Miracle-dz/UniFAR-FTUs huggingface dataset:
Striatal neurons are recruited dynamically into collective representations of self-initiated and learned actions in freely-moving mice

Striatal spiny projection neurons are hyperpolarized-at-rest (HaR) and are driven to spike threshold by a small number of powerful inputs – an input-output configuration that is detrimental to response reliability. Because the striatum is important for habitual behaviors and goal-directed learning, we conducted microendoscopic imaging in freely-moving mice that express a genetically-encoded calcium indicator sparsely in striatal HaR neurons to compare their response reliability during self-initiated movements and operant conditioning. The sparse expression was critical for longitudinal studies of response reliability, and for studying correlations among HaR neurons while minimizing spurious correlations arising from contamination by the background neuropil signal. We found that HaR neurons are recruited dynamically into action representation through a moment-by-moment formation of distinct cell assemblies. While individual neurons respond with little reliability, the population response remained reliable. Moreover, we found evidence for the formation of correlated cell assemblies during conditioned (but not innate) behaviors, but this correlation was independent of the distance between neurons.
A Method of Multi-UAV Cooperative Task Assignment Based on Reinforcement Learning

With the increasing complexity of UAV application scenarios, the performance of a single UAV cannot meet the mission requirements. Many complex tasks need the cooperation of multiple UAVs. How to coordinate UAV resources becomes the key to mission completion. In this paper, a task model including multiple UAVs and unknown obstacles is constructed, and the model is transformed into a Markov decision process (MDP). In addition, considering the influence of strategies among UAVs, a multiagent reinforcement learning algorithm based on SAC algorithm and centralized training and decentralized execution framework, MA-SAC (Multi-Agent Soft Actor-Critic), is proposed to solve the MDP. Simulation results show that the algorithm can effectively deal with the task allocation problem of multiple UAVs in this scenario, and its performance is better than other multiagent reinforcement learning algorithms.
Differentiating Problematic from Habitual Instagram Use: A Uses and Grats 2.0 Perspective

The popularity of Instagram among young adults has raised concerns about the excessive use of Instagram. Is it the kind of problematic media use that takes over one’s life or just habitual ritualized use characterized by mindless scrolling? How can we differentiate between the two based on the use of Instagram features and perceived gratifications from the technology itself? Drawing on the theoretical framework of Uses and Gratifications (U&G) and U&G 2.0, we conducted a survey study (N = 482) to answer these questions. Our findings show that habitual Instagram use is positively related to the use of lurking- and connection-related features and the gratification of play. By contrast, problematic Instagram use is positively correlated with the use of broadcasting-related features and the gratification of novelty. Theoretical implications for differentiating problematic from habitual Instagram use and practical implications for designing ethical interfaces, which consider the digital well-being of users, are discussed.
The Effect of Information Asymmetry and Debt on Accrual Earnings Management: Firm Size as a Moderating Variable

One of the opportunistic manager practices is earnings management. This attitude arises because of the high asymmetry of information between the principal and manager, weak regulation and control. If information asymmetry is high, stakeholders do not have sufficient resources, incentives or access to relevant information to monitor managers' actions. Through the size of the company is expected to see how big the influence of information asymmetry and debt on earnings management actions. This research is included in the third TKT measurement, namely proof-of-concept of important functions and/or characteristics analytically and experimentally. This study aims to examine the impact of the interaction of information asymmetry and debt on earnings management through accrual of working capital with firm size as a moderating variable. This study uses quantitative methods with crosssectional data. This study uses secondary data in the form of financial statements of consumption sector manufacturing companies listed on the Indonesia Stock Exchange (IDX) from 2015 to 2021. The results prove that DER has a positive effect on earnings management and company size is able to moderate the effect of DER on earnings management. However, information asymmetry has no effect on earnings management and firm size is not able to moderate the effect of information asymmetry on earnings management.
Web-based English Language Learning: A Review from Web 1.0 to Web 3.0

Since the internet network's establishment, the World Wide Web is considered as the most important tool used by billions of people across the world. It allows people to share the media, read, write, publish and interact with others via the internet. Since its advent, the World Wide Web has evolved from a read-only medium technological network to the collaborative one where learners are interlinked in various ways. Through its various stages of evolution, English language learning was transformed from simple to an interactive, collaborative, and learner-centered environment. This paper will present the conceptual review of the literature, in chronological order, on how English language learning has evolved over the past years, particularly from web 1.0 to web 3.0. It also aims to present the analysis of the evolution of the World Wide Web (www) technologies from a learner perspective; therefore, it will elucidate various web-based tools, technologies, trends, and applications used by learners in their education.
The Status of Moroccan High School Students’ Environmental Knowledge

This paper examines the status of Moroccan high school students’ environmental knowledge. It aims to test the level of students’ knowledge regarding a range of factual and conceptual knowledge of topics related to local and global environmental issues which high school students are supposed to know. The study adopted a quantitative research design and the research sample was made of 524 female and male students belonging to eight public and private high schools in the city Agadir and the suburbs. The descriptive statistics of the knowledge test’s scores revealed that students demonstrated insufficient degrees of environmental knowledge both at the levels of factual and conceptual questions. Meanwhile, the results were also indicative of the absence of positive or negative correlations between students’environmental knowledge and their age, gender, stream, school type and grade. Finally, this paper suggests ways to boost students’ environmental knowledge to improve their pro-environmental attitudes and behaviors.
Successful pregnancy in an end-stage renal disease woman on chronic hemodialysis

Successful conception in a female patient on hemodialysis (HD) is considered a high-risk pregnancy and associated with maternal and fetal complications. Thus, most such pregnancies lead to abortion or termination to preserve maternal health. Here, we report a successfully-delivered case of a 19th-week-diagnosed pregnancy in a 35-year-old mother with end-stage renal disease (ESRD). We present the case of a 35-year-old female with ESRD, diagnosed 10 years ago secondary to glomerulonephritis. The patient underwent the deceased–donor renal transplantation once, which unfortunately was rejected. During the initial tests for second-time kidney transplantation, a human chorionic gonadotropin-beta (beta-hCG) positive with a level of 9953 mIU/mL was reported, suggesting the pregnancy. The transvaginal ultrasonography confirmed the pregnancy at an approximate gestational age of 19 weeks. As a result, the patient underwent four and half hours of intensive HD five times a week and continued until 36 weeks of pregnancy. At 36 weeks, the patient presented to Shariati hospital, Tehran, Iran, with low- back pain. Consequently, a cesarean section (C/S) was performed, and the baby boy was born with a nine of ten Apgar score. Although successful pregnancy is possible for women with ESRD, it requires special multidisciplinary care. Intensive HD and regular fetal monitoring have improved the pregnancy outcome in this population. However, the risk of severe complications is still for the health of the mother and her offspring.
Robust video steganography framework based on channel-space attention

Abstract. In robust video steganography, a message is embedded into a video such that video distortions are avoided while producing a stego video of imperceptible difference from the cover video. Traditional techniques achieved robustness against particular distortions but are complicated in computation and design, and rely on different compression standards. Nowadays, deep-learning-based methods can achieve impressive visual quality and robustness to attacks. We propose a framework with a channel-space attention mechanism for robust video steganography. The framework is composed of depthwise separable convolution layers that can learn channel-space segments for embedding and extraction. The secret messages are distributed across channel-space scales to increase imperceptibility and robustness to distortions. This end-to-end solution is trained with the 3-player game approach to conducting robust steganography, where three networks compete. Two of these handle embedding and extraction operations, while the third network simulates attacks and detection from a steganalyst as an adversarial network. Comparative results versus recent research show that our method is more robust against compression and video distortion attacks. Peak signal-to-noise ratio and structural similarity index were used for evaluating visual quality and demonstrate the imperceptibility of our method.
Game-Theory Modeling for Social Welfare Maximization in Smart Grids

In this paper, we study the Stackelberg game-based evolutionary game with two players, generators and energy users (EUs), for monetary profit maximization in real-time price (RTP) demand response (DR) systems. We propose two energy strategies, generator’s best-pricing and power-generation strategy and demand’s best electricity-usage strategy, which maximize the profit of generators and EUs, respectively, rather than maximizing the conventional unified profit of the generator and EUs. As a win–win strategy to reach the social-welfare maximization, the generators acquire the optimal power consumption calculated by the EUs, and the EUs obtain the optimal electricity price calculated by the generators to update their own energy parameters to achieve profit maximization over time, whenever the generators and the EUs execute their energy strategy in the proposed Stackelberg game structure. In the problem formulation, we newly formulate a generator profit function containing the additional parameter of the electricity usage of EUs to reflect the influence by the parameter. The simulation results show that the proposed energy strategies can effectively improve the profit of the generators to 45% compared to the beseline scheme, and reduce the electricity charge of the EUs by 15.6% on average. Furthermore, we confirmed the proposed algorithm can contribute to stabilization of power generation and peak-to-average ratio (PAR) reduction, which is one of the goals of DR.
In situ synthesis of heterogeneous NiSe2/MoSe2 nanocomposite for high‐efficiency electrocatalytic hydrogen evolution reaction

Developing high‐efficiency electrocatalysts for hydrogen evolution reaction (HER) has been considered as a crucial way to reduce energy loss in water‐splitting. Herein, heterogeneous NiSe2/MoSe2 (H‐NiSe2/MoSe2) nanocomposite constructed by metallic NiSe2 nanocrystallites embedded in few‐layer MoSe2 nanosheets has been in situ synthesized by a simple hydrothermal reaction. As an HER electrocatalyst, it delivers superior performance with an ultra‐small onset overpotential of 103 mV, a small overpotential of 147 mV at 10 mA cm−2, a low Tafel slope of 43.5 mV dec−1, and long‐term stability up to 18 h in 0.5 M H2SO4. The superior performance for HER can be ascribed to the unique embedded nanostructure, which can not only improve the overall conductivity of the nanocomposite but also greatly increase the exposed active sites. Furthermore, the synergistic effect of NiSe2 and MoSe2 hybrid facilitates the rapid electrons transfer from the electrode to the exposed MoSe2 edges to take part in the HER reaction, and thus promotes the HER performance. This study provides a simple route for fabricating bi‐metallic selenides‐based nanocomposite as an efficient and stable electrocatalyst for HER.
The applications of machine vision in raw material and production of wood products

Machine vision has been developed nearly for 70 years and been widely applied in electronics, automotive manufacturing, food processing, etc. With deepening study of its theory and technology in forestry industry, the industry of wood products is moving steadily toward the goal of automated identification and production to improve the manufacturing intelligence of enterprises. In this study, theoretical and algorithmic research on image acquisition, feature extraction, recognition, and classification involved in machine vision-based wood recognition technology were analyzed on the basis of its global development. The applications of machine vision in the wood materials, such as the identification of tree species, wood inspection and classification, defects detection of wood product, surface analysis of wood color, and quality control of furnishing products were thoroughly analyzed. The development trend of machine vision in the production and management of wood materials was considered in the current development of wood and furnishing enterprises. These results lay a solid foundation for wood science research, and intelligent manufacture of wooden furniture, and efficient development of greener and cleaner production of the furniture industry, which could improve the environmental effect of the wood products and furniture and make a great contribution for the carbon goal of “30-60” in China.
Bottom‐up effects of plant quantity and quality on arthropod diversity across multiple trophic levels in a semi‐arid grassland

Plant quantity and quality can independently affect the diversity of the entire arthropod communities and multiple arthropod taxa in grassland ecosystems. However, it remains unclear how these effects on arthropod taxa at one trophic level propagate through food web to influence the diversity of higher trophic levels. We performed a monoculture experiment with 15 herbaceous species in the Inner Mongolian grassland to investigate how natural variations in plant productivity and host leaf traits affect herbivore taxon richness, which, in turn, affects predator taxon richness. For herbivores, plant productivity indirectly promoted herbivore taxon richness by increasing herbivore biomass, which was attributed to the increases in the richness of dominant sucking herbivores and endophytes with high food requirements. However, the high plant quality indicator (e.g. high leaf protein, phosphorus and water contents, and high leaf protein to carbohydrate ratio) directly increased, whereas the low plant quality indicator (e.g. high leaf lignin content) directly decreased herbivore taxon richness. Taxon richness of chewing and sucking herbivores with specific feeding modes (tearing or sucking mouthparts) showed strong positive responses to increasing plant quality. For predators, herbivore taxon richness, rather than herbivore biomass, mainly mediated the positive effects of plant productivity and the high plant quality indicator, but the negative effect of the low plant quality indicator, on predator taxon richness. At the feeding guild level, the taxon richness of parasitoids, other predators and spiders exhibited positive responses to different herbivores, which was attributed to their different diet preferences. Predator diversity could be promoted by prey partitioning among predator guilds facilitating species coexistence. At the family level, the taxon richness of most predator families was positively correlated with that of more than one herbivore family, suggesting that high predator diversity may be caused by balanced diets owing to high prey diversity. Synthesis. Natural variations in plant quantity and quality can substantially affect the diversity of herbivores and cascade up the food web to affect predators. Specificity and mechanisms of feeding have a large impact on the responses of arthropod guilds at each trophic level.
Detection and segmentation of melanoma skin cancer in dermoscopy images using modified Alexnet convolutional neural network‐morphological methodology

Melanoma is most vicious and dangerous type of skin cancer that its early detection can save patients' lives. Computer‐aided methods can be used for this early detection with acceptable performance. In this article, the melanoma skin cancer dermoscopy images are detected and cancer regions are segmented using the proposed modified AlexNet convolutional neural network‐morphological segmentation (CNN‐MS) method. This proposed work consists of enhancement stage, feature matrix construction stage, CNN classification stage, and segmentation stage. In enhancement stage, the skin pixels are contrast enhanced by histogram equalization technique and then non‐sub sampled contourlet transform (NSCT) is applied on the enhanced skin image to obtain the decomposed coefficients. Further, local ternary pattern (LTP) features are computed from the enhanced image and the feature matrix (FM) is constructed by integrating the NSCT coefficients and the LTP features. This constructed FM is fed to the CNN model for the classification of Melanoma skin cancer images from the normal skin images. Finally, morphological functions are applied on the classified melanoma skin image to segment the cancer regions. The performance of this developed model for skin cancer detection is analyzed on international skin imaging collaboration (ISIC) dataset images and achieves 98.11% of Se, 98.5% of Sp, 99.11% of Acc, and 0.96 of MCC. Also, the performance of this developed model for skin cancer detection is analyzed on SIIM‐ISIC dataset images and achieves 98.04% Se, 98.46% Sp, 99.12% Acc, and 0.97 MCC.
EZ Water and the Origin of Life

To create life, the first step should logically be the formation of the condensed system that defines a cell. If the original contents were dispersed widely, then those components would require condensation. Absent the needed condensation forces, those prime substances would have remained scattered, with no particular proclivity to form a cell. Energy is needed for the above-described process. Without energy for the splitting of water molecules, EZ cannot build. The required energy comes from light. Particularly effective, we found, is infrared light. The impacted water is presumably its EZ fraction, whose crystal-like structure allows for information-storage capability. Ordinary liquid water has no such capability: its randomly oriented, rapidly fluctuating molecules would be expected to show no capacity for retention of information. EZ water, on the other hand, seems practically “designed” to carry information.
An Overview of Composite Standard Elastic-Net Distribution Based on Complex Wavelet Coefficients

Statistical tools have changed significantly in the past decade; when the maximum likelihood method is usually applied, it provides an inaccurate solution due to its unsuitable properties and causes problems in fit. However, the current Ordinary Least Square (OLS) Model is more reliable in specific situations where the estimation is based on the slope only. On the other hand, some methods depend on the slope, and the intercept is recommended. These methods can be described as thresholding rules. Another often preferred method is the posterior mean (PM) technique. This procedure depends on two parts, the first part is the likelihood, and the other is the prior distribution, where the second part plays a significant role in the estimation. In this article, the standard elastic-net distribution is assumed as the prior part, which consists of two parts, the first being the normal distribution and the second being the double exponential distribution. The reason why this model is used is that wavelet tools have different levels of resolution. Thus, this model may provide a more accurate estimation for the wavelet coefficients, which might be estimated using a normal or double exponential distribution. In the past, some properties of elastic-net penalized were introduced and discussed. However, more properties are introduced for this distribution. In addition, two models based on the elastic-net method are demonstrated, involving the point mass prior. The first model combines normal as likelihood, and elastic-net distributions as prior, while the other combines the double exponential distribution as likelihood with the elastic-net distribution as prior. Moreover, the level-dependent components are estimated at each resolutions. A simulated investigation is studied using the Markov Chain Monte Carlo (MCMC) tool to estimate the underlying features, where real data are involved and modelled using the proposed methods. A stationary wavelet basis is also applied. As a result, the proposed procedure reduces noise levels which may be helpful since noise levels often corrupt real data, usually a significant cause of most numerical estimation problems.
Cu‐Isotope Evidence for Subduction Modification of Lithospheric Mantle

Ultramafic xenoliths from southeastern Arizona, USA, provide evidence for Cu‐isotope heterogeneity in the lithospheric mantle. We report new data on Type I (Cr‐, Mg‐rich) peridotites, but also the first Cu‐isotope data for Fe‐Ti‐Al‐rich Type II pyroxenite (±amphibole) xenoliths. Whole rock δ65Cu values of the pyroxenites and cryptically metasomatized Type I lherzolites range to isotopically heavier compositions than asthenospheric mantle (i.e., up to +1.44‰ and +1.12‰, respectively, vs. ∼0‰ ± 0.2‰). Copper leached from the xenoliths using aqua regia, assumed to be hosted in interstitial sulfides, is even more variable (δ65Cu −0.78 to +3.88‰), indicating considerable isotopic heterogeneity within individual samples. Host basalts have low δ65Cu (−0.23‰ to −1.30‰), so basalt—xenolith interactions are not responsible for the compositional variations observed. While mass‐dependent fractionation may be partly responsible, metasomatism by fluids derived from recycled crustal materials is the predominant control on isotopic variations observed. Amphibole megacrysts and amphiboles separated from Type II amphibole‐bearing clinopyroxenite have normal, mantle‐like 18O/16O ratios but H‐isotope compositions (δ2HSMOW −82‰ to −45‰) that range between that of nominally anhydrous mantle (−80 ± 10‰) and seawater (0‰). Host basalts are also enriched in 34S relative to depleted asthenospheric mantle, having δ34SCDT values up to +8‰, i.e., compositions commonly attributed to a component of recycled seawater or hydrated oceanic crust. These new data suggest that formation of Type II metasomes in the lithospheric mantle beneath the Basin and Range Province was associated with subduction of the Farallon plate and not alkali basalt magmatism associated with Basin and Range extension.