Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Fast and Precise Pixel-to-Torque Control: A Platform for Reproducible Research of Learning on Hardware

In the field, robots often need to operate in unknown and unstructured environments, where accurate sensing and state estimation (SE) become a major challenge. Cameras have been used with great success in mapping and planning in such environments [1] as well as complex but quasi-static tasks, such as grasping [2], but are rarely integrated into the control loop for unstable systems. Learning pixel-to-torque control promises to enable robots to flexibly handle a wider variety of tasks. While reinforcement learning (RL) offers a solution in principle, learning pixel-to-torque control for unstable systems that require precise and high-bandwidth control still presents a significant practical challenge, and best practices have not been established. Part of the reason is that many of the most auspicious tools, such as deep neural networks (DNNs), are opaque: the cause for success on one system is difficult to interpret and generalize.
Adsorption and Sustained Delivery of Small Molecules from Nanosilicate Hydrogel Composites

Two-dimensional nanosilicate particles (NS) have shown promise for the prolonged release of small-molecule therapeutics while minimizing burst release. When incorporated in a hydrogel, the high surface area and charge of NS enable electrostatic adsorption and/or intercalation of therapeutics, providing a lever to localize and control release. However, little is known about the physio-chemical interplay between the hydrogel, NS, and encapsulated small molecules. Here, we fabricated polyethylene glycol (PEG)-NS hydrogels for the release of model small molecules such as acridine orange (AO). We then elucidated the effect of NS concentration, NS/AO incubation time, and the ability of NS to freely associate with AO on hydrogel properties and AO release profiles. Overall, NS incorporation increased the hydrogel stiffness and decreased swelling and mesh size. When individual NS particles were embedded within the hydrogel, a 70-fold decrease in AO release was observed compared to PEG-only hydrogels, due to adsorption of AO onto NS surfaces. When NS was pre-incubated and complexed with AO prior to hydrogel encapsulation, a >9000-fold decrease in AO release was observed due to intercalation of AO between NS layers. Similar results were observed for other small molecules. Our results show the potential for use of these nanocomposite hydrogels for the tunable, long-term release of small molecules.
Identification, Characterization, and Expression Analysis of Spondin-Like and Fasciclin-Like Genes in Neopyropia yezoensis, A Marine Red Alga

Extracellular matrix (ECM) proteins play crucial roles in the regulation of cell proliferation and differentiation. We identified homologous genes encoding ECM proteins that are known to associate with integrins in animal cells in red macroalga Neopyropia yezoensis. Four genes encoding spondin domain-containing proteins (NySPLs) and eight genes encoding fasciclin domain-containing proteins (NyFALs) from N. yezoensis were selected for bioinformatics and expression analysis in order to obtain insights into the roles of ECM proteins for the life cycle. NySPLs had eight β-strands with two contiguous α-helices, which were similar to those of the F-spondin domain of animals. NyFALs had conserved H1 and H2 motifs and a YH motif between the H1 and H2 regions. Quantitative reverse transcription polymerase chain reaction showed that NySPL1–3 and NyFAL8 transcripts were highly accumulated in mature gametophytes that formed the spermatia. Furthermore, expressions of all NySPLs were upregulated in response to the ethylene precursor 1-aminocylopropane-1-carboxylic acid that induces gametogenesis. NyFAL1, 4 were highly expressed in sporophytes, whereas NyFAL2, 3, 5, 6, and 7 were overexpressed in gametophytes, especially at the vegetative stage. These findings facilitate future research on ECM architecture in the unique life cycles of red macroalgae.
Precise Marketing Strategy Optimization of E-Commerce Platform Based on KNN Clustering

With the development of computer technology and the arrival of the era of artificial intelligence, the analysis of user demand bias is of great significance to the operation optimization of e-commerce platforms. Combined with CS domain signaling data, IP packet data of PS domain, and customer CRM data provided by operators, this research studies each dimension index of operator user portrait, after that the operator user portrait platform is divided into some individual subunits, and then the corresponding data mining technology is carried out to study the implementation scheme of each subunit. The system can process and mine multidimensional data of operators’ users and form user portraits on the basis of user data aggregation. Finally, based on the operator user portrait platform studied in this paper, the operator user data are analyzed from both the user’s mobile phone use behavior and user consumption behavior. Furthermore, the application value of this research in the precision marketing and personalized service of operators is illustrated.
Global Disparities in Hepatitis B Elimination—A Focus on Africa

In 2016, WHO member states at the World Health Assembly adopted a Global Health Sector Strategy that included a policy of eliminating viral hepatitis. Clear targets were established to assist in achieving this by 2030. The strategy, while achievable, has exposed existing global disparities in healthcare systems and their ability to implement such policies. Compounding this, the regions with most disparity are also those where the hepatitis B prevalence and disease burden are the greatest. Foundational to hepatitis B elimination is the identification of both those with chronic infection and crucially pregnant women, and primary prevention through vaccination. Vaccination, including the birth dose and full three-dose coverage, is key, but complete mother-to-child transmission prevention includes reducing the maternal hepatitis B viral load in the third trimester where appropriate. Innovations and simplified tools exist in order to achieve elimination, but what is desperately required is the will to implement these strategies through the support of appropriate investment and funding. Without this, disparities will continue.
Hemocyte proteome of the Lake Baikal endemic Eulimnogammarus verrucosus (Crustacea: Amphipoda) sheds light on immune-related proteins

Hemocytes are cells circulating in the hemolymph and playing an important role in crustacean immunity. These cells not only function as phagocytes but also express immune compounds to the hemolymph. Here we obtained hemocyte proteome of the endemic amphipod (Amphipoda, Crustacea) Eulimnogammarus verrucosus from Lake Baikal, the first hemocyte proteome of an amphipod, using liquid chromatography/tandem mass spectrometry (LC-MS/MS). A total of 1152 unique proteins were discovered with LC-MS/MS. We discovered both proteins directly involved in the immune response, such as pattern recognition proteins (C-type lectins), and compounds with antimicrobial activity (ctenidin and anti-lipopolysaccharide factor/scygonadin). Moreover, hemocyanins which may act as a phenoloxidase and C-type lectins were among the most diverse protein groups in the hemocyte proteome. The obtained data can be useful for further studies of immune components and mechanisms in Baikal amphipods.
Research on Coal Level Detecting System of Mine Coal Bunker

According to the characteristics of the uneven surface coal and complex environment,the paper introduced some kinds of coal level detecting methods: ultrasonic level meter,capacitance level meter,radiation level meter and image processing level meter etc.Their respective scope and insufficiency is presented.At last,the paper provided some improved methods of coal level detecting system.
Oral Immunogenicity of Enterotoxigenic Escherichia coli Outer Membrane Vesicles Encapsulated into Zein Nanoparticles Coated with a Gantrez® AN–Mannosamine Polymer Conjugate

Enterotoxigenic Escherichia coli (ETEC) represents a major cause of morbidity and mortality in the human population. In particular, ETEC infections affect children under the age of five from low-middle income countries. However, there is no licensed vaccine against this pathogen. ETEC vaccine development is challenging since this pathotype expresses a wide variety of antigenically diverse virulence factors whose genes can be modified due to ETEC genetic plasticity. To overcome this challenge, we propose the use of outer membrane vesicles (OMVs) isolated from two ETEC clinical strains. In these OMVs, proteomic studies revealed the presence of important immunogens, such as heat-labile toxin, colonization factors, adhesins and mucinases. Furthermore, these vesicles proved to be immunogenic after subcutaneous administration in BALB/c mice. Since ETEC is an enteropathogen, it is necessary to induce both systemic and mucosal immunity. For this purpose, the vesicles, free or encapsulated in zein nanoparticles coated with a Gantrez®–mannosamine conjugate, were administered orally. Biodistribution studies showed that the encapsulation of OMVs delayed the transit through the gut. These results were confirmed by in vivo study, in which OMV encapsulation resulted in higher levels of specific antibodies IgG2a. Further studies are needed to evaluate the protection efficacy of this vaccine approach.
Diagnosis for Slight Bearing Fault in Induction Motor Based on Combination of Selective Features and Machine Learning

Induction motors are widely used in industry and are essential to industrial processes. The faults in motors lead to high repair costs and cause financial losses resulting from unexpected downtime. Early detection of faults in induction motors has become necessary and critical in reducing costs. Most motor faults are caused by bearing failure. Machine learning-based diagnostic methods are proposed in this study. These methods use effective features. First, load currents of healthy and faulty motors are measured while the rotating speed is changing continuously. Second, experiments revealed the relationship between the magnitude of the amplitude of specific signals and the rotating speed, and the rotating speed is treated as a new feature. Third, machine learning-based diagnoses are conducted. Finally, the effectiveness of machine learning-based diagnostic methods is verified using experimental data.
Predicting Cutting Force and Primary Shear Behavior in Micro-Textured Tools Assisted Machining of AISI 630: Numerical Modeling and Taguchi Analysis

The cutting tool heats up during the cutting of high-performance super alloys and it negatively affects the life of the cutting tool. Improved tool life can enhance both the machinability and sustainability of the cutting process. To improve the tool life preferably cutting fluids are utilized. However, the majority of cutting fluids are non-biodegradable in nature and pose harmful threats to the environment. It has been established in the metal cutting literature that introducing microgrooves at the cutting tool rake face can significantly reduce the coefficient of friction (COF). Reduction in the COF promotes anti-adhesive behavior that improves the tool life. The current study numerically investigates the orthogonal cutting process of AISI 630 Stainless Steel using different micro grooved cutting tools. Results of the numerical simulations point to the positive influence of micro grooves on tool life. The results of the main effects found that the cutting temperature was decreased by approximately 10% and 7% with rectangular and triangular micro grooved tools, respectively. Over machining performance indicated that rectangular micro groove tools provided comparatively better performance.
Molecular Mechanisms in Pentanucleotide Repeat Diseases

The number of neurodegenerative diseases resulting from repeat expansion has increased extraordinarily in recent years. In several of these pathologies, the repeat can be transcribed in RNA from both DNA strands producing, at least, one toxic RNA repeat that causes neurodegeneration by a complex mechanism. Recently, seven diseases have been found caused by a novel intronic pentanucleotide repeat in distinct genes encoding proteins highly expressed in the cerebellum. These disorders are clinically heterogeneous being characterized by impaired motor function, resulting from ataxia or epilepsy. The role that apparently normal proteins from these mutant genes play in these pathologies is not known. However, recent advances in previously known spinocerebellar ataxias originated by abnormal non-coding pentanucleotide repeats point to a gain of a toxic function by the pathogenic repeat-containing RNA that abnormally forms nuclear foci with RNA-binding proteins. In cells, RNA foci have been shown to be formed by phase separation. Moreover, the field of repeat expansions has lately achieved an extraordinary progress with the discovery that RNA repeats, polyglutamine, and polyalanine proteins are crucial for the formation of nuclear membraneless organelles by phase separation, which is perturbed when they are expanded. This review will cover the amazing advances on repeat diseases.
Sequential bilingual heritage children's L1 attrition in lexical retrieval: Age of acquisition versus language experience

Abstract This study investigated the unresolved issue of potential sources of heritage language attrition. To test contributing effects of three learner variables – age of second language acquisition, length of residence, and language input – on heritage children's lexical retrieval accuracy and speed, we conducted a real-time word naming task with 68 children (age 11–14 years) living in South Korea who spoke either Chinese or Russian as a heritage language. Results of regression analyses showed that the participants were less accurate and slower in naming target words in their heritage language as their length of residence in Korea and the amount of Korean input increased. The age of Korean acquisition did not significantly influence their performance. These findings support the claim that heritage speakers’ language experience is a more reliable predictor of first language attrition than age of acquisition. We discuss these findings in light of different approaches to explaining language attrition.
Microalgae as a Sustainable, Natural-Oriented and Vegan Dermocosmetic Bioactive Ingredient: The Case of Neochloris oleoabundans

“Vegan” and “sustainable” characteristics are strong claim trends behind the development of innovative skincare, fragrances, and makeup products. This created a need in the market for compliant ingredients. To date, there have been no records evidencing the use of the microalgae Neochloris oleoabundans (NA) in dermocosmetics. Therefore, we studied the applicability of such a natural compound in this context. NA was cultivated, and the scavenging activity (SA) of the NA extracts was evaluated. The highest SA was from the aqueous extract (54.8% ± 2.1%), being higher than that of the positive control. Two hydrogels were prepared with 1.0% ammonium acryloyldimethyltaurate/VP copolymer: (1) control gel; and (2) gel with a 1.0% NA aqueous extract. In vivo experiments were performed in healthy male and female volunteers with skin phototypes of II–IV. The stratum corneum (SC) hydration and the transepidermal water loss (TEWL) were measured in the forearm of participants to determine their biocompatibility. This parameter was determined by skin bioengineering measurements, confirming that SC hydration and TEWL were not affected by the samples. The laser Doppler measurements results showed a delayed erythema onset in the sites, where the NA hydrogel was applied. The results confirmed the biocompatibility and the anti-inflammatory activity of an innovative ingredient derived from microalgae suitable for a natural and vegan lifestyle.
Enhancing innovative delivery in schools using design thinking

The pandemic has created challenges in all sectors of the economy and education. Traditional teaching approaches seem futile in the new context, thus the need to constantly reinvent the delivery to meet the fast-paced changes in the education domain. Hence, Design Thinking (DT) is an alternative approach that might be useful in the given context. DT is known to be a human-centric approach to innovative problem-solving processes. DT could be employed in the delivery process to develop twenty-first-century skills and enhance creativity and innovation, in an attempt to identify alternative solutions. The study explores the role of design thinking (DT) in the form of empathy, thinking process, gamified lessons and curriculum enhancement, which leads to innovative delivery among teachers. It enhances and facilitates innovative content delivery by leveraging creativity. The study targeted 131 teachers, whereby 61 are primary school teachers and 70 are secondary school teachers. A questionnaire constituting of 23 close-ended questions using the 5-point Likert scale was used to collect data. Data was analyzed using SmartPLS to establish relationships between DT and Innovative Delivery in schools. The data was further analyzed to seek co-relations between the DT steps and the successful transformation of content delivery by teachers. The study established a framework for the application of design thinking for teachers as the primary support in developing activities for their students. It shows that thinking process, gamifying lessons and curriculum enhancement have positive significance in innovative delivery, whereas empathy did not show a significant positive relationship. The outcome of this study will help fill the gap towards creating an interesting method of delivery in schools and constantly innovating the method to suit the evolving generation. This insight is crucial for the Ministry of Education and policymakers to enhance teachers’ ability to innovatively deliver content to students.
Models for Assessing Strategies for Improving Hospital Capacity for Handling Patients during a Pandemic

Abstract Objective: The aim of this study was to investigate the performance of key hospital units associated with emergency care of both routine emergency and pandemic (COVID-19) patients under capacity enhancing strategies. Methods: This investigation was conducted using whole-hospital, resource-constrained, patient-based, stochastic, discrete-event, simulation models of a generic 200-bed urban U.S. tertiary hospital serving routine emergency and COVID-19 patients. Systematically designed numerical experiments were conducted to provide generalizable insights into how hospital functionality may be affected by the care of COVID-19 pandemic patients along specially designated care paths, under changing pandemic situations, from getting ready to turning all of its resources to pandemic care. Results: Several insights are presented. For example, each day of reduction in average ICU length of stay increases intensive care unit patient throughput by up to 24% for high COVID-19 daily patient arrival levels. The potential of 5 specific interventions and 2 critical shifts in care strategies to significantly increase hospital capacity is also described. Conclusions: These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, collaborate with other health care facilities, or request external support, thereby increasing the likelihood that arriving patients will find an open staffed bed when 1 is needed.
Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning

In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method. The proposed method exploits spatial and spectral correlations in HSI data using sparse Bayesian learning and compressive sensing (CS). By utilizing a simultaneously L&S data model, we employ the information of the principal components and Bayesian learning to reconstruct the hyperspectral images. The simulation results demonstrate that the proposed method is superior to LRMR and SS&LR methods in terms of reconstruction accuracy and computational burden under the same signal-to-noise tatio (SNR) and compression ratio.
Overlapping Decentralized Control Strategies of Building Structure Vibration Based on Fault-Tolerant Control under Seismic Excitation

The vibration control system of a building structure under a strong earthquake can be regarded as a large complex system composed of a series of overlapping subsystems. In this paper, the overlapping decentralized control of building structure vibration under seismic excitation is studied. Combining the overlapping decentralized control method, H∞ control algorithm, and passive fault-tolerant control method, a passive fault-tolerant overlapping decentralized control method based on the H∞ control algorithm is proposed. In this paper, the design of robust H∞ finite frequency passive fault-tolerant static output feedback controller for each subsystem is studied. The fault matrix of the subcontroller is expressed by a polyhedron with finite vertices. In order to reduce the influence of external disturbance on the controlled output, the finite frequency H∞ control is adopted and the Hamiltonian matrix is avoided. In this paper, the passive fault-tolerant overlapping decentralized control method based on H∞ control algorithm is applied to the vibration control system of the four-story building structure excited by the Hachinohe seismic wave. One drive is set on each layer of the structure, and a total of four drives are set. Select the driver fault factor of 0.5 or 1 and the frequency band [0.3, 8] Hz. The overlapping decentralized control scheme and 16 fault-tolerant fault matrices are designed, and the numerical comparison results are given. The results show that both overlapping decentralized control strategy and multioverlapping decentralized control strategy have achieved good control results. Due to the different number of subsystems and overlapping information, the overlapping decentralized control scheme increases the flexibility of controller setting and reduces the computational cost.
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 7.4 Madoi earthquake: an archetype bilateral slip‐pulse rupture arrested at a splay fault

We combine measurements of ground deformation from Synthetic Aperture Radar images, high-rate GNSS and tele-seismic waveforms to study the rupture kinematics of the Madoi earthquake, which occurred in eastern Tibet on May 21, 2021 and reached a moment magnitude Mw 7.4. The data show nearly pure left-lateral motion along a 170 km long rupture and a total duration of 36s. The earthquake initiated near the middle of the main segment and evolved in a bilateral slip pulse rupture which propagated at a sub-Rayleigh speed of 2.6-2.8 km/s. In our model, slip is concentrated at depth of less than ~15 km and reaches a maximum of 4.2 m. The rupture arrested ~10 s after branching on the extensional splay faults at both extremities. The branching onto the splay faults and the eventual arrest of the rupture is used to provide constraints on the fault frictional properties.