Efficient Management Research Articles

Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.

Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.

TSO-DSO interface for Net Zero: the German case of 50Hertz and its DSO neighbour TEN Thüringer Energienetz*

As Germany advances towards its energy transition goals, the imperative to expand and restructure its power grid infrastructure becomes increasingly evident. The current infrastructure lacks the capacity to meet future demands, highlighting the pressing need for action. Escalating challenges related to system security and grid reliability underscore the critical importance of fostering close coordination between Distribution System Operators (DSOs) and Transmission System Operators (TSOs). Collaboration is particularly crucial in control areas such as for instance 50Hertz’s in north-eastern Germany, where efficient management of energy fluctuations is essential for the successful transition to high renewable energy usage. Regulatory strategies aimed at facilitating this transition must prioritize a strong collaboration between DSOs and TSOs necessary for stability, optimizing operations, and securing the network. This article is intended to illustrate how a regulatory framework for such TSO-DSO cooperation in north-eastern and eastern Germany for congestion management could be necessary, in accordance with the next grid development plans. Regulatory gaps associated with such interface will be identified and policy recommendations derived from them will address the challenges and opportunities linked to congestion management and the utilization of smart grid technologies.

Intelligent smelting process, management system: Efficient and intelligent management strategy by incorporating large language model

Intelligent smelting process, management system: Efficient and intelligent management strategy by incorporating large language model

The Influence of Strategic Implementation Practices on the Performance of NGOs in Mombasa County

Running non-governmental organizations still has its fair share of challenges. Managerial efficiency is still wanting, its performance very low with most of them witnessing increased corruption cases. This study aimed to assess the influence of strategic implementation practices on the performance of NGOs in Mombasa County, Kenya. The study was guided by the strategic fit theory. The target population comprised 906 respondents. Data from the board members were collected using questionnaires, while interviews were conducted to collect data from presidents and treasurers. Piloting of research instruments was conducted among 10.0% of the sample size of respondents in the neighboring Mombasa County to establish validity, reliability, credibility, and dependability. Data analysis began by identifying common themes from the respondents’ descriptions of their experiences. Qualitative data was analyzed thematically along the objectives and presented in narrative forms, while quantitative data was analyzed descriptively using frequencies, percentages, mean, and standard deviation, and inferentially using ANOVA with the help of Statistical Packages for Social Science (SPSS Version 27). To enhance their strategic implementation practices, NGOs are advised to bolster collaborations with stakeholders, improve resource mobilization, assess internal and external factors, and engage employees in the planning and execution of strategies.

Examine the Relationship Between the Professional Competency of IA and the Effectiveness of RM in Improving Compliance in Iraq's Public Organization

The importance of internal audits has escalated in the current dynamic and intricate economic and financial landscape. It plays a crucial role in assisting management in attaining its goals by offering unbiased and independent assurance regarding the efficiency of risk management, the sufficiency of internal control, and the accuracy of financial reporting. It is widely known that Iraq has seen instances of fraud and mismanagement of public funds in recent years, highlighting a systemic flaw in the regulatory structure and the auditing profession inside public sector organizations. However, by enhancing compliance through the fundamental pillars of optimizing the efficiency of internal audits and the efficacy of risk management, we can envision a future where the misuse of public funds and various forms of corruption are minimized. This, in turn, will lead to improved adherence to relevant laws and the production of reliable financial statements that can be used for decision-making by appropriate stakeholders. It is the role of government agencies to prioritize these improvements, as they have the power and responsibility to do so. We created a questionnaire of three dimensions and 45 paragraphs to fulfil the research objectives. A sample of internal auditors in sectoral organizations was randomly selected, comprising 283 respondents. We employed both primary and multiple regression analyses to examine the hypotheses. The study concluded that there needs to be a strategy for continuing education for internal audit employees, which indicates a lack of sufficient interest in this topic and the lack of clear strategies and policies for risk management.

Smart e-waste management: a revolutionary incentive-driven IoT solution with LPWAN and edge-AI integration for environmental sustainability.

Managing e-waste involves collecting it, extracting valuable metals at low costs, and ensuring environmentally safe disposal. However, monitoring this process has become challenging due to e-waste expansion. With IoT technology like LoRa-LPWAN, pre-collection monitoring becomes more cost-effective. Our paper presents an e-waste collection and recovery system utilizing the LoRa-LPWAN standard, integrating intelligence at the edge and fog layers. The system incentivizes WEEE holders, encouraging participation in the innovative collection process. The city administration oversees this process using innovative trucks, GPS, LoRaWAN, RFID, and BLE technologies. Analysis of IoT performance factors and quantitative assessments (latency and collision probability on LoRa, Sigfox, and NB-IoT) demonstrate the effectiveness of our incentive-driven IoT solution, particularly with LoRa standard and Edge AI integration. Additionally, cost estimates show the advantage of LoRaWAN. Moreover, the proposed IoT-based e-waste management solution promises cost savings, stakeholder trust, and long-term effectiveness through streamlined processes and human resource training. Integration with government databases involves data standardization, API development, security measures, and functionality testing for efficient management.

Nutrients recovery from livestock wastewater by batch and gas bubble-column studies with biochar, nano-composite material, and ammonium magnesium phosphate hydrate

The breeding of livestock raises substantial environmental concerns, especially the efficient management of nutrients and pollution. This research is designed to assess the potency of char and modified char in diluting nutrient concentrations in livestock wastewater. The characteristics of graphene oxide, struvite, and calcium-modified char were inspected, defining their efficacy in both batch and bed-column investigations of nutrient sorption. Various factors, including sorption capacity, time of contact, ion levels, a decrease in ion levels over time, and sorption kinetics, have been considered, along with their appropriateness for respective models. The first evaluation of the options concluded that 600 °C char was better since it exhibited higher removal efficiency. Modified char sorption data at 600 °C was used to adjust the models “PSOM, Langmuir”, and “Thomas”. The models were applied to both batch and bed-column experiments. The maximum phosphate sorption was 110.8 mg/g, 85.73 mg/g, and 82.46 mg/g for B-GO, B-S, and B-C modified chars respectively, in the batch experiments. The highest phosphate sorption in column experiments, at a flow rate of 400 μl/min, was 51.23 mg per 10 g of sorbent. This corresponds to a sorption rate of 5.123 mg/g. B-GO and B-S modified chars showed higher sorption capacities; this was observed in both the batch and bed-column studies. This displayed the capability of graphene oxide and struvite-modified chars for efficient ion and nutrient uptake, whether in single or multi-ion environments, making them a very good candidate for nutrient filtration in livestock wastewater treatment. Additionally, B-GO char enhanced the sorption of phosphate, resulting in augmented seed germination and seedling growth. These results reveal that B-GO char can be used as a possible substitute for chemical fertilizers.

Smart e-waste management: a revolutionary incentive-driven IoT solution with LPWAN and edge-AI integration for environmental sustainability.

Managing e-waste involves collecting it, extracting valuable metals at low costs, and ensuring environmentally safe disposal. However, monitoring this process has become challenging due to e-waste expansion. With IoT technology like LoRa-LPWAN, pre-collection monitoring becomes more cost-effective. Our paper presents an e-waste collection and recovery system utilizing the LoRa-LPWAN standard, integrating intelligence at the edge and fog layers. The system incentivizes WEEE holders, encouraging participation in the innovative collection process. The city administration oversees this process using innovative trucks, GPS, LoRaWAN, RFID, and BLE technologies. Analysis of IoT performance factors and quantitative assessments (latency and collision probability on LoRa, Sigfox, and NB-IoT) demonstrate the effectiveness of our incentive-driven IoT solution, particularly with LoRa standard and Edge AI integration. Additionally, cost estimates show the advantage of LoRaWAN. Moreover, the proposed IoT-based e-waste management solution promises cost savings, stakeholder trust, and long-term effectiveness through streamlined processes and human resource training. Integration with government databases involves data standardization, API development, security measures, and functionality testing for efficient management.

One Method for Predicting Satellite Communication Terminal Service Demands Based on Artificial Intelligence Algorithms

This paper presents a traffic demand prediction method based on deep learning algorithms, aiming to address the dynamic traffic demands in satellite communication and enhance resource management efficiency. Integrating Seq2Seq and LSTM networks, the method improves prediction accuracy and applicability, especially for mobile terminals such as aviation and maritime ones. Unlike traditional approaches, it does not require extensive statistical data and can be generalized to real-world systems, providing stable long-term traffic demand predictions. This study utilizes real-world flight data mapped to corresponding satellite beams, allowing the precise prediction of beam-specific traffic demands. The results show that aggregating historical demand data for beams with similar trends achieves an average predictive Mean Squared Error (MSE) of 0.0007 and a maximum MSE fluctuation of 0.014, significantly outperforming predictions based on average values in terms of stability and accuracy. This novel solution for resource management in satellite communication ensures efficient and accurate long-term traffic demand predictions.

A Reconstruction Methodology of Dynamic Construction Site Activities in 3D Digital Twin Models Based on Camera Information

Digital twin technology significantly enhances construction site management efficiency; however, dynamically reconstructing site activities presents a considerable challenge. This study introduces a methodology that leverages camera data for the 3D reconstruction of construction site activities. The methodology was initiated using 3D scanning to meticulously reconstruct the construction scene and dynamic elements, forming a model base. It further integrates deep learning algorithms to precisely identify static and dynamic elements in obstructed environments. An enhanced semi-global block-matching algorithm was then applied to derive depth information from the imagery, facilitating accurate element localization. Finally, a near-real-time projection method was introduced that utilizes the spatial relationships among elements to dynamically incorporate models into a 3D base, enabling a multi-perspective view of site activities. Validated by simulated construction site experiments, this methodology showcased an impressive reconstruction accuracy reaching up to 95%, this underscores its significant potential in enhancing the efficiency of creating a dynamic digital twin model.

Efficient Management of Containers for Software Defined Vehicles

Containerization technology, such as Docker, is gaining in popularity in newly established software-defined vehicle architectures (SDVA). However, executing those containers can quickly become computationally expensive in constrained environments, given the limited CPU, memory, and energy resources in the Electric Control Units (ECU) of SDVA. Consequently, the efficient management of these containers is crucial for enabling the on-demand usage of the applications in the vehicle based on the available resources while considering several constraints and priorities, including failure tolerance, security, safety, and comfort. In this paper, we propose a dynamic software container management approach for constrained environments such as embedded devices/ECUs in SDVA within smart cars. To address the conflicting objectives and constraints within the vehicle, we design a novel search-based approach based on multi-objective optimization. This approach facilitates the allocation, movement, or suspension of containers between ECUs in the cluster. Collaborating with our industry partner, Ford Motor Company, we evaluate our approach using different real-world software-defined scenarios. These scenarios involve using heterogeneous clusters of ECU devices in vehicles based on real-world software containers and use-case studies from the automotive industry. The experimental results demonstrate that our scheduler outperforms existing scheduling algorithms, including the default Docker scheduler -Spread- commonly used in automotive applications. Our proposed scheduler exhibits superior performance in terms of energy and resource cost efficiency. Specifically, it achieves a 35% reduction in energy consumption in power-saving mode compared to the scheduler employed by Ford Motor Company. Additionally, our scheduler effectively distributes workload among the ECUs in the cluster, minimizing resource usage, and dynamically adjusts to the real-time requirements and constraints of the car environment. This work will serve as a fundamental building block in the automotive industry to efficiently manage software containers in smart vehicles considering constraints and priorities in the real world.

Efficient Management of Containers for Software Defined Vehicles

Containerization technology, such as Docker, is gaining in popularity in newly established software-defined vehicle architectures (SDVA). However, executing those containers can quickly become computationally expensive in constrained environments, given the limited CPU, memory, and energy resources in the Electric Control Units (ECU) of SDVA. Consequently, the efficient management of these containers is crucial for enabling the on-demand usage of the applications in the vehicle based on the available resources while considering several constraints and priorities, including failure tolerance, security, safety, and comfort. In this paper, we propose a dynamic software container management approach for constrained environments such as embedded devices/ECUs in SDVA within smart cars. To address the conflicting objectives and constraints within the vehicle, we design a novel search-based approach based on multi-objective optimization. This approach facilitates the allocation, movement, or suspension of containers between ECUs in the cluster. Collaborating with our industry partner, Ford Motor Company, we evaluate our approach using different real-world software-defined scenarios. These scenarios involve using heterogeneous clusters of ECU devices in vehicles based on real-world software containers and use-case studies from the automotive industry. The experimental results demonstrate that our scheduler outperforms existing scheduling algorithms, including the default Docker scheduler -Spread- commonly used in automotive applications. Our proposed scheduler exhibits superior performance in terms of energy and resource cost efficiency. Specifically, it achieves a 35% reduction in energy consumption in power-saving mode compared to the scheduler employed by Ford Motor Company. Additionally, our scheduler effectively distributes workload among the ECUs in the cluster, minimizing resource usage, and dynamically adjusts to the real-time requirements and constraints of the car environment. This work will serve as a fundamental building block in the automotive industry to efficiently manage software containers in smart vehicles considering constraints and priorities in the real world.

RESEARCH ON THE IMPACT OF TELEWORK ON WORK-LIFE BALANCE IN JAPANESE HUMAN RESOURCES MANAGEMENT

To increase workers' own Work-life Balance (WLB) satisfaction under telework in Japan, a high level of interest in the workplace or monitoring, a reduced telework workload, autonomy in the performance of duties, and interdependence of duties are effective. High interest from the workplace or monitoring and reduced telework workload also increases the level of satisfaction with the company's WLB measures. Telework in Japanese companies presents different challenges from job description-based job performance systems, and we need to explore effective ways of using telework and improving its efficiency in Japanese-style Human Resources Management (HRM) in the future.

RESEARCH ON THE IMPACT OF TELEWORK ON WORK-LIFE BALANCE IN JAPANESE HUMAN RESOURCES MANAGEMENT

To increase workers' own Work-life Balance (WLB) satisfaction under telework in Japan, a high level of interest in the workplace or monitoring, a reduced telework workload, autonomy in the performance of duties, and interdependence of duties are effective. High interest from the workplace or monitoring and reduced telework workload also increases the level of satisfaction with the company's WLB measures. Telework in Japanese companies presents different challenges from job description-based job performance systems, and we need to explore effective ways of using telework and improving its efficiency in Japanese-style Human Resources Management (HRM) in the future.

Configuration based on Industry 4.0 technologies as a step towards an affordable smart warehouse

The rise of Industry 4.0, marked by its focus on digitalization and intelligent technologies, is redefining traditional warehouse operations within smart cities. This new era leverages tools like Artificial Intelligence (AI) and the Internet of Things (IoT) to create automated solutions that address the complex challenges faced by modern urban centers. While smart cities grapple with interconnected issues, traditional methods often fall short. Data-driven, technology-powered solutions offer a more efficient approach. In the field of logistics, innovative warehouse solutions are emerging to address the growing complexities of the supply chain. One major challenge faced by many small businesses is inaccurate inventory knowledge, leading to inefficient warehouse management. This research proposes a cost-effective solution that utilizes Industry 4.0 technologies, specifically AI and IoT, to optimize inventory control and reorder point forecasting. This approach aims to democratize warehouse efficiency in smart cities by making it accessible to businesses of all sizes. This paves the way for a “smarter” future, upgrading warehouses with intelligent yet affordable solutions.

A novel MTCMOS based 8T2M NVSRAM design for low power applications with high temperature endurance

Abstract This research investigates, for the first time, a novel eight-transistor-two-memristor (8T2M) nonvolatile 
static random access memory (NVSRAM) with 7-nm technology. The key innovation in this design lies 
in the incorporation of multiple-threshold complementary metal oxide semiconductor (MTCMOS) 
technology with power gating technique, which enables efficient power management and enhanced 
performance with low leakage current. The implementation of multiple threshold voltage levels allows 
for dynamic control of transistor behavior, optimizing power consumption and read/write speeds. As 
compared to a traditional six-transistor (6T) static random access memory (SRAM) cell, it has been 
ascertained that there is a 33% enhancement in the read margin and an 18% improvement in the write 
margin. Moreover, the delay for read, write ‘0’ and write ‘1’ is also minimized by 63.89%, 37.99% and 
42.77%. Furthermore, the power attenuation is also reduced for read and write by 63.02% and 81.6%, 
respectively with respect to a conventional SRAM.

Confined Crystallization Polyether-Based Flexible Phase Change Film for Thermal Management.

Phase change materials (PCMs) possess the potential to regulate temperature by utilizing their thermal properties to absorb and release heat. Nevertheless, the application of PCMs in thermal management is constrained by issues such as liquid leakage and limited flexibility. In this study, we propose a novel approach to address these challenges by incorporating a pore structure within nanofibers to confine the crystallization of phase change molecules, thereby enhancing the flexibility of the composite material. Additionally, inspired by the adaptive mechanisms observed in plants, we have developed a form stable PCM based on polyether, which effectively mitigates the issue of liquid leakage at higher temperatures. Despite being a solid-liquid PCM at its core, this material exhibits molecular-scale flow and macroscopic shape stability as a result of intermolecular forces. The composite film material possesses remarkable flexibility, efficient thermal management capabilities, adjustable phase transition temperature, and the ability to undergo repeated processing and utilization. Consequently, it holds promising potential for applications in personal thermal energy management.

The role of knowledge management to improve organizational performance through organizational ambidexterity within the uncertainties

PurposeThis study highlights the significance of knowledge management in the relationship between organizational ambidexterity and organizational performance within the context of Indonesian SMEs.Design/methodology/approachThe study adopts a quantitative research approach, employing a survey questionnaire to collect data from a sample of SMEs operating in Indonesia. Structural Equation Modeling using Partial Least Square is used to investigate the relationship between variables.FindingsThe findings of this study show that in the context of small and medium enterprises, the variables of environmental and technological uncertainty are not driving factors in organizational ambidexterity. In contrast, market uncertainty has a significant effect on organizational ambidexterity. Moreover, it is also found that knowledge management does not support the variables of environmental, market, and technological uncertainty on organizational ambidexterity in small and medium enterprises. The results show that knowledge management plays a significant role in organizational ambidexterity. It also shows that knowledge management could support a significant way between organizational ambidexterity and organizational performance.Originality/valueThe findings of this study give insights for SMEs to attain a sustainable competitive edge in a dynamic business landscape by implementing efficient knowledge management techniques that bolster their ambidextrous capacities.

Confined Crystallization Polyether-Based Flexible Phase Change Film for Thermal Management.

Phase change materials (PCMs) possess the potential to regulate temperature by utilizing their thermal properties to absorb and release heat. Nevertheless, the application of PCMs in thermal management is constrained by issues such as liquid leakage and limited flexibility. In this study, we propose a novel approach to address these challenges by incorporating a pore structure within nanofibers to confine the crystallization of phase change molecules, thereby enhancing the flexibility of the composite material. Additionally, inspired by the adaptive mechanisms observed in plants, we have developed a form stable PCM based on polyether, which effectively mitigates the issue of liquid leakage at higher temperatures. Despite being a solid-liquid PCM at its core, this material exhibits molecular-scale flow and macroscopic shape stability as a result of intermolecular forces. The composite film material possesses remarkable flexibility, efficient thermal management capabilities, adjustable phase transition temperature, and the ability to undergo repeated processing and utilization. Consequently, it holds promising potential for applications in personal thermal energy management.

Numerically simulating the interconnected nature of the road-soil-pipe infrastructure

Successful operation of infrastructure, such as the road, rail, and utility networks, are fundamental to modern living, and failure of these can have significant consequences. Yet they are likely, when in proximity, to interact, and failure of one can cause cascade failure of others. Equally, all are supported by the ground, which can also experience changes in properties as the infrastructure deteriorates. Research into the interconnected nature of these infrastructure is not new, but much focuses on the structural behaviour of the human-made components (road structures, pipes, etc). This paper presents a numerical model developed to systematically evaluate the impacts of simulated pipe leakage on the surrounding ground and pavement layer above. The model outputs indicate that the road surface experiences increased strains due to weakening ground conditions around the leaking pipe and these are exacerbated by asymmetrical traffic loading. Findings indicate that leakage can cause differential settlements (observed both at the surface and pipe levels), which could cause localised deterioration of the pavement material (due to tensile cracking, etc.) precipitating surface anomalies like cracks or potholes. This suggests that understanding the root-cause of road surface deterioration is critical to efficient long-term management of road networks so that the symptoms are not remediated whilst the root-cause remains. This research provides a crucial step towards enhancing predictive maintenance and calls for further investigation into the long-term geotechnical impacts of leakage, to develop robust repair and maintenance frameworks that address underlying causes of road surface anomalies.