Real-time Knowledge Research Articles

Latency Reduction in Immersive Systems through Request Scheduling with Digital Twin Networks in Collaborative Edge Computing

Immersive systems, which offer immersive interactive experiences by tightly coupling the real and virtual worlds, encompassing Augmented Reality (AR) and Virtual Reality (VR), have undergone rapid development in recent years. The inherent latency issue of immersive systems poses considerable challenges for AR/VR applications that are highly sensitive to delay. To address this challenge, we propose utilizing edge collaboration as an efficient means to schedule AR/VR requests, owing to the inherent ability of edge nodes to seamlessly coordinate within the same geographical region and optimally allocate request resources to minimize latency. However, when it comes to edge nodes across different regions, a significant hurdle emerges. These nodes often lack real-time knowledge about cached resources across other regions, making cross-regional edge collaboration for AR/VR request scheduling an extremely challenging problem. We formally define and prove the NP-hardness of this problem. To tackle this complexity, we introduce a Federated Digital Twin Model Construction (FedDT), which establishes a global digital twin network connecting different edge nodes. This network simulates and reflects the resource status and availability of each edge node in real time. Subsequently, we devise an extended Dijkstra algorithm to determine the shortest path between AR/VR requests and edge nodes, taking into account current network conditions to optimize service delay. Both theoretical analysis and experimental results confirm that compared with baseline methods, our proposed solution consistently achieves an average delay reduction of 36.78% across various scenarios, effectively demonstrating its superior performance.

The Application of Large Language Models in Gastroenterology: A Review of the Literature.

Large language models (LLMs) are transforming the medical landscape by enhancing access to information, diagnostics, treatment customization, and medical education, especially in areas like Gastroenterology. LLMs utilize extensive medical data to improve decision-making, leading to better patient outcomes and personalized medicine. These models are instrumental in interpreting medical literature and synthesizing patient data, facilitating real-time knowledge for physicians and supporting educational pursuits in medicine. Despite their potential, the complete integration of LLMs in real-life remains ongoing, particularly requiring further study and regulation. This review highlights the existing evidence supporting LLMs' use in Gastroenterology, addressing both their potential and limitations. Recent studies demonstrate LLMs' ability to answer questions from physicians and patients accurately. Specific applications in this field, such as colonoscopy, screening for colorectal cancer, and hepatobiliary and inflammatory bowel diseases, underscore LLMs' promise in improving the communication and understanding of complex medical scenarios. Moreover, the review discusses LLMs' efficacy in clinical contexts, providing guideline-based recommendations and supporting decision-making processes. Despite these advancements, challenges such as data completeness, reference suitability, variability in response accuracy, dependency on input phrasing, and a lack of patient-generated questions underscore limitations in reproducibility and generalizability. The effective integration of LLMs into medical practice demands refinement tailored to specific medical contexts and guidelines. Overall, while LLMs hold significant potential in transforming medical practice, ongoing development and contextual training are essential to fully realize their benefits.

Hepatitis C virus genotypes among population with reported risk factors in Assam, north-east India: Emergence of genotype-8.

Background & objectives Hepatitis C virus (HCV) exhibits extensive genetic diversity in infected hosts. There are few published reports of HCV genotype (GT) distribution from the north-east Indian States lying close to the 'Golden Triangle' known for illicit drug trafficking. Real-time knowledge of HCVGT distribution is important for studies on epidemiologic aspects and virus evolution and for the development of new target-specific, direct-acting antiviral drugs. This study aims to examine the distribution of HCVGTs and their subtypes in different risk groups from Assam, north-east India. Methods It is a hospital-based cross-sectional study. Plasma samples reactive for anti-HCV antibody in enzyme-linked immunosorbent assay (ELISA) were subjected to viral load test and genotyping by real-time Reverse Transcription-Polymerase Chain Reaction (RT-PCR) or characterization of non-structural protein NS5B region by nested PCR. Nucleotide sequences were subjected to phylogenetic analysis. Results The most common HCVGT detected was GT-3 (95.89%), followed by GT-1 (3.42%), GT-6xa (0.34%) and GT-8 (0.34%). The mean age of subjects was 30.24 yr, and males outnumbered females. The most commonly associated risk factor was injecting drug use (IDU) (74.31%), followed by tattooing and/or piercing (33.22%), transfusion of blood/blood products (10.27%), and haemodialysis (9.25%). Co-infection with human immunodeficiency virus (HIV) was found in 17.8 per cent, and with Hepatitis B virus (HBV) in 3.42 per cent of the cases. Interpretation & conclusions The detection of HCVGT-8 makes this the first report from Assam and the second from India as per the authors' knowledge. This may indicate strain's endemic nature in India. The increasing trend of HCV infection among young IDUs and HCV-HIV co-infection indicates the need for enhancing surveillance and intensified prevention efforts among young adults.

A Framework of Real-time Knowledge Capture and Formalization for Model-based Design with Spoken Annotation and Design Operations

Abstract Knowledge capture without an extra burden on engineers is one of the most challenging issues in the engineering domain. This study proposes a framework of real-time knowledge capture and systematic formalization for the model-based design (MBD), the systems design approach where a simulation is a key component. The framework records the screen of the modeling tool as video data and spoken annotation in which an engineer verbally explains the rationale of the modeling conducted with Modelica-based software commonly used for MBD. The captured modeling tool log and spoken annotation are divided into chunks, each corresponding to a design operation, e.g., defining functions, defining system components, setting model elements, setting model parameters, and running simulations. gIBIS, the argumentation model for representing design rationale, represents the logical structure of the design process. We define design operation templates at the system and subsystem levels, which act as rules to convert the modeling tool log into gIBIS format systematically. We conduct a design case of a spinach harvesting machine with MBD software to verify the proposed framework. The results show that through non-intrusive recording of the design process, the proposed framework is effective in capturing knowledge that can be used to help understand how a model was built.

A two-for-one deal: Targeting nominal GDP to create a supply-shock robust inflation target

A key tenet of modern central banking says that if inflation expectations are anchored then monetary policy should “look through” the inflationary effects of supply shocks that do not permanently affect potential output. However, the inflation surge of 2021–2022 vividly demonstrated that this standard monetary policy prescription is difficult to implement in practice since it requires a real-time knowledge of what shocks are driving changes in inflation while requiring a credible nominal anchor. This paper argues that a workaround solution to these problems is for central banks to stabilize the growth path of nominal GDP. Doing so allows central banks to look through short-term inflation movements caused by temporary supply shocks while still anchoring medium-run inflation. Therefore, central banks that target nominal GDP also are effectively targeting medium-run inflation. This two-for-one targeting deal not only solves the knowledge problem while keeping inflation anchored but can be implemented via standard Taylor-like rules. This paper shows how to operationalize such a framework for the case of the United States.

Machine learning-driven real-time identification of large-space building fires and forecast of temperature development

In firefighting, real-time knowledge of fire dynamics is crucial yet often unavailable. The building geometry and measured gas temperatures are integrated to ascertain fire states and predict temperature evolution, utilizing a machine learning framework that combines long short-term memory networks and transfer learning. The model is pre-trained on datasets with 1000 samples from parametric fire models and 200 from field simulations, enabling real-time predictions with on-site data. Numerical simulations in portal frame buildings demonstrate over 95% accuracy in identifying fire states and 90% in predicting gas temperatures. A method using correlation coefficients and standard deviations effectively detects damaged thermocouples with over 96% accuracy, given a damage ratio below 30%. Validation in two real fire scenarios showed more than 92% accuracy in locating fires and over 89% in forecasting gas temperatures 20 min ahead, which costs 2.14 s and 1.83 s, respectively. The machine learning framework also exhibits robustness to changes in ventilation conditions by making temperature predictions using reliability theory. The proposed framework provides clear information about the state and development of the fire to firefighters and is useful for promoting smart firefighting.

Sky Image Classification Based on Transfer Learning Approaches.

Cloudy conditions at a local scale pose a significant challenge for forecasting renewable energy generation through photovoltaic panels. Consequently, having real-time knowledge of sky conditions becomes highly valuable. This information could inform decision-making processes in system operations, such as determining whether conditions are favorable for activating a standalone system requiring a minimum level of radiation or whether sky conditions might lead to higher energy consumption than generation during adverse cloudy conditions. This research leveraged convolutional neural networks (CNNs) and transfer learning (TL) classification techniques, testing various architectures from the EfficientNet family and two ResNet models for classifying sky images. Cross-validation methods were applied across different experiments, where the most favorable outcome was achieved with the EfficientNetV2-B1 and EfficientNetV2-B2 models boasting a mean Accuracy of 98.09%. This study underscores the efficacy of the architectures employed for sky image classification, while also highlighting the models yielding the best results.

Multi input multi output lateral dynamics control of an electric vehicle

Full electric vehicles with multiple and independently controlled powertrains allow for an improvement of vehicle handling capabilities both in steady state and in transient manoeuvres. This paper focuses on active lateral dynamics control of an electric vehicle equipped with 4 in-wheel motors and active rear steering. An integral terminal sliding mode controller (ITSMC) is derived starting from the linearized single track model with the addition of the rear wheel steering angle. The controller has a multi-input multi-output structure and is designed to track vehicle yaw rate and sideslip angle reference quantities through torque vectoring and active rear steering actuation. A novel approach for calculating reference sideslip angle and yaw rate using a logistic function is also presented in this paper. The ITSMC relies on real time knowledge of sideslip angle which cannot be measured in the real vehicle, thus it is estimated through the addition of an extended Kalman filter to the control loop. The performance of the controller is tested with VI-CarRealTime 14 degrees of freedom nonlinear model both for steady state and transient manoeuvres. Simulation results show a good tracking of the reference value with no chattering issues and with an improved behaviour if compared to a sliding mode controller from literature.

Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery

Abstract Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional histopathology examination (gold standard) to assess margin status (positive or negative) in patients undergoing surgery for BCa. A multivariate model was trained using pathology-validated mass spectra from ex vivo resection specimens from 11 patients, including 36 spectra from invasive cancer, and 118 spectra from normal breast adipose. This model was subsequently tested retrospectively on intraoperative data from 25 BCa cases that were conducted using navigated REIMS. Navigation was facilitated by an electromagnetic sensor placed on the cautery. To record the position of the tumor, a localization wire fitted with an electromagnetic sensor was placed into the tumor using ultrasound prior to surgery, and a 3D map of the tumor was created. This enabled real-time knowledge of the cautery position relative to the tumor during surgery. Spectra from the plume classified as BCa were mapped onto a display of the tumor region and compared with the pathology report. Our multivariate classifier exhibited >90% accuracy on cross-validation, driven by an elevated ratio of glycerophospholipid:triglyceride in cancer as compared with normal adipose. In the intraoperative testing cohort, 4/150 margins were assessed by pathology as positive, all of which were correctly identified by REIMS (Sensitivity=100%). Notably, two of these cases were positive for ductal carcinoma in situ. 146/150 margins were negative, of which 131/146 negative margins were consistent with histopathology (Specificity=90%). There were 13 negative margins that were determined to be positive by REIMS (false positive), of which 8 margins were noted as either ‘close margins’ (with cancer cells being detected within 1 mm of the inked margin on histopathology), or as high-density normal breast tissue. Other normal tissue such as skin and muscle also exhibited high phospholipid content but these spectra could be rationalized by relative distance from the tumor region using the navigation data and/or information provided by the surgeon in real-time. We have demonstrated the importance of spatio-temporal tracking and histopathology to validate intraoperative REIMS. Furthermore, we have shown the feasibility of navigated REIMS in identifying margins containing cancer in real-time during BCa surgery, where a wider excision may be desirable. Citation Format: Martin Kaufmann, Amoon Jamzad, Tamas Ungi, Jessica R Rodgers, Teaghan Koster, Chris Yeung, Josh Ehrlich, Alice Santilli, Mark Asselin, Natasja Janssen, Julie McMullen, Kathryn Solberg, Joanna Cheesman, Alessia Di Carlo, Kevin Yi Mi Ren, Sonal Varma, Shaila Merchant, Cecil Jay Engel, G Ross Walker, Andrea Gallo, Doris Jabs, Parvin Mousavi, Gabor Fichtinger, John F Rudan. Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-23-07.

Isolating pollen signals from laser diode aerosol Optical Particle Counter (OPC) data through positive matrix factorization (PMF) and Unmix receptor models

Pollen, a significant natural bioaerosol and allergen for sensitized individuals, is expected to increase in prevalence due to climate change. Mitigating allergy symptoms involves avoiding pollen exposure and pre-medication, emphasizing the importance of real-time knowledge of localized ambient air pollen concentrations. Laser diode Optical Particle Counters (OPCs) are commonly used for monitoring particle number concentrations in ambient air. This study explores the hypothesis that OPCs can monitor pollen but may struggle to distinguish them from other particles. We aimed to isolate the pollen signal from collective particle number concentrations using source apportionment models, specifically Positive Matrix Factorization (PMF) and Unmix, applied to multiple bin OPC data. The pollen signals isolated using PMF show slightly better correlation values than those isolated using Unmix. PMF-derived pollen signals exhibit strong correlations with Holoptelea (r = 0.64) and total pollen (r = 0.54) concentrations, while a moderate correlation is observed with Poaceae (r = 0.47). Exclusion of low pollen events strengthens correlations for Holoptelea and Poaceae to very strong (r = 0.87) and strong (r = 0.67), respectively. Although both model types effectively isolate the pollen signal, metrics suggest that Unmix has the potential for more accurate predictions of both moderate and extreme pollen events simultaneously. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root Mean Square Error (RRMSE) metrics for Holoptelea are 46.2 grains m−3, 72.4 grains m−3, and 15.3; for Poaceae, 3.9 grains m−3, 4.9 grains m−3, and 13.0; and for total pollen, 43.5 grains m−3, 72.1 grains m−3, and 14.1. This study represents a significant development in the use of source apportionment models and ambient OPCs for real-time pollen monitoring, offering a cost-effective alternative to conventional automated pollen sensors. Despite challenges, the proposed methodology provides a practical and accessible solution for pollen monitoring, contributing to the advancement of bioaerosol monitoring technologies.

The Role of Trust as a Driver of Private-Provider Participation in Disease Surveillance: Cross-Sectional Survey From Nigeria.

Recognition of the importance of valid, real-time knowledge of infectious disease risk has renewed scrutiny into private providers' intentions, motives, and obstacles to comply with an Integrated Disease Surveillance Response (IDSR) framework. Appreciation of how private providers' attitudes shape their tuberculosis (TB) notification behaviors can yield lessons for the surveillance of emerging pathogens, antibiotic stewardship, and other crucial public health functions. Reciprocal trust among actors and institutions is an understudied part of the "software" of surveillance. We aimed to assess the self-reported knowledge, motivation, barriers, and TB case notification behavior of private health care providers to public health authorities in Lagos, Nigeria. We measured the concordance between self-reported notification, TB cases found in facility records, and actual notifications received. A representative, stratified sample of 278 private health care workers was surveyed on TB notification attitudes, behavior, and perceptions of public health authorities using validated scales. Record reviews were conducted to identify the TB treatment provided and facility case counts were abstracted from the records. Self-reports were triangulated against actual notification behavior for 2016. The complex health system framework was used to identify potential predictors of notification behavior. Noncompliance with the legal obligations to notify infectious diseases was not attributable to a lack of knowledge. Private providers who were uncomfortable notifying TB cases via the IDSR system scored lower on the perceived benevolence subscale of trust. Health care workers who affirmed "always" notifying via IDSR monthly reported higher median trust in the state's public disease control capacity. Although self-reported notification behavior was predicted by age, gender, and positive interaction with public health bodies, the self-report numbers did not tally with actual TB notifications. Providers perceived both risks and benefits to recording and reporting TB cases. To improve private providers' public health behaviors, policy makers need to transcend instrumental and transactional approaches to surveillance to include building trust in public health, simplifying the task, and enhancing the link to improved health. Renewed attention to the "software" of health systems (eg, norms, values, and relationships) is vital to address pandemic threats. Surveys with private providers may overestimate their actual participation in public health surveillance.

Integrating structure time series forecasting and multicriteria decision analysis for adaptive operational risk assessment: an empirical study using real-time data

This study propose a framework for integrating Multi-Criteria Decision Analysis (MCDA) and Structure Time Series (STS) prediction for multivariate operational risk assessment often with highly dynamic risk determinants. In particular, by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as an MCDA method, the framework is able to prioritize risk determinants according to their inherent uncertainties’ impacts on their respective operational objectives, and by employing Seasonal Autoregressive Integrated Moving Average (SARIMA) as an STS technique, the framework emphasizes real-time knowledge utilization for iteratively reducing uncertainty. By integrating SARIMA and TOPSIS, the framework aims to construct a multivariate operational risk assessment profile that is prioritized and continuously updated by the latest data and knowledge. Based on the proposed framework, the study constructs a mathematical model coded in Python to perform an empirical assessment of 161 countries’ operational risk using real-time data from the Armed Conflict Location & Event Data Project’s. A comprehensive analysis of the model’s functionality, quality, and sensitivity based on the assessment result is provided. Conclusions and limitations are also discussed, highlighting the model’s theoretical novelty and practical implications.

Dynamic Measurement and Prediction of Sulfur Hexafluoride Gas Weight Based on Non-Ideal Gas State Equation and Improved Random Forest

Real-time knowledge of sulfur hexafluoride (SF6) gas weight can effectively manage and maintain the power equipment using SF6. Firstly, the gas pressure and temperature sensors are utilized to measure SF6 gas pressure and temperature dynamically. Secondly, the measured gas pressure and temperature values are converted to the SF6 gas weight using equations for calculating non-ideal gas states. Finally, the dynamic prediction model of the SF6 gas weight is developed by combining the improved chimp optimization algorithm and random forest algorithm. The dynamic prediction model is trained using measured pressure and temperature data as inputs and the calculated SF6 gas weight as outputs. The trained prediction model enables the dynamic prediction of SF6 gas weight. The experimental results show that the mean absolute error, root mean square error, and mean absolute percentage error of the proposed prediction model reaches 51.54 kg, 103.59 kg, and 11.62%.

Verification of drone formation relative positioning technology based on Beidou

High precision drone formation flight requires real-time knowledge of the three-dimensional position and velocity information of the carrier. The Beidou navigation system, due to its all-weather and high-precision characteristics, can provide good relative position results for drones. However, when drones move at high speeds and in complex environments, The Beidou navigation receiver may experience various errors, leading to poor positioning results or even inability to locate. This paper provides targeted technical design for the reliability of Beidou relative positioning in complex environments during drone flight, monitors navigation data in real time to improve the fixed reliability of overall ambiguity, and uses BeiDou signal simulator to simulate and generate observation data for analysis and processing. The test results show that this method improves the continuity and reliability of the fixed ambiguity of Beidou, and can meet the requirements of real-time formation flight of drone.

Overview of Health-Monitoring Technology for Long-Distance Transportation Pipeline and Progress in DAS Technology Application.

There are various health issues associated with the different stages of long-distance pipeline transportation. These issues pose potential risks to environmental pollution, resource waste, and the safety of human life and property. It is essential to have real-time knowledge of the overall health status of pipelines throughout their entire lifecycle. This article investigates various health-monitoring technologies for long-distance pipelines, providing references for addressing potential safety issues that may arise during long-term transportation. This review summarizes the factors and characteristics that affect pipeline health from the perspective of pipeline structure health. It introduces the principles of major pipeline health-monitoring technologies and their respective advantages and disadvantages. The review also focuses on the application of Distributed Acoustic Sensing (DAS) technology, specifically time and space continuous monitoring technology, in the field of pipeline structure health monitoring. This paper discusses the process of commercialization development of DAS technology, the main research progress in the experimental field, and the open research issues. DAS technology has broad application prospects in the field of long-distance transportation pipeline health monitoring.

REMOVED: SAMAS 2: Structural health and ballistic impact monitoring and prognosis on a military helicopter

The development of a Structural Health Monitoring and Prognosis (SHMP) tool for corrosion degradation and bullet impact damages is matter of SAMAS 2, a project coordinated by the European Defense Agency (EDA) and funded by 2 EDA Member States (Italy and Poland). The aim of SAMAS 2 is to increase the machine availability by directly monitoring the corrosion degradation as well as detecting the ballistic impact occurrence and - in case of crack nucleation - monitoring its propagation inside the structure, concept at the basis of Structural Health Monitoring (SHM) systems. In this way it would be possible to get a real time knowledge about the damage situation, thus setting a Condition Based Maintenance (CBM). The current paper is a summary of the advances in the project, covering all the involved engineering areas, from the adopted sensor network to the FEM simulation based on Reduced Order Modelling techniques and coupling with Machine Learning algorithms (Digital-Twin model), thus coming to the definition of a robust methodology for SHM design. The possibilities for result exploitation, as well as the points requiring further advances, are going to be presented.

ТРАНСФОРМАЦІЯ ТЕХНОЛОГІЙ КОМУНІКАЦІЙ ВІРТУАЛЬНИХ КОМАНД В ІТ-СФЕРІ

The article studies the issue of transformation of communication technologies of virtual teams in the IT industry. It has been established that with the globalization of the IT industry and the spread of geographically distributed teams in this area, there is a demand for more complex and functional communication technologies. The emergence of collaboration platforms such as Slack and Microsoft Teams has proven to provide virtual teams with centralized environments for communication, document sharing mechanisms, and real-time collaboration management. It was determined that the integration of feature-rich video conferencing tools such as Zoom, Cisco Webex, BlueJeans created visibility and a sense of physical presence in virtual interaction, allowing to go beyond traditional boundaries, facilitating personal discussions, presentations and meetings within teams. Communication technologies facilitate unprecedented communication between geographically distributed teams that transcend borders and time zones. Virtual IT teams are able to communicate seamlessly, collaborate on projects, and share knowledge and experience in real time. Collaboration platforms, video conferencing tools have transformed operations in the IT industry by simplifying work processes and accelerating decision-making processes. It was concluded that the transformation of communication technologies of virtual teams in the IT industry contributes to: increasing work efficiency through better and more effective interaction between virtual teams; ensuring uninterrupted cooperation even when the team members are physically located at a great distance; overcoming geographical limitations; ensuring interaction with talented specialists from different parts of the world; increasing flexibility through adaptation of working hours and working conditions to the individual needs of the team; rapid implementation and sharing of innovative ideas and solutions within the team. As technology continues to evolve, the IT industry can remain flexible and adaptable, taking into account new trends to streamline operations and meet the changing demands of a dynamic marketplace.

Exploring the Academic–Industry Collaboration in Knowledge Sharing for Supplier Selection: Digitalizing the OEM

Increasing reliance on digital technologies has led to a significant shift in how businesses operate, with many now relying heavily on digital platforms for effective planning, communication, sales, marketing, supply chain, and logistics management. In this context, knowledge sharing platforms enable academic–industry collaboration in which exchange of ideas, opinions, experience, and expertise brings collective intelligence in cooperative learning ecosystem thereby expediting decision making. However, establishing long-term commitment among the partners, allocation of time and resources for sharing tacit knowledge, collaboration among partners with different strategic priorities, and real-time knowledge sharing capabilities are essential for effective and rapid learning in knowledge sharing platforms. The present article will examine these benefits and challenges in knowledge sharing and its impact on supplier selection platforms in Asian automakers. The findings of this article will be helpful for researchers and practitioners intending to explore the role of cooperation in knowledge sharing and digital transformation amid competitive environment prevalent in the automotive industry. The potential supplier database is first examined for qualifying the capability requirements put forth in this article and further prioritized using a multicriteria decision-making technique and analytic hierarchy process. The article results reveal that the manufacturer has highly prioritized firms’ financial transparency for supplier evaluation followed by the suppliers’ cost control, quality control, and manufacturing capabilities. The article has significant theoretical and practical implications for developing robust supplier evaluation criteria for automobile industry and a digital ecosystem for original equipment manufacturers in making supplier related decisions.

Leveraging Salesforce Analytics for Enhanced Business Intelligence

Salesforce Analytics is a strong business intelligence (BI) solution that turns raw data into actionable insights. Today's data-driven world requires fast, reliable data analysis for business choices. Salesforce Analytics' broad range of solutions helps organizations use data for better decision-making, operational efficiency, and strategic planning. Integration of Salesforce Analytics into company processes has several benefits. First, it gives organizations real-time knowledge to adapt quickly to market developments and client requests. Organizations may track KPIs and trends using customisable dashboards, automatic reporting, and predictive analytics. Real-time visibility empowers decision-makers to prevent concerns from becoming major ones. Salesforce Analytics' unified data access and analysis platform improves departmental cooperation. Team members may easily exchange insights and reports, breaking down silos and promoting data-driven culture. On-demand report creation and sharing guarantees that all stakeholders have the same information, resulting in better aligned and informed decision-making. Customer relationship management is another important Salesforce Analytics function. The software analyzes touchpoint data to help companies understand their consumers. Businesses may detect client preferences, forecast behavior, and tailor marketing by using this data, improving customer happiness and loyalty. Salesforce's AI-powered analytics help foresee client wants and personalize offerings. Salesforce Analytics also improves forecasting and planning. Organizations may forecast future performance better by evaluating previous data and patterns. This skill is crucial in sales forecasting, inventory management, and financial planning, where precise projections ensure operational efficiency and profitability. Another benefit of Salesforce Analytics is third-party data integration. To get a complete picture of their operations, businesses may mix data from social media, email marketing, and e-commerce platforms. Integration improves strategic choices and corporate results by enabling more complete analysis. The platform's versatility lets analytics tools be customized to match corporate demands, boosting its BI solution value. Data security is crucial in the digital era, and Salesforce Analytics takes it seriously. The platform protects critical company data using encryption, access restrictions, and industry standards. Businesses may use their data confidently because of this security assurance. Finally, Salesforce Analytics improves business intelligence by offering real-time insights, collaboration, customer knowledge, and precise forecasting and planning. In today's fast-paced corporate world, its ability to interact with third-party data sources and secure data analysis makes it a useful tool. Salesforce Analytics will allow data-driven decision-making and strategic planning to help companies accomplish their objectives and expand sustainably as they navigate the contemporary market.

After action review of the COVID-19 pandemic response in North West province, South Africa.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused coronavirus disease 2019 (COVID-19) pandemic with major disruptions globally. Northwest Province Department of Health (NWDoH) in South Africa set up comprehensive epidemiological emergency response plans for preventing, finding, containing and stopping the spread of COVID-19 in accordance with the National Disaster Management Act. This After-Action Report (AAR) describes the provincial response to the pandemic from September 2020 to October 2022. The AAR was conducted using the World Health Organization AAR methodology. Focus groups discussed five items: coordination, leadership and governance; epidemiology, surveillance and laboratory; case management and continuity of essential services; risk communication and community engagement and COVID-19 vaccination. The timely establishment and activation of provincial intergovernmental and intersectoral coordinating structures led to effective coordination, resource mobilisation, leadership, decision-making and intervention. The effective communication in the department and other stakeholders resulted in improved surveillance data quality, timelier response and increased ownership of data. Dissemination, training and implementation of case management protocols ensured standardised case management. The multi-channel information dissemination targeting different audiences empowered people with real-time knowledge on the infection and encouraged health-seeking behaviours. The AAR demonstrated the importance of coordinated epidemiological, laboratory and communication response that requires significant public health reserve capacity in peacetime for rapid expansion in an emergency. This review contributes to the body of knowledge emerging from the COVID-19 pandemic and provides guidance on enhanced public health response to future emergencies.