Interface Management Research Articles

Management Tools for Random Demand Inventory and Supply

A mathematical management model’s added value is obtained only after the design and implementation of a user-friendly operating and usage tool. Fol-lowing work on developing an automated inventory management system and/or supplies, a dynamic model for the rational management of product stocks was established. Its implementation aims to limit or eliminate over-stocking and/or stock depletion. The orderable quantity prediction tool based on a settable and preset time period demonstrates the added value of incorporating probabilistic mathematical principles into supply management processes. In this context, this article discusses aspects of the design and implementation of random demand management algorithms based on Mar-kov chains. The goal is to forecast the state or behavior of goods marketing company’s product stocks and to develop a user supply management inter-face. The latter’s functional application will ultimately demonstrate the ac-curacy of the model. This paper also looks at how to use Markov chains to predict the reliability of any technical device, as well as how to implement an automated system with the desired technical specifications.

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

Data centralization can potentially increase Internet of Things (IoT) usage. The trend is to move IoT devices to a centralized server with higher memory capacity and a more robust management interface. Hence, a larger volume of data will be transmitted, resulting in more network security issues. Cloud IoT offers more advantages for deploying and managing IoT systems through minimizing response delays, optimal latency, and effective network load distribution. As a result, sophisticated network attack strategies are deployed to leverage the vulnerabilities in the extensive network space and exploit user information. Several attempts have been made to provide network intrusion detection systems (IDS) to the cloud IoT interface using machine learning and deep learning approaches on dedicated IDS datasets. This paper proposes a transfer learning IDS based on the Convolutional Neural Network (CNN) architecture that has shown excellent results on image classification. We use five pre-trained CNN models, including VGG16, VGG19, Inception, MobileNet, and EfficientNets, to train on two selected datasets: CIC-IDS2017 and CSE-CICIDS2018. Before the training, we carry out preprocessing, imbalance treatment, dimensionality reduction, and conversion of the feature vector into images suitable for the CNN architecture using Quantile Transformer. Three best-performing models (InceptionV3, MobileNetV3Small, and EfficientNetV2B0) are selected to develop an ensemble model called efficient-lightweight ensemble transfer learning (ELETL-IDS) using the model averaging approach. On evaluation, the findings show that the ELETL-IDS outperformed existing state-of-the-art proposals in all evaluation metrics, reaching 100% in accuracy, precision, recall, and F-score. We use Matthew’s Correlation Coefficient (MCC) to validate this result and compared it to the AUC-ROC, which maintained an exact value of 0.9996. To this end, our proposed model is lightweight, efficient, and reliable enough to be deployed in cloud IoT systems for intrusion detection.

Breadcrumb-inspired construction of liquid metal@BN core-shell microparticles for highly thermal conductive elastomeric composites with excellent flexibility and stability

The rapid development of wearable electronics urges for flexible thermal management materials with higher service performance and stability of operation under high-complexity environmental stresses. A liquid metal embedded elastomer (LMEE) offers a combined solution with both high flexibility and thermal conductivity to heat dissipation of wearable electronics. But the potential leakage risk of the liquid metal (LM) in LMEE under external force severely limits the application of LMEE in the field of flexible thermal management. Inspired by a traditional Chinese snack, “fried milk”, we herein modify the two-dimensional boron nitride (BN) by thiolation, so as to develop LM@BN core-shell particles through the formation of a protective layer around micron-sized LM particles using thiol-metal interactions, before the flexible thermal management material with high stability is finally prepared by the combination between the LM@BN particles and a silicone rubber elastomer. The results demonstrated that the LM@BN core-shell structure can effectively maintain the balance between the tensile property, the thermal conductivity, and the stability of the material, thereby facilitating the future application of LM-elastomer composites with regard to flexible thermal management and thermal interface materials.

Decision Support System to Determine Higher Education Resource Needs Using Accreditation Assessment Standards

Accreditation is an activity to evaluate the eligibility of programs and academic units based on predetermined criteria. In an educational institution, accreditation is an important thing to do to improve quality in the field of education because the aim is to improve quality which includes all aspects of education in the form of science, administration, as well as teaching, and educational staff. College managers really need a decision support system that can produce several alternative strategic decisions quickly and accurately. The existence of decision support system tools used to make strategic decisions makes the manager's work lighter and the results are more accurate. In this study, a decision support system approach was applied to determine the resource requirements of tertiary institutions using accreditation assessment standards consisting of a management subsystem model, data management subsystem, and user interface management subsystem. The result of the research is a design of a decision support system that can be used in an educational institution for determining the need of higher education resources.

OVERVIEW OF THE METHODS AND TOOLS FOR SITUATION IDENTIFICATION AND DECISION-MAKING SUPPORT IN THE CYBERPHYSICAL SYSTEM «SMART HOUSE»

The technology of a smart house is mostly understood as a system that combines a number of subsystems that provide comfortable living conditions for residents in the room and make it possible to significantly reduce energy costs. A house is called smart if it has a certain computer or control system for managing engineering equipment. "Smart House" should be designed so that all services can be integrated with each other with minimal costs (in terms of finances, time and effort), and their maintenance would be organized in an optimal way.
 The "Smart House" system should competently allocate resources, reduce operating costs and provide a clear control and management interface. Such an intelligent system should be able to recognize specific planned and emergency situations occurring in the home and respond to them (make decisions) in accordance with the given program. Therefore, today the urgent task is to recognize the situation and support decision-making in the "Smart House" cyber-physical system.
 The conducted overview of methods and tools for situation identification and decision-making support in the "Smart House" cyber-physical system showed that: in existing solutions, situation recognition occurs only for one of the groups of the system of managed housing functions or does not occur at all; existing solutions provide decision-making support for only one of the groups of the system of managed housing functions or do not provide it at all; the available solutions do not provide for the possibility of assessing the sufficiency of information for decision-making in the "Smart House" cyber-physical system.
 Therefore, there is a need to develop such methods and tools for situation identification and decision-making support in the "Smart House" cyber-physical system, which would: perform situation recognition for all 5 groups of the system of managed housing functions; provide the decision-making support for all 5 groups of the system of managed housing functions; perform an assessment of the sufficiency of information for making all decisions in the "Smart House" cyber-physical system, which will be the focus of the authors' further efforts.

MAYA: Exploring multiform attributes of node to align YANG data models

Nowadays Network Configuration Protocol (NETCONF) has become an essential part of network management for its flexibility and scalability. Vendors intend to use NETCONF to replace Command-Line Interface (CLI) and Simple Network Management Protocol (SNMP) to manage devices. Yet Another Next Generation (YANG) models are tailored to model NETCONF protocol messages. However, there exists heterogeneity issue with YANG models as a consequence of vendors proposing proprietary YANG models which differ from each other in structure or content. Thus, managing network devices from different vendors requires expert knowledge and plenty of resources. In this paper, we present MAYA, a solution to automatically accomplish alignment of YANG models from different vendors or organizations by exploring multiform node attributes. In MAYA, different semantic similarity techniques are used to measure distance between different attributes, such as name, description and type, in nodes from various YANG models. A customized SMP based matching algorithm for YANG models alignment is proposed to generate mapping relations between models based on the semantic similarity. The real cases analysis and experiments show that MAYA is able to meet the demands in production on the problem of YANG model alignment.

MicrobeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation.

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.

Exploring the Impact Mechanism of Interface Management of Prefabricated Construction Projects

Prefabricated construction (PC) is gaining more acceptance worldwide as a sustainable construction method due to its advantages in energy savings, emissions reduction, and cleaner production. However, the inherent geographical and organizational fragmentation of PC projects causes numerous interface issues. Effective interface management is key to the success of PC projects. Thereby, this study aims to explore how interface management is affected by various factors. Based on the literature review, the hypothesis of interaction between different factors is deduced and put forward, and the conceptual model of the interaction path of influencing factors in interface management of PC projects is established. Based on 117 valid questionnaires, an empirical test based on PLS-SEM analysis was conducted. According to the results, 10 paths affecting the performance of interface management were verified. The 7 factors in the conceptual model can all have an impact on the performance of interface management through direct or indirect paths. Among them, information communication, trust and cooperation, technology and management ability have a greater direct impact on interface management, while technical environment, information communication and contract relationship have a greater total impact on interface management. This study reveals the influence mechanism of interface management of PC projects, which can bridge the existing research shortcomings and provide a reference for solving interface management problems in practice to promote the development of PC.

Ten Best Practices For Designing An Online Course

We have worked in positions in higher education that required us to lead, design, and teach online courses that emphasized on curriculum and instructional design best practices. In our roles, we interacted with experienced and inexperienced faculty members to develop courses. We witnessed the challenges faculty members faced when designing and delivering online courses. The purpose of this article is to place emphasis on ten best practices for designing an online course to support faculty members who teach online. We highlighted the design process by underlining the importance of: (i) course overview: description and rationale, (ii) designing measurable learning goals and outcomes, (iii) designing inclusive course content, (iv) designing inclusive instruction, (v) engaging the learner, (vi), designing class section outline and course schedule, (vii) making online course accessible, (viii) designing assessment and rubrics for instruction, (ix) technology interface, and (x) learning management system. Keywords: online course, online teaching, educational technology, higher education, https://orcid.org/0000-0001-8444-8619

Intelligent network planning tool for location optimization of unmanned aerial vehicle base stations using geographical images

Nowadays, automated and intelligent solutions are required by different services and applications to satisfy current industrial necessities. Internet of things/robotic devices have been found useful in the collection and processing of data for pattern recognition, analyzing and anticipating incident information, optimization, and provision of better and timely decision-making to enable network providers to measure the quality of services, thereby ensuring efficiency. Telecommunication systems need reliable and fast internet networks for connected devices and other business activities; thus, technologies are being explored to determine efficient ways of planning and installing base stations. Recently, possibilities of deploying unmanned aerial vehicles (UAVs) as base stations had been investigated. Although successes have been recorded, not much work has been done on network planning tools for location optimization of UAV base stations (UAV-BS) in 5G and beyond networks to the best of our knowledge. In this paper, an intelligent network planning tool (iNPT) for location optimization of UAV-BS has been developed with different channel models and network planning parameters. The developed tool has resources to suggest a better positioning of the UAV-BS using the simulated annealing meta-heuristic to optimize their locations. The tool also includes a battery management interface that can calculate the battery dissipation with reference to the received and transmitted power of the UAV-BS and display the estimated time of flight for the UAV-BS. Geographical information, such as the height of a specific region, obtained from a map is considered in the simulations. The results obtained using the COST231-Hata, Hata path loss, log distance, and two-ray ground reflection models have been presented in this paper. Based on the experimental results, the developed simulator can be useful in network planning tasks to improve coverage areas using UAV-BSs. In addition, the proposed tool has a friendly interface that provides geographical images, and the BSs and UAVs optimized locations.

Software design patterns for data management features in web-based information systems

In complex information systems, some features may recur hundreds of times. Therefore, identifying such features and suggesting suitable design solutions for them can simplify the development and maintenance of such complex systems. In that regard, this work introduces five design patterns that were utilized to develop data management features that recurred many times in several web-based information systems used to manage enterprise and student data at the German Jordanian University. In this context, a software design pattern describes a solution to design repeating software features. The proposed design patterns are documented in a general manner using UML diagrams to enable utilizing them in different web development platforms and to allow their development using popular object-oriented programming languages. In particular, the suggested patterns seek to solve the following software features: flexible user interface for data management, reusable module for dependent dropdown filters, table data lazy loading, unified modules to handle data addition and editing, and page state restoration when navigating between related pages. Not to mention, the validation results show that the discussed design patterns were used hundreds of times while implementing six information systems for the university. Specifically, one of the patterns was utilized more than 700 times. Additionally, as it seems that some of the design patterns in this work were not investigated in related work.

Design and Discussion on Information Management and Control System of Agricultural Machinery and Equipment

In order to solve the problem of low development efficiency of agricultural mechanization information network system, a design and discussion method of agricultural machinery equipment information management and control system is proposed. This paper introduces the technologies required to realize the agricultural machinery operation management control system: Android development technology required to develop mobile phone positioning software, Internet map API for displaying location data, and construction of agricultural machinery resource allocation model for the realization of the scheduling module. Different objective functions are set, and the model results in different situations are obtained through experimental data. The experimental data are in the case of schedulable agricultural machinery, if the target is short-distance short-distance operation, it can save time by 0.16 h, and the existing M1 model is in Changying. Village work, the work end time is about 2.5 hours; M4 and M2 models work in the Xuzhuang Village, and the work end time is about 0.5 h; M3 model works in the Jiangtang Village, and the calculation results for different goals are as follows: M1 model first dispatches short-distance operations; the M3 type is dispatched first for short-distance operations; the M4 type operating in Xuzhuang Village is given priority to dispatch short-distance and short-distance operations. According to the analysis of system requirements, each submodule of the system is designed, including the function description and realization method of the module. The history track query module, mobile phone positioning module, and agricultural machinery scheduling module are introduced in detail. Using the Android development technology required by mobile phone positioning software, the information management of user interface, server system, and database is realized, which provides conditions for the collection of agricultural machinery and equipment information and the generation of information management control.

Distributed Price-Based Power Management for Multibuses DC Nanogrids EEMS

Nanogrids for single consumers have gained importance based on the development of standards for prosumers’ electrical installations (PEIs) such as the IEC 60364 series. All distributed energy resources (DERs) are usually integrated into a single place using one or more dc buses to reduce power conversion losses in those systems. Also, an electrical energy management system (EEMS) is required for the nanogrids smart operation, where droop controls and state machines have been considered for power management. The drawback of these strategies is that the power management is poor when droop controls are considered at the second layer because all DERs must contribute to the dc bus voltage regulation. In addition, the state machines are limited due to their reduced flexibility. The main contribution of this article is a distributed price-based power management (d-PBPM) proposed to overcome these challenges. The d-PBPM is based on dc bus signaling and the price theory, and no droop control is required in the second layer, improving the management efficiency. Experimental results show that the d-PBPM makes the nanogrids’ EEMS scalable to new DERs and new dc buses, considering the addition of distributed sources, dc buses, and energy price to an existing nanogrid. The experimental setup includes a dual dc bus nanogrid with photovoltaic (PV) systems, energy storage, loads management, and ac grid interface.

Early Life Family Care for and With Migrants: The Development of a Transcultural Transition Mentoring Competence Profile

Early Life Family Care (ELFC) has so far neglected the needs of migrant families, partly due to professionals’ lack of transcultural knowledge required to address these specific needs. Simultaneously, migrants face employment barriers, including the non-recognition of qualifications and competences. The EU-funded project addressed both these issues by developing an ELFC mentoring training scheme for migrants, who can use their existing and acquired competences to co-operate with parents in challenging transition phases (e.g. becoming new parents). The article discusses one key pillar of the project: the development of a ‘competence profile’. This involved identifying both, ELFC needs of families, which can be addressed by mentoring, as well as the corresponding competences required by mentors. For this purpose, 25 semi-structured interviews with ELFC professionals and migrant parents were conducted. The results show migrant families’ need for skilled support in the management of administrative, interface, relationship, participation and boundary issues.

Any lessons to learn? Pathways and impasses towards health system resilience in post-pandemic times.

The COVID-19 pandemic has been an ultimate challenge for health systems as a whole rather than just single sectors (e.g. hospital care). Particularly, interface management between health system sectors and cooperation among stakeholders turned out to be crucial for an adequate crisis response. Dealing with such interfaces, it is argued in the literature, demands from health care systems to become resilient. One way to analyse this is to focus on the ways in which bottlenecks in health systems are dealt with during the pandemic. This paper investigates six bottlenecks, including overburdened public health agencies, neglected nursing homes and insufficient testing capacities that have been encountered in the health systems of Germany, Sweden and the Netherlands during the pandemic. Based on empirical findings we identify and critically discuss preliminary lessons in terms of health system resilience, an increasingly popular theoretical concept that frames crises as an opportunity for health system renewal. We argue that in practice health system resilience is hindered by path dependencies of national health systems and, owed to the crisis, interim policies that lack ambition for broader reforms.

A Quench Detection and Monitoring System for Superconducting Magnets at Fermilab

A quench detection system was developed for protecting and monitoring the superconducting (SC) solenoids for the Muon-to-Electron Conversion Experiment (Mu2e) at Fermilab. The quench system was designed for a high level of dependability and long-term continuous operation. It is based on three tiers: Tier-1, FPGA-based Digital Quench Detection (DQD); Tier-2, Analog Quench Detection (AQD); and Tier-3, the quench controls and data management system. The Tier-1 and Tier-2 systems are completely independent and fully redundant. The Tier-3 system is based on National Instruments (NI) cRIO and provides the user interface for quench controls and data management. It is independent from Tiers 1 & 2. The DQD provides both quench detection and quench characterization (monitoring) capability. Both DQD and AQD have built-in high voltage isolation and user programmable gains and attenuations. The DQD and AQD also includes user configured current dependent thresholding and validation times. A 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> article of the three-tier system was fully implemented on the new Fermilab magnet test stand for the HL-LHC Accelerator Upgrade Project (AUP). It successfully provided quench protection and monitoring (QPM) for a cold superconducting bus test in November 2020. The Mu2e quench detection design has since been implemented for production testing of the AUP magnets. A detailed description of the system along with results from the AUP superconducting bus test will be presented.

Exploring the Impact Mechanism of Interface Management Performance of Sustainable Prefabricated Construction: The Perspective of Stakeholder Engagement

Prefabricated construction (PC) activities are geographically fragmented, temporally disrupted, resulting in numerous and complex interfaces. It is stakeholder collaboration by integrating diverse resources within the PC industry to potentially address the factors that impact interface management performance. Previous studies have explored the impact factors of interface management performance without deeply considering the stakeholder and their linkages with the impact factors of interface management. Therefore, this study used a two-mode social network to investigate the impact of the interface management performance of sustainable PC from a stakeholder perspective. Firstly, 24 factors impact interface management of sustainable PC, as well as 12 stakeholders with power to address the factors, were identified based on a literature review and interviews with experts. Subsequently, Stakeholder-factors relationships were judged by a designed questionnaire. Then, the centrality and core-periphery structure analysis methods were adopted to study the network. The findings revealed that developers, general contractors, subcontractors, designers, and suppliers are the core stakeholders, with improved collaboration between these five stakeholders (42%) able to address 18 core factors (75%). The factors that have the most significant impact on the interface management performance of prefabricated construction include understanding and trust, communication and learning, and cooperative attitudes among participants, the effectiveness and timeliness of information communication, formal interface management processes, technical innovation, and the perfection of standards and specifications. By prioritizing these factors, the complexity of the network can be successfully decreased and interface management performance can be improved. This study not only contributes to identifying the impact mechanism of stakeholders on the factors of interface management performance, but also contributes to promoting stakeholder cooperation to improve the sustainability of prefabricated construction.

MMKP: A mind mapping knowledgebase prototyping tool for precision medicine.

BackgroundWith significant advancements in the area of precision medicine, the breadth and complexity of the relevant knowledge in the field has increased significantly. However, the difficulty associated with dynamic modelling and the disorganization of such knowledge hinders its rapid development potential.ResultsTo overcome the difficulty in using the relational database model for dynamic modelling, and to aid in the organization of precision medicine knowledge, we developed the Mind Mapping Knowledgebase Prototyping (MMKP) tool. The MMKP implements a novel design that we call a “polymorphic foreign key”, which allows the establishment of a logical linkage between a single table field and a record from any table. This design has advantages in supporting dynamic changes to the structural relationships in precision medicine knowledge. Knowledge stored in MMKP is presented as a mind map to facilitate human interaction. When using this tool, medical experts may curate the structure and content of the precision knowledge in a flow that is similar to the human thinking process.ConclusionsThe design of polymorphic foreign keys natively supports knowledge modelling in the form of mind mapping, which avoids the hard-coding of medical logic into a rigid database schema and significantly reduces the workload that is required for adapting a relational data model to future changes to the medical logic. The MMKP tool provides a graphical user interface for both data management and knowledgebase prototyping. It supports the flexible customization of the data field constraints and annotations. MMKP is available as open-source code on GitHub: https://github.com/ZjuLiangsl/mmkp.

Exploring fish assemblage structure, feeding guild, and water quality in a typical river-reservoir interface of tropical large reservoir environment, Central India.

The present study aims to understand the spatio-temporal dynamics in habitat ecology and fish assemblage and determine the relationship of habitat transition and fish distribution in a large riverine-reservoir cascade system (Sardar Sarovar Reservoir part in Madhya Pradesh). This study was carried out along a 125-km riverine-reservoir interface (RRI) during 2018-2020. The study showed presence of a total of 110 fish species (104 spp. in upstream main Narmada River stretch excluding reservoirs, 56 in RRI, and 39 in Sardar Sarovar reservoir). We recorded 50 species common in upstream riverine stretch of Narmada and the RRI while RRI uniquely harbored 3 native species (Chitala chitala, Labeo boga, Salmostoma phulo) not previously reported from Narmada River stretch upstream. The similarity percentage (SIMPER) analysis showed presence of S. phulo causing 27.91%, Schistura dayi (3.71%), and Rita pavimentata (3.41%) of dissimilarity among Maheshwar (S1), Rajghat (S2), Koteshwar (S3) non-confluence zone, and Kakrana (S4)-confluence zone. The analysis of data indicated disappearance of some species like deccan peninsular carp Labeo fimbriatus, Mahseer Tor khudree which was earlier reported but was absent in recent years. The statistical correlation of environmental variables with observed abundances offish feeding guilds showed significant positive correlation with specific conductivity and total dissolved solid content of the water. The canonical correspondence analysis (CCA) indicated association among omnivorous fishes of the reservoir with water temperature; total dissolved solids; specific conductivity; total alkalinity; chlorophyll; and NO3-N. The herbivore fishes were found to be more associated with magnesium, dissolved oxygen, and phosphorus content of the water while insectivorous fishes were more associated with the silicate and transparency of the water. The abundance of omnivores species indicated dominance of generalist fish species rather than specialists in the interface region of reservoir showing conservation and ecological importance of the ecosystem. The baseline information generated on fish assemblage and ecological perspectives of the river reservoir interface and science-based management recommendations formulated in the study are critical for sustaining fish diversity, promoting fisheries enhancement, and management planning of tropical large reservoirs.

Job Precariousness: Supply Chain Management in a Waste Pickers Association

Waste pickers are key players within municipal solid waste management and an important link in the recycling chain. Waste pickers perform their activities in precarious conditions: they work in bad weather; run the risk of traffic accidents; come in contact with insects that transmit disease and suffer from weight overload. These conditions are aggravated by the lack of labor rights and guarantees, as well as the instability of income and exploitation in commercial processes receiving derisory prices for materials. In this context, the present research seeks, in an interdisciplinary way, the supply chain management interface with the aspects that constitute the precariousness of the pickers’ work, aiming to identify and foster proposals for infrastructure and supply chain improvements of an association of recyclable waste pickers through dialogue and construction with the workers concerned. For this, the ergonomic work analysis was used for information gathering and diagnosis that will subsidize the construction of a process map through the SIPOC tool. From the documentary analysis of the project, the result of part of the association’s production process was reached, that is, its recovery of recyclables in 2018, which reached a volume of 141,893.7 kg. This study sought to provide moments of reflective discussion with the pickers about the organizational processes of the association, in order to collectively construct proposals that enable better working conditions for them.