bluuebunny/crossref_metadata_2025 huggingface dataset:
SF-Adapter
Authors: Kang Hua , Hu Qingyong , Zhang Qian
Published: January 2024

Wearable sensor-based human activity recognition (HAR) has gained significant attention due to the widespread use of smart wearable devices. However, variations in different subjects can cause a domain shift that impedes the scaling of the recognition model. Unsupervised domain adaptation has been proposed as a solution to recognize activities in new, unlabeled target domains by training the source and target data together. However, the need for accessing source data raises privacy concerns. Source-free domain adaptation has emerged as a practical setting, where only a pre-trained source model is provided for the unlabeled target domain. This setup aligns with the need for personalized activity model adaptation on target local devices. As the edge devices are resource-constrained with limited memory, it is crucial to take the computational efficiency, i.e., memory cost into consideration. In this paper, we develop a source-free domain adaptation framework for wearable sensor-based HAR, with a focus on computational efficiency for target edge devices. Firstly, we design a lightweight add-on module called adapter to adapt the frozen pre-trained model to the unlabeled target domain. Secondly, to optimize the adapter, we adopt a simple yet effective model adaptation method that leverages local representation similarity and prediction consistency. Additionally, we design a set of sample selection optimization strategies to select samples effective for adaptation and further enhance computational efficiency while maintaining adaptation performance. Our extensive experiments on three datasets demonstrate that our method achieves comparable recognition accuracy to the state-of-the-art source free domain adaptation methods with fewer than 1% of the parameters updated and saves up to 4.99X memory cost.
Reflected Reality
Authors: Zhou Qiushi , Syiem Brandon Victor , Li Beier , Goncalves Jorge , Velloso Eduardo
Published: January 2024

We propose Reflected Reality: a new dimension for augmented reality that expands the augmented physical space into mirror reflections. By synchronously tracking the physical space in front of the mirror and the reflection behind it using an AR headset and an optional smart mirror component, reflected reality enables novel AR interactions that allow users to use their physical and reflected bodies to find and interact with virtual objects. We propose a design space for AR interaction with mirror reflections, and instantiate it using a prototype system featuring a HoloLens 2 and a smart mirror. We explore the design space along the following dimensions: the user's perspective of input, the spatial frame of reference, and the direction of the mirror space relative to the physical space. Using our prototype, we visualise a use case scenario that traverses the design space to demonstrate its interaction affordances in a practical context. To understand how users perceive the intuitiveness and ease of reflected reality interaction, we conducted an exploratory and a formal user evaluation studies to characterise user performance of AR interaction tasks in reflected reality. We discuss the unique interaction affordances that reflected reality offers, and outline possibilities of its future applications.
LocCams
Authors: Gu Yangyang , Chen Jing , Wu Cong , He Kun , Zhao Ziming , Du Ruiying
Published: January 2024

Unlawful wireless cameras are often hidden to secretly monitor private activities. However, existing methods to detect and localize these cameras are interactively complex or require expensive specialized hardware. In this paper, we present LocCams, an efficient and robust approach for hidden camera detection and localization using only a commodity device (e.g., a smartphone). By analyzing data packets in the wireless local area network, LocCams passively detects hidden cameras based on the packet transmission rate. Camera localization is achieved by identifying whether the physical channel between our detector and the hidden camera is a Line-of-Sight (LOS) propagation path based on the distribution of channel state information subcarriers, and utilizing a feature extraction approach based on a Convolutional Neural Network (CNN) model for reliable localization. Our extensive experiments, involving various subjects, cameras, distances, user positions, and room configurations, demonstrate LocCams' effectiveness. Additionally, to evaluate the performance of the method in real life, we use subjects, cameras, and rooms that do not appear in the training set to evaluate the transferability of the model. With an overall accuracy of 95.12% within 30 seconds of detection, LocCams provides robust detection and localization of hidden cameras.
LoCal
Authors: Zhang Duo , Zhang Xusheng , Xie Yaxiong , Zhang Fusang , Wang Xuanzhi , Li Yang , Zhang Daqing
Published: January 2024

Millimeter wave (mmWave) radar excels in accurately estimating the distance, speed, and angle of the signal reflectors relative to the radar. However, for diverse sensing applications reliant on radar's tracking capability, these estimates must be transformed from radar to room coordinates. This transformation hinges on the mmWave radar's location attribute, encompassing its position and orientation in room coordinates. Traditional outdoor calibration solutions for autonomous driving utilize corner reflectors as static reference points to derive the location attribute. When deployed in the indoor environment, it is challenging, even for the mmWave radar with GHz bandwidth and a large antenna array, to separate the static reference points from other multipath reflectors. To tackle the static multipath, we propose to deploy a moving reference point (a moving robot) to fully harness the velocity resolution of mmWave radar. Specifically, we select a SLAM-capable robot to accurately obtain its locations under room coordinates during motion, without requiring human intervention. Accurately pairing the locations of the robot under two coordinate systems requires tight synchronization between the mmWave radar and the robot. We therefore propose a novel trajectory correspondence based calibration algorithm that takes the estimated trajectories of two systems as input, decoupling the operations of two systems to the maximum. Extensive experimental results demonstrate that the proposed calibration solution exhibits very high accuracy (1.74 cm and 0.43° accuracy for location and orientation respectively) and could ensure outstanding performance in three representative applications: fall detection, point cloud fusion, and long-distance human tracking.
RLoc
Authors: Zhang Tianyu , Zhang Dongheng , Wang Guanzhong , Li Yadong , Hu Yang , sun Qibin , Chen Yan
Published: January 2024

In recent years, decimeter-level accuracy in WiFi indoor localization has become attainable within controlled environments. However, existing methods encounter challenges in maintaining robustness in more complex indoor environments: angle-based methods are compromised by the significant localization errors due to unreliable Angle of Arrival (AoA) estimations, and fingerprint-based methods suffer from performance degradation due to environmental changes. In this paper, we propose RLoc, a learning-based system designed for reliable localization and tracking. The key design principle of RLoc lies in quantifying the uncertainty level arises in the AoA estimation task and then exploiting the uncertainty to enhance the reliability of localization and tracking. To this end, RLoc first manually extracts the underutilized beamwidth feature via signal processing techniques. Then, it integrates the uncertainty quantification into neural network design through Kullback-Leibler (KL) divergence loss and ensemble techniques. Finally, these quantified uncertainties guide RLoc to optimally leverage the diversity of Access Points (APs) and the temporal continuous information of AoAs. Our experiments, evaluating on two datasets gathered from commercial off-the-shelf WiFi devices, demonstrate that RLoc surpasses state-of-the-art approaches by an average of 36.27% in in-domain scenarios and 20.40% in cross-domain scenarios.
Thermal Earring
Authors: Xue Qiuyue Shirley , Liu Yujia , Breda Joseph , Springston Mastafa , Iyer Vikram , Patel Shwetak
Published: January 2024

Body temperature is an important vital sign which can indicate fever and is known to be correlated with activities such as eating, exercise and stress. However, continuous temperature monitoring poses a significant challenge. We present Thermal Earring, a first-of-its-kind smart earring that enables a reliable wearable solution for continuous temperature monitoring. The Thermal Earring takes advantage of the unique position of earrings in proximity to the head, a region with tight coupling to the body unlike watches and other wearables which are more loosely worn on extremities. We develop a hardware prototype in the form factor of real earrings measuring a maximum width of 11.3 mm and a length of 31 mm, weighing 335 mg, and consuming only 14.4 uW which enables a battery life of 28 days in real-world tests. We demonstrate this form factor is small and light enough to integrate into real jewelry with fashionable designs. Additionally, we develop a dual sensor design to differentiate human body temperature change from environmental changes. We explore the use of this novel sensing platform and find its measured earlobe temperatures are stable within ±0.32 °C during periods of rest. Using these promising results, we investigate its capability of detecting fever by gathering data from 5 febrile patients and 20 healthy participants. Further, we perform the first-ever investigation of the relationship between earlobe temperature and a variety of daily activities, demonstrating earlobe temperature changes related to eating and exercise. We also find the surprising result that acute stressors such as public speaking and exams cause measurable changes in earlobe temperature. We perform multi-day in-the-wild experiments and confirm the temperature changes caused by these daily activities in natural daily scenarios. This initial exploration seeks to provide a foundation for future automatic activity detection and earring-based wearables.
EarSE
Authors: Duan Di , Chen Yongliang , Xu Weitao , Li Tianxing
Published: January 2024

Speech enhancement is regarded as the key to the quality of digital communication and is gaining increasing attention in the research field of audio processing. In this paper, we present EarSE, the first robust, hands-free, multi-modal speech enhancement solution using commercial off-the-shelf headphones. The key idea of EarSE is a novel hardware setting---leveraging the form factor of headphones equipped with a boom microphone to establish a stable acoustic sensing field across the user's face. Furthermore, we designed a sensing methodology based on Frequency-Modulated Continuous-Wave, which is an ultrasonic modality sensitive to capture subtle facial articulatory gestures of users when speaking. Moreover, we design a fully attention-based deep neural network to self-adaptively solve the user diversity problem by introducing the Vision Transformer network. We enhance the collaboration between the speech and ultrasonic modalities using a multi-head attention mechanism and a Factorized Bilinear Pooling gate. Extensive experiments demonstrate that EarSE achieves remarkable performance as increasing SiSDR by 14.61 dB and reducing the word error rate of user speech recognition by 22.45--66.41% in real-world application. EarSE not only outperforms seven baselines by 38.0% in SiSNR, 12.4% in STOI, and 20.5% in PESQ on average but also maintains practicality.
Laser-Powered Vibrotactile Rendering
Authors: Su Yuning , Jin Yuhua , Wang Zhengqing , Shi Yonghao , Huang Da-Yuan , Han Teng , Yang Xing-Dong
Published: January 2024

We investigate the feasibility of a vibrotactile device that is both battery-free and electronic-free. Our approach leverages lasers as a wireless power transfer and haptic control mechanism, which can drive small actuators commonly used in AR/VR and mobile applications with DC or AC signals. To validate the feasibility of our method, we developed a proof-of-concept prototype that includes low-cost eccentric rotating mass (ERM) motors and linear resonant actuators (LRAs) connected to photovoltaic (PV) cells. This prototype enabled us to capture laser energy from any distance across a room and analyze the impact of critical parameters on the effectiveness of our approach. Through a user study, testing 16 different vibration patterns rendered using either a single motor or two motors, we demonstrate the effectiveness of our approach in generating vibration patterns of comparable quality to a baseline, which rendered the patterns using a signal generator.
Do I Just Tap My Headset?
Authors: Khurana Anjali , Glueck Michael , Chilana Parmit K.
Published: January 2024

A variety of consumer Augmented Reality (AR) applications have been released on mobile devices and novel immersive headsets over the last five years, creating a breadth of new AR-enabled experiences. However, these applications, particularly those designed for immersive headsets, require users to employ unfamiliar gestural input and adopt novel interaction paradigms. To better understand how everyday users discover gestures and classify the types of interaction challenges they face, we observed how 25 novices from diverse backgrounds and technical knowledge used four different AR applications requiring a range of interaction techniques. A detailed analysis of gesture interaction traces showed that users struggled to discover the correct gestures, with the majority of errors occurring when participants could not determine the correct sequence of actions to perform or could not evaluate their actions. To further reflect on the prevalence of our findings, we carried out an expert validation study with eight professional AR designers, engineers, and researchers. We discuss implications for designing discoverable gestural input techniques that align with users' mental models, inventing AR-specific onboarding and help systems, and enhancing system-level machine recognition.
Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones
Authors: Pillai Arvind , Nepal Subigya Kumar , Wang Weichen , Nemesure Matthew , Heinz Michael , Price George , Lekkas Damien , Collins Amanda C. , Griffin Tess , Buck Benjamin , Preum Sarah Masud , Cohen Trevor , Jacobson Nicholas C. , Ben-Zeev Dror , Campbell Andrew
Published: January 2024

Speech-based diaries from mobile phones can capture paralinguistic patterns that help detect mental illness symptoms such as suicidal ideation. However, previous studies have primarily evaluated machine learning models on a single dataset, making their performance unknown under distribution shifts. In this paper, we investigate the generalizability of speech-based suicidal ideation detection using mobile phones through cross-dataset experiments using four datasets with N=786 individuals experiencing major depressive disorder, auditory verbal hallucinations, persecutory thoughts, and students with suicidal thoughts. Our results show that machine and deep learning methods generalize poorly in many cases. Thus, we evaluate unsupervised domain adaptation (UDA) and semi-supervised domain adaptation (SSDA) to mitigate performance decreases owing to distribution shifts. While SSDA approaches showed superior performance, they are often ineffective, requiring large target datasets with limited labels for adversarial and contrastive training. Therefore, we propose sinusoidal similarity sub-sampling (S3), a method that selects optimal source subsets for the target domain by computing pair-wise scores using sinusoids. Compared to prior approaches, S3 does not use labeled target data or transform features. Fine-tuning using S3 improves the cross-dataset performance of deep models across the datasets, thus having implications in ubiquitous technology, mental health, and machine learning.
AdaStreamLite
Authors: Wei Yuheng , Xiong Jie , Liu Hui , Yu Yingtao , Pan Jiangtao , Du Junzhao
Published: January 2024

Streaming speech recognition aims to transcribe speech to text in a streaming manner, providing real-time speech interaction for smartphone users. However, it is not trivial to develop a high-performance streaming speech recognition system purely running on mobile platforms, due to the complex real-world acoustic environments and the limited computational resources of smartphones. Most existing solutions lack the generalization to unseen environments and have difficulty to work with streaming speech. In this paper, we design AdaStreamLite, an environment-adaptive streaming speech recognition tool for smartphones. AdaStreamLite interacts with its surroundings to capture the characteristics of the current acoustic environment to improve the robustness against ambient noise in a lightweight manner. We design an environment representation extractor to model acoustic environments with compact feature vectors, and construct a representation lookup table to improve the generalization of AdaStreamLite to unseen environments. We train our system using large speech datasets publicly available covering different languages. We conduct experiments in a large range of real acoustic environments with different smartphones. The results show that AdaStreamLite outperforms the state-of-the-art methods in terms of recognition accuracy, computational resource consumption and robustness against unseen environments.
Controlled Release Bilayer Floating Effervescent and Noneffervescent Tablets Containing Levofloxacin and Famotidine
Authors: Tufail Muhammad , Shah Kifayat Ullah , Khan Ikram Ullah , Khan Kamran Ahmad , Shah Shefaat Ullah , Rashid Faisal , Khan Jahangir , Alshammari Abdulrahman , Alasmari Abdullah F. , Riaz Muhammad Shahid
Published: January 2024

The present study is aimed at designing bilayer‐floating tablets to improve the drug concentration in the stomach for enhanced therapeutic efficacy. The tablets are comprised of an upper layer of levofloxacin (466.5 mg) and a lower layer of famotidine (133.5 mg). Five formulations (F1‐F5) were developed by using hydroxypropyl methylcellulose grades (K4M, K15M, and K100M) along with Carbopol 934. In the case of the effervescent system (F1‐F3), sodium bicarbonate was added to impart buoyancy to the tablets; while in the case of noneffervescent formulations (F4 & F5), guar gum and xanthan gum were incorporated to induce flotation and swelling and retard the release of a drug. The precompression characteristics of tablets depict the suitability of all formulation powder for direct compression. The ATR‐FTIR analyses have shown that the components of both effervescent and noneffervescent tablets are compatible with each other. The total weight of each tablet was 600 mg, with a weight variation of about ≤10 mg. Both the layers were smooth and flat with a thickness ranging from 3.16 ± 0.04 to 3.54 ± 0.01 mm. The diameters of prepared floating tablets were about 15 mm, optimum for oral administration. After adjusting the tablet’s hardness to 6‐7 kg/cm2, its friability was found to be <0.35 percent. The mean drug content of the formulations was above 90%. The floating lag time of all formulations (F2‐F5) was below 25 seconds, except F1 which took almost 50 seconds to start floating on the surface of gastric content due to its higher density. The total floating time of effervescent (F1‐F3) and noneffervescent formulations was in the range of 15‐25 hours, thereby providing sufficient time to complete drug release and absorption in the gastric area. The total floating time of noneffervescent formulations was higher (p ≤ 0.05) than effervescent formulations due to efficient wettability and swelling characteristics. The release of drugs from both layers of noneffervescent tablets was significantly controlled when compared to the effervescent system, and an anomalous non‐Fickian diffusion was found for the drug release. The stability study of the optimized formulation proved the integrity and stability of the developed formulation. Thus, developed formulations are deemed suitable for controlled codelivery of active pharmaceutical ingredients for the effective treatment of H. pylori.
Research on Nonroutine Problems: A Hybrid Didactical Design for Overcoming Student Learning Obstacles
Authors: Sukarma I. K. , Isnawan Muhamad G. , Alsulami Naif M.
Published: January 2024

The multiplication of fractions was generally considered easy, but it became challenging when presented as a nonroutine problem. One solution to these challenges was integrating technology into learning. Therefore, the study is aimed at proposing a hybrid module as a solution. The design in this research was a didactical design research. The study involved 56 participants, aged 13–18 years, mainly female students from the Sasak tribe in an Indonesian junior high school. Researchers employed fraction comprehension tests, in-depth interviews, and a hybrid hypothesis module as primary instruments. For learning outcomes, NVivo-12-assisted thematic analysis was used, and for learning obstacles, retrospective-based qualitative analysis was used. The study findings revealed several factors that caused learning obstacles, including conceptual ontogenic, epistemological factors, and students’ restricted Internet access. The hybrid hypothesis module proved effective in assisting students by providing additional problem contexts and scaffolding, addressing previous learning obstacles. After implementation, there was a redesign of the hybrid module in the form of additional scaffolding to help students construct the concepts studied. This research concluded that the application of technology in the form of hybrid modules was able to minimize barriers to student learning.
Health and Characteristics of Australian Apple Growing Soils
Authors: Hardie Marcus , Oliver Garth , Cotching William , Walker Brianna , Lancaster Rachel , Swarts Nigel
Published: January 2024

Despite being the highest value fruit crop in Australia, little is known about the types and condition or “health” of Australia’s apple growing soils. This study is unique in being the first to report the condition and characteristics of Australia’s apple growing soils; it provides essential baseline data for future monitoring of soil health in apple production systems, as well as soil physical and chemical data required for the development of perennial soil-tree-climate models. Soil chemical and physical properties were measured at 34 orchards, across five states. Soils were assessed for water retention, hydraulic conductivity, bulk density, macroporosity, organic carbon, CEC, ESP, pH, and EC. Despite high to very high levels of organic carbon, most topsoils were moderately to poorly structured. Around one-third to half of all sites showed evidence of poor aeration or impeded drainage, whilst 10 of the 34 sites were prone to nutrient leaching. Plant available soil water (PAWC) varied greatly between sites from 31 mm to 170 mm from 0 to 60 cm depth and between sites within the same soil order. Whilst topsoils had high to very high levels of organic carbon (average: 2.46%), they were otherwise poorly structured, with higher than expected bulk density (average: 1.32 g/cm3) and lower than expected air capacity (average: 9.97%) and macroporosity (average: 1.75%). Subsoils were also found to have little soil water availability (average: 15.39 mm/100 mm), low air capacity (average: 5.28%), and low CEC (average: 8.12 cmol (+) kg−1). Notably, 10 of the 34 sites had less than 6 cmol (+) kg−1 CEC throughout the entire soil profile, indicating potential risk of nutrient leaching. This study indicates that apple growing soils require careful management to improve topsoil structure, and to maintain or increase soil carbon, as well as use of soil moisture sensors to schedule irrigation. In addition, some sites also require improved subsoil drainage and care to ensure fertigation and irrigation do not result in leaching of nutrients beneath the root zone.
Molecular Insights into the Breast and Prostate Cancer Cells in Response to the Change of Extracellular Zinc
Authors: Barman Shital K. , Sen Monokesh K. , Mahns David A. , Wu Ming J. , Malladi Chandra S.
Published: January 2024

Zinc dyshomeostasis is manifested in breast and prostate cancer cells. This study attempted to uncover the molecular details prodded by the change of extracellular zinc by employing a panel of normal and cancerous breast and prostate cell lines coupled with the top-down proteomics with two-dimensional gel electrophoresis followed by liquid chromatography-tandem mass spectrometry. The protein samples were generated from MCF-7 breast cancer cells, MCF10A normal breast cells, PC3 prostate cancer cells, and RWPE-1 normal prostate cells with or without exogenous zinc exposure in a time course (T0 and T120). By comparing the cancer cells vs respective normal epithelial cells without zinc treatment (T0), differentially expressed proteins (23 upregulated and 18 downregulated in MCF-7 cells; 14 upregulated and 30 downregulated in PC3 cells) were identified, which provides insights into the intrinsic differences of breast and prostate cancer cells. The dynamic protein landscapes in the cancer cells prodded by the extracellular zinc treatment reveal the potential roles of the identified zinc-responsive proteins (e.g., triosephosphate isomerase, S100A13, tumour proteins hD53 and hD54, and tumour suppressor prohibitin) in breast and prostate cancers. This study, for the first time, simultaneously investigated the two kinds of cancer cells related to zinc dyshomeostasis, and the findings shed light on the molecular understanding of the breast and prostate cancer cells in response to extracellular zinc variation.
Table S7 from Early Cancer Detection in Li–Fraumeni Syndrome with Cell-Free DNA
Authors: Wong Derek , Luo Ping , Oldfield Leslie E. , Gong Haifan , Brunga Ledia , Rabinowicz Ron , Subasri Vallijah , Chan Clarissa , Downs Tiana , Farncombe Kirsten M. , Luu Beatrice , Norman Maia , Sobotka Julia A. , Uju Precious , Eagles Jenna , Pedersen Stephanie , Wellum Johanna , Danesh Arnavaz , Prokopec Stephenie D. , Stutheit-Zhao Eric Y. , Znassi Nadia , Heisler Lawrence E. , Jovelin Richard , Lam Bernard , Lujan Toro Beatriz E. , Marsh Kayla , Sundaravadanam Yogi , Torti Dax , Man Carina , Goldenberg Anna , Xu Wei , Veit-Haibach Patrick , Doria Andrea S. , Malkin David , Kim Raymond H. , Pugh Trevor J.
Published: January 2024

Supplemental Table 7 – Clinical Findings
Supplementary Table 4 from SUV39H1 Ablation Enhances Long-term CAR T Function in Solid Tumors
Authors: López-Cobo Sheila , Fuentealba Jaime R. , Gueguen Paul , Bonté Pierre-Emmanuel , Tsalkitzi Kyriaki , Chacón Irena , Glauzy Salomé , Bohineust Armelle , Biquand Ariane , Silva Lisseth , Gouveia Zelia , Goudot Christel , Perez Franck , Saitakis Michael , Amigorena Sebastian
Published: January 2024

Gene signatures used in the study. Related to Figures 3, 4, 5, S4, S5, S6, and S7.
Data from AMG 509 (Xaluritamig), an Anti-STEAP1 XmAb 2+1 T-cell Redirecting Immune Therapy with Avidity-Dependent Activity against Prostate Cancer
Authors: Nolan-Stevaux Olivier , Li Cong , Liang Lingming , Zhan Jinghui , Estrada Juan , Osgood Tao , Li Fei , Zhang Hanzhi , Case Ryan , Murawsky Christopher M. , Estes Bram , Moore Gregory L. , Bernett Matthew J. , Muchhal Umesh , Desjarlais John R. , Staley Binnaz K. , Stevens Jennitte , Cooke Keegan S. , Aeffner Famke , Thomas Oliver , Stieglmaier Julia , Lee Jae-Lyun , Coxon Angela , Bailis Julie M.
Published: January 2024

Abstract
The tumor-associated antigen STEAP1 is a potential therapeutic target that is expressed in most prostate tumors and at increased levels in metastatic castration-resistant prostate cancer (mCRPC). We developed a STEAP1-targeted XmAb 2+1 T-cell engager (TCE) molecule, AMG 509 (also designated xaluritamig), that is designed to redirect T cells to kill prostate cancer cells that express STEAP1. AMG 509 mediates potent T cell–dependent cytotoxicity of prostate cancer cell lines in vitro and promotes tumor regression in xenograft and syngeneic mouse models of prostate cancer in vivo. The avidity-driven activity of AMG 509 enables selectivity for tumor cells with high STEAP1 expression compared with normal cells. AMG 509 is the first STEAP1 TCE to advance to clinical testing, and we report a case study of a patient with mCRPC who achieved an objective response on AMG 509 treatment.
Significance:
Immunotherapy in prostate cancer has met with limited success due to the immunosuppressive microenvironment and lack of tumor-specific targets. AMG 509 provides a targeted immunotherapy approach to engage a patient's T cells to kill STEAP1-expressing tumor cells and represents a new treatment option for mCRPC and potentially more broadly for prostate cancer.

See related commentary by Hage Chehade et al., p. 20.

See related article by Kelly et al., p. 76.

This article is featured in Selected Articles from This Issue, p. 5
Dietary Folate and Cofactors Accelerate Age-dependent p16 Epimutation to Promote Intestinal Tumorigenesis
Authors: Yang Li , Peery Robert C. , Farmer Leah M. , Gao Xia , Zhang Yiqun , Creighton Chad J. , Zhang Lanjing , Shen Lanlan
Published: January 2024

The extent to which non-genetic environmental factors, such as diet, contribute to carcinogenesis has been long debated. One potential mechanism for the effects of environmental factors is through epigenetic modifications that affect gene expression without changing the underlying DNA sequence. However, the functional cooperation between dietary factors and cancer-causing epigenetic regulation is largely unknown. Here, we use a mouse model of age-dependent p16 epimutation, in which the p16 gene activity is directly controlled by promoter DNA methylation. We show p16 epimutation is modulated by folate and cofactors in dietary supplementation, which leads to increased colon cancer risk. Importantly, our findings provide functional evidence concerning the safety of folate fortification in the general population.

Significance:

Our study demonstrates that dietary folate and cofactors modulate tumor-suppressor gene methylation to increase intestinal tumorigenesis. Our findings highlight the need for monitoring the long-term safety of folate fortification in high-risk individuals.
Obstructive Sleep Apnea Does Not Exclude Polycythemia Vera: A Case Report
Authors: Afana Mohammad S. , Abu-Tineh Mohammad , Alshurafa Awni , Ahmed Khalid , Abdulgayoom Mohammed , Yassin Mohamed A.
Published: January 2024

Introduction: Polycythemia vera (PV) is one of the myeloproliferative neoplasms (MPN) diagnosed by World Health Organization (WHO) criteria 2016, which requires the presence of 3 major criteria: high hemoglobin/hematocrit, bone marrow findings, and Janus Kinase 2 (JAK2) mutation or two major and one minor criteria, including erythropoietin (EPO) level. However, in clinical practice, difficulties in diagnosis can arise as it may be masked by secondary causes for erythrocytosis such as smoking or obstructive sleep apnea (OSA). Case Presentation: Here, we report a 55-year-old gentleman, morbidly obese with OSA on home continuous positive airway pressure (CPAP) machine, who was incidentally found to have polycythemia. Further evaluation confirmed the diagnosis of PV. Conclusion: PV can be masked by the assumption of secondary polycythemia based on history. This underscores the importance of screening such cohort through JAK2 and EPO testing to avoid missing PV.
Trends in sentiment of Twitter users towards Indonesian tourism: analysis with the k-nearest neighbor method
Authors: Purnama Harahap Eka , Dwi Purnomo Hindriyanto , Iriani Ade , Sembiring Irwan , Nurtino Tio
Published: January 2024

This research analyzes the sentiment of Twitter users regarding tourism in Indonesia using the keyword "wonderful Indonesia" as the tourism promotion identity. The aim of this study is to gain a deeper understanding of the public sentiment towards "wonderful Indonesia" through social media data analysis. The novelty obtained provides new insights into valuable information about Indonesian tourism for the government and relevant stakeholders in promoting Indonesian tourism and enhancing tourist experiences. The method used is tweet analysis and classification using the K-nearest neighbor (KNN) algorithm to determine the positive, neutral, or negative sentiment of the tweets. The classification results show that the majority of tweets (65.1% out of a total of 14,189 tweets) have a neutral sentiment, indicating that most tweets with the "wonderful Indonesia" tagline are related to advertising or promoting Indonesian tourism. However, the percentage of tweets with positive sentiment (33.8%) is higher than those with negative sentiment (1.1%). This study also achieved training results with an accuracy rate of 98.5%, precision of 97.6%, recall of 98.5%, and F1-score of 98.1%. However, reassessment is needed in the future as Twitter users' sentiment can change along with the development of Indonesian tourism itself.
Sentiment analysis of online licensing service quality in the energy and mineral resources sector of the Republic of Indonesia
Authors: Hizqil Ahmad , Ruldeviyani Yova
Published: January 2024

The Ministry of Energy and Mineral Resources of the Republic of Indonesia regularly assessed public satisfaction with its online licensing services. User rated their satisfaction at 3.42 on a scale of 4, below the organization's average of 3.53. Evaluating public service performance is crucial for quality improvement. Previous research relied solely on survey data to assess public satisfaction. This study goes further by analyzing user feedback in text form from an online licensing application to identify negative aspects of the service that need enhancement. The dataset spanned September 2019 to February 2023, with 24,112 entries. The choice of classification methods on the highest accuracy values among decision tree, random forest, naive bayes, stochastic gradient descent, logistic regression (LR), and k-nearest neighbor. The text data was converted into numerical form using CountVectorizer and term frequency-inverse document frequency (TF-IDF) techniques, along with unigrams and bigrams for dividing sentences into word segments. LR bigram CountVectorizer ranked highest with 89% for average precision, F1-score, and recall, compared to the other five classification methods. The sentiment analysis polarity level was 36.2% negative. Negative sentiment revealed expectations from the public to the ministry to improve the top three aspects: system, mechanism, and procedure; infrastructure and facilities; and service specification product types.
Application of remote monitoring of biosignals and geolocation with a Wearable for patients with sequelae of the coronavirus
Authors: Linder Rubiños Jimenez Santiago , Garcia Perez Mario Alberto , Chávez Gallegos Eduardo Nelson , Velasquez Jimenez Linett Angélica , Alarcon Cueva Niko Alain , Sanchez Cabrera Mauro Bernardo
Published: January 2024

Several patients who have overcome coronavirus disease (COVID-19) have been left with cardiovascular and pulmonary sequelae and most medical centers lack a remote monitoring system for each patient that notifies them of any complications during rehabilitation. The objective of this research was to implement a Wearable that monitors the patient's health and alerts in case of detecting any anomaly. For this reason, a Wearable was developed that displays the patient's heart rate, oxygen saturation level and body temperature on the Light Emitting Diode (LCD) and the application mobile, sending an alert and geolocation message if anomalies are detected in vital signs. The standard deviation of heart rate, temperature and oxygen saturation was obtained, which was 1.4930, 0.1558 and 0.4364 in the rest stage, respectively, and 6.3442, 0.2365 and 0.9186 in the physical activity stage respectively with a maximum duration of 42 hours and 52 minutes of battery, managing to send alert messages and store the information in the cloud, which allows to conclude that the Wearable can facilitate the management of the database and the location of the patient, that the measurement error increases with physical activity, and that battery life varies with the number of biosignal readings per hour.
A review of wireless body area networks
Authors: Haszerila Wan Hassan Wan , Mohd Ali Darmawaty , Mohd Sultan Juwita , Kassim Murizah
Published: January 2024

The widespread adoption of wireless body area network (WBAN) in healthcare presents opportunities to meet the increasing demand for medical services. WBAN enables continuous real-time (RT) monitoring through biomedical sensor nodes positioned in or around a patient's body, collecting vital physiological data. In addition, WBAN imposes stringent criteria for energy efficiency and reliability throughout data collection and transmission. This review paper places significant emphasis on the fundamental concept and essential characteristics of WBAN technology. First, the WBAN features, including architecture, sensor nodes types and network topology is presented. Then, the study explores a wide variety of communication standards and multiple access (MA) mechanism deployed in this technology. Moreover, it discusses open research and challenging issues such as heterogeneous traffic, quality of services (QoS), energy consumption, reliability, interference management, and human body movements effect. Finally, the paper is concluded, and future directions are identified in this evolving field of human health monitoring technology.
Design analysis of moth-flame optimized fault tolerant technique for minimally buffered network-on-chip router
Authors: Sumithra Subramanian , Lakshminarayanan Venkatara Nagaiyanallur , Suresh Kumar Subramani , Purushothaman Ramaiah , Kokulavani Kathiresan , Gowri Velankanni
Published: January 2024

A network on a chip is a solitary silicon chip utilized to perform the communication characteristics of large-scale (LSI) to very large-scale integration (VLSI) systems. Network-on-chip (NoC) architecture includes links, network interfaces (NI), and routers to unite with external memories or processors. NoC is designed to flow messages from the source module to the destination module through several links involving routing decisions.
 The design of NoC is complex and the buffer section’s expensiveness creates problems while providing secured data service. Moreover, routers and links in NoC setups are liable to faults. This work introduces a minimal buffered router, and the faults in the network are optimized using moth flame optimized (MFO) fault-tolerant technique. The software named Xilinx ISE design suite 14.5 is employed for the minimum buffered router model.
 The suggested scheme is operated with less area, low power (0.241 mW), and high speed (965.261 Megahertz (MHz)) when matched with previous works.
Topic prediction modelling on social media content using machine learning
Authors: Dewi Aisha Izmi , Ayu Wulandhari Lili
Published: January 2024

The simplicity to deliver an opinion about companies or institutions via social media has resulted in both positive and negative judgments. Through social media all positive and negative information will be easily found and spread. It is concerned that negative information will lead to negative public opinion. If this occurs, the company will suffer from a lack of trust, which will harm the company's reputation. Thus, to monitor uncontrolled issues, a company wants to know what topics or opinions are developing in the community. Therefore, the topic modelling using latent dirichlet allocation (LDA) is proposed to identify topics that are being discussed on social media. The findings of this study got the coherence score of 0.558 and based on the direct human judgment, the model got an average 80% correctly. The findings of this study reveal 4 topics groups that represent the corporate social media content. These findings offer information to companies about the latest topics or opinions that are currently developing in society which could provide recommendations related to decision-making on current issues thus increasing the trust and reliability towards the company.
Deep neural networks approach with transfer learning to detect fake accounts social media on Twitter
Authors: Ridho Lubis Arif , Prayudani Santi , Luthfi Hamzah Muhammad , Yusnida Lase Yuyun , Lubis Muharman , Al-Khowarizmi Al-Khowarizmi , Ardi Hutagalung Gabriel
Published: January 2024

The massive use of social media makes people take actions that have a negative impact on cyberspace, such as creating fake accounts that aim to commit crimes such as spam and fraud to spread false information. Fake accounts are difficult to detect in the traditional way because fake accounts always use photos, names, and unreal information, there are several criteria that can identify a fake account such as no information, few followers, and minimal activity. In the traditional model, it is difficult to detect fake accounts on many Twitters social media accounts, so the application of the deep learning model with the convolutional neural network (CNN) algorithm and the application of deep learning can help detect fake accounts. This study will use data on Twitter social media so that this research produces good accuracy for the scenarios described at the methodology stage. This research produces an accuracy of 86% for the deep learning model with the CNN algorithm, and with the traditional model, it produces an accuracy of 51% while the use of transfer learning produces an accuracy of 93.9%.
Impedance analysis of squirrel-cage induction motor at high harmonics condition
Authors: Skamyin Aleksandr , Shklyarskiy Yaroslav , Lobko Kirill , Dobush Vasiliy , Sutikno Tole , Hatta Jopri Mohd
Published: January 2024

This study examines different methodologies for representing an asynchronous motor at higher harmonics by utilizing its equivalent circuit. Several approaches were compared based on simulation modeling. An experimental investigation was conducted in a laboratory setting using asynchronous electric motors with power ratings of 1.5 kW and 5.5 kW. The purpose of this investigation was to examine the characteristics of impedance in the presence of high-harmonic conditions. The generation of higher harmonics was achieved through the utilization of a precisely regulated thyristor rectifier in conjunction with a thyristor power controller. The findings indicate that the load on the shaft solely impacts the resistance at the fundamental harmonic, while the resistance at higher harmonics remains unaffected by the operating mode of the asynchronous motor (AM).
Big data vehicle density management in vehicular ad-hoc network
Authors: Tantaoui Mouad , Moukhafi Mehdi , Chana Idriss
Published: January 2024

Smart city project is today a domain of interest to community research which play well-known role in road traffic management. Data exchange became complicated in terms of capacity in the intelligent transport system (ITS), and without the raise of big data, the treatment is very difficult to manage. vehicular ad-hoc network (VANETs) faces many challenges mainly the voluminous data generated by different actors of VANET environment.
 We propose a real time anomalies detection system in an instantaneous way with parallel data treatment. The system method intends to compute precisely vehicle density at each section on each road, which help to handle the traffic and forward to vehicles information about the road and the best safe path to reach their destination. Also, we build anomalies prediction system based on machine learning framework, it is a good solution for avoiding traffic congestion and limiting the risk of accidents. The simulation results demonstrate that the proposed system method reduces congestion greatly by taking into account the load balancing and therefore avoids saturation and reduces accidents. It should also be noted that the results obtained show that the system is characterized by low latency and high accuracy.
Review of learning management systems: history, types, advantages, and challenges
Authors: Taha Al-Dhief Fahad , Al Nasser Ali , Mohamed Tharikh Shafazawana , Al Nasser Hassan , Abdul Ghaffar Al-Mosleh Ali , Abbas Abbood Albadr Musatafa , Razaq Mohamed Alsemawi Majid
Published: January 2024

As technological advancements continue in the higher education domain, the systems of higher learning management in universities and institutions have witnessed significant attention from researchers and developers. The technologies of these systems can present extremely useful tools and provide many advantages to enhance learning and increase the students’ performance. However, there are some issues and challenges that face these systems and need to be highlighted. Moreover, there is a need to review the advantages of such systems in order to motivate other countries to adopt, use, and develop the learning systems. Therefore, this paper gives an overview of the learning management systems (LMSs) in universities and institutes. Furthermore, it presents the history of LMSs, usages, systems types, advantages, challenges and issues.
Retinal lesions classification for diabetic retinopathy using custom ResNet-based classifier
Authors: Ajith Kumar Silpa , Satheesh Kumar James
Published: January 2024

Failure to diagnose and treat retinal illnesses on time might lead to irreversible blindness. The focus is on three common retinal lesions associated with diabetic retinopathy (DR): microaneurysms (MAs), haemorrhages, and exudates. The proposed solution leverages deep learning, employing a customized residual network (ResNet) based classifier trained on real-time retinal images meticulously annotated and graded by ophthalmologists. Annotation noise was a significant obstacle addressed by downsampling and augmenting the data. Compared to cutting-edge techniques, this one performs better with test-set accuracy of 93.34% across all classes. This approach holds great promise for enhancing early detection and treatment of DR by automating the recognition of these vital retinal abnormalities. The ability to automatically classify these symptoms can aid clinicians in making more precise diagnosis and starting treatments sooner. This research shows that deep learning-based approaches are highly effective, especially when combined with a customised ResNet-based classifier and thorough pre-processing steps. We observed that this method provides the ability to better the lives of patients and lower the rate of permanent blindness resulting from retinal disorders.
Performance analysis of GaN based dual active bridge converter for electric vehicle charging application
Authors: Snehalika Snehalika , Patel Ranjeeta , Kumar Panigrahi Chinmoy
Published: January 2024

The research work proposes a Gallium Nitride (GaN) based dual active bridge (DAB) converter for electric vehicle (EV) charging applications. The wide bandgap semiconductor device, GaN is implemented in the DAB topology of an Isolated Bidirectional DC-DC converter (IBDC). GaN-based DC-DC converters lead to higher efficiency, smaller size, faster charging, and reduced heat generation improving the overall performance of the EV charging system. The performance characteristics of GaN based DAB converter is analyzed both in simulation and hardware for EV charging application. The same analysis is extended to a Si based DAB converter and comparative results are presented. A 14.25 kW GaN based DAB and
 10.5 kW Si based DAB is designed and evaluated using LTspice XVII software and a scaled-down prototype of 0.612 kW GaN based DAB and 0.25 kW Si based DAB is presented for experimental validation. Result analysis under similar operating conditions indicated an improvement of 36% more output power transfer on the GaN based DAB over the traditional Si based DAB. The developed prototype showcased improved levels of power, voltage, and current under same operating conditions. The power transmission in the developed DAB based IBDC topology is controlled using the single-phase-shift (SPS) control strategy.