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With the rapid growth of the Internet of Things (IoT) and the emergence of big data, handling massive amounts of data has become a major challenge. Traditional approaches involve sending raw data to cloud data centers for cleaning, processing, and interpretation using data warehouse tools. However, this study introduces BlueEdge, a fog edge mobile application that aims to shift the cleaning and preprocessing tasks from the cloud to the edge. We compare BlueEdge with four popular data cleaning tools (WinPure, DoubleTake, WizSame, and DQGlobal) that operate within data warehouse architectures, such as Hadoop servers. The comparison considers criteria such as time consumption, resource utilization (memory and CPU), and tool performance. BlueEdge utilizes Natural Language Processing (NLP) techniques, including those from the Natural Language Toolkit (NLTK) and Python packages, to connect with a real-time database. As shown in our results, the accuracy values that BlueEdge showed ranged between 72 and 95% across 6 categories of name-based duplicate detection tasks, proving its competitive performance in mobile edge environments. The validation of the framework was done using a larger dataset of 146 error cases with statistically significant values having confidence interval of between 3.4% to 5.8. Statistical comparison indicates consistently significant changes ( p < 0.05) compared to baseline settings of four commercial tools with large effect sizes ( Cohen d: 0.89- 1.34). BlueEdge takes care of data duplication elimination services such as using different spelling and pronunciation (78.4%, CI: 73.1–83.7%), misspellings (72.0%, CI: 66.2–77.8%), name abbreviations (90.5%, CI: 86.1–94.9%), honorific prefixes (95.2%, CI: 91.8–98.6%), common nicknames (76.2%, C The reliable performance of edge-based data cleaning is verified through cross-validation analysis (81.7% ± 2.3%), the results of which prove the consistency of its activity. Additionally, BlueEdge utilizes a minimal bandwidth of only 5000 bytes per edge on mobile phones, unlike data warehouses that require 10,000–60,000 bytes on Hadoop machines. Additionally, BlueEdge is designed to reduce the time taken for data cleaning to 1 s at the data edge, unlike the standard 4–30 s it normally takes for data warehouses. The blue edge is easy to use without authorization of the mobile devices, where the application is conducted free of charge. The framework was validated through controlled experimental testing and real-world deployment at an IT services company, achieving an overall ITSQM quality score of 8.9/10 and demonstrating practical effectiveness in organizational settings. This foundation has been further enhanced with neural network-based classification approaches, which are currently under peer review.

How do you run your warehouse with SAP S/4HANA? This comprehensive guide has the answers! Begin by setting up your embedded Extended Warehouse Management (EWM) system using organizational structures and master data. Then master your essential processes such as goods issue and receipt, putaway, picking, and taking inventory. Bring everything together with information on advanced tasks like cross-docking, value-added services, kitting, and integration with SAP TM and SAP GTS!-- Provided by publisher.

This study explores enhancing decision-making processes in inventory management and production operations by integrating a developed system. The proposed solution improves the decision-making process, managing the material supply of the product and inventory management in general. Based on the researched issues, the shortcomings of modern enterprise resource planning systems (ERPs) were considered in the context of Warehouse 4.0. Based on the problematic areas of material accounting in manufacturing enterprises, a typical workplace was taken as a basis, which creates a gray area for warehouse systems and does not provide the opportunity of quality-managing the company’s inventory. The main tool for collecting and processing data from the workplace was the neural network. A mobile application was proposed for processing and converting the collected data for the decision-maker on material management. The YOLOv8 convolutional neural network was used to identify materials and production parts. A laboratory experiment was conducted using 3D-printed models of commercially available products at the SmartTechLab laboratory of the Technical University of Košice to evaluate the system’s effectiveness. The data from the network evaluation was obtained with the help of the ONNX format of the network for further use in conjunction with the C++ OpenCV library. The results were normalized and illustrated by diagrams. The designed system works on the principle of client–server communication; it can be easily integrated into the enterprise resource planning system. The proposed system has potential for further development, such as the expansion of the product database, facilitating efficient interaction with production systems in accordance with the circular economy, Warehouse 4.0, and lean manufacturing principles.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877–0.892 vs. C-statistic = 0.871; 95%CI = 0.863–0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

PurposeThe purpose of this paper is twofold: first, to discuss key challenges associated with the use of either simulation or real-world application of intelligent autonomous vehicles (IAVs) in supply network operations; and second, to provide a theoretical and empirical evidence-based methodological framework that supports the integrated application of conceptualisation, simulation, emulation and physical application of IAVs for the effective design of digital supply networks.Design/methodology/approachFirst, this study performs a critical review of the extant literature to identify major benefits and shortcomings related to the use of either simulation modelling or real-word application of physical IAVs. Second, commercial and bespoke software applications, along with a three-dimensional validation and verification emulation tool, are developed to evaluate an IAV’s operations in a conceptual warehouse. Third, a commercial depth-sensor is used as a test bed in a physical setting.FindingsThe results demonstrate that conceptual and simulation modelling should be initially used to explore alternative supply chain operations in terms of ideal performance while emulation tools and real-world IAV test beds are eminent in validating preferred digital supply chain design options.Research limitations/implicationsThe provided analysis framework was developed using literature evidence along with experimental work and research experience, without consulting any industry experts. In addition, this study was developed based on the application of a single physical device application as a test bed and, thus, the authors should further progress with the testing of a physical IAV in an industrial warehouse.Practical implicationsThe study provides bespoke simulation modelling and emulation tools that can be useful for supply chain practitioners in effectively designing network operations.Originality/valueThis work contributes in the operations management field by providing both a multi-stage methodological framework and a practical “toolbox” for the proactive assessment and incorporation of IAVs in supply network operations.

Background This study was designed for the research and development (R&D) and application of a storage inflow and outflow management system enabling departments to perform efficient, scientific, and information-based consumable management. Methods In the endocrinology department of a hospital, expert and R&D teams in consumable management were set up, and an information-based storage inflow and outflow management system for consumables was designed and developed. The system was operated on a personal computer and was divided into three modules: public consumables, bed consumables, and quality control management. The functions of the system included storage inflow and outflow, early warnings, response to user queries, and statistics on consumables. Data were derived from the hospital information system (HIS,ZHIY SOFTWARE HIS VERSION4.0) and a questionnaire survey. Economic indicators, work efficiency of consumable management, nurse burnout, consumable stockroom management, and staff satisfaction were compared under manual management, Excel-based management, and the consumable storage inflow and outflow management system. The results of the questionnaire were analysed using the R software, version 4.1.0. Results Dates were obtained from manual management, Excel-based management and the consumable storage inflow and outflow management system. Under these three methods, the daily prices of department consumables per bed were 53.43 ± 10.27 yuan, 38.65 ± 8.56 yuan, and 31.98 ± 7.36 yuan, respectively, indicating that the new management system reduced costs for the department. The time spent daily on consumable management was shortened from 119.5 (106.75, 123.5) min to 56.5 (48.5, 60.75) to 20 (17.25, 24.25) min. Nurses’ emotional fatigue and job indifference scores, respectively, decreased from 22.90 ± 1.65 and 8.75 ± 1.25 under manual management to 19.70 ± 1.72 and 6.90 ± 1.37 under Excel-based management and to 17.20 ± 2.04 and 6.00 ± 1.30 under the novel system; the satisfaction of the warehouse keeper and collection staff, respectively, increased from 76.62% and 80.78% to 91.6% and 90.5% to 98.8% and 98.5% under the three successive systems. Conclusions The storage inflow and outflow management system achieved produced good results in the storage and classification of consumables.

In the last decade, simulation software as a tool for managing and controlling business processes has received a lot of attention. Many of the new software features allow businesses to achieve better quality results using optimisation, such as genetic algorithms. This article describes the use of modelling and simulation in shipment and sorting processes that are optimised by a genetic algorithm’s involvement. The designed algorithm and simulation model focuses on optimising the duration of shipment processing times and numbers of workers. The commercially available software Tecnomatix Plant Simulation, paired with a genetic algorithm, was used for optimisation, decreasing time durations, and thus selecting the most suitable solution for defined inputs. This method has produced better results in comparison to the classical heuristic methods and, furthermore, is not as time consuming. This article, at its core, describes the algorithm used to determine the optimal number of workers in sorting warehouses with the results of its application. The final part of this article contains an evaluation of this proposal compared to the original methods, and highlights what benefits result from such changes. The major purpose of this research is to determine the number of workers needed to speed up the departure of shipments and optimise the workload of workers.

Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA--the Biological Network Analyzer--a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/.

Background One of the primary obstacles to measure clinical quality is the lack of configurable solutions to make computers understand and compute clinical quality indicators. The paper presents a solution that can help clinical staff develop clinical quality measurement more easily and generate the corresponding data reports and visualization by a configurable method based on openEHR and Clinical Quality Language (CQL). Methods First, expression logic adopted from CQL was combined with openEHR to express clinical quality indicators. Archetype binding provides the clinical information models used in expression logic, terminology binding makes the medical concepts consistent used in clinical quality artifacts and metadata is regarded as the essential component for sharing and management. Then, a systematic approach was put forward to facilitate the development of clinical quality indicators and the generation of corresponding data reports and visualization. Finally, clinical physicians were invited to test our system and give their opinions. Results With the combination of openEHR and CQL, 64 indicators from Centers for Medicare & Medicaid Services (CMS) were expressed for verification and a complicated indicator was shown as an example. 68 indicators from 17 different scenes in the local environment were also expressed and computed in our system. A platform was built to support the development of indicators in a unified way. Also, an execution engine can parse and compute these indicators. Based on a clinical data repository (CDR), indicators were used to generate data reports and visualization and shown in a dashboard. Conclusion Our method is capable of expressing clinical quality indicators formally. With the computer-interpretable indicators, a systematic approach can make it more easily to define clinical indicators and generate medical data reports and visualization, and facilitate the adoption of clinical quality measurements.