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In this work, the device modeling of n-i-p architecture based on FTO/WSe2/ CsSn x Ge 1 - x I 3 x = 0.5 /Spiro-OMeTAD/Au was performed using solar cell capacitance simulator-one dimensional (SCAPS-1D) tool. All simulations were performed in the 1.5G AM sunlight spectrum. The effect of various parameters such as defect density, thickness of absorber layer and work function of contact electrode have been investigated. The effect of temperature, series and shunt resistance have also been analyzed. After optimization of photovoltaic parameters, we obtained power conversion efficiency 18.51%, fill factor 79.41%, open circuit voltage (Voc) 0.8458 V and short-circuit current density (Jsc) 27.562 mA/cm2. The modeling of CsSn x Ge 1 - x I 3 x = 0.5 based devices open the door for the fabrication of feasible and highly efficient perovskite solar cells. [ABSTRACT FROM AUTHOR]

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.

The molecular design and conformations of hole-transporting materials (HTM) have unravelled a strategy to enhance the performance of environmentally sustainable perovskite solar cells (PSC). Several attempts have been made and several are underway for improving the efficiency of PSCs by designing an efficient HTM, which is crucial to preventing corrosion, facilitating effective hole transportation, and preventing charge recombination. There is a need for a potential alternative to the current market-dominating HTM due to its high cost of production, dopant requirements, moisture sensitivity, and low stability. Among several proposed HTMs, molecules derived from thiophene exhibit unique behaviour, such as the interaction with under-coordinated Pb2+, thereby facilitating the passivation of surface defects in the perovskite layer. In addition, coupling a suitable side chain imparts a hydrophobic character, eventually leading to the development of a moisture-sensitive and highly stable PSC. Furthermore, thiophene-backboned polymers with ionic pendants have been employed as an interfacial layer between PSC layers, with the backbone facilitating efficient charge transfer. This perspective article comprehensively presents the design strategy, characterization, and function of HTMs associated with thiophene-derived molecules. Hence, it is observed that thiophene-formulated HTMs have an enhanced passivation effect, good performance in an open-circuit environment, longevity, humidity resistance, thermostability, good hole extraction, and mobility in a dopant-free condition. For a better understanding, the article provides a comparative description of the activity and function of thiophene-based small molecules and polymers and their effect on device performance. [ABSTRACT FROM AUTHOR]

This paper examines the problem of identifying network anomalies when processing data streams in industrial systems. A network anomaly refers to a malicious signature and the current context: network environment and topology, routing parameters, and node characteristics. As a result of the study, it is proposed to use a neocortex model that supports the memory mechanism to detect network anomalies. [ABSTRACT FROM AUTHOR]

This study considers the problem of detecting network anomalies caused by computer attacks in the networks of the industrial Internet of things. To detect anomalies, a new method is proposed, built using a hierarchical temporal memory (HTM) computation model based on the neocortex model. An experimental study of the developed method of detecting computer attacks based on the HTM model showed the superiority of the developed solution over the LSTM analog. The developed prototype of the anomaly detection system provides continuous training on unlabeled data sets in real time, takes into account the current network context, and applies the accumulated experience by supporting the memory mechanism. [ABSTRACT FROM AUTHOR]

The photovoltaic performances of n-i-p or inverted p-i-n BaSi2 homojunction solar cells are limited by the parasitic high light absorption of n-BaSi2 or p-BaSi2 window layer. In this work, diverse electron transport materials (ETMs) and hole transport materials (HTMs) are employed instead of BaSi2 as window layers to promote the performances of BaSi2 solar cells, and the effects of different ETMs and HTMs were simulated and comprehensively studied. For n-ETM/i-BaSi2/p-BaSi2 solar cells, the solar cell with ZnO exhibits the highest efficiency of 28.21%. As increasing the thicknesses of ETM layer from 5 nm to 100 nm, the efficiency for n-BaSi2/i-BaSi2/p-BaSi2 solar cell decreases seriously from 24.52% to 15.99%, while the solar cells with wide band gap ETMs including SnO2, TiO2 and ZnO show neglectable change in performances. The performances n-ETM/i-BaSi2/p-BaSi2 solar cells are affected by donor like defects more than acceptor like defects in i-BaSi2. For p-HTM/i-BaSi2/n-BaSi2 solar cells, employing Cu2O, CuI, CuSCN or NiO as HTM layer produces evident valence band discrepancies at HTM/i-BaSi2 interfaces. Heavy doping in HTM layer is necessary desired to overcome the energy band barriers. As increasing the thicknesses of HTM layer from 5 nm to 100 nm, the efficiency for p-BaSi2/i-BaSi2/n-BaSi2 solar cell also decreases from 24.52% to 15.99%, while the solar cells with wide band gap HTMs including CuI, CuSCN and NiO show neglectable change, and the efficiency for p-Cu2O/i-BaSi2/n-BaSi2 solar cells decreases slightly from 25.86% to 24.88%. The performances of p-HTM/i-BaSi2/n-BaSi2 solar cells are affected by acceptor like defects more than donor like defects in i-BaSi2. [ABSTRACT FROM AUTHOR]

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.

Purpose: This study measures the impact of implementation of the Improved Decentralized Maintenance Model on the functionality and availability of medical equipment in Burundi's resource constrained health care system with hypothesis that the model has a measurable impact. Methods: The initial model was tested in 2 provinces before being improved and extended to the provinces of Bujumbura and Rumonge. It is composed by 4 interrelated main axes (Maintenance management, Human, Financial and Material resources,). This study analyses the effects of this implementation between October 2020 and February 2022. Results: The rate of medical equipment functional in service has increased substantially, respectively +8% in the province of Bujumbura and 2% in the province of Rumonge, and the rate of medical equipment requiring maintenance has fallen by 9.7% (Bujumbura) and 2% (Rumonge). The provinces implementing the model have convincing results in terms of functionality with scores of at least 80%, while the national average is 64%. Conclusions: There is a notable positive change between 2020 and 2022 in the functionality of medical equipment and a decrease in equipment awaiting maintenance, and therefore in its availability. This study proves that a sustainable implementation of a decentralized maintenance model is feasible and highly useful in low-resource settings due to its affordability. The context of a low-resource country is a challenge in itself, but the model developed shows that by tackling the low-resource levels of preventive maintenance, we can have some impact on the functionality of the equipment and therefore on the quality of care. [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.

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]

In the last years, the rapid emergence of photovoltaic technology has introduced a clean and renewable energy source, the significant potential of photovoltaic devices lies in their ability to transform light energy into electrical energy using cost-effective and eco-friendly methods. Organic materials have achieved notable increases in power conversion efficiency through chemical functionalization, proving advantages such as low-cost synthesis, high purity, and good charge transport properties in perovskites solar cells (PSCs). Challenges are still, including the stability and efficiency of organic materials, however, advances in material chemistry and structural optimization offer promising solutions that could significantly enhance the performance and commercial viability of PSCs. The integration of organic dyes for passivators, hole and electron transport layers in PSCs has shown improvements in device efficiency and stability, offering potential for increased efficiency and reduced production costs. [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]

• DFT and TD-DFT modelling and extensive analysis of eight carbazole-based molecules as HTMs in perovskite solar cells. • Deep HOMO energy levels guarantee proper alignment with the perovskite material. • Acceptor core groups enhance charge transport properties. • Flatness is beneficial when the core consisted of two cycles, particularly for MP 2 P 2 M. • MP 2 P 2 M is distinguished by its superior charge transfer properties. This study mainly focuses on DFT and TD-DFT modelling and extensive analysis of eight carbazole-based molecules (MM, MPM, MP 2. 5 M, MP 2. 6 M, MP 3. 5 M, MPPM, MP 2 P 2 M, and MP 3 P 3 M) as Hole Transporting Materials in Perovskite Solar cells. These compounds have in common the arm N3,N3,N6,N6-tetrakis(4-methoxyphenyl)-9H-carbazole-3,6-diamine (M) characterized by its strong ability to carry holes, low cost, and good stability. The molecule MM presents two arms without acceptor core, while other compounds present two arms separated by different acceptor cores: phenyl, biphenyl, pyridine or bipyridine. All under-probe compounds were favored candidates for HTMs in perovskite solar cells due to their excellent HOMO delocalization, reduced hole reorganization energies, lower chemical reactivity, longer radiative lifetimes, and spontaneous solvation processes. Flatness proved beneficial when the core consisted of two cycles, particularly for MP 2 P 2 M. The results showed that the HOMO levels were primarily influenced by M fragments in most of the molecules, while the LUMO levels were distributed across both the carbazole units and the cores. All molecules exhibited a more stable LUMO compared to that of Spiro-OMETAD, with MP 2 P 2 M showing a notably stable level, resulting in a narrower gap. The study found that, except for MP 2 P 2 M, these materials did not interfere with the perovskite layer and effectively filled pores. The incorporation of acceptor core groups improved the conjugation backbone and electronic states, significantly enhancing charge transport properties, particularly in molecules with a double ring structure. Furthermore, adding a pyridine center greatly increased solubility, especially in MP 3 P 3 M, suggesting that acceptor core inclusion benefits the solubility of the investigated HTMs. The use of two rings core was shown to enhance light harvesting efficiency, boost photocurrent generation, and improve charge transfer performance, particularly in MP 2 P 2 M, which is distinguished by its superior flatness, solubility, and charge transfer properties. [ABSTRACT FROM AUTHOR]

High efficiency perovskite solar cells are usually achieved with expensive hole transport materials (HTM), such as PTAA and Spiro-OMeTAD. To bring this technology to industry it is essential to find alternative HTM. In this work, we show results with the inexpensive inorganic semiconductor CuSCN as HTM in inverted device structure. We first studied the interaction between CuSCN and the perovskite solution by performing solubility test and found out that CuSCN can be dissolved in perovskite solution. Then devices were fabricated using single-step deposition technique and sequential deposition technique were first a PbI2 layer is first deposited that could overcome the potential incompatibility of the perovskite solution with the CuSCN. Solar cells with Voc > 1000 mV were successfully fabricated with the 2-step process but demonstrated lower efficiencies compared to the 1-step process due to a lower Jsc. [ABSTRACT FROM AUTHOR]

Poly(3-hexylthiophene) (P3HT) has garnered significant attention as a novel hole transport material (HTM). Principally, its cost-effective synthesis, excellent hole conductivity, and stable film morphology make it one of the most promising HTMs for perovskite solar cells (PSCs). However, the efficiency of PSCs employing P3HT remains less than ideal, primarily due to the mismatch of energy levels and insufficient interface contact between P3HT and the perovskite film. In this work, 2,9-dimethyl-4,7-diphenyl-1,10-phenanthroline (BCP) was inserted into the P3HT/perovskite interface for effectively alleviating the recombination loss. BCP could effectively anchor uncoordinated Pb 2+ and establish π-π stacking interactions with P3HT. These interactions not only neutralize flaws to reduce energy depletion but also enhance the configuration of P3HT, aiding in carrier transfer. Consequently, the BCP-modified device achieved an efficiency of 19.27%, which is significantly superior to the control device (12%).

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.

• Five Phenothiazine-based hole transporting molecules were designed for efficient perovskite solar devices. • DFT calculations, structural geometrical analysis, and electronic properties were performed. • The molecules were found to be better hole-transporters for perovskite building blocks. • Improved VOC and power conversion efficiencies, indicating potential for future efficient PSCs devices. Perovskite solar cells are the emerging photovoltaics devices due to their high efficiency because of broad range light absorption and better charge carrier and separation properties. However, in the past few decades their material degradation and instability in the device operation accompanying low power conversion efficiency were the major drawbacks. To address these shortcomings, here we designed five Phenothiazine based novel hole-transporting molecules of d -Pi-A type with improved photovoltaic parameters for efficient perovskite solar devices. Detailed DFT calculation analysis with MPW1PW91 functions and 6–31 G basis set were performed for all molecules. Structural geometrical analysis along with density of states and transition density matrix analysis were extensively performed. Their electronic properties including HOMO and LUMO energies including energy gap, electron affinity, and ionization potential were also explored to elucidate the charge transfer characteristics. The lower values of reorganization in relation to reference molecules show that all designed molecules are better hole-transporters for perovskite building blocks. Lastly the devices photovoltaic parameter calculations for all the designed systems revealed that their Fill factor (FF), VOC and resultant power conversion efficiencies were significantly improved (18.63–22.81 % than its reference counterpart with PCE of 16.7 %) demonstrating their great potential for the future efficient PSCs devices. [ABSTRACT FROM AUTHOR]

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.Inverted Bulk heterojunctions (Ag/MoO3/PCDTBT-PC70BM/ZnO/ITO) Organic Solar cells, based on Organic (Polymer) materials is fabricated and characterized in this work. PCDTBT-PC70BM was synthesized by chloroform, chlorobenzene and odichlorobenzene (organic solvent). Surface morphology of ZnO and PCDTBT-PC70BM were studied. Bulk heterojunctions of active material are formed by the mixture of PCDTBT donor and PC70BM an acceptor in a random manner. For Sufficient transportation of charge carrier (electron and hole), hole transport (HT) and electron transport (ET) layers was deposited. ZnO is used as an ETM and synthesized by using Sol-Gel technique. MoO3 thin film deposited over the active material, enhances hole transformation because of band gap tuning with Ag and active materials. Absorbance and Photoluminescence spectra of polymer material with different organic solvents were studied and results were discussed in this work. o-dichlorobenzene enhance the absorption of PCDTBT/PC70BM. At 400 nm, 90% of sun light is absorbed, and 70% absorption is figure out in 500- 550nm wavelength. The Photo-luminescence of PCDTBT/PC70BM thin film in different organic solvents is ranging from 650nm to 750nm. At 700nm, 20% is shown for chloroform, 40% for chlorobenzene and highest 80% is achieved by o-dichlorobenzene. J-V value is obtained from a solar simulator which irradiates the sun spectrum 1.5 AM, for all the devices having cell area 0.045 cm². For concentration (1:1) ratio in different organic solvents like chloroform, chlorobenzene and o-dichlorobenzene, (3.5, 4.2, and 5.8) %, PCE were obtained respectively. [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.

An ultimate goal of placing fire detection systems in buildings and structures is to allow for the rapid detection of fire and accurate faster than real time prediction of ensuing fire behavior so that relevant information can be delivered to the appropriate stakeholders. In the near-term, development of detection systems with decreased detection time, better discrimination against nuisance and false alarms, and real-time monitoring of the fire state is a critical interim step. Building comfort and efficiency systems are increasingly incorporating a greater quantity of sensors and these sensors are installed at a greater density than any fire sensor with the exception of the sprinkler. While currently used primarily for building management purposes, the application of these, or similar types of building sensors, for rapid fire detection, fire state determination, and fire forecasting offers great potential. This paper discusses the potential benefits of the application of Hierarchical Temporal Memory algorithms for fire state determination in a continuous learning environment based on its application to a series of live fire experiments. [ABSTRACT FROM AUTHOR]

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]

Recurrent Neural Networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) are often deployed as neural network-based predictors for time series data. Recently, Hierarchical Temporal Memory (HTM), a machine learning technology attempting to simulate the human brain's neocortex, has been proposed as another approach to time series data prediction. While HTM has gained a lot of attention, little is known about the actual performance compared to the more common RNNs. The only performance comparison between the two, performed at the company behind HTM, shows they perform similarly. In this article, we present a more in-depth performance comparison, involving more extensive hyperparameter tuning and evaluation on more scenarios. Surprisingly, our results show that both LSTM and GRUs can outperform HTM by over 30% at lower runtime. Furthermore, we show that HTM requires explicitly timestamped data to recognize daily and weekly patterns, while LSTM only needs the raw sequential data to predict such time series accurately. Finally, our experiments indicate that the temporally aware components of all considered predictors contribute nothing to the prediction accuracy. We further strengthen this claim by presenting equally or better performing Multilayer Perceptrons conceptually similar to the HTM and LSTM, disregarding their temporal aspects. [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.

Six materials were used as an interlayer at the anode side (anode interlayer [AIL]) of an archetypical planar heterojunction organic solar cell (OSC). In addition to two conventional wide bandgap hole transport materials (HTMs), tris(4‐carbazol‐9‐ylphenyl)amine (TCTA) and trans‐4,4′‐bis[N‐(naphthalen‐1‐yl)‐N‐phenylamino]stilbene (NPAE), we explore four narrow bandgap materials, bis(biphenylaminospiro)‐fumaronitrile (PhSPFN), bis(N‐(naphthalen‐1‐yl)‐N‐phenylamino)anthraquinone (NPAAnQ), bis‐(di(2‐fluorophenyl)aminospiro)‐fumaronitrile (FPhSPFN), and bis[4‐(N‐(pyren‐1‐yl)‐N‐phenylamino)phenyl]fumaronitrile (PyPAFN), the energy levels of which essentially align with the ones of SubPc, the active light‐absorbing material of the OSC study herein. By using a narrow bandgap AIL, universally enhanced short‐circuit current density and power conversion efficiencies (PCEs) have been achieved. In addition, one of these materials, FPhSPFN, results in a PCE of 5.13%, which is the highest reported value for SubPc solar cells with a similar architecture. This is ascribed to the formation of an otherwise passive exciton‐blocking interface. Furthermore, this demonstrates that charge selectivity by way of a high‐lying lowest unoccupied molecular orbital (LUMO) energy level is not a prerequisite for successful AIL design. As such, in terms of energy level alignment and bandgap energies, we establish a viable alternative approach toward interface and interlayer material design. [ABSTRACT FROM AUTHOR]

The strategy of ion doping in metal oxide is generally used to enhance the photoanode film feature, attain a suitable energy band and enhance carrier mobility. Here, a rare earth element erbium was doped into TiO 2 electron transport layer (ETL) by adding erbium trinitrate into the titanium precursor solution. In the present investigation, an attempt has been made to fabricate perovskite sensitized erbium doped TiO 2 nanoparticle (Er3+@TNPs) photoanode solar cell with different hole transport materials (HTMs) and graphite coated counter electrode. The perovskite sensitized solar cell (PSSC) built with CH 3 NH 3 SnI 3 sensitized Er3+@TNPs photoanode and spiro-MeOTAD HTM shows an impressive photovoltaic performance had the power conversion efficiency (PCE) of 11.54% when compared to the undoped TiO 2 photoanode PSSC. The results of this investigation indicate that the rare earth ion doping could be a promising method for producing effective ETL and enhanced performance PSSCs. • This work is mainly focused to increase the power conversion efficiency of photovoltaic devices using efficient photoanode structure, competence perovskite material with spiro-MeOTAD hole transport material. • The results showed in the present investigation, the device structure and the materials used in this study, significantly satisfied our focus of interest. [ABSTRACT FROM AUTHOR]

• Overview of CzDMPA based hole transporting materials (HTMs) for perovskite solar cells (PCSs) is discussed. • Classification of DMPA-containing carbazole based HTMs is given. • Research trends in synthetic pathways and structure-property relationship are reviewed. • Photovoltaic performance and stability in PCSs are summarized. Perovskite solar cells (PSC) based on metal-organic halide perovskites have emerged as one of the most promising photovoltaic technologies. Power conversion efficiency (PCE) has increased remarkably over the last few years from less than 10% up to certified efficiencies exceeding 23%. Selective interfacial layers play a critical role in charge extraction processes. In order to obtain high efficiencies, a hole transporting material (HTM) is required. Huge research efforts have been made to design efficient and low-cost materials. Carbazole-based HTMs are among the most promising new HTMs, in particular when they are substituted with dimethoxydiphenylamine (DMPA) units. This review gives an overview of the different classes of DMPA-containing carbazole-based HTMs and highlights the synthetic pathways, the structure-property relationship and the photovoltaic performances obtained in PSC devices. The relevance of structural variations and chemical doping processes is discussed with regard to efficiency and stability issues. [ABSTRACT FROM AUTHOR]