Miracle-dz/UniFAR-FTUs huggingface dataset:
Auditory discrimination in aging bilinguals vs. monolinguals with and without hearing loss

Demands for effective assessments of speech perception specific to the aging brain are increasing, as the impacts of hearing loss on an individual’s functional health, socialization, and cognition have become more widely recognized. Understanding the mechanisms behind the optimal function of the aging brain in relation to speech and language is challenging, especially in the bilingual population where the language learning and language interference processes could be mistaken for perceptual difficulty. Age-related presbycusis is unavoidable, and the contributions of this sensorineural hearing loss (SNHL) process on impaired speech recognition are not completely understood. This lack of understanding of the effects of aging and bilingual language competency on speech perception can act as a barrier to successful auditory rehabilitation. The present study investigated the effects of aging on vowel sound discrimination in adult listeners (age 50+) with the following characteristics: American English (AE) monolinguals with normal hearing, simultaneous or early sequential Spanish-English (SE) bilinguals with normal hearing, and AE monolinguals with SNHL (AE-SNHL). The goal was to identify the differences in vowel sound discrimination performance between the monolingual and bilingual aging populations to guide future language assessments and intervention processes. English vowel discrimination was assessed using an AXB discrimination task in quiet and using the Quick Speech in Noise (QuickSIN) test. SE bilinguals were outperformed by AE and AE-SNHL monolinguals, suggesting SE bilinguals primarily use their L1 acoustic properties to discriminate speech segments. No significant difference was found in QuickSIN performance between the bilingual and the monolingual groups, but there was a significant difference between AE and AE-SNHL. In conclusion, vowel discrimination was affected by interference with the native language, while performance in the noise condition was affected by hearing loss. The results of this study contribute to our understanding of the age-related speech processing deficits from three different aging groups regarding the cognitive control system.
ENHANCES THE ROBUSTNESS OF DEEP LEARNING MODELS USING ROBUST SPARSE PCA TO DENOISE ADVERSARIAL IMAGES

Những năm gần đây đã chứng kiến sự phát triển nhanh chóng của trí tuệ nhân tạo. Các ứng dụng học sâu đã được phát triển rộng rãi trong cuộc sống như nhận dạng đối tượng, nhận diện khuôn mặt, vận hành xe tự động, y học,… Tuy nhiên, các hệ thống này ẩn chứa nhiều rủi ro từ các cuộc tấn công đối kháng vào các mô hình học sâu. Kẻ tấn công sử dụng hình ảnh có chứa nhiễu loạn rất nhỏ khó có thể nhận thấy và có thể đánh lừa các mô hình học sâu. Nhiều nghiên cứu đã chỉ ra rằng mẫu đối kháng phần lớn được tạo ra từ việc thêm nhiễu loạn vào hình ảnh sạch. Trong bài báo này, các tác giả đề xuất sử dụng phương pháp phân tích thành phần đặc trưng hình ảnh theo cách thưa thớt để khử nhiễu cho các ảnh đối kháng. Với kết quả thực nghiệm, các tác giả đã chứng minh rằng phương pháp PCA thưa mạnh có hiệu quả trong việc lựa chọn và phân loại các đặc điểm chính của ảnh để loại bỏ nhiễu loạn không mong muốn có trong ảnh đầu vào. Hình ảnh sau khi khử nhiễu đã được mô hình học máy phân lớp chính xác.
Using Model Cards for ethical reflection on machine learning models: an interview-based study

How do tools designed for documenting machine learning models contribute to developers’ ethical reflection? We set out to answer this question regarding Model Cards, a tool proposed for such purpose. We conducted a thematic analysis of eight semi-structured interviews based on speculative design sessions. Each participant assumed the role of developer of an artificial intelligence model in one of two scenarios: loan applications or university admissions. We found evidence that designers may have been selective about which ethical issues – from among those they had reflected on – they recorded in the Model Cards. While participants were hesitant to grant full autonomy to the artifact to be developed, we identified they still tended to rely on a third party (outside the design team) to mediate the relationship between the system and other stakeholders. Our findings contribute to our understanding of documentation tools, their epistemic value, and how they can be leveraged to engage in a more ethically informed design process of artificial intelligence systems.
Hydrogeochemistry and isotope geochemistry of Velenje Basin groundwater

The geochemical and isotopic composition of groundwater in the Velenje Basin, Slovenia, was investigated between the years 2014 to 2015 to identify the geochemical processes in the major aquifers (Pliocene and Triassic) and the water–rock interactions. Thirty-eight samples of groundwater were taken from the aquifers, 19 in the mine and 19 from the surface. Groundwater in the Triassic aquifer is dominated by HCO3 –, Ca2+ and Mg2+ with δCDIC values in the range from -19.3 to -2.8 ‰, indicating degradation of soil organic matter and dissolution of carbonate minerals. In contrast, groundwater in the Pliocene aquifers is enriched in Mg2+, Na+, Ca2+, K+, and Si, and has high alkalinity, with δCDIC values in the range of -14.4 to +4.6 ‰. Based on the δ CDIC values in all the aquifers (Pliocene and Triassic), both processes influence the dissolution of carbonate minerals and dissolution of organic matter and in the Pliocene aquifers, methanogenesis as well. Based on Principal Component Analysis (PCA), and on geochemical and isotopic data we conclude that the following types of groundwater in Velenje Basin are present: Triassic aquifers with higher pH and lower conductivity and chloride, Pliocene, Pliocene 1 and Pliocene 2 aquifers with lower pH and higher conductivity and chloride contents, and Pliocene 3 and Pliocene 2, 3 aquifers with the highest pH values and lowest conductivities and chloride contents. 87Sr/86Sr tracer was used for the first time in Slovenia to determine geochemical processes (dissolution of silicate versus carbonate fraction) in Velenje Basin groundwater of different aquifers dewatering Pliocene and Triassic strata. 87Sr/86Sr values range from 0.70820 to 0.71056 in groundwater of Pliocene aquifers and from 0.70808 to 0.70910 in groundwater of the Triassic aquifer. This indicates that dissolution of the carbonate fraction prevails in both aquifers, while in Pliocene aquifers, an additional silicate weathering prevails with higher 87Sr/86Sr isotope ratios.
Conventional weight loss therapy in morbid obesity during COVID-19 pandemic: degree of burdens at baseline and treatment efficacy

Introduction COVID-19 affected global physical and psychological health. The purpose of this study was to explore the pandemics impact on health-related quality of life (HRQoL), mental health (anxiety, depression, and perceived stress) and eating behavior in people with severe obesity participating in a multimodal conservative behavioral weight loss (BWL) program conducted via videoconferencing. Additionally, the efficacy of the six-month BWL program in a virtual video-based setting during the pandemic was examined. Methods 297 participants of a face-to-face multimodal behavioral weight loss program prior to the pandemic (PrePAN, May 2014–September 2019) and 146 participants of the in terms of content same intervention in a videoconference-based setting during the pandemic (PAN, July 2020–April 2022) were questioned and compared using standardized questionnaires for HRQoL, symptoms of depressive and anxiety disorders, perceived stress, and eating behavior at baseline and at the end of treatment. Results Symptoms for anxiety, depression and perceived stress were similar between PrePAN and PAN at baseline. In addition, PAN tended to show lower disinhibition of eating behavior and feelings of hunger than PrePAN. During the pandemic, the BWL intervention resulted in body weight loss (67%) or stabilization (16%) in most of the participants. It also contributed by improving physical HRQoL, lower worries, and improved eating behaviors compared to baseline. Conclusion During the COVID-19 pandemic, baseline mental health of people with morbid obesity was not worse than before the pandemic. Additionally, the BWL intervention in the virtual video-based setting stabilized and improved physical and mental health during the COVID-19 pandemic.
An Efficient and Lightweight Detection Model for Forest Smoke Recognition

Massive wildfires have become more frequent, seriously threatening the Earth’s ecosystems and human societies. Recognizing smoke from forest fires is critical to extinguishing them at an early stage. However, edge devices have low computational accuracy and suboptimal real-time performance. This limits model inference and deployment. In this paper, we establish a forest smoke database and propose a model for efficient and lightweight forest smoke detection based on YOLOv8. Firstly, to improve the feature fusion capability in forest smoke detection, we fuse a simple yet efficient weighted feature fusion network into the neck of YOLOv8. This also greatly optimizes the number of parameters and computational load of the model. Then, the simple and parametric-free attention mechanism (SimAM) is introduced to address the problem of forest smoke dataset images that may contain complex background and environmental disturbances. The detection accuracy of the model is improved, and no additional parameters are introduced. Finally, we introduce focal modulation to increase the attention to the hard-to-detect smoke and improve the running speed of the model. The experimental results show that the mean average precision of the improved model is 90.1%, which is 3% higher than the original model. The number of parameters and the computational complexity of the model are 7.79 MB and 25.6 GFLOPs (giga floating-point operations per second), respectively, which are 30.07% and 10.49% less than those of the unimproved YOLOv8s. This model is significantly better than other mainstream models in the self-built forest smoke detection dataset, and it also has great potential in practical application scenarios.
Advancing Drug Delivery Paradigms: Polyvinyl Pyrolidone (PVP)-Based Amorphous Solid Dispersion for Enhanced Physicochemical Properties and Therapeutic Efficacy

Background: The current challenge in drug development lies in addressing the physicochemical issues that lead to low drug effectiveness. Solubility, a crucial physicochemical parameter, greatly influences various biopharmaceutical aspects of a drug, including dissolution rate, absorption, and bioavailability. Amorphous solid dispersion (ASD) has emerged as a widely explored approach to enhance drug solubility. Objective: The objective of this review is to discuss and summarize the development of polyvinylpyrrolidone (PVP)-based amorphous solid dispersion in improving the physicochemical properties of drugs, with a focus on the use of PVP as a novel approach. Methodology: This review was conducted by examining relevant journals obtained from databases such as Scopus, PubMed, and Google Scholar, since 2018. The inclusion and exclusion criteria were applied to select suitable articles. Results: This study demonstrated the versatility and efficacy of PVP in enhancing the solubility and bioavailability of poorly soluble drugs. Diverse preparation methods, including solvent evaporation, melt quenching, electrospinning, coprecipitation, and ball milling are discussed for the production of ASDs with tailored characteristics. Conclusion: PVP-based ASDs could offer significant advantages in the formulation strategies, stability, and performance of poorly soluble drugs to enhance their overall bioavailability. The diverse methodologies and findings presented in this review will pave the way for further advancements in the development of effective and tailored amorphous solid dispersions.
Differential expression of slow and fast-repriming tetrodotoxin-sensitive sodium currents in dorsal root ganglion neurons

Sodium channel Nav1.7 triggers the generation of nociceptive action potentials and is important in sending pain signals under physiological and pathological conditions. However, studying endogenous Nav1.7 currents has been confounded by co-expression of multiple sodium channel isoforms in dorsal root ganglion (DRG) neurons. In the current study, slow-repriming (SR) and fast-repriming (FR) tetrodotoxin-sensitive (TTX-S) currents were dissected electrophysiologically in small DRG neurons of both rats and mice. Three subgroups of small DRG neurons were identified based on the expression pattern of SR and FR TTX-S currents. A majority of rat neurons only expressed SR TTX-S currents, while a majority of mouse neurons expressed additional FR TTX-S currents. ProTx-II inhibited SR TTX-S currents with variable efficacy among DRG neurons. The expression of both types of TTX-S currents was higher in Isolectin B4-negative (IB4−) compared to Isolectin B4-positive (IB4+) neurons. Paclitaxel selectively increased SR TTX-S currents in IB4− neurons. In simulation experiments, the Nav1.7-expressing small DRG neuron displayed lower rheobase and higher frequency of action potentials upon threshold current injections compared to Nav1.6. The results suggested a successful dissection of endogenous Nav1.7 currents through electrophysiological manipulation that may provide a useful way to study the functional expression and pharmacology of endogenous Nav1.7 channels in DRG neurons.
Human-Robot Collaboration Through a Multi-Scale Graph Convolution Neural Network With Temporal Attention

Collaborative robots sensing and understanding the movements and intentions of their human partners are crucial for realizing human-robot collaboration. Human skeleton sequences are widely recognized as a kind of data with great application potential in human action recognition. In this letter, a multi-scale skeleton-based human action recognition network is proposed, which leverages a spatio-temporal attention mechanism. The network achieves high-accuracy human action prediction by aggregating multi-level key point features of the skeleton and applying the spatio-temporal attention mechanism to extract key temporal information features. In addition, a human action skeleton dataset containing eight different categories is collected for a human-robot collaboration task, where the human activity recognition network predicts skeleton sequences from a camera and the collaborating robot makes collaborative actions based on the predicted actions. In this study, the performance of the proposed method is compared with state-of-the-art human action recognition methods and ablation experiments are performed. The results show that the multi-scale spatio-temporal graph convolutional neural network has an action recognition accuracy of 94.16%. The effectiveness of the method is also verified by performing human-robot collaboration experiments on a real robot platform in a laboratory environment.
IoT-Based Shoe for Enhanced Mobility and Safety of Visually Impaired Individuals

This research paper presents the design, development, and evaluation of an Internet of Things (IoT)- based shoe system to enhance the mobility and safety of visually impaired individuals. The proposed shoe leverages IoT technologies, embedded sensors, and wireless communication to provide real-time information and assistance to blind individuals during their daily activities. The system encompasses a wearable shoe device equipped with sensors, a microcontroller unit, and a companion mobile application that relays important data and alerts the user. The effectiveness of the IoT-based shoe is evaluated through a series of user tests and feedback surveys. The results demonstrate the potential of this innovative solution to empower blind individuals, improve their independence, and promote a safer environment for their navigation.
Identify Industrial Work Practices in the Food and Beverage Product Department of Kayumas Seminyak Resort

This research Identify Industrial Work Practices in the Food and Beverage Product Department of Kayumas Seminyak Resort. It can be used as a guide to improve the quality of work and creativity in entering the tourism industry. Increasing student knowledge in the Food and Beverage products field and implementing training will be helpful later when facing the industrial world. This research is descriptive qualitative research. The document used in this research is taken from Industrial Work Practices in the Food and Beverage Product Department of Kayumas Seminyak Resort Students' report. After carrying out industrial work practices, the researcher gained a lot of useful work experience in the form of theories and practical knowledge regarding the Food and Beverage Product field and the ability to work together. In addition, the author has carried out one of the requirements for completing the Food and Beverage Product Study program by carrying out industrial work practices. Keywords: Industrial Work Practices, Food and Beverage Product Department, Quality of work, Tourism Industry, Kayumas Seminyak Resort.
Simultaneous Identification of Multiple Parameters in Wireless Power Transfer Systems Using Primary Variable Capacitors

This paper proposes a novel approach to simultaneously identifying multiple critical parameters in a wireless power transfer (WPT) system, such as the resonant frequency, mutual inductance, and load resistance, solely from the primary side. The key is to adopt a primary-side-switch-controlled capacitor (SCC) to ensure that the imaginary part of the input impedance is only caused by the secondary-side reflected impedance at three predesigned frequencies. The DSP controller then samples and processes the primary voltage and current using a gradient descent algorithm to derive the above parameters. After the identification, the SCC adjusts its equivalent capacitance based on the secondary-side practical resonant frequency to ensure a zero-phase angle (ZPA), thereby significantly improving the compatibility of the WPT system with unknown receivers. Compared to the previous frequency-sweeping method, the proposed approach is simpler and more suitable for deployment on the controller. Finally, experimental results demonstrate that the identification error of mutual inductance and resonant frequency are within 7.5% and 2.68%, respectively.
Glycogen Synthase Kinase-3 Inhibition by CHIR99021 Promotes Alveolar Epithelial Cell Proliferation and Lung Regeneration in the Lipopolysaccharide-Induced Acute Lung Injury Mouse Model

Acute respiratory distress syndrome (ARDS) is a life-threatening lung injury that currently lacks effective clinical treatments. Evidence highlights the potential role of glycogen synthase kinase-3 (GSK-3) inhibition in mitigating severe inflammation. The inhibition of GSK-3α/β by CHIR99021 promoted fetal lung progenitor proliferation and maturation of alveolar epithelial cells (AECs). The precise impact of CHIR99021 in lung repair and regeneration during acute lung injury (ALI) remains unexplored. This study intends to elucidate the influence of CHIR99021 on AEC behaviour during the peak of the inflammatory phase of ALI and, after its attenuation, during the repair and regeneration stage. Furthermore, a long-term evaluation was conducted post CHIR99021 treatment at a late phase of the disease. Our results disclosed the role of GSK-3α/β inhibition in promoting AECI and AECII proliferation. Later administration of CHIR99021 during ALI progression contributed to the transdifferentiation of AECII into AECI and an AECI/AECII increase, suggesting its contribution to the renewal of the alveolar epithelial population and lung regeneration. This effect was confirmed to be maintained histologically in the long term. These findings underscore the potential of targeted therapies that modulate GSK-3α/β inhibition, offering innovative approaches for managing acute lung diseases, mostly in later stages where no treatment is available.
Preface: Security and safety in physical layer systems

This special topic mainly focuses on the progress of physical-layer security (PLS) technologies and their potential applications for the future beyond ﬁfth-generation (B5G) and sixth-generation (6G) networks. The tremendous growth in connectivity and the ubiquity of wireless communications have resulted in an unprecedented awareness of the importance of security and privacy. Achieving secure and trusted communications is vital for future intelligent connected applications, especially life-critical vehicle-to-everything (V2X) applications. However, the heterogeneous, dynamic and decentralized architecture of these networks leads to diﬃculties for cryptographic key management and distribution. By exploiting the physical characteristics of devices, wireless channels and noise, PLS oﬀers reliable solutions against eavesdropper attacks as complementary approaches to cryptographic techniques
Applying the Technology Acceptance Model to Understand the Use and Application of ChatGPT for English Language Learning by South Korean University Students

This study delves into the determinants influencing South Korean university students studying required college English courses to adopt the use of ChatGPT for their English language learning. A hypothesized technology acceptance model (TAM) utilizing six pertinent variables, including external factors, perceived ease of use, perceived usefulness, attitudes toward the use of technology, behavioral intention, and actual use, was adopted. A total of 122 college students from one university in South Korea participated in the study, providing data through an online questionnaire. Structural equation modeling was employed for the data analysis. The study tested eight hypotheses based on the study model, all of which demonstrated statistical significance. Notably, all five of the factors exhibited direct positive effects on the actual use of the technology. This research contributes to our understanding of the trajectory toward technology acceptance by exploring the perspectives of students in South Korea. It also discusses the implications these findings have on the future evolution of English as a foreign language (EFL) education at the university level.
The influence of streamers’ physical attractiveness on consumer response behavior: based on eye-tracking experiments

Live streaming marketing has become a popular topic of academic research. However, relatively few studies have been conducted in terms of the physical attractiveness of streamers, and even fewer studies have analyzed the changes in cognitive-emotional mechanisms that affect consumer response behavior. Based on SOR theory and cognitive-emotional system theory, this study proposes a theoretical research model and analyzes the internal mechanism of streamers’ physical attractiveness affecting consumers’ response using a combination of eye-tracking experiments and questionnaires. The results showed that: compared to streamers with lower physical attractiveness, consumers pay longer attention to streamers and products with higher physical attractiveness, and their response behaviors (continued watching intention, continued engagement intention, and purchase intention) are more active; compared to consumers with low involvement, consumers with high involvement pay longer attention to the product and perceive the process for a longer period of time; and quasi-social interaction and the flow experience play the role of a chain mediator between streamers’ physical attractiveness and consumers’ response behaviors. This study not only has certain theoretical significance for expanding the applicable boundaries of the physical appearance halo effect, but also has important practical significance for live broadcasting e-commerce companies to effectively adopt visual marketing and enhance customer retention.