Miracle-dz/UniFAR-FTUs huggingface dataset:
Parasocial Interactions in Digital Tourism: Attributes of Live Streamers and Viewer Engagement Dynamics in South Korea

This study investigates the use of live streaming as a strategic tool in the tourism industry, with a focus on the attributes of live streamers that influence viewer engagement, particularly in the context of South Korea’s digital landscape. The purpose of this research is to understand how the attractiveness, trustworthiness, and expertise of live streamers can affect the parasocial relationships—characterized by perceived friendship and trust—between viewers and streamers and how these relationships subsequently influence viewer loyalty and cooperation intentions. A quantitative research methodology was employed, utilizing a structured online survey distributed by a leading market research agency in South Korea. The survey targeted a diverse demographic to ensure a comprehensive analysis of digital consumer behavior in the tourism sector. Data were analyzed using IBM SPSS Statistics 28.0 and IBM SPSS Amos 28.0, employing structural equation modeling to test the hypothesized relationships. The results revealed that while attractiveness and trustworthiness impact perceived friendship, only expertise significantly affects trust for a travel live streamer. Furthermore, perceived friendship plays a crucial role in fostering loyalty to and cooperation with the streamer, having practical implications for the tourism industry in terms of crafting marketing strategies and training digital ambassadors. Th study extends parasocial interaction theory to the digital travel domain, providing original insights into virtual tourist behavior and highlighting live streaming’s significant contribution to viewer engagement. This research has limitations in its geographical focus on South Korea, suggesting the need for cross-cultural studies to validate the findings. Overall, this study offers valuable contributions to the academic literature and practical guidance for the tourism industry, emphasizing the importance of digital personalities in post-pandemic tourism recovery strategies.
Movement predictability modulates sensorimotor processing

Introduction An important factor for optimal sensorimotor control is how well we are able to predict sensory feedback from internal and external sources during movement. If predictability decreases due to external disturbances, the brain is able to adjust muscle activation and the filtering of incoming sensory inputs. However, little is known about sensorimotor adjustments when predictability is increased by availability of additional internal feedback. In the present study we investigated how modifications of internal and external sensory feedback influence the control of muscle activation and gating of sensory input. Methods Co-activation of forearm muscles, somatosensory evoked potentials (SEP) and short afferent inhibition (SAI) were assessed during three object manipulation tasks designed to differ in the predictability of sensory feedback. These included manipulation of a shared object with both hands (predictable coupling), manipulation of two independent objects without (uncoupled) and with external interference on one of the objects (unpredictable coupling). Results We found a task-specific reduction in co-activation during the predictable coupling compared to the other tasks. Less sensory gating, reflected in larger subcortical SEP amplitudes, was observed in the unpredictable coupling task. SAI behavior was closely linked to the subcortical SEP component indicating an important function of subcortical sites in predictability related SEP gating and their direct influence on M1 inhibition. Discussion Together, these findings suggest that the unpredictable coupling task cannot only rely on predictive forward control and is compensated by enhancing co-activation and increasing the saliency for external stimuli by reducing sensory gating at subcortical level. This behavior might serve as a preparatory step to compensate for external disturbances and to enhance processing and integration of all incoming external stimuli to update the current sensorimotor state. In contrast, predictive forward control is accurate in the predictable coupling task due to the integrated sensory feedback from both hands where sensorimotor resources are economized by reducing muscular co-activation and increasing sensory gating.
A Comprehensive Study on the Hydrogeochemical and Isotope Characteristics and Genetic Mechanism of Geothermal Water in the Northern Jinan Region

Geothermal water (GW) resources are highly valued as clean, renewable energy sources. In this study, a comprehensive analysis of water chemistry and isotope data from 25 GW samples was conducted to gain insights into the hydrochemical characteristics and formation mechanisms of the GW in the northern Jinan region (NJR). Statistical analysis and hydrochemical methods were employed for relevant analysis. The findings reveal that the GW in the NJR exhibits high salinity, with an average total dissolved solids (TDS) concentration of 9009.00 mg/L. The major ions identified are Na+ and Cl−, with mean concentrations of 2829.73 mg/L and 4425.77 mg/L, respectively, resulting in a hydrochemical type of Cl−Na. The analysis of δ2H and δ18O isotopes indicates that the GW originates from atmospheric precipitation that undergoes deep cycling and interaction with older groundwater. The composition of 3H suggests that the GW in the NJR is a mixture of waters, while radiocarbon dating (14C) suggests that the recharge of the GW may have occurred in the late Pleistocene era. The GW in the NJR is classified as partially equilibrated waters. The temperature range of geothermal reservoirs is 57.13 to 99.74 °C. The hydrochemical components primarily result from water–rock interactions, including silicate weathering, cation exchange, as well as carbonate weathering and the dissolution of halite and gypsum. Moreover, taking into account the hydrogeological conditions, hydrochemistry, and isotope analysis, a conceptual model of the geothermal reservoir in the NJR was developed. The research findings serve as a valuable reference and foundation for the development and utilization of geothermal resources in the Jinan region. These originate from the Taiyi mountains in the south or the Taihang mountains in the west, and experience deep circulation and long runoff times. This study provides a reference for the sustainable development and utilization of regional geothermal resources.
Research on application of model ensemble in sports image classification based on environmental information

In recent years, a deep convolutional neural network has been widely used in the field of image classification. However, training a satisfactory network is very arduous, not only to tune the hyper-parameters in the network but also to avoid the overfitting problem caused by a deep neural network. Another point is that it is difficult for a neural network to learn subtle details without human annotation. Therefore this paper proposed a preferable classification algorithm for sports classification tasks that combines the deep neural network with the object detection algorithm to obtain the prediction result. In this paper, the author compared the differences between classification directly using neural networks and a modified model ensemble approach to classification from a holistic perspective, as well as elaborating the advantages and disadvantages of the two approaches. The conclusion shows that the use of the improved model ensemble classification algorithm performs better than the direct use of neural networks and also achieves a high degree of accuracy in the test set.
Case Report: Supramalleolar osteotomy to preserve joint function and delay the development of Charcot neuroarthropathy of the ankle

Background Charcot neuroarthropathy (CN) is a severe disease that primarily affects the foot and ankle. Conservative treatment with total contact casts is suitable for early stages, but surgery is necessary for complications such as ulceration, malalignment, infection, or severe pain. The ankle instability caused by excessive axial load may require arthrodesis. However, preserving joint function in young patient can significantly enhance the quality of life. Case report A 33-year-old woman underwent open reduction and internal fixation after the right tibia and fibula fractures following a fall while walking. She developed severe pain and deformity in her right ankle after full weightbearing. After further evaluation, she was diagnosed with Charcot neuroarthropathy (CN) of the right ankle. The patient declined arthrodesis and opted for a supramalleolar osteotomy (SMO) instead 18 months after the initial surgery. The SMO procedure involved correcting the hindfoot malalignment through osteotomy and fixation. Although she experienced skin necrosis, the patient eventually achieved satisfactory outcomes with improvements in pain, deformity, and functionality of the ankle. Radiographic measurements showed positive realignment, and the patient reported a significant improvement in her quality of life at the final follow-up. Conclusions The SMO procedure could potentially be considered as an option to preserve ankle function and delay the disease development of CN for young patients. The restored foot stability and hindfoot alignment can help improve patients' quality of life.
Epilepsy in Africa: a multifaceted perspective on diagnosis, treatment, and community support

Epilepsy is the most widespread neurological disorder in Africa, affecting an estimated 25 million people. The disorder is characterized by recurrent seizures, which can be caused by a variety of factors, including past trauma, central nervous system infections, and genetic disorders. Diagnosis and treatment of epilepsy are challenging in African patients due to several factors, including the low socioeconomic status of the residents and limited access to appropriate medication. Phenobarbital remains the only drug widely available to patients, but it is not always effective and can have significant side effects. In addition to the medical challenges, individuals with epilepsy also face a lot of social stigmas in Africa. Widespread superstitions and false beliefs lead to prejudices against these people, making it difficult for them to live fulfilling social lives. With the development of new treatment modalities, such as gene therapy, stem cell therapy, and P-glycoprotein inhibitors, it is more important now than ever to increase the research output for the African region to create the best possible treatment and maximize patient outcomes.
Hemoparasites Do Not Affect Life-History Traits and Cellular Immune Response in Treefrog Hosts Boana cordobae

Simple Summary Unicellular blood parasites are common in amphibians all over the world. It is poorly understood whether the hemoparasite load can cause disease or affect life-history traits. Our study on the neotropical treefrog Boana cordobae from Argentina quantified the hemoparasite load in 37 individuals and focused on their cellular immune response (leukocyte profile) and the potential effects on the life-history traits size, body condition, and age. Thirty frogs were infected by either hemogregarines or trypanosomes or both in high intensities, a prevalence unprecedented in other anuran hosts. Yet, none of them showed externally visible signs of disease or aberrant behavior. The leukocyte profile did not differ significantly between parasite-free controls and infected frogs. Age-adjusted size, body condition, and age were not affected significantly by hemoparasite load, but most parasite-free frogs were young first-breeders, suggesting that infections take place after sexual maturation of the hosts. Vectors transmitting trypanosomes and/or hemogregarines to B. cordobae remain to be identified and studied in detail. Abstract We provide the first evidence for hemoparasites in the endemic Cordoba treefrog Boana cordobae. We collected 37 adult frogs at 1200 m a.s. l. in the Comechingones Mountains in the Córdoba province (Argentina). Each individual was sexed, then snout–vent length and body mass were recorded, a toe was collected for skeletochronological age determination, and a slide with a blood smear was prepared for hemoparasite screening, before releasing the frogs in situ. A total of 81% (n = 30) of the frogs were infected by hemogregarines and trypanosomes with a high intensity of infections. Dactylosoma was found for the first time in Argentina. Hemoparasites had no significant effect on the leukocyte profile, which we assessed from the May–Grünwald–Giemsa-stained blood smears. The neutrophils/lymphocytes ratio, indicative of stress, was insignificantly higher (0.06) in parasitized frogs than in parasite-free individuals (0.04). Infected frogs were larger than the controls, but this effect vanished when correcting size data for age. Young frogs (first-breeders) dominated the age distribution of parasite-free individuals, suggesting that infection of frogs takes usually place after sexual maturation. Vectors transmitting hemoparasites to B. cordobae remain to be identified. We demonstrate that moderate to high intensities of hemoparasites do not significantly affect the cellular immune response of B. cordobae, or any of the life-history traits studied, nor did they show any external sign of disease.
Factors Affecting Online Buying Behavior with Technology Adapting Curve using Analytic Hierarchy Process (AHP)

Purpose: Many factors influence customer’s online buying behavior. The need to identify these factors and their priorities in the mind of the customer when they are purchasing online is crucial for a marketer. This paper exemplifies the priorities of these factors under the technology adaptive curve (TAC). A comparative analysis of adaptation technology in between the first two stages of TAC place in this study. Methodology: The Analytic Hierarchy Process (AHP) used to know consumer priorities under MCDM (Multi-CriteriaDecision Making). To get the saturated factors data total of fifty-seven studies were analyzed. To assign the weight to selected factors, a bipolar questionnaire survey was conducted. For further analysis of AHP, the expert choice software used to know the relative importance of selected factors. Findings: Results of AHP show that the priorities of factors that influence online shopping behavior change with respect to TAC phases. The findings of this study are helpful to managers in the case of technology adoption in the consumer market, and many others can get benefit from this. Conclusion: A specific segment of customers has the same behavior to adopt the technology. This study must be considered before introducing and the second phase of TAC especially in Pakistan.
A Nested–Nested Sparse Array Specially for Monostatic Colocated MIMO Radar with Increased Degree of Freedom

This paper mainly investigates the problem of direction of arrival (DOA) estimation for a monostatic MIMO radar. Specifically, the proposed array, which is called a nested–nested sparse array (NNSA), is structurally composed of two nested subarrays, a NA with N1+N2 elements and a sparse NA, respectively, with N3+N4 elements. The design process of NNSA is optimized into two steps and presented in detail. Setting NNSA as transmitter/receiver arrays, we derive the closed-form expression of consecutive DOFs and calculate the mutual coupling coefficient. Eventually, extensive simulations are carried out and the results verify the superiority of the proposed array over the previous arrays in terms of consecutive DOFs, array aperture and mutual coupling effect.
UHF RFID Microwave Sensor Tag Design for an RSSI-Based Machine Learning Assisted Binary Ethanol–Water Mixture Characterization

This article proposes a novel microwave sensor-based UHF radio frequency identification (RFID) tag design for the dielectric parameter characterization of binary ethanol–water liquid samples with different concentrations. The sensor tag is designed on an FR-4 substrate with the Impinj M6 Dura chip. The fabricated prototype has an overall physical size of <inline-formula> <tex-math notation="LaTeX">$82.1\times19.2$ </tex-math></inline-formula> mm. The mixture samples dropped into the middle part of the microwave sensor tag change the RSSI value from the RFID system, the main parameter to be associated with the water ratio and dielectric parameters in the binary mixture to be used in machine learning (ML). Several ML regression methods have been used for ethanol–water characterization. The GPR model obtained a promising result with <inline-formula> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> = 0.98, RMSE <inline-formula> <tex-math notation="LaTeX">$=4.08$ </tex-math></inline-formula>, and MAE = 0.85 for the water ratio in the mixture, <inline-formula> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> = 0.98, RMSE = 3.02, and MAE = 0.66 for the dielectric constant real part, and <inline-formula> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> = 0.99, RMSE = 0.26, and MAE = 0.05 for the dielectric constant imaginary part of the mixture, and the model yielded the highest predictive performance among the four ML models. According to the obtained ML metrics, the water ratio in the binary mixture, the dielectric constant real and imaginary parts of the mixture using the RSSI received over the RFID system, the distance, and the RFID frequency have been successfully characterized. The proposed design has the technical potential to be used as characterization equipment for binary ethanol–water samples with low cost, high precision, and reusable features with ML assistance.
Analyzing Effective Factors of Online Learning Performance by Interpreting Machine Learning Models

Analyzing the effective factors influencing online learning performance is a research topic that has garnered significant attention. Traditional approaches, such as multiple regression and structural equation models, tend to assume linearity, while non-linear machine learning models lack interpretability. To address this gap, we propose a framework that interprets machine learning models to analyze the effective factors of online learning performance. By applying this framework to four benchmark datasets of online learning, we examine the differential impact of various factors on performance, explore the interactions among these factors, and identify the key factors for representative learners. Our findings indicate that: 1) non-linear machine learning models, particularly Decision Regression, offer better representation of the non-linear relationship between effective factors and online learning performance compared to classical multivariate regression; 2) the factor of online learning behavior exerts a greater influence on performance than demographic features, academic background, or online curriculum design; 3) online learning behavior features exhibit additional interaction effects on performance; and 4) learners with medium performance are influenced by diverse effective factors, with active participation in online learning activities emerging as the most crucial means to improve performance. Interpreting machine learning models presents an innovative approach for analyzing the effective factors of online learning performance, which can be extended to other factor analysis studies. The results of this research provide valuable insights for optimizing machine learning models in predicting online learning performance and enhancing learner outcomes.
3D Point Cloud Denoising Based on Color Attribute

Collecting or transmitting point cloud data is often subject to noise, which can potentially affect the accuracy of geometry and color representation in different spatial domains. This study addresses the denoising problem specifically for 3D point cloud color data and proposes two dedicated denoising algorithms based on the characteristics of noise in different spatial domains. In the denoising process, the RGB color space is first transformed into the YUV color space for further denoising operations. Surface smoothing is achieved by employing either a median filter or a bilateral filter based on the impact of noise on spatial information. These algorithms, built upon 2D image processing techniques, offer two key contributions: 1) color correction on spatial points to enhance denoising performance, and 2) the use of low-complexity filters while maintaining comparable filtering effectiveness, resulting in nearly a twofold reduction in processing time.
Toward Hardware-Assisted Malware Detection Utilizing Explainable Machine Learning: A Survey

Hardware joined the battle against malware by introducing secure boot architectures, malware-aware processors, and trusted platform modules. Hardware performance indicators, power profiles, and side channel information can be leveraged at run-time via machine learning for continuous monitoring and protection. The explainability of these machine learning algorithms may play a crucial role in interpreting their results and avoiding false positives. In this paper, we present an eagle eye on the state of the art of these components: we examine secure architectures and malware-aware processors, such as those implemented in the RISC-V Instruction Set Architecture and Reduced Instruction Set Computer (RISC). We categorize hardware-assisted solutions increased by machine learning for classification. We survey recently proposed software-assisted and hardware-assisted explainability algorithms in our context. In the discussion, we suggest that (1) safe architectures that guarantee secure device boot are a must, (2) Side-channel approaches are challenging to integrate into embedded systems, yet they show promise in terms of efficiency, (3) malware-aware processors provide valuable features for malware detection software, and (4) Without explainability, malware detection software is error-prone and can be easily bypassed.
A High-Performance and P-Type FeFET-Based Non-Volatile Latch

Non-volatile memory has a significant future in the Internet of Things and computation-in-memory applications. Among them, non-volatile memories using emerging FeFET devices have garnered significant attention. This paper proposes a novel P-type FeFET -based non-volatile latch. This design takes advantage of the unique characteristics of a P-type FeFET device to achieve non-volatility with no additional control signals. The Cadence simulation tool Virtuoso verifies that our proposed design has correct functioning with excellent power, area, and delay performance compared to state-of-the-art designs.