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Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners.

The article aims to explore the public’s fear of data being misused when using European Covid-19 contact-tracing applications. The point of departure lies in considering the research question of whether the Covid-19 pandemic has influenced the platformisation of traditional institutions, i.e., whether the design of Europe’s proximity-tracing applications mimic the dataintensive web services of commercial platforms, namely commercial APIs and their data policies, in order to bypass the right to privacy. We accordingly argue that is vital to address the public’s fears of governmental and corporate dataveillance as well as data misuse while using such apps. The investigation entails of a critical analysis of the Exposure Notification System framework designed by Apple and Google (or GAEN) and the #OstaniZdrav application. The article rejects the justification of the public’s fear of governmental dataveillance, while recognising the possibility of corporate data misuse. Keywords: Covid-19 contact-tracing application, right to privacy, GDPR, API, protocol, metadata, dataveillance

In the era of market globalisation, the quality of products has become a key factor for success in the manufacturing industry. The growing demand for customised products requires a corresponding adjustment of processes, leading to frequent and necessary changes in production control. Quality inspection has been historically used by the manufacturing industry to detect defects before customer delivery of the end product. However, traditional quality methods, such as quality inspection, suffer from large limitations in highly customised small batch production. Frameworks for quality inspection have been proposed in the current literature. Nevertheless, full exploitation of the Industry 4.0 context for quality inspection purpose remains an open field. Vice-versa, for quality inspection to be suitable for Industry 4.0, it needs to become fast, accurate, reliable, flexible, and holistic. This paper addresses these challenges by developing a multi-layer quality inspection framework built on previous research on quality inspection in the realm of Industry 4.0. In the proposed framework, the quality inspection system consists of (a) the work-piece to be inspected, (b) the measurement instrument, (c) the actuator that manipulates the measurement instrument and possibly the work-piece, (d) an intelligent control system, and (e) a cloud-connected database to the previous resources; that interact with each other in five different layers, i.e., resources, actions, and data in both the cyber and physical world. The framework is built on the assumption that data (used and collected) need to be validated, holistic and on-line, i.e., when needed, for the system to effectively decide upon conformity to surpass the presented challenges. Future research will focus on implementing and validating the proposed framework in an industrial case study.

The learning ecosystem is the unified whole formed by education and its surrounding environment, including human elements such as the internal school education system and organization and non-human factors such as the external soft and challenging environment. However, the global COVID-19 outbreak in 2020 has led to large-scale home-based learning among students, which has broken the original ecological balance of learning. The interaction between the four elements of the traditional instructional system cannot explain all the teaching behavior. Based on the research perspective of large-scale home-based learning, this paper proposes to add family and technology into the original teaching system framework to form a new family-school linkage instructional System framework, including school education, family education, online teaching, and other types of education, improve the learning ecosystem and provide new thinking for the education and teaching in the post-epidemic era.

Transactive energy plays a key role in the operation and energy management of future power systems. However, the conventional operational mechanism, which follows a centralized design, is often less secure, vulnerable to malicious behaviors, and suffers from privacy leakage. In this work, we introduce blockchain technology in transactive energy to address these challenges. Specifically, we develop a novel blockchain-based transactive energy framework for prosumers and design a decentralized energy trading algorithm that matches the operation of the underlying blockchain system. We prove that the trading algorithm improves the individual benefit and guarantees the socially optimal performance, and thus incentivizes prosumers to join the transactive energy platform. Moreover, we evaluate the feasibility of the transactive energy platform throughout the implementation of a small-scale network of Internet of Things (IoT) devices and extensive simulations using real-world data. Our results show that this blockchain-based transactive energy platform is feasible in practice, and the decentralized trading algorithm reduces the user's individual cost by up to 77% and lowers the overall cost by 24%.

Different asymmetries in Okun's law for 21 OECD countries over the period 1989-2019 are studied in the paper. To this end, an extended Okun equation accommodating possibly differentiated responsiveness to output and unemployment fluctuations is formulated applicable not only at the level of a whole economy, but also separately to gender-specific parts of the labor market in a system framework. For most of the countries, Okun's law asserts itself with a greater magnitude in years when output recedes than in those when it grows. In nearly all the countries, Okun's law is found stronger with decreases in unemployment and weaker or offset with rising unemployment. Finally, in the majority of the countries, male unemployment is more sensitive to output fluctuations than female unemployment, or there is no significant difference whatsoever. (c) 2020 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved. V článku se studují různé asymetrie v Okunově zákoně pro 21 zemí OECD za období 1989 - 2019. K tomuto účelu je formulována rozšířená Okunova rovnice zohledňující potenciálně diferencovanou odezvu vůči fluktuacím ve výstupu a nezaměstnanosti, která je aplikovatelná nejen na úrovni celé ekonomiky, pro genderově specifické části trhu práce v duchu systému rovnic. Pro většinu zemí se Okunův zákon projevuje s vyšší silou v letech, kdy výstup klesá, než v letech, kdy výstup roste. Ve skoro všech zemích se zjistil silnější Okunův zákon při poklesu nezaměstnanosti a slabší nebo tlumený s rostoucí nezaměstnaností. Pro většinu zemí je nezaměstnanost mužů senzitivnější na fluktuace ve výstupu než nezaměstnanost žen, případně se neukazuje žádný signifikantní efekt.

The study examines the role of environmental, behavioral and technological variables in predicting customer behavioral intentions to adopt the Bank of Uganda Internet Banking System (BBS Connect) by integrating three existing frameworks of Task Technology Fit (TTF), Unified Theory of Acceptance & Use of Technology (UTAUT) and Initial Trust Model (ITM). In doing so, the study follows a comprehensive approach to address Internet banking adoption intention. Partial Least Squares (PLS) Structural Equation Modelling is used to analyze the data collected from BBS Connect users. From the empirical findings, we are able to establish the significant contribution of technology characteristics and task characteristics in facilitating TTF. Initial trust was found to be facilitated by structural assurance and not by familiarity with the Bank of Uganda. The statistical results support the significant association of initial trust, performance expectancy and social influence with behavioral intentions to adopt BBS Connect. The approach used in the study incorporates three well established theories of technology acceptance. This results in a high explanatory power of our research model at 73.2% of behavioural intention to adopt the BBS Connect System. We provide theoretical and practical implications that are useful for Internet banking in Uganda and Africa at large.

Millimeter-wave (mmWave) radar, as an emerging technique, is increasing in popularity for human activity recognition. In contrast to traditional sensors and radars, mmWave radars give detailed information on objects from the range domain to the Doppler domain. The short wavelength allows mmWave radars to achieve a high resolution and a small antenna size, but also makes them prone to noise. In this article, we present a system framework for human detection and tracking using mmWave radars. We show that mmWave radars have good performance in indoor environments, with over 90% sensitivity. We show that using a single radar can raise a large number of false alarms due to unstable data and noise. However, with two radars the precision of the system can be improved significantly.

Eight years ago we have designed and implemented an initial prototype of an agent-based glider pilot support system (the GliderAgent). Our aim was to validate correctness of the initial assumption that an agent-based system, combined with sensor data, can help pilots in various situations that occur during a flight. For instance, the GliderAgent was capable of detecting certain dangers, warn the pilot and, autonomously, send notification(s) to the ground station. Due to the continuous and rapid development of mobile technologies and sensors, our initial prototype has evolved. First, we moved the system, from an emulated environment, to real devices. Second, a semantic-rule-based decision making system was integrated with the GliderAgent, to analyze feeds from sensors (altitude, temperature, blood pressure, etc.) and, based on received information, to trigger appropriate behaviors. The result of our work provided a foundations for development of a general-purpose, cyber-physical system framework, which will be described in this paper. Furthermore, the developed system illustrates an interesting approach to integration of heterogeneous IoT devices (potentially also IoT platforms).

The current investigation was intended to improve the assay strategy for concurrent estimation of telmisartan and atorvastatin by utilizing instrument high performance liquid chromatography (HPLC), mobile phase of methanol:water, and 250 nm detection wavelength. The retention times were established to be 2.4 minutes for telmisartan and 3 minutes for atorvastatin. The analytical technique for both the drugs showed linearity over the range of 20 to 80 ppm of the target concentration. The recuperation was seen as 99.9% for both drugs. Both system framework and strategy precision were seen as precise and well inside the range. The detection limit of 0.05 for telmisartan and 0.019 for atorvastatin was found. The planned method was found to be simple, explicit, specific, linear, and rugged. Hence, it can be utilized for routine examination of telmisartan and atorvastatin in mass bulk and pharmaceutical formulations.

In this paper, we consider the problem of estimating the L2-gain under a given feedback law for linear discrete-time systems subject to actuator saturation. The basic idea is to use the linear parameter varying system framework to model the saturation nonlinearity. It is shown that the conditions can be expressed as a set of linear matrix inequalities. Furthermore, it is proved that the conditions are guaranteed to be less conservative than several existing solutions in the literature. One numerical example is presented to illustrate the effectiveness of the proposed method.

Accurate prediction and early identification of student at-risk of attrition are of high concern for higher educational institutions (HEIs). It is of a great importance not only to the students but also to the educational administrators and the institutions in the areas of improving academic quality and efficient utilisation of the available resources for effective intervention. However, despite the different frameworks and various models that researchers have used across institutions for predicting performance, only negligible success has been recorded in terms of accuracy, efficiency and reduction of student attrition. This has been attributed to the inadequate and selective use of variables for the predictive models. This paper presents a multi-dimensional and an integrated system framework that involves considerable learners' input and engagement in predicting their academic performance and intervention in HEIs. The purpose and functionality of the framework are to produce a comprehensive, unbiased and efficient way of predicting student performance that its implementation is based upon multisources data and database system. It makes use of student demographic and learning management system (LMS) data from the institutional databases as well as the student psychosocial-personality (SPP) data from the survey collected from the student to predict performance. The proposed approach will be robust, generalizable, and possibly give a prediction at a higher level of accuracy that educational administrators can rely on for providing timely intervention to students.

Driving phobia is a widespread anxiety disorder in modern society. Driving phobia disorders often cause difficulties in people&rsquo, s professional and social activities. A growing trend for treating driving phobia is to apply virtual reality exposure therapy (VRET). We refined the system&rsquo, s performance based on the previous research publication and have conducted a VRET pre-test research study with treatment and control group subjects to demonstrate the effectiveness of VRET. Some systemic problems were discovered in the first published experiment. For example, the experimental process and the virtual reality (VR) driving scenarios had to be modified to reflect realistic scenarios causing the fear of driving. These issues were identified and improvements made and verified in this research. A total of 130 subjects completed the driving behavior survey. Thirty subjects were randomly drawn from the subjects with high driving fear questionnaire scores and were invited to participate in the treatment experiments. The latest research presents the refined VRET for driving phobia disorders, including the revised system framework, the main modules and integration, and the subjects&rsquo, biodata collection, management, and analysis. The experiment results provide strong evidence that the refined VRET design helps subjects overcome driving phobias. The subjects&rsquo, subjective distress and fear of driving are reduced significantly through the implementation of VRET.

Binary defocusing technique can effectively break the limitation of hardware speed, which has been widely used in the real-time three-dimensional (3D) reconstruction. In addition, fusion technique can reduce captured images count for a 3D scene, which helps to improve real-time performance. Unfortunately, it is difficult for binary defocusing technique and fusion technique working simultaneously. To this end, our research established a novel system framework consisting of dual projectors and a camera, where the position and posture of the dual projectors are not strictly required. And, the dual projectors can adjust defocusing level independently. Based on this, this paper proposed a complementary decoding method with unconstrained dual projectors. The core idea is that low-resolution information is employed for high-resolution phase unwrapping. For this purpose, we developed the low-resolution depth extraction strategy based on periodic space-time coding patterns and the method from the low-resolution order to high-resolution order of fringe. Finally, experimental results demonstrated the performance of our proposed method, and the proposed method only requires three images for a 3D scene, as well as has strong robustness, expansibility, and implementation.

Gestational Diabetes Mellitus (GDM) is the most common complication of pregnancy and potentially causes problemsfor both mother and baby, including risks of developing type 2 diabetes (T2D). Compared to other Americans, Indigenous youth have more risk factors and higher rates of GDM. We developed Stopping GDM a GDM risk reduction and reproductive health program for American Indian adolescent females. To further deepen the cultural strengths of the program we draw on constructs from Indigenous ways of knowing, such as Ancestral Knowledge System (AKS). We conducted a secondary analysis on an existing qualitative dataset collected during the Stopping GDM study using AKS as a secondary framework. Key constructs from AKS include: relationships, place-based knowledge, generational networks, holistic approaches, language, cultural heritage, and critical reflection. This dataset includes focus groups with AI adolescent girls (age 12-20) (n=13); their adult female caregivers (n=9); and AI women with a history of GDM or T2D (n=5) from two urban sites. All focus groups were moderated by a trained qualitative researcher with a notetaker and semi-structured moderator guide and analyzed using Atlas.ti. Four primary themes supported by the AKS framework constructs emerged: 1) the important and unique role of women in Native communities; 2) traditional ways as they relate to women, reproductive health, and wellness; 3) support for women’s wellness and reproduction; 4) social determinants as challenges for Indigenous communities in promoting healthy behaviors. These findings will be used to support expansion of Stopping GDM to increase its cultural relevance for a broader Indigenous priority audience which includes Native Hawaiian and Alaska Native communities. Disclosure D. Charron-prochownik: None. R. Soon: None. S. A. Stotz: None. A. F. Fischl: None. K. L. Gonzales: None. M. Mau: None. K. R. Moore: None. Funding National Institutes of Health (UL1TR001857 to D.C-P.); National Institute of Nursing Research (1R01NR014831-01A1 to D.C-P., K.R.M.)

Melting temperature has great influence on the high temperature properties and working temperature limits of ultra-high temperature ceramics (UHTCs) In order to bypass the challenge in the measurement of ultra-high melting points, this paper proposed a novel method to predict UHTCs melting temperature via machine learning. A dataset including more than ten thousand melting temperature data has been established, which covers 8 elements and most of the known non-oxide UHTCs. We built up an element to ceramic system framework by back propagation artificial neural network (BPANN) with the accuracy approaching to 90% and the correlation coefficients approaching to 0.95. Our work provides a probability to get the high accuracy melting temperature of UHTCs, and a more convenient way to develop novel materials with higher working temperature. The given case of melting temperature prediction of Hf-C-N ceramics proves the generality of the artificial neural network (ANN). An inter-validation of melting temperature prediction using our network with materials thermodynamics and density functional theory (DFT) has been demonstrated, indicating that our network is of powerful prediction ability.

In order to solve the automatic generation and evolution of personalized curriculum, the method of using genetic algorithm to realize the evolution of personalized learning content is proposed to solve the dynamic personalized needs of users. Through the research and implementation of personalized curriculum generation technology, firstly, the structures of curriculum generation, genetic algorithm and curriculum scene, as well as the method and technology of personalized curriculum generation and evolution system framework are described in detail. Then, the framework structure based on genetic algorithm is determined, and the user model is updated. Finally, experiments are carried out based on genetic algorithm. The research on the experiment of automatic generation and evolution of personalized curriculum shows that the application of genetic algorithm in the process of curriculum generation and evolution makes students' learning content evolve with the change of their knowledge state in the process of learning, effectively promotes students' interest in learning, and improves learning efficiency and effect.

Research on incubation models indicates that incubators and accelerators are crucial catalysts for the development of start-ups. To facilitate start-ups in financial markets, several regulatory authorities have adopted a new incubation model called a ‘regulatory sandbox’. Regulatory sandboxes enable eligible applicants to test their technology-enabled financial solutions for a certain period of time (subject to conditions the regulator imposes). As such, these instruments allow innovation while preventing severe instability in financial markets caused by systemic risk. Despite their importance, management research has devoted little attention to studying how sandboxes operate as a new incubation model. In our abductive study, we adopt the activity system framework and a qualitative analysis approach to investigate the activities of five leading sandboxes and compare them with the activities of other incubation models. The data analysis yielded an activity model with three design elements (achieving membership, participating and detaching) and one design theme (improving connectedness). Thus, sandboxes are characterized by providing regulatory guidance and facilitating access to testing across international jurisdictions, distinguishing them from both generic and specialized incubation models. Our primary contribution to the incubation literature is extending the knowledge of a unique incubation model through a set of theoretical propositions. publishedVersion

In modeling cell culture growth using unstructured model, two types of equations are normally used: logistic and Monod. However, these two equations are known for their limitations to model death phase of cell culture growth and to account for dead cells accumulation data. In this paper, we present a modeling framework whereby both Logistic and Monod equations can be used in a single set of equations system to overcome these limitations. First, it can be shown that the increase of total cell population that consists of viable and dead cells follows a logistic growth pattern with its own intrinsic growth rate and total carrying capacity. Furthermore, a hybrid Logistic-Monod equation with first-order decay kinetics can be used to model viable cell growth data with decline phase effectively. With this paradigm, a pseudo-rate equation can be written to account for dead cells accumulation data using population balancing with a simple understanding that dead cell population is simply the difference between total and viable cells. These equations can be adjoined with substrate consumption and product generation rate equations to depict complete batch growth data that covers exponential growth and death phases. This modeling framework has been fitted successfully to fit batch growth data of two cell lines from published literature with complete depictions of dead cell accumulation and cell viability profiles. The implication of this modeling framework for chemostat culture performance analysis is further investigated.

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to solve the AI black-box mystery in the healthcare industry. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained 6 key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first-level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.