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En France, la notion de filière forêt-bois s’est progressivement imposée pour désigner l’ensemble des acteurs et des activités d’exploitation forestière et de transformation du bois. Dans un contexte de mondialisation des marchés et de réorganisation politique, cette catégorie d’analyse monolithique semble remise en question à travers l’émergence de formes de développement et de gouvernance territorialisées. En interrogeant le concept de filière appliqué à la forêt et au bois, nous cherchons à mettre en évidence le caractère construit et multiscalaire de cette dernière. À travers deux cas d’étude pris dans le nord-est de la France – la multiplication des initiatives de valorisation des bois locaux et la contractualisation des ventes de bois en forêt publique – nous montrerons que le territoire est devenu un enjeu stratégique mais aussi que la coexistence de différentes visions des rapports filière-territoire peut être génératrice de tensions dans un secteur déjà fragmenté. In France, the specific term filière has gradually become accepted as the customary notion encompassing both the actors of the forestry sector and their activities, from forest operations to timber processing and manufacturing of final products. In a context of market globalization and readjustment of public policies, this approach is now questioned by the emergence of new forms of territorial development and governance. The article proposes to reconsider the generic definition of the forestry value-chain, by including social and spatial dimensions into the analysis. Using two case-studies located in north-eastern France – (1) the growing interest for locally based value-creation through product certification and (2) the shift from auction mechanism to supply contracts in timber sales from public forests – we show that territorial features have now become of strategic concern for private and public stakeholders. Besides, we highlight that seemingly contradictory types of logic (territorial versus sectoral) may also raise tensions in an already fragmented sector.

Nous étudions le problème de la décision de la cohérence de réseaux de contraintes dans le cadre de combinaisons de formalismes qualitatifs. Nous proposons un cadre formel qui englobe les intégrations lâches, le raisonnement multiéchelle et une forme de raisonnement spatio-temporel. En particulier, nous identifions des conditions suffisantes assurant la polynomialité de la décision de la cohérence, et nous appliquons ces résultats pour trouver des sous-classes traitables. We study the problem of consistency checking for constraint networks over combined qualitative formalisms. We propose a framework which encompasses loose integrations, multiscale reasoning and a form of spatio-temporal reasoning. In particular, we identify sufficient conditions ensuring the polynomiality of consistency checking, and we use them to find tractable subclasses.

Multi-scale structures, as found in nature (e.g., bone and bamboo), hold the promise of achieving superior performance while being intrinsically lightweight, robust, and multi-functional. Recent years have seen a rapid development in topology optimization approaches for designing multi-scale structures, but the field actually dates back to the seminal paper by Bendsøe and Kikuchi from 1988 (Computer Methods in Applied Mechanics and Engineering 71(2): pp. 197–224). In this review, we intend to categorize existing approaches, explain the principles of each category, analyze their strengths and applicabilities, and discuss open research questions. The review and associated analyses will hopefully form a basis for future research and development in this exciting field.

Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruction networks mainly improve the model performance by increasing the network depth. However, blindly increasing the network depth can easily lead to gradient disappearance or gradient explosion, increasing the difficulty of training. This report proposes a new pyramidal multi-scale residual network (PMSRN) that uses hierarchical residual-like connections and dilation convolution to form a multi-scale dilation residual block (MSDRB). The MSDRB enhances the ability to detect context information and fuses hierarchical features through the hierarchical feature fusion structure. Finally, a complementary block of global and local features is added to the reconstruction structure to alleviate the problem that useful original information is ignored. The experimental results showed that, compared with a basic multi-scale residual network, the PMSRN increased the peak signal-to-noise ratio by up to 0.44 dB and the structural similarity to 0.9776.

Implementation of wildfire- and climate-adaptation strategies in seasonally dry forests of western North America is impeded by numerous constraints and uncertainties. After more than a century of resource and land use change, some question the need for proactive management, particularly given novel social, ecological, and climatic conditions. To address this question, we first provide a framework for assessing changes in landscape conditions and fire regimes. Using this framework, we then evaluate evidence of change in contemporary conditions relative to those maintained by active fire regimes, i.e., those uninterrupted by a century or more of human-induced fire exclusion. The cumulative results of more than a century of research document a persistent and substantial fire deficit and widespread alterations to ecological structures and functions. These changes are not necessarily apparent at all spatial scales or in all dimensions of fire regimes and forest and nonforest conditions. Nonetheless, loss of the once abundant influence of low- and moderate-severity fires suggests that even the least fire-prone ecosystems may be affected by alteration of the surrounding landscape and, consequently, ecosystem functions. Vegetation spatial patterns in fire-excluded forested landscapes no longer reflect the heterogeneity maintained by interacting fires of active fire regimes. Live and dead vegetation (surface and canopy fuels) is generally more abundant and continuous than before European colonization. As a result, current conditions are more vulnerable to the direct and indirect effects of seasonal and episodic increases in drought and fire, especially under a rapidly warming climate. Long-term fire exclusion and contemporaneous social-ecological influences continue to extensively modify seasonally dry forested landscapes. Management that realigns or adapts fire-excluded conditions to seasonal and episodic increases in drought and fire can moderate ecosystem transitions as forests and human communities adapt to changing climatic and disturbance regimes. As adaptation strategies are developed, evaluated, and implemented, objective scientific evaluation of ongoing research and monitoring can aid differentiation of warranted and unwarranted uncertainties.

Aiming at the problems of low efficiency and poor accuracy in the product surface defect detection. In this paper, an online surface defects detection method based on YOLOV3 is proposed. Firstly, using lightweight network MobileNetV2 to replace the original backbone as the feature extractor to improve network speed. Then, we propose an extended feature pyramid network (EFPN) to extend the detection layer for multi-size object detection and design a novel feature fusing module (FFM) embedded in the extend layer to super-resolve features and capture more regional details. In addition, we add an IoU loss function to solve the mismatch between classification and bounding box regression. The proposed method is used to train and test on the hot rolled steel open dataset NEU-DET, which contains six typical defects of a steel surface, namely rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. The experimental results show that our method achieves a satisfactory balance between performance and consumption and reaches 86.96% mAP with a speed of 80.96 FPS, which is more accurate and faster than many other algorithms and can realize real-time and high-precision inspection of product surface defects.

In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several critical issues that need to be addressed in the HSIC task, such as the lack of labeled training samples, which constrains the classification accuracy and generalization ability of CNNs. To address this problem, a deep multi-scale attention fusion network (DMAF-NET) is proposed in this paper. This network is based on multi-scale features and fully exploits the deep features of samples from multiple levels and different perspectives with an aim to enhance HSIC results using limited samples. The innovation of this article is mainly reflected in three aspects: Firstly, a novel baseline network for multi-scale feature extraction is designed with a pyramid structure and densely connected 3D octave convolutional network enabling the extraction of deep-level information from features at different granularities. Secondly, a multi-scale spatial–spectral attention module and a pyramidal multi-scale channel attention module are designed, respectively. This allows modeling of the comprehensive dependencies of coordinates and directions, local and global, in four dimensions. Finally, a multi-attention fusion module is designed to effectively combine feature mappings extracted from multiple branches. Extensive experiments on four popular datasets demonstrate that the proposed method can achieve high classification accuracy even with fewer labeled samples.

MXenes as an emerging two‐dimensional (2D) material have attracted tremendous interest in electrochemical energy‐storage systems such as supercapacitors. Nevertheless, 2D MXene flakes intrinsically tend to lie flat on the substrate when self‐assembling as electrodes, leading to the highly tortuous ion pathways orthogonal to the current collector and hindering ion accessibility. Herein, a facile strategy toward multi‐scale structural engineering is proposed to fabricate high‐performance MXene hydrogel supercapacitor electrodes. By unidirectional freezing of the MXene slurry followed by a designed thawing process in the sulfuric acid electrolyte, the hydrogel electrode is endowed with a three‐dimensional (3D) open macrostructure impregnated with sufficient electrolyte and H+‐intercalated microstructure, which provide abundant active sites for ion storage. Meanwhile, the ordered channels bring through‐electrode ion and electron transportation pathways that facilitate electrolyte infiltration and mass exchange between electrolyte and electrode. Furthermore, this strategy can also be extended to the fabrication of a 3D‐printed all‐MXene micro‐supercapacitor (MSC), delivering an ultrahigh areal capacitance of 2.0 F cm–2 at 1.2 mA cm–2 and retaining 1.2 F cm–2 at 60 mA cm–2 together with record‐high energy density (0.1 mWh cm–2 at 0.38 mW cm–2). A strategy of multi‐scale structural engineering is proposed to fabricate high‐performance MXene hydrogel supercapacitor electrode with both vertically ordered macrostructure and the proton‐intercalated microstructure. This strategy also can be extended to the fabrication of 3D‐printed all‐MXene micro‐supercapacitor, delivering an ultrahigh areal capacitance together with record‐high energy density.