (UAVs), or drones, has emerged as a popular technical challenge in recent years, since it is related to many use case scenarios from environmental monitoring to search and rescue. It faces a number of difficulties mainly due to image acquisition and contents, and processing constraints. Since drones’ flying conditions constrain image acquisition, human subjects may appear in images at variable scales, orientations, and occlusion, which makes action recognition more difficult. We explore low-resource methods for ML (machine learning)-based action recognition using a previously collected real-world dataset (the “Okutama-Action” dataset). This dataset contains representative situations for action recognition, yet is controlled for image acquisition parameters such as camera angle or flight altitude. We investigate a combination of object recognition and classifier techniques to support single-image action identification. Our architecture integrates YoloV5 with a gradient boosting classifier; the rationale is to use a scalable and efficient object recognition system coupled with a classifier that is able to incorporate samples of variable difficulty. In an ablation study, we test different architectures of YoloV5 and evaluate the performance of our method on Okutama-Action dataset. Our approach outperformed previous architectures applied to the Okutama dataset, which differed by their object identification and classification pipeline: we hypothesize that this is a consequence of both YoloV5 performance and the overall adequacy of our pipeline to the specificities of the Okutama dataset in terms of bias–variance tradeoff.

We developed a robust object-level change detection method that could capture distinct scene changes in an image pair with viewpoint differences. To achieve this, we designed a network that could detect object-level changes in an image pair. In contrast to previous studies, we considered the change detection task as a graph matching problem for two object graphs that were extracted from each image. By virtue of this, the proposed network more robustly detected object-level changes with viewpoint differences than existing pixel-level approaches. In addition, the network did not require pixel-level change annotations, which have been required in previous studies. Specifically, the proposed network extracted the objects in each image using an object detection module and then constructed correspondences between the objects using an object matching module. Finally, the network detected objects that appeared or disappeared in a scene using the correspondences that were obtained between the objects. To verify the effectiveness of the proposed network, we created a synthetic dataset of images that contained object-level changes. In experiments on the created dataset, the proposed method improved the F1 score of conventional methods by more than 40%. Our synthetic dataset will be available publicly online.

To endow robots with the flexibility to perform a wide range of tasks in diverse and complex environments, learning their controller from experience data is a promising approach. In particular, some recent meta-learning methods are shown to solve novel tasks by leveraging their experience of performing other tasks during training. Although studies around meta-learning of robot control have worked on improving the performance, the safety issue has not been fully explored, which is also an important consideration in the deployment. In this paper, we firstly relate uncertainty on task inference with the safety in meta-learning of visual imitation, and then propose a novel framework for estimating the task uncertainty through probabilistic inference in the task-embedding space, called PETNet. We validate PETNet with a manipulation task with a simulated robot arm in terms of the task performance and uncertainty evaluation on task inference. Following the standard benchmark procedure in meta-imitation learning, we show PETNet can achieve the same or higher level of performance (success rate of novel tasks at meta-test time) as previous methods. In addition, by testing PETNet with semantically inappropriate or synthesized out-of-distribution demonstrations, PETNet shows the ability to capture the uncertainty about the tasks inherent in the given demonstrations, which allows the robot to identify situations where the controller might not perform properly. These results illustrate our proposal takes a significant step forward to the safe deployment of robot learning systems into diverse tasks and environments.

Abstract The M13 bacteriophage, a nature‐inspired environmentally friendly biomaterial, is used as a perovskite crystal growth template and a grain boundary passivator in perovskite solar cells. The amino groups and carboxyl groups of amino acids on the M13 bacteriophage surface function as Lewis bases, interacting with the perovskite materials. The M13 bacteriophage‐added perovskite films show a larger grain size and reduced trap‐sites compared with the reference perovskite films. In addition, the existence of the M13 bacteriophage induces light scattering effect, which enhances the light absorption particularly in the long‐wavelength region around 825 nm. Both the passivation effect of the M13 bacteriophage coordinating to the perovskite defect sites and the light scattering effect intensify when the M13 virus‐added perovskite precursor solution is heated at 90 °C prior to the film formation. Heating the solution denatures the M13 bacteriophage by breaking their inter‐ and intra‐molecular bondings. The denatured M13 bacteriophage‐added perovskite solar cells exhibit an efficiency of 20.1% while the reference devices give an efficiency of 17.8%. The great improvement in efficiency comes from all of the three photovoltaic parameters, namely short‐circuit current, open‐circuit voltage, and fill factor, which correspond to the perovskite grain size, trap‐site passivation, and charge transport, respectively.

Abstract Recently, Gaiotto and Rapčák (GR) proposed a new family of the vertex operator algebra (VOA) as the symmetry appearing at an intersection of five-branes to which they refer as Y algebra. Procházka and Rapčák, then proposed to interpret Y algebra as a truncation of affine Yangian whose module is directly connected to plane partitions (PP). They also developed GR’s idea to generate a new VOA by connecting plane partitions through an infinite leg shared by them and referred it as the web of W-algebra (WoW). In this paper, we demonstrate that double truncation of PP gives the minimal models of such VOAs. For a single PP, it generates all the minimal model irreducible representations of W-algebra. We find that the rule connecting two PPs is more involved than those in the literature when the U(1) charge connecting two PPs is negative. For the simplest nontrivial WoW, N $$ \mathcal{N} $$ = 2 superconformal algebra, we demonstrate that the improved rule precisely reproduces the known character of the minimal models.

Situational awareness by Unmanned Aerial Vehicles (UAVs) is important for many applications such as surveillance, search and rescue, and disaster response. In those applications, detecting and locating people and recognizing their actions in near real-time can play a crucial role for preparing an effective response. However, there are currently three main limitations to perform this task efficiently. First, it is currently often not possible to access the live video feed from a UAV's camera due to limited bandwidth. Second, even if the video feed is available, monitoring and analyzing video over prolonged time is a tedious task for humans. Third, it is typically not possible to locate random people via their cellphones. Therefore, we developed the Person-Action-Locator (PAL), a novel UAV-based situational awareness system. The PAL system addresses the first issue by analyzing the video feed onboard the UAV, powered by a supercomputer-on-a-module. Specifically, as a support for human operators, the PAL system relies on Deep Learning models to automatically detect people and recognize their actions in near real-time. To address the third issue, we developed a Pixel2GPS converter that estimates the location of people from the video feed. The result - icons representing detected people labeled by their actions - is visualized on the map interface of the PAL system. The Deep Learning models were first tested in the lab and demonstrated promising results. The fully integrated PAL system was successfully tested in the field. We also performed another collection of surveillance data to complement the lab results.

The reduction of fullerene (C60) with sodium dispersion in the presence of an excess amount of dipropyl sulfate was found to yield highly propylated fullerene, C60(nC3H7)n (max. n = 24), and C60(nC3H7)20 was predominantly generated as determined by mass spectroscopy.

Perovskite solar cells (PSCs) are regarded as the next-generation thin-film energy harvester, owing to their high performance. However, there is a lack of studies on their encapsulation technology, which is critical for resolving their shortcomings, such as their degradation by oxygen and moisture. It is determined that the moisture intrusion and the heat trapped within the encapsulating cover glass of PSCs influenced the operating stability of the devices. Therefore, we improved the moisture and oxygen barrier ability and heat releasing capability in the passivation of PSCs by adding multi-walled carbon nanotubes to the epoxy resin used for encapsulation. The 0.5 wt% of carbon nanotube-added resin-based encapsulated PSCs exhibited a more stable operation with a ca. 30% efficiency decrease compared to the ca. 63% decrease in the reference devices over one week under continuous operation. Specifically, the short-circuit current density and the fill factor, which are affected by moisture and oxygen-driven degradation, as well as the open-circuit voltage, which is affected by thermal damage, were higher for the multi-walled carbon nanotube-added encapsulated devices than the control devices, after the stability test.

The structure of crystalline [60]fullerene with a lithium cation inside (Li+@C60) was determined by synchrotron radiation X-ray diffraction measurements to understand the electrostatic and thermal properties of the encapsulated Li+ cation. Although the C60 cages show severe orientation disorder in [Li+@C60](TFPB−)·C4H10O and [Li+@C60](TFSI−)·CH2Cl2, the Li+ cations are rather ordered at specific positions by electrostatic interactions with coordinated anions outside the C60 cage. The Li+@C60 molecules in [Li+@C60](ClO4−) with a rock-salt-type cubic structure are fully disordered with almost uniform spherical shell charge densities even at 100 K by octahedral coordination of ClO4− tetrahedra and show no orientation ordering, unlike [Li+@C60](PF6−) and pristine C60. Single-bonded (Li+@C60−)2 dimers in [Li+@C60−](NiOEP)⋅CH2Cl2 are thermally stable even at 400 K and form Li+–C bonds which are shorter than Li+–C bonds in [Li+@C60](PF6−) and suppress the rotational motion of the Li+ cations.

Recent expansion of intelligent gadgets, such as smartphones and smart watches, familiarizes humans with sensing their activities. We have been developing a road accessibility evaluation system inspired by human sensing technologies. This paper introduces our methodology to estimate road accessibility from the three-axis acceleration data obtained by a smart phone attached on a wheelchair seat, such as environmental factors, e.g., curbs and gaps, which directly influence wheelchair bodies, and human factors, e.g., wheelchair users’ feelings of tiredness and strain. Our goal is to realize a system that provides the road accessibility visualization services to users by online/offline pattern matching using impersonal models, while gradually learning to improve service accuracy using new data provided by users. As the first step, this paper evaluates features acquired by the DCNN (deep convolutional neural network), which learns the state of the road surface from the data in supervised machine learning techniques. The evaluated results show that the features can capture the difference of the road surface condition in more detail than the label attached by us and are effective as the means for quantitatively expressing the road surface condition. This paper developed and evaluated a prototype system that estimated types of ground surfaces focusing on knowledge extraction and visualization.