Miracle-dz/UniFAR-FTUs huggingface dataset:
Shape spaces from morphing

In computer graphics, models of three-dimensional shapes are nowadays mainly represented as meshes. A mesh contains a set of vertices describing geometric positions (and other attributes such as color, etc.) and topological information describing edges containing vertices and forming faces. Meshes are universal in the sense that they can represent every shape with arbitrary precision (assuming infinite space to store the description). In many applications one deals not only with one single mesh but with many meshes. The most prominent example are geometric animations, which is typically stored a set of meshes describing the shape over time. We like to exploit the idea of a shape space, where shapes are described as the combination of a few base shapes. Here, base shapes are meshes, and all combinations are meshes. We start exploring this idea by looking at the simple case of only two base meshes. The main idea of this work is to use morphing techniques to generate the family of shapes described as the combination of two base shapes. Morphing techniques are used to generate smooth transitions from one object to another. They have become popular and widespread in the special effects industry but have applications in many areas such as medical imaging and scientific visualization. We can say that a morph represents the family of shapes generated by two base shapes, i.e. the space is one dimensional. By adding a third base shape and morphing between an element of the family resulting from the first two base shapes we add another dimension. This process can be repeated to add any number of dimensions. Such spaces of shapes allow to represent each shape in the space with a vector of scalars not longer than the number of base objects spanning the space. Assuming the number of base shapes is relatively small with respect to the amount of information needed to describe a single shape, this is an extremely compact and meaningful way of describing a shape. Why is the representation meaningful? Imagine a set of faces (smiling, frowning, blinking, staring, etc.) comprising the base of a space. If we want to generate a particular expression we simply describe the face in terms of the features we want. The modeling process is intuitive and simple. In addition, if such a face has to be stored or communicated only the small vector is needed. The major aim of the dissertation is to build spaces of polyhedral objects and demonstrate their usefulness in practical applications. However, at the current state of science even morphing between two polyhedral objects is a difficult process. For that reason, a large part of the work is dedicated to generating morph sequences between two meshes. Potential applications discussed in detail include geometric animations and information visualization.
ScholarWorks @ UTRGV ScholarWorks @ UTRGV

Latin America and the Caribbean (LAC) have limited facilities and professionals trained to diagnose, treat, and support people with dementia and other forms of cognitive impairment. The situation for people with dementia is poor, and worsening as the proportion of elderly in the general population is rapidly expanding. We reviewed existing initiatives and provided examples of actions taken to build capacity and improve the effectiveness of individuals, organizations, and national systems that provide treatment and support for people with dementia and their caregivers. Regional barriers to capacity building and the importance of public engagement are highlighted. Existing programs need to disseminate their objectives, accomplishments, limitations, and overall lessons learned in order to gain greater recognition of the need for capacity-building programs.
Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain

Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process. The FMEA approach, however, requires domain experts to manually analyse the FMEA model to derive risk-reducing actions that should be applied. In this paper, we provide a formal framework to allow for automatic planning and acting in FMEA models. More specifically, we cast the FMEA model into a Markov decision process which can then be solved by existing solvers. We show that the FMEA approach can not only be used to support medical experts during the modelling process but also to automatically derive optimal therapies for the treatment of patients.
Using Co-Design in mHealth Systems Development: A Qualitative Study with Experts in Co-design and mHealth System Development (Preprint)

BACKGROUND The proliferation of mobile devices has enabled new ways of delivering health services through mobile health systems. Researchers and practitioners have emphasized that the design of such systems is a complex endeavor with various pitfalls, including limited stakeholder involvement in design processes and integration into existing system landscapes. Co-design is an approach to address these pitfalls. Despite a rich body of literature on co-design methodologies, limited research exists to guide the co-design of mHealth systems. OBJECTIVE The objectives of our study was to (1) contextualize an existing co-design framework to mHealth applications and (2) derive guidelines to address common challenges of co-designing mHealth systems. METHODS We conducted an exploratory qualitative study consisting of 16 semi-structured interviews with co-design method experts (8) and mHealth system developers (8). Data were analyzed using thematic analysis. RESULTS The contextualized framework captures important considerations of the mHealth context, including dedicated prototyping and implementation phases. Additionally, seven guidelines were developed: (1) specificity of targeted mHealth context, (2) immersion in mHealth context, (3) health behavior change, (4) co-design facilitators, (5) post-design advocates, (6) health-specific evaluation criteria, and (7) usage data and contextual research to understand impact. CONCLUSIONS System designers encounter unique challenges when engaging in mHealth development. We hope that the contextualized framework and guidelines will serve as a shared frame of reference to facilitate interdisciplinary collaboration at the nexus of information technology and health research.
Influence of Chlorella vulgaris Microalgae on the Performance of Membrane Bioreactors for Industrial Wastewater Treatment

This research was conducted to evaluate the Effect of Hydraulic Residence Time and Organic Loading Rate in a Microalgal Membrane Bioreactor for Wastewater Treatment. One method of treating industrial wastewater combines an anoxic procedure with a Moving Bed Biofilm Reactor (MBBR). In this study, process engineering is combined with microalgae to boost dissolved oxygen levels and counteract the rise in sludge. This study combines process engineering with microalgae to mitigate growing sludge and enhance wastewater quality as measured by dissolved oxygen levels. MLSS 1000 mg/L, Chlorella vulgaris microalgae 106 L, and 1.75 kg of urea and TSP were the microalgae concentrations utilized. The quality of industrial wastewater is positively impacted by adding microalgae in the MBBR process. The quality of industrial wastewater is positively affected by adding microalgae in the MBBR process. Adding microalgae caused the dissolved oxygen level to rise over the minimal threshold. Additionally, when wastewater is being treated, microalgae might lessen the chance of increasing sludge in the MBBR.
Central Lancashire Online Knowledge (CLoK)

—Objective: Our study was designed to develop a customisable, wearable, and comfortable medical device – the so-called “MyPAD” that monitors the fullness of the bladder, triggering an alarm indicating the need to void, in order to prevent badwetting – i.e., treating Nocturnal Enuresis (NE) at the pre-void stage using miniaturised mechatronics with Artificial Intelligence (AI) . Methods and procedures: The developed features include: multiple bespoke ultrasound (US) probes for sensing, a bespoke electronic device housing custom US electronics for signal processing, a bedside alarm box for processing the echoed pulses and generating alarms, and a phantom to mimic the human body. The validation of the system is conducted on the tissue-mimicking phantom and volunteers using Bidirectional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM-RNN) and Reinforcement Learning (RL). Results: A Se value of 99% and a Sp value of 99.5% with an overall accuracy rate of 99.3% are observed. Conclusion: The obtained results demonstrate successful empirical evidence for the viability of the device, both in monitoring bladder expansion to determine voiding need and in reinforcing the continuous learning and customisation of the device for bladder control through consecutive uses. Clinical impact: MyPAD will treat the NE better and efficiently against other techniques currently used (e.g., post-void alarms) and will i) replace those techniques quickly considering sufferers’ condition while being treated by other approaches, and ii) enable children to gain control of incontinence over time and consistently have dry nights. Category
Automatic Association of Web Directories to Word Senses

We describe an algorithm that combines lexical information (from WordNet 1.7) with Web directories (from the Open Directory Project) to associate word senses with such directories. Such associations can be used as rich characterizations to acquire sense-tagged corpora automatically, cluster topically-related senses and detect sense specializations. The algorithm is evaluated for the 29 nouns (147 senses) used in the Senseval 2 competition, obtaining 148 (word sense,Web directory) associations covering 88% of the domain-specific word senses in the test data with 86% accuracy. The richness of Web directories as sense characterizations is evaluated in a supervised Word Sense Disambiguation task using the Senseval 2 test suite. The results indicate that, when the directory/word sense association is correct, the samples automatically acquired from the Web directories are nearly as valid for training as the original Senseval 2 training instances. The results support our hypothesis that Web directories are a rich source of lexical information: cleaner, more reliable and more structured than the full Web as a corpus.
Evidence of recent and ongoing admixture in the U.S. and influences on health and disparities.

Many researchers in genetics and social science incorporate information about race in their work. However, migrations (historical and forced) and social mobility have brought formerly separated populations of humans together, creating younger generations of individuals who have more complex and diverse ancestry and race profiles than older age groups. Here, we sought to better understand how temporal changes in genetic admixture influence levels of heterozygosity and impact health outcomes. We evaluated variation in genetic ancestry over 100 birth years in a cohort of 35,842 individuals with electronic health record (EHR) information in the Southeastern United States. Using the software STRUCTURE, we analyzed 2,678 ancestrally informative markers relative to three ancestral clusters (African, East Asian, and European) and observed rising levels of admixture for all clinically-defined race groups since 1990. Most race groups also exhibited increases in heterozygosity and long-range linkage disequilibrium over time, further supporting the finding of increasing admixture in young individuals in our cohort. These data are consistent with United States Census information from broader geographic areas and highlight the changing demography of the population. This increased diversity challenges classic approaches to studies of genotype-phenotype relationships which motivated us to explore the relationship between heterozygosity and disease diagnosis. Using a phenome-wide association study approach, we explored the relationship between admixture and disease risk and found that increased admixture resulted in protective associations with female reproductive disorders and increased risk for diseases with links to autoimmune dysfunction. These data suggest that tendencies in the United States population are increasing ancestral complexity over time. Further, these observations imply that, because both prevalence and severity of many diseases vary by race groups, complexity of ancestral origins influences health and disparities.
Investigating Anomalies in Compute Clusters: An Unsupervised Learning Approach

As compute clusters continue to grow in scale and complexity, the frequency of detected anomalies in their operation significantly increases. Timely detection of anomalous events is vital to maintain system efficiency and availability. This study presents an attention-based graph neural network (GNN) for detecting anomalies in clusters at the compute node level and for providing detailed root cause analysis. We show the effectiveness of attention-based GNNs to accurately detect and localize anomalies on real-world datasets
MIT Open Access Articles Segmentation of nerve bundles and ganglia in spine MRI using particle filters

Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.
How diabetes-related information from social media is (not) discussed in clinical consultations

In today ’ s digital world, people with type 1 and 2 diabetes turn to peers on social media to access and share information. Some studies have addressed how such information is discussed in clinical consultations, but conceptual nuances are needed to account for the different ways information is discussed. In this article, we draw on semi-structured interviews with 19 clinicians and 25 people with diabetes to examine how diabetes-related information from social media is discussed in Danish outpatient clinical consultations. The data were collected from September 2020 to January 2021. We conceptualise how these discussions fall on a continuum of (dis)engagement with social media information represented by three metaphorical concepts: parallel world, border zone, and trading zone. On one end, social media resembles a parallel world disconnected from clinical consultations: people with diabetes do not bring up social media information and clinicians do not invite them to discuss it. The middle of the continuum is represented by a border zone in which people with diabetes present social media information and clinicians ’ reactions can either push back, maintaining social media as a parallel world, or support the formation of a trading zone. On the other end, clinical consultations resemble a trading zone: clinicians are open to social media information, invite people with diabetes to discuss it and acknowledge the value of social media. Furthermore, these discussions are often characterised by negotiation in which different perspectives are exchanged. We discuss the benefits and challenges of moving from the parallel world to the trading zone, arguing that discussions about social media information may help clinicians learn what people with diabetes gain from online peer interactions and enable them to offer their expertise to support people with diabetes as they navigate a complex world of online information.
Close packing effects on clean and dirty snow albedo and associated climatic implications

Previous modeling of snow albedo, a key climate feedback parameter, follows the independent scattering approximation (ISA) such that snow grains are considered as a number of separate units with distances longer than wavelengths. Here we develop a new snow albedo model for widely observed close‐packed snow grains internally mixed with black carbon (BC) and demonstrate that albedo simulations match closer to observations. Close packing results in a stronger light absorption for clean and BC‐contaminated snow. Compared with ISA, close packing reduces pure snow albedos by up to ~0.05, whereas it enhances BC‐induced snow albedo reduction and associated surface radiative forcing by up to 15% (20%) for fresh (old) snow, with larger enhancements for stronger structure packing. Finally, our results suggest that BC‐snow albedo forcing and snow albedo feedback (climate sensitivity) are underestimated in previous modeling studies, making snow close packing consideration a necessity in climate modeling and analysis.
PhysioCHI: Towards Best Practices for Integrating Physiological Signals in HCI

Recently, we saw a trend toward using physiological signals in interactive systems. These signals, offering deep insights into users' internal states and health, herald a new era for HCI. However, as this is an interdisciplinary approach, many challenges arise for HCI researchers, such as merging diverse disciplines, from understanding physiological functions to design expertise. Also, isolated research endeavors limit the scope and reach of findings. This workshop aims to bridge these gaps, fostering cross-disciplinary discussions on usability, open science, and ethics tied to physiological data in HCI. In this workshop, we will discuss best practices for embedding physiological signals in interactive systems. Through collective efforts, we seek to craft a guiding document for best practices in physiological HCI research, ensuring that it remains grounded in shared principles and methodologies as the field advances.
SUBSISTENCE HUNTING FOR TURTLES IN NORTHWESTERN ECUADOR Aprovechamiento de subsistencia de la fauna de tortugas en el noroccidente de Ecuador

We describe the subsistence exploitation of an entire turtle fauna in Esmeraldas Province, Ecuador. We collected firsthand accounts and witnessed a number of capture techniques used by rural Afroecuadorian and Chachi inhabitants of the Cayapas-Santiago river basin. The diversity of techniques indicated a practical knowledge of the ecology of the species. Chelydra acutirostris, Kinosternon leucostomum, Rhinoclemmys annulata, melanosterna, and R. nasuta were captured and eaten. Poziando involved cleaning pools in a stream bed during the relatively dry season by removing live plants, organic detritus, and then seining with baskets; we observed R. melanosterna and K. leucostomum captured in this way. Pitfall traps baited with fruit were used to catch R. melanosterna during forays on land. Basket traps (“canasto tortuguero”) with a wooden slat funnel across the opening are floated with balsa lashed to the sides. Banana or Xanthosoma leaf bait in the basket traps caught R. melanosterna, R. nasuta, and K. leucostomum. Marshy areas were probed for R. melanosterna and K. leucostomum. Direct capture by hand was also common. Turtles were relished as food items; all turtles captured were consumed, usually in soup or stew. Use of turtles for food in the region was pervasive, perhaps because fish and game populations were depleted.
Operation optimization of CFB boiler based on data-driven and pattern mining

: In operation control of thermal power plant boilers, there are many problems, such as various controllable operation parameters, difficult mechanism modeling and deviation between operation parameters and design values, which make it difficult to optimize the operation of boilers driven by data. Thus, a pattern mining method is applied to analyze the historical operation data of the distributed control system (DCS) of the related boiler monitoring points. Reasonable fuzzy partition matrices are constructed and matrix operations are performed to mine the fuzzy association patterns that match the boiler's high-efficiency operation under load changes, thereby to guide the operation optimization of boiler. By taking the 130 t/h circulating fluidized bed boiler of a thermal power plant in Dongguan as the research object, the historical data of its DCS are extracted. The data are analyzed by the method of this paper and the control optimization strategy implied in operation is mined. The optimization results show that, this method can provide rapid and reasonable support for the economic operation of the boiler.
Development of a Digital Health Intervention to Support Patients on a Waitlist for Orthopedic Specialist Care: Co-Design Study

Background The demand for orthopedic specialist consultations for patients with osteoarthritis in public hospitals is high and continues to grow. Lengthy waiting times are increasingly affecting patients from low socioeconomic and culturally and linguistically diverse backgrounds who are more likely to rely on public health care. Objective This study aimed to co-design a digital health intervention for patients with OA who are waiting for an orthopedic specialist consultation at a public health service, which is located in local government areas (LGAs) of identified social and economic disadvantage. Methods The stakeholders involved in the co-design process included the research team; end users (patients); clinicians; academic experts; senior hospital staff; and a research, design, and development agency. The iterative co-design process comprised several key phases, including the collation and refinement of evidence-based information by the research team, with assistance from academic experts. Structured interviews with 16 clinicians (female: n=10, 63%; male: n=6, 38%) and 11 end users (age: mean 64.3, SD 7.2 y; female: n=7, 64%; male: n=4, 36%) of 1-hour duration were completed to understand the requirements for the intervention. Weekly workshops were held with key stakeholders throughout development. A different cohort of 15 end users (age: mean 61.5, SD 9.7 y; female: n=12, 80%; male: n=3, 20%) examined the feasibility of the study during a 2-week testing period. The System Usability Scale was used as the primary measure of intervention feasibility. Results Overall, 7 content modules were developed and refined over several iterations. Key themes highlighted in the clinician and end user interviews were the diverse characteristics of patients, the hierarchical structure with which patients view health practitioners, the importance of delivering information in multiple formats (written, audio, and visual), and access to patient-centered information as early as possible in the health care journey. All content was translated into Vietnamese, the most widely spoken language following English in the local government areas included in this study. Patients with hip and knee osteoarthritis from culturally and linguistically diverse backgrounds tested the feasibility of the intervention. A mean System Usability Scale score of 82.7 (SD 16) was recorded for the intervention, placing its usability in the excellent category. Conclusions Through the co-design process, we developed an evidence-based, holistic, and patient-centered digital health intervention. The intervention was specifically designed to be used by patients from diverse backgrounds, including those with low health, digital, and written literacy levels. The effectiveness of the intervention in improving the physical and mental health of patients will be determined by a high-quality randomized controlled trial.