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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]

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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]

Perovskite solar cells (PSCs) are emerging as promising candidates for next-generation photovoltaic technologies, primarily due to their high efficiency and cost-effective manufacturing. A critical challenge in maximizing PSC performance lies in achieving consistent and efficient charge extraction and transport. This study investigates the energy alignment and electron transport in a ZnSe-based electron transport layer (ETL), focusing on their optical and electrical properties to enhance the efficiency of lead-free double perovskite solar cells. We utilize the 1D Solar Cell Capacitance Simulator (SCAPS-1D) to evaluate a lead-free double perovskite (Cs2BiAgI6) light absorber, noted for its environmental compatibility. By strategically manipulating key parameters such as doping density and defect concentration, along with optimizing layer thickness, we significantly enhance the power conversion efficiency (PCE) of Cs2BiAgI6-based double PSCs. Particularly, the incorporation of ZnSe as the ETL leads to an optimized PCE of 30.19%. We examine the impact of three different hole transport layers (HTLs) on device performance: MoO3, Cu2O, and spiro-OMeTAD. We also investigate how fluctuations in operating temperature affect the PCE of Cs2BiAgI6 in real time. Our findings highlight the critical role of the ETL in improving charge transport, offering valuable insights for the development and practical application of high-performance double PSCs.

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]

In this study, the primary focus was on enhancing the performance of Photovoltaic devices by modifying the ETL and HTL transport layers. We conducted a comprehensive analysis of efficiency and fill factor variations resulting from adjustments in key device parameters, notably the active layer thickness. The HTL layer employed materials such as CdTe (Cadmium telluride) and CISSe (Copper indium sulfur selenide), while the ETL layer utilized CdS (Cadmium sulfide), ZnO (Zinc oxide), SnOx (Stannous oxalte), SnO2 (Tin oxide), and TiO2 (Titanium dioxide). Additionally, Silicon (Si) was incorporated into our structure. Our highest efficiency recorded was 27.38%, marking a significant achievement for our proposed cell design. In summary, our simulation results underscore the promising performance of the CdTe/CISSe/Si/CdS/ZnO structure, yielding an efficiency of 27.38%, an open-circuit voltage (Voc) of 0.8136V, a short-circuit current density (Jsc) of 41.17428 mA/cm², and a fill factor (FF) of 79.36%. The discussions presented herein suggest that our proposed Photovoltaic (PV) Solar Cell holds great potential for adequate performance and improved power conversion efficiency, making it a compelling choice for solar energy applications. [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]

Data-driven medical research requires explainable, robust models that can handle the noisy, high-dimensional nature of electronic healthcare data while adequately communicating the results to physicians. Additionally, metadata sharing, and reproducible dataset preparation are needed to increase data quality and interoperability of privacy-sensitive patient data. In this work, we present GraphXplore, a tool for visual data exploration, automatic metadata extraction and data transformation. It enables explainable, easy-to-use exploratory data analysis paired with dataset preparation and metadata annotation accessible for physicians. The tool is implemented as a Python package and graphical user interface application tailored to the needs of medical researchers and modularly integrable into hospital data warehouse infrastructures.

Hive can store massive data through extended clusters, far exceeding the expansion and storage capabilities of traditional databases, and has become a major tool for building data warehouses in the era of big data. Based on Hive technology, this paper studies the enterprise-level data warehouse architecture composed of data storage layer, Hive data warehouse layer and application layer. Then, the paper studies data warehouse tools, uses HDFS for underlying storage and MapReduce for computing engine, and compares Hive data warehouse with relational database. Then, the ETL process is studied. The data goes through three stages: extraction, conversion and loading, and finally the data flows from the source to the target end. Finally, the system function and test are studied, the subsystems including data processing, data management, data migration and data analysis are constructed, and the core functions are tested. The test results were consistent with the expected results and met the delivery standards. [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]

Proses pengambilan keputusan yang cepat dan tepat merupakan kebutuhan utama dalam lingkungan bisnis yang dinamis. Namun, banyak perusahaan freight forwarder menghadapi permasalahan dalam efisiensi pengambilan keputusan karena ketidaksempurnaan proses pengolahan data di dalam data warehouse. Untuk mengatasi permasalahan tersebut. Penelitian ini bertujuan untuk mengoptimalkan proses Data Warehouse (DWH) guna meningkatkan produktivitas dalam pengambilan keputusan di perusahaan freight forwarder di Indonesia. Dalam konteks lingkungan bisnis yang semakin kompleks dan dinamis, kebutuhan akan informasi yang tepat waktu dan akurat sangat penting untuk mendukung pengambilan keputusan yang efektif. DWH telah menjadi solusi populer untuk mengintegrasikan dan menganalisis data dari berbagai sumber guna mendukung proses pengambilan keputusan. Business Process Optimization (BPO) diterapkan untuk menganalisis dan merancang ulang proses bisnis yang terkait dengan pengolahan data, sehingga meningkatkan efisiensi dan akurasi pengolahan data dalam DWH. Dalam penelitian ini, digunakan model optimasi yang dirancang untuk memaksimalkan efisiensi proses ETL dan meningkatkan kinerja sistem Online Analytical Processing (OLAP) serta Online Transaction Processing (OLTP). Metode-metode ini diharapkan dapat meningkatkan kualitas dan kecepatan pengambilan keputusan, serta efisiensi operasional perusahaan freight forwarder. Hasil dari penelitian ini diharapkan dapat memberikan produktivitas dan daya saing bagi perusahaan di industri logistik.

Este estudio aborda la aplicación de la metodología Cross-Industry Standard Process for Data Mining (CRISP-DM) en el análisis de los gases disueltos en aceite de transformadores de potencia, siendo este un componente crítico en los sistemas eléctricos. La adopción de este método estructurado de seis fases permitió evaluar de forma integral la condición de las unidades de transformación del sistema eléctrico ecuatoriano a partir del análisis de datos de inversión y expansión del sector, así como del estudio de 1 099 perfiles DGA (Dissolved Gas Analysis) obtenidos de una población de 153 transformadores ubicados en las distintas regiones de Ecuador continental. Los hallazgos descritos en este trabajo tienen el potencial de mejorar significativamente las estrategias y políticas de inversión y mantenimiento. Además, se propone la adopción de técnicas de automatización en el proceso de clasificación DGA, utilizando modelos de aprendizaje supervisado para potenciar la confiabilidad y eficiencia del servicio público de energía. Los resultados sugieren que este enfoque no solo mejora el diagnóstico dentro de las actividades de mantenimiento, sino que también proporciona una base sólida para trazar una hoja de ruta hacia una gestión predictiva de los activos, lo que se traduce en una mejora sustancial de la confiabilidad del sistema eléctrico nacional.

The ability to transform data is essential to support strategic decision-making in a company or organization. Data transformation can be done by utilizing data warehouse technology. Therefore, it is necessary to know the description of data warehouses that use the Extract, Transform, and Load (ETL) process. This research will focus on implementing Datawarehouse at TechTrove, an e-commerce company using Pentaho Data Integration (PDI). Star Schema organizes data marts and Online Analytical Processing (OLAP) to optimize data warehouse tasks. Business Intelligence (BI) tools are critical in extracting valuable insights and showcasing the platform's analytical capabilities in customer behavior analysis, product evaluation, sales monitoring, and inventory management. This research transformed raw data into a strategic plan to support decisions in E-Commerce companies.

Knowing that nowadays information is one of the strongest weapons a company can have, it has a relevant role in the decision-making part of the company. Building the right structures and using the right tools for these structures is essential for a certain company or business to be a leader in the field where it operates. Building IT infrastructure in business is a must for businesses today, one of the most innovative areas today is undoubtedly Business Intelligence. BI has restructured the company's decision-making processes, becoming the main pillar on which decision-making is based. This paper sheds light on the main concepts related to BI, also it focuses on the main techniques of Business Intelligence, BI techniques, and analysis of the current situation of use and involvement of BI in Albanian companies.

This study aims to analyse the performance of MAPbI3-based perovskite solar cells (PSCs) by integrating machine learning (ML) models with the SCAPS-1D simulator. An extensive dataset of 28,182 PSCs, combinations of six-electron transport layers, ten-hole transport layers, and MAPbI3 absorber layer by varying thickness of each layer, has been generated in the SCAPS-1D simulator. In this research work, among those eight ML models, the XGBoost algorithm shows high accuracy for predicting the power conversion efficiency (PCE) of the cell, achieving root mean square error (RMSE) of 0.052 and a coefficient of determination (R2) of 0.999. Using Pearson correlation and Shapley Additive Explanations (SHAP), the most effective configuration for high-performance PSCs was identified by evaluating parameter significance. SCAPS-1D simulations revealed an optimal configuration comprising 200nm WS2, 900nm MAPbI3, and 500nm CBTS thin layer, achieving a PCE of 24.34%. Further adjustments in doping densities increased the PCE to 34.65%. This research highlights the critical importance of precise material and structural optimization to improve PSC performance. The integration of ML with traditional simulation techniques provides a robust foundation for PSC research, supporting further experimental validation and potential large-scale applications, ultimately advancing more efficient and durable photovoltaic technologies.

Elpasolites are expected to be promising photovoltaic semiconductor alternatives to lead-based perovskite semiconductors. Concern over the stability and toxicity of lead halide perovskites has led to an increase in interest in the environmental friendly elpasolite Cs2BiAgI6 (Cesium Bismuth Silver Iodide). An extensive numerical analysis of the Cs2BiAgI6 based elpasolite solar cell (ESC) model with the standard n-i-p nanostructured planar architecture ITO/TiO2/Cs2BiAgI6/CIGS/NiO/Au is presented in this article. Numerous device parameters, such as temperature, series resistance, thickness and the defect density of the dual-graded light absorber layers, electron transport layer (ETL), and hole transport layer (HTL), have all been thoroughly studied. The device numerical modeling and simulation is carried out using the well-known SCAPS-1D simulation tools (version 3.3.10). Under AM1.5G illumination, achieved a Voc of 1.2277 V, Jsc of 34.342 mA/cm2, and fill factor of 86.25% with power conversion efficiency of 36.36% through parameter optimization, which is much higher than the highest reported values found in the literature.

CsSnI3 is a promising lead-free material that shows potential as a substitute for lead-based material in the development of ecologically benign perovskite solar cells (PSCs) due to its low cost, high efficiency, and excellent thermal stability. This research is intended to enhance the efficiency of CsSnI3-based PSCs by employing numerical simulation through the One Dimensional Solar Cell Capacitance Simulator (SCAPS-1D) to optimize the optoelectronic properties of the electron transport layer (ETL), absorber layer, hole transport layer (HTL), and different interface layers. An investigation was conducted to evaluate the influence of different ETLs namely WS2, ZnSe, C60 and PCBM on the performance of CsSnI3-based PSCs utilizing poly (3-hexylthiophene-2,5-diyl) (P3HT) as the HTL. Moreover, the influence of variations in the thickness, doping density, and defect density of the absorber layer, ETL, and HTL on key photovoltaic parameters including power conversion efficiency (PCE), short-circuit current density ( $\text{J}_{SC}$ ), open circuit voltage ( $\text{V}_{OC}$ ), and fill factor (FF) was observed. The findings revealed that depending on the ETL employed, the PCE varies between the structures. For structures using WS2, ZnSe, C60, and PCBM as ETL, the corresponding PCE values are 31.63%, 29.64%, 29.75%, and 29.62%. Additionally, for all PSC structures, the effects of interface defect, temperature, series-shunt resistance, capacitance-voltage characteristics, and Mott-Schottky plot have been observed, and the corresponding current density-voltage (J-V), quantum efficiency (QE), generation, and recombination rates have been computed. Finally, the outcomes of this analysis were compared with prior research conducted on CsSnI3-based PSCs, thereby offering significant perspectives for their advancement and commercial viability.

Abstract 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.

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]

Knowing that nowadays information is one of the strongest weapons a company can have, it has a relevant role in the decision-making part of the company. Building the right structures and using the right tools for these structures is essential for a certain company or business to be a leader in the field where it operates. Building IT infrastructure in business is a must for businesses today, one of the most innovative areas today is undoubtedly Business Intelligence. BI has restructured the company's decision-making processes, becoming the main pillar on which decision-making is based. This paper sheds light on the main concepts related to BI, also it focuses on the main techniques of Business Intelligence, BI techniques, and analysis of the current situation of use and involvement of BI in Albanian companies. [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]