Miracle-dz/UniFAR-FTUs huggingface dataset:
Medieval Mouths in Context: Biocultural and Multi-Scalar Considerations of the Mouth and the Case of Late-Medieval Villamagna, Italy

THIS PAPER EXAMINES THE CULTURAL FRAMEWORK and material evidence for teeth and oral health in later medieval Europe, using as a case study the bioarchaeological analysis of an excavated cemetery in central Italy (Villamagna). It proffers an alternative approach to the study of human skeletal material by reframing the questions that bioarchaeologists normally ask about mouths. Instead of stopping at, ‘how much disease? ’ or ‘what state of health? ’, here, ‘how did the mouth relate to individuals’ experiences of their world, and how might scientific information about health and disease provide insight into wider aspects of life, society and economy? ’ is asked. This paper points to a range of cultural understandings around the mouth which were changing in the High and Later Middle Ages (c 1000–1400), namely: the Bible and changing explanations for the relationships between mouth, heart, confession and experience of the divine; an evolving understanding of medicine and medical principles; and new forms of saintly intervention involved in healthcare. Detailed osteobiographies of two adults from Villamagna illustrate shaped individual experiences and the ways in which oral condition reflects and use-patterns and lifeways common to such communities.
Identifying Support for Knowledge-Intensive Processes in BPMN and its Extensions

Context: Business process modeling is widely used to increase the competitive advantage of organizations and Business Process Model and Notation (BPMN) is the standard language for modeling such processes. However, it is often extended to handle domain-specific processes or enhance the language itself. Problem: Applications of process-oriented approaches have reached new challenging, knowledge-intensive scenarios, such as healthcare, case management, and others. For these scenarios, it is common to observe functions involving collaborative resources and processes that are little structured, which do not have the same level of predictability as a structured work routine. This fact led to the definition of a new class of business processes, Knowledge-Intensive Processes (KIP). Solution: This paper investigates which requirements related to the modeling of KIP are met by the standard BPMN language and its extensions proposed in the literature. IS Theory: The paper explores the interaction between Structured Process Modeling Theory and Organizational Information Processing Theory applied to the context of business process management, especially related to knowledge-intensive processes in organizations. Method: We performed an analysis of the adherence of standard BPMN to the KIP modeling requirements and a systematic literature review identifying BPMN extensions, analyzing these proposals regarding their adherence to the same requirements. Summarization of Results: Twelve BPMN extensions were analyzed and compared according to a set of criteria. These extensions were obtained from the SCOPUS digital library and guided the conduct of the research. Contributions and Impact in the IS area: This paper is the first evaluation of BPMN and its extensions regarding KIP requirements. Moreover, we identified the main trends in the development of BPMN extensions related to KIP and some opportunities for future researches.
Scalable Riemann Solvers with the Discontinuous Galerkin Method for Hyperbolic Network Simulation

We develop a set of highly efficient and effective computational algorithms and simulation tools for fluid simulations on a network. The mathematical models are a set of hyperbolic conservation laws on edges of a network, as well as coupling conditions on junctions of a network. For example, the shallow water system, together with flux balance and continuity conditions at river intersections, model water flows on a river network. The computationally accurate and robust discontinuous Galerkin methods, coupled with explicit strong stability preserving Runge-Kutta methods, are implemented for simulations on network edges. Meanwhile, linear and nonlinear scalable Riemann solvers are being developed and implemented at network vertices. These network simulations result in tools that are added to the existing PETSc and DMNetwork software libraries for the scientific community in general. Simulation results of a shallow water system on a Mississippi river network with over one billion network variables are performed on an extreme-scale computer using up to 8,192 processor with an optimal parallel efficiency. Further potential applications include traffic flow simulations on a highway network and blood flow simulations on a arterial network, among many others.
Scalable Multi-FPGA Design of a Discontinuous Galerkin Shallow-Water Model on Unstructured Meshes

FPGAs are fostering interest as energy-efficient accelerators for scientific simulations, including for methods operating on unstructured meshes. Considering the potential impact on high-performance computing, specific attention needs to be given to the scalability of such approaches. In this context, the networking capabilites of FPGA hardware and software stacks can play a crucial role to enable solutions that go beyond a traditional host-MPI and accelerator-offload model. In this work, we present the multi-FPGA scaling of a discontinuous Galerkin shallow water model using direct low-latency streaming communication between the FPGAs. To this end, the unstructured mesh defining the spatial domain of the simulation is partitioned, the inter-FPGA network is configured to match the topology of neighboring partitions, and halo communication is overlapped with the dataflow computation pipeline. With this approach, we demonstrate strong scaling on up to eight FPGAs with a parallel efficiency of >80% and execution times per time step of as low as 7.6 μs. At the same time, with weak scaling, the approach allows to simulate larger meshes that would exceed the local memory limits of a single FPGA, now supporting meshes up to more than 100,000 elements and reaching an aggregated performance of up to 6.5 TFLOPs. Finally, a hierarchical partitioning approach allows for better utilization of the FPGA compute resources in some designs and, by mitigating limitations posed by the communication topology, enables simulations with up to 32 partitions on 8 FPGAs.
Analysis of Political Sentiment Demonstrations on Twitter under COVID-19 Pandemic

Context: In the last decades, there has been a significant expansion of the Internet among the world’s population and, together with this growth, social network platforms have consolidated as a trend and established themselves as social and political environments due to their functionalities, low costs and growing number of users. Problem: Given the presented context, it is very important to identify, analyze and bring knowledge about political demonstrations that have taken place on social networks to understand this new era of social and political activism on digital platforms. Solution: In this research, political demonstrations on Twitter were studied with the central objective of bringing knowledge about how they have been organized on this platform. Particularly, in the context of restrictions on the circulation of people resulting from the peak period of the COVID-19 pandemic. IS Theory: This work was elaborated based on concepts of Graph Theory. Method: Posts made on Twitter were monitored and collected during an established period of time, and then the most prominent virtual political demonstrations were selected for analysis. The analysis of the results were carried out with a quantitative approach along with qualitative observations made during the period of data collection. Summary of Results: Based on a defined methodology, it was possible to identify the network structures and actors of the political demonstrations selected for analysis and to show the similarities between these structures. Contributions and Impact in the IS area: The main contributions of this work bring knowledge about the organization of political demonstrations and how related messages are distributed on Twitter, and present an asynchronous data collection methodology.
Description of dynamic images: a systematic mapping for the understanding and transparency of information that may be perceptible visually impaired citizens

Context: With the advent of the Web and the advancement of information and communication technologies (ICTs), society began to keep itself informed of facts and everyday events and can be connected anywhere on the planet. However, a portion of society, such as the visually impaired, still face major challenges in Information Systems (IS) on the Web regarding access to information in dynamic images. Problem: These citizens, in general, cannot understand the information described in these images, so that they can build a mental model. You need to provide alternative ways of describing them. Solution: In this article, studies were explored that could help in the description of dynamic images under the pillars of understanding, transparency of information and perception for the construction of SIs for the visually impaired. SI theory: This work follows the protocols adopted for systematic mapping directed to meta-analysis and hermeneutics. Method: A Systematic Mapping of the Literature was used following the steps of planning, conducting and analyzing the results. Summary Results: From the search repositories, 1019 studies were captured. After applying the evaluation criteria, 11 relevant studies were accepted. The results revealed that there are few studies in the development of technologies that use methods, models and techniques for the description of dynamic images. Contributions to IS: As a contribution, this work exposes research gaps for the creation of ISs that include descriptions in dynamic images for the understanding and transparency of perceptible information to people with visual impairments. In addition to instigating research in Human-Computer Interaction aligned with the GranDSI-BR 2016-2026.
STAR: A Cache-based Stream Warehouse System for Spatial Data

The proliferation of mobile phones and location-based services has given rise to an explosive growth in spatial data. To enable spatial data analytics, spatial data needs to be streamed into a data stream warehouse system that can provide real-time analytical results over the most recent and historical spatial data in the warehouse. Existing data stream warehouse systems are not tailored for spatial data. In this article, we introduce the STAR system. STAR is a distributed in-memory data stream warehouse system that provides low-latency and up-to-date analytical results over a fast-arriving spatial data stream. STAR supports both snapshot and continuous queries that are composed of aggregate functions and ad hoc query constraints over spatial, textual, and temporal data attributes. STAR implements a cache-based mechanism to facilitate the processing of snapshot queries that collectively utilizes the techniques of query-based caching (i.e., view materialization) and object-based caching. Moreover, to speed up processing continuous queries, STAR proposes a novel index structure that achieves high efficiency in both object checking and result updating. Extensive experiments over real datasets demonstrate the superior performance of STAR over existing systems.
The Use of Users’ Personal Data to Improve Online Retail Services: An Analysis from a Systematic Literature Review

Context: The rise of online retail systems is increasingly evident, bringing users the convenience of shopping anywhere and at better prices than physical stores. Problem: As part of the online shopping process, several user data are frequently collected at registration or while browsing and making purchases. However, it is not always clear to the online consumer how this data is used and what benefits this sharing brings. Solution: This study aims to understand what personal data is being collected from online consumers, how it is being used, and the positive and negative impacts on the services provided to the user from the sharing of personal data. IS Theory: This work was conceived under the aegis of Argumentation Theory, presenting data (facts and opinions) from primary studies on the subject, evidence that proves the legitimacy of these data, and conclusions in the form of assertions or statements. Method: The research has a descriptive character and qualitative analysis, applying the systematic literature review method. Summary Results: From 52 studies, the results showed a great diversity of personal and browsing data collected, mainly during the purchase process, and used to define behavior patterns and offer personalized services. As a negative impact, there is a concern with data privacy and a lack of awareness of how online seller uses personal data. IS Contributions and Impact: As a main contribution, we highlight an enlightening discussion of how consumer data is used to improve online retail services, identifying quality studies on the topic, and supporting IS researchers.
StyleGAN as a Deconvolutional Operator for Large Eddy Simulations

We present a novel deconvolution operator for Large Eddy Simulation (LES) of turbulent flows based on the latest StyleGAN deep learning networks. We exploit the flexibility of this architecture in separating the different layers of the GAN generator, which can be seen as instantaneous fields of the LES. These can be moved in time via integrating the corresponding filtered Navier-Stokes (NS) equations. The subgrid-scale (SGS) stress tensor is obtained from the reconstructed field, rather than ad-hoc turbulence models. We trained a StyleGAN-based network (MSG-StyleGAN) with 5000 images of a decaying 2D-Homogeneous Isotropic Turbulence (2D-HIT) starting at ReΛ = 60 using a 256x256 grid mesh size. We then reconstructed a DNS simulation, point by point, using a 32x32 resolution via research into the latent space of the GAN until the difference between internal fields and LES fields are within a given tolerance. Results show convergence towards the ground truth DNS solution as the tolerance approaches zero.
Characterizing the Job-task-skill Pattern of Job Requirements with Job Advertisement Mining

Understanding job requirements is essential for establishing and optimizing employability-oriented education programs. Most relevant research focus on clarifying the skill requirement of an occupational field. In this research, we argue that job tasks serve as a bridge between a job and the required skills, and we provide a method for investigating the job-task-skill pattern of job requirements using text mining on publicly available job advertisements. To provide this, we: 1) collect data on thousands of job advertisements through web crawling and scraping; 2) categorize the jobs through title analysis; 3) identify the topic of both tasks and skills through word co-occurrence network clustering; and 4) systematically analyze the characteristics and internal relationships between job roles, tasks, and the required skills. A test case was conducted in the context of China's big data sector, and the findings show that the proposed strategy is viable, practical, and instructive.
Mobile application for the possible detection of melanoma in the skin by digital photography: A preliminary feasibility study

Early detection of skin cancer is essential for its treatment. In this sense, there are numerous techniques and instruments for medical personnel to make the respective diagnosis; however, these instruments can be expensive or simply not available. Therefore, this study proposes a mobile application for the possible detection of melanoma in the skin by digital photography, which has achieved 96% accuracy in the tests performed using 80 images of skin lesions of which 40 are benign and 40 malignant. The application takes the image and sends it to the server for processing, which is based on the criteria of asymmetry and irregularity of the border of the photographed sample.
EPAComp: An Architectural Model for EPA Composition

Context: Complex Event Processing (CEP) architectures present high applicability in Realtime Streaming Analytics (RTSA) by extracting and generating valuable information from continuous data feeds, like in stock markets, traffic, and patient monitoring. Problem: Although guidelines and models for CEP architectures have been proposed, the composition of its inter-operable elements in charge of processing events, known as Event Processing Agent (EPA), is challenging for software architects. Solution: This work proposes EPAComp, a model that covers this gap and addresses large-scale processing requirements through features such as stream-based constructions and specialized EPAs. IS Theory: We employed the Representation theory to create a model representing information systems for event processing. Method: The model was applied in a real case experiment to create a solution to collect streams of events from around 200 systems and to provide a dashboard for monitoring their usage. Besides, industry experts qualitatively evaluated the proposal. Results: The experiment results show an application of the model to handle heterogeneous data in a scalable and efficient manner according to indicators regarding performance, the assertiveness of processed output, degree of cohesion, and coupling of components. The qualitative results present that experts asserted EPAComp capabilities fit RTSA requirements. Contributions: An architectural model for EPA composition that enhances the literature by (i) representing static and dynamic EPA compositions through arrangements of specific aggregation structures; (ii) defining the state-of-the-art event processing strategies in CEP; and, (iii) organizing the hierarchy of EPA types.
Sex differences in the evaluation of proteinuria using the urine dipstick test

Background The urine protein dipstick test is widely used, but false-positive and false-negative results may occur. This study aimed to compare the urine protein dipstick test with a urine protein quantification method. Methods The data were extracted using the Abbott Diagnostic Support System, which analyzes the inspection results using multiple parameters. This study included 41,058 specimens tested using the urine dipstick test and protein creatinine ratio from patients aged ≥18 years. The proteinuria creatinine ratio was classified according to the Kidney Disease Outcomes Quality Initiative guidelines. Results Urine protein on the dipstick test was negative in 15,548 samples (37.9%), trace in 6,422 samples (15.6%), and ≥1+ in 19,088 samples (46.5%). Among the trace proteinuria samples, A1 (<0.15 g/gCr), A2 (0.15–0.49 g/gCr), and A3 (≥0.5 g/gCr) category proteinuria accounted for 31.2, 44.8, and 24.0% of samples, respectively. All trace proteinuria specimens with a specific gravity of <1.010 were classified as A2 and A3 category proteinuria. In the trace proteinuria cases, women had a lower specific gravity and a higher percentage of A2 or A3 category proteinuria than men. The sensitivity in the “dipstick proteinuria” ≥ trace” group was higher than that in the “dipstick proteinuria ≥ 1+” group within the lower specific gravity group. The sensitivity in the “dipstick proteinuria ≥ 1+” group was higher for men than for women, and the sensitivity in the “dipstick proteinuria ≥ trace” group was higher than that in the “dipstick proteinuria ≥ 1+” group for women. Conclusion Pathological proteinuria assessment requires caution; this study suggests that evaluating the specific gravity of urine specimens with trace proteinuria is essential. Particularly for women, the sensitivity of the urine dipstick test is low, and caution is needed even with trace specimens.
Relationships between anthropometric and body composition characteristics and age in Polish women over 60 as affected by their socioeconomic and health status and physical activity levels

Background: Little is known about changes in anthropometric and body composition (A&BC) characteristics during the aging process. Research indicates that body composition can be linked with socioeconomic status (SES), health status (HS), and physical activity (PA) levels. Aim: The aim of this study was to evaluate age-related changes in A&BC characteristics in female seniors aged 60+ in view of their SES, HS, and PA levels. Methods: The survey was conducted in November and December 2022 on a total of 661 female seniors. A questionnaire survey was conducted to obtain information about the participants’ socioeconomic status (chronic diseases, health status, marital status, membership in social organizations, financial status, place of residence, education). The respondents’ PA levels were assessed with the International Physical Activity Questionnaire (IPAQ), and their A&BC characteristics were determined in a bioelectrical impedance analysis with the InBody 270 body composition analyzer. The relationships between A&BC characteristics and age were evaluated based on the values of the Pearson correlation coefficient (r). Results: The mean values of Percent Body Fat (PBF), Body Mass Index (BMI), and the waist-hip ratio (WHR) were relatively high (37.2%, 28.5 kg/m2, and 0.8, respectively) and indicative of overweight and gynoid obesity. A higher number of significant negative correlations between A&BC characteristics and age were observed in seniors with lower values of SES, HS, and PA, which points to more rapid involutional changes in this group of respondents. A segmental analysis also revealed significantly lower values of fat-free mass (FFM) and body fat mass (BFM) (both indicators were calculated in percentage and kg), in particular in the upper limbs, in women with lower SES, HS, and PA levels. Conclusion: Environmental factors, including biological, physiological, environmental, psychological, behavioral, and social factors, are significantly associated with aging in women. Age-related changes in A&BC characteristics tend to proceed more rapidly in female seniors with low values of SES and HE and insufficient PA levels.
A novel training program: laparoscopic versus robotic-assisted low anterior resection for rectal cancer can be trained simultaneously

Background Current expectations are that surgeons should be technically proficient in minimally invasive low anterior resection (LAR)—both laparoscopic and robotic-assisted surgery. However, methods to effectively train surgeons for both approaches are under-explored. We aimed to compare two different training programs for minimally invasive LAR, focusing on the learning curve and perioperative outcomes of two trainee surgeons. Methods We reviewed 272 consecutive patients undergoing laparoscopic or robotic LAR by surgeons A and B, who were novices in conducting minimally invasive colorectal surgery. Surgeon A was trained by first operating on 80 cases by laparoscopy and then 56 cases by robotic-assisted surgery. Surgeon B was trained by simultaneously performing 80 cases by laparoscopy and 56 by robotic-assisted surgery. The cumulative sum (CUSUM) method was used to evaluate the learning curves of operative time and surgical failure. Results For laparoscopic surgery, the CUSUM plots showed a longer learning process for surgeon A than surgeon B (47 vs. 32 cases) for operative time, but a similar trend in surgical failure (23 vs. 19 cases). For robotic surgery, the plots of the two surgeons showed similar trends for both operative times (23 vs. 25 cases) and surgical failure (17 vs. 19 cases). Therefore, the learning curves of surgeons A and B were respectively divided into two phases at the 47th and 32nd cases for laparoscopic surgery and at the 23rd and 25th cases for robotic surgery. The clinicopathological outcomes of the two surgeons were similar in each phase of the learning curve for each surgery. Conclusions For surgeons with rich experience in open colorectal resections, simultaneous training for laparoscopic and robotic-assisted LAR of rectal cancer is safe, effective, and associated with accelerated learning curves.