Miracle-dz/UniFAR-FTUs huggingface dataset:
A Human-Like Shared Driving Strategy in Lane-Changing Scenario Using Cooperative LPV/MPC

Linear quadratic (LQ) game has been widely used in shared driving to resolve conflicts between drivers and driving automation systems (DAS). However, the system model in LQ game is assumed to be linear time-invariant, which does not consider the effect of changing longitudinal speed on the lateral control. A novel cooperative LPV/MPC approach is proposed, which characterizes the longitudinal and lateral coupled vehicle control in a dynamic game. The stability of the cooperative LPV/MPC system is analyzed in the presence of inconsistent targets of driver and DAS. Driver behavior is studied using 2,701 lane-changing cases extracted from naturalistic driving data (NDD). A human-like risk assessment method is achieved according to the driver behavior characteristics. Finally, a shared driving strategy in lane-changing scenarios is presented based on the cooperative LPV/MPC algorithm and the human-like risk assessment method. Simulation validations show that the shared driving strategy can ensure vehicle stability when longitudinal control, lateral control and dynamic game are performed simultaneously. In addition, the shared driving strategy can realize the driving weight gradual handover, which can help to better assist the driver in risk avoidance.
The propagation of small amplitude nonlinear waves in a strongly inhomogeneous medium

A small amplitude wave is propagated into a semi-infinite, strongly inhomogeneous, nonlinear elastic material. The inhomogeneity is chosen, as in functionally graded materials, so that a closed form exact solution is possible within linear theory. The relationship with the approximate solution described by geometrical acoustics, when the inhomogeneity is slowly varying, is readily seen. The nonlinear result is derived by adjusting the linear characteristic as in Whitham’s nonlinearization technique. Shocks may then occur.
Anti-Inflammatory Effects of Adiponectin Receptor Agonist AdipoRon against Intervertebral Disc Degeneration

Adiponectin, a hormone secreted by adipocytes, has anti-inflammatory effects and is involved in various physiological and pathological processes such as obesity, inflammatory diseases, and cartilage diseases. However, the function of adiponectin in intervertebral disc (IVD) degeneration is not well understood. This study aimed to elucidate the effects of AdipoRon, an agonist of adiponectin receptor, on human IVD nucleus pulposus (NP) cells, using a three-dimensional in vitro culturing system. This study also aimed to elucidate the effects of AdipoRon on rat tail IVD tissues using an in vivo puncture-induced IVD degeneration model. Analysis using quantitative polymerase chain reaction demonstrated the downregulation of gene expression of proinflammatory and catabolic factors by interleukin (IL)-1β (10 ng/mL) in human IVD NP cells treated with AdipoRon (2 μM). Furthermore, western blotting showed AdipoRon-induced suppression of p65 phosphorylation (p < 0.01) under IL-1β stimulation in the adenosine monophosphate-activated protein kinase (AMPK) pathway. Intradiscal administration of AdipoRon was effective in alleviating the radiologic height loss induced by annular puncture of rat tail IVD, histomorphological degeneration, production of extracellular matrix catabolic factors, and expression of proinflammatory cytokines. Therefore, AdipoRon could be a new therapeutic candidate for alleviating the early stage of IVD degeneration.
Learning-based dynamic connectivity maintenance for UAV-assisted D2D multicast communication

Unmanned aerial vehicles (UAVs) enable flexible networking functions in emergency scenarios. However, due to the movement characteristic of ground users (GUs), it is challenging to capture the interactions among GUs. Thus, we propose a learning-based dynamic connectivity maintenance architecture to reduce the delay for the UAV-assisted device-to-device (D2D) multicast communication. In this paper, each UAV transmits information to a selected GU, and then other GUs receive the information in a multi-hop manner. To minimize the total delay while ensuring that all GUs receive the information, we decouple it into three subproblems according to the time division on the topology: For the cluster-head selection, we adopt the Whale Optimization Algorithm (WOA) to imitate the hunting behavior of whales by abstracting the UAVs and cluster-heads into whales and preys, respectively; For the D2D multi-hop link establishment, we make the best of social relationships between GUs, and propose a node mapping algorithm based on the balanced spanning tree (BST) with reconfiguration to minimize the number of hops; For the dynamic connectivity maintenance, Restricted Q-learning (RQL) is utilized to learn the optimal multicast timeslot. Finally, the simulation results show that our proposed algorithms perform better than other benchmark algorithms in the dynamic scenario.
Self-Supervised Nodes-Hyperedges Embedding for Heterogeneous Information Network Learning

The exploration of self-supervised information mining of heterogeneous datasets has gained significant traction in recent years. Heterogeneous graph neural networks (HGNNs) have emerged as a highly promising method for handling heterogeneous information networks (HINs) due to their superior performance. These networks leverage aggregation functions to convert pairwise relations-based features from raw heterogeneous graphs into embedding vectors. However, real-world HINs contain valuable higher-order relations that are often overlooked but can provide complementary information. To address this issue, we propose a novel method called Self-supervised Nodes-Hyperedges Embedding (SNHE), which leverages hypergraph structures to incorporate higher-order information into the embedding process of HINs. Our method decomposes the raw graph structure into snapshots based on various meta-paths, which are then transformed into hypergraphs to aggregate high-order information within the data and generate embedding representations. Given the complexity of HINs, we develop a dual self-supervised structure that maximizes mutual information in the enhanced graph data space, guides the overall model update, and reduces redundancy and noise. We evaluate our proposed method on various real-world datasets for node classification and clustering tasks, and compare it against state-of-the-art methods. The experimental results demonstrate the efficacy of our method. Our code is available at https://github.com/limengran98/SNHE.
Advances and Challenges of Stimuli-Responsive Nucleic Acids Delivery System in Gene Therapy

Gene therapy has emerged as a powerful tool to treat various diseases, such as cardiovascular diseases, neurological diseases, ocular diseases and cancer diseases. In 2018, the FDA approved Patisiran (the siRNA therapeutic) for treating amyloidosis. Compared with traditional drugs, gene therapy can directly correct the disease-related genes at the genetic level, which guarantees a sustained effect. However, nucleic acids are unstable in circulation and have short half-lives. They cannot pass through biological membranes due to their high molecular weight and massive negative charges. To facilitate the delivery of nucleic acids, it is crucial to develop a suitable delivery strategy. The rapid development of delivery systems has brought light to the gene delivery field, which can overcome multiple extracellular and intracellular barriers that prevent the efficient delivery of nucleic acids. Moreover, the emergence of stimuli-responsive delivery systems has made it possible to control the release of nucleic acids in an intelligent manner and to precisely guide the therapeutic nucleic acids to the target site. Considering the unique properties of stimuli-responsive delivery systems, various stimuli-responsive nanocarriers have been developed. For example, taking advantage of the physiological variations of a tumor (pH, redox and enzymes), various biostimuli- or endogenous stimuli-responsive delivery systems have been fabricated to control the gene delivery processes in an intelligent manner. In addition, other external stimuli, such as light, magnetic fields and ultrasound, have also been employed to construct stimuli-responsive nanocarriers. Nevertheless, most stimuli-responsive delivery systems are in the preclinical stage, and some critical issues remain to be solved for advancing the clinical translation of these nanocarriers, such as the unsatisfactory transfection efficiency, safety issues, complexity of manufacturing and off-target effects. The purpose of this review is to elaborate the principles of stimuli-responsive nanocarriers and to emphasize the most influential advances of stimuli-responsive gene delivery systems. Current challenges of their clinical translation and corresponding solutions will also be highlighted, which will accelerate the translation of stimuli-responsive nanocarriers and advance the development of gene therapy.
Margin-Aware Adaptive-Weighted-Loss for Deep Learning Based Imbalanced Data Classification

In supervised learning algorithms, the class imbalance problem often leads to generating results biased towards the majority classes. Present methods used to deal with the class imbalance problem ignore a principal aspect of separating the overlapping classes. This is the reason why most of these methods are prone to overfit on the training data. To this end, we propose a novel loss function, namely margin-aware adaptive-weighted loss. Here, we first use the large margin softmax to leverage intraclass compactness and interclass separability. Further to learn an unbiased representation of the classes, we put forward a dynamically weighted loss for imbalanced data classification. This weight dynamically adapts on every minibatch based on the inverse class frequencies. In addition, it takes care of the hard-to-train samples by using the confidence scores to learn discriminative hidden representations of the data. The overall framework is found to be effective when evaluated on the following two widely used datasets: 1) Canadian Institute for Advanced Research (CIFAR)-10 and 2) Fashion-MNIST. Additional experiments on human against machine and Asia Pacific tele-ophthalmology society 2019 blindness detection datasets prove the robustness of our methodology.
Sakli Markov Model Karisimlari için Spektral Ö˘ grenme Spectral Learning of Mixtures of Hidden Markov Models

Ozetce —Bu calismada, Sakli Markov Modeli (SMM) olarak modellenen zaman serilerinin topaklandirilmasi icin yeni bir yontem onerilmektedir. Topaklardaki SMM parametrelerinin guncellenmesi icin son yillarda yapay ogrenme literaturunde populer olmaya baslayan sakli degisken modelleri icin spektral ogrenme yontemleri kullanilmaktadir. Spektral yontemler, alisilagelmis beklenti-enbuyutme yaklasiminin aksine, sakli degisken modellerinde tek adimda parametre kestirimi yapmamizi saglar. Bu sebeple, onerdigimiz yontem hesap karmasikligi bakimindan alisilagelmis yontemlerle SMM-topaklandirmaya gore hesap karmasikligi bakimindan daha ucuzdur.
Identification of candidate genes that regulate the trade-off between seedling cold tolerance and fruit quality in melon (Cucumis melo L.)

Abstract Trade-offs between survival and growth are widely observed in plants. Melon is an annual, trailing herb that produces economically valuable fruits that are traditionally cultivated in early spring in China. Melon seedlings are sensitive to low temperatures, and thus usually suffer from cold stress during the early growth period. However, little is known about the mechanism behind the trade-offs between seedling cold tolerance and fruit quality in melon. In this study, a total of 31 primary metabolites were detected from the mature fruits of eight melon lines that differ with respect to seedling cold tolerance; these included 12 amino acids, 10 organic acids, and 9 soluble sugars. Our results showed that concentrations of most of the primary metabolites in the cold-resistant melons were generally lower than in the cold-sensitive melons; the greatest difference in metabolite levels was observed between the cold-resistant line H581 and the moderately cold-resistant line HH09. The metabolite and transcriptome data for these two lines were then subjected to weighted correlation network analysis, resulting in the identification of five key candidate genes underlying the balancing between seedling cold tolerance and fruit quality. Among these genes, CmEAF7 might play multiple roles in regulating chloroplast development, photosynthesis, and the ABA pathway. Furthermore, multi-method functional analysis showed that CmEAF7 can certainly improve both seedling cold tolerance and fruit quality in melon. Our study identified an agriculturally important gene, CmEAF7, and provides a new insight into breeding methods to develop melon cultivars with seedling cold tolerance and high fruit quality.
Estimating the Global Influence of Cover Crops on Ecosystem Service Indicators in Croplands With the LPJ‐GUESS Model

Cover crops (CCs) can improve soil nutrient retention and crop production while providing climate change mitigation co‐benefits. However, quantifying these ecosystem services across global agricultural lands remains inadequate. Here, we assess how the use of herbaceous CCs with and without biological nitrogen (N) fixation affects agricultural soil carbon stocks, N leaching, and crop yields, using the dynamic global vegetation model LPJ‐GUESS. The model performance is evaluated with observations from worldwide field trials and modeled output further compared against previously published large‐scale estimates. LPJ‐GUESS broadly captures the enhanced soil carbon, reduced N leaching, and yield changes that are observed in the field. Globally, we found that combining N‐fixing CCs with no‐tillage technique could potentially increase soil carbon levels by 7% (+0.32 Pg C yr−1 in global croplands) while reducing N leaching loss by 41% (−7.3 Tg N yr−1) compared with fallow controls after 36 years of simulation since 2015. This integrated practice is accompanied by a 2% of increase in total crop production (+37 million tonnes yr−1 including wheat, maize, rice, and soybean) in the last decade of the simulation. The identified effects of CCs on crop productivity vary widely among main crop types and N fertilizer applications, with small yield changes found in soybean systems and highly fertilized agricultural soils. Our results demonstrate the possibility of conservation agriculture when targeting long‐term environmental sustainability without compromising crop production in global croplands.
ALTERNATING EULER SUMS AND SPECIAL VALUES OF THE WITTEN MULTIPLE ZETA FUNCTION ATTACHED TO $\mathfrak {so}(5)$

Abstract We study the Witten multiple zeta function associated with the Lie algebra $\so $. Our main result shows that its special values at nonnegative integers are always expressible by alternating Euler sums. More precisely, every such special value of weight w at least 2 is a finite ℚ-linear combination of alternating Euler sums of weight w and depth at most 2, except when the only nonzero argument is one of the two last variables, in which case ζ(w−1) is needed.
NONLINEAR INVARIANCE OF FLAT CONNECTION IN GAUGE GRAVITY

: At this paper one studies the spinning gravitational ﬁeld formed by a torsion free and ﬂat connection. In existing such a connection i.e the Maurer - Cartan 1 - form ω on a tangent bundle, the nonlinear invariance of the term tr [ ω ∧ ∗ ω ] gives the spin current form. Also one sees that the action integral of the gravitational ﬁeld is bonded such that Λ( ∼ 1G ) ≤ R M ( L Mat + L Gr ) < ∞ depending of the vacuum energy density Λ 0 and gravitational constant G.
Hubungan Fleksibilitas Otot Hamstring Dengan Nyeri Punggung Bawah Pada Pasien Poli Fisioterapi Di Rumah Sakt Petrokimia Gresik

ABSTRACTHeppy Romdhani Yudhaningrumi, 2022, The Relationship Of Hamstring Muscle Flexibility With Lower Back Pain Patients In Poly Physiotherapy Petrokimia Gresik Hospital, Angria Pradita (Pembimbing I), Agung Hadi E (Pembimbing II) Low back pain which is one of the musculoskeletal disorders is an accumulation of pain in the context of work and clinically may be caused by work or can be exacerbated by work activities. This condition hampers people's ability to perform various types of work inside or outside the home and interferes with mobility. The risk of low back pain is related to the lack of hamstring flexibility which causes susceptibility to musculoskeletal injury. This study aims to identify the relationship between hamstring muscle flexibility and low back pain.This study has 40 respondents. The design of this study uses a type of correlation with a cross sectional approach. Bivariate analysis in this study used Kendall's Tau.The results of this study are that most of the flexibility of the hamstring muscle is below the average of 62.5%, most of the respondents' low back pain is mild pain of 62.5%, and there is a relationship between hamstring muscle flexibility and low back pain in Physiotherapy Poly at the Petrokimia Gresik Hospital with p-value = 0.000 (p<0.05). This research is expected to be input for additional information based on data and theory to improve the quality of physiotherapy services by educating the public about how to maintain hamstring muscle flexibility and handling low back pain correctly. Keyword: hamstring muscle flexibility, low back pain, physiotherapy
Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning

Land Use and Land Cover (LULC) classification using remote sensing data is a challenging problem that has evolved with the update and launch of new satellites in orbit. As new satellites are launched with higher spatial and spectral resolution and shorter revisit times, LULC classification has evolved to take advantage of these improvements. However, these advancements also bring new challenges, such as the need for more sophisticated algorithms to process the increased volume and complexity of data. In recent years, deep learning techniques, such as convolutional neural networks (CNNs), have shown promising results in this area. Training deep learning models with complex architectures require cutting-edge hardware, which can be expensive and not accessible to everyone. In this study, a simple CNN based on the LeNet architecture is proposed to perform LULC classification over Sentinel-2 images. Simple CNNs such as LeNet require less computational resources compared to more-complex architectures. A total of 11 LULC classes were used for training and validating the model, which were then used for classifying the sub-basins. The analysis showed that the proposed CNN achieved an Overall Accuracy of 96.51% with a kappa coefficient of 0.962 in the validation data, outperforming traditional machine learning methods such as Random Forest, Support Vector Machine and Artificial Neural Networks, as well as state-of-the-art complex deep learning methods such as ResNet, DenseNet and EfficientNet. Moreover, despite being trained in over seven million images, it took five h to train, demonstrating that our simple CNN architecture is only effective but is also efficient.
Diversity of soil mites (Acari: Mesostigmata) in streamside mountain forests

Habitat loss and fragmentation are among the most influential factors responsible for recent biodiversity decline. The key to understanding the changes in ecosystem functioning is the analysis of the relationship between species diversity and environmental gradients such as those found near streamside forests. We used soil mites (Mesostigmata) as model species as they are predators in soil ecosystems, they occur in high relative abundance, their communities are species‐rich and are robust toward sampling and extraction methods. The main goals of this study were to (1) check how the plant community and associated environmental variables (light conditions, litter accumulation, and soil acidity) along mountain streams affect soil Mesostigmata abundance and diversity, and (2) to analyze the distance from mountain streams to which the ecosystem functions (Mesostigmata diversity) are affected, based on a distance gradient. We systematically established 31 study plots (500 m2) every 200 m along the watercourses, representing three habitat types along the stand composition gradient (ash‐sycamore [Fraxinus excelsior–Acer pseudoplatanus], European beech [Fagus sylvatica], and Norway spruce [Picea abies] forests) for measurements of soil litter thickness, soil acidity, and light conditions. We extracted soil Mesostigmata mites from samples in Tullgren funnels and identified them to the species level. We found that Mesostigmata abundance in forests decreased in the following order: Norway spruce forest > European beech forest > ash‐sycamore forest. Additionally, changes in Shannon diversity were associated with light conditions. In conclusion, streamside mountain forests maintained abundant, diverse and species‐rich Mesostigmata communities, which were associated with a plant community‐light condition gradient. Additionally, more abundant mite communities occurred in forests with higher soil pH values, and under higher light conditions.
Exploring religious/spiritual pathways between practical wisdom and depression: Testing the importance of the divine relationship in later life

ABSTRACT The last several decades have witnessed the topic of wisdom gaining momentum in the field of positive psychology. In this study, we focus on the potentially important role of religion/spirituality, specifically beliefs about God, as a mediating mechanism underlying the relationship between practical wisdom and depression among older adults. Drawing on a nationally representative sample of older adults from the 2013 wave of the Religion, Aging, and Health Survey (n = 1,497), our results show that practical wisdom is associated with lower depressive symptoms. We also document that three God-related constructs, God-mediated control, trust in God, and gratitude towards God each partially explained the relationship between wisdom and well-being. Taken together, Christian conceptions of God as a personal, divine being, the ultimate attachment figure who is an unconditional source of love and support for believers, may be a target of older adults who have cultivated practical wisdom.
The iMHere 2.0 System for Family Caregivers of Older Adults: A Focus Group

Background: Family caregivers with continuous caregiving responsibilities are at increased risk for adverse physical and mental health outcomes. In response to the challenges of caregiving, a mobile health system (iMHere 2.0) was developed to support caregivers. The study's objective was to gather feedback from family caregivers of older adults on the current features of iMHere 2.0 and to formulate design criteria for future iterations of the system. Methods: An exploratory qualitative study with thematic analyses of focus group feedback. Findings: A total of 10 caregivers of older adults participated in a focus group. Five themes emerged: (1) Monitoring health data, (2) Setting up customized reminders, (3) Supporting care coordination, (4) Balancing security and multiple user access, and (5) Disseminating iMHere 2.0 into the community, along with some potential barriers to implementation. Conclusions: Design criteria were developed to provide a framework for iterative design and development of the iMHere system to support caregivers of older adults.
Contrasting Behaviorist and Constructivist Perspectives 
on Learning for Students with Emotional and Behavioral 
Disorders

: Behaviorism and constructivism provide different understandings of the learning process. This paper critically analyses these two learning theories in terms of how they organize and support learning and compares the different manifestations of their application with students with emotional and behavioral disorders (EBD). The author's own experiences are also used to analyze teaching practice based on these two learning theories. The article concludes by suggesting that authentic teaching situations should combine the two teaching methods and be tailored to students’ needs