Miracle-dz/UniFAR-FTUs huggingface dataset:
MultimodalHD : Federated Learning Over Heterogeneous Sensor Modalities using Hyperdimensional Computing

—Federated Learning (FL) has gained increasing interest as a privacy-preserving distributed learning paradigm in recent years. Although previous works have addressed data and system heterogeneities in FL, there has been less exploration of modality heterogeneity , where clients collect data from various sensor types such as accelerometer, gyroscope, etc. As a result, traditional FL methods assuming uni-modal sensors are not applicable in multimodal federated learning (MFL). State-of-the-art MFL methods use modality-specific blocks, usually recurrent neural networks, to process each modality. However, executing these methods on edge devices proves challenging and resource-intensive. A new MFL algorithm is needed to jointly learn from heterogeneous sensor modalities while operating within limited resources and energy. We propose a novel hybrid framework based on Hyperdimensional Computing (HD) and deep learning, named MultimodalHD , to learn effectively and efficiently from edge devices with different sensor modalities. MultimodalHD uses a static HD encoder to encode raw sensory data from different modalities into high-dimensional low-precision hyper-vectors. These multimodal hypervectors are then fed to an attentive fusion module for learning richer representations via inter-modality attention. Moreover, we design a proximity-based aggregation strategy to alleviate modality interference between clients. MultimodalHD is designed to fully utilize the strengths of both worlds: the computing efficiency of HD and the capability of deep learning. We conduct experiments on multimodal human activity recognition datasets. Results show that MultimodalHD delivers comparable (if not better) accuracy compared to state-of-the-art MFL algorithms, while being 2x - 8x more efficient in terms of training time. Our code is available online 1 .
LIR-Net:Learnable Iterative Reconstruction Network for Fan Beam CT Sparse-View Reconstruction

In computed tomography (CT), although sparse sampling of projections effectively mitigates radiation problems, the quality of CT images is severely compromised. Recovering high-quality CT images from sparsely sampled data is a challenging task. Recently, “Iterative Theory + Deep Learning” schemes have shown promising results in CT reconstruction tasks. In this paper, we propose an Iterative Reconstruction Network Model based on learnable projection operators (LIR-NET). Unlike existing image domain iteration schemes that fuse information from the projection domain, LIR-Net achieves joint optimization and iteration of dual-domain data. The dual-domain subnetwork uses a replaceable and lightweight U-net. We propose an Enhanced Aligned Loss (EAL) scheme to speed up convergence in the projection domain subnet. A designed last-iteration Sparse Truth Bootstrap (STB) module improves data distortion, while Bit-plane Codec (BC) is combined to enhance the noise discovery and removal capability of the image domain subnet. Extensive experiments based on the dataset from the “Low Dose CT Image and Projection Data (LDCT-and-Projection data)” show that our approach outperforms the best available solutions in both quantitative and qualitative evaluations. Moreover, the robustness of the proposed scheme has been tested and validated on an additional dataset.
Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise

This paper begins by exploring the challenge of event-triggered state estimations in nonlinear systems, grappling with packet dropout and correlated noise. A communication mechanism is introduced that determines whether to transmit measurement values based on whether event-triggered conditions are violated, thereby minimizing redundant communication data. In designing the filter, noise decorrelation is initially conducted, followed by the integration of the event-triggered mechanism and the unreliable network transmission system for state estimator development. Subsequently, by combining the three-degree spherical–radial cubature rule, the numerical implementation steps of the proposed state estimation framework are outlined. The performance estimation analysis highlights that by adjusting the event-triggered threshold appropriately, the estimation performance and transmission rate can be effectively balanced. It is established that when there is a lower bound on the packet dropout rate, the covariance matrix of the state estimation error remains bounded, and the stochastic stability of the state estimation error is also confirmed. Ultimately, the algorithm and conclusions that are proposed in this paper are validated through a simulation example of a target tracking system.
Feasibility Study on Menstrual Cycles With Fitbit Device (FEMFIT): Prospective Observational Cohort Study

Background Despite its importance to women’s reproductive health and its impact on women’s daily lives, the menstrual cycle, its regulation, and its impact on health remain poorly understood. As conventional clinical trials rely on infrequent in-person assessments, digital studies with wearable devices enable the collection of longitudinal subjective and objective measures. Objective The study aims to explore the technical feasibility of collecting combined wearable and digital questionnaire data and its potential for gaining biological insights into the menstrual cycle. Methods This prospective observational cohort study was conducted online over 12 weeks. A total of 42 cisgender women were recruited by their local gynecologist in Berlin, Germany, and given a Fitbit Inspire 2 device and access to a study app with digital questionnaires. Statistical analysis included descriptive statistics on user behavior and retention, as well as a comparative analysis of symptoms from the digital questionnaires with metrics from the sensor devices at different phases of the menstrual cycle. Results The average time spent in the study was 63.3 (SD 33.0) days with 9 of the 42 individuals dropping out within 2 weeks of the start of the study. We collected partial data from 114 ovulatory cycles, encompassing 33 participants, and obtained complete data from a total of 50 cycles. Participants reported a total of 2468 symptoms in the daily questionnaires administered during the luteal phase and menses. Despite difficulties with data completeness, the combined questionnaire and sensor data collection was technically feasible and provided interesting biological insights. We observed an increased heart rate in the mid and end luteal phase compared with menses and participants with severe premenstrual syndrome walked substantially fewer steps (average daily steps 10,283, SD 6277) during the luteal phase and menses compared with participants with no or low premenstrual syndrome (mean 11,694, SD 6458). Conclusions We demonstrate the feasibility of using an app-based approach to collect combined wearable device and questionnaire data on menstrual cycles. Dropouts in the early weeks of the study indicated that engagement efforts would need to be improved for larger studies. Despite the challenges of collecting wearable data on consecutive days, the data collected provided valuable biological insights, suggesting that the use of questionnaires in conjunction with wearable data may provide a more complete understanding of the menstrual cycle and its impact on daily life. The biological findings should motivate further research into understanding the relationship between the menstrual cycle and objective physiological measurements from sensor devices.
Searching for the Virulence-contributing Genes of the Magnaporthe oryzae by Transcriptome Analysis

Magnaporthe oryzae is a fungal pathogen that causes rice blast. Plant metabolites such as plant hormones and phytoalexin can promote or inhibit the rice blast infection. To study the effect of plant metabolites on M. oryzae, we selected salicylic acid (SA), abscisic acid (ABA), and a phytoalexin sakuranetin to treat M. oryzae grown on the medium. Through the analysis of transcriptome data, 185 and 38 genes, 803 and 156 genes, and 1525 and 428 genes were up- or down-regulated after SA, ABA, or sakuranetin treatment. Among these differentially expressed genes (DEGs), most of them were annotated to the cellular process and metabolic process in the biological process category and binding and catalytic activity in the molecular function category by GO analysis. According to KEGG pathway analysis, metabolism is the pathway with the highest number of DEGs, and the main enriched pathway is carbohydrate, lipid, and amino acid metabolism. In addition, we also found two ABA-induced up-regulated genes that may contribute to M. oryzae infection from the transcriptome data. We verified their expressions in M. oryzae that infected rice.
Cranberry supplementation improves physiological markers of performance in trained runners

[Purpose] Cranberries have the highest polyphenol and antioxidant capacity among fruits and vegetables and may protect against exercise-induced free radical production, consequently improving performance. This study aimed to investigate the effect of polyphenol-rich cranberry extract (CE) on time-trial performance and lactate response following exercise. [Methods] A total of 14 trained runners were tested at i) baseline, ii) 2 h following an acute CE dose (0.7 g/kg of body mass), and iii) 4 weeks after daily supplement consumption (0.3 g/kg of body mass). At each time point, runners performed a 1500-m race followed by a 400-m race where the live vastus lateralis oxygenation changes were determined by near-infrared spectroscopy and blood lactate was measured at rest and 1 and 3 min after each trial. The Shapiro-Wilk test and repeated-measures analysis of variance were used to establish significance (P <0.05). [Results] Cranberry supplementation over 28 d improved aerobic performance during the 1500-m time trial, whereas the acute dose had no effect. More specifically, muscle reoxygenation rates were significantly faster after 28 d compared to baseline (P = 0.04; η2 = 0.29), and a trend towards slower deoxygenation rate was observed (P = 0.13; η2 = 0.20). Chronic CE consumption also buffered the post-exercise lactate response for the 400-m race (P = 0.01; η2 = 0.27), while no effects were seen for the longer race. [Conclusion] Our results suggest that cranberry supplementation may have ergogenic effects, as it improves physiological markers of performance during short- and long-distance running.
Sandbox Data Science: Culturally Relevant K-12 Computing

in culturally responsive computing (CRC) provide viable frames for how to design learning experiences that encourage learner access, empowerment, and personal interests—key features for spurring field diversity through learning. With this imperative and framing in mind, we share our project called “Coding Like a Data Miner” (CLDM), which leverages a social media-based application programming interface (API) to teach learners how to gather, process (or wrangle), analyze and then communicate insights learned from “big data” sets. We describe this design as sandbox data science (SDS)—an approach to computing-based data science that is consistent with CRC perspectives with demonstrated promise in broadening participation and enhancing productivity in computer science education. In this article, we share insights into our rationale and the theoretical perspectives that drive our curricular design. We then provide an overview of the curriculum with case examples of the sorts of pursuits that can be taken up by learners in this context. Finally, we reflect on CLDM and design principles that make SDS a viable approach to broadening computing-based data science participation and productivity. This curriculum and accompanying resources are publicly available for review, use and adaptation at www.abclearninglab. com/cldm.
31. PRELIMINARY DINOFLAGELLATE BIOSTRATIGRAPHY FOR THE MIDDLE EOCENE TO LOWER OLIGOCENE FROM THE SOUTHWEST ATLANTIC OCEAN

A preliminary examination of Paleogene dinoflagellate cysts from core holes drilled on DSDP Leg 71, Falkland Plateau, indicates that dinoflagellates may be useful for recognizing the Eocene/Oligocene boundary in the southwestern Atlantic Ocean. In addition, data from Hole 511 provide evidence for establishing tentative limits to the age of type material for several dinoflagellate taxa described by Wilson (1967) from erratics at McMurdo Sound, Antarctica. Illustrations and brief comments about morphology or taxonomic relationships are presented for all taxa discussed in this chapter, including informal and undescribed forms.
A Baseline-Free Electromechanical Impedance Resonance Method for Measuring the Modulus of Elasticity of Concrete Cubes Using Surface-Bonded PZT Patches

The modulus of elasticity of concrete (Ec) is an essential parameter commonly used in concrete design, concrete curing monitoring, and deterioration evaluation and damage detection of concrete structures. A quick and reliable in situ determination of Ec helps in accurate decision-making for construction and maintenance. The electromechanical impedance (EMI) technique using surface-bonded lead zirconate titanate (PZT) patches (referred to as SBP) has become a popular nondestructive method for monitoring concrete structures due to ease of operations. The existing research mainly utilized baseline-dependent approaches to monitor the changes (e.g., hardening or damage evolution) of concrete structures. However, relevant baselines are greatly influenced by the status of PZT sensors and bonding layers, limiting the practical applications of this technique. This paper presents a baseline-free EMI resonance method for measuring Ec of concrete without using prior baseline data for the first time. Numerical and experimental studies on standard concrete cubes (100 mm in width) with SBP were conducted to examine the proposed method. A dimensionless physical quantity was first proposed to view the EMI signals. Then, resonance peaks highly related to concrete properties but insensitive to the status of the sensor system were selected, physically described, and correlated to the concrete parameters through numerical analyses. Finally, experimental validations, covering the measurement of Ec, repeatability of the chosen resonance peaks, and temperature effects, were conducted to illustrate the proposed method’s stability, accuracy, and sensitivity.
Analysis of Human Performance and Potential Application of Virtual Reality (VR) Shooting Games as a Shooting Training Simulator for Military Personnel

Shooting is one of the essential abilities that military personnel must regularly train. One technology that can be applied to the military shooting sector is the Virtual Reality (VR) shooting game. This technology is a shooting training simulator for Military Personnel. This study compares the shooting performance of military personnel in both real and virtual environments. The researchers analyzed the learning curve of the shooting performance, measured the degree of reality, immersive level, and usability of the VR shooting games, and tested the potential application of the simulator. The result showed that the shooting accuracy and precision in real and virtual shooting conditions do not significantly differ. This means that using a VR shooting game simulator can represent the training conditions in the actual shooting range. The shooting speed in virtual environments is related to the stages of the shooter (position, breath control, aiming, and trigger control), which are influenced by human performance factors (shock, vibration, and gun explosions). In addition, the shooting performance when practicing virtual shooting increased significantly, proving a learning curve for adapting to virtual environments in the simulator. VR shooting games had a total SUS score of 81.1, categorized as Grade A usability or excellent usability. Based on the results of the Pearson correlation test, there is a strong positive relationship between the SUS questionnaire and Presence Questionnaire (0.908), SUS and Immersive Tendencies Questionnaire (0.802), and Presence Questionnaire and Immersive Tendencies Questionnaire (0.814). Good usability in VR shooting games positively influences the degree of reality and can make participants feel a high presence when shooting virtually. Thus, VR shooting games are appropriate to support military shooting training for military personnel.
Observations of Harbor Porpoise in the Vicinity of Acoustic Alarms on a Set Gill Net

Large and small-scale fishery experiments have demonstrated that attaching acoustic devices (pingers) on gill nets reduces harbor porpoise (Phocoenaphocoena) entanglement and mortality. However, the mechanism for mortality reduction is unknown. We conducted shorebased observations of a set gill net that was alternately set with and without alarms for 2to 5-day periods during 27 days between 11 July 6 August 1996. When the acoustic alarms were attached to the net, harbor porpoise were displaced by an acoustic buffer with a radius of at least 125 m around the net (P <O.Ol), which demonstrated the effectiveness of pingers in reducing entanglement.
Design of Preamplifier for Ultrasound Transducers

In diagnostic ultrasound imaging applications, preamplifiers are used as first-stage analog front-end amplifiers for ultrasound transducers because they can amplify weak acoustic signals generated directly by ultrasound transducers. For emerging diagnostic ultrasound imaging applications, different types of preamplifiers with specific design parameters and circuit topologies have been developed, depending on the types of the ultrasound transducer. In particular, the design parameters of the preamplifier, such as the gain, bandwidth, input- or output-referred noise components, and power consumption, have a tradeoff relationship. Guidelines on the detailed design concept, design parameters, and specific circuit design techniques of the preamplifier used for ultrasound transducers are outlined in this paper, aiming to help circuit designers and academic researchers optimize the performance of ultrasound transducers used in the diagnostic ultrasound imaging applications for research directions.
An economic analysis of productivity in organ transplantation in Brazil just before and after the COVID-19 pandemic

We evaluate the productivity of the Brazilian states and the Federal District in transforming potential organ donors into actual donations in the years immediately before and after the COVID-19 pandemic (2019-2022). The Brazilian National Health System (SUS) which is one of the largest public transplant systems in the world, provides full coverage of all costs involved in organ donation, transplants, and post-transplant. We applied Ordinary Least Squares Regression in data from 2019-2022 and the results indicate that there is significant room for improvement in terms of converting potential donors into actual donors. The number of donors with organs transplanted decreased during the pandemic as the productivity of the transplant was affected, although it seems that the structure of the Brazilian transplant system and its technology were not significantly affected permanently.
STEM-PjBL and creativity of science learning students in elementary schools

Science learning is a lesson that studies nature and life. Science learning opens up opportunities for students to ask questions, develop new ideas, and build new skills, so this research aims to describe the implementation of STEM-PjBL, measure student creativity after implementing STEM-PjBL, and measure the influence of STEM-PjBL in science learning. This research uses a quantitative approach with the Pretest-Posttest Nonequivalent Control Group Design type. In this design, there are two groups: the experimental and the control groups. Data collection techniques use observation. The subjects used were 58 students, consisting of 28 as an experimental group and 30 as a control group. Data analysis techniques include a paired sample t-test and an independent sample t-test with a significance level of 5%. There is an increase in student creativity in the learning process. The results of the paired sample t-test show that there are significant differences in student creativity after the STEM-PjBL action in learning. The results of the independent sample t-test show that there are significant differences in STEM-PjBL actions in the learning process. STEM-PjBL has an influence on student creativity in the learning process.
Obesity and Cancer Rehabilitation for Functional Recovery and Quality of Life in Breast Cancer Survivors: A Comprehensive Review

Simple Summary Obesity is a global health challenge closely linked to breast cancer (BC). Recognizing this intricate relationship, our research explores the multifaceted connections between obesity and BC. The aim of this narrative review was to underline the key role of a personalized rehabilitation approach targeting the connection between obesity and BC. By comprehending these links, we aim to enhance risk assessment, improve survivorship care, and contribute valuable insights to the broader field of cancer rehabilitation. Moreover, tailored and technology-driven methods show potential insight into enhancing the effectiveness of rehabilitation in cancer patients with obesity. Through this research, we seek to pave the way for effective strategies addressing the challenges faced by obese individuals with BC. Abstract Obesity is a global health challenge with increasing prevalence, and its intricate relationship with cancer has become a critical concern in cancer care. As a result, understanding the multifactorial connections between obesity and breast cancer is imperative for risk stratification, tailored screening, and rehabilitation treatment planning to address long-term survivorship issues. The review follows the SANRA quality criteria and includes an extensive literature search conducted in PubMed/Medline, Web of Science, and Scopus. The biological basis linking obesity and cancer involves complex interactions in adipose tissue and the tumor microenvironment. Various mechanisms, such as hormonal alterations, chronic inflammation, immune system modulation, and mitochondrial dysfunction, contribute to cancer development. The review underlines the importance of comprehensive oncologic rehabilitation, including physical, psychological, and nutritional aspects. Cancer rehabilitation plays a crucial role in managing obesity-related symptoms, offering interventions for physical impairments, pain management, and lymphatic disorders, and improving both physical and psychological well-being. Personalized and technology-driven approaches hold promise for optimizing rehabilitation effectiveness and improving long-term outcomes for obese cancer patients. The comprehensive insights provided in this review contribute to the evolving landscape of cancer care, emphasizing the importance of tailored rehabilitation in optimizing the well-being of obese cancer patients.