Search Results (51)

Extracting causal relations from natural language texts is crucial for uncovering causality, and most existing causal relation extraction models are single-task learning-based models, which can not comprehensively address attributes such as part-of-speech tagging and chunk analysis. However, the characteristics of words with multi-domains are more relevant for causal relation extraction, due to words such as adjectives, linking verbs, etc., bringing more noise data limiting the effectiveness of the single-task-based learning methods. Furthermore, causalities from diverse domains also raise a challenge, as existing models tend to falter in multiple domains compared to a single one. In light of this, we propose a multi-task sequence tagging model, MPC−CE, which utilizes more information about causality and relevant tasks to improve causal relation extraction in noised data. By modeling auxiliary tasks, MPC−CE promotes a hierarchical understanding of linguistic structure and semantic roles, filtering noise and isolating salient entities. Furthermore, the sparse sharing paradigm extracts only the most broadly beneficial parameters by pruning redundant ones during training, enhancing model generalization. The empirical results on two datasets show 2.19% and 3.12% F1 improvement, respectively, compared to baselines, demonstrating that our proposed model can effectively enhance causal relation extraction with semantic features across multiple syntactic tasks, offering the representational power to overcome pervasive noise and cross-domain issues. Full article

Schizophrenia is a chronic and severe psychiatric disorder affecting approximately 1% of the global population, characterized by disrupted synaptic plasticity and brain connectivity. While substantial evidence supports its classification as a neurodevelopmental disorder, non-canonical neurodegenerative features have also been reported, with increasing attention given to astrocytic dysfunction. Overall, in this study, we explore the role of astrocytes as a structural and functional link between neurodevelopment and neurodegeneration in schizophrenia. Specifically, we examine how astrocytes contribute to forming an aberrant substrate during early neurodevelopment, potentially predisposing individuals to later neurodegeneration. Astrocytes regulate neurotransmitter homeostasis and synaptic plasticity, influencing early vulnerability and disease progression through their involvement in Ca²⁺ signaling and dopamine–glutamate interaction—key pathways implicated in schizophrenia pathophysiology. Astrocytes differentiate via nuclear factor I-A, Sox9, and Notch pathways, occurring within a neuronal environment that may already be compromised in the early stages due to the genetic factors associated with the ‘two-hits’ model of schizophrenia. As a result, astrocytes may contribute to the development of an altered neural matrix, disrupting neuronal signaling, exacerbating the dopamine–glutamate imbalance, and causing excessive synaptic pruning and demyelination. These processes may underlie both the core symptoms of schizophrenia and the increased susceptibility to cognitive decline—clinically resembling neurodegeneration but driven by a distinct, poorly understood molecular substrate. Finally, astrocytes are emerging as potential pharmacological targets for antipsychotics such as clozapine, which may modulate their function by regulating glutamate clearance, redox balance, and synaptic remodeling. Full article

Network pruning is a deep learning model compression technique aimed at reducing model storage requirements and decreasing computational resource consumption. However, mainstream pruning techniques often encounter challenges such as limited precision in feature selection and a diminished feature extraction capability. To address these issues, we propose an information extraction-based sparse stripe pruning (IESSP) method. This method introduces an information extraction module (IEM), which enhances stripe selection through a mask-based mechanism, promoting inter-layer interactions and directing the network’s focus toward key features. In addition, we design a novel loss function that links output loss to stripe selection, enabling an effective balance between accuracy and efficiency. This loss function also supports the adaptive optimization of stripe sparsity during training. Experimental results on benchmark datasets demonstrate that the proposed method outperforms existing techniques. Specifically, when applied to prune the VGG-16 model on the CIFAR-10 dataset, the proposed method achieves a 0.29% improvement in accuracy while reducing FLOPs by 75.88% compared to the baseline. Full article

Bud-notching in pear varieties with weak-branches enhances branch development, hormone distribution, and germination, promoting healthier growth and improving early yield. To examine the regulatory mechanisms of endogenous hormones on lateral bud germination in Pyrus spp. (cv. ‘Huangguan’) (Pyrus bretschneideri Rehd.), juvenile buds were collected from 2-year-old pear trees. Then, a comprehensive study, including assessments of endogenous hormones, germination and branching rates, RNA-seq analysis, and gene function analysis in these lateral buds was conducted. The results showed that there was no significant difference in germination rate between the control and bud-notching pear trees, but the long branch rate was significantly increased in bud-notching pear trees compared to the control (p < 0.05). After bud-notching, there was a remarkable increase in IAA and BR levels in the pruned section of shoots, specifically by 141% and 93%, respectively. However, the content of ABA in the lateral buds after bud-notching was not significantly different from the control. Based on RNA-seq analysis, a notable proportion of the differentially expressed genes (DEGs) were linked to the plant hormone signal transduction pathway. Notably, the brassinosteroid signaling pathway seemed to have the closest connection with the branching ability of pear with the related genes encoding BRI1 and CYCD3, which showed significant differences between lateral buds. Finally, the heterologous expression of PyCYCD3 has a positive regulatory effect on the increased Arabidopsis growth and branching numbers. Therefore, the PyCYCD3 was identified as an up-regulated gene that is induced via brassinosteroid (BR) and could act as a conduit, transforming bud-notching cues into proliferative signals, thereby governing lateral branching mechanisms in pear trees. Full article

The sustainability of the primary sector is closely linked to meeting the demand for seeds using agro-industrial waste and bioresidues. Sustainability is a multidimensional concept focused on achieving environmental health, social justice, and economic viability. To this end, an experiment was designed based on a combination of biotechnological strategies accessible to many individuals. The first strategy involves the use of compost and vermicompost as cultivation substrates; the second is the in vitro acclimatization of potato plants to these substrates; and the third is the incorporation of Trichoderma asperellum into these substrates to determine the synergistic effect of both. The compost used in this work came from sewage sludge from an agri-food company (Cp); a dining room and pruning waste from a university campus (Cu); and vermicomposted coffee pulp waste (Cv). Each sample was mixed with coconut fiber (Fc) in proportions of 100, 75, 50, and 25%. In the resulting mixtures, María Bonita variety vitroplants were planted and placed in a greenhouse. The biometric response in the three cases indicated a dependence on the type of compost and the proportion of the coconut fiber mixture. The inoculation of Trichoderma asperellum with sewage sludge compost increased stem thickness (42.58%) and mini-tuber weight (6.74%). In contrast, uninoculated treatments showed the best performance in terms of the number of mini-tubers. A 50:50 mixture of sewage sludge compost with coconut fiber and without inoculation of Trichoderma asperellum was the best treatment for the production of pre-basic seeds of the María Bonita potato variety. The use of composted agricultural waste and bioresidues is shown as a valid and low-cost alternative for the sector, even independently of the incorporation of additional inoculants. Full article

Musculoskeletal Disorders (MSDs) stand as a prominent cause of injuries in modern agriculture. Scientific research has highlighted a causal link between MSDs and awkward working postures. Several methods for the evaluation of working postures, and related risks, have been developed such as the Rapid Upper Limb Assessment (RULA). Nevertheless, these methods are generally applied with manual measurements on pictures or videos. As a consequence, their applicability could be scarce, and their effectiveness could be limited. The use of wearable sensors to collect kinetic data could facilitate the use of these methods for risk assessment. Nevertheless, the existing system may not be usable in the agricultural and vine sectors because of its cost, robustness and versatility to the various anthropometric characteristics of workers. The aim of this study was to develop a technology capable of collecting accurate data about uncomfortable postures and repetitive movements typical of vine workers. Specific objectives of the project were the development of a low-cost, robust, and wearable device, which could measure data about wrist angles and workers’ hand positions during possible viticultural operations. Furthermore, the project was meant to test its use to evaluate incongruous postures and repetitive movements of workers’ hand positions during pruning operations in vineyard. The developed sensor had 3-axis accelerometers and a gyroscope, and it could monitor the positions of the hand–wrist–forearm musculoskeletal system when moving. When such a sensor was applied to the study of a real case, such as the pruning of a vines, it permitted the evaluation of a simulated sequence of pruning and the quantification of the levels of risk induced by this type of agricultural activity. Full article

Grapevine trunk diseases (GTDs) are currently limiting grapevine productivity in many vineyards worldwide. As no chemical treatments are registered to control GTDs, biocontrol agents are being tested against these diseases. Esquive^® WP, based on the fungus Trichoderma atroviride I-1237 strain, is the first biocontrol product registered in France to control GTDs. In this study, we determine whether, following grapevine pruning wound treatments with Esquive^® WP, changes occurred or not in the indigenous microbial communities that are colonizing grapevine wood. Over a 6-year period, Esquive^® WP was applied annually to pruning wounds on three grapevine cultivars located in three different regions. Wood samples were collected at 2 and 10 months after the Esquive^® WP treatments. Based on MiSeq high-throughput sequencing analyses, the results showed that specific microbial communities were linked to each ‘region/cultivar’ pairing. In certain cases, a significant modification of alpha diversity indexes and the relative abundance of some microbial taxa were observed between treated and non-treated grapevines 2 months after Esquive^® WP treatment. However, these modifications disappeared over time, i.e., 10 months post-treatment. This result clearly showed that Esquive^® WP pruning wood treatment did not induce significant changes in the grapevine wood’s microbiome, even after 6 years of recurrent applications on the plants. Full article

Worldwide, different strategies are being developed in order to ensure optimum conditions for the development and growth of economic competitiveness, as well as for increasing the quality of life and environmental protection. All these strategies are closely linked to the development and modernization of systems for producing energy from clean and renewable sources. In this context, the present paper presents the results of research regarding the evaluation of the sustainability of briquette production using biomass resulting from vine winter pruning as the raw material. An analysis of the scientific literature indicates that nearly 8 Mt of biomass would result from the over 7.4 million hectares of vine plantations in the world, biomass that could be valorized through densification in order to produce solid biofuels with a lower calorific value of more than 17 MJ/kg. This study examines the production of briquettes from vineyard winter pruning with consideration of two types of densification technologies: baling and natural drying of the tendrils, and collection, shredding, and artificial drying of the lignocellulose debris. The quality indices and energy consumption and energy efficiency of the briquettes were evaluated to determine their feasibility as an alternative fuel source. When designing the scientific endeavor, the following aspects were considered: defining the aim and objectives of the research; designing the research algorithm; collecting, preparing, and conditioning the biomass; conducting a chemical analysis of the briquettes; and evaluating the energy consumption and energy efficiency for producing the briquettes, taking into account two drying methods (natural and artificial drying). In the meantime, some specific laboratory equipment was designed and built for the artificial drying of biomass, evaluation of mechanical durability, measurement of energy consumption, etc. Analysis of the experimental data has led to the conclusion that the agricultural waste from vine pruning can constitute an important and sustainable source of energy in the form of briquettes that fulfill most of the requirements imposed by international standards. Full article

Satellite edge computing (SEC) plays an increasing role in earth observation, due to its global coverage and low-latency computing service. In SEC, it is pivotal to offload diverse observation-data-processing tasks to the appropriate satellites. Nevertheless, due to the sparse intersatellite link (ISL) connections, it is hard to gather complete information from all satellites. Moreover, the dynamic arriving tasks will also influence the obtained offloading assignment. Therefore, one daunting challenge in SEC is achieving optimal offloading assignments with consideration of the dynamic delay-sensitive tasks. In this paper, we formulate task offloading in SEC with delay-sensitive tasks as a mixed-integer linear programming problem, aiming to minimize the weighted sum of deadline violations and energy consumption. Due to the limited ISLs, we propose a fully-decentralized method, called the PI-based task offloading (PITO) algorithm. The PITO operates on each satellite in parallel and only relies on local communication via ISLs. Tasks can be directly offloaded on board without depending on any central server. To further handle the dynamic arriving tasks, we propose a re-offloading mechanism based on the match-up strategy, which reduces the tasks involved and avoids unnecessary insertion attempts by pruning. Finally, extensive experiments demonstrate that PITO outperforms state-of-the-art algorithms when solving task offloading in SEC, and the proposed re-offloading mechanism is significantly more efficient than existing methods. Full article

Weight thresholding (WT) is a method intended to decrease the number of links within weighted networks that may otherwise be excessively dense for network science applications. WT aims to remove links to simplify the network by holding most of the features of the original network. Here, we test the robustness and the efficacy of the node attack strategies on real-world networks subjected to WT that remove links of higher weight (strong links). We measure the network robustness along node removal with the largest connected component (LCC). We find that the real-world networks under study are generally robust when subjected to WT. Nonetheless, WT with strong link removal changes the efficacy of the attack strategies and the rank of node centralities. Also, WT with strong link removal may trigger a more significant change in the node centrality rank than WT by removing weak links. Network science research with the aim to find important/influential nodes in the network has to consider that simplifying the network with WT methodologies may change the node centrality. Full article

Light plays a crucial role in the growth of fruit trees, influencing not only nutrient absorption but also fruit appearance. Therefore, understanding fruit tree canopy light transmittance is essential for agricultural and forestry practices. However, traditional measurement methods, such as using a canopy analyzer, are time-consuming, labor-intensive, and susceptible to external influences, lacking convenience and automation. To address this issue, we propose a novel method based on point clouds to estimate light transmittance, with the Leaf Area Index (LAI) serving as the central link. Focusing on apple trees, we utilized handheld LiDAR for three-dimensional scanning of the canopy, acquiring point cloud data. Determining the optimal voxel size at 0.015 m via standardized point cloud mean spacing, we applied the Voxel-based Canopy Profile method (VCP) to estimate LAI. Subsequently, we established a function model between LAI and canopy light transmittance using a deep neural network (DNN), achieving an overall correlation coefficient R² of 0.94. This model was then employed to estimate canopy light transmittance in dwarfed and densely planted apple trees. This approach not only provides an evaluation standard for pruning effects in apple trees but also represents a critical step towards visualizing and intelligentizing light transmittance. Full article

In a world essentializing communication for human connection, the deaf community encounters distinct barriers. Sign language, their main communication method is rich in hand gestures but not widely understood outside their community, necessitating interpreters. The existing solutions for sign language recognition depend on extensive datasets for model training, risking overfitting with complex models. The scarcity of details on dataset sizes and model specifics in studies complicates the scalability and verification of these technologies. Furthermore, the omission of precise accuracy metrics in some research leaves the effectiveness of gesture recognition by these models in question. The key phases of this study are Data collection, Data preprocessing, Feature extraction using CNN and finally transfer learning-based classification. The purpose of utilizing CNN and transfer learning is to tap into pre-trained neural networks for optimizing performance on new, related tasks by reusing learned patterns, thus accelerating development and improving accuracy. Data preprocessing further involves resizing of images, normalization, standardization, color space conversion, augmentation and noise reduction. This phase is capable enough to prune the image dataset by improving the efficiency of the classifier. In the subsequent phase, feature extraction has been performed that includes the convolution layer, feature mapping, pooling layer and dropout layer to obtain refined features from the images. These refined features are used for classification using ResNet. Three different datasets are utilized for the assessment of proposed model. The ASL-DS-I Dataset includes a total of 5832 images of hand gestures whereas, ASL-DS-II contains 54,049 images and ASL-DS-III dataset includes 7857 images adopted from specified web links. The obtained results have been evaluated by using standard metrics including ROC curve, Precision, Recall and F-measure. Meticulous experimental analysis and comparison with three standard baseline methods demonstrated that the proposed model gives an impressive recognition accuracy of 96.25%, 95.85% and 97.02% on ASL-DS-I, ASL-DS-II and ASL-DS-III, respectively. Full article

Mical family enzymes are unusual actin regulators that prime filaments (F-actin) for disassembly via the site-specific oxidation of M44/M47. Filamentous actin acts as a substrate of Mical enzymes, as well as an activator of their NADPH oxidase activity, which leads to hydrogen peroxide generation. Mical enzymes are required for cytokinesis, muscle and heart development, dendritic pruning, and axonal guidance, among other processes. Thus, it is critical to understand how this family of actin regulators functions in different cell types. Vertebrates express six actin isoforms in a cell-specific manner, but MICALs’ impact on their intrinsic properties has never been systematically investigated. Our data reveal the differences in the intrinsic dynamics of Mical-oxidized actin isoforms. Furthermore, our results connect the intrinsic dynamics of actin isoforms and their redox state with the patterns of hydrogen peroxide (H₂O₂) generation by MICALs. We documented that the differential properties of actin isoforms translate into the distinct patterns of hydrogen peroxide generation in Mical/NADPH-containing systems. Moreover, our results establish a conceptual link between actin stabilization by interacting factors and its ability to activate MICALs’ NADPH oxidase activity. Altogether, our results suggest that the regulatory impact of MICALs may differ depending on the isoform-related identities of local actin networks. Full article

The nonlinear Volterra equalizer has been proved to be able to solve the problem of nonlinear distortion, but it has high computational complexity and is difficult to implement. In this paper, a simplified second-order Volterra nonlinear equalizer designed for intensity modulation/direct detection systems based on direct modulated laser is proposed and demonstrated, taking into account the characteristics of the system. It has been proved that the received signal of direct modulation laser/direct detection system can be expressed in Volterra series form, but its form is too complex, and the device parameters should also be considered. We re-derived it and obtained a more concise form. At the same time, we proposed a method to simplify the second-order Volterra nonlinear equalizer without relying on device parameters. The performance of the proposed Volterra nonlinear equalizer is evaluated experimentally on a 56 Gb/s 4-ary pulse amplitude modulation link implemented by using a 1.55 µm direct modulation laser. The results show that, compared with the traditional Volterra nonlinear equalizer, the receiver sensitivity of the equalizer is only reduced by 0.2 dB at most, but the complexity can be reduced by 50%; compared with diagonally pruned Volterra nonlinear equalizers, the complexity of the equalizer is the same, but the reception sensitivity can be improved by 0.5 dB. Full article

With the improvement of satellite autonomy, multi-satellite cooperative mission planning has become an important application. This requires multiple satellites to interact with each other via inter-satellite links to reach a consistent mission planning scheme. Considering issues such as inter-satellite link failure, external interference, and communication delay, algorithms should minimize communication costs as much as possible. The CBBA algorithm belongs to a fully distributed multi-agent task allocation algorithm, which has been introduced into multi-satellite autonomous task planning scenarios and achieved good planning results. This paper mainly focuses on the communication problem, and proposes an improved algorithm based on it, which is called c-CBBA. The algorithm is designed with task preemption strategy and single-chain strategy to reduce the communication volume. The task preemption strategy is an accelerated convergence mechanism designed for the convergence characteristics of CBBA, while the single-chain strategy is a communication link pruning strategy designed for the information exchange characteristics of satellites. Experiments in various scenarios show that the algorithm can effectively reduce communication volume while ensuring the effectiveness of task planning. Full article