Miracle-dz/UniFAR-FTUs huggingface dataset:
Navigating the Last Mile: The Demand Effects of Click-and-Collect Order Fulfillment

Many retailers are rushing into the click-and-collect (C&C) format, where shoppers place orders online and pick up the goods themselves later. The authors study the demand implications of C&C and postulate how different ways of organizing this format—each with its own convenience features—appeal to households with different shopper characteristics. Using two data sets, each covering the introduction of two C&C fulfillment types by a major grocery retailer in a large number of local markets, the authors compare the impact of in-store fulfillment (pickup at existing stores), near-store fulfillment (pickup at outlets adjoining stores), and stand-alone fulfillment (pickup at free-standing locations). The authors find that the shift in online consumer spending significantly differs between the three order fulfillment types, as does the impact on total spending. No one order fulfillment type systematically dominates; the effects depend heavily on shopper characteristics. The study’s results provide guidance on which C&C fulfillment type(s) to operate under what conditions and caution retailers not to take the easy in-store route routinely.
Research on Arm Stroke Track Positioning in Backstroke Training Based on Layered Teaching Method

Traditional physical education teaching method is one-way in which teacher's output physical education teaching tasks and methods to students, execute teaching contents, feedback teaching effects and evaluate them centrally. Under this teaching mode, by analyzing the distribution of stroke track of backstroke training arm, combining with students' subjective initiative, the stroke track of backstroke training arm is located and identified, and hierarchical teaching method is adopted to detect stroke track of backstroke training arm and design teaching plan. This paper puts forward a method of positioning the stroke track of backstroke training arm based on layered teaching method. A three-dimensional visual surface reconstruction model of the visual image of the stroke movement of backstroke training arm is constructed. Combining with the sampling results of the visual characteristics of the stroke movement of backstroke training arm, the visual tracking and block matching of the visual image of the stroke movement of backstroke training arm are carried out. The edge contour of arm radian of the stroke movement image of backstroke training arm is detected by template automatic matching and wavelet multi-scale decomposition method. The visual space region fusion model of the visual image of the backstroke training arm stroke movement is established, and the arc trajectory of the arm stroke movement visual image of the backstroke training arm is automatically located according to the regularized quantitative feature analysis results. The simulation results show that this method has high accuracy in positioning the stroke trajectory of backstroke training arm, and improves the ability of tracking and detecting the stroke trajectory of backstroke training arm.
An ICT governance analysis for the digital and smart transformation of Brazilian municipalities

Brazilian municipalities face challenges to provide efficient public services to their local community. One reason is that cities in the country are different in geography, development, economics, education, and technology environments. In the context of digital transformation, cities are promoting innovative trends by adopting smart cities initiatives, in which Information and Communication Technology (ICT) represents a transversal enabler. Studies and frameworks were applied worldwide to characterize cities' smartness, but few focused on ICT governance, especially in subnational governments. From this perspective, this research, based on the fundamentals of digital government, ICT governance, and smart cities, analyzed ICT governance requisites and practices in fifteen Brazilian municipalities best ranked in the Smart City Connected index. The methodology procedures encompassed cities' official websites and transparency portals searches and information requests based on Brazilian Access Information Law. The results evidenced that most Brazilian cities lack a smart city program approach, several cities did not implement ICT governance mechanisms and were not compliant with the Information Access Law, and ICT governance was not conceived as active transparency in some cities portals. Future studies should focus on developing an ICT governance catalog practices for Brazilian's smart cities and a methodology for ICT governance open database as a smart city approach.
Experts Evaluation of Usability for Digital Solutions Directed at Older Adults: a Scoping Review of Reviews

Background: it is important to standardize the evaluation and reporting procedures across usability studies to guide researchers, facilitate comparisons, and promote high-quality studies. A first step to standardizing is to have an overview of how experts-based usability evaluation studies are reported across the literature. Objectives: to describe and synthesize the procedures of usability evaluation by experts that are being reported to conduct inspection usability assessments of digital solutions relevant for older adults. Methods: a scoping review of reviews was performed using a five-stage methodology to identify and describe relevant literature published between 2009 and 2020 as follows: i) identification of the research question; ii) identification of relevant studies; iii) select studies for review; iv) charting of data from selected literature; and v) collation, summary, and report of results. The research was conducted on five electronic databases: PubMed, ACM Digital Library, IEEE, Scopus, and Web of Science. The articles that met the inclusion criteria were identified, and data extracted for further analysis, including evaluators, current usability inspection methods, and instruments to support usability inspection methods. Results: a total of 3958 articles were identified. After a detailed screening, 12 reviews matched the eligibility criteria. Conclusion: overall, we found a variety of unstandardized procedures and a lack of detail on some important aspects of the assessment, including a thorough description of the evaluators and of the instruments used to facilitate the inspection evaluation such as heuristics checklists. These findings suggest the need for a consensus framework on the experts’ assessment of usability that informs researchers and allows standardization of procedures.
Using Artificial Intelligence Techniques for Evidence-Based Decision Making in Government: Random Forest and Deep Neural Network Classification for Predicting Harmful Algal Blooms in New York State

Advances in artificial intelligence techniques have shown tremendous potential as a decision support tool for government agencies. However, recent studies typically highlight the combination of large-scale datasets and high-performance computing technologies, which is frequently far away from the reality of many public agencies that still heavily rely on their legacy systems and labor-intensive practices. Using that non-ideal organizational and technical infrastructure, they have to face very complex problems and propose policy solutions. Harmful algal blooms (HABs) are one of such problems. HABs have increasingly become a serious environmental issue in the United States. However, the rapid and sporadic growth of algae and the current standard relying on manual sampling weaken the agencies' response. To overcome this limitation, in this study, we attempt to bridge advanced AI technologies and current government practice by examining the potential of artificial intelligence by comparing the performance of linear probability, random forest, and deep neural network algorithms in predicting HABs with manual sampling data. By integrating manually-sampled HABs data with predictors from publicly-available datasets (land use, weather, and drought), we demonstrate that random forest and deep neural network (DNN) algorithms improve the specificity of the prediction, increasing the true negative rate. Albeit not ideal, we believe that this approach can benefit public agencies that are forced to respond to HABs with limited resources for investing in improving legacy systems. Accurate prediction with limited data could still be useful for certain government decisions, even when the mechanisms of causality are not totally clear. AI techniques have the potential to improve these predictive capabilities.
A Service-oriented Framework for Developing Personalized Patient Care Plans for COVID-19

The COVID-19 pandemic has identified weaknesses and stresses in the existing healthcare and governance system, even in the most developed countries. Given the scale and scope of the pandemic, existing healthcare systems are heavily resource constrained, and home-based isolation has been considered as a potential first step for reducing both the disease spread and the stress on the healthcare system. However, the needs and requirements of home-based isolation are extremely unique for each patient, depending on their medical condition and comorbidities, family responsibilities, and environmental constraints. Therefore, it is necessary to develop personalized patient care plans to ensure that the needs of each patient are appropriately met. In this paper we propose a service oriented framework that allows dynamic composition and management of such plans assuming existence of an appropriate knowledge base and availability of web-services interfaces of the underlying systems of caregivers and service providers. We develop a prototype implementation to show the feasibility of the proposed framework and discuss the challenges/issues in deploying such a system in practice.
A Research on Guangzhou Dialect's Negative Transfer on British English Pronunciation by Speech Analyzer Software Praat and Ear Recognition Method

In order to explore the negative transfer of Guangzhou dialect on the British English pronunciation, this paper takes English major freshmen in Nanfang College of Sun Yat-sen University as respondents to participate into a phonetic test and analyzes their English pronunciation of vowels, consonants, stress, rhythm, and intonation, based on the data collected by a speech analyzer software, Praat and the evaluation done by foreign teachers (i.e. Ear Recognition Method). In conclusion, it is found that the respondents’ pronunciation of segmental phoneme and suprasegmental phoneme was negatively affected by Guangzhou dialect in different levels. In connection with the major findings, the authors provide four suggestions for English learners with Guangzhou dialect as mother tongue to improve their English pronunciation.
Apple Siri (input) + Voice Over (output) = a de facto marriage: An exploratory case study with blind people

People who are blind or have severe low vision (BLVP) often rely on synthesized voice (output) to interact with computers. Thanks to Voice Assistants (VAs), BLVP can now use voice commands to interact (input) with a range of devices. Yet, very little is known about how they use VAs. This exploratory paper reports on semi-structured, face-to-face interviews with (N=10) legally blind adults, including typhlotechnicians, who teach other BLVP to use digital technologies and may themselves be blind people. Whilst the current impact of VAs on our everyday lives focuses on aiding in the completion of simple day-to-day activities, the results show that the ‘couple’ Apple Siri and Voice Over has a strong, positive impact on the everyday lives of our participants. They reported using VAs mostly as a tool, not as a social actor, and that productivity was more important for them than privacy in their everyday use of Siri. Implications for design and research are outlined.
A domain analysis of the literature citing National Longitudinal Study of Adolescent to Adult Health (Add-Health)

With the movement of open data in scholarly community, data become increasingly important in research work. Data repository play the mediator between users and it encourage scientific discoveries among research work. In order to make the depository usage more understandable, a domain analysis of scientific literature citing National Longitudinal Study of Adolescent to Adult Health (Add-Health) was conducted. This study proposes using data citation as a novel way of defining a knowledge domain. Descriptive statistics and citation statistics of the journal articles were analyzed over time by observing depository usage. Furthermore, co-word network analysis and topic modeling were performed in order to identify major research themes in the domain. Similarities of the intellectual structure revealed by these two clustering approaches were reported. The main research themes derived from Add-Health, as reflected in its citing literature included family, obesity, violent victimization, delinquency, substances use, ethnic difference, and sexual behavior. The implication and limitation of the results were discussed.
Inclusion through proactive public services: findings from the Netherlands: Classifying and designing proactivity through understanding service eligibility and delivery processes

The COVID-19 pandemic highlights the dependence on digital public service delivery in many nations. The intensified use of digital public services also shifted the spotlight to accessibility and reactive design of digital public services. Inspired by the high level of proactivity provided in commercial digital services, policy-makers are looking for guidance on employing the vast amount of (personal) data available at various public agencies to proactively aid citizens during important life events. Proactivity, however, is a very complex multi-level concept with a myriad of case-specific forms and conditions and is not always desired. Moreover, there is little guidance in the literature on how to classify the level of proactivity and design more proactive public services. The objective of this paper is to provide guidance for classifying, understanding, and designing proactivity. Drawing on previous conceptualizations in literature, this paper introduces a proactivity classification framework that is substantiated using empirical cases from the Netherlands. We found that fully proactive services are not always desired or possible due to public service characteristics. The two key variables in this framework – service eligibility and service delivery – were used to propose design principles for increasing public services' proactivity. The principles were validated and prioritized by four public service innovators. Policy-makers looking to enhance inclusivity through service proactivity can start by classifying current services and integrating the design principles in their innovation roadmap.
Spiral Cycle Implementation for Designing an All-Terrain Teleoperated Robot in Honduras

The purpose of this work is to expose the design of an electronic RF communication system that allows movement control of an all-terrain robot. For the development of this device, a spiral methodology is used, allowing the development and analysis of four stages of the robot. This study starts with RF communication with a microcontroller, implementation of PID control, 2-degree-of-freedom arm control, and field testing. Significant results are obtained in the development of a PI to control the movement of the robot at a maximum distance of 450 meters and autonomy of 2 hours. Finally, it is concluded that the spiral methodology facilitated the planning and execution of the electronic design for the robot. The PID minimized the error between the communication of the user and the robot. The robot could be used to monitor agro-industrial farms in Honduras.
Smartphone-based Biofeedback Games for Shielding of Immune System against COVID-19: The CovidShield Game Suite design approach

Game-based biofeedback applications on smartphone devices can be played anywhere, giving users, at the same time, a tangible and real-time external measure of their behavior/response. The CovidShield Game Suite (CGS) design process approach presented here, as part of the CovidShield project, introduces a holistic interactive approach to shield the human immune system against COVID-19, by targeting optimal control of all the relevant factors, i.e., physical and mental, in an unobtrusive, gamified and user friendly, low-cost way, allowing for personalization and adaptation to each user's physiological and mental profile. From the methodological point of view, to facilitate the visualization of the constructed breathing game-scenarios, the storyboarding technique was adopted. Overall, the realization of the CGS sets the basis for establishing a holistic framework that could improve and/or change the quality of life of the user/player by following the learning process of breathing patterns that the CGS can offer.
Perspectives of Visually Impaired Visitors on Museums: Towards an Integrative and Multisensory Framework to Enhance the Museum Experience

Although the growing concern to make museums accessible to individuals with visual impairments, their participation in these institutions is still limited, given the multiple barriers they often experience and the lack of assistive technologies to promote access. This research investigates the perspectives of 72 blind and partially sighted individuals on enhancing their visiting experience in museums. A co-created framework to improve visitors’ autonomy is proposed, concluding that sensory, intellectual and physical access must be integrated into the pre, on-site and post phases of visiting museums.
RHIZOSPHERE CHEMISTRY IN ACID FOREST SOILS THAT DIFFER IN THEIR DEGREE OF AL‐SATURATION OF ORGANIC MATTER

&NA; The objective of this research was to study the rhizosphere of tree species of acid forest soils derived from amphibolite (AMP) and granite (GR) rocks, so that the influence of the type of pedogenesis (generated by these materials) on its chemistry could be established. Umbric A1 horizons were sampled in three AMP soils (Lithic Hapludands, Andic Dystrudept), under oak, pine, and eucalyptus stands, and in three nearby GR soils (Typic Dystrudepts) under identical stands. The A1 GR horizons had a lower pH (4.2 vs 4.6), and greater aqueous soluble Al (0.40 vs 0.06 mmol kg−1) and NH4Cl‐extractable Al contents (6.8 vs 2.6 cmolc kg−1) than the A1 AMP horizons. Mean values of sodium pyrophosphate extractable‐Al (Alp) and ammonium oxalate extractable‐Al (Alo) in the A1 AMP horizons were 12.0 and 16.2 g kg−1, respectively, and the molar ratio between Alp and sodium pyrophosphate extractable‐C (Cp) ranged between 0.1 and 0.2. Mean values of Alp, Alo, and Alp/Cp in the A1 GR horizons were significantly (P < 0.05) smaller (5.2 g kg−1, 5.3 g kg−1, and 0.06, respectively). In the A1 GR horizons, we observed a lower pH (significant at P < 0.05) in the rhizosphere than in the bulk soils, a pattern which was not observed in the A1 AMP horizons. The balance of nutritive cations and anions between rhizosphere and bulk soils did not explain the patterns of pH change. Based on the relationships found among organic acids, soil pH, and Alp/Cp, we propose that, in the A1 GR horizons, where organic matter is Al‐undersaturated, the increased organic acidity produced in the rhizosphere cannot be completely buffered by Al alkalinity, whereas in the AMP soils, where mineral amorphous Al coexists with Al‐humus complexes, organic compounds become stabilized with reactive Al surfaces, maintaining organic acidity at low levels, even in the rhizosphere (whenever the stabilization capacity of these surfaces is not exceeded). Our results imply that both variations in organic matter content and the degree of metal saturation of the organic matter need to be considered when investigating the causes behind changes in soil pH in the rhizosphere of acid forest soils.
Nodule Slices Detection based on Weak Labels with a Novel Deep Learning Method

Early detection of lung nodules is essential to the diagnosis and treatment of lung cancer. In this paper, we proposed an improved method to automatically identify the slices with lung nodules from computed tomography (CT). This deep learning-based method aimed to serve as a tool for the fast screening of lung nodules, in order to reduce CT reading time for radiologists. The proposed deep learning model combined convolutional neural networks (CNN) and variable length bidirectional Long Short-Term Memory networks (LSTM). It relied on a supervised learning approach that only required slice labels on the training dataset. The labels indicated the CT slices that contained a nodule, but not the exact location of the nodule. The proposed method was evaluated on two datasets with 5-fold cross-validation. The first dataset was collected from two 3A grade hospitals in China. It contained 1726 CT volumes (positives vs. negatives, 1:1). Each volume was labeled by at least three radiologists with more than five years of experience. The second dataset was the publicly available LIDC-IDRI database containing 888 scans, which underwent a two-phase annotation process by four experienced radiologists. For the first dataset, our method reached a high detection sensitivity of 88.2% with 0.5 false positives per CT volume. For the second dataset, we achieved a high sensitivity of 86.9% with an average of 0.8 false positives per subject. The results demonstrated that the proposed method achieved high sensitivity and specificity in identifying CT slices with lung nodules. Moreover, this study revealed that the proposed method has promising potential in reducing radiologists’ CT reading time, which only required slice labels on the training data for easy implementation.
CovidSense: A Smartphone-based Initiative for Fighting COVID-19 Spreading

Coronavirus Disease COVID-19 is a new strain of coronavirus, first identified in clusters with pneumonia-like symptoms with manifestation mainly of fever (>37.8oC), runny nose, dry cough, anosmia, loss of taste, and fatigue. Testing for COVID-19 can be carried out on samples obtained by various methods, including nasopharyngeal swab or sputum sample. Results are generally available within a few hours to one day. Taking into consideration the worldwide COVID-19 pandemic impact (more than 35.500.000 incidences and more than 1.000.000 deaths so far), it is clear that there is an immediate need to devise an effective tool that can be used both at the hospital and home sites to provide a reliable risk index of confronting COVID-19. Here, the potentialities of a smartphone-based app, namely CovidSense, that could provide a reliable COVID-19 risk index to the user before performing any relevant clinical test, monitor his/her symptoms and provide guidance for further actions are presented. CovidSense is a holistic approach for COVID-19 risk assessment that captures both the status of the respiratory system, via acquisition and AI-based analysis of breathing and coughing sounds, along with the Autonomic Nervus System/Immune System status, via the capturing and analysis of the Heart Rate Variability. The analytical description and functionality of the CovidSense included here manifests the usefulness of such approach in continuous symptoms monitoring, allowing people with early symptoms, quarantined and sensitive groups, such as older adults and patients with comorbidities, to become active participants in the effort of fighting the spread of COVID-19, by monitoring their status and get engaged in appropriate preventative actions. CovidSense paves the way for solutions using technological artifacts, such as smartphones, to foster inclusiveness and joint efforts in successfully addressing global health problems, such as the COVID-19 pandemic.