Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotions, Simulation, and Abstract Art

Some people feel emotions when they look at abstract art. This article presents a ‘simulation’ theory that predicts which emotions they will experience, including those based on their aesthetic reactions. It also explains the mental processes underlying these emotions. This new theory embodies two precursors: an account of how mental models represent perceptions, descriptions, and self-reflections, and an account of the communicative nature of emotions, which distinguishes between basic emotions that can be experienced without knowledge of their objects or causes, and complex emotions that are founded on basic ones, but that include propositional contents. The resulting simulation theory predicts that abstract paintings can evoke the basic emotions of happiness, sadness, anger, and anxiety, and that they do so in several ways. In mimesis, models simulate the actions and gestures of people in emotional states, elicited from cues in the surface of paintings, and that in turn evoke basic emotions. Other basic emotions depend on synaesthesia, and both association and projection can yield complex emotions. Underlying viewers’ awareness of looking at a painting is a mental model of themselves in that relation with the painting. This self-reflective model has access to knowledge, enabling people to evaluate the work, and to experience an aesthetic emotion, such as awe or revulsion. The comments of artists and critics, and experimental results support the theory.
Identification and Fine Mapping of a Locus Related to Leaf Up-Curling Trait (Bnuc3) in Brassica napus

Leaf trait is an important target trait in crop breeding programs. Moderate leaf curling may be a help for improving crop yield by minimizing the shadowing by leaves. Mining locus for leaf curling trait is of significance for plant genetics and breeding researches. The present study identified a novel rapeseed accession with up-curling leaf, analyzed the up-curling leaf trait inheritance, and fine mapped the locus for up-curling leaf property (Bnuc3) in Brassica napus. Genetic analysis revealed that the up-curling leaf trait is controlled by a single dominant locus, named BnUC3. We performed an association study of BnUC3 with single nucleotide polymorphism (SNP) markers using a backcross population derived from the homozygous up-curling leaf line NJAU-M1295 and the canola variety ‘zhongshuang11’ with typical flat leaves, and mapped the BnUC3 locus in a 1.92 Mb interval of chromosome A02 of B. napus. To further map BnUC3, 232 simple sequence repeat (SSR) primers and four pairs of Insertion/Deletion (InDel) primers were developed for the mapping interval. Among them, five SSR markers and two InDel markers were polymorphic. By these markers, the mapping interval was narrowed to 92.0 kb using another F2 population. This fine mapping interval has 11 annotated genes among which BnaA02T0157000ZS were inferred to be candidate casual genes for up-curling leaf based on the cloned sequence analysis, gene functionality, and gene expression analysis. The current study laid a foundational basis for further elucidating the mechanism of BnUC3 and breeding of variety with up-curling leaf.
Mixer-Based Semantic Spread for Few-Shot Learning

Key semantics can come from everywhere on an image. Semantic alignment is a key part of few-shot learning but still remains challenging. In this paper, we design a Mixer-Based Semantic Spread (MBSS) algorithm that employs a mixer module to spread the key semantic on the whole image, so that one can directly compare the processed image pairs. We first adopt a convolutional neural network to extract features from both support and query images and separate each of them into multiple Local Descriptor-based Representations (LDRs). The LDRs are then fed into the mixer for semantic spread, where every LDR attracts complementary information from its peers. In this way, the objective semantic is made spread on the whole image in a data-driven manner. The overall pipeline is supervised by a voting-based loss, guaranteeing a good mixer. Visualization results validate the feasibility of our mixer. Comprehensive experiments on three benchmark datasets, miniImageNet, tieredImageNet, and CUB, show that our algorithm achieves the state-of-the-art performance in both 5-way 1-shot and 5-way 5-shot settings.
Shopping Cart Abandonment among Young Adult in Malaysia: Exploring the Drivers of Cart Abandonment and Consumer Behaviour’s

Online shopping has been around for decades; however, only recently it becomes mainstream, especially during a pandemic. Due to the rise of online shopping activities, the rate of shopping cart abandonment also increases concurrently. This issue damages the e-tailer's business. It causes trillions of dollar losses annually and generates problems such as high advertising costs, signals a poor user experience and impacts website analytics. Thus, this research is conducted to delve into this topic and explain this behaviour among young adults. A self-administered online questionnaire has been conducted and distributed to people in their 20's to '30s, and 255 completed questionnaires were obtained. However, only 252 respondents were analyzed. Regression and correlation analysis documented that organizational tools, entertainment value, and perceived cost were the underlying factors leading to young adults' shopping carts abandonment. These findings will help the e-tailers to understand young adult behaviour towards online shopping and can be treated as indications to lessen shopping cart abandonment.
A fast viscosity forward-backward algorithm for convex minimization problems with an application in image recovery

The purpose of this paper is to invent an accelerated algorithm for the convex minimization problem which can be applied to the image restoration problem. Theoretically, we first introduce an algorithm based on viscosity approximation method with the inertial technique for finding a common fixed point of a countable family of nonexpansive operators. Under some suitable assumptions, a strong convergence theorem of the proposed algorithm is established. Subsequently, we utilize our proposed algorithm to solving a convex minimization problem of the sum of two convex functions. As an application, we apply and analyze our algorithm to image restoration problems. Moreover, we compare convergence behavior and efficiency of our algorithm with other well-known methods such as the forward-backward splitting algorithm and the fast iterative shrinkage-thresholding algorithm. By using image quality metrics, numerical experiments show that our algorithm has a higher efficiency than the mentioned algorithms.
A semantic parsing pipeline for context-dependent question answering over temporally structured data

Abstract We propose a new setting for question answering (QA) in which users can query the system using both natural language and direct interactions within a graphical user interface that displays multiple time series associated with an entity of interest. The user interacts with the interface in order to understand the entity’s state and behavior, entailing sequences of actions and questions whose answers may depend on previous factual or navigational interactions. We describe a pipeline implementation where spoken questions are first transcribed into text which is then semantically parsed into logical forms that can be used to automatically extract the answer from the underlying database. The speech recognition module is implemented by adapting a pre-trained long short-term memory (LSTM)-based architecture to the user’s speech, whereas for the semantic parsing component we introduce an LSTM-based encoder–decoder architecture that models context dependency through copying mechanisms and multiple levels of attention over inputs and previous outputs. When evaluated separately, with and without data augmentation, both models are shown to substantially outperform several strong baselines. Furthermore, the full pipeline evaluation shows only a small degradation in semantic parsing accuracy, demonstrating that the semantic parser is robust to mistakes in the speech recognition output. The new QA paradigm proposed in this paper has the potential to improve the presentation and navigation of the large amounts of sensor data and life events that are generated in many areas of medicine.
Evaluation of Immunomodulatory Responses and Changed Wound Healing in Type 2 Diabetes—A Study Exploiting Dermal Fibroblasts from Diabetic and Non-Diabetic Human Donors

The dermis is the connective layer between the epidermis and subcutis and harbours nerve endings, glands, blood vessels, and hair follicles. The most abundant cell type is the fibroblast. Dermal fibroblasts have a versatile portfolio of functions within the dermis that correspond with different types of cells by either direct contact or by autocrine and paracrine signalling. Diabetic skin is characterized by itching, numbness, ulcers, eczema, and other pathophysiological changes. These pathogenic phenotypes have been associated with the effects of the reactive glucose metabolite methylglyoxal (MGO) on dermal cells. In this study, dermal fibroblasts were isolated from diabetic and non-diabetic human donors. Cultured dermal fibroblasts from diabetic donors exhibited reduced insulin-induced glucose uptake and reduced expression of the insulin receptor. This diabetic phenotype persists under cell culture conditions. Secretion of IL-6 was increased in fibroblasts from diabetic donors. Increased secretion of IL-6 and MIF was also observed upon the treatment of dermal fibroblasts with MGO, suggesting that MGO is sufficient for triggering these immunomodulatory responses. Remarkably, MIF treatment resulted in decreased activity of MGO-detoxifying glyoxalase-1. Given that reduced glyoxalase activity results in increased MGO levels, these findings suggested a positive-feedback loop for MGO generation, in which MIF, evoked by MGO, in turn blocks MGO-degrading glyoxalase activity. Finally, secretion of procollagen Type I C-Peptide (PICP), a marker of collagen production, was reduced in fibroblast from diabetic donors. Remarkably, treatment of fibroblasts with either MGO or MIF was sufficient for inducing reduced PICP levels. The observations of this study unravel a signalling network in human dermal fibroblasts with the metabolite MGO being sufficient for inflammation and delayed wound healing, hallmarks of T2D.
Gait Analysis Platform for Measuring Surgery Recovery

Gait analysis has evolved significantly during last years due to the great development of the Medical Internet of Things (MIoT) platforms that allow an easy integration of sensors (inertial, magnetic and pressure in our case) to the complex analytics required to compute, not only relevant parameters, but also meaningful indexes. In this paper, we extend a previous development based on a fully wireless pair of insoles by implementing an updated version with more reliable and user-friendly devices, smartphone app and web front-end and back-end. We also extend previous work focused on fall analysis (with the corresponding fall risk index or FRI) with the proposal of a new surgery recovery index (SRI) to account for the individual speed recovery speed that can be measured either at clinical facilities or at home in a telemedicine environment or while doing daily life activities. This new index can be personalized for different types of surgeries that affect gait such as hip, knee, etc. This paper presents the case of hip recovery and is built on top of the clinical standard SPPB test and allows obtaining quantitative parameters directly from the sensors.
A Review of Current Publications Trend on Missing Data Imputation Over Three Decades: Direction and Future Research

Missing value or sometimes synonym as missing data, is an unavoidable issue when collecting data. It is uncontrollable and happen in almost any research fields. Hence, this study focused on identifying the current publications trend on missing data imputation techniques (1991- 2021) specifically in classification problems using bibliometric analysis. Most importantly, this research aims to uncover the potential missing data imputation methods. Two software were used; VOSViewer and Harzing Publish or Perish. Based on the Scopus database extracted in June 2021, the findings indicate an emerging trend in missing data imputation research to date, while there are two imputation methods that get the most attention; the random forest and the nearest neighbor methods.
Bankfull shear velocity predicts embeddedness and silt cover in gravel streambeds

Excess fine sediment (<2 mm) deposition on gravel streambeds can degrade habitat quality for stream biota. Two measures of fine sediment deposition include embeddedness and silt cover (<62.5 μm). Embeddedness measures fine sediment in interstitial pore spaces, whereas silt cover, primarily deposited during low flows, measures fine sediment draped on the streambed's surface. Here, we demonstrate that a baseline level of embeddedness and a maximum value of silt cover can be predicted from bankfull shear velocity, which can be estimated from river channel and streamflow characteristics, independently of knowing the sediment supply. We derive an equation for bankfull shear velocity that only requires knowing bankfull flow, channel width, and channel slope, which can be readily obtained in the United States from freely available, remotely sensed data. We apply this methodology to data collected at 30 sites in the Piedmont region of Virginia and North Carolina. This work is an important step in developing statistical models of stream ecosystems in which geophysical variables can predict embeddedness and silt cover, which commonly limit biotic assemblages.
Performance of Electrocoagulation Processes for the Removal of COD and Ammonia from High Salinity Landfill-leachate using Iron or Aluminum Electrodes

Landfill leachate is characterized by a large fraction of low biodegradability organics. The present work aimed to evaluate and optimize the electrocoagulation (EC) process to remove chemical oxygen demand (COD) and NH4-N from highsalinity landfill leachate. The study was conducted using iron or aluminum electrodes while different parameters such as reaction time, current density, and pH were evaluated on the process's efficiency. The experimental results showed that by applying a current density of 12 mAcm-2, the COD removal after 240 minute electro-processing time was 78.5 % and 72.5% for iron and aluminum electrodes respectively. Also, the removal of NH4-N after 240 minutes at a current density of 12 mA cm2 was 62 % and 54% for iron and aluminum electrodes respectively. This study demonstrates the feasibility of electrocoagulation to treat high-salinity landfill leachate without the need for a supporting electrolyte.
Smart Grid Technologies as a Concept of Innovative Energy Development: Initial Proposals for the Development of Ukraine

The formation of the concept of Smart Grid is associated with a number of issues, including theoretical and methodological. One of the main problems in forming such theory is to build its basis, the starting point for the development of which is the definition of Smart Grid as a systems of views concept on the future of power engineering, the principles of operation and technological basis of which undergoes significant changes compared to modern energy. The paper is aimed at reviewing and developing directions and approaches to the definition of Smart Grid in combination with machine learning mechanisms, highlighting their diverse and common nature to develop a holistic innovative energy development. In this paper, a study of the energy sector in Ukraine was conducted. Its efficiency and innovative development are considered. Problems with the implementation of Smart Grid technology, which arise when using alternative sources and monitoring and administration systems, were highlighted. The ways of mathematical formulation of the Smart Grid optimization problem are determined using the Data Science approach based on the machine learning system and neural networks. Big Data processing methods, Data Mining, statistical methods, artificial intelligence methods, and Machine Learning are analyzed. The design and development of databases and application software will be done using the Data Science method. Smart-technologies will take over the processes of control, accounting and diagnostics of assets, which will provide promising opportunities for self-recovery of the power system, as well as efficient operation of fixed assets. With the introduction of Smart Grid technologies for the Ukrainian power industry, significant fundamental changes will take place. This is the transition from centralized methods of generation and transmission of electricity to distributed networks with the ability to control energy production facilities and network topology at any point, including at the consumer level. Replacement of centralized demand forecasting according to the methodology of active consumer influence becomes an element and subject of the management system. A high-performance information and computing infrastructure will be built as the core of the energy system. This approach creates the preconditions for the widespread introduction of new devices that increase the maneuverability and controllability of the equipment. The creation of next-generation operational applications (SCADA/EMS/NMS) allows the use of innovative algorithms and methods of power system management, including its new active power elements.
The challenge of time-resolved flow investigation of a one-stage centrifugal compressor with a non-symmetric volute

A single stage industrial centrifugal compressor including vaneless diffuser and a nonsymmetric volute is investigated with regard to the development of flow instabilities and rotating stall. A deeper understanding of flow phenomena that might be a precursor for stall inception would help to predict unstable operation already during the design phase of a compressor stage, as today the design is commonly supported by CFD analysis providing performance data and flow information at an early stage of the design process. Due to the complex flow path geometry of the volute a sophisticated instrumentation concept was developed allowing for unsteady pressure measurement with synchronized high-resolution flow field measurement. This paper summarizes the challenges and the steps taken during rig design in terms of integrating non-intrusive, high resolution pressure measurement sensors along with access for optical techniques such as time-resolved PIV and L2F to investigate unsteady flow phenomena and stall inception occurring at reduced speed operation. Results of performance measurements and surge testing are presented and compared to CFD simulations. Exemplary results of the extensive database obtained by optical techniques in combination with dynamic pressure measurements are provided.
(ZMA)-transform method

Abstract In this action, we study a new integral transform identical to laplace, ZZ and Sumudu transforms are started. (ZMA)–Transform is asymptotic to ZZ transforms with some modification. The feature of this transform solves the differential equations (DE) smoothly and effectively without complications and with guaranteed results, moreover presented application for initial value problem (IVP) show that the transformation. In addition to this, I prepared a table for these transfers.
Characterization of Structural and Physicochemical Properties of an Exopolysaccharide Produced by Enterococcus sp. F2 From Fermented Soya Beans

The present study sought to isolate a novel exopolysaccharide (EPS-F2) from Enterococcus sp. F2 through ethanol precipitation, anion-exchange, and gel-filtration chromatography and characterize the physicochemical properties by spectral techniques. EPS-F2 was identified as a neutral homo-exopolysaccharide composed of only glucose with a high molecular weight of 1.108 × 108 g/mol. It contained →6)-α-D-Glcp-(1→ linkage in the main chain and →3, 6)-α-D-Glcp-(1→ branch chain). Moreover, EPS-F2 possessed excellent thermal stability (266.6°C), water holding capacity (882.5%), oil holding capacity (1867.76%), and emulsifying activity against various edible oils. The steady shear experiments exhibited stable pseudo plasticity under various conditions (concentrations, temperatures, and pHs). The dynamic oscillatory measurements revealed that EPS-F2 showed a liquid-like behavior at a low concentration (2.5%), while a solid-like behavior at high concentrations (3.0 and 3.5%). Overall, these results suggest that EPS-F2 could be a potential alternative source of functional additives and ingredients and be applied in food industries.
Cancer Cell Membrane-Coated Nanoparticles with a Yolk-Shell Structure Augment Cancer Chemotherapy.

Despite rapid advancements in antitumor drug delivery, insufficient intracellular transport and subcellular drug accumulation are still issues to be addressed. Cancer cell membrane (CCM)-camouflaged nanoparticles (NPs) have shown promising potential in tumor therapy due to their immune escape and homotypic binding capacities. However, their efficacy is still limited due to inefficient tumor penetration and compromised intracellular transportation. Herein, a yolk-shell NP with a mesoporous silica nanoparticle (MSN)-supported PEGylated liposome yolk and CCM coating, CCM@LM, was developed for chemotherapy and exhibited a homologous tumor-targeting effect. The yolk-shell structure endowed CCM@LM with moderate rigidity, which might contribute to the frequent transformation into an ellipsoidal shape during infiltration, leading to facilitated penetration throughout multicellular spheroids (MCSs) in vitro (up to a 23.3-fold increase compared to the penetration of membrane vesicles). CCM@LM also exhibited a cellular invasion profile mimicking an enveloped virus invasion profile. CCM@LM was directly internalized by membrane fusion, and the PEGylated yolk (LM) was subsequently released into the cytosol, indicating the execution of an internalization pathway similar to that of an enveloped virus. The incoming PEGylated LM further underwent efficient trafficking throughout the cytoskeletal filament network, leading to enhanced perinuclear aggregation. Ultimately, CCM@LM, which coencapsulated low-dose doxorubicin (DOX) and the PARP inhibitor mefuparib hydrochloride (MPH), exhibited a significantly stronger antitumor effect than the first-line chemotherapeutic drug Doxil. Our findings highlight that NPs that can undergo facilitated tumor penetration and robust intracellular trafficking have a promising future in cancer chemotherapy.