Online Operations Research Articles

Improving older adults’ accessibility to the web using real-time online interactive guides

Internet use by older adults is increasing and has the potential to improve their mental and social wellbeing, however it is still low compared to other age groups for various online activities. The objective of this study was to develop and evaluate a computerized expert system, GoldNet, which functions as a human expert and guides the user, step-by-step and in real time, when performing online operations. A user study was conducted on 30 older adults (over 65 years old) that attend an adult daycare center. The participants were all novice users with no prior experience using the Internet. A stratified randomized between participants experimental design was used to evaluate users' performance and satisfaction before and after receiving guidance, after practice, and after a two-week period without controlled practice. Participants were assigned to one of three study groups according to the guidance they received: GoldNet guides, video guidance, or guidance from a personal human tutor. During the experiment participants’ eye movements were monitored. The results indicate that by using GoldNet's automated guides, older adults can perform online tasks with high effectiveness, efficiency, and user satisfaction, performing comparably to users trained by a personal human tutor and significantly better than users relying on video guidance. GoldNet was also found beneficial as a refresher tool after a period without controlled training.

INAP: An integrated network analysis pipeline for microbiome studies.

Integrated network analysis pipeline (iNAP) is an online analysis pipeline for generating and analyzing comprehensive ecological networks in microbiome studies. It is implemented in two sections, that is, network construction and network analysis, and integrates many open-access tools. Network construction contains multiple feasible alternatives, including correlation-based approaches (Pearson's correlation and Spearman's rank correlation along with random matrix theory, and sparse correlations for compositional data) and conditional dependence-based methods (extended local similarity analysis and sparse inverse covariance estimation for ecological association inference), while network analysis provides topological structures at different levels and the potential effects of environmental factors on network structures. Considering the full workflow, from microbiome data set to network result, iNAP contains the molecular ecological network analysis pipeline and interdomain ecological network analysis pipeline (IDENAP), which correspond to the intradomain and interdomain associations of microbial species at multiple taxonomic levels. Here, we describe the detailed workflow by taking IDENAP as an example and show the comprehensive steps to assist researchers to conduct the relevant analyses using their own data sets. Afterwards, some auxiliary tools facilitating the pipeline are introduced to effectively aid in the switch from local analysis to online operations. Therefore, iNAP, as an easy-to-use platform that provides multiple network-associated tools and approaches, can enable researchers to better understand the organization of microbial communities. iNAP is available at http://mem.rcees.ac.cn:8081 with free registration.

Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side.

As an efficient way to integrate multiple distributed energy resources (DERs) and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads (TCLs), energy storage systems (ESSs), price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process (MDP) process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor–critic (Memory A3C, M-A3C) with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training. The multithreaded working feature of M-A3C can efficiently learn the resource priority allocation on the demand side of the microgrid and improve the flexible scheduling of the demand side of the microgrid, which greatly reduces the input cost. Comparison of the researched cost optimization results with the results obtained with the proximal policy optimization (PPO) algorithm reveals that the proposed algorithm has better performance in terms of convergence and optimization economics.

Economic Anxiety and the Performance of SMEs during COVID-19: A Cross-National Study in Kuwait

The focal aim of the project was to assess the economic anxiety (EA) and the performance of small and medium enterprises (SMEs) during partial and full lockdowns in Kuwait. The challenges facing the SMEs during COVID-19 and the potential solutions were also explored. The call for this vital investigation was due to the global economic fallout and the shocking drop within the marketplace caused by the COVID-19 pandemic. A descriptive approach was used for online survey design to collect datasets from 147 SMEs spanning all governorates of Kuwait in the period between March and June 2021. It included sociodemographic data, economic anxiety perception, potential challenges and solutions to SMEs, and SMEs’ performance. The data analysis using SPSS 25 showed that 78.2% of the SMEs were affected directly by the COVID-19 pandemic, and about 83% were affected negatively by the COVID-19 pandemic. In comparison, only 12.2% experienced a positive impact, mainly medical, technology, social media, food supplies, and delivery or logistics industries. With great concerns of SMEs for all dimensions related to economic anxiety (with an average of around 3.95), the greatest concerns were the financial and cash flow, followed by labor shortage (an average between 4.51 and 5.00). The results also showed that most of the performance indicators for the SMEs were low (with an average of less than or equal to 2.5), and more than 66% of them worked fewer hours during the pandemic; the number of operating hours was dropped dramatically. More than 74% of the SMEs used technology in more than 20% of their activities, representing an increase in using technologies of about 44%, and about 25.2% used social networks in more than 80% of their activities. The performance of SMEs is also found to be significantly and positively correlated with the economic anxiety levels, with a correlation coefficient of 0.186. The findings revealed significant and crucial outcomes for policymaking, decision-makers, and governmental agencies to build recovery plans and proper actions needed to manage the consequences caused by the disaster against the economic and other developments within the context of SMEs. Overall, there is a clear need to find ways and customize operations to adapt to the new work modes that require social distancing, online operations, and site management. In addition, new alternative modes of SMEs work follow to compensate for the lower working hours from the office and increased online working from home.

Development of a movable standing wave resonant test system for fundamental power couplers with an extraordinary power gain

Off-line high-power tests of the fundamental power couplers prior to their on-line operations are of importance for ensuring their operating reliability and stability. To test the couplers using a limited power supply effectively, a movable standing wave (SW) resonant test system with an extraordinary power gain has been developed at Institute of Modern Physics, Chinese Academy of Sciences (IMP, CAS). The system consists of a movable resonator having two movable shorts for the enhancement and movement of the SW field, and a tunable secondary coupler for feeding power into the resonator without reflection. The proof-of-principle structure of the system has been built and tested both at the low-power and at the high-power levels. The low-power test demonstrates that the moving range of the SW resonant field is over half a wavelength which ensures that the fundamental couplers can be tested by SW field at all reflected phases, and the system can provide a power gain ranging from 50 to 94, corresponding to 200--376 ($4\ifmmode\times\else\texttimes\fi{}50--4\ifmmode\times\else\texttimes\fi{}94$) of power gain in the case of traveling wave resonant ring system. Two types of multipacting inside the couplers were observed during the high-power tests and the mechanism of their influences on the power gain was analyzed. This movable and high-power-gain solution can be beneficial for the promotion of SW resonant test systems for fundamental couplers.

Survival strategies of producers involved in short food supply chains following the outbreak of COVID-19 pandemic: A Hungarian case-study.

Survival strategies of producers involved in short food supply chains following the outbreak of COVID-19 pandemic: A Hungarian case-study.

Javnozdravstveni nadzor nad legionarskom bolešću povezanom sa putovanjima u Evropi i Crnoj Gori

The aim of this study is to show how public health surveillance of travel-associated Legionnaires' disease was conducted in Europe, as well as in Montenegro. The estimations of the European Centre for Disease Prevention and Control (ECDC) indicate that every year about one thousand tourists from Europe get infected by legionellae during their holiday and stay at hotels across the continent. The European Legionnaires' Disease Surveillance Network (ELDSNet) was established with the aim to detect, control and prevent cases, clusters and sources of outbreaks of travel-associated Legionnaires' disease within the European Union (EU) and European Economic Area (EEA), and to assist with detection and response to outbreaks outside the EU/EEA. Sharing information leads to concrete and timely actions of member states of ELDSNet, so that residents of the EU/EEA who travel abroad are protected. ECDC recommends a set of measures that should be followed by public health authorities and tourist sector of the country which was visited by the traveler who caught this disease. If the measures are not carried out, information is published online and European tour operators can use the information when booking holiday packages abroad. Operating procedures provide a set of common measures which should be followed by all EU/ EEA member states involved in the protection of their residents against Legionnaires' disease. Each case of Legionnaires' disease which is reported in tourists, who stayed in Montenegro, is epidemiologically analyzed according to the operating procedures of ELDS Network.

SL-Hyper-FleX: a cognitive and flexible-bandwidth optical datacom network by self-supervised learning [Invited

This paper presents a cognitive flexible-bandwidth optical interconnect architecture for datacom networks. The proposed architecture leverages silicon photonic reconfigurable all-to-all switch fabrics interconnecting top-of-rack switches arranged in a Hyper-X-like topology with a cognitive control plane for optical reconfiguration by self-supervised learning. The proposed approach makes use of a clustering algorithm to learn the traffic patterns from historical traces. We developed a heuristic algorithm for optimizing the intra-pod connectivity graph for each identified traffic pattern. Further, to mitigate the scalability issue induced by frequent clustering operations, we parameterized the learned traffic patterns by a support vector machine classifier. The classifier is trained offline by self-labeled data to enable the classification of traffic matrices during online operations, thereby facilitating cognitive reconfiguration decision making. The simulation results show that compared with a static all-to-all interconnection, the proposed approach can improve the throughput by up to1.62×while reducing the end-to-end packet latency and flow completion time by up to3.84×and20×, respectively.

Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods.

Fruits provide various vitamins to the human body. The chemical properties of fruits provide useful information to researchers, including determining the ripening time of fruits and the lack of nutrients in them. Conventional methods for determining the chemical properties of fruits are destructive and time-consuming methods that have no application for online operations. For that, various researchers have conducted various studies on non-destructive methods, which are currently in the research and development stage. Thus, the present paper focusses on a non-destructive method based on spectral data in the 200–1100-nm region for estimation of total soluble solids and BrimA in Gala apples. The work steps included: (1) collecting different samples of Gala apples at different stages of maturity; (2) extracting spectral data of samples and pre-preprocessing them; (3) measuring the chemical properties of TSS and BrimA; (4) selecting optimal (effective) wavelengths using artificial neural network-simulated annealing algorithm (ANN-SA); and (5) estimating chemical properties based on partial least squares regression (PLSR) and hybrid artificial neural network known as the imperialist competitive algorithm (ANN-ICA). It should be noted that, in order to investigate the validity of the methods, the estimation algorithm was repeated 500 times. In the end, the results displayed that, in the best training, the ANN-ICA predicted the TSS and BrimA with correlation coefficients of 0.963 and 0.965 and root mean squared error of 0.167% and 0.596%, respectively.

Automated Playbook for UAV Traffic Management Based on Spatiotemporal Scenario Data

This paper develops a decision framework to automate the playbook for UAS traffic management (UTM) under uncertain environmental conditions based on spatiotemporal scenario data. Motivated by the traditional air traffic management (ATM) which uses the playbook to guide traffic using pre-validated routes under convective weather, the proposed UTM playbook leverages a database to store optimal UAS routes tagged with spatiotemporal wind scenarios to automate the UAS trajectory management. Our perspective is that the UASs, and many other modern systems, operate in spatiotemporally evolving environments, and similar spatiotemporal scenarios are tied with similar management decisions. Motivated by this feature, our automated playbook solution integrates the offline operations, online operations and a database to enable real-time UAS trajectory management decisions. The solution features the use of similarity between spatiotemporal scenarios to retrieve offline decisions as the initial solution for online fine tuning, which significantly shortens the online decision time. A fast query algorithm that exploits the correlation of spatiotemporal scenarios is utilized in the decision framework to quickly retrieve the best offline decisions. The online fine tuning adapts to trajectory deviations and subject to collision avoidance among UASs. The solution is demonstrated using simulation studies, and can be utilized in other applications, where quick decisions are desired and spatiotemporal environments play a crucial role in the decision process.

Trend of Economy and Business Development in a Composite Environment

The Journal of Business Administration Research has been focusing on the research and development of global business models and management, contributing highly valued information for professionals engaged in scientific research, application innovation, and enterprise operation. The global economy and business development have been facing unprecedented challenges in seeking new economic models and renewing business models and management practices since the outbreak of COVID-19 in 2020. Traditional industries are forced to transform the standardized management that has already become an essential condition, into smarter, more simplified, and highly effective management to adapt to an environment with mature applications of the Internet of Things (IoT), shifting from offline to online operations, and focusing more on operations at the user end instead of at the platform end. The goal is to integrate resources and improve operation efficiency more effectively by upgrading industries, lowering human resource requirements, and simplifying management models and production processes.

Mobile payment and mobile applications (Apps) behaviors for online recommendations

A mobile application (App) is an application designed to run on a smartphone, tablet, or other mobile device. With the continuous change of mobile payment applications in smart phones and the support of the banking system, the global mobile payment population is increasing. This study examines the behaviors of Taiwan mobile payment and apps users, a total of 1,176 valid questionnaire data is divided into six sections with 29 items for a database design. This study develops a data mining approach, including clustering analysis and association rules, based on a relational database. Thus, this study shows that mobile payment not only can provide payment service but is also a critical mobile application platform for online business. Finally, we show that as users of mobile payment and apps gain additional demand and consumption ability, online operators can gradually put together mobile payment business models to enable future electronic commerce online recommendations.

From Hierarchical Control to Flexible Interactive Electricity Services: A Path to Decarbonization

We propose to conceptualise electric energy systems as complex dynamical systems using physically intuitive multilayered energy modelling as the basis for systematic diverse technology integration, and control in on-line operations. It is shown that such modelling exhibits unique structure which comes from the conservation of instantaneous power (P) and of instantaneous reactive power ( _Q), (interaction variables (intVar)) at the interfaces of subsystems. The intVars are used as a means to model and control the interactive zoomed-out inter-modular (inter-area, inter-component) system dynamics. Control co-design can then be pursued using these models so that the primary control shapes intVars of its own module by using its own lowlevel detailed technology-specific model and intVar info exchange with the neighbours. As a result, we describe how the proposed approach can be used to support orderly evolution from today’s hierarchical control to a platform enabling flexible interactive protocols for electricity services. The potential for practical use of the proposed concepts is far-reaching and transparent. All that needs to be conceived is that intVar characterising any intelligent Balancing Authority (iBA) is a generalisation of today’s Area Control Error (ACE) characterising net energy balance of a Balancing Authority (BA). An iBA can be any subsystem with its own sub-objectives, such as distributed energy resources (DERs) comprising customers and grid forming microgrids; distribution systems; transmission systems; Independent System Operators (ISOs); and, ultimately, electric energy markets within large interconnection. Several industry problems are described as particular sub-problems of general interactive electricity services. These formulations help one compare models and assumptions used as part of current solutions, and propose enhanced solutions. Most generally, feasibility and stability conditions can be introduced for ensuring feasible power flow solutions, regulated frequency and voltage and orderly power exchange across the iBAs.

Spread Estimation With Non-Duplicate Sampling in High-Speed Networks

Per-flow spread measurement in high-speed networks has many practical applications. It is a more difficult problem than the traditional per-flow size measurement. Most prior work is based on sketches, focusing on reducing their space requirements in order to fit in on-chip cache memory. This design allows the measurement to be performed at the line rate, but it suffers from expensive computation for spread queries (unsuitable for online operations) and large errors in spread estimation for small flows. This paper complements the prior art with a new spread estimator design based on an on-chip/off-chip model. By storing traffic statistics in off-chip memory, our new design faces a key technical challenge to design an efficient online module of non-duplicate sampling that cuts down the off-chip memory access. We first propose a two-stage solution for non-duplicate sampling, which is efficient but cannot handle well a sampling probability that is either too small or too big. We then address this limitation through a three-stage solution that is more space-efficient. Our analysis shows that the proposed spread estimator is highly configurable for a variety of probabilistic performance guarantees. We implement our spread estimator in hardware using FPGA. The experiment results based on real Internet traffic traces show that our estimator produces spread estimation with much better accuracy than the prior art, reducing the mean relative (absolute) error by about one order of magnitude. Moreover, it increases the query throughput by around three orders of magnitude, making it suitable for supporting online queries in real time.

Field determination of inorganic mercury in seawaters by a portable dual-channel and purge-and-trap system with atomic fluorescence spectrometry

ABSTRACT An on-line flow injection mercury analyser (FIMA) for inorganic mercury (Hg) analysis in environmental waters is presented. The FIMA is a portable dual-channel and purge-and-trap system combining aqueous reduction with stannous chloride (SnCl2), two-stage gold (Au) amalgamation, thermal desorption of Hg0 and final detection with cold vapour atomic fluorescence spectrometry (CVAFS). Simultaneous on-line operations of sample loading and analysis make analytical throughput and accuracy improved and contamination eliminated. Results of system optimisation and reliability regarding the determination of inorganic Hg2+ are described. Analytical performances of both channels were the same in terms of data reliability. Low procedural blanks were obtained from laboratory and field trip (≤5 pg, n = 30). Excellent calibrations (r2 > 0.99) with a high precision and good stability (RSD <5%, n = 30) were obtained. Detection limits and reproducibility of the methods have been estimated as ≤0.05 ng L−1 and <5% (0.5 ng L−1, n = 30). The system was finally validated by measurements comparison of the two channels (r2 > 0.98), and analysis of a certified reference material (coastal seawater BCR-579, recoveries 101 ± 4%, n = 10). Overall, a sample throughput of ~10 samples per hour with two-channel detections of FIMA can be achieved. The onboard analysis of inorganic Hg species (e.g. elemental, reactive, and total Hg) in surface seawaters of the East China Sea (ECS) are also successfully achieved. The portable FIMA is fast, easy, and robust for reliable monitoring of Hg in natural seawaters.

Revamp your onboarding experience to better serve incoming students

A disconnect. That's what was occurring for adult and transfer students from the time they were admitted to Carlow University until classes started. “When I was promoted from University Registrar to Assistant Provost and supervisor of the Student Hub — which includes the Registrar's Office, financial aid, and student accounts, I noticed that the coordinator of advising position that was in place to work with this incoming population of students wasn't meeting their needs,” said Jason Krall, Assistant Provost for Online and Academic Operations. “Sure the position helped faculty advisors with advising‐related matters, but what I was seeing was a true gap in these students getting off to a strong start when it came to onboarding.”

Mobile Payment and Mobile Application (App) Behavior for Online Recommendations

A mobile application (App) is an application designed to run on a smartphone, tablet, or other mobile device. With the continuous change of mobile payment applications in smart phones and the support of the banking system, the global mobile payment population is increasing. This study examines the behaviors of Taiwan mobile payment and apps users, a total of 1,176 valid questionnaire data is divided into six sections with 29 items for a database design. This study develops a data mining approach, including clustering analysis and association rules, based on a relational database. Thus, this study shows that mobile payment not only can provide payment service but is also a critical mobile application platform for online business. Finally, we show that as users of mobile payment and apps gain additional demand and consumption ability, online operators can gradually put together mobile payment business models to enable future electronic commerce online recommendations.

Investigating online social media users’ behaviors for social commerce recommendations

Online social media create virtual communities and network platforms that people use to create, share, and exchange opinions, views and experiences. With social networks, social commerce not only relies on commerce, but online social media can also promote the sale of goods or services online. Many online operators have begun to use recommendation systems to analyze customer purchase history and identify individual products that customers may purchase. This enables the company to send product information to consumers to attract their attention. In addition, consumers have a higher purchase rate for recommended products based on consumer data. Based on a survey in Taiwan society, this study uses the questionnaire survey method to collect data on a relational database. This study investigates Taiwan online social media users’ behaviors using data mining methods, including clustering analysis and association rules. Clustering analysis is to investigate possible profiles of users and association rules are to find knowledge patterns and rules of user profiles, online social media usage motivation/preferences and social commerce behavior in order to generate social commerce recommendations in terms of social technology development in the modern society.

A machine-learning approach to synthesize virtual sensors for parameter-varying systems

This paper introduces a novel model-free approach to synthesize virtual sensors for the estimation of dynamical quantities that are unmeasurable at runtime but are available for design purposes on test benches. After collecting a dataset of measurements of such quantities, together with other variables that are also available during on-line operations, the virtual sensor is obtained using machine learning techniques by training a predictor whose inputs are the measured variables and the features extracted by a bank of linear observers fed with the same measures. The approach is applicable to infer the value of quantities such as physical states and other time-varying parameters that affect the dynamics of the system. The proposed virtual sensor architecture — whose structure can be related to the Multiple Model Adaptive Estimation framework — is conceived to keep computational and memory requirements as low as possible, so that it can be efficiently implemented in embedded hardware platforms.The effectiveness of the approach is shown in different numerical examples, involving the estimation of the scheduling parameter of a nonlinear parameter-varying system, the reconstruction of the mode of a switching linear system, and the estimation of the state of charge (SoC) of a lithium-ion battery.

PROPOSED MODELS FOR CSR MARKETING COMMUNICATIONS ON FACEBOOK BASED ON COMPANY-CAUSE FIT AND CONSUMER RESPONSES

Businesses should use Corporate Social Responsibility (CSR) marketing communications to show genuine support for stakeholders affected by the Covid19 pandemic. Two forms of CSR marketing communications were focused on: cause-related marketing (CRM) and cause sponsorship (CS). This research looked into how CRM and CS impact customer responses to: a) like/join the company's Facebook page, b) exchange CSR activities through eWOM (electronic word-of-mouth), and c) intention to buy the company's products and services (purchase intention). The company-cause fit was the dependent variable that was tested against the three responses. A survey-based within-subjects experiment of CRM and CS x 2 (good fit / bad fit) was conducted on active Facebook users aged 18 to 64 years old. Ten (10) pre-selected firms from the Philippines' Top 30 Businesses were listed, These companies released both CRM and CS Facebook posts. The CRM posts were shown to half of the sample size (n=136), while the CS posts were shown to the other half (n=136) and rated by a questionnaire. This research suggested models based on regression analysis and modeling that would advise companies how to better conduct CRM and CS online operations in order to maximize investments, especially during periods of crisis like the Covid19 pandemic.