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The pernicious and stability concerns of lead based perovskite solar cells have limited the commercialization. The lead-free Cesium based double perovskite could be a viable answer to these issues. It exhibits encouraging optoelectronic properties, high environmental stability and low toxicity. In this present work a theoretical analysis of Cesium based double perovskite solar cell using Spiro-OMETAD as hole transport layer and SnO2 as ETL has been studied. The double perovskite Cs2AgBiBr6 has good optical and electronic features. For this study, a device structure of FTO/SnO2/Cs2AgBiBr6/Spiro-OMeTAD/Cu was simulated. The Solar Cell Capacitance Simulator (SCAPS-1D) was used for one dimensional simulation and analysis. The optimized active layer of 0.3 µm was used for the study. PCE, Voc, Jsc and FF were obtained using Spiro OMETAD as HTL and SnO2 as ETL. The maximum PCE of 10.6675% and the maximum quantum efficiency of 86.17025% were found at 275 K working temperature. The simulation results obtained in this study are encouraging. It will provide insightful guidance in replacing commonly used toxic Pb-based perovskite with eco- friendly, highly efficient inorganic double perovskite solar cell [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Decision support systems (DSS) play a pivotal role as computerized tools, guiding and enhancing decision-making processes vital for organizational progress. This research focuses on developing a system tailored for dynamic decision-making, particularly emphasizing the integration of data mining technology. Decision algorithms and neural networks are discussed in depth, providing a comprehensive understanding of the analytical tools crucial for effective decision support. Additionally, the research sheds light on potential risks, ensuring a nuanced view of challenges that may impact the development of DSS. A significant portion of the study is dedicated to the design of DSS architecture and the strategic integration of data mining within the database. The proposed development stages for a business information system, ranging from feasibility study to release, serve as a structured framework for practical implementation. Details within each stage, including data analysis, cleaning, and module development, are meticulously examined. Emphasis is placed on critical steps such as system design, database design, and extract, transform, load (ETL) process design, elucidating their importance in the holistic development of DSS. The conclusion reinforces the paramount importance of leveraging data mining technology in the process of developing decision support systems. [ABSTRACT FROM AUTHOR]

Cs2AgBiBr6 is a promising lead‐free double perovskite solar cells (PSCs) material. Its full potential has yet to be realized due to issues with its large band gap and the optimization of the alignment of the electron transport layer (ETL) and hole transport layer (HTL). The photovoltaic performance of Cs2AgBiBr6‐based devices has been optimized using ZnO, IGZO, TiO2, WS2, PCBM, and C60 ETLs and Cu2O, CuScN, CuSbS2, NiO, P3HT, PEDOT: PSS, Spiro MeOTAD, CuI, CuO, V2O5, CBTS, and CFTS HTLs. It has been observed by simulation study that Cs2AgBiBr6‐based devices exhibit remarkably high photoconversion efficiency when combined with certain ETLs. To better understand the performance, we examine how the best device structures are affected by the absorber and ETL thickness, ETL carrier density, series and shunt resistance, generation, and recombination rate. The findings suggest that TiO2 and ZnO ETLs, in conjunction with CBTS HTL, exhibit good potential for producing high‐efficiency (η > 13%) Cs2AgBiBr6‐based heterojunction solar cells with an ITO/ETL/Cs2AgBiBr6/CBTS/Au device structure. Optimization of the valence band offset (VBO) at the CBTS/Cs2AgBiBr6 interface reveals that reduced VBO value has a beneficial impact on the performance of the solar cell. This modeling work gives a prospective route for manufacturing lead‐free Cs2AgBiBr6 PSCs. [ABSTRACT FROM AUTHOR]

Many industries and academia have devoted a lot of effort and money to creating and/or using good extract-transform-load (ETL) software suitable for their data analysis purposes since it is considered a key to their success. As a result, we find the valuable interventions of research efforts based on ETL software are divided according to well-known approaches such as Business Intelligence, Big Data, and/or Semantic. As a result, problems arise in keeping up with changes and handling the significant diversity in features across these approaches. Which results in disorientation in the finding, evaluation, and choice of an ETL for industries and academia facing their approaches needs. These problems inspire us to provide a contribution that uses the systematic-literature-review (SLR) method to collect 207 papers from three databases, namely, ScienceDirect, Springer, and IEEE, dated from 2010 to 2022, grouped based on both ETL approaches and their commonly used criteria, afterwards using an existing method that automatically identifies the adequate multicriteria method for this study, which gives us the analytical-hierarchy-process method to provide the best research paper according to the requirements of scientific literature. The result implies the great significance of this study in multiple ways, providing a global idea of research papers about ETL approaches, allowing customers to eliminate uncertainty from selecting an ETL according to their specific approach needs, preferences, and interests, and also enabling future researchers and developers of ETL to decide when to focus and how to make innovative contributions to fill gaps in the literature. [ABSTRACT FROM AUTHOR]

A planar architecture double perovskite solar cell (DPSC) has been proposed and modeled employing Pb free La2NiMnO6 absorber layer. In present work, fluorine-doped tin oxide (FTO) is employed as transparent electrode, tungsten disulfide (WS2) is used as ETL, cuprous oxide (Cu2O) as HTL and La2NiMnO6 material is utilized as an absorber layer using the simulation program Solar Cell Capacitance Simulator-One Dimensional (SCAPS-1D). Notably, the configuration of the proposed device is FTO/WS2/La2NiMnO6/Cu2O/Au which demonstrates power conversion efficiency (PCE) of 11.41%. Several parameters affecting device performance such as absorber thickness, defect density, series and shunt resistance, interface defect density and various back metal contacts are studied and optimized. The structure exhibits PCE 18.89%, fill factor (FF) 85.52%, open-circuit voltage (Voc) 0.7919 V, and current density (Jsc) 27.89 mA/cm2. The results of the current work might be beneficial for researchers in designing low-cost, highly efficient, and non-toxic inorganic DPSC. [ABSTRACT FROM AUTHOR]

In recent years, perovskite solar cell (PSC) has gained a lot of attention due to its inexpensive cost of the fabrication process and great power conversion efficiency. The structure of perovskite contains an active layer sandwich between electron transport layer and hole transport layer (HTL). The contact layers in solar cell play a significant role in overall performance of solar cell. Many work have been reported on methylammonium lead halide s(MAPbX3) with various HTL. But the interface between MAPbX3 with organic CuInSe2 is not studied yet. In this study, we developed an interface between perovskite solar cell and inorganic hole transport layer, CuInSe2. In our proposed structure ITO/WS2/CH3NH3PbI3/CuInSe2/Au we used methylammonium lead iodide (CH3NH3PbI3) as a halide absorber layer and evaluated various parameters: efficiency, open circuit voltage, short circuit current and fill factor using SCAPS-1D software. In our findings we observed that the inorganic HTL CuInSe2 and methylammonium iodide absorber shows best result. The obtained results for PSC are, efficiency (19.33%), open circuit voltage, (VOC) (0.88) V, short circuit current (JSC) (26.91) mA/cm2 and fill factor (80.85%).Thus the CuInSe2 HTL with halide absorber layer can be good candidate for perovskite solar cell. [ABSTRACT FROM AUTHOR]

To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications, a method has been proposed that employs the Extract-Transform-Load (ETL) approach to create an X-ray dataset of contraband items. Initially, X-ray scatter image data is collected and cleaned. Using Kafka message queues and the Elasticsearch (ES) distributed search engine, the data is transmitted in real-time to cloud servers. Subsequently, contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for quick image retrieval. The method of integrating targets with backgrounds has enhanced the X-ray contraband image data, increasing the number of positive samples. Finally, an Airport Customs X-ray dataset (ACXray) compatible with customs business scenarios has been constructed, featuring an increased number of positive contraband samples. Experimental tests using three datasets to train the Mask Region-based Convolutional Neural Network (Mask RCNN) algorithmandtestedon400 real customs images revealed that the recognition accuracy of algorithms trained with Security Inspection X-ray (SIXray) and Occluded Prohibited Items X-ray (OPIXray) decreased by 16.3% and 15.1%, respectively, while the ACXray dataset trained algorithm's accuracy was almost unaffected. This indicates that the ACXray dataset-trained algorithm possesses strong generalization capabilities and is more suitable for customs detection scenarios. [ABSTRACT FROM AUTHOR]

The object of research is the process of automating systems for structuring data from several sources. The subject of the research is methods and algorithms for implementing a complete system for automated and parallel processing, validation and structuring of data. One of the most problematic areas is the merging of databases with different structures and several common fields into a generalized structure. The research was aimed at developing a system to increase the efficiency of automation of big data processing. As a result of the work, optimization methods were studied, the influence of their internal parameters on the operation of algorithms was analyzed, their main advantages and disadvantages were determined, and software was developed in which the corresponding methods were implemented. An algorithm for structuring data before processing has been obtained. Data structuring is achieved by performing the «mapping» operation. Mapping can take place by indexes of already cleaned data or using a defined dictionary with a given set of keys, which allows not to care about the sequence of storing values and their possible shift. The practical significance of the developed system lies in the improvement of methods of collecting and processing information for the purpose of its further validation, cleaning and accumulation in the following categories: geographic addresses and geo-coordinates, validation and automated addition of a mobile phone number to the international format, processing of car numbers (in modern and outdated format), VIN code of the engine and car brand, validation of urls of social networks, passport data and processing of personal data. Compared to similar methods for processing large volumes of data, the possibility of splitting the input file or stream into separate parts was used, the cleaned data from which is combined at the end of the system operation. Thanks to this, it is possible to process data whose size exceeds the available volume of the device’s RAM, and the method of working with loosely structured text files in CSV format has been improved. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) have attracted increasing research interest, but their performance depends on both the choice of materials and the process used. The materials can typically be treated in solution, which makes them well suited for roll-to-roll processing methods, but their deposition under ambient conditions requires overcoming some challenges to improve stability and efficiency. In this review, we highlight the latest advancements in photonic curing (PC) for perovskite materials, as well as for hole transport layer (HTL) and electron transport layer (ETL) materials. We present how PC parameters can be used to control the optical, electrical, morphological, and structural properties of perovskite HTL and ETL layers. Emphasizing the significance of these advancements for perovskite solar cells could further highlight the importance of this research and underline its essential role in creating more efficient and sustainable solar technology. [ABSTRACT FROM AUTHOR]

In many healthcare facilities, the prescription of drugs is done only in a semi-structured manner, using free-text fields where various information is often mixed. Therefore, automatic processing, especially for secondary use such as research purposes, is often challenging. This paper compares various approaches that identify and classify the various parts of these free-text fields in German language, namely simple Levenshtein-based, rule-based and CRF (conditional random field)-based approaches. Our results show that a F1-score >90% can be achieved with both the rule-based and the CRF-based approach, with the CRF-based approach even reaching nearly 95% [ABSTRACT FROM AUTHOR]

This paper explores the challenges and lessons learned during the mapping of HL7 v2 messages structured using custom schema to openEHR for the Medical Data Integration Center (MeDIC) of the University Hospital, Schleswig- Holstein (UKSH). Missing timestamps in observations, missing units of measurement, inconsistencies in decimal separators and unexpected datatypes were identified as critical inconsistencies in this process. These anomalies highlight the difficulty of automating the transformation of HL7 v2 data to any standard, particularly openEHR, using off-the-shelf tools. Addressing these anomalies is crucial for enhancing data interoperability, supporting evidence-based research, and optimizing clinical decision-making. Implementing proper data quality measures and governance will unlock the potential of integrated clinical data, empowering clinicians and researchers and fostering a robust healthcare ecosystem. [ABSTRACT FROM AUTHOR]

In Germany, the standard format for exchange of clinical care data for research is HL7 FHIR. Graph databases (GDBs), well suited for integrating complex and heterogeneous data from diverse sources, are currently gaining traction in the medical field. They provide a versatile framework for data analysis which is generally challenging for raw FHIR-formatted data. For generation of a knowledge graph (KG) for clinical research data, we tested different extract-transform-load (ETL) approaches to convert FHIR into graph format. We designed a generalised ETL process and implemented a prototypic pipeline for automated KG creation and ontological structuring. The MeDaX-KG prototype is built from synthetic patient data and currently serves internal testing purposes. The presented approach is easy to customise to expand to other data types and formats. [ABSTRACT FROM AUTHOR]

To systematically and comprehensively identify data issues in large clinical datasets, we adopted a harmonized data quality assessment framework with Python scripts before integrating the data into FHIR® for secondary use. We also added a preliminary step of categorizing data fields within the database scheme to facilitate the implementation of the data quality framework. As a result, we demonstrated the efficiency and comprehensiveness of detecting data issues using the framework. In future steps, we plan to continually utilize the framework to identify data issues and develop strategies for improving our data quality. [ABSTRACT FROM AUTHOR]

Charge balance is one of the most important factors for realizing high performance organic light emitting devices (OLEDs). In this work, we provide a novel strategy to improve the charge balance in OLEDs by optimizing the hole injection layer (HIL) as well as the electron transporting layer (ETL) and thereby controlling the charge carrier supplies in the device. First, we develop a p‐dopant material (PD02), with a lowest unoccupied molecular orbit (LUMO) of −4.63 eV, much shallower than that of the commercial material (PD01) of which the LUMO is −5.04 eV. Nevertheless, this enables us to modulate the supply of holes to the emissive layer through tuning doping concentration. We demonstrate that device performances are significantly improved by employing such a scheme. With a 23% molar doping of PD02, a bottom emission red OLED achieves an external quantum efficiency (EQE) of over 30%, an operating voltage of 3.4 V and a LT95 ~15,000 h at 10 mA/cm2, with a Digital Cinema Initiative P3 (DCI‐P3) chromaticity of CIE (X, Y) = (0.68, 0.32). Moreover, the efficiency roll‐off is suppressed up till ~3500 cd/m2, a desirable feature in display applications. The lateral conductivity of by using such HIL is also found to be much lower than that of PD01, resulting in reduced crosstalk among RGB pixels. Next, a new electron transporting material (ETM‐02) with a deep LUMO of −2.86 eV is also introduced to further optimize the charge balance. Although devices with ETM‐02 shows lower voltage and higher EQE, lifetime is compromised. In order to improve lifetime, additional fine tuning of the charge balance is essential. Finally, a second p‐dopant PD03 with a LUMO of −4.91 eV is added to the HIL to further extend the modulation flexibility in the hole injection. A double‐layer HIL consisting of 8 nm of HTM:16% PD02 and 2 nm of HTM:3% PD03, where the former is in contact with anode, is adopted in the device structure. The bottom emission deep red device achieve EQE over 30%, an operating voltage of 3.2 V and an improved LT95 ~13,000 h at 10 mA/cm2 with a BT.2020 range chromaticity of CIE (X, Y) = (0.701, 0.299). In the double HIL configuration, the introduction of PD03 provides one more parameter for tuning and therefore improves the overall device performances. [ABSTRACT FROM AUTHOR]

Traditional data warehouses (DWs) have played a key role in business intelligence and decision support systems. However, the rapid growth of the data generated by the current applications requires new data warehousing systems. In big data, it is important to adapt the existing warehouse systems to overcome new issues and limitations. The main drawbacks of traditional Extract–Transform–Load (ETL) are that a huge amount of data cannot be processed over ETL and that the execution time is very high when the data are unstructured. This paper focuses on a new model consisting of four layers: Extract–Clean–Load–Transform (ECLT), designed for processing unstructured big data, with specific emphasis on text. The model aims to reduce execution time through experimental procedures. ECLT is applied and tested using Spark, which is a framework employed in Python. Finally, this paper compares the execution time of ECLT with different models by applying two datasets. Experimental results showed that for a data size of 1 TB, the execution time of ECLT is 41.8 s. When the data size increases to 1 million articles, the execution time is 119.6 s. These findings demonstrate that ECLT outperforms ETL, ELT, DELT, ELTL, and ELTA in terms of execution time. [ABSTRACT FROM AUTHOR]

In big data, analysis data is collected from different sources in various formats, transforming into the aspect of cleansing the data, customization, and loading it into a Data Warehouse. Extracting data in other formats and transforming it to the required format requires transformation algorithms. This transformation stage has redundancy issues and is stored across any location in the data warehouse, which increases computation costs. The main issues in big data ETL are handling high-dimensional data and maintaining similar data for effective data warehouse usage. Therefore, Extract, Transform, Load (ETL) plays a vital role in extracting meaningful information from the data warehouse and trying to retain the users. This paper proposes hybrid optimization of Swarm Intelligence with a tabu search algorithm for handling big data in a cloud-based architecture-based ETL process. This proposed work overcomes many issues related to complex data storage and retrieval in the data warehouse. Swarm Intelligence algorithms can overcome problems like high dimensional data, dynamical change of huge data and cost optimization in the transformation stage. In this work for the swarm intelligence algorithm, a Grey-Wolf Optimizer (GWO) is implemented to reduce the high dimensionality of data. Tabu Search (TS) is used for clustering the relevant data as a group. Clustering means the segregation of relevant data accurately from the data warehouse. The cluster size in the ETL process can be optimized by the proposed work of (GWO-TS). Therefore, the huge data in the warehouse can be processed within an expected latency. [ABSTRACT FROM AUTHOR]

The extraction, transformation, and loading (ETL) process is a crucial and intricate area of study that lies deep within the broad field of data warehousing. This specific, yet crucial, aspect of datamanagement fills the knowledge gap between unprocessed data and useful insights. Starting with basic information unique to this complex field, this study thoroughly examines the many issues that practitioners encounter. These issues include the complexities of ETL procedures, the rigorous pursuit of data quality, and the increasing amounts and variety of data sources present in themodern data environment. The study examines ETL methods, resources, and the crucial standards that guide their assessment in the midst of this investigation. These components form the foundation of data warehousing and act as a safety net to guarantee the dependability, accuracy, and usefulness of data assets. This publication takes on the function of a useful guide for academics, professionals, and students, despite the fact that it does not give empirical data. It gives students a thorough grasp of the ETL paradigm in the context of data warehousing and equips them with the necessary skills to negotiate the complex world of data management. This program equips people to lead effective data warehousing initiatives, promoting a culture of informed decision-making and datadriven excellence in a world where data-driven decision-making is becoming more and more important. [ABSTRACT FROM AUTHOR]

In this work, our aim was to design and numerical simulation of a solar cell using the SCAPS-1D simulation program. The studied solar cell has an N-I-P type structure, with its active layer based on a hybrid (organic–inorganic) semiconductor called "methylammonium tin triiodide perovskite", CH3NH3SnI3, which is known as MASnI3. This semiconductor is known for its efficiency in the field of photovoltaic thanks to its good properties such as high absorption, direct bang-gap, and facilities of elaboration. The objective of this study was primarily focused on improving the performance of the perovskite solar cells, specifically enhancing their reproducibility and stability, as they tend to degrade rapidly. To achieve this, we proposed the use of ZnO and Spiro-OMeTAD as charge transport layers (ETL and HTL, respectively) and varying the thickness of the active layer to obtain the optimal parameters that ensure the proper functioning of the cell. [ABSTRACT FROM AUTHOR]

In this study, we introduced the weak electron-accepting oxazole derivative 4,5-diphenyl-2-(4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)phenyl)oxazole (TPO) into both anthracene and pyrene moieties of a dual core structure. Ultimately, we developed 2-(4-(6-(anthracen-9-yl)pyren-1-yl)phenyl)-4,5-diphenyloxazole (AP-TPO) as the substitution on the second core, pyrene, and 4,5-diphenyl-2-(4-(10-(pyren-1-yl)anthracen-9-yl)phenyl)oxazole (TPO-AP) as the substitution on the first core, anthracene. Both materials exhibited maximum photoluminescence wavelengths at 433 and 443 nm in solution and emitted deep blue light with high photoluminescence quantum yields of 82% and 88%, respectively. When used as the emitting layer in non-doped devices, TPO-AP outperformed AP-TPO, achieving a current efficiency of 5.49 cd/A and an external quantum efficiency of 4.26% in electroluminescence. These materials introduce a new category of deep blue emitters in the organic light-emitting diodes field, combining characteristics related to the electron transport layer. [ABSTRACT FROM AUTHOR]

We synthesized zinc oxide nanoparticles (ZnO NPs) by meticulously controlling both temperature and reaction times, allowing us to fine-tune their crystalline properties, morphology, and particle dimensions. This analysis confirmed the existence of a mixture of rod and sphere shapes (ZnO-I), including rod-shaped NPs with an average size of 14.8 nm × 5.2 nm and spherical NPs with an average diameter of 5.27 nm. We subsequently incorporated these synthesized ZnO NPs into organic light-emitting diode (OLED) devices for red, green, and blue colors, utilizing them as the electron injection layer through a solution-based process. The green OLED device using ZnO-I exhibited a promising current efficiency of 4.02 cd/A and an external quantum efficiency of 1.47%. [ABSTRACT FROM AUTHOR]

The results of gene expression analysis based on p-value can be extracted and sorted by their absolute statistical significance and then applied to multiple similarity scores of their gene ontology (GO) terms to promote the combination and adjustment of these scores as essential predictive tasks for understanding biological/clinical pathways. The latter allows the possibility to assess whether certain aspects of gene function may be associated with other varieties of genes, to evaluate regulation, and to link them into networks that prioritize candidate genes for classification by applying machine learning techniques. We then detect significant genetic interactions based on our algorithm to validate the results. Finally, based on specifically selected tissues according to their normalized gene expression and frequencies of occurrence from their different biological and clinical inputs, a reported classification of genes under the subject category has validated the abstract (glomerular diseases) as a case study. [ABSTRACT FROM AUTHOR]

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Business intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable improved decision-making. A modern BI architecture typically consists of a data warehouse made up of one or more data marts that consolidate data from several operational databases. BI further incorporates a combination of analytics, data management, and reporting tools, together with associated methodologies for managing and analyzing data. An important goal of BI initiatives is to improve business decision-making for organizations to increase revenue, improve operational efficiency, and gain a competitive advantage. In this article, we analyze qualitatively various prominent business intelligence (BI) frameworks in the literature and develop a comprehensive BI framework from these. Through the technique of qualitative propositions, we identify the properties, respective advantages, and possible disadvantages of the said BI frameworks to develop a comprehensive framework aimed mainly at data management, incorporating the advantages and eliminating the disadvantages of the individual frameworks. The BI landscape is vast, so as a limitation, we note that the new framework is conceptual; hence, no implementation or any quantitative measurement is performed at this stage. That said, our work exhibits originality since it combines numerous BI frameworks into a comprehensive framework, thereby contributing to conceptual BI framework development. As part of future work, the new framework will be formally specified, followed by a practical phase, namely, conducting case studies in the industry to assist companies in their BI applications. [ABSTRACT FROM AUTHOR]

Recent breakthroughs based on lead (Pb) halide perovskites have inspired extensive study into low-cost photovoltaics to overcome the major challenges that come in this space: stability and toxicity. This work undertook the device configuration of a lead-free perovskite solar cell (PSC) to adopt (CH3NH3SnBr3) methylammonium tin bromide perovskite as an absorber layer. The perovskite solar cell was built with the perovskite CH3NH3SnBr3 as the light absorber layer, Spiro-OMeTAD as the hole transport layer, and a comparative evaluation of various electron transport layers (ETLs) was performed depending on conduction band offsets. CdZnS excelled the other suggested ETLs, with a 24.05% power conversion efficiency. The efficiency of the solar cell was also improved by modifying the doping density in the ETL, the perovskite light absorber layer's thickness, and the HTL layer. The ETL based doping concentrations for the CdZnS (1021 cm−3) and Spiro-OMeTAD (1020 cm−3) were discovered to be the most effective for displaying increased efficiency. 600 nm thick perovskite layer was acceptable for the efficient design. These findings represent the photovoltaic device's best performance; it will be employed to design PSC with high efficiency. [ABSTRACT FROM AUTHOR]

Perovskite solar cells have become a popular alternative to traditional silicon solar cells due to their potential to provide high-efficiency, low-cost, and lightweight solar energy harvesting solutions. However, the multilayer architecture of perovskite solar cells demands careful investigation of the interaction and interfacing between the various layers, as they play a crucial role in determining the overall performance of the cell. In this context, the present work aims at analyzing the coupling between a spongy transparent electron-transporting layer (ETL) and perovskite in a formulation CsPbI 3 :EuI 2 . The ETL used in this work is a transparent mesoporous TiO 2 layer called "gig-lox" (grazing incidence angle geometry–local oxidation), which has been optimized to boost the interfacing with the perovskite for achieving a highly interconnected blend of materials. The gig-lox TiO 2 ETL shows a high surface wettability with respect to the perovskite solution, especially after pre-annealing at 500 ° C, and this enables the perovskite material to deeply infiltrate throughout it. The surface wettability of the gig-lox TiO 2 has been estimated by contact angle measurements, while the deep infiltration of the perovskite material has been demonstrated through X-ray diffraction and transmission electron microscopy analyses. Thanks to the achieved deep infiltration, the photo-generated charge injection from the perovskite into the mesoporous oxide is enhanced with respect to the use of a planar compact oxide, as shown by the photoluminescence measurements. The mainstay of the approach resides in the ETL that is deposited by a solvent-free sputtering method and is up-scalable for high industrial throughput. [ABSTRACT FROM AUTHOR]

The most important obstacles in industrialization of the perovskite solar cell are presence of lead toxicity and stability. In this article, a theoretical analysis of eco-friendly and stable CsSn0.5Ge0.5I3 based PSC has been presented. Different factors influencing the cell performance such as operating temperature, defect concentration, back contact of work function, parasitic resistance and thickness of perovskite layer have been investigated. In the present work, cesium tin germanium triiodide has been used as a cost-effective light absorber layer in place of counterpart of lead. After optimizing thickness of CsSn0.5Ge0.5I3 at 700 nm, the parameters such as fill factor, short current density, efficiency, and open-circuit voltage are 64.98%, 26.872 mA/cm2, 16.12% and 0.923 V, respectively. The proposed result will open a remarkable way for research community to develop lead-free perovskite solar cells. [ABSTRACT FROM AUTHOR]

Information Technology (IT) and its advancements change the education environment. Conventional classroom education has been transformed into a modernized form. Education field decision-makers are always searching for new technologies that provide fast solutions to support Education Decision Support Systems (EDSS). There is a significant need for an effective decision support system to utilize student data which helps the university in making the right decisions. The Electronic learning system (e-learning) provides a live forum for faculties and students to connect with learning portals and virtually execute educational activities. Even though these modern approaches support the education system, active student participation still needs to be improved. Moreover, accurately measuring student performance using collected attributes remains difficult for parents and teachers. Therefore, this paper seeks to understand and predict student performance using effective data processing and a deep learning-based decision model. The implementation of EDSS starts with data preprocessing, Extraction-Transformation-Load (ETL), a data mart area to store the extracted data with Online Analytical Processing (OLAP) processing, and decision-making using Deep Graph Convolutional Neural Network (DGCNN). The statistical evaluation is based on the student dataset from the Kaggle repository. The analyzed results depict that the proposed EDSS model on an independent data mart with efficient decision support and OLAP provides a better platform to make academic decisions and help educators to make necessary decisions notified to the students. [ABSTRACT FROM AUTHOR]

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Formamidinium (NH2CHNH2+) lead-based perovskite materials are becoming one of the suitable materials for planar heterojunction solar cell due to its wide bandgap tunability feature. In this work, simulation of formamidinium lead-based perovskite solar cell device is performed using SCAPS-1D software where bandgap of the absorber layer has been varied along with the other parameters like electron affinity, conduction & valence band effective density of states and thickness. With those variations, photovoltaic performance parameters e.g., open circuit voltage ( V OC ), current density ( J SC ), efficiency (η ), fill factor ( FF ), and Quantum efficiency ( QE ) have been investigated. The effect of thickness variation of absorber layer, SnO2 and ZnO as a different ETL material and effect of gold (Au)/2-D MXenes Ti 3 C 2 T x as a rear electrode have been explored and depicted its effects on overall photovoltaic performance parameters. Further, the effects of series/shunt resistance ( R Series and R Shunt ) have also been simulated using SCAPS-1D software. [ABSTRACT FROM AUTHOR]

Motivated form the performance and utilization of organic light emitting diodes (OLEDs) in advanced flexible optoelectronic devices, this research article reports the structure optimization of ITO/NPD/Alq3/TPBi/Bphen/LiF/Al based OLED. For the designed OLED, the computational investigation of its electrical properties (charge densities, I–V and J–V characteristics), recombination rates along the growth direction and optical parameters with the help of Shockley–Read–Hall recombination model, Poisson equations, drift and diffusion equations and carrier continuity equation have been studied. As per the data taken from the I–V and J–V characteristics, the operating (threshold) voltage of designed OLED was found as ~ 3.5 V. From the calculation, it was confirmed that the e–h recombination rate is highest (~ 4.5 × 1028/m3) in emissive (EML) region (~ 260 nm–275 nm). In optical parameters, the study has been made on input emission spectrum of the emissive (EML) layer (Alq3) which demonstrates the maximum intensity corresponding to wavelength of ~ 475 nm (visible region). Further, the external quantum efficiency with variable applied voltage has also been studied with the information on the mechanism of efficiency drop on a particular voltage. The behaviours of the recombination prefactors β1 and β2 for the designed OLED have also been studied with the variable applied range of voltages. The outcomes of the study suggest the usage of optimized OLED structure in the advanced and flexible optoelectronics in the visible region. [ABSTRACT FROM AUTHOR]

The surge in data volume necessitates the integration of Resource Description Framework (RDF) data within corporate environments. While Extract, Transform, Load (ETL) processes exhibit proficiency with conventional data sources, their scalability diminishes when applied to large and highly varied data sources, inclusive of RDF data. The latter constitutes a wealth of knowledge that, when harnessed via data warehouse technology, can augment corporate value in a fiercely competitive milieu. The advent of platforms like polystore offers opportunities for advanced hardware deployment. ETL processes necessitate two crucial phases: Partitioning and data allocation. Concurrently, the scientific community is spurred to innovate ETL processes that support real-time analytics. This study proposes a novel architecture for ETL processes, named Open-Scala-ETL (Os-ETL). Equipped with a method for deploying a data warehouse based on a polystore, Os-ETL enables real-time analysis. The primary objective of the Os-ETL solution is to resolve the complexities in deploying a graph structure data warehouse on a polystore--a process that involves partitioning and data allocation. Os-ETL is a distributed solution that supports both batch and streaming processing using the Spark framework. Scala scripts are executed within this framework to partition RDF graphs and distribute the resultant fragments across various sites. The implementation of Os-ETL is based on Apache Spark, with ETL deployment on a Spark SQL polystore. This solution empowers companies with data warehouse technology to improve performance, scalability, and latency between a data warehouse and its data sources. The approach has been assessed and validated using largescale, heterogeneous data, encompassing the LUBM benchmark, CSV files, an Oracle database, and a Neo4j graph database. The results corroborate its superior performance in terms of scalability and optimization. [ABSTRACT FROM AUTHOR]

Business Intelligence plays a vital role in helping organizations to extract meaningful insights from their data and make informed decisions. However, choosing the right data modeling approach can be challenging, as different methodologies have unique advantages and limitations. The focus of the current paper is to provide an insight into the Data Vault architecture, which is an emerging approach to data modeling, compared to the established methods of Kimball and Inmon. The paper conducted a comparative analysis of these methodologies, with a particular emphasis on the benefits of Data Vault. This study highlights the advantages of the Data Vault model in managing data from multiple sources and its compatibility with agile implementation practices. Overall, this paper sheds light on the relevance of Data Vault in contemporary data management practices. [ABSTRACT FROM AUTHOR]

With the aim of achieving high efficiency, cost-effectiveness, and reliability of solar cells, several technologies have been studied. Recently, emerging materials have appeared to replace Si-based cells, seeking economic fabrication of solar cells. Thin-film solar cells (TFSCs) are considered strong candidates for this mission, specifically perovskite-based solar cells, reporting competitive power convergence efficiencies reaching up to 25.7%. Substantial efforts have been invested in experimental and research work to surpass the Si-based cells performance. Simulation analysis is a major tool in achieving this target by detecting design problems and providing possible solutions. Usually, a TFSC adopts p-i-n heterojunction architecture by employing carrier transport materials along with the absorber material in order to extract the photogenerated electrons and holes by realizing a built-in electric field. Eventually, this dependency of conventional heterojunction TFSCs on carrier transport layers results in cost-ineffective cells and increases the possibility of device instability and interface problems. Thus, the design of p-n homojunction TFSCs is highly desirable as an essential direction of structural innovation to realize efficient solar cell operation. In this review, a summary of the fundamentals of TFSC materials, recent design and technology progress, and methodologies for improving the device performance using experimental research studies will be discussed. Further, simulation analysis will be provided by demonstrating the latest research work outcomes, highlighting the major achievements and the most common challenges facing thin film homojunction solar cell structures and the methods to improve them. [ABSTRACT FROM AUTHOR]

Due to its unique photoelectric properties, MXene has a wide range of potential applications. Herein, different MXene contents (0, 0.10, 0.15, 0.20, 0.25, 0.4 wt%) of SnO2–MXene nanocomposites are used as the electron transport layer. The introduction of MXene not only inhibits SnO2 agglomeration and induces the vertical growth of CsPbIBr2, but also passivates defects of the SnO2/CsPbIBr2 interface. It effectively increases the power conversion efficiency from 8.98% to 11.21%. In addition, even after 1000 h of storage with a relative humidity of more than 50% in the ambient air, the unsealed device still maintains about 92% of its initial efficiency. This strategy provides a promising way to improve the efficiency of solar cells. [ABSTRACT FROM AUTHOR]

Recently our world has recognized a very important revolution in all sectors of life; this evolution comes following the good exploitation of the data generated by intensive human communication, which uses social networks and computer tools. NoSQL systems appear to resolve some limitations of relational databases in the management of BigData. This situation has created a crucial need to migrate relational databases to NoSQL systems, especially for companies that want to keep their old data accumulated for years, which describes their experiences, market studies, and the behavior of customers, competitor positions, and future strategic alignment. Our contribution comes to developing our complete approach to migrating the relational database to MongoDB. This article will start with an introduction, after showing related work with a discussion then we move on to present our analysis and modeling methodology, during it, we develop our models and meta-models of the two systems: source and destination of migration. After we present our global approach called "TMSDRDND", which divides its treatment into two layers: "TSRSNLayer" and "MDRSNLayer". The first deals with the transformation of the structure and the semantic data, and the second takes care of data migration using an ETL to be developed according to a specific concept and architecture and exploiting the results of the first layer. During these two layers, we treat three axes, each of which processes a part of the RDB according to their nature: data stored in tables, data carried on the structure, and data coming from the semantics of relational databases. [ABSTRACT FROM AUTHOR]

Business intelligence (BI) is a set of technologies and strategies allowing analyzing data and providing information to help in the decision-making process. Today, BI is used in many sectors and shows marvelous results. In this paper, we are interested in supporting decision-making in higher education. BI concepts when integrated to the process of academic affairs can make significant improvement. The present paper aims at describing a BI solution to support academic affairs at Taibah University. Using BI, we can take advantages of a set of analytical tools that support decision-making for different types of users (students, faculty members, administrators and decision makers). The proposed BI solution consists essentially of three mains tasks: (1) collecting data from different sources using the three operations (Extract, Transform and Load), (2) proposing a multidimensional solution that describes the academic processes, and (3) visualizing results through a set of dashboards and reports. Experiments are made using the SQL Server Data Tools. Three steps are detailed which are: SQL Server Integration Services, SQL Server Analysis Services and SQL Server Reporting Services. The proposed solution provides many statistical and predictive indicators needed in academic tasks. [ABSTRACT FROM AUTHOR]

Conventional lead halide Perovskite Solar Cells (PSC) have toxicity and stability issues. Therefore it is crucial to look for lead-free inorganic perovskite material, such as La2NiMnO6, RbGeI3, CsGeI3, Cs2AgBiBr6 and others. There has been much work in the area of PSC using Cs2AgBiBr6 as an absorber, However, due to some critical issues of Cs2AgBiBr6, such as the film's broad bandgap which limits its capacity to absorb light, leading to corresponding PSC being typically restricted to around 4% efficiency. In this work, a lead-free PSC with Cs2AgBiBr6 as the absorber has been engineered to resolve the issues by considering the experimental works in the literature to increase efficiency to 6.3% from a maximum 4.48% as reported in the literature. The output response of both design approaches, as well as the potentiality of future designs, are investigated in terms of output parameters, i.e., Open-Circuit Voltage (VOC), Short-circuit Current density (JSC), Fill Factor (FF), and Power Conversion Efficiency (PCE). Besides, this work also focuses on eliminating Sulphur from ETLs by working on several sulphides and oxide-based Electron Transport Layers (ETLs). Several solar cell device structures have been analyzed for their numerical simulation with sulphide ETLs such as ZnS, WS2, CdS, CdZnS and oxide ETLs such as TiO2, ZnO, WO3, IGZO. To progress towards eco-friendly PSCs, alternatives to sulphide, several transparent oxide alternatives (TiO2, ZnO, WO3, IGZO) have been considered as ETLs. These ETLs have been further doped with Mg to enhance the performance parameters. Mg-doped based ETLs additionally behaving as Hole-blocking layers (HBLs) in corresponding PSCs leads to comparatively significantly better performance in a number of aspects, including VOC (1.21 V), PCE (5.74%), JSC, and FF. The solar cell design has been optimized for high performance through various techniques such as varying ETL and absorber thickness, interface defects variation, series/shunt resistance, band to band recombination, and bandgap grading through doping. Also an effective electric field (Eeff) that depicts the conduction's impact has been calculated. Best performing device among different designs among PSCs with sulphide and oxide-based ETLs has been ZnS/Cs2AgBiBr6/Cu2O (PCE-6.3%) and Mg-doped (20%) ZnO/Cs2AgBiBr6/Cu2O (PCE-5.74%). These results may help researchers in their efforts to find the best-suited materials for the design of high-performance PSCs in the future. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) are intriguing and viable challengers among all solar photovoltaic (PV) technologies worldwide due to their high conversion efficiency and simple manufacturing procedure. PSCs are currently thought to be very promising for the future generation of PV technology, which can make it easier to solving the rising energy demand. However, commercial mass production of PSCs is still far away due to their stability, and eco‐friendly material issues. Extrinsic degradation is one of the vital issues for PSCs and various approaches are proposed and developed by researchers towards improve the quality and the stability of perovskite and other active materials for a PSC with longer lifetime. In this article, we conduct a systematic study to identify the major intrinsic and extrinsic degradation factors, and the degradation mechanism of PSCs. We investigate the potential approaches related to improving the stability and performance of the PSCs including encapsulation, interfacial layer engineering, additive engineering, ion engineering, and fabrication techniques. We also briefly point out the recent promising approaches to improve stability and power conversion efficiency (PCE) under various harsh conditions. This review will provide a better insight into the present scenario of the PSC as well. [ABSTRACT FROM AUTHOR]

This work presents the assessment of tin oxide (SnO2) electron transport layer (ETL)‐based quantum dot solar cell for improved efficiency (>20%). The proposed solar cell consists of a solid layer of lead sulfide (PbS) treated with PbS‐TBAI (tetrabutylammonium iodide) as absorber layer and PbS CQD treated with 1,2‐ethanedithiol (PbS‐EDT) as hole transport layer (HTL). The high level of efficiencies is achieved by varying the doping concentration of ETL and HTL, which affect the mobility of carriers (both electrons and holes). The optimization is carried out with a doping concentration (for ETL and HTL) of 1 × 1019 cm−3. The said doping reflected >20% conversion efficiency. PCE, fill‐factor (FF), Voc, and Jsc were analyzed for all the performed variations. [ABSTRACT FROM AUTHOR]

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The lead halide-based perovskite solar cells have attracted much attention in the photovoltaic industry due to their high efficiency, easy manufacturing, lightweight, and low cost. However, these lead halide-based perovskite solar cells are not manufactured commercially due to lead-based toxicity. To investigate lead-free inorganic perovskite solar cells (PSCs), we investigated a novel Cs3Bi2I9-based perovskite configuration in SCAPS-1D software using different hole transport layers (HTLs). At the same time, WS2 is applied as an electron transport layer (ETL). Comparative analysis of the various design configurations reveals that ITO/WS2/Cs3Bi2I9/PEDOT:PSS/Au offers the best performance with 20.12% of power conversion efficiency (PCE). After optimizing the thickness, bandgap, defect density, and carrier density, the efficiency of the configuration is increased from 20.12 to 24.91%. Improvement in other performance parameters such as short circuit current (17.325 mA/cm2), open circuit voltage (1.5683 V), and fill factor (91.66%) are also observed after tuning different attributes. This investigation indicates the potential application of Cs3Bi2I9 as a lead-free and stable perovskite material that can contribute to improving the renewable energy sector. [ABSTRACT FROM AUTHOR]

The monitoring of physical and logical networks is essential for the high availability of 5G/6G networks. This could become a challenge in 5G/6G deployments due to the heterogeneity of the optical layer. It uses equipment from multiple vendors, and, as a result, the protocols and methods for gathering monitoring data usually differ. Simultaneously, to effectively support 5G/6G networks, the optical infrastructure should also be dense and ensure high throughput. Thus, vast numbers of photonic transceivers operating at up to 400 Gbps are needed to interconnect network components. In demanding optical solutions for 5G and beyond, enterprise-class equipment will be used—for example, high-capacity and high-density optical switches based on the SONiC distribution. These emerging devices produce vast amounts of data on the operational parameters of each optical transceiver, which should be effectively collected, processed, and analyzed. The aforementioned circumstances may lead to the necessity of using multiple independent monitoring systems dedicated to specific optical hardware. Apache NiFi can be used to address these potential issues. Its high configurability enables the aggregation of unstandardized log files collected from heterogenous devices. Furthermore, it is possible to configure Apache NiFi to absorb huge data streams about each of the thousands of transceivers comprising high-density optical switches. In this way, data can be preprocessed by using Apache NiFi and later uploaded to a dedicated system. In this paper, we focus on presenting the tool, its capabilities, and how it scales horizontally. The proven scalability is essential for making it usable in optical networks that support 5G/6G networks. Finally, we propose a unique optimization process that can greatly improve the performance and make Apache NiFi suitable for high-throughput and high-density photonic devices and optical networks. We also present some insider information on real-life implementations of Apache NiFi in commercial 5G networks that fully rely on optical networks. [ABSTRACT FROM AUTHOR]

The effectiveness of the Business Intelligence (BI) system mainly depends on the quality of knowledge it produces. The decision-making process is hindered, and the user's trust is lost, if the knowledge offered is undesired or of poor quality. A Data Warehouse (DW) is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better decisions. The Extract, Transform, and Load (ETL) process is the backbone of a DW system, and it is responsible for moving data from source systems into the DW system. The more mature the ETL process the more reliable the DW system. In this paper, we propose the ETL Maturity Model (EMM) that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge produced. The EMM is made up of five levels of maturity i.e., Chaotic, Acceptable, Stable, Efficient and Reliable. Each level of maturity contains Key Process Areas (KPAs) that have been endorsed by industry experts and include all critical features of a good ETL system. Quality Objectives (QOs) are defined procedures that, when implemented, resulted in a high-quality ETL process. Each KPA has its own set of QOs, the execution of which meets the requirements of that KPA. Multiple brainstorming sessions with relevant industry experts helped to enhance the model.EMMwas deployed in two key projects utilizing multiple case studies to supplement the validation process and support our claim. This model can assist organizations in improving their current ETL process and transforming it into a more mature ETL system. This model can also provide high-quality information to assist users inmaking better decisions and gaining their trust. [ABSTRACT FROM AUTHOR]

The project "Collaboration on Rare Diseases" CORD-MI connects various university hospitals in Germany to collect sufficient harmonized electronic health record (EHR) data for supporting clinical research in the field of rare diseases (RDs). However, the integration and transformation of heterogeneous data into an interoperable standard through Extract-Transform-Load (ETL) processes is a complex task that may influence the data quality (DQ). Local DQ assessments and control processes are needed to ensure and improve the quality of RD data. We therefore aim to investigate the impact of ETL processes on the quality of transformed RD data. Seven DQ indicators for three independent DQ dimensions were evaluated. The resulting reports show the correctness of calculated DQ metrics and detected DQ issues. Our study provides the first comparison results between the DQ of RD data before and after ETL processes. We found that ETL processes are challenging tasks that influence the quality of RD data. We have demonstrated that our methodology is useful and capable of evaluating the quality of real-world data stored in different formats and structures. Our methodology can therefore be used to improve the quality of RD documentation and to support clinical research. [ABSTRACT FROM AUTHOR]

The growth of information technology has opened the gate for the organizations to maintain their data in various forms and at various volumes. This increases the volume and dimension of data being maintained. However, they store their data in their data servers or in cloud environment. Such data have been used to generate various intelligence to support various problems. To support such analysis process, different data have been used and the big data comes to play in this part. Optimizing techniques to help improve the process of ETL could greatly help in real-time analysis of data. ETL optimization could be achieved through several factors simplest being increasing the frequency of the process. Other ways to achieve optimization are through the use of various architectures, programming models, intelligence in transformation and security. To improve the performance of ETL, an efficient dynamic multi-variant relational intelligent ETL framework has been presented in this article. The distributed approach maintains various ontology's and data dictionaries which have been dynamically updated by different threads of ETL process. Initially, the process is start by applying the extraction process which extracts the data from different sources and finds set of dimensions and their characteristics. Such data extracted have been verified over the data dictionary. Further, the relational score has been measured for each data source with the existing one. Similarly, the method computes the value of multi-variant relational similarity (MVRS) for the data obtained from a single source. This will be performed by different threads of ETL process. According to the value of MVRS, the method performs map reduce and merging of data. According to the value of MVRS the method selects the data node and merges the data to store in the data warehouse. The threads of ETL are capable of reading the changes in data dictionaries and ontology's to iterate the process of transformation and loading. The method improves the performance of ETL with least time complexity and higher performance. [ABSTRACT FROM AUTHOR]

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Real-time extract transform load (ETL) is the integral part of increasing demand of faster business decisions targeting large number of modern applications. Multi-source unstructured data stream extraction and transformation using disk data in distributed environment are the building blocks of real-time ETL due to volume and velocity of data. Therefore designing an architecture for basic building blocks for real-time ETL remains a major challenge. In this paper, we focus primarily to expedite stream-disk joins during transformation phase of ETL that is considered most expensive operator in stream processing due to frequent disk access. We propose an architecture for real-time ETL to ingest unstructured stream of data from multi-sources, without having to worry about the structure of data sources, and transform them after joining with distributed disk data. We also present a novel data pipeline stream-disk join that uses partition-based input and best-effort in-memory database technique reducing frequent disk access. The proposed architecture addresses the challenges of stream data loss, ignored un-matching streams, disk overhead and real-time processing for distributed environment. The experimental results obtained using stream generator and real-world datasets on local and distributed machines show that proposed architecture yields significantly improved throughput especially for large number of stream tuples with large datasets. [ABSTRACT FROM AUTHOR]