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This paper sets up a small open new Keynesian economy model with constrained households and incomplete markets to address the driving forces of business cycles in Korea. It shows that there exists a substantial fraction of constrained households who cannot have access to financial market. Furthermore, the estimated model reveals that a TANK model is better than a RANK model in explaining business cycles in Korea. The effect of domestic productivity shock on Korean economy has dominated in the variations of output, while the contribution of the foreign productivity shock to the variations of output and inflation has increased after the Asian financial crisis. The monetary policy shock has dominated the variation of inflation at short and medium horizons.

Perovskite solar cells have garnered the attention of researchers in recent years as they have been able to attain high efficiency within a short time. In the present study, the hybrid organic inorganic lead iodide perovskite has been used as the sensitizer. Successful fabrication of a hybrid lead halide perovskite sensitized solar cell (PSC) with copper iodide as a medium for transporting holes (HTM) has been performed in a sandwich structure. The synthesized CH3NH3PbI3 material and HTM have undergone various characterization processes, such as XRD, UV–Vis, SEM, and EDX. The present work is based on the synthesis of perovskite, HTM, and polymer electrolyte and their application in a PSC at room ambient following the sandwich structure. A planar WE/TiO2/CH3NH3PbI3/HTM/electrolyte/CE structure was fabricated through solution-processed spin-coating. The electrical properties of the PSC were measured through a solar simulator, and it attained an efficiency of 13.64%, with V oc = 0.63 V and Jsc = 33 mA cm 2 with fill factor = 0.65. This work shows the sandwich-structured fabrication of the PSC in ambient condition with a considerably good efficiency which represents the novelty of the work. [ABSTRACT FROM AUTHOR]

An immense volume of digital documents exists online and offline with content that can offer useful information and insights. Utilizing topic modeling enhances the analysis and understanding of digital documents. Topic modeling discovers latent semantic structures or topics within a set of digital textual documents. The Internet of Things, Blockchain, recommender system, and search engine optimization applications use topic modeling to handle data mining tasks, such as classification and clustering. The usefulness of topic models depends on the quality of resulting term patterns and topics with high quality. Topic coherence is the standard metric to measure the quality of topic models. Previous studies build topic models to generally work on conventional documents, and they are insufficient and underperform when applied to web content data due to differences in the structure of the conventional and HTML documents. Neglecting the unique structure of web content leads to missing otherwise coherent topics and, therefore, low topic quality. This study aims to propose an innovative topic model to learn coherence topics in web content data. We present the HTML Topic Model (HTM), a web content topic model that takes into consideration the HTML tags to understand the structure of web pages. We conducted two series of experiments to demonstrate the limitations of the existing topic models and examine the topic coherence of the HTM against the widely used Latent Dirichlet Allocation (LDA) model and its variants, namely the Correlated Topic Model, the Dirichlet Multinomial Regression, the Hierarchical Dirichlet Process, the Hierarchical Latent Dirichlet Allocation, the pseudo-document based Topic Model, and the Supervised Latent Dirichlet Allocation models. The first experiment demonstrates the limitations of the existing topic models when applied to web content data and, therefore, the essential need for a web content topic model. When applied to web data, the overall performance dropped an average of five times and, in some cases, up to approximately 20 times lower than when applied to conventional data. The second experiment then evaluates the effectiveness of the HTM model in discovering topics and term patterns of web content data. The HTM model achieved an overall 35% improvement in topic coherence compared to the LDA. [ABSTRACT FROM AUTHOR]

Lead-based perovskite solar cells (PSCs) have drawn much research attention over the years due to their impressive light-to-power conversion efficiency (PCE), low temperature with easy manufacturing, tolerance to defects, high absorption coefficient and low cost. In this paper, the effect of absorber thickness, absorber band gap, absorber doping concentration, and electron transport material (ETM) thickness of PSC with two inorganic hole transport materials (HTM) was investigated using one-dimensional solar capacitance simulation (SCAPS-1D) software. Results obtained indicates that solar cell containing CuI as HTM performed better than that with NiO. A power conversion efficiency of 16.65%, fill factor (FF) of 82.43%, current density (Jsc) of 24.83 mA/cm2 and voltage (Voc) of 0.83 V were obtained for CuI with an enhancement of 1.15 times in PCE, 1.10 in Jsc and 1.17 in FF over the initial device and PCE of 15.74%, FF of 74.69 %, Jsc of 27.22 mA/cm2 and Voc of 0.77 V for NiO with an enhancement of 1.20 times in PCE, 1.25 times in Jsc and 1.15 in FF when compared with the initial device. The result therefore, shows that CuI as HTM performed better than NiO and implies that critical selection of the absorber parameters is a very key factor to enhance solar cell devices.

An immense volume of digital documents exists online and offline with content that can offer useful information and insights. Utilizing topic modeling enhances the analysis and understanding of digital documents. Topic modeling discovers latent semantic structures or topics within a set of digital textual documents. The Internet of Things, Blockchain, recommender system, and search engine optimization applications use topic modeling to handle data mining tasks, such as classification and clustering. The usefulness of topic models depends on the quality of resulting term patterns and topics with high quality. Topic coherence is the standard metric to measure the quality of topic models. Previous studies build topic models to generally work on conventional documents, and they are insufficient and underperform when applied to web content data due to differences in the structure of the conventional and HTML documents. Neglecting the unique structure of web content leads to missing otherwise coherent topics and, therefore, low topic quality. This study aims to propose an innovative topic model to learn coherence topics in web content data. We present the HTML Topic Model (HTM), a web content topic model that takes into consideration the HTML tags to understand the structure of web pages. We conducted two series of experiments to demonstrate the limitations of the existing topic models and examine the topic coherence of the HTM against the widely used Latent Dirichlet Allocation (LDA) model and its variants, namely the Correlated Topic Model, the Dirichlet Multinomial Regression, the Hierarchical Dirichlet Process, the Hierarchical Latent Dirichlet Allocation, the pseudo-document based Topic Model, and the Supervised Latent Dirichlet Allocation models. The first experiment demonstrates the limitations of the existing topic models when applied to web content data and, therefore, the essential need for a web content topic model. When applied to web data, the overall performance dropped an average of five times and, in some cases, up to approximately 20 times lower than when applied to conventional data. The second experiment then evaluates the effectiveness of the HTM model in discovering topics and term patterns of web content data. The HTM model achieved an overall 35% improvement in topic coherence compared to the LDA.

The high efficiency and low cost of production of perovskite solar cells (PSCs) based on organic-inorganic halides have attracted the attention of researchers. However, due to the intricacy in the synthesis of Spiro-OMeTAD and the high cost of gold (Au) utilized as the back contact (BC), have affected its viability for commercialization. In this present study, a simulation was performed with and without HTM utilizing different metal contacts (Ag, Cr, Cu, Au, Ni and Pt). SCAPS-1D, a software program in one dimension, was used to conduct the simulation. A systematic analysis was done to determine how the metal back contact's work functions affected the PSC both with and without HTM. The outcomes demonstrate that the PSCs' photovoltaic performance is significantly influenced by the metal contact's work function (WF). The best metal contact for HTM and HTM-free devices was Pt, with a metal work function of 5.65 eV. The initial power conversion efficiencies (PCEs) for the two configurations were 26.229% for HTM-free and 25.608% for HTM-based device. A number of parameters, including absorber thickness, interface defect density, and electron transport material (ETM) thickness, were varied to obtain optimal values of 0.8 μm for both HTM and HTM-free PSCs, 1005 cm−2 for both HTM and HTM-free PSCs, and 0.01 μm for both HTM and HTM-free PSCs. These values were then used to simulate the final HTM and HTM-free devices with a PCE of 27.423%, current density (Jsc) of 27.546 mA/cm2, open circuit voltage (Voc) of 1.239 V, and fill factor (FF) of 80.347% for HTM-free whereas PCE of 26.767% with Jsc of 27.545 mA/cm2, Voc of 1.250 V, and FF of 77.733% for HTM based. These outcomes reflect outstanding enhancement of ∼1.05 and ∼1.07 times in PCE and Jsc over unoptimized cells with and without HTM.

The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly detection using deep learning is therefore mainly constrained to generative models such as generative adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer from general deep learning issues and are hard to properly train. In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM architecture specifically for anomaly detection in complex videos such as surveillance footage. We have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation results and online learning capabilities prove the great potential of using our system for real-time unsupervised anomaly detection in complex videos. [ABSTRACT FROM AUTHOR]

The Spiro-OMeTAD is an excellent candidate for application as hole transport material (HTM), but its high hygroscopicity, inclination to crystallize, and fragility to moisture and heat make it unsuitable for solar cells. Thus, it is of interest to investigate other HTM candidates. In this paper, the use of p-type InGaAs as hole transport material (HTM) has been suggested to enhance the performance of perovskite-based solar cells (PSC). The simulation of a hybrid CH3NH3PbI3/InGaAs planar heterojunction perovskite solar cell is performed using the Silvaco ATLAS simulator. In order to confirm the predictability of the proposed simulation methodology, the conventional ITO/TiO2/MAPbI3/Spiro-OMeTAD structure is simulated, and shows good coherence with experimental results. The proposed design using InGaAs as HTM outperforms the conventional device in terms of short-circuit current density (JSC) of 37.2 mA/cm2, open-circuit voltage (VOC) of 1 V, fill factor (FF) of 80% and high value of efficiency. In addition, the findings show that with In content of x = 0.7 the efficiency will improve to reach a value of about 30%. [ABSTRACT FROM AUTHOR]

As demand grows for efficient and affordable photovoltaic technology, investigating alternative materials becomes crucial. Tin-based perovskite has a low bandgap, great optoelectronic properties, and higher carrier mobility. It has become an interesting absorber layer for perovskite solar cells (PSCs). However, improving their device performance presents challenges. Copper-based hole transport materials (HTM) and zinc-based electron transport materials (ETM) offer low cost, easy fabrication, and high electrical conductivity. This research analyzes the impacts of these transport materials on the FASnI 3 absorber layer using numerical modeling in SCAPS-1D. Through simulation analysis, the study thoroughly examines the effect of these materials on PSC performance parameters such as QE curve, layer thickness, defect density, interface defect, and doping concentration. Moreover, this study identifies the most efficient PSC configurations, achieving good performance by ITO/ZnSe/ FASnI 3 / Cu 2 O /Au with a V oc of 2.05 V, J sc of 29.316 mA/cm2, FF of 53.37 %, and power conversion efficiency (PCE) of 32.13 %, providing valuable insights in optimizing FASnI 3 solar cells and advancing next-generation solar energy conversion technologies. • SCAPS 1D simulation on perovskite solar cell. • Tin-based perovskite has emerged as encouraging absorber layer in perovskite solar cells. • Cu-based HTM and Zn-based ETM offer low cost, easy fabrication, and high electrical conductivity. • Using different HTL materials such as Cu 2 O , CuSbS 2 , and Zn-based ETL have been identified as potential candidates to enhance the performance of FASnI 3. • A study identifies good performance by ITO/ZnSe/ FASnI 3 / Cu 2 O /Au with a V oc of 2.05 V, J sc of 29.316 mA/cm2, FF of 53.37 %, and PCE of 32.13 %, [ABSTRACT FROM AUTHOR]

For manually operated machine tools, the accuracy of the machine tool structure limits the accuracy of the parts produced. Such is not necessarily the case with computer numerically controlled (CNC) machine tools. This concept may not be immediately obvious to the engineering undergraduate. The method of error compensation is presented here in a manner that is accessible to the undergraduate engineering student. A homogeneous transformation matrix (HTM) model quantifies the geometric errors of a machine tool, which can be compensated for in software. The mathematical treatment is reduced to only essential elements to emphasize physical understanding. A key feature of this presentation is the application of the model to a three-axis milling machine. This illustration enables the undergraduate student to grasp the concept with ease. Another feature is that the entire model is developed from first principles, which does not require the student to invoke any empirical relationships. Three solved numerical problems illustrate the application of the model to practical situations. Information provided here may be used by the teacher as a template to introduce this subject at the undergraduate level.

In just a few years, the efficiency of perovskite-based solar cells (PSCs) has risen to 25.8%, making them competitive with current commercial technology. Due to the inherent advantage of perovskite thin films that can be fabricated using simple solution techniques at low temperatures, PSCs are regarded as one of the most important low-cost and mass-production prospects. The lack of stability, on the other hand, is one of the major barriers to PSC commercialization. The goal of this review is to highlight the most important aspects of recent improvements in PSCs, such as structural modification and fabrication procedures, which have resulted in increased device stability. The role of different types of hole transport layers (HTL) and the evolution of inorganic HTL including their fabrication techniques have been reviewed in detail in this review. We eloquently emphasized the variables that are critical for the successful commercialization of perovskite devices in the final section. To enhance perovskite solar cell commercialization, we also aimed to obtain insight into the operational stability of PSCs, as well as practical information on how to increase their stability through rational materials and device fabrication. [ABSTRACT FROM AUTHOR]

Perovskite Solar Cells (PSCs) being the most advanced photovoltaic technology today have attracted global attention due to its solution processability and cost effectiveness. In this paper, we reported the performance of perovskite solar cells by integrating Reduced Graphene Oxide (rGO) as a buffer layer. The combined effect of Ultraviolet visible (UV-vis) spectroscopy, X-ray Diffractometer (XRD), Raman spectroscopy, Scanning Electron Microscopy (SEM), Four Point Probe and Solar Simulator were used to explore the absorbance, crystallinity, morphological, resistivity and current voltage behavior of the photoanodes and devices. The rGO was deposited on top the mesoporous Titanium dioxide (m-TiO2) (Device B), on methylammonium lead triiodide (CH3NH3PbI3) perovskite absorber (Device C) and on m-TiO2 & CH3NH3PbI3 perovskite absorber (Device D). The reference device (Device A) was fabricated without rGO as a bench mark for comparison. From the results obtained, the devices with rGO show significant improvement over the device lacking rGO. The reference device gave a Short Circuit Current Density (Jsc) of 8.151 mAcm-2, Open Circuit Voltage (Voc) of 0.575 V, Fill Factor (FF) of 74.4 % and Power Conversion Efficiency (PCE) of 3.49 %. The best performing device was the one with rGO incorporated on both m-TiO2 and CH3NH3PbI3. The device gave a Jsc of 8.737 mAcm-2, Voc of 0.836 V, FF of 68.1% and PCE of 4.98 %. The device experienced an enhancement of ~7.20 % and 42.69 % in Jsc and PCE over the pristine device. This investigation aids the fundamental understanding of the effect of Graphene Materials (GRMs) on the optoelectronic properties of PSCs, and it further confirms the promising prospects of achieving low-cost, efficient and stable PSCs for commercialization with graphene materials. [ABSTRACT FROM AUTHOR]

The application of machine learning algorithms to the detection of fraudulent credit card transactions is a challenging problem domain due to the high imbalance in the datasets and confidentiality of financial data. This implies that legitimate transactions make up a high majority of the datasets such that a weak model with 99% accuracy and faulty predictions may still be assessed as high-performing. To build optimal models, four techniques were used in this research to sample the datasets including the baseline train test split method, the class weighted hyperparameter approach, and the undersampling and oversampling techniques. Three machine learning algorithms were implemented for the development of the models including the Random Forest, XGBoost and TensorFlow Deep Neural Network (DNN). Our observation is that the DNN is more efficient than the other 2 algorithms in modelling the under-sampled dataset while overall, the three algorithms had a better performance in the oversampling technique than in the undersampling technique. However, the Random Forest performed better than the other algorithms in the baseline approach. After comparing our results with some existing state-of-the-art works, we achieved an improved performance using real-world datasets.

The article analyses the research related to the issue of detecting anomalies in current medical data. The work aims to develop an information system for detecting data anomalies in the format of time series, such as metrics, with the ability to visualize the results for expert evaluation. The process of detecting anomalies by machine learning tools for detecting anomalies in the flow data is investigated. A system for detecting anomalies in metrics using the HTM model is built. According to the model results, obtained satisfactory accuracy for detecting anomalies at standard network parameters, the estimation of anomalies differed clearly for different aggregation intervals. The OPF Client functionality was used to build the HTM model, which allowed to achieve speed and simplicity in its construction. The obtained results allow the expert to choose the best of the size of the intervals as well as the parameters of the models. This choice is necessary because the presence of the anomaly depends on expert knowledge in a particular field. [ABSTRACT FROM AUTHOR]

Abstract: Perovskite solar cells have become an attractive subject in the solar energydevice area. During ten years of development, the energy conversion efficiency has beenimproved from 2.2% to more than 22%, and it still has a very good potential for furtherenhancement. In this paper, a numerical model of the perovskite solar cell with thestructure of glass/ FTO/ TiO2/ H3NH3PbI3/ HTM/Au by using Silvaco Atlas software ispresented. The effect of hole transport material characteristics, including hole mobilityand band gap offset of organic and inorganic HTM layers such as Spiro-MOeTAD, CuOand Cu2O on the performance of PSCs are investigated. The simulation results reveal thatwith increase of hole mobility in hole transport layer, the cell efficiency is increases.Meanwhile, the solar cell exhibits a better performance by using inorganic materials likeCuO and Cu2O as hole transport layer, than by using Spiro-MOeTAD, particularly theefficiency reaches 22.12% when Cu2O is used.

The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly detection using deep learning is therefore mainly constrained to generative models such as generative adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer from general deep learning issues and are hard to properly train. In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM architecture specifically for anomaly detection in complex videos such as surveillance footage. We have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation results and online learning capabilities prove the great potential of using our system for real-time unsupervised anomaly detection in complex videos.

In a vehicular environment, by becoming connected, vehicles are subject to more threats in comparison to traditional information systems, with the difference that, as a cyber-physical system, anomalies and intrusions could have repercussions in the physical world. In this work, we have developed an ontological anomaly-detection approach (OADA). The anomalies studied in this work mainly concern: network scans, DNS tunnel attacks, and telemetry data anomalies. Our contribution relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic, such as flow or latency. They are subject to the process of detecting anomalies and intrusions, carried out thanks to the algorithm with HTM. For each entry, the algorithm produces a score that allows us to decide if a window is abnormal and to lift an alert if that is the case. We evaluated our system using a communications and anomalies emulation tool. We use the corpus of data produced thanks to Autobot. We seek to determine from among the best scores of Matthews correlation coefficient (MCC) and Detection efficiency score (DES) which were the parameters for which HTM detects all anomalies with the greatest possible coverage. The obtained results prove that HTM can detect all anomalies for each window duration. [ABSTRACT FROM AUTHOR]

In just a few years, the efficiency of perovskite-based solar cells (PSCs) has risen to 25.8%, making them competitive with current commercial technology. Due to the inherent advantage of perovskite thin films that can be fabricated using simple solution techniques at low temperatures, PSCs are regarded as one of the most important low-cost and mass-production prospects. The lack of stability, on the other hand, is one of the major barriers to PSC commercialization. The goal of this review is to highlight the most important aspects of recent improvements in PSCs, such as structural modification and fabrication procedures, which have resulted in increased device stability. The role of different types of hole transport layers (HTL) and the evolution of inorganic HTL including their fabrication techniques have been reviewed in detail in this review. We eloquently emphasized the variables that are critical for the successful commercialization of perovskite devices in the final section. To enhance perovskite solar cell commercialization, we also aimed to obtain insight into the operational stability of PSCs, as well as practical information on how to increase their stability through rational materials and device fabrication.

Many models based on the operation of the neocortex, which is the center of brain intelligence, are emerging. The Hierarchical Temporal Memory (HTM) model is a unique intermediate level model of the neocortex’s layered substructures. The hypothesis is that these layers build temporal models of sequences of observations and/or motor signals, i.e., build a sequence memory. Implementations of this model exist in Python, C++ and Java. However, those implementations are quite cumbersome to use, as they depend on many other packages. This paper presents a lean, standalone, easy to modify MATLAB implementation. The performance results from processing the Numenta Anomaly Benchmark (NAB) demonstrate the fidelity of matlabHTM.

• A bio-inspired learning system for trajectory anomaly detection is proposed. • The method is tested on 2D object trajectories and 3D air signature verification. • An ELM-HTM fusion framework is used. Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose a new bio-inspired learning model for a single-class classifier to detect abnormality in video object trajectories. The method uses a simple but dynamic extreme learning machine (ELM) and hierarchical temporal memory (HTM) together referred to as ELM-HTM in an unsupervised way to learn and classify time series patterns. The method has been tested on trajectory sequences in traffic surveillance to find abnormal behaviors such as high-speed, unusual stops, driving in wrong directions, loitering, etc. Experiments have also been performed with 3D air signatures captured using sensors and used for biometric authentication(forged/genuine). The results indicate a significant gain over training time and classification accuracy. The proposed method outperforms in predicting long-time patterns by observing small steps with an average accuracy gain of 15% as compared to the state-of-the-art HTM. The method has applications in detecting abnormal activities in videos by learning the movement patterns as well as in biometric authentication. [ABSTRACT FROM AUTHOR]

With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not applicable for continuous online learning and analyzing. In this paper, a new online intrusion scenario discovery framework is proposed and the intelligence algorithm HTM (Hierarchical Temporal Memory) is employed to improve the performance of the online learning ability of the system. The proposed framework can discover and model intrusion scenarios, and the constructed model keeps evolving with the variance of the data. Additionally, a series of data preprocessing methods are introduced to enhance its adaptability to the noisy and twisted data. The experimental results show that the framework is effective in intrusion scenario discovery, and the discovered scenario is more concise and accurate than our previous work. [ABSTRACT FROM AUTHOR]

Copper oxide (CuxO) films are considered to be an attractive hole-transporting material (HTM) in the inverted planar heterojunction perovskite solar cells due to their unique optoelectronic properties, including intrinsic p-type conductivity, high mobility, low-thermal emittance, and energy band level matching with the perovskite (PS) material. In this study, the potential of reactive sputtered CuxO thin films with a thickness of around 100 nm has been extensively investigated as a promising HTM for effective and stable perovskite solar cells. The as-deposited and annealed films have been characterized by using X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Photoluminescence (PL), UV-Vis spectroscopy, and Hall-effect measurement techniques. The significant change in structural and optoelectronic properties has been observed as an impact of the thermal annealing process. The phase conversion from Cu2O to CuO, including grain size increment, was observed upon thermal annealing. The transmittance and optical bandgap were found to vary with the films’ crystallographic transformation. The predominant p-type conductivity and optimum annealing time for higher mobility have been confirmed from the Hall measurement. Films’ optoelectrical properties were implemented in the complete perovskite solar cell for numerical analysis. The simulation results show that a 40 min annealed CuxO film yields the highest efficiency of 22.56% with a maximum open-circuit voltage of 1.06 V.

In this work, easy and simple structured perovskite solar cells (PSCs) are designed and characterized. Our effort was to reduce the cost of the fabrication of such PSC devices, first by using an inexpensive starting precursor (aqueous methylamine solution) for the perovskite materials and second by design in a PSC structure free of the expensive hole transport layer (HTL). The CH3NH3PbI3 perovskite sols were deposited onto a conductive FTO glass using the spin coating technique followed by heating at 100 °C for 10 min. The structure of the films was characterized by X-ray diffraction (XRD) and their optical properties by UV–VIS spectrophotometry and photoluminescence (PL). The obtained phase confirmed the formation of a tetragonal perovskite structure. Two different solvents have been used, dimethylformamide (DMF) and dimethyl sulfoxide (DMSO). The effect of the type and the concentration of the used solvent DMF and DMSO on the performance of the solar cells have been investigated. It was found that a 40% concentration of the perovskite material resulted in the optimum film thickness that gives the best photoelectric performance. The DMF-based PSC assembled solar cell exhibited the best performance with an open circuit voltage of 750 mV, a photocurrent density of 12.5 mA/cm2, and an overall photon to electric conversion efficiency of 5.7%; all these results are higher than those of cells made with DMSO. Highlights: Easy and simple structured perovskite solar cells free of the hole transport layer were designed for efficient low-cost organolead halide perovskites solar cells by using inexpensive precursors. The influence of the concentration and the type of the solvent of the obtained perovskite materials on the performance of the solar cell have been investigated. For both the used solvents (DMF and DMSO), it was found that a 40% concentration is the optimum to obtain better performance for the DMF-based PSC. An open circuit voltage of 750 mV, a photocurrent density of 12.5 mA/cm2 with an overall photon to electric conversion efficiency of 5.7% was obtained. [ABSTRACT FROM AUTHOR]

With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not applicable for continuous online learning and analyzing. In this paper, a new online intrusion scenario discovery framework is proposed and the intelligence algorithm HTM (Hierarchical Temporal Memory) is employed to improve the performance of the online learning ability of the system. The proposed framework can discover and model intrusion scenarios, and the constructed model keeps evolving with the variance of the data. Additionally, a series of data preprocessing methods are introduced to enhance its adaptability to the noisy and twisted data. The experimental results show that the framework is effective in intrusion scenario discovery, and the discovered scenario is more concise and accurate than our previous work.

Abstract We aim to form stable representations of temporal sequences with key focus on semantic learning and streaming data. The state of the art in the Hierarchical Temporal Memory is represented by Numenta's recently published "ColumnPooler" which emulates functionality of cortical L2/3 layer, forms stable allocentric representations of temporal sequences and/or objects, and has been applied to sensory-motor learning. Our designed experiments evaluate the ColumnPooler for such task and uncover its current limitations. Presented "Motion Memory" design defines needed modifications in order to be effectively used for sequence representation, namely: Semantic distance between the representations; Online learning on streams; ability to represent time; and representation of motion from static sensor. One of the main problems with the current design is the lack of semantic meaning in (continuously updated) representations of the object. The proposed improvement enables MotionMemory to do unsupervised learning on streaming data and resulting representation have semantic meaning, this has many practical applications in sensory processing (ie. vision), or hierarchical learning. [ABSTRACT FROM AUTHOR]

In this study, six small carbazole-based molecules are investigated for usage as hole transport materials (HTMs) in perovskite solar cells. Among these compounds, two molecules based on 9-(4-(thiophen-2-yl)phenyl)-9H-carbazole thiophene-phenyle and carbazole (M 1 and M 2) were already synthesized, and four new molecules are designed by substituting carbazole, in positions 3,6 and 2,7, with methoxyphenyl (P 1 and P 2) and dimethoxyphenylamine (E 1 and E 2). Theoretical methods used in the calculations included density functional theory and time-dependent density functional theory. FMOs of all under-probe molecules are well positioned to ensure accurate alignment and prevent charge recombination at the perovskite material interface. The molecules' absorbance in the area below 404 nm shows that HTMs cannot compete with perovskite materials in an inverted configuration of a device. Reorganization energies indicate that M 1 , P 1,2 and E 1,2 are more favourable to be HTM, while M 2 shows a favourable electron transfer; it can be used as an electron transfer material (ETM). The results demonstrate that hole-electron couples can easily separate for any under-exanimated molecules, simplifying hole transport and enhancing the short-circuit (J SC). Additionally, DMPA-based molecules (E 1,2) may display chemical instability because of their poor hardness and the local distribution of charge in electrostatic potential maps. [ABSTRACT FROM AUTHOR]

We demonstrate for the first-time liquid exfoliated few layers of 2D Black phosphorus (BP) nanosheets as a hole transporting material (HTM) for perovskite based solar cells. The photoelectron spectroscopy in air (PESA) measurements confirm the low laying valence band level of BP nanosheets (−5.2 eV) favourable for hole injection from CH 3 NH 3 PbI 3 (MAPbI 3 ). Our results show that ∼25% improvement in power conversion efficiency (PCE) of η = 16.4% for BP nanosheets + Spiro-OMeTAD as an HTM as compared to spiro-OMeTAD (η = 13.1%). When BP nanosheets are exclusively utilised as an HTM, a PCE of η = 7.88% is noted, an improvement over the 4% PCE values observed for HTM free devices. Photoluminescence (PL) quenching of MAPbI 3 and impedance measurements further confirm the charge extraction ability of BP nanosheets. The structural and optical characterization of liquid exfoliated BP nanosheets is discussed in detail with the aid of transmission electron microscopy, Raman spectroscopy, absorption spectroscopy and photo-electron spectroscopy. [ABSTRACT FROM AUTHOR]

The organometal perovskite solar cells have shown stupendous development and have reached a power conversion efficiency (PCE) of 22.1%. However, the toxicity of lead in perovskite solar cells is a major challenge towards their incorporation into photovoltaic devices and thus needs to be addressed. Tin perovskite (CH3NH3SnI3) have attracted a lot of attention recently and could be a viable alternative material to replace lead perovskite in thin film solar cells. A detail understanding of effects of each component of a solar cell on its output performance is needed to further develop the technology. In this work, we performed a numerical simulation of a planar heterojunction tin based perovskite solar cell using SCAPS (Solar Cell Capacitance Simulator). Results revealed that thickness and defect density of the absorber material strongly influence the PCE of the device. Various types of hole transporting material (HTM) were compared and analysed to improve the performance of the solar cell. Parameters such as hole mobility and acceptor density of HTM also signified dependence on PCE of the device. These results indicate the possibility to design, fabricate and enhance the performance of tin based perovskite solar cells. [ABSTRACT FROM AUTHOR]

This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody’s (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb’s three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy.

The high efficiency and low cost of production of perovskite solar cells (PSCs) based on organic-inorganic halides have attracted the attention of researchers. However, due to the intricacy in the synthesis of Spiro-OMeTAD and the high cost of gold (Au) utilized as the back contact (BC), have affected its viability for commercialization. In this present study, a simulation was performed with and without HTM utilizing different metal contacts (Ag, Cr, Cu, Au, Ni and Pt). SCAPS-1D, a software program in one dimension, was used to conduct the simulation. A systematic analysis was done to determine how the metal back contact's work functions affected the PSC both with and without HTM. The outcomes demonstrate that the PSCs' photovoltaic performance is significantly influenced by the metal contact's work function ( W ). The best metal contact for HTM and HTM-free devices was Pt, with a metal work function of 5.65 eV. The initial power conversion efficiencies (PCEs) for the two configurations were 26.229% for HTM-free and 25.608% for HTM-based device. A number of parameters, including absorber thickness, interface defect density, and electron transport material (ETM) thickness, were varied to obtain optimal values of 0.8 μm for both HTM and HTM-free PSCs, 10 F ). The best metal contact for HTM and HTM-free devices was Pt, with a metal work function of 5.65 eV. The initial power conversion efficiencies (PCEs) for the two configurations were 26.229% for HTM-free and 25.608% for HTM-based device. A number of parameters, including absorber thickness, interface defect density, and electron transport material (ETM) thickness, were varied to obtain optimal values of 0.8 μm for both HTM and HTM-free PSCs, 10 0 for both HTM and HTM-free PSCs, and 0.01 μm for both HTM and HTM-free PSCs. These values were then used to simulate the final HTM and HTM-free devices with a PCE of 27.423%, current density ( 5  cm -2 for both HTM and HTM-free PSCs, and 0.01 μm for both HTM and HTM-free PSCs. These values were then used to simulate the final HTM and HTM-free devices with a PCE of 27.423%, current density ( J ) of 1.239 V, and fill factor (FF) of 80.347% for HTM-free whereas PCE of 26.767% with sc ) of 27.546 mA/cm 2 , open circuit voltage ( V of 1.250 V, and FF of 77.733% for HTM based. These outcomes reflect outstanding enhancement of ∼1.05 and ∼1.07 times in PCE and oc over unoptimized cells with and without HTM.J sc of 27.545 mA/cm 2 , V oc of 1.250 V, and FF of 77.733% for HTM based. These outcomes reflect outstanding enhancement of ∼1.05 and ∼1.07 times in PCE and J sc over unoptimized cells with and without HTM., Competing Interests: Authors have declared that there was no conflict of interest., (© 2023 The Authors.)

This paper proposes a geometric error model for the three-axis machine tool using the homogeneous transformation matrix. An error-prediction model for the three-axis CNC machine tools is presented. This model takes the geometric errors into consideration, using the homogeneous coordinate transformation matrix(HTM) to analyze the geometric error model, which can change the tool's complex position and orientation changes in space into matrix calculations to make the model simpler and more intuitive. A simple method using the cubic polynomial functions to fit the geometric error components is proposed. Using this method, the high-order, non-linear spatial geometric problem can be transformed into algebraic equation. To large extent, the difficulty of calculation is reduced. The QC20 ball-bar is used to measure, analyze and separate all the geometric error components. A new kind of particle swarm algorithm based on the genetic algorithm is developed to solve the algebra equation. [ABSTRACT FROM AUTHOR]

Background: Glaucoma is the largest cause of irreversible blindness affecting more than 60 million people globally. The disease is defined as a gradual loss of peripheral vision due to death of Retinal Ganglion Cells (RGC). The RGC death is largely influenced by the rate of aqueous humor production by ciliary processes and its passage through the trabecular meshwork (TM) in the anterior part of the eye. Primary open angle glaucoma (POAG), the most common subtype, is a genetically complex disease. Multiple genes and many loci have been reported to be involved in POAG but taken together they explain less than 10% of the patients from a genetic perspective warranting more studies in different world populations. The purpose of this study was to perform genome-wide search for common variants associated with POAG in an east-Indian population. Methods: The study recruited 746 POAG cases and 697 controls distributed into discovery and validation cohorts. In the discovery phase, genome-wide genotype data was generated on Illumina Infinium 660 W-Quad platform and the significant SNPs were genotyped using Illumina GGGT assay in the second phase. Logistic regression was used to test association in the discovery phase to adjust for population sub-structure and chi-square test was used for association analysis in validation phase. Publicly available expression dataset for trabecular meshwork was used to check for expression of the candidate gene under cyclic mechanical stress. Western blot and immunofluorescence experiments were performed in human TM cells and murine eye, respectively to check for expression of the candidate gene. Results: Meta-analysis of discovery and validation phase data revealed the association of rs7916852 in MPP7 gene (p = 5.7×10-7) with POAG. We have shown abundant expression of MPP7 in the HTM cells. Expression analysis shows that upon cyclic mechanical stress MPP7 was significantly down-regulated in HTM (Fold change: 2.6; p = 0.018). MPP7 protein expression was also found to be enriched in the ciliary processes of the murine eye. Conclusion: Using a genome-wide approach we have identified MPP7 as a novel candidate gene for POAG with evidence of its expression in relevant ocular tissues and dysregulation under mechanical stress possibly mimicking the disease scenario. [ABSTRACT FROM AUTHOR]

Hybrid organometal halide perovskites have attracted much attention these past four years as the new active layer for photovoltaic applications. Researches are now intensively focused on the stability issues of these solar cells, the process of fabrication and the design of innovative materials to produce efficient perovskite devices. In this review, we highlight the recent progress demonstrated in 2015 in the design of new π-conjugated organic materials used as hole transporters in such solar cells. Indeed, several of these "synthetic metals" have been proposed to play this role during the last few years, in an attempt to replace the conventional 2,2′,7,7′-tetrakis-(N,N-di-4-methoxyphenylamino)-9,9′-spirobifluorene (Spiro-OMeTAD) reference. Organic compounds have the benefits of low production costs and the abundance of raw materials, but they are also crucial components in order to address some of the stability issues usually encountered by this type of technology. We especially point out the main design rules to reach high efficiencies. [ABSTRACT FROM AUTHOR]

Hybrid organometal halide perovskites have attracted much attention these past four years as the new active layer for photovoltaic applications. Researches are now intensively focused on the stability issues of these solar cells, the process of fabrication and the design of innovative materials to produce efficient perovskite devices. In this review, we highlight the recent progress demonstrated in 2015 in the design of new π-conjugated organic materials used as hole transporters in such solar cells. Indeed, several of these “synthetic metals” have been proposed to play this role during the last few years, in an attempt to replace the conventional 2,2′,7,7′-tetrakis-(N,N-di-4-methoxyphenylamino)-9,9′-spirobifluorene (Spiro-OMeTAD) reference. Organic compounds have the benefits of low production costs and the abundance of raw materials, but they are also crucial components in order to address some of the stability issues usually encountered by this type of technology. We especially point out the main design rules to reach high efficiencies.