Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Stakeholder Game Relationships and Behaviors to Facilitate Recycled Resource Management: A Systematic Framework

Understanding the game relationships and behaviors of stakeholders is the key to effectively promoting recycled resource management. The insights from past research are relatively limited and lack a holistic and comprehensive understanding to guide recycled resource management practice in China. To this end, this study systematically reviews and summarizes the stakeholder game relationships and behaviors in recycled resource management, based on which a systematic framework for analyzing stakeholder game relationships and behaviors in recycled resource management is proposed. To further explain this framework, cross-sectional and longitudinal analyses of the game relationships among and within stakeholder groups are conducted. The proposed systematic framework is a unification and deepening of existing research perspectives on stakeholder games in recycled resource management, and has more practical guidance for promoting recycled resource management in China.
Ecohydrologic considerations for modeling of stable water isotopes in a small intermittent watershed

Naturally occurring stable water isotope tracers provide useful information for hydrologic model development and calibration. Existing models include varied approaches concerning unsaturated zone percolation mixing (preferential versus matrix flow) and evapotranspiration (ET) partitioning. We assess the impact of unsaturated zone simplifying assumptions when simulating the Shale Hills Watershed, a small (7.9 ha), temperate, forested watershed near Petersburg, Pennsylvania, USA, with a relatively simple model. We found that different model structures/assumptions and parameterizations of unsaturated zone percolation had substantial impacts on the agreement between simulated and observed unsaturated‐zone water isotopic signatures. We show that unsaturated zone percolation mixing primarily affects the unsaturated zone δ18O and δ2H during winter and spring and that percolation was best represented as a combination of both preferential and matrix flow. We evaluate the importance and implications related to the partitioning of ET into evaporation and transpiration and demonstrated that incorporation of a plant growth model for ET partitioning substantially improved reproduction of observed hydrologic isotopic patterns of the unsaturated zone during the spring season. We show that unsaturated zone percolation mixing and ET partitioning approaches do not substantially influence stream δ18O and δ2H and conclude that observed streamflow isotopic data is not always a strong predictor of model performance with respect to intrawatershed processes.
An Online Model-Free Reinforcement Learning Approach for 6-DOF Robot Manipulators

Controlling 6 Degrees-of-Freedom (DoF) robotic manipulators in an online, model-free manner poses significant challenges due to their complex coupling, non-linearities, and the need to account for unmodeled dynamics. This paper introduces a model-free adaptive approach for real-time control of a 6 DoF “EPSON” robotic manipulator, without requiring any prior knowledge of the manipulator’s dynamics. Initially, we lay out the framework for an optimal control solution. A performance index is introduced, leveraging error dynamics and correction control signals, offering the capability to incorporate high-order error dynamics without the need to explicitly derive error trajectories. The order of error dynamics is determined by the chosen number of error samples. We assume a kernel-based solution structure aligning with the performance index, resulting in a temporal difference equation. This equation can be optimized to formulate a model-free control strategy. Subsequently, a reinforcement learning approach is adopted to approximate the underlying strategy. Infeasible exact solutions are overcome by employing a value iteration mechanism to adapt the actor-critic structures within an adaptive critics framework. To validate the proposed approach, it is compared against a conventional proportional-integral controller. A Unified Robot Description Format file is generated to facilitate the import of the robotic manipulator into the MATLAB Simulink environment, enabling its control. Ultimately, the proposed method yields superior results in terms of the dynamic characteristics of the response, demonstrating its effectiveness over the conventional approach.
Transcriptome Sequencing Unveils a Molecular-Stratification-Predicting Prognosis of Sarcoma Associated with Lipid Metabolism

Sarcomas are heterogeneous connective tissue malignancies that have been historically categorized into soft tissue and bone cancers. Although multimodal therapies are implemented, many sarcoma subtypes are still difficult to treat. Lipids play vital roles in cellular activities; however, ectopic levels of lipid metabolites have an impact on tumor recurrence, metastasis, and drug resistance. Thus, precision therapies targeting lipid metabolism in sarcoma need to be explored. In this study, we performed a comprehensive analysis of molecular stratification based on lipid metabolism-associated genes (LMAGs) using both public datasets and the data of patients in our cohort and constructed a novel prognostic model consisting of squalene epoxidase (SQLE) and tumor necrosis factor (TNF). We first integrated information on gene expression profile and survival outcomes to divide TCGA sarcoma patients into high- and low-risk subgroups and further revealed the prognosis value of the metabolic signature and immune infiltration of patients in both groups, thus proposing various therapeutic recommendations for sarcoma. We observed that the low-risk sarcoma patients in the TCGA-SARC cohort were characterized by high proportions of immune cells and increased expression of immune checkpoint genes. Subsequently, this lipid metabolic signature was validated in four external independent sarcoma datasets including the CHCAMS cohort. Notably, SQLE, a rate-limiting enzyme in cholesterol biosynthesis, was identified as a potential therapeutic target for sarcoma. Knockdown of SQLE substantially inhibited cell proliferation and colony formation while promoting the apoptosis of sarcoma cells. Terbinafine, an inhibitor of SQLE, displayed similar tumor suppression capacity in vitro. The prognostic predictive model and the potential drug target SQLE might serve as valuable hints for further in-depth biological, diagnostic, and therapeutic exploration of sarcoma.
SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation

The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.
SDC: Spatial Depth Completion for Outdoor Scenes

Depth completion is a crucial computer vision task that aims to fill in missing or incomplete depth values in a depth map. In this paper, we propose SDC: Spatial Depth Completion for Outdoor Scenes. Our approach leverages a two-stage architecture with a spatial feature extractor (SFE) to utilize multi-scale features for accurate depth completion effectively. The proposed method incorporates attention mechanisms, including the Efficient Position Attention Module (EPAM) and Channel Attention Module (CAM), to adaptively fuse depth map features and improve the accuracy of depth completion. Additionally, the Pearson loss function is employed to further enhance the accuracy of the completed depth maps. Experimental results on the KITTI depth completion benchmark demonstrate that our method achieves comparable or better results than traditional depth completion methods while significantly reducing the number of parameters. The proposed SDC model shows great potential in practical applications of depth completion, with its ability to effectively fuse multiscale features and compact model size.
Research on Improvements to the VRnetzer Platform for Combat Network Analysis

VRnetzer is a three-dimensional platform proposed by Australian scholars to analyze complex biological networks, which can support doctors to independently analyze the network and trace the cause of disease. If VRnetzer can be introduced into combat network analysis, it will effectively support commanders to grasp the structural characteristics of combat networks and assist them in decision-making. [2]This article proposes an improved method for VRnetzer platform for combat network analysis, which mainly enables the improvement of VRnetzer platform: first, it can display links, second, it can display different combat units in the combat network using different link nodes, and third, it can import detailed information corresponding to nodes. Finally, this method can successfully achieve the functions of VRnetzer data link display, corresponding information import, and type differentiation, which can provide support for the display of complex combat networks in three-dimensional space.
Suicidal Tendency and Depression Diagnosing Medical Agent Using fNIRS and VFT

Functional Near-Infrared Spectroscopy (fNIRS) is a technique for measuring blood flow in the brain, specifically focusing on changes in the frontal lobe. It has found valuable applications in psychiatry, particularly in diagnostic processes. This study explores the potential of fNIRS data and verbal fluency test (VFT) data, both collected during fNIRS measurements, as tools for diagnosing the severity of major depressive disorder, a significant mental health condition. Beyond merely detecting depression, our research introduces a medical support agent model to identify signs of suicidal tendencies in individuals with severe depression. By integrating data collection, preprocessing techniques, feature extraction, and multimodal classification methods for fNIRS and VFT data, our study suggests these artificial intelligence-based medical agents could enhance diagnostic accuracy and provide valuable support in clinical judgments.
Mechanistic Insight in Permeability through Different Membranes in the Presence of Pharmaceutical Excipients: A Case of Model Hydrophobic Carbamazepine

The present study reports the effects of two pharmaceutical excipients of differing natures—non-ionic surfactant pluronic F127 (F127) and anionic sulfobutylether-β-cyclodextrin (SBE-β-CD)—on the permeation of the model compound, carbamazepine (CBZ). The permeability coefficients of CBZ at three concentrations of the excipients were measured through two different artificial barriers: hydrophilic cellulose membrane (RC) and lipophilic polydimethylsiloxane–polycarbonate membrane (PDS). The equilibrium solubility of CBZ in F127 and SBE-β-CD solutions was determined. The micellization, complexation, and aggregation tendencies were investigated. Systemically increasing the solubility and the reduction of permeation upon the excipients’ concentration growth was revealed. The quantitative evaluation of the permeability tendencies was carried out using a Pratio parameter, a quasi-equilibrium mathematical mass transport model, and a correction of permeability coefficients for the free drug concentration (“true” permeability values). The results revealed the mutual influence of the excipient properties and the membrane nature on the permeability variations.
Charcot–Marie–Tooth Disease with Myelin Protein Zero Mutation Presenting as Painful, Predominant Small-Fiber Neuropathy

Charcot–Marie–Tooth disease (CMT) rarely presents with painful symptoms, which mainly occur in association with myelin protein zero (MPZ) gene mutations. We aimed to further characterize the features of painful neuropathic phenotypes in MPZ-related CMT. We report on a 58-year-old woman with a longstanding history of intermittent migrant pain and dysesthesias. Examination showed minimal clinical signs of neuropathy along with mild changes upon electroneurographic examination, consistent with an intermediate pattern, and small-fiber loss upon skin biopsy. Genetic testing identified the heterozygous variant p.Trp101Ter in MPZ. We identified another 20 CMT patients in the literature who presented with neuropathic pain as a main feature in association with MPZ mutations, mostly in the extracellular MPZ domain; the majority of these patients showed late onset (14/20), with motor-nerve-conduction velocities predominantly in the intermediate range (12/20). It is hypothesized that some MPZ mutations could manifest with, or predispose to, neuropathic pain. However, the mechanisms linking MPZ mutations and pain-generating nerve changes are unclear, as are the possible role of modifier factors. This peculiar CMT presentation may be diagnostically misleading, as it is suggestive of an acquired pain syndrome rather than of an inherited neuropathy.
Fast Image-free high precision target tracking using single pixel detection

The fast location tracking of moving targets is of great significance for navigation and guidance. The image-based tracking methods usually require massive sampling and data storage. In order to realize the fast tracking of the target, we propose an image-free method based on single pixel detection. The proposed method uses Fourier slice theorem to directly obtain the one-dimensional projection information of the target. At extremely low sampling, the one-dimensional projection curve is simplified into a gently changing crest, but the peak of the curve at this time still contains the central coordinate information of the target. By post-processing the projection curve at different sampling rates and selecting appropriate tracking characteristics, the accuracy can be maintained while further reducing the sampling rate and simplifying the calculation. In addition, the background subtraction is utilized to remove the effect of background information, so that the target can still be tracked in a complex background. The effectiveness of the proposed method in different background conditions is verified by simulations. Under the spatial resolution of 256×256 pixels, the sampling rate of the proposed method can be reduced to 0.012%, which greatly shortens the sampling time. At the same time, the proposed method has small error with less than one pixel in the horizontal and vertical directions.
Understanding the intricacies of risky indebtedness, impulse buying and perceived risk in buy-now-pay-later adoption

PurposeThis study aims to establish that the relationship between the risky indebtedness behavior (RIB) of consumers and their attitude toward adopting buy-now-pay-later (BNPL) is not immediate but is mediated through impulse buying. Moreover, it explores how perceived risk moderates the association between the attitude to adopt BNPL and its adoption intention.Design/methodology/approachThis study used the existing theoretical and empirical evidence to propose a model and validated it using the data collected from 339 young shoppers in India. Analysis of data is conducted using partial least squares structural equation modeling.FindingsThe study results show that consumers’ RIB is not directly related to their attitude toward BNPL. However, impulse buying fully mediates this relationship, influencing the attitude toward BNPL. Impulse buying and attitude serially mediate the relationship between RIB and BNPL adoption intention. Further, in the context of BNPL, perceived risk strengthens the attitude-intention gap.Practical implicationsThis study advises policymakers and BNPL providers to carefully assess users’ creditworthiness to prevent those already in debt from entering into a detrimental loop.Originality/valueThis study provides novel perspectives on consumer’s RIB and BNPL within the Indian context. The study additionally identifies the mediating influence of impulse buying and the moderating effect of perceived risk on BNPL adoption intention.
A Multimodal Approach for Bridge Inspection

With the exacerbation of bridge aging issues, the demand for efficient and cost-effective bridge inspection solutions becomes increasingly urgent. Currently, most methods for surface damage detection on bridges employ single-modal, image-based object detection models. Despite their overall effectiveness on specific datasets, such methods frequently encounter error detection issues during actual inspection processes. This study proposes a multimodal (image and text) model for surface damage detection in bridge structures by combining the CLIP model with the YOLOv8 model. By conducting tests on the composite concrete bridge of the Jiaozhou Bay Bridge, Qingdao, the effectiveness of bridge detection using drones is validated, and the common issue of false positive detections in traditional object detection models is successfully addressed.
Why does a leader’s other-oriented perfectionism lead employees to do bad things? Examining the role of moral disengagement and moral identity

Introduction Moral disengagement is an essential concept in organizational behavioral ethics, as it is strongly related to employee behaviors and attitudes. What is not clear, however, is which leader traits are directly associated with employees’ moral disengagement and which are indirectly associated with unethical behavior. This study draws on a social cognitive perspective that links leaders’ other-oriented perfectionism (LOOP) with unethical employee behavior. Specifically, we propose that LOOP provides employees with excuses and encouragement to engage in unethical pro-organizational behavior (UPB). Methods We analyzed data collected from 266 full-time employees at two-time points, and used mediated and moderated structural equation models to test the hypotheses, and the findings largely support our claims. Results The results suggest that LOOP effectively promotes employees’ involvement in UPB. Moderated mediation tests suggest that the positive indirect impact of LOOP on employees’ unethical behavior via moral disengagement was attenuated by higher employees’ moral identity. Discussion In summary, the results indicate that when leaders emphasize only perfection and make unrealistic demands on their employees, the latter perceive that engaging in unethical behavior is demanded by the leader, that the responsibility is not theirs, and thus they are more willing to engage in unethical behavior. This study discusses the implications of these findings from both practical and theoretical perspectives.
Integrating Ethos into Resilient Change Facilitation in Ukrainian Realia

Purpose. This viewpoint paper explores the practice of change facilitation, aiming to unveil how societies can nurture resilience in the face of imminent challenges. It synthesizes Ukrainian real-life experiences, revealing the principles behind various facilitation approaches to fostering resilient change in demanding contexts. Methodology. The study's methodology entails the synthesis and conceptualization of diverse professional experiences in Ukrainian realia, unravelling the underlying ethos behind various change facilitation approaches. By integrating these insights into broader perspectives and communities of practice, the research aims to offer a holistic perspective on fostering resilient change. Findings. The results section succinctly presents the distilled findings, offering a thought-provoking array of ideas specifically tailored for facilitators operating within high-stakes and extreme-risk and existential environments. Limitations/Implications. Due to the research approach, findings may lack generalizability, necessitating further testing of the presented propositions. Practical Implications. The study concludes by highlighting the potential impact of its professional interpretations on the fields of facilitation and social cohesion. The paper's content encompasses an exploration of facilitation, encompassing Ethos, Logos, and Pathos elements. It highlights the multifaceted role of a facilitator and presents three distinct approaches to implementing change: the knowledge and experience-driven approach, the inspiration-based approach, and the strategy-oriented approach tailored to fragile crisis contexts. Originality/Value. The paper delves into the existential challenges and ethically justified opportunities inherent in adopting the combined approach of facilitation, underlining the nuanced dynamics inherent in resilient change facilitation.
Aircraft Noise and the Risk of Stroke.

BACKGROUND There have been many individual studies on the question whether air- craft noise is a risk factor for stroke, but until now there has not been any summary of the current state of the evidence of adequately high methodological quality. METHODS In a systematic review and meta-analysis (PROSPERO registry number CRD42013006004), we evaluated the relation between address-based aircraft noise exposure and the incidence of stroke. A systematic literature search was performed in the MEDLINE, EMBASE, and BIOSIS databases including publications up to August 2017. Two of the authors, working independently of each other, screened the titles, abstracts, and full texts for eligible articles and evaluated the quality of the included studies on a three-level scale. The change of risk per 10 dB increase in the weighted mean aircraft noise level (LDEN) was calculated. LDEN is a noise level indicator with additional weighting of evening and nighttime noise. RESULTS Of the nine studies that met the inclusion criteria, seven were suitable for inclusion in the meta-analysis. The result of the meta-analysis indicated a relative stroke risk of 1.013 (95% confidence interval, [0.998; 1.028]) per 10 dB increase in LDEN, corresponding with an estimated 1.3% increase in the risk of stroke for each additional 10 dB of aircraft noise. The underlying studies were of poor to medium quality. The analyses of the studies included adjustments for various combinations of confounders, including age, sex, ethnicity, and socioeconomic status. CONCLUSION The present meta-analysis indicates that aircraft noise increases the risk of stroke, even if the overall finding just fails to reach statistical significance. The differing measures of exposure in the included studies, the lack of differentiation be- tween ischemic and hemorrhagic stroke, and the lack of consideration of maximum noise levels are all factors that may have led to a marked underestimation of the risk of stroke.