bluuebunny/crossref_metadata_2025 huggingface dataset:
Combining Neural Networks with Logic Rules
Authors: Zhang Lujiang
Published: March 2023

How to utilize symbolic knowledge in deep learning is an important problem. Deep neural networks are flexible and powerful, while symbolic knowledge has the virtue of interpretability and intuitiveness. It is necessary to combine the two together to inject symbolic knowledge into neural networks. We propose a novel approach to combine neural networks with logic rules. In this approach, task-specific supervised learning and policy-based reinforcement learning are performed alternately to train a neural model until convergence. The basic idea is to use supervised learning to train a deep model and use reinforcement learning to propel the deep model to meet logic rules. In the process of the policy gradient reinforcement learning, if a predicted output of a deep model meets all logical rules, the deep model is given a positive reward, otherwise, it is given a negative reward. By maximizing the expected rewards, the deep model can be gradually adjusted to meet logical constraints. We conduct experiments on the tasks of named entity recognition. The experimental results demonstrate the effectiveness of our method.
State-space of the Vasicek model for long-term bonds with Kalman filter
Authors: Mawonike Romeo , Ikpe Dennis , Gyamerah Samuel Asante
Published: March 2023

In this paper, a time-consistent and arbitrage-free state space for the one-, two-, and three-factor Vasicek models for long-term bonds is constructed. To account for the uncertainty in long-term bond yields, we propose a stochastic time-dependent mean-reversion model. The state-space model allows for the computation of measurement errors from observed yields. Appropriate state and measurement linear equations are derived to allow the use of the Kalman filter for model implementation. Based on weekly South African Government Bonds from February 2010 to February 2021, we give parameter estimates for the one-factor, two-factor, and three-factor models. The results from the study show that the developed model can fit the term structure of very long-term bonds.
eat2pic
Authors: Nakamura Yugo , Nakaoka Rei , Matsuda Yuki , Yasumoto Keiichi
Published: March 2023

Given the complexity of human eating behaviors, developing interactions to change the way users eat or their choice of meals is challenging. In this study, we propose an interactive system called eat2pic designed to encourage healthy eating habits such as adopting a balanced diet and eating more slowly, by refraining the task of selecting meals into that of adding color to landscape pictures. The eat2pic system comprises a sensor-equipped chopstick (one of a pair) and two types of digital canvases. It provides fast feedback by recognizing a user's eating behavior in real time and displaying the result on a small canvas called "one-meal eat2pic." Moreover, it also provides slow feedback by displaying the number of colors of foods that the user consumed on a large canvas called "one-week eat2pic." The former was designed and implemented as a guide to help people eat more slowly, and the latter to encourage people to select more balanced menus. Through two user studies, we explored the experience of interaction with eat2pic, in which users' daily eating behavior was reflected in a series of "paintings," that is, images produced by the automated system. The experimental results suggest that eat2pic may provide an opportunity for reflection in meal selection and while eating, as well as assist users in becoming more aware of how they are eating and how balanced their daily meals are. We expect this system to inspire users' curiosity about different diets and ways of eating. This research also contributes to expanding the design space for products and services related to dietary support.
The Tale of a Complicated Relationship
Authors: Terzimehić Nađa , Aragon-Hahner Sarah , Hussmann Heinrich
Published: March 2023

Smartphones have gotten under public scrutiny due to their ostensible negative impact on users' well-being. Nonetheless, users and related work report positive aspects of smartphones, too. We investigated this discrepancy through the prism of the emotional user-smartphone relationship by having people write love/breakup letters to their smartphones. We gathered 82 letters - 42 before and 40 during the COVID-19 pandemic. We found a mixed nature regarding the distribution of love and breakup letters and associated emotions based on the revisited OCC-model of emotions - with a slight shift towards the negative emotional spectrum during the COVID-19 pandemic. Furthermore, we performed an extensive qualitative analysis of 819 user statements extracted from the letters, resulting in a connection of emotions to 17 smartphone features and eight themes of real-life consequences of smartphone use. We then identified eight common patterns of this connection, classified as smartphone roles. The collected letters mostly model a complex user-smartphone relationship, comprising different roles depending on users' inner and outer context. We discuss how HCI could help in shaping the complex user-smartphone relationship in future research and suggest supporting a healthy balance between users' daily life and smartphone use.
Hierarchical Clustering-based Personalized Federated Learning for Robust and Fair Human Activity Recognition
Authors: Li Youpeng , Wang Xuyu , An Lingling
Published: March 2023

Currently, federated learning (FL) can enable users to collaboratively train a global model while protecting the privacy of user data, which has been applied to human activity recognition (HAR) tasks. However, in real HAR scenarios, deploying an FL system needs to consider multiple aspects, including system accuracy, fairness, robustness, and scalability. Most existing FL frameworks aim to solve specific problems while ignoring other properties. In this paper, we propose FedCHAR, a personalized FL framework with a hierarchical clustering method for robust and fair HAR, which not only improves the accuracy and the fairness of model performance by exploiting the intrinsically similar relationship between users but also enhances the robustness of the system by identifying malicious nodes through clustering in attack scenarios. In addition, to enhance the scalability of FedCHAR, we also propose FedCHAR-DC, a scalable and adaptive FL framework which is featured by dynamic clustering and adapting to the addition of new users or the evolution of datasets for realistic FL-based HAR scenarios. We conduct extensive experiments to evaluate the performance of FedCHAR on seven datasets of different sizes. The results demonstrate that FedCHAR could obtain better performance on different datasets than the other five state-of-the-art methods in terms of accuracy, robustness, and fairness. We further validate that FedCHAR-DC exhibits satisfactory scalability on three large-scale datasets regardless of the number of participants.
Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical Settings
Authors: Gao Chenyang , Marsic Ivan , Sarcevic Aleksandra , Gestrich-Thompson Waverly , Burd Randall S.
Published: March 2023

In clinical settings, most automatic recognition systems use visual or sensory data to recognize activities. These systems cannot recognize activities that rely on verbal assessment, lack visual cues, or do not use medical devices. We examined speech-based activity and activity-stage recognition in a clinical domain, making the following contributions. (1) We collected a high-quality dataset representing common activities and activity stages during actual trauma resuscitation events-the initial evaluation and treatment of critically injured patients. (2) We introduced a novel multimodal network based on audio signal and a set of keywords that does not require a high-performing automatic speech recognition (ASR) engine. (3) We designed novel contextual modules to capture dynamic dependencies in team conversations about activities and stages during a complex workflow. (4) We introduced a data augmentation method, which simulates team communication by combining selected utterances and their audio clips, and showed that this method contributed to performance improvement in our data-limited scenario. In offline experiments, our proposed context-aware multimodal model achieved F1-scores of 73.2±0.8% and 78.1±1.1% for activity and activity-stage recognition, respectively. In online experiments, the performance declined about 10% for both recognition types when using utterance-level segmentation of the ASR output. The performance declined about 15% when we omitted the utterance-level segmentation. Our experiments showed the feasibility of speech-based activity and activity-stage recognition during dynamic clinical events.
X-CHAR
Authors: Jeyakumar Jeya Vikranth , Sarker Ankur , Garcia Luis Antonio , Srivastava Mani
Published: March 2023

End-to-end deep learning models are increasingly applied to safety-critical human activity recognition (HAR) applications, e.g., healthcare monitoring and smart home control, to reduce developer burden and increase the performance and robustness of prediction models. However, integrating HAR models in safety-critical applications requires trust, and recent approaches have aimed to balance the performance of deep learning models with explainable decision-making for complex activity recognition. Prior works have exploited the compositionality of complex HAR (i.e., higher-level activities composed of lower-level activities) to form models with symbolic interfaces, such as concept-bottleneck architectures, that facilitate inherently interpretable models. However, feature engineering for symbolic concepts-as well as the relationship between the concepts-requires precise annotation of lower-level activities by domain experts, usually with fixed time windows, all of which induce a heavy and error-prone workload on the domain expert. In this paper, we introduce X-CHAR, an eXplainable Complex Human Activity Recognition model that doesn't require precise annotation of low-level activities, offers explanations in the form of human-understandable, high-level concepts, while maintaining the robust performance of end-to-end deep learning models for time series data. X-CHAR learns to model complex activity recognition in the form of a sequence of concepts. For each classification, X-CHAR outputs a sequence of concepts and a counterfactual example as the explanation. We show that the sequence information of the concepts can be modeled using Connectionist Temporal Classification (CTC) loss without having accurate start and end times of low-level annotations in the training dataset-significantly reducing developer burden. We evaluate our model on several complex activity datasets and demonstrate that our model offers explanations without compromising the prediction accuracy in comparison to baseline models. Finally, we conducted a mechanical Turk study to show that the explanations provided by our model are more understandable than the explanations from existing methods for complex activity recognition.
ThermoFit
Authors: Wang Guanyun , Yang Yue , Guo Mengyan , Zhu Kuangqi , Yan Zihan , Cui Qiang , Zhou Zihong , Ji Junzhe , Li Jiaji , Luo Danli , Pan Deying , Fan Yitao , Han Teng , Tao Ye , Sun Lingyun
Published: March 2023

Smart orthoses hold great potential for intelligent rehabilitation monitoring and training. However, most of these electronic assistive devices are typically too difficult for daily use and challenging to modify to accommodate variations in body shape and medical needs. For existing clinicians, the customization pipeline of these smart devices imposes significant learning costs. This paper introduces ThermoFit, an end-to-end design and fabrication pipeline for thermoforming smart orthoses that adheres to the clinically accepted procedure. ThermoFit enables the shapes and electronics positions of smart orthoses to conform to bodies and allows rapid iteration by integrating low-cost Low-Temperature Thermoplastics (LTTPs) with custom metamaterial structures and electronic components. Specifically, three types of metamaterial structures are used in LTTPs to reduce the wrinkles caused by the thermoforming process and to permit component position adjustment and joint movement. A design tool prototype aids in generating metamaterial patterns and optimizing component placement and circuit routing. Three applications show that ThermoFit can be shaped on bodies to different wearables. Finally, a hands-on study with a clinician verifies the user-friendliness of thermoforming smart orthosis, and technical evaluations demonstrate fabrication efficiency and electronic continuity.
Feeling the Temperature of the Room
Authors: Haliburton Luke , Schött Svenja Yvonne , Hirsch Linda , Welsch Robin , Schmidt Albrecht
Published: March 2023

Thermal signals have been explored in HCI for emotion-elicitation and enhancing two-person communication, showing that temperature invokes social and emotional signals in individuals. Yet, extending these findings to group communication is missing. We investigated how thermal signals can be used to communicate group affective states in a hybrid meeting scenario to help people feel connected over a distance. We conducted a lab study (N=20 participants) and explored wrist-worn thermal feedback to communicate audience emotions. Our results show that thermal feedback is an effective method of conveying audience engagement without increasing workload and can help a presenter feel more in tune with the audience. We outline design implications for real-world wearable social thermal feedback systems for both virtual and in-person communication that support group affect communication and social connectedness. Thermal feedback has the potential to connect people across distances and facilitate more effective and dynamic communication in multiple contexts.
FeverPhone
Authors: Breda Joseph , Springston Mastafa , Mariakakis Alex , Patel Shwetak
Published: March 2023

Smartphones contain thermistors that ordinarily monitor the temperature of the device's internal components; however, these sensors are also sensitive to warm entities in contact with the device, presenting opportunities for measuring human body temperature and detecting fevers. We present FeverPhone --- a smartphone app that estimates a person's core body temperature by having the user place the capacitive touchscreen of the phone against their forehead. During the assessment, the phone logs the temperature sensed by a thermistor and the raw capacitance sensed by the touchscreen to capture features describing the rate of heat transfer from the body to the device. These features are then used in a machine learning model to infer the user's core body temperature. We validate FeverPhone through both a lab simulation with a skin-like controllable heat source and a clinical study with real patients. We found that FeverPhone's temperature estimates are comparable to commercial off-the-shelf peripheral and tympanic thermometers. In a clinical study with 37 participants, FeverPhone readings achieved a mean absolute error of 0.229 °C, a limit of agreement of ±0.731 °C, and a Pearson's correlation coefficient of 0.763. Using these results for fever classification results in a sensitivity of 0.813 and a specificity of 0.904.
Soil Moisture Sensing with UAV-Mounted IR-UWB Radar and Deep Learning
Authors: Ding Rong , Jin Haiming , Xiang Dong , Wang Xiaocheng , Zhang Yongkui , Shen Dingman , Su Lu , Hao Wentian , Tao Mingyuan , Wang Xinbing , Zhou Chenghu
Published: March 2023

Wide-area soil moisture sensing is a key element for smart irrigation systems. However, existing soil moisture sensing methods usually fail to achieve both satisfactory mobility and high moisture estimation accuracy. In this paper, we present the design and implementation of a novel soil moisture sensing system, named as SoilId, that combines a UAV and a COTS IR-UWB radar for wide-area soil moisture sensing without the need of burying any battery-powered in-ground device. Specifically, we design a series of novel methods to help SoilId extract soil moisture related features from the received radar signals, and automatically detect and discard the data contaminated by the UAV's uncontrollable motion and the multipath interference. Furthermore, we leverage the powerful representation ability of deep neural networks and carefully design a neural network model to accurately map the extracted radar signal features to soil moisture estimations. We have extensively evaluated SoilId against a variety of real-world factors, including the UAV's uncontrollable motion, the multipath interference, soil surface coverages, and many others. Specifically, the experimental results carried out by our UAV-based system validate that SoilId can push the accuracy limits of RF-based soil moisture sensing techniques to a 50% quantile MAE of 0.23%.
Spectral-Loc
Authors: Wang Yanxiang , Hu Jiawei , Jia Hong , Hu Wen , Hassan Mahbub , Uddin Ashraf , Kusy Brano , Youssef Moustafa
Published: March 2023

For indoor settings, we investigate the impact of location on the spectral distribution of the received light, i.e., the intensity of light for different wavelengths. Our investigations confirm that even under the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. By exploiting this observation, we propose Spectral-Loc, a novel indoor localization system that uses light spectral information to identify the location of the device. With spectral sensors finding their way into the latest products and applications, such as white balancing in smartphone photography, Spectral-Loc can be readily deployed without requiring any additional hardware or infrastructure. We prototype Spectral-Loc using a commercial-off-the-shelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength sub-bands. We benchmark the localization accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations over two different indoor spaces, a meeting room, and a large office space, demonstrate that the use of light spectral information significantly reduces the localization error for the different percentiles.
Behavior Modeling Approach for Forecasting Physical Functioning of People with Multiple Sclerosis
Authors: Antar Anindya Das , Kratz Anna , Banovic Nikola
Published: March 2023

Forecasting physical functioning of people with Multiple Sclerosis (MS) can inform timely clinical interventions and accurate "day planning" to improve their well-being. However, people's physical functioning often remains unchecked in between infrequent clinical visits, leading to numerous negative healthcare outcomes. Existing Machine Learning (ML) models trained on in-situ data collected outside of clinical settings (e.g., in people's homes) predict which people are currently experiencing low functioning. However, they do not forecast if and when people's symptoms and behaviors will negatively impact their functioning in the future. Here, we present a computational behavior model that formalizes clinical knowledge about MS to forecast people's end-of-day physical functioning in advance to support timely interventions. Our model outperformed existing ML baselines in a series of quantitative validation experiments. We showed that our model captured clinical knowledge about MS using qualitative visual model exploration in different "what-if" scenarios. Our work enables future behavior-aware interfaces that deliver just-in-time clinical interventions and aid in "day planning" and "activity pacing".
Augmented Reality Supported Learning for Cultural Heritage of Taiwan in On-Site and Off-Site Environments: The Case of a Daxi Old Street
Authors: Chiang Kai-Chun , Weng Cathy , Rathinasabapathi Abirami , Chen Hsuan , Su Jin-Hwei
Published: March 2023

This study aims to explore whether the integration of Augmented Reality (AR) technology used in community-based art education can improve the participants’ learning effectiveness and to compare its effects in two different learning environments (on-site and off-site). A quasi-experimental research design has been conducted to check the effectiveness of integrating AR activities in terms of learners’ effectiveness. The sample for this study includes 40 and 64 participants in on-site and off-site groups, respectively. The on-site group visited the real environment and viewed AR-integrated materials such as posters and rhymes; the off-site group visited the school exhibition and viewed the same materials as the on-site group. The data was obtained through motivation, learning satisfaction, technology acceptance, and learning effectiveness scales designed by the researchers. Descriptive statistics and an independent
t
-test were utilized for the data analysis. There was a significant difference in learning motivation, learning effectiveness, and learning satisfaction between on-site and off-site groups; however, there was no significant difference in the mean of technology acceptance. This study finds that the use of AR digital materials facilitated the visitors’ experience of cultural heritage and improved participants learning effectiveness, and particularly learning satisfaction and learning motivation, more so in the on-site experience than in the off-site experience. Results also revealed that there was a significant difference in gender and age toward learning motivation and learning effectiveness. With respect to participants with different artistic and educational backgrounds, the results showed no significant differences in learning motivation, learning effectiveness, learning satisfaction, and technology acceptance. Finally, recommendations for future practices and research are discussed.
A Small Increase in Serum Creatinine within 48 h of Hospital Admission Is an Independent Predictor of In-Hospital Adverse Outcomes in Patients with ST-Segment Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention: Findings from the Improving Care for Cardiovascular Disease in the China Project
Authors: Zhu Jiajia , Liu Wenxian , Li Jiang , Ma Changsheng , Zhao Dong
Published: March 2023

Background. Acute kidney injury (AKI) is a common complication of percutaneous coronary intervention (PCI) that has been associated with high morbidity and mortality in patients with STEMI. Acute tubular damage may be reflected by serum creatinine (Scr) values that do not meet the criteria for AKI. Methods. This analysis included 19,424 patients from the Improving Care for Cardiovascular Disease in China, Acute Coronary Syndrome Project (n = 5,221 (36.8%), patients with a small increase in Scr within 48 h of hospitalization; n = 14,203 patients with no increase in Scr). The primary outcome was the incidence of major adverse cardiovascular events (MACE). Secondary outcomes included the incidence of massive hemorrhage, in-hospital death, atrial fibrillation, heart failure, cardiogenic shock, cardiac arrest, and stroke. Logistic regression analysis was used to evaluate associations between a small increase in Scr within 48 h of hospitalization (>0.1 to <0.3 mg/dl) and MACE or massive hemorrhage during hospitalization. Results. Patients with a small increase in Scr within 48 h of hospitalization were significantly more likely to experience MACE (11.2% vs. 9.1%;

P
<
0.001

) or massive hemorrhage (3.2% vs. 2.2%;

P
<
0.001

) compared to patients with no increase in Scr, but there was no significant difference in in-hospital mortality (0.8% vs. 0.9%;

P
=
0.301

). Logistic regression analysis showed that a small increase in Scr within 48 h of hospital admission was a risk factor for MACE (OR, 1.168; 95% CI, 1.044–1.306;

P
=
0.006

) or massive hemorrhage (OR, 1.413; 95% CI, 1.164–1.715;

P
<
0.001

). Other risk factors included age ˃65 years, history of heart failure, use of glycoprotein IIb/IIIa inhibitors, aspirin or ACEI/ARB, LVEF <40%, Killip class III-IV, and increased SBP and heart rate. Conclusion. A small increase in Scr during hospitalization in patients with STEMI undergoing primary PCI that does not meet the criteria for AKI is a risk factor for in-hospital adverse outcomes. This effect is maintained in patients with normal Scr at hospitalization. Trial Registration. Clinical trial registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT02306616.
Yield Response of Hybrid and Open Pollinated Maize (Zea mays L.) Varieties to Different Levels of Fertilizer Nitrogen under Rain-Fed Conditions in the Bono Region of Ghana
Authors: Ansu Eric , Gyasi Santo Kwadwo , Khalid Abdul Aziz , Abdulai Muntala , Ntiamoah Afreh Daniel , Atakora Kwabena
Published: March 2023

Background. The experiment was undertaken at Nsapor, a suburb of Berekum municipality in the Bono Region of Ghana, from March 2019 to November 2019 to determine the suitable rate of fertilizer nitrogen application to optimize seed yield and yield attributes of Pannar 12 and Omankwa maize varieties in a semi deciduous agroecology of Ghana. Soil fertility is low in Ghana because of factors such as rampant annual bushfires, short fallow periods as a result of high human populations, continuous cropping, deforestation, and improper mining activities. There is also little information on crop variety and site-specific fertilizer recommendations in Ghana, resulting in inappropriate use of fertilizers by most Ghanaian farmers, culminating in low crop yields. Methods. Hybrid (Pannar 12) and open pollinated (Omankwa) maize varieties were treated with four rates of fertilizer nitrogen obtained from NPK 15-15-15 (0 kgN/ha, 90 kgN/ha, 120 kgN/ha and 150 kgN/ha) and laid out in a factorial combination in a randomized complete block design with three replicates. Results. Application of 150 kg/ha of fertilizer N to Pannar 12 variety resulted in grain yields of 6146 kg/ha and 6095 kg/ha in the major and minor rainy seasons, respectively. The results also showed that application of 120 kg/ha of fertilizer N to Omankwa variety gave grain yields of 4635 kg/ha and 5286 kg/ha in the major and minor rainy seasons, respectively. To optimize the grain yield of maize, farmers could use NPK 15-15-15 fertilizer as a source of nitrogen and apply 120 kgN/ha to Omankwa variety and 150 kgN/ha to Pannar 12 variety in both major and minor rainy seasons.
Study on the Mechanical Properties of Flexible Prefabricated Highway Structural Systems considering the Effect of Two-Way Fluid-Structure Interaction
Authors: Huang Liang , Ma Wenbo , Li Haotian , Xu Shizhan , Song Zebin , Hou Yujie
Published: March 2023

The rainfall often causes changes in the physical performance of the embankment, which leads to disasters such as embankment collapse and road surface settlement and cracking. Past research has tended to be limited to traditional embankment seepage models and is mostly based on the assumption that surface precipitation is uniform and rainfall intensity is constant. To adapt to the changes and development of the times, based on the water leakage mode of the flexibly assembled highway embankment, a new rainfall calculation model of the reinforced soil embankment is constructed and combined with the solid-fluid coupling physics theory, the sedimentation amount and slope stability of the embankment are analyzed at multiple levels, the mechanical properties of the new embankment under rainfall conditions are summarized, the factors affecting the settlement of the embankment and the stability of the slope are explored, and the reference model is provided for the construction of the new embankment, and the existing construction process is improved through multilevel cause analysis. We improve and develop the existing theoretical system to reduce the probability of road diseases, improve the efficiency of road construction, and reduce the cost of road maintenance.
Energy-Economic-Environmental Analysis of On-Grid Solar System for Electricity and Hydrogen Production at Homemade Scale: Effect of Different Shades
Authors: Raeisi H. A. , Sadeghzadeh S. M.
Published: March 2023

As an energy source, solar cells can be central to peak load control in different power companies. Hydrogen production can be cost-effective if the generated electricity is unusable. Shades reduce solar cell current and fundamentally alter panel function, making it necessary to restore panels to optimum power generation. Owing to the issues outlined in this paper, the energy-economic-environmental effect of a shade detection algorithm was examined for different shade scenarios, and domestic-scale solar hydrogen production was evaluated. As the effect of the partial shade detection algorithm on the amount of solar hydrogen production has not been investigated quantitatively so far, this issue was discussed in this study. The algorithm and laboratory equipment developed by the authors were used to detect the partial shadow. Thanks to the respected reviewer, the following sentence was added to the abstract. The analyses in HOMER V2.81 software used 20-year average data for a residential building in the city of Tehran obtained from the NASA website. The results recommended that the designed algorithm increases hydrogen production in different scenarios between 5.8% and 32.9%. The results also revealed that the designed algorithm significantly lessen the pollutants between 100 kg/year and 240 kg/year.
Ascorbic Acid Content and Antioxidant Activities of White and Brown Teff [Eragrostic tef (Zucc.)Trotter] Grains and Injera
Authors: Yisak Hagos , Belete Andargie , Chandravanshi Bhagwan Singh , Redi-Abshiro Mesfin , Yaya Estifanos Ele
Published: March 2023

Teff [Eragrostis tef (Zuccagni) Trotter] is a cereal grain originating in Ethiopia as a staple food for millions of people. Its grain is a gluten-free superfood and got acceptance as a medicinal ingredient. Therefore, it is worthwhile to determine the antioxidative activities and L-ascorbic acid contents of teff grain and its baked food (injera). This study aimed to determine the ascorbic acid contents and antioxidant activities in the aqueous extract of the white and brown teff grains and their injera samples using iodimetric titration and UV-Vis spectrophotometric methods, respectively. The ascorbic acid contents in the white and brown teff ranged from 67.9–112.6 mg/100 g and 69.2–117.2 mg/100 g, respectively, and those in injera of the selected teff samples ranged from 30.5–32.9 mg/100 g and 37.3–43.0 mg/100 g, respectively. The antioxidant activities ranged from 1.26–7.04 μmol AAE/g for the white teff grains, 1.44–6.29 μmol AAE/g for the brown teff grains, 1.81–2.47 μmol AAE/g for white teff injera, and 3.89–4.86 μmol AAE/g for the brown teff injera samples. Findings of the present study have revealed that white teff and brown teff grains and their injera were found to have a higher content of ascorbic acid than commonly consumed grains and vegetables. No significant difference (α = 0.05) has been observed between the two varieties of teff grains with respect to the ascorbic acid content and antioxidant activities. However, there was a statistically significant difference (α = 0.05) in the ascorbic acid content and antioxidant activities between the teff grains and their injera samples. Therefore, this study indicated that teff grains and injera are rich in ascorbic acid content and antioxidant activities as compared to other cereal grains and are very crucial for human nutrition and health.
Efficacy and Safety of Bedside Removal of Tunnelled Hemodialysis Catheter by Noninterventional Nephrologists among Adult Patients in the King Abdulaziz University Hospital Hemodialysis Centre in Jeddah: A Retrospective Cohort Study
Authors: Kashgary Abdullah , Almuhyawi Razan A. , Alhijri Reem R. , Ba Durayq Aseel M. , Alnagrani Wed B. , Alharbi Arwa J. , Al Khalaf Hamidah M. , Obaid Haya S. , Fadel Ahmed Zaky , Abdelsalam Mostafa
Published: March 2023

This study aimed to assess the efficacy and safety of bedside removal of tunnelled hemodialysis catheter (TDC) by noninterventional nephrologists among adult patients. It is a retrospective study that involved 53 patients from March 2020 to February 2022 at the King Abdulaziz University Hospital (KAUH) Hemodialysis Centre in Jeddah, Saudi Arabia. Of the 53 participants, 60.4% were male and 40.6% female, and their mean age was 50.94 ± 18.89 years. The most common comorbidities were hypertension (HTN) in 47 (88.7%), diabetes mellitus (DM) in 24 (45.3%), and DM and HTN together in 23 (43.4%) patients. The most common site of TDC removal was the right internal jugular vein (77.4%). In 84.9% of the cases, the TDC was removed as an inpatient procedure, and in the majority of the cases (64.2%), the TDC was removed by a noninterventional nephrologist. The most common reasons for TDC removal were sepsis or clinical concerns for infection (64.2%) and TDC not needed (20.8%) due to recovery of the renal function or access maturation. Most patients (96.2%) suffered no complications; only one of 34 (%) patients with catheter removal by a noninterventional nephrologist had bleeding, which required more observation and monitoring before discharge on the same day. Our study revealed that the bedside TDC removal was well tolerated with a minimal complication rate.
Simulating the Spread of Peste des Petits Ruminants in Kazakhstan Using the North American Animal Disease Spread Model
Authors: Yessenbayev Kairat , Mukhanbetkaliyev Yersyn , Yessembekova Gulzhan , Kadyrov Ablaikhan , Sultanov Akhmetzhan , Bainiyazov Aslan , Bakishev Temirlan , Nkamwesiga Joseph , Korennoy Fedor , Abdrakhmanov Sarsenbay
Published: March 2023

In this study, we simulated the potential spread of Peste des Petits Ruminants (PPR) between small ruminant (SR) farms in the Republic of Kazakhstan (RK) in case of the disease’s introduction into the country. The simulation was based on actual data on the location and population of SR farms in the RK using the North American Animal Disease Spread Model (NAADSM). The NAADSM employs the stochastic simulations of the between-farm disease spread predicated on the SIR compartmental epidemic model. The most important epidemiological indicators of PPR, demography of SR farms, and livestock management characteristics in the RK were used for model parameterization. This article considers several scenarios for the initial introduction of PPR into the territory of Kazakhstan, based on previously identified high-risk regions and varying sizes of initially infected farms. It is demonstrated that the duration and size of the outbreak do not depend on the size of initially infected farms but rather depend on the livestock concentration and number of farms in the affected area. This implies that the outbreak may affect the largest number of farms in the case of introduction of the disease into farms in southern Kazakhstan. However, even in the most unfavorable scenario, the total number of affected farms does not exceed 2.4% of all SR farms in the RK. The size of the affected area is, in most cases, no larger than an averaged 2-level administrative division’s size, which suggests the scale of a local epidemic. The chosen model provides ample opportunity to study the impact of different control and prevention measures on the spread of PPR as well as to assess the potential economic damage.
A Compact Frequency- and Power-Dividing Ratio-Tunable Quadrature Coupler
Authors: Zhang Yuxia , Tan Xiangguan , Xiang Qingxin
Published: March 2023

This paper presents a compact quadrature coupler with a tunable frequency and power-dividing ratio. Wide tunable frequency and power-dividing ratio are achieved by using the novel tunable unit instead of the transmission line sections in a traditional varactor-based quadrature coupler. Closed-form equations are derived for design parameters. For verification, a quadrature coupler is designed based on the given parameters, which demonstrate the tunable frequency of 2.0 GHz to 6.0 GHz and the tunable power-dividing ratio of -20 dB to 7.2 dB at 3 GHz. Finally, a microstrip tunable quadrature coupler is fabricated and measured. The measurements agree well with simulations. Under the frequency-tunable state, the measured 3-dB working frequency of this coupler can be continuously adjusted from 2.0 GHz to 4.7 GHz. And during the adjustment process, the return loss and isolation are always >15 dB. In the power-dividing ratio-tunable state, the adjustment range of the measured power-dividing ratio is from -14 to 6.0 dB at 3 GHz. Moreover, the return loss and isolation are always maintained at >20 dB.
A Hybrid GCN and Filter‐Based Framework for Channel and Feature Selection: An fNIRS‐BCI Study
Authors: Zafar Amad , Dad Kallu Karam , Atif Yaqub M. , Ali Muhammad Umair , Hyuk Byun Jong , Yoon Min , Su Kim Kwang
Published: March 2023

In this study, a channel and feature selection methodology is devised for brain‐computer interface (BCI) applications using functional near‐infrared spectroscopy (fNIRS). A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction phase, the performance of two filter‐based feature selection algorithms, (i) the minimum redundancy maximum relevance (mRMR) and (ii) ReliefF, is investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, and kurtosis) are used, whereas the conventional support vector machine (SVM) is utilized as a classifier for training and testing. The proposed methodology is validated using an available online dataset of motor imagery (left‐ and right‐hand), mental arithmetic, and baseline tasks. First, the efficacy of the proposed methodology is shown for two‐class BCI applications (i.e., left‐ vs. right‐hand motor imagery and mental arithmetic vs. baseline). Second, the proposed framework is applied to four‐class BCI applications (i.e., left‐ vs. right‐hand motor imagery vs. mental arithmetic vs. baseline). The results show that the number of appropriate channels and features was significantly reduced, resulting in a significant increase in classification accuracy for both two‐class and four‐class BCI applications, respectively. Furthermore, both mRMR (i.e., 87.8% for motor imagery, 87.1% for mental arithmetic, and 78.7% for four‐class) and ReliefF (i.e., 90.7% for motor imagery, 93.7% for mental arithmetic, and 81.6% for four‐class) yielded high average classification accuracy (p < 0.05). However, the results of the ReliefF algorithm are more stable and significant.
Sustained, Effortless Weight Loss after Damage to the Left Frontoinsular Cortex: A Case Report
Authors: Reasoner Benjamin D. , Boes Aaron D. , Geerling Joel C.
Published: March 2023

This case report highlights a possible consequence of damage to the left frontoinsular region. A 53-year-old woman with chronic obesity and headaches presented with seizure, leading to the discovery and resection of a large sphenoid wing meningioma. Postoperative brain imaging revealed loss of the left frontoinsular cortex and portions of the underlying white matter, claustrum, and striatum. Throughout her adult life, this patient had tried and failed to lose weight, but after surgery, she no longer desired to eat large meals, and without effort, her body mass index decreased from 38.6 (85th percentile) to 24.9 (25th percentile). Combined with previous research implicating the insular cortex in interoception, appetite, and drug-related urges, her reduced hunger and effortless weight loss after resection of the left frontoinsular cortex suggest that this region of the human brain may play a role in hunger-related urges that contribute to overeating.
X-Linked Hydrocephalus with New L1CAM Pathogenic Variants: Review of the Most Prevalent Molecular and Phenotypic Features
Authors: Ahmed Rania R. , Medhat Amina M. , Hamdy Germine M. , Effat Laila K.E. , Abdel-Hamid Mohamed S. , Abdel-Salam Ghada M.H.
Published: March 2023

Introduction: The underlying molecular defects of congenital hydrocephalus are heterogeneous and many isolated forms of hydrocephalus remain unsolved at the molecular level. Congenital hydrocephalus in males associated with agenesis of the corpus callosum is a notable characteristic of L1CAM gene which is by far the most common genetic etiology of congenital hydrocephalus. Methods and Results: Sequencing of the L1CAM gene on 25 male patients/fetuses who had been presented with hydrocephalus revealed 6 patients and two fetuses with different hemizygous pathogenic variants. Our study identified 4 novel variants and 4 previously reported. The detection rate was 32%, and all the variants were shown to be maternally inherited. Nonsense variants were detected in 3 patients, while missense variants were detected in 2 patients. Frameshift, silent, and splicing variant, each was detected in 1 patient. The clinical manifestations of the patients are in line with those frequently observed including communicating hydrocephalus and agenesis of the corpus callosum. Moreover, rippled ventricles with subdural collection and asymmetry of ventricles after shunt operation were seen in 1 patient and 2 patients, respectively. In addition, abnormal basal ganglia were found in 4 patients which seems to be an additional distinct new finding. We also describe a patient with novel nonsense variant with the rare association of Hirschsprung’s disease. This patient displayed additionally multiple porencephalic cysts and encephalomalacia secondary to hemorrhage due to repeated infections after shunt operation. The patients with the missense variants showed long survival, while those with truncating variants showed poor prognosis. Conclusion: This report adds knowledge of novel pathogenic variants to the L1CAM variant database. Furthermore, we evaluated the clinical and imaging data of these patients.
Descrição do ovo, larva e pupa de anopheles (arthuromyia) gilesi (neiva, 1908)
Authors: Deane L. M. , Deane M. P. , Causey R.
Published: March 2023

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Mais uma nova espécie sulamericana de nycteribiidae (diptera)
Authors: Guimarães Lindolpho R.
Published: March 2023

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Das sieht ja ganz anders aus, wie fühlst du dich denn? Teaching Modal Particles ja and denn with the Queer Eye Germany series: a didactic model based on a Descriptive Format
Authors: Aquino Marceli Cherchiglia
Published: March 2023

This article proposes a linguistically based framework that can be used for the teaching of Modal Particles (MPs) using contextualized examples of authentic interactions from the Netflix's series Queer Eye Germany. For the description of  MPs ja and denn we employ the Descriptive Format (Diewald et al. 2017), an interlinguistic schematic table which provides a definition of the morphological, syntactic, semantic and pragmatic characteristics of MPs. The format provides a linguistic framework for the development of a didactic material for teaching MPs, that is divided into three stages (Ende et al. 2013): introduction, elaboration and production. Therefore, the activities of the didactic model will be carried out in two phases: (i) the description of the formal and pragmatic characteristics of MPs ja and denn through the Descriptive Format; (ii) the elaboration of a didactic sequence with reception and production tasks using occurrences of MPs in authentic communicative contexts. This proposal aims to fill gaps in the teaching of MPs that takes into account a metalinguistic reflection (Aquino 2020), by which students can critically reflect on linguistic norms and the sociocultural reality of the target as well as of their own language.
Evidence of non-selective lexical access to second and third language in unbalanced multilinguals: an N400 study on the processing of interlingual homographs
Authors: Lameira Marina , Bezerra Felipe , Toassi Pâmela , Cravo André , Carthery-Goulart Maria Teresa
Published: March 2023

Bilinguals and multilinguals commonly encounter words in their multiple languages which share some linguistic aspects. Among those are interlingual homographs, or words that have the exact same orthography in two different languages. The current study examined, through a semantic judgment task in English with ERP recording, how multilinguals (speakers of Brazilian Portuguese, English and German) accessed the meanings of interlingual homographs that belonged to their dominant and non-dominant foreign languages compared to a control group of Brazilian Portuguese-English bilinguals. The findings demonstrated that multilinguals were slower to respond to the English-German interlingual homographs as compared to control stimuli (no homographs). The results also demonstrated that, when the interlingual homographs were semantically related to their targets in the non-target language, there were significantly more errors and a higher RT than in unrelated conditions. Additionally, only the bilinguals presented the typical N400 effect for unrelated conditions, suggesting that the co-activation of the non-target language due to interlingual homographs modulated this ERP in the multilingual group. Our results provide support for the Bilingual Interactive Activation plus model and suggest that literature findings on interference between first and second languages also hold for second and third languages.
Entre Gritos bárbaros e jornais: a vida poética de Moacyr de Almeida
Authors: Queiroz Mario Cesar Newman de
Published: March 2023

Moacyr de Almeida (1902-1925), muito jovem, entre 1916 e 1925, se torna poeta renomado, presença constante na imprensa carioca, onde também trabalhou, por meio de notícias de eventos e recitais, ou por meio de poesias impressas. Morre em 01 de maio de 1925, aos 23 anos de idade, e, com ajuda de amigos, seu irmão Pádua de Almeida, também jornalista e poeta, publica em setembro do mesmo ano o livro que o poeta morto deixara organizado, Gritos Bárbaros. A edição em livro é póstuma; em vida do autor, apenas poemas impressos em jornais. O cotejo entre a impressão póstuma e as em vida ressalta, por vezes, a existência de diferenças significativas. Tem-se, então, um problema de ecdótica a analisar: privilegia-se a edição dos poemas em livro, sob confiança cega da autoridade do irmão? Ou privilegia-se a edição desses poemas tal como publicados com o poeta em vida? E, ainda, as publicações nos periódicos são um caso de eliminatio, tal como as três edições da obra do poeta, sem preocupação crítica efetiva, trataram? Este trabalho enceta uma abordagem com fins de anunciar o início de uma edição crítica da obra do poeta carioca.