Miracle-dz/UniFAR-FTUs huggingface dataset:
MSFNet: A Multi-Scale Space-Time Frequency Fusion Network for Motor Imagery EEG Classification

Motor imagery electroencephalogram (MI-EEG) classification is essential in brain-computer interface (BCI), and many classification methods have been proposed recently. However, the MI-EEG classification accuracy of the public dataset still has room for improvement, and designing a suitable model to extract and fuse the multi-modality features efficiently is crucial. In this study, we proposed a Multi-scale Space-time Frequency fusion Network (MSFNet) to improve the MI-EEG classification accuracy. The MSFNet comprises data acquisition and preprocessing, multi-scale time-conv fusion unit, multi-scale frequency-conv fusion unit, feature fusion, and classification. Multi-scale time-conv fusion unit can extract multi-scale spatiotemporal features, and multi-scale frequency-conv fusion unit can extract five frequency sub-band features. These two features were concatenated to complete the multi-modality features fusion, and MI-EEG was classified. Average accuracy, kappa value, and F1 score were adopted as the evaluation metric, and BCI Competition 2008 IV 2a and High Gamma datasets were employed to demonstrate the effectiveness of the MSFNet. The superiority of this proposed model was demonstrated by comparison against the state-of-the-art methods, and the classification result is the highest. Overall, we achieved an average accuracy of 80.47%, kappa value of 0.783, and F1 score of 0.743 in the BCI Competition 2008 IV 2a dataset, and we achieved an average accuracy of 93.56%, kappa value of 0.933, and F1 score of 0.915 in High Gamma dataset. This model realized the extraction and efficient fusion of spatiotemporal and frequency domain features and the high-precision MI-EEG classification, which has an important application value.
Does Personal Innovativeness Affect Sustainable Waste Behaviour? The Mediating Role of Creative Reuse

Purpose – The success of businesses and governments in developing and implementing sustainable waste management policies depends on the habits of individual users. Innovative personality traits, creative reuse, and sustainable waste behaviours of product and service users play a significant role in achieving circular economy practices. This study aims to investigate the mediating role of creative reuse in the effect of personal innovativeness on sustainable waste behaviour. Design/methodology/approach – Primary data was used in the research, and data were collected via questionnaires from individuals living in Türkiye over 18 years old. The research data was obtained by creating a link containing the survey questions in the electronic environment and sending it to the participants via the Internet through various social media platforms. 373 people participated in the research. The structural equation model was applied as the research method. SPSS and SmartPLS 4 package programs were used for statistical analysis. Findings – It has been found that creative reuse has a partial mediating effect between personal innovativeness and sustainable waste behaviour. Research limitations/implications – In future studies, the model can be expanded by including different variables in the research model. Moreover, suppose the survey is applied to a wider audience with more diverse socio-economic characteristics and in different age groups. In that case, strategies can be developed for people at different levels by analyzing according to age groups. Furthermore, similar studies can be conducted in different countries, allowing for cross-country comparisons to be made. Practical implications – The main research results emphasize the importance for organizations to consider boosting users’ personal innovation traits by offering creativity-enhancing practices such as open innovation platforms. Furthermore, it can be suggested that considering the significance of revising sustainable production strategies by innovating through collaboration platforms with users. Originality/value – This study has a unique value that contributes to the field of sustainability by combining personal innovativeness and creativity, which are an essential part of the innovation literature, with reuse, a vital element of the circular economy.
Adverse neurobehavioral changes with reduced blood and brain cholinesterase activities in mice treated with statins

Background and Aim: Pleiotropic effects of hypolipidemic statins with behavioral outcomes have been suggested in humans and laboratory animals. There is limited information on the neurobehavioral effects of statins in mice. The aim of the present study was to examine changes in neurobehavioral performance and cholinesterase (ChE) activity in mice after high doses of three commonly used statins (atorvastatin, simvastatin, and rosuvastatin). Materials and Methods: Two hours after vehicle (control) or statin dosing at 250, 500, 750, or 1000 mg/kg orally, each mouse was subjected to 5 min open-field activity, negative geotaxis at an angle of 45°/60 s, 5 min head pocking, and forced swimming endurance. Plasma, erythrocyte, and brain ChE activities were determined spectrophotometrically 2 and 24 h after oral dosing of statins at 500 and 1000 mg/kg. Results: The statins variably, but dose-dependently and significantly (p < 0.05) delayed the latency to move in the open-field arena, decreased locomotion and rearing, reduced head pocking, and delayed negative geotaxis performance. However, statins significantly increased the duration of forced swimming and decreased the duration of immobility in the swimming tank. Statins significantly and dose-dependently decreased plasma, erythrocyte, and brain ChE activity 2 and 24 h after dosing. Plasma and brain ChE activities recovered by 5%–32.9% and 5.7%–14.4% 24 h later from the 2 h ChE values, respectively. Conclusion: High doses of statins differentially modulate neurobehavioral outcomes in mice in association with reduced plasma, erythrocyte, and brain ChE activity. Plasma or erythrocyte ChE may be used for biomonitoring of the adverse/therapeutic effects of statins.
Detection of Porang Plant Diseases and Pests (Amorphophallus Muelleri) Based on Leaf Imagery Utilizing DCNN Transfer Learning

Produk olahan dari umbi tanaman porang (Amorphophallus muelleri) selalu diminati di kawasan Asia: Jepang, China, Korea, serta negara kawasan Australia. Umbi tanaman porang dapat digunakan sebagai bahan baku industri kosmetik dan juga memiliki potensi untuk mencegah berbagai penyakit manusia, karena memiliki kandungan glaukoma yang tinggi. Untuk mendapatkan umbi porang yang berkualitas baik, banyak petani menghadapi berbagai penyakit seperti penyakit busuk daun, virus mozaik (mosaik konjac) dan serangan hama pada daun tanaman porang. Dalam studi ini, diajukan sebuah arsitektur deep learning untuk klasifikasi penyakit daun pada tanaman porang. Kinerja dari model CNN Custome dibandingkan dengan model deep learning lainnya. Semua model dilatih pada kumpulan data asli dan data augmentasi dari 1000 gambar. Pendekatan Transfer Learning digunakan untuk melatih semua model deep learning. Hasil pengujian dataset menunjukkan bahwa model arsitektur EfficientNetV2M mencapai skor tertinggi dibandingkan dengan model deep learning lainnya pada dataset augmentasi dengan akurasi sebesar 98,44%. Sedang ResNet50 pada dataset asli dengan nilai 97,66% pada dataset asli, sedangkan CNN Custome dengan akurasi sebesar 89,06% dan 85,94% untuk semua dataset.
Pioneering the Future: A Trailblazing Review of the Fusion of Computational Fluid Dynamics and Machine Learning Revolutionizing Plasma Catalysis and Non-Thermal Plasma Reactor Design

The advancement of plasma technology is intricately linked with the utilization of computational fluid dynamics (CFD) models, which play a pivotal role in the design and optimization of industrial-scale plasma reactors. This comprehensive compilation encapsulates the evolving landscape of plasma reactor design, encompassing fluid dynamics, chemical kinetics, heat transfer, and radiation energy. By employing diverse tools such as FLUENT, Python, MATLAB, and Abaqus, CFD techniques unravel the complexities of turbulence, multiphase flow, and species transport. The spectrum of plasma behavior equations, including ion and electron densities, electric fields, and recombination reactions, is presented in a holistic manner. The modeling of non-thermal plasma reactors, underpinned by precise mathematical formulations and computational strategies, is further empowered by the integration of machine learning algorithms for predictive modeling and optimization. From biomass gasification to intricate chemical reactions, this work underscores the versatile potential of plasma hybrid modeling in reshaping various industrial processes. Within the sphere of plasma catalysis, modeling and simulation methodologies have paved the way for transformative progress. Encompassing reactor configurations, kinetic pathways, hydrogen production, waste valorization, and beyond, this compilation offers a panoramic view of the multifaceted dimensions of plasma catalysis. Microkinetic modeling and catalyst design emerge as focal points for optimizing CO2 conversion, while the intricate interplay between plasma and catalysts illuminates insights into ammonia synthesis, methane reforming, and hydrocarbon conversion. Leveraging neural networks and advanced modeling techniques enables predictive prowess in the optimization of plasma-catalytic processes. The integration of plasma and catalysts for diverse applications, from waste valorization to syngas production and direct CO2/CH4 conversion, exemplifies the wide-reaching potential of plasma catalysis in sustainable practices. Ultimately, this anthology underscores the transformative influence of modeling and simulation in shaping the forefront of plasma-catalytic processes, fostering innovation and sustainable applications.
Sentiment Classification of COVID-19 Issues on Twitter Using Indonesia Sentiment Lexicon and Machine Learning-Based Approaches (Case Study: Indonesia, March-May 2020)

This study examines sentiment analysis related to COVID-19 in Indonesia (March-May 2020) using InSet Lexicon as training data in supervised machine learning models. The dataset comprises 7,967 tweets, divided into 90% training data and 10% testing data. The results reveal that Support Vector Machine (SVM) and Random Forest (RF) are the most effective methods, achieving accuracy above 80%, with SVM reaching 87% and RF at 86%. InSet Lexicon itself attains an accuracy of 75%, a macro average of 69%, and a weighted average of 74%, making it an effective alternative for large-scale data labeling. Research recommendations support further development of InSet Lexicon for sentiment classification and expansion of the lexicon for foreign languages to enhance sentiment analysis accuracy in a global context. This study provides valuable insights into understanding public sentiment regarding crucial issues such as COVID-19 in Indonesia.
BOLD cardiorespiratory pulsatility in the brain: from noise to signal of interest

Functional magnetic resonance imaging (fMRI) based on the Blood Oxygen Level Dependent (BOLD) contrast has been extensively used to map brain activity and connectivity in health and disease. Standard fMRI preprocessing includes different steps to remove confounds unrelated to neuronal activity. First, this narrative review explores how signal fluctuations due to cardiac and respiratory activity, usually considered as “physiological noise” and regressed out from fMRI time series. However, these signal components bear useful information about some mechanisms of brain functioning (e.g., glymphatic clearance) or cerebrovascular compliance in response to arterial pressure waves. Aging and chronic diseases can cause stiffening of the aorta and other main arteries, with a reduced dampening effect resulting in greater transmission of pressure impulses to the brain. Importantly, the continuous hammering of cardiac pulsations can produce local alterations of the mechanical properties of the small cerebral vessels, with a progressive deterioration that ultimately affects neuronal functionality. Second, the review emphasizes how fMRI can study the brain patterns most affected by cardiac pulsations in health and disease with high spatiotemporal resolution, offering the opportunity to identify much more specific risk markers than systemic factors based on measurements of the vascular compliance of large arteries or other global risk factors. In this regard, modern fast fMRI acquisition techniques allow a better characterization of these pulsatile signal components due to reduced aliasing effects, turning what has been traditionally considered as noise in a signal of interest that can be used to develop novel non-invasive biomarkers in different clinical contexts.
The impact of the pandemic on mothers and children, with a focus on syndemic effects on black families: the “Prenatal to Preschool” study protocol

Introduction Racism, a known social determinant of health, affects the mental health and well-being of pregnant and postpartum women and their children. Convincing evidence highlights the urgent need to better identify the mechanisms and the ways in which young children’s development and mental health are adversely impacted by their mothers’ experiences of racism. With the additional stressor of the COVID-19 pandemic, the criticality of improving knowledge of these domains has never been starker. The proposed project will address these questions and move the field forward to create targeted, culturally informed preventative interventions, thus achieving mental health equity for all children and families. Methods This prospective research is a cohort study that will longitudinally observe the outcomes of a cohort of mothers and their children recruited during the initial phase of the COVID-19 pandemic. Data will be parent/caregiver questionnaires assessing mental health, racism, support, and resilience at multiple time points with the first beginning at 24 months, clinical interviews with mothers, electronic medical records of mothers, and videotaped dyadic interactions at child age 24 and 48 months. A subset of Black participants will be asked to participate in qualitative interviews at child age 36 months. Results Analyze will be performed within and across Black and Non-Latino/a/e/x white (NLW) groups, and comparing mothers and fathers/secondary caregivers. Descriptive and multivariate analyzes will be run to better characterize how young children’s development and mental health may be adversely impacted by their caregiver’s experiences of racism. Discussion This prospective longitudinal mixed-methods study evaluates the simultaneous effects of the COVID-19 pandemic and racism on mothers and their developing children to characterize cross-racial differences, providing insight into risk and resilience factors in early development and the peripartum period.
Characteristics and immune checkpoint status of radioiodine-refractory recurrent papillary thyroid carcinomas from Ukrainian Chornobyl Tissue Bank donors

Introduction The radioiodine-refractory (RAI-R) recurrent papillary thyroid carcinomas (PTCs) are more frequent in elderly patients and have an unfavorable prognosis. Data on the prevalence and characteristics of RAI-R recurrent PTCs in patients of young and middle age with or without a history of radiation exposure in childhood are poorly described. The aim of the current study was: i) to determine the frequency of RAI-R recurrent PTCs among donors of the Chornobyl Tissue Bank (CTB) and analyze the clinicopathological features of primary tumors (PTs), primary metastases (PMTSs), recurrent metastases (RMTSs) and risk factors for RMTS, and ii) to determine the immune checkpoint status (ICS) of the RAI-R recurrent PTCs and to assess the factors associated with ICS positivity. Methods Sixty RAI-R recurrent PTCs (46 exposed to radiation and 14 non-exposed, 2.5% of all cases registered with the CTB) from the Ukrainian patients aged up to 48 years were identified. Results The clinicopathological characteristics of the PTs moderately to weakly resembled those of the PMTS and RMTS from the same patients while the metastatic tissues were highly similar. The multivariate model of RMTS included the dominant solid-trabecular growth pattern of the PT, cystic changes, N1b metastases, and the probability of a causation (POC) of PTC by radiation as risk factors. Among these factors, the lateral PMTS (N1b) had the strongest effect. The longer period of latency (a POC component) was the second statistically significant characteristic. ICS percent agreement between the PT and RAI-R RMTS was 91.5%; 23.7% of PTs and 28.8% of RMTSs had positive ICS (positive PD-L1 tumor epithelial cells (TECs) and positive PD-L1/PD1 tumor-associated immune cells). ICS positivity of PTs was associated with pronounced oncocytic changes and high density of the p16INK4A-positive TECs in the invasive areas of PTs. In RMTSs, ICS positivity was associated with pronounced oncocytic changes and Ki-67 labeling index ≥ 4.5% of PTs, and the dominant solid-trabecular growth pattern, Ki-67 labeling index ≥ 7.6% and p16INK4A-positivity of RMTS. Discussion The findings are of clinical relevance and may be useful for developing individual treatment approaches for patients with RAI-R recurrent PTCs possibly involving immunotherapy.
Flexible Coatings Facilitate pH-Targeted Drug Release via Self-Unfolding Foils: Applications for Oral Drug Delivery

Ingestible self-configurable proximity-enabling devices have been developed as a non-invasive platform to improve the bioavailability of drug compounds via swellable or self-unfolding devices. Self-unfolding foils support unidirectional drug release in close proximity to the intestinal epithelium, the main drug absorption site following oral administration. The foils are loaded with a solid-state formulation containing the active pharmaceutical ingredient and then coated and rolled into enteric capsules. The coated lid must remain intact to ensure drug protection in the rolled state until targeted release in the small intestine after capsule disintegration. Despite promising results in previous studies, the deposition of an enteric top coating that remains intact after rolling is still challenging. In this study, we compare different mixtures of enteric polymers and a plasticizer, PEG 6000, as potential coating materials. We evaluate mechanical properties as well as drug protection and targeted release in gastric and intestinal media, respectively. Commercially available Eudragit® FL30D-55 appears to be the most suitable material due to its high strain at failure and integrity after capsule fitting. In vitro studies of coated foils in gastric and intestinal media confirm successful pH-triggered drug release. This indicates the potential advantage of the selected material in the development of self-unfolding foils for oral drug delivery.
Cognitive Hubs and Spatial Redistribution

1 Model Details 4 1.1 Household Decisions . 4 1.2 Firms . 5 1.2. 1 Intermediate Goods Producers . 5 1.2. 2 Final Goods . 6 1.2. 3 Derivation of Prices . 7 1.2. 4 Trade Shares . 8 1.3 Market Clearing and Aggregation at the Industry and City Level . 9 1.4 Definition of Equilibrium . 10 1.5 Aggregate Equilibrium . 11
N-gram Language Model for Chinese Function-word-centered Patterns

N-gram language modelling, a proven and effective method in NLP, is widely used to calculate the probability of a sentence in natural language. Language pattern is a linguistic level between word/character and sentence, which exists in pattern grammar. In this research, the approach of language model and language pattern are combined for the first time, and language patterns are studied by use of the N-gram model. Chinese function-word-centered patterns are extracted from the LCMC corpus, and aligned into pattern chains. The language model is trained from these chains to investigate the properties and distribution of Chinese function words, the interaction of content words and function words, and the interaction between patterns. The results indicate that there are approximately 10,000 function-word-centered patterns in the texts, which are distributed exponentially. This research summarizes the most common function-word-centered patterns and content-word-centered patterns, and discusses the interactions of patterns based on corpus data. The bigram language model of these patterns reflects the restrictions of function words. In addition, the research adopts an innovative method to visualize the interactions between patterns. This research fills the research gap between word/character and sentence and reveals basic Chinese pattern categories and the interactions between patterns, which makes a significant contribution to Chinese linguistic research, and improves the efficiency of NLP.
Does Osteopathic Heart-Focused Palpation Modify Heart Rate Variability in Stressed Participants with Musculoskeletal Pain? A Randomised Controlled Pilot Study

Heart rate variability (HRV) describes fluctuations in time intervals between heartbeats and reflects autonomic activity. HRV is reduced in stressed patients with musculoskeletal pain and improved after osteopathic manipulative treatment and mind–body interventions. Heart-focused palpation (HFP) combines manual and mind–body approaches to facilitate relaxation. This randomised controlled pilot study investigated the feasibility and sample size for a future randomised controlled trial and the effect of a single treatment with HFP or sham HFP (SHAM) on short-term HRV. A total of Thirty-three adults (47.7 ± 13.5 years old) with stress and musculoskeletal pain completed the trial with acceptable rates of recruitment (8.25 subjects per site/month), retention (100%), adherence (100%), and adverse events (0%). HFP (n = 18), but not SHAM (n = 15), significantly increased the root mean square of successive RR interval differences (p = 0.036), standard deviation of the NN intervals (p = 0.009), and ratio of the low-frequency to high-frequency power band (p = 0.026). HFP and SHAM significantly decreased the heart rate (p < 0.001, p = 0.009) but not the stress index and ratio of the Poincaré plot standard deviation along and perpendicular to the line of identity (p > 0.05). A power analysis calculated 72 participants. Taken together, the study was feasible and HFP improved HRV in stressed subjects with musculoskeletal pain, suggesting a parasympathetic effect.
Quantifying the uncertainty in fault plane solutions inferred from S/P amplitude ratios

We study the problem of the moment tensor inversion of a double-couple microseismic source from observed S/P amplitude ratios. The emphasis of this work is on uncertainty quantification that includes the effect of the uncertain event location. We use a Bayesian approach to quantify the uncertainty of the fault plane solution. The posterior distribution is effectively calculated by sampling from the posterior distribution of the event location, and performing a moment-tensor inversion using individual samples. The uncertainty in the reconstructed moment tensor depends on the receiver geometry, signal noise, and the true moment tensor. After a suitable transformation of the input data, the problem can be reduced to a classical least-squares estimation problem.