Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Robust and Efficient Musculoskeletal Modeling Using Distributed Physics-Informed Deep Learning

This article develops a novel distributed framework based on physics-informed deep learning for robust and efficient musculoskeletal modeling in nonstationary scenarios, which could simultaneously strengthen the robustness and generalization, and reduce the time cost of model training. Without loss of generality, we utilize surface electromyogram (sEMG)-based muscle forces and joint angle prediction as an example to demonstrate the proposed distributed framework. Specifically, the whole collected sEMG data are first divided into several subdomains, and then a corresponding number of physics-informed deep-learning-based local models is built using these grouped data. Finally, all the local models are integrated into a global model to obtain the final predictions. Moreover, weights inversely proportional to the training errors of local models are added to the corresponding local models to reduce and control the negative effects of unknown factors. Different from existing distributed modeling methods, the proposed distributed framework embeds the prior physics knowledge, i.e., the equation of motion, into local models to regularize loss functions of deep neural networks, it thus could overcome limitations of the conventional data-driven and physics-based musculoskeletal models while preserving their advantages. The local-global distributed modeling mechanism could locally achieve better representation for subdomains while preserving the global performance and reducing the computational cost and memory requirements. In addition, the embedded prior physics knowledge enables local models to reflect physical or physiological mechanisms during the training process, which could alleviate the overfitting problem and reduce the need for the number of training data, thus the global model is more robust and better generalizes to the unseen data. Comprehensive experiments on six healthy subjects demonstrate the feasibility and effectiveness of the proposed distributed framework.
Chemical Signatures & Social Interactions: Implications of west Fergusson Island obsidian at Hopo, east of the Vailala River (Gulf of Papua), Papua New Guinea

Eleven artefacts made of west Fergusson Island obsidian were recovered in beach sands inland from today’s coastline east of the Vailala River in the Gulf of Papua. These artefacts were found some 900 km by sea southwest of their geological source location. They extend the known western limit for incoming obsidian along the south coast of mainland PNG by 155 km. Here we discuss the chemical signatures of these artefacts towards an interpretation of social relations between peoples living on the south coast of PNG after c. 2600 cal BP.
Effectiveness of the Eutectic Mixture of Local Anaesthetics Cream in the Management of Arteriovenous Fistula Needle Insertion Pain in Patients Undergoing Haemodialysis

This review aimed to assess the effectiveness of the eutectic mixture of local anaesthetics (EMLA) cream in the management of arteriovenous fistula (AVF) needle insertion pain in adult patients undergoing haemodialysis (HD) compared with other alternative interventions. The main search was conducted in November 2020 and updated in December 2021. In the search strategy, keywords and synonyms were used and multiple databases were searched with no date limitation to ensure a comprehensive search that would yield all studies relevant to the review and minimise location bias. A total of 209 studies were found in this search and filtered. After filtering through these studies, only five studies were finally included in the review. EMLA-cream was found to be effective in reducing AVF needle insertion pain among adult patients undergoing HD. Despite EMLA cream’s effectiveness in reducing HD needle insertion pain and its fewer side effects, the findings of the included studies should be interpreted with caution, as there are some limitations, and further research is required.
The Cultural Development of the Northwest Coast

EARLY RECONSTRUCTIONS of North Pacific development rested on three conclusions already defined by the time of Harlan I. Smith's archaeological surveys published in 1899. ' These three initial assumptions concerned: (a) relationships between the Northwest Coast and the American Arctic and/or Asia; (b) the significance of an old climax of culture on the lower Fraser River; and (c) the westward movement of cultural traits from the interior tothe coast. When Drucker viewed the Northwest Coast in 1943 from the materials ofhis survey of northern sites, he accepted each of these,2 and one or more of them have been variously involved in all reconstructions t date. More recent work has led to two new formulations both of which are primarily relevant to the third initial assumption, i.e.to the interchange of coastal and interior traits. These are: (1) the recognition of a coastal stone-chipping tradition of some age, and (2) the reconstruction of a prehistoric cultural province in the foothill regions of the Cascades and the Coast Range. The first of these rests on new archaeological data. It was presaged in the excavation fWakemap mound, strengthened by the Suquamish finds and confirmed by the stratigraphy of Cattle Point.3 The total archaeological picture in which this coastal province finds its place is reviewed briefly below. Some of its implications are also examined but complete exploitation of this new orientation must await further xcavation. Unlike the preceding, the second recent formulation relies largely on ethnographic data. Certain similarities have been noted4 within the western longitudinal subarea of the Plateau, Inland Puget Sound, and the middle Fraser. " It is here suggested that hese areas formed the nucleus of a single prehistoric foothill province and, further, that acceptance of such a reconstruction serves to explain both the position of the Bella Coola and the distribution of the massive stone sculpture of the Pacific Northwest. A reconstruction willvary somewhat s the view shifts from one focal point to another and in this paper the Northwest Coast is seen consistently from the
Chemical Composition, Functional and Anticancer Properties of Carrot

Plants are a valuable source of drugs for cancer treatment. Daucus carota has been investigated for its health properties. In particular, Daucus carota L. subsp. Sativus, the common edible carrot root, has been found to be rich in bioactive compounds such as carotenoids and dietary fiber and contains many other functional components with significant health-promoting features, while Daucus carota L. subsp. Carrot (Apiacae), also known as wild carrot, has been usually used for gastric ulcer therapy, diabetes, and muscle pain in Lebanon. Here, we review the chemical composition of Daucus carota L. and the functional properties of both edible and wild carrot subspecies. Then, we focus on compounds with anticancer characteristics identified in both Daucus carota subspecies, and we discuss their potential use in the development of novel anticancer therapeutic strategies.
Green Plants and Nature: Natural Wastewater Treatment Plants

 Abstract— Our nature and natural resources are the real teachers of our engineers. In wastewater engineering, one of the most sophisticated methods of wastewater treatment is the tertiary treatment or biological treatment. Methods such as biodisc, biofiltration and activated sludge use tiny organisms to remove organic and inorganic pollutants from wastewater. Other higher levels of organisms, such as plants, have been also used for polluted water purification. In phytoremediation, with its different methods of purification, plants are used to remove pollutants from wastewater. Other organisms such as algae, shellfish and fish can be also applied for wastewater treatment. In this paper, the results of the research on using organisms to remove pollutants such as heavy metals from different kinds of wastewaters are presented. The researches were either based on the measurements on nature, or pilot studies in the laboratory. We used water hyacinth to remove nitrate, phosphate, BOD5 and COD from municipal wastewater. The common reed and some other aquatic plants were used for the purification of wastewaters discharged from oil industries and gas purification factories. The concentrations of metals were measured in tissues of organisms exposed to polluted waters in nature; organisms such as algae, fish, shellfish, plants and worms. High concentrations of metals were observed in the organism tissues, suggesting they are favorable for wastewater treatment. The results suggest that a combination of methods would be the most appropriate and in most cases economic method for the treatment of different types of wastewater.
Synapse-associated protein 102 – a highly mobile MAGUK predominate in early synaptogenesis

Neurodevelopmental and neurodegenerative disorders are primarily characterized by serious structural and functional changes in excitatory glutamatergic synapses in the brain, resulting in many synaptic deficits and aberrant synapse loss. It is a big challenge to reverse these synaptic impairments as a treatment for neurological diseases in the field. Extensive research on glutamate receptors as therapeutic targets has been done but with little success shown in human trials. PSD-95-like MAGUK proteins perform a pivotal role in regulating the trafficking and stability of glutamate receptors that are important to postsynaptic structure and function. MAGUK and MAGUK-modulated synaptic pathways are becoming promising candidates for developing therapeutic targets. As a MAGUK protein, SAP102 is not understood well compared to PSD-95. Here, we review the current research on SAP102 including its synaptic functions and regulation, especially its expression and functions in the early stage of synaptogenesis and the association with neurodevelopmental disorders. This review presents valuable information for future structural and functional studies of SAP102 to reveal its roles in young and mature neurons. It provides clues for developing potential remedies to reverse synaptic impairments and strategies to grow new neurons.
Speech Emotion Recognition and Deep Learning: An Extensive Validation Using Convolutional Neural Networks

The domain of Speech Emotion Recognition (SER) has experienced a tremendous revolution due to the outbreak of deep learning, which has contributed, as in many other research areas, to a significant boost in terms of model accuracy. SER refers to a branch of Human-Computer Interaction (HCI), which deals with recognizing emotional states from human speech. Although being a thriving field of research, SER still poses several non-trivial challenges, mainly due to the lack of shared best practices and high-quality datasets that can make the developed models suitable for their application in real environments. In this paper, we implement a CNN-based model combined with a Convolutional Attention Block, and conduct a series of experiments involving a selection of four English datasets popularly used for SER applications: RAVDESS, TESS, CREMA-D, and IEMOCAP. After testing the proposed pipeline on individual datasets, achieving a mean accuracy of 83%, 100%, 68% and 63% respectively, we perform an extensive cross-validation between common emotional classes belonging to single datasets or combinations of them, with the aim to investigate the generalization abilities of the extracted features.
Effect of Processing Methods on Antinutritional Factors (Oxalate, Phytate, and Tannin) and Their Interaction with Minerals (Calcium, Iron, and Zinc) in Red, White, and Black Kidney Beans

The purpose of this study was to assess how different processing techniques affected mineral compositions, antinutritional factors, and their interactions in red, white, and black kidney beans consumed in Ethiopia. Mineral contents were found to be 41–44, 58–78, and 112–126 mg Ca/100 g in the raw, soaked, and cooked samples, respectively. Iron content in the raw, soaked and cooked samples were found to be 2.77–2.97, 1.94–2.20 and 2.87–3.28 mg Fe/100 g, respectively, showing 26–30% loss on soaking followed by 33–48% increase on cooking. While Zn content in the raw, soaked and cooked samples were found to be 2.47–3.26, 3.34–4.68 and 2.83–3.31 mg Zn/100 g, respectively, showing 35–43% increase on soaking followed by 15–29% decrease on cooking. In the case of antinutrients, both treatments showed incredible decrements. Phytate in the raw samples was 178-179 mg/100 g and showed a 12–16% decrement on soaking and a 37-38% decrement up on cooking, oxalate was 1.5–1.8 mg/100 g in the raw samples and showed a 4.4–13% decrement during treatments, and tannin in the raw samples was 102–160 mg/100 g and showed a 23–30% decrement on soaking, followed by 21–41% during cooking. Phytate : Ca and oxalate : Ca molar ratios in soaked and cooked samples were within the critical values in the raw samples. In contrast, phytate : Zn and Ca × phytate : Zn in all treatments were found to be within the critical value, confirming the good bioavailability of zinc in all the samples, while phytate : Fe was found over the critical value, showing its poor availability.
Multiview Feature Learning and Multilevel Information Fusion for Joint Classification of Hyperspectral and LiDAR Data

With the rapid development of earth observation technology, the fusion of multisource remote sensing (RS) data has become an important research field in land cover classification. Particularly, the fusion of hyperspectral image (HSI) and light detection and ranging (LiDAR) data, which can provide complementary information to each other, has attracted an increasing attention for land cover classification tasks. However, most of the existing methods focus on multisource information fusion on feature level and ignore the discrepancy of the land covers with the same label among different kinds of viewpoints, which limit the further improvement of classification performance. So, this article proposes a novel HSI-LiDAR classification method based on multiview feature learning (MVFL) and multilevel information fusion (MLIF), which consists of a multiview data representation (MVDR) strategy, a multibranch dual-channel graph convolutional networks (MB-DCGCNs) model, and a progressively high-confidence label assignment (PHCLA) scheme. MVDR is designed by jointly utilizing multiple attributes of spatial information and multisource RS data to re-express multisource land covers with diversity and complementarity. The MB-DCGCNs model, which aims at integrating spectral–spatial–elevation information on feature level, is employed on the above multiview re-expressed HSI-LiDAR (MVR-HL) data to explore land cover feature representations from different views. Furthermore, a PHCLA scheme is proposed to classify the land covers with high-reliability by a combination of decision-level label prediction and pixel-level label assignment. Comparative experiments on two benchmark datasets with several state-of-the-art classification methods validate the effectiveness and superior performance of the proposed method.
Palladium Catalysts Supported in Microporous Phosphine Polymer Networks

A new set of microporous organic polymers (POPs) containing diphosphine derivatives synthesized by knitting via Friedel–Crafts has been attained. These amorphous three-dimensional materials have been prepared by utilizing diphosphines, 1,3,5-triphenylbenzene, and biphenyl as nucleophile aromatic groups, dimethoxymethane as the electrophilic linker, and FeCl3 as a promoting catalyst. These polymer networks display moderate thermal stability and high microporosity, boasting BET surface areas above 760 m2/g. They are capable of coordinating with palladium acetate, using the phosphine derivative as an anchoring center, and have proven to be highly efficient catalysts in Suzuki–Miyaura coupling reactions involving bromo- and chloroarenes under environmentally friendly (using water and ethanol as solvents) and aerobic conditions. These supported catalysts have achieved excellent turnover numbers (TON) and turnover frequencies (TOF), while maintaining good recyclability without significant loss of activity or Pd leaching after five consecutive reaction cycles.
Microseismic record de‐noising using a sparse time‐frequency transform

This work presents a noise attenuation technique based upon applying a sparsity constraint to a time-frequency transform. It is demonstrated that the solution obtained from applying the sparsity constraint rather than the more common minimum norm constraint produces a superior noise attenuated signal. The sparsity constrained transformation is achieved by finding a sparse representation of the input data in terms of a dictionary of complex Ricker wavelets. The utilization of a complex wavelet dictionary possesses the advantage that signals with arbitrary phase can be represented with enhanced sparsity. Examples with synthetic and real microseismicity data illustrate the capacity of the technique to attenuate ambient noise in microseismic records with low signal-to-noise ratio.
Assessing Spatial Knowledge and Mental Map Development Under Virtual Training Conditions

Mental maps are the core of wayfinding because they contain representations of point, route, and configuration related spatial knowledge. Currently, methods spatial knowledge do not clearly separate these theoretically disparate facets. The purpose of this research is to examine the relationships between point-related, route-related, and configuration-related spatial knowledge tests administered to participants in the context of training to comprehend a virtual environment. Results indicated that measures which are often interchanged under the assumption that they are reflective of spatial knowledge may in fact measure different and unrelated constructs. Futher, analyses showed that measures that are commonly used to tap point-related and route-related knowledge may have different influences on the development of configuration knowledge, and little to none on the ability for individuals to leverage their holistic mental map for wayfinding.
Characterization of the bacterial microbiome of non-hematophagous bats and associated ectoparasites from Brazil

Introduction Bats, along with their ectoparasites, harbor a wide diversity of symbiotic and potential pathogenic bacteria. Despite the enormous diversity of bats (181 species), few studies aimed to investigate the bacterial microbiome of Brazilian chiropterans and associated ectoparasites. This study aimed to characterize the bacterial microbiome of non-hematophagous bats and associated Streblidae flies and Macronyssidae and Spinturnicidae mites in the state of Mato Grosso do Sul, midwestern Brazil. Methods Oral and rectal swabs were collected from 30 bats (Artibeus lituratus [n = 13], Artibeus planirostris [n = 9], Eptesicus furinalis [n = 5], Carollia perspicillata [n = 2], and Platyrrhinus lineatus [n = 1]). In addition, a total of 58 mites (15 Macronyssidae and 43 Spinturnicidae) and 48 Streblidae bat flies were collected from the captured bats. After DNA extraction and purification, each sample’s bacterial composition was analyzed with metagenomic sequencing. Results The microbiome composition of both oral and rectal bat swab samples showed that Gammaproteobacteria was the most abundant bacterial class. Spiroplasma, Wolbachia and Bartonella represented the most abundant genera in Streblidae flies. While Wolbachia (Alphaproteobacteria) was the most abundant genus found in Spinturnicidae, Arsenophonus (Gammaproteobacteria) was found in high abundance in Macronyssidae mites. In addition to characterizing the microbiome of each sample at the class and genus taxonomic levels, we identified medically significant bacteria able to infect both animals and humans in oral (Streptococcus and Anaplasma) and rectal swabs (Enterobacter, Klebsiella, Escherichia, Enterococcus, Streptococcus), Macronyssidae (Anaplasma, Bartonella, Ehrlichia) and Spinturnicidae (Anaplasma, Bartonella) mites as well as Streblidae flies (Spiroplasma, Bartonella). Discussion and conclusion Besides expanding the knowledge on the bacterial microbiome of non-hematophagous bats and Streblidae flies from Brazil, the present work showed, for the first time, the bacterial community of bat-associated Macronyssidae and Spinturnicidae mites.
Active Tectonics of the Aegean Region

The Aegean Sea and its surrounding regions comprise one of the most rapidly deforming parts of the Alpine-Himalayan mountain belt. Though the deformation of the belt as a whole is related to thc northward move­ ment of Africa, Arabia, and India relative to Eurasia, the tectonics of the Aegean region itself is dominated by strike-slip and extensional motions. The actively deforming part of the Aegean region is not large (about 700 x 700 km2) compared with that in Asia, yet it has had an influence on Our views of continental tectonics disproportionate to its size, and comparable perhaps only to Tibet. Fundamental contributions from the Aegean have been made to many topics, including:
Improved LTE-R Access Authentication Scheme Based on Blockchain and Secgear

High-speed railway (HSR) is developing toward automation and intelligence with the motivation of improved safety. The automatic train control system is deeply committed to HSR safety, and depends on long term evolution for railway (LTE-R). The authentication scheme is the primary contributor among all security measures to secure communication in LTE-R. We found vulnerabilities in the authentication mechanism used in LTE-R, such as key leakage, inability to resist privileged user attacks, and large authentication latency. To address these vulnerabilities, we propose a secure and efficient access authentication scheme for LTE-R. Unlike other studies, Our proposal capitalizes on the utilization of blockchain to establish a decentralized authentication system, effectively eliminating the single point of collapse vulnerability. Recognizing the public nature of the blockchain, we integrate the secgear framework to provide robust privacy protection measures. Furthermore, our scheme augments LTE-R access authentication by introducing a handover authentication phase and a password change phase, introducing additional security attributes for enhanced protection. In addition, we have conducted fleshed-out security analysis and simulation experiments, which show that our scheme is able to achieve privacy protection with acceptable efficiency.
Enhance Auditory Experience by Identifying User Needs and Classifying User Types While Driving Electric Vehicles

The auditory experience of driving electric vehicles (EVs) has been studied as a means of conveying vehicle status information and improving user satisfaction. However, the driving experience and user preferences may vary depending on the driving context, including user characteristics. Thus, this study aims to 1) classify users based on their characteristics and 2) investigate their auditory experience preferences while driving EVs. For this purpose, 40 participants conducted questionnaires about their characteristics and performed a think-aloud task while driving 15.6 km in real EVs. As a result, three user characteristics and two user types were identified using factor analysis and K-means clustering, respectively. Text-formed think-aloud data were analyzed through network analysis to obtain insights for designing usercentered driving sound for EVs. These findings can contribute to the strategic management of EV sound design.