Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Plasmonic Hybrids: Efficient and Selective Photocatalysts

Important efforts are currently under way in order to implement plasmonic phenomena in the growing field of photocatalysis, striving for improved efficiency and reaction selectivity. A significant fraction of such efforts have been focused on distinguishing, understanding and enhancing specific energy transfer mechanisms from plasmonic nanostructures to their environment. Herein we report a synthetic strategy that brings together two of the main physical mechanisms driving plasmonic photocatalysis into an engineered system by rationally combining the photochemical features of energetic charge carriers and the electromagnetic field enhancement inherent to the plasmonic excitation. We do so by creating hybrid photocatalysts that integrate multiple plasmonic resonators in a single entity, controlling their joint contribution through spectral separation and differential surface functionalization. This strategy allows us to study the combination of different photosensitization mechanisms when activated simultaneously. Our results show that hot electron injection can be combined with an energy transfer process mediated by near-field interaction, leading to a significant increase of the final photocatalytic response of the material. In this manner, we overcome the limitations that hinder photocatalysis driven only by a single energy transfer mechanism, and move the field of plasmonic photocatalysis closer to energy-efficient applications. Furthermore, our multimodal hybrids offer a test system to probe the properties of the two targeted mechanisms and open the door to wavelength-selective photocatalysis and novel tandem reactions.
“I Feel Therefore I Decide”: Effect of Negative Emotions on Temporal Discounting and Probability Discounting

Temporal and probability discounting are considered two fundamental constructs in economic science, as they are associated with phenomena with major societal impact and a variety of sub-optimal behaviors and clinical conditions. Although it is well known that positive and negative affective states bear important cognitive/behavioral consequences, the effect of emotional experiences on decision-making remains unclear due to the existence of many conflicting results. Inspired by the need to understand if and to what extent the current COVID-19 pandemic has determined changes in our decision-making processes by means of the unusual, prolonged experience of negative feelings, in this study we investigate the effect of anger, fear, sadness, physical and moral disgust on intertemporal and risky choices. Results show that all emotions significantly increase subjects’ preferences for immediate rewards over delayed ones, and for risky rewards over certain ones, in comparison to a “neutral emotion” condition, although the magnitude of the effect differs across emotions. In particular, we observed a more pronounced effect in the case of sadness and moral disgust. These findings contribute to the literature on emotions and decision-making by offering an alternative explanation to the traditional motivational appraisal theories. Specifically, we propose that the increased preference for immediate gratification and risky outcomes serves as a mechanism of self-reward aimed at down-regulating negative feelings and restore the individual’s “emotional balance”.
Intelligent scaling for 6G IoE services for resource provisioning

The proposed research motivates the 6G cellular networking for the Internet of Everything’s (IoE) usage empowerment that is currently not compatible with 5G. For 6G, more innovative technological resources are required to be handled by Mobile Edge Computing (MEC). Although the demand for change in service from different sectors, the increase in IoE, the limitation of available computing resources of MEC, and intelligent resource solutions are getting much more significant. This research used IScaler, an effective model for intelligent service placement solutions and resource scaling. IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. The research also highlights several challenges geared by architectonics that submerge an Intelligent Scaling and Placement module.
The wavenumber imaging of fiber waviness in hybrid glass–carbon fiber reinforced polymer composite plates

Out-of-plane waviness is one of the most common defects which degenerates the strength, stiffness, and fatigue life of hybrid glass–carbon fiber–reinforced polymer composites (FRPs). An accurate and high-speed non-destructive testing method is highly desired for large composite structures in industries. Ultrasonic phased array is a great candidate for such application. This paper applies the wavenumber algorithm to image the waviness in hybrid FRP plates which are a multi-layered medium. The central frequency of 5 MHZ is chosen in order to maximize the ply resonance. Transducers are migrated virtually to each interface between glass and carbon plies in order to overcome the difficulty of wave propagation analysis in such multi-layered system. The wavenumber algorithm demonstrates a better computational performance compared to that of the traditional total focusing method (TFM) in time domain up to 6 times. The glass ply depth and waviness angle can be more accurately presented with relative errors less than 1.5% and 14.8%, respectively. In addition, the resin-rich defect characterization is also achievable with a maximum error of 14.4%.
Evaluation of Weight Decay Regularization Techniques for Stock Price Prediction using Gated Recurrent Unit Network

Stock price forecasting in the field of interest for many stock investors to earn more profit from stock trading. Nowadays, machine learning researchers are also involved in this research field so that fast, accurate and automatic stock price forecasting can be achieved. This research paper evaluated GRU network’s performance with weight decay reg-ularization techniques for predicting price of stocks listed NEPSE. Three weight decay regularization technique analyzed in this research work were (1) L1 regularization (2) L2 regularization and (3) L1_L2 regularization. In this research work, six randomly selected stocks from NEPSE were experimented. From the experimental results, we observed that L2 regularization could outperform L1 and L1_L2 reg-ularization techniques for all six stocks. The average MSE obtained with L2 regularization was 4.12% to 33.52% lower than the average MSE obtained with L1 regularization, and it was 10.92% to 37.1% lower than the average MSE obtained with L1_L2 regularization. Thus, we concluded that the L2 regularization is best choice among weight regularization for stock price prediction.
Engineered Fully Human Single-Chain Monoclonal Antibodies to PIM2 Kinase

Proviral integration site of Moloney virus-2 (PIM2) is overexpressed in multiple human cancer cells and high level is related to poor prognosis; thus, PIM2 kinase is a rational target of anti-cancer therapeutics. Several chemical inhibitors targeting PIMs/PIM2 or their downstream signaling molecules have been developed for treatment of different cancers. However, their off-target toxicity is common in clinical trials, so they could not be advanced to official approval for clinical application. Here, we produced human single-chain antibody fragments (HuscFvs) to PIM2 by using phage display library, which was constructed in a way that a portion of phages in the library carried HuscFvs against human own proteins on their surface with the respective antibody genes in the phage genome. Bacterial derived-recombinant PIM2 (rPIM2) was used as an antigenic bait to fish out the rPIM2-bound phages from the library. Three E. coli clones transfected with the HuscFv genes derived from the rPIM2-bound phages expressed HuscFvs that bound also to native PIM2 from cancer cells. The HuscFvs presumptively interact with the PIM2 at the ATP binding pocket and kinase active loop. They were as effective as small chemical drug inhibitor (AZD1208, which is an ATP competitive inhibitor of all PIM isoforms for ex vivo use) in inhibiting PIM kinase activity. The HuscFvs should be engineered into a cell-penetrating format and tested further towards clinical application as a novel and safe pan-anti-cancer therapeutics.
Wearable Physiological Multi-Vital Sign Monitoring System With Medical Standard

In recent years, due to increased attention of individual physical health condition, the demand on portable multi-vital signs monitoring devices has shown an explosive growth. This work designed a portable, multi-vital signs monitoring system, consisting of chest unit and wrist unit, with size of 32 mm $\times32$ mm and diameter of 30 mm, respectively. The designed system is capable of transmitting measured electrocardiograph (ECG), pulse wave and body temperature signal to the mobile devices wirelessly by Bluetooth low energy (BLE) technique. The heart rate, pulse oximetry (SpO2) and respiratory rate are extracted through time domain algorithm. The chest and wrist units can continuously monitor body vital signs for more than 12 and 24 hours, respectively. Besides, the experiment results reveal that the chest unit is able to monitor patients with heart rate abnormalities such as atrial fibrillation and premature beats. The typical heart diseases can be detected in real-time. Meanwhile, the wrist unit clearly detects the changes of the physical parameters of the subjects under different physiological conditions. The accuracy of the designed system is consistent with that of the commercial medical instrument counterpart, with further advantages of compact, portable and patient-friendly natures.
Process-Induced Changes in the Quantity and Characteristics of Grain Dietary Fiber

Daily use of wholegrain foods is generally recommended due to strong epidemiological evidence of reduced risk of chronic diseases. Cereal grains, especially the bran part, have a high content of dietary fiber (DF). Cereal DF is an umbrella concept of heterogeneous polysaccharides of variable chemical composition and molecular weight, which are combined in a complex network in cereal cell walls. Cereal DF and its distinct components influence food digestion throughout the gastrointestinal tract and influence nutrient absorption and other physiological reactions. After repeated consumption of especially whole grain cereal foods, these effects manifest in well-demonstrated health benefits. As cereal DF is always consumed in the form of processed cereal food, it is important to know the effects of processing on DF to understand, safeguard and maximize these health effects. Endogenous and microbial enzymes, heat and mechanical energy during germination, fermentation, baking and extrusion destructurize the food and DF matrix and affect the quantity and properties of grain DF components: arabinoxylans (AX), beta-glucans, fructans and resistant starch (RS). Depolymerization is the most common change, leading to solubilization and loss of viscosity of DF polymers, which influences postprandial responses to food. Extensive hydrolysis may also remove oligosaccharides and change the colonic fermentability of DF. On the other hand, aggregation may also occur, leading to an increased amount of insoluble DF and the formation of RS. To understand the structure–function relationship of DF and to develop foods with targeted physiological benefits, it is important to invest in thorough characterization of DF present in processed cereal foods. Such understanding also demands collaborative work between food and nutritional sciences.
An Ironic Pairing: Enlisting Technology to Bring the Benefits of Nature to Counseling Students

ABSTRACT This qualitative and phenomenological study investigated the experiences of eleven counselors-in-training (CITs) exposed to four live computer-projected scenes and sounds of nature (running stream, snowy mountain landscape, campfire in the forest, and ocean setting) at the start of their practicum classes. The researchers sought to explore the subjective experiences of the CIT’s exposed to the technology-driven natural environment at the beginning of four consecutive practicum meetings. The findings suggest that technology can be harnessed to catalyze many of the same benefits that prior counselor education-based research indicates can be derived from engagement with the natural world: a decrease in negative emotional states that block learning and engagement, an increased sense of connectedness to class peers, and an enhanced recognition of the importance of self-care and personal wellness. Implications for counselor education programs, counselor educators, and counseling students are explored.
Activation of transient receptor potential vanilloid 4 is involved in pressure overload-induced cardiac hypertrophy

Previous studies, including our own, have demonstrated that transient receptor potential vanilloid 4 (TRPV4) is expressed in hearts and implicated in cardiac remodeling and cardiac dysfunction. However, the effects of TRPV4 on pressure overload-induced cardiac hypertrophy remain unclear. In this study, we found that TRPV4 expression was significantly increased in mouse hypertrophic hearts, human failing hearts, and neurohormone-induced hypertrophic cardiomyocytes. Deletion of TRPV4 attenuated transverse aortic constriction (TAC)-induced cardiac hypertrophy, cardiac dysfunction, fibrosis, inflammation, and the activation of NFκB - NOD-like receptor pyrin domain-containing protein 3 (NLRP3) in mice. In vitro, TRPV4 inhibition decreased the neurohormone-induced cardiomyocyte hypertrophy and the increase of intracellular Ca2+ concentration. TRPV4 agonist triggered Ca2+ influx and evoked the phosphorylation of Ca2+/calmodulin-dependent protein kinase II (CaMKII) but these effects were abolished by removing extracellular Ca2+ or TRPV4 inhibition. More importantly, TAC or neurohormone stimulation-induced CaMKII phosphorylation was significantly blocked by TRPV4 inhibition. Finally, we showed that CaMKII inhibition significantly inhibited the phosphorylation of NFκB induced by TRPV4 activation. Our results suggest that TRPV4 activation contributed to pressure overload-induced cardiac hypertrophy. This effect was associated with upregulated Ca2+/ CaMKII mediated the activation of NFκB-NLRP3. Thus, TRPV4 may represent a potential therapeutic drug target for cardiac hypertrophy.
Hoxb5 reprograms murine multipotent blood progenitors into hematopoietic stem cell-like cells

The expression of transcription factor Hoxb5 specifically marks the functional hematopoietic stem cells (HSC) in mice. However, our recent work demonstrated that ectopic expression of Hoxb5 exerted little effect on HSC but could convert B cell progenitors into functional T cells in vivo. Thus, cell type- and development stage-specific roles of Hoxb5 in hematopoietic hierarchy await more extensive exploration. Here, with a mouse strain engineered with conditional expression of Hoxb5, we unveiled that induced expression of Hoxb5 in mouse multipotent progenitor cells (MPP) led to the generation of a de novo Sca1+cKit+Mac1+CD48+ (Mac1+CD48+SK) cell type, which has the ability to repopulate long-term multi-lineage hematopoiesis in serial transplant recipients. RNA-seq analyses showed that Mac1+CD48+SK cells exhibited an acquired machinery of DNA replication and cell division, which resembled nature fetal liver HSC cells (FL HSC). In short, our current study uncovers that Hoxb5 is able to empower MPP with self-renewal potential, thereby providing new strategies to reprogram blood progenitor cells into HSC-like cells.
Geographic Information Sciences : A Journal of the Association of Chinese Professionals in Geographic Information Systems

Spatially fragmented patterns of development have led to an interest in growth control measures aimed at altering the fragmented pattern and creating incentives to cluster development in the landscape. Due to the spatially dependent nature of land values, a geographic information system is used with statistical software to visualize and analyze the spatial pattern of land values in a fast growing suburban county of Washington DC. Spatial statistical measures of correlation and the semivariogram are used to measure the degree of spatial correlation and the distance over which the residuals of the hedonic land value model are correlated. These results are used in a spatial econometric framework to more efficiently draw inference on the effects of growth controls on the spatial pattern of land value. Hedonic analysis reveals that open space and rural preservation are implicitly positively capitalized into newly developed land values through zoning regulations.
AI-Based Quality Control of Wood Surfaces with Autonomous Material Handling

The theory and applications of Smart Factories and Industry 4.0 are increasing the entry into the industry. It is common in industry to start converting exclusive parts, of their production, into this new paradigm rather than converting whole production lines all at once. In Europe and Sweden, recent political decisions are taken to reach the target of greenhouse gas emission reduction. One possible solution is to replace concrete in buildings with Cross Laminated Timber. In the last years, equipment and software that have been custom made for a certain task, are now cheaper and can be adapted to fit more processes than earlier possible. This in combination, with lessons learned from the automotive industry, makes it possible to take the necessary steps and start redesigning and building tomorrows automated and flexible production systems in the wood industry. This paper presents a proof of concept of an automated inspection system, for wood surfaces, where concepts found in Industry 4.0, such as industrial Internet of things (IIoT), smart factory, flexible automation, artificial intelligence (AI), and cyber physical systems, are utilized. The inspection system encompasses, among other things, of the shelf software and hardware, open source software, and standardized, modular, and mobile process modules. The design of the system is conducted with future expansion in mind, where new parts and functions can be added as well as removed.
Using RuGPT3-XL Model for RuNormAS competition

The paper presents a fine-tuning methodology of the RuGPT3-XL (Generative Pretrained Transformer-3 for Russian) language model for the normalization of text spans task. The solution is presented in a competition for two tasks: Normalization of Named Entities (Named entities) and Normalization of a wider class of text spans, including the normalization of different parts of speech (Generic spans). The best solution has achieved 0.9645 accuracy on the Generic spans task and 0.9575 on the Named entities task. The presented solutions are in the public domain at https://github.com/RussianNLP/RuNormAS-solution
Botulinum Neurotoxins in Central Nervous System: An Overview from Animal Models to Human Therapy

Botulinum neurotoxins (BoNTs) are potent inhibitors of synaptic vesicle fusion and transmitter release. The natural target of BoNTs is the peripheral neuromuscular junction (NMJ) where, by blocking the release of acetylcholine (ACh), they functionally denervate muscles and alter muscle tone. This leads them to be an excellent drug for the therapy of muscle hyperactivity disorders, such as dystonia, spasticity, and many other movement disorders. BoNTs are also effective in inhibiting both the release of ACh at sites other than NMJ and the release of neurotransmitters other than ACh. Furthermore, much evidence shows that BoNTs can act not only on the peripheral nervous system (PNS), but also on the central nervous system (CNS). Under this view, central changes may result either from sensory input from the PNS, from retrograde transport of BoNTs, or from direct injection of BoNTs into the CNS. The aim of this review is to give an update on available data, both from animal models or human studies, which suggest or confirm central alterations induced by peripheral or central BoNTs treatment. The data will be discussed with particular attention to the possible therapeutic applications to pathological conditions and degenerative diseases of the CNS.
The Cohesin Complex and Its Interplay with Non-Coding RNAs

The cohesin complex is a multi-subunit protein complex initially discovered for its role in sister chromatid cohesion. However, cohesin also has several other functions and plays important roles in transcriptional regulation, DNA double strand break repair, and chromosome architecture thereby influencing gene expression and development in organisms from yeast to man. While most of these functions rely on protein–protein interactions, post-translational protein, as well as DNA modifications, non-coding RNAs are emerging as additional players that facilitate and modulate the function or expression of cohesin and its individual components. This review provides a condensed overview about the architecture as well as the function of the cohesin complex and highlights its multifaceted interplay with both short and long non-coding RNAs.
SAR vehicle recognition via scale-coupled Incep_Dense Network (IDNet)

ABSTRACT Vehicle target recognition of synthetic aperture radar (SAR) is one of the critical tasks of image interpretation. Convolutional neural networks (CNNs) are increasingly used in SAR images and make great progress in single-scale recognition under standard operating conditions (SOCs). However, the classical CNNs have the limitation of single model design and single scale, which is unfavourable for the recognition of multi-scale targets. This paper puts forward the design idea of scale coupling with both parallel convolution and dense connection, and designs an Incep_Dense module, which can learn and express scale information from different convolution paths and different feature levels simultaneously. Based on the Incep_Dense module, we built Incep_Dense network (IDNet) for SAR image vehicle target recognition. Furthermore, we add special layers to make IDNets more standardized and practical. Experiments show that the proposed IDNet achieves the optimal accuracy of 99% in the ten classifications of MSTAR and less than 1.5% accuracy degradation on the constructed multi-scale dataset. It has excellent multi-scale learning and representation capabilities with better performance than classical CNNs. It also performs well under the extended operating conditions (EOCs) of MSTAR and Gotcha with good generalization ability and robustness.
Online Knowledge Extraction and Preference Guided Multi-Objective Optimization in Manufacturing

The integration of simulation-based optimization and data mining is an emerging approach to support decision-making in the design and improvement of manufacturing systems. In such an approach, knowledge extracted from the optimal solutions generated by the simulation-based optimization process can provide important information to decision makers, such as the importance of the decision variables and their influence on the design objectives, which cannot easily be obtained by other means. However, can the extracted knowledge be directly used during the optimization process to further enhance the quality of the solutions? This paper proposes such an online knowledge extraction approach that is used together with a preference-guided multi-objective optimization algorithm on simulation models of manufacturing systems. Specifically, it introduces a combination of the multi-objective evolutionary optimization algorithm, NSGA-II, and a customized data mining algorithm, called Flexible Pattern Mining (FPM), which can extract knowledge in the form of rules in an online and automatic manner, in order to guide the optimization to converge towards a decision maker’s preferred region in the objective space. Through a set of application problems, this paper demonstrates how the proposed FPM-NSGA-II can be used to support higher quality decision-making in manufacturing.