Analytical Power Research Articles

Functional and analytical recapitulation of osteoclast biology on demineralized bone paper

Osteoclasts are the primary target for osteoporosis drug development. Recent animal studies revealed the crucial roles of osteoblasts in regulating osteoclastogenesis and the longer lifespans of osteoclasts than previously thought with fission and recycling. However, existing culture platforms are limited to replicating these newly identified cellular processes. We report a demineralized bone paper (DBP)-based osteoblast culture and osteoclast assay platform that replicates osteoclast fusion, fission, resorption, and apoptosis with high fidelity and analytical power. An osteoid-inspired DBP supports rapid and structural mineral deposition by osteoblasts. Coculture osteoblasts and bone marrow monocytes under biochemical stimulation recapitulate osteoclast differentiation and function. The DBP-based bone model allows longitudinal quantitative fluorescent monitoring of osteoclast responses to bisphosphonate drug, substantiating significantly reducing their number and lifespan. Finally, we demonstrate the feasibility of humanizing the bone model. The DBP-based osteo assay platforms are expected to advance bone remodeling-targeting drug development with improved prediction of clinical outcomes.

Study of optical properties of DM–DM planar interface by incorporating non-linearity and NID space

Study of the optical characteristics of materials, objects and geometries has wider role and applications in science and engineering. Reflection and transmission coefficients are used to study the optical properties of planar geometries. Primary aim of the presented work is to study the reflection and transmission of second harmonic generation in non-integer dimensional environment. Nonlinear medium in our considered geometry is of second harmonic generation type hence we only studied the second order reflection and transmission characteristics. Solution of the nonlinear wave equation is obtained by using slowly varying amplitude approximation and method of separation of variables in NID space is used to study how the presence of NID medium alter the electromagnetic waves. As a special case of the original geometry we also studied the changes in the behavior of the coefficients when an epsilon negative and a mu negative NID mediums are used as a reflected half space. In all the cases impact of the non-linearity and the noninteger dimensions on the unknown coefficients are studied separately. It is studied that higher amplitude of the reflection coefficient can be achieved by using a dielectric magnetic NID medium compared to epsilon negative NID medium. On the other hand it is also noted that for epsilon near zero medium, amplitude of the reflection coefficient reaches zero for specific values of the nonlinear permeability and incident angle whereas, for mu negative NID medium amplitude of both the coefficients increases (decreases) by increasing the values of ϵic(ϵrc). It is also noted that dimensions of the NID mediums can be used as a controlling factor for the amplitude of the coefficients by keeping the same shape. For the verification of the analytical results nonlinear power law is used.

The Role of Artificial Intelligence (AI) and Data Analytics in Enhancing Guest Personalization in Hospitality

Purpose: The main objective of this study was to explore the role of Artificial Intelligence (AI) and data analytics in enhancing guest personalization.
 Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
 Findings: The findings revealed that there exists a contextual and methodological gap relating to the role of Artificial Intelligence (AI) and data analytics in enhancing guest personalization. Preliminary empirical review revealed the significant potential of AI and data analytics in transforming the hospitality industry by enhancing guest personalization. By offering personalized experiences that align with individual preferences, hotels can not only improve guest satisfaction but also drive revenue growth and customer loyalty. However, it is imperative for the industry to navigate the ethical considerations associated with data privacy to ensure that the benefits of personalization are realized without compromising guest trust and privacy. The findings of this study provide valuable insights for hoteliers, service providers, and policymakers looking to harness the power of AI and data analytics to create exceptional guest experiences in the evolving landscape of hospitality.
 Unique Contribution to Theory, Practice and Policy: The Technology Acceptance Model (TAM), Service Quality theory and Customer Relationship Management theory (CRM) may be used to anchor future studies on Artificial Intelligence and data analytics in modern hospitality. This study recommended for investing in robust AI and data analytics infrastructure, gathering comprehensive guest data, implementing AI driven personalization algorithms, empowering staff with AI tools and continuously monitoring and adapting.

Temperature Sensor Integration into the Node-RED Platform for Transformer Monitoring

Transformers must be continuously monitored to guarantee dependable and effective performance. The combination of Internet of Things (IoT) technology and sophisticated data processing systems opens up new possibilities for improved transformer monitoring in this context. To enable real-time monitoring of transformer conditions, this study focuses on the integration of IoT temperature sensors into the Node-RED platform. The suggested method gathers temperature information from several locations inside the scale laboratory transformer using IoT temperature sensors. The Node-RED platform receives the collected data and processes, analyzes, and visualizes it in real time. Comprehensive monitoring is made possible by the platform, allowing operators to quickly identify any unusual temperature swings or potential problems inside the transformer. For older transformers built with normal Kraft paper, the rated hot spot temperature is either 90°C according to IEEE or 97°C according to IEC. The Node-RED platform’s incorporation of IoT devices not only provides real-time monitoring capabilities but also offers the flexibility to implement predictive maintenance strategies. By harnessing the power of data analytics and visualization, operators can make informed decisions to optimize transformer performance, extend its lifespan, and minimize downtime. The results of this study demonstrate the effectiveness of the IoT temperature sensor integration into the Node-RED platform for laboratory transformer monitoring. Based on the measurement winding temperature at the transformer has reached 90 ˚C at full load, and then the system starts to activate the blower to cool the transformer. The approach contributes to the advancement of transformer management practices, ensuring the reliability and efficiency of critical energy infrastructure.

Acceptance Testing of Used Cascade Impactor Stages Based on Pressure Drop Measurements in a Flow System Managed with a Critical Flow Venturi: Part II-Quantification of the Test Uncertainty Ratio for Pass/Fail Decision-Making under Pharmacopeial Constraints.

Background: The pressure drop at any cascade impactor stage is related to the open area of nozzles at that stage. Pressure drop measurement therefore can potentially test whether the nozzles of a given stage are within the range specified for continued use for testing of inhalable drug products. Previous such efforts, however, have been hindered by the measurement precision required for making a pass/fail decision about these used impactors. In this study, we articulate the error analysis for a pressure drop measurement system managed with a critical flow venturi (CFV) and show that the resultant uncertainty in the effective diameter of used Next Generation Impactor (NGI) and Andersen-type impactor stages is generally small compared to the specification range. This result enables the user to make a pass/fail decision regarding suitability for continued use. Methods: We develop the equations governing the relationship between stage pressure drop and the effective diameter of each stage of a used impactor. These equations show that pressure drop measurements can indicate only the change (if any) in the effective diameter between a previous measurement and the current measurement. Propagation-of-error principles therefore show that the uncertainty of both measurements affects the resulting uncertainty. Results: The test uncertainty ratio (analytical power) of a CFV-managed pressure drop measurement system exceeds six for all but stage one of the NGI and for stages -1 and -2 of the Andersen-type impactor. The stage-one nozzle of the NGI is readily qualified with a Class X pin. Conclusions: The CFV-managed flow system described in Part I is sufficiently precise to enable a decision to be made about whether used impactor nozzles are suitable for continued use for testing of registered inhalable drug products. Examination of the industrial viability of the technology will require long-term testing in real-world settings with comparison to optical inspection methods.

Predictive Dispatch of Volunteer First Responders: Algorithm Development and Validation.

Smartphone-based emergency response apps are increasingly being used to identify and dispatch volunteer first responders (VFRs) to medical emergencies to provide faster first aid, which is associated with better prognoses. Volunteers' availability and willingness to respond are uncertain, leading in recent studies to response rates of 17% to 47%. Dispatch algorithms that select volunteers based on their estimated time of arrival (ETA) without considering the likelihood of response may be suboptimal due to a large percentage of alerts wasted on VFRs with shorter ETA but a low likelihood of response, resulting in delays until a volunteer who will actually respond can be dispatched. This study aims to improve the decision-making process of human emergency medical services dispatchers and autonomous dispatch algorithms by presenting a novel approach for predicting whether a VFR will respond to or ignore a given alert. We developed and compared 4 analytical models to predict VFRs' response behaviors based on emergency event characteristics, volunteers' demographic data and previous experience, and condition-specific parameters. We tested these 4 models using 4 different algorithms applied on actual demographic and response data from a 12-month study of 112 VFRs who received 993 alerts to respond to 188 opioid overdose emergencies. Model 4 used an additional dynamically updated synthetic dichotomous variable, frequent responder, which reflects the responder's previous behavior. The highest accuracy (260/329, 79.1%) of prediction that a VFR will ignore an alert was achieved by 2 models that used events data, VFRs' demographic data, and their previous response experience, with slightly better overall accuracy (248/329, 75.4%) for model 4, which used the frequent responder indicator. Another model that used events data and VFRs' previous experience but did not use demographic data provided a high-accuracy prediction (277/329, 84.2%) of ignored alerts but a low-accuracy prediction (153/329, 46.5%) of responded alerts. The accuracy of the model that used events data only was unacceptably low. The J48 decision tree algorithm provided the best accuracy. VFR dispatch has evolved in the last decades, thanks to technological advances and a better understanding of VFR management. The dispatch of substitute responders is a common approach in VFR systems. Predicting the response behavior of candidate responders in advance of dispatch can allow any VFR system to choose the best possible response candidates based not only on ETA but also on the probability of actual response. The integration of the probability to respond into the dispatch algorithm constitutes a new generation of individual dispatch, making this one of the first studies to harness the power of predictive analytics for VFR dispatch. Our findings can help VFR network administrators in their continual efforts to improve the response times of their networks and to save lives.

Determining the Degree of Appeal to Aristotelian Rhetoric

Here, we contribute to the extant literature by proposing and testing a model for determining a degree of appeal to Aristotelian Rhetoric. We employed content analysis as a methodology to analyse data from debate transcripts. Our core findings confirm the applicability and analytical power of the proposed model. Three ranking dimensions, low, moderate and robust appeal, were proven to systematically and comprehensively drive debate analysis. They demonstrated the underlying mechanism of Aristotelian Rhetoric in understanding the utilisation of persuasion strategy in a debate. They also demonstrated how a conclusion can be drawn on the overall appeal and intensity when the three rhetoric are collectively weighed and ranked. We therefore concluded that the validated model addresses the research gap in perfecting the traditional Aristotelian Rhetoric in the contemporary period.

AI-Driven Learning Analytics in STEM Education

In recent years, the integration approach of Artificial Intelligence (AI) is called for many disciplines, it also STEM education has paved the way for transformative advancements. This paper provides an example of AI-driven learning analytics within the context of STEM education. It provides a thorough analysis of the AI-driven STEM curriculum and its associated paradigm. Additionally, it highlights the obstacles and possible threats that educators and institutions face when implementing technological innovations in the classroom. The serves as a valuable resource for educators, researchers, and policymakers seeking to harness the power of AI-driven learning analytics to enhance STEM education. The transformative potential of AI is now shaping the future of STEM learning environments while advocating for a responsible and ethical approach to data-driven education. Ethical concerns and moral considerations should be discussed in school AI and STEM education.

68 Integrating Enviromics, Genomics, and Machine Learning for Precision Breeding of Resilient Beef Cattle

Abstract Animal breeding is one of the main pillars of livestock production. Statistics, computer science, and genomics have transformed productivity in the industry. Another round of breakthroughs is expected to come from harnessing the power of big data and machine learning analytics to address the complex interaction between animal genetics and the environment. Developing methods that enable precise selection decisions for animals in diverse production environments is expected to result in significant productivity gains and a reduction in welfare issues, while also mitigating the environmental impact of suboptimal allocation of resources to ill-adapted animals. Due to climate uncertainty, an understanding of these genetic and environmental interactions will be critical for decision-making. Our multi-institutional group initiated a project to generate a comprehensive enviromics data lake to develop and apply novel methods for breeding more adapted and resilient beef cattle for varying environments. Using GIS technology and integrating various sources of environmental information from publicly available databases and satellite imaging, detailed descriptions of soil, climate, forage, and weather conditions will be created for thousands of U.S. farms, representing millions of cattle, with phenotypic and genotypic data. Farms will be comprehensively described in terms of their facilities and management practices through surveys. Machine learning and artificial intelligence techniques will then be used to predict future animal performance for precision livestock management. The proposed models will also be biologically validated by measuring direct indicators of animal resilience. Results will be communicated to beef cattle producers and industry groups to foster the implementation of environment-aware approaches into routine genetic evaluations. In this talk, we will discuss this ongoing project in detail and present some preliminary results.

A Novel Approach of Reducing Energy Consumption by utilizing Big Data analysis in Mobile Cloud Computing

With the rapid proliferation of smart mobile devices and increasing adoption of cloud computing services, energy efficiency has become an important issue in mobile cloud environments. High energy consumption not only results in higher operational costs but also creates sustainability concerns related to cloud infrastructure and services. This paper proposes leveraging big data techniques such as machine learning and predictive analytics to optimize resource allocation and reduce energy consumption in mobile cloud computing. The massive amount of data on factors like user behavior, mobility patterns, network availability, and resource utilization can provide key insights to improve energy efficiency. We present an intelligent predictive framework to forecast mobile cloud resource demands and enable dynamic scaling of cloud configurations aligned to current needs. By proactively adapting cloud resources based on learned models and detected usage patterns, over-provisioning and under-utilization can be minimized. Specifically, we demonstrate how clustering, classification, regression, and times series models derived from contextual usage data can significantly improve energy efficiency when integrated with mobile cloud management systems. The proposed approaches are validated experimentally using simulated workloads and real-world trajectory data sets. Results indicate average energy savings of 42% and up to 62% for certain user groups compared to conventional cloud resource allocation techniques. This work provides an important contribution toward building more sustainable and energy efficient mobile cloud computing systems to meet the mobility and computing demands of the future through the transformative power of big data analytics.

AMP | Optimizing M&A Outcomes: Harnessing the Power of Big Data Analytics and Natural Language Processing

AMP | Optimizing M&A Outcomes: Harnessing the Power of Big Data Analytics and Natural Language Processing

Provision of ramp-rate limitation from distribution networks to transmission systems as preventive action towards frequency disturbances

The high proliferation of Converter-Interfaced Renewable Energy Sources (CIRES) poses several challenges to electric power system operators regarding frequency stability and control. Some of these issues are related with the high active power Ramp Rates (RRs) of CIRES, stemming from their inertia-less and stochastic nature. To deal with such problems, Transmission System Operators (TSOs) often commit excessive amounts of conventional spinning reserves, while in weak Transmission Systems (TSs) regulations are imposed, aiming to mitigate RRs of CIRES. Moreover, several technical studies have proposed control approaches for RR Limitation (RRL). Nevertheless, the influence of RRL on frequency quality and mainly the impact of RRL control capability of small-scale CIRES, connected to Distribution Networks (DNs), on frequency deviations have not been fully explored and quantified. To fill this gap, a detailed investigation is performed in this paper. For this purpose, simulations are conducted on a simplified System Frequency Response (SFR) model as well as on an analytical power system model. The latter is a modified version of the IEEE 9-bus Test System, consisting of three conventional Synchronous Generators (SGs) and three Medium Voltage (MV) DNs, which include CIRES equipped with SuperCapacitors (SCs), thus presenting RRL control capability. To thoroughly evaluate the impact of RRL on frequency dynamics, a set of metrics is introduced and frequency duration curves are created for various scenarios. Finally, the influence of several factors, such as the total system inertia, the RRL target value, the SC size, etc, is quantified by using the proposed indices.

Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis

BackgroundLong COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) that present with similar symptoms.MethodsWe used a combinatorial analysis approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms. We compared two subpopulations of long COVID patients from Sano Genetics’ Long COVID GOLD study cohort, focusing on patients with severe or fatigue dominant phenotypes. We evaluated the genetic signatures previously identified in an ME/CFS population against this long COVID population to understand similarities with other fatigue disorders that may be triggered by a prior viral infection. Finally, we also compared the output of this long COVID analysis against known genetic associations in other chronic diseases, including a range of metabolic and neurological disorders, to understand the overlap of pathophysiological mechanisms.ResultsCombinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis. Of these, 9 genes have prior associations with acute COVID-19, and 14 were differentially expressed in a transcriptomic analysis of long COVID patients. A pathway enrichment analysis revealed that the biological pathways most significantly associated with the 73 long COVID genes were mainly aligned with neurological and cardiometabolic diseases.Expanded genotype analysis suggests that specific SNX9 genotypes are a significant contributor to the risk of or protection against severe long COVID infection, but that the gene-disease relationship is context dependent and mediated by interactions with KLF15 and RYR3.Comparison of the genes uniquely associated with the Severe and Fatigue Dominant long COVID patients revealed significant differences between the pathways enriched in each subgroup. The genes unique to Severe long COVID patients were associated with immune pathways such as myeloid differentiation and macrophage foam cells. Genes unique to the Fatigue Dominant subgroup were enriched in metabolic pathways such as MAPK/JNK signaling. We also identified overlap in the genes associated with Fatigue Dominant long COVID and ME/CFS, including several involved in circadian rhythm regulation and insulin regulation. Overall, 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patient from UK Biobank.Among the 73 genes associated with long COVID, 42 are potentially tractable for novel drug discovery approaches, with 13 of these already targeted by drugs in clinical development pipelines. From this analysis for example, we identified TLR4 antagonists as repurposing candidates with potential to protect against long term cognitive impairment pathology caused by SARS-CoV-2. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS.ConclusionThis study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies. These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID.

DEVELOPMENT OF A DIGITAL MARKETING STRATEGY FOR A UKRAINIAN AGRICULTURAL BERRY COMPANY IN INTERNATIONAL MARKETS

In today's globalised business environment, Ukrainian agribusinesses, particularly those in the berry sector, face a dynamic and competitive international market. To succeed in this environment, these companies need to develop effective digital marketing strategies that resonate with a global audience. This research focuses on the intricacies and imperatives of forming a robust digital marketing strategy for Ukrainian agri-berry companies operating in international markets. The study begins by delving into the specifics of the berry sector, highlighting its unique characteristics, challenges and potential. It highlights the importance of berries in a global context, emphasising their role as health-conscious consumers seek nutritious and sustainable options. Next, the research explores the digital landscape, highlighting the central role of digital platforms, social media and e-commerce in modern marketing. It highlights the power of data analytics and artificial intelligence in understanding consumer behaviour and tailoring marketing efforts. One of the key findings is the need to understand the cultural nuances and preferences of international target markets. It presents case studies of successful digital marketing campaigns by Ukrainian agricultural berry companies in different global regions. The purpose of this article is to comprehensively examine the formation of digital marketing strategies for Ukrainian agribusinesses specialising in berries as they navigate the complex landscape of international markets. It aims to shed light on the unique challenges and opportunities faced by these companies, highlighting the importance of digital marketing in reaching global consumers. Through in-depth research, case studies and analysis, this article aims to provide valuable insights and practical guidance for Ukrainian agri-berry companies, enabling them to develop effective and tailored digital marketing strategies that will enhance their competitiveness and success on the international stage. Methodology. The methodology used in this article involves a multifaceted approach designed to comprehensively examine the formation of digital marketing strategies for Ukrainian agri-berry companies operating in international markets. Extensive research was conducted to review the existing literature on digital marketing strategies, international market entry and the agricultural sector in Ukraine. Comparative analysis was carried out to identify patterns, trends and best practices in the digital marketing strategies of Ukrainian agrarian berry companies compared to their international competitors. Several case studies were conducted to analyse real-life examples of Ukrainian agrarian berry companies that have ventured into international markets. These case studies provided valuable insights into the challenges and successes of different digital marketing strategies. Results. The survey conducted as part of this study sheds light on the current landscape of digital marketing strategies among Ukrainian agricultural berry companies in the context of international markets. A significant percentage of companies surveyed showed a significant gap in their digital presence compared to international competitors. Many Ukrainian agri-berry companies have yet to fully exploit the potential of digital marketing. This limited use represents a missed opportunity to optimise marketing efforts. An interesting trend has emerged in terms of social media platforms. While most companies use Facebook for marketing, there is a growing interest in diversifying to platforms such as Instagram and LinkedIn in line with international marketing trends. Respondents often cited challenges associated with internationalisation, such as adapting marketing content for different markets, navigating diverse regulations and overcoming language barriers. Despite the challenges, a significant number of companies reported a positive return on investment (ROI) from their digital marketing efforts. This shows that with the right strategies, international markets can be very lucrative. There is room for improvement: а recurring theme in respondents' responses was the recognition that there is significant room for improvement in their digital marketing strategies. Many respondents expressed their intention to invest more in digital marketing and seek professional advice. Thus, the survey results highlight both the opportunities and challenges faced by Ukrainian agricultural berry companies in their efforts to develop effective digital marketing strategies for international markets. These findings will form the basis for the practical recommendations outlined in this study. Practical implications. The practical implications of this research highlight the need for Ukrainian agri-berry companies to prioritise digital marketing strategies for international expansion. By improving their digital presence, leveraging data analytics and diversifying across relevant platforms, these companies can overcome challenges and capitalise on the significant opportunities presented by international markets. Ukrainian agri-berry companies can use the findings to revise their digital marketing strategies, focusing on data-driven approaches and diversified platforms. By addressing the challenges of internationalisation and investing wisely, these companies can increase their global market presence and profitability. Value/originality. The value and originality of this article lies in a comprehensive study of the formation of digital marketing strategies for Ukrainian agricultural berry companies in international markets. This topic is extremely relevant in today's globalised business environment, where digital marketing plays a key role in a company's success. First, this article contributes to the existing body of knowledge by specifically addressing the unique challenges faced by Ukrainian agricultural berry companies.

Harnessing the Power of Cloud-Based Big Data Analytics for E-Government Advancement in Morocco: A Catalyst for Development

Harnessing the Power of Cloud-Based Big Data Analytics for E-Government Advancement in Morocco: A Catalyst for Development

Smart city healthcare delivery innovations: a systematic review of essential technologies and indicators for developing nations

BackgroundIn recent times, the concept of smart cities has gained remarkable traction globally, driven by the increasing interest in employing technology to address various urban challenges, particularly in the healthcare domain. Smart cities are proving to be transformative, utilizing an extensive array of technological tools and processes to improve healthcare accessibility, optimize patient outcomes, reduce costs, and enhance overall efficiency.MethodsThis article delves into the profound impact of smart cities on the healthcare landscape and discusses its potential implications for the future of healthcare delivery. Moreover, the study explores the necessary infrastructure required for developing countries to establish smart cities capable of providing intelligent health and care services. To ensure a comprehensive analysis, we employed a well-structured search strategy across esteemed databases, including PubMed, OVID, EMBASE, Web of Science, and Scopus. The search scope encompassed articles published up to November 2022, resulting in a meticulous review of 22 relevant articles.ResultsOur findings provide compelling evidence of the pivotal role that smart city technology plays in elevating healthcare delivery, forging a path towards improved accessibility, efficiency, and quality of care for communities worldwide. By harnessing the power of data analytics, Internet of Things (IoT) sensors, and mobile applications, smart cities are driving real-time health monitoring, early disease detection, and personalized treatment approaches.ConclusionSmart cities possess the transformative potential to reshape healthcare practices, providing developing nations with invaluable opportunities to establish intelligent and adaptable healthcare systems customized to their distinct requirements and limitations. Moreover, the implementation of smart healthcare systems in developing nations can lead to enhanced healthcare accessibility and affordability, as the integration of technology can optimize resource allocation and improve the overall efficiency of healthcare services. It also may help alleviate the burden on overburdened healthcare facilities by streamlining patient care processes and reducing wait times, ensuring that medical attention reaches those in need more swiftly.

Harnessing the power of real-time analytics and reverse ETL: Strategies for unlocking data-driven insights and enhancing decision-making

Harnessing the power of real-time analytics and reverse ETL: Strategies for unlocking data-driven insights and enhancing decision-making

Artificial Intelligence (AI) Architecture for Integrated Smart Digital Banking System

Using artificial intelligence, physicists have condensed a daunting quantum problem that until recently required 100,000 equations into a task of as few as four equations - all without sacrificing accuracy. Artificial intelligence is becoming the most current achievement of the computing world. The human brain is to some extent represented and spliced with the ability to solve problems. The purpose of this study is to describe in detail how the power of artificial intelligence with its complex system can help the needs of digital technology in the banking sector. The research method used is the elaboration of great thoughts and facts about artificial intelligence. Scientific data is interpreted with analytical power that is as precise as possible, so as to produce a description that meets the logic of structured thinking. The data is taken from relevant and up-to-date literature, the work of scientists who have been disseminated in various weighty scientific publications at the world level. The results of the study found how artificial intelligence has folded the complexity of the problem into a simple solution. Artificial intelligence in the banking world becomes an instrument that integrates consumers, smartphones, databases, security systems, financial services authorities, agencies, law enforcement officers, and various derivative digital systems.

Reading Big Data by Machine Learning: The Used of Computer Science for Human Life

Machine learning (ML) models use big data to learn and improve predictability and performance automatically through experience and data, without being programmed to do so by humans. Artificial Intelligence (AI) techniques are being increasingly deployed in finance, in areas such as asset management, algorithmic trading, credit underwriting or blockchain-based finance, enabled by the abundance of available data and by affordable computing capacity. The purpose of this study is to describe in detail how the power of artificial intelligence with its complex system can help the needs of digital technology in the banking sector. The research method used is the elaboration of great thoughts and facts about artificial intelligence. Scientific data is interpreted with analytical power that is as precise as possible, so as to produce a description that meets the logic of structured thinking. The data is taken from relevant and up-to-date literature, the work of scientists who have been disseminated in various weighty scientific publications at the world level. The report can help policy makers to assess the implications of these new technologies and to identify the benefits and risks related to their use. It suggests policy responses that that are intended to support AI innovation in finance while ensuring that its use is consistent with promoting financial stability, market integrity and competition, while protecting financial consumers. Emerging risks from the deployment of AI techniques need to be identified and mitigated to support and promote the use of responsible AI. Existing regulatory and supervisory requirements may need to be clarified and sometimes adjusted, as appropriate, to address some of the perceived incompatibilities of existing arrangements with AI applications.

APPLICATION OF THE PROBLEM BASED INSTRUCTION (PBI) LEARNING MODEL ON TUBE MATERIALS TO IMPROVE STUDENT LEARNING OUTCOMES

This research aims 1) to describe tube learning by applying the Problem Based Instruction (PBI) learning model, 2) to describe improving student learning outcomes through implementing the Problem Based Instruction (PBI) learning model. This research is Classroom Action Research (PTK). The instruments used are (a) Teacher (Researcher) and Student Observation Sheets, (b) Interview Guide Sheets, (c) Documentation, (d) Field Notes, (e) Learning Results Tests. The research subjects were class IX students at SMP Swatsta Imanuel Bekasi with a total of 20 students. The research results obtained show that the Problem Based Instruction (PBI) learning model can improve student learning outcomes, especially in tube material. This is because by implementing the Problem Based Instruction (PBI) learning model students are more active in learning, understand the concept of the material better, are able to solve mathematical problems, students' analytical power towards mathematical problems increases, they are more critical in solving problems and the group learning carried out makes Students are more open in expressing their ideas. Based on the results of this research, it can be concluded that using the Problem Based Instruction (PBI) learning model can improve student learning outcomes, especially in tube material. The Problem Based Instruction (PBI) learning model should be used as an alternative in improving student learning outcomes in mathematics learning.