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This paper studies the problem of exponential stability of second moments of large-scale distribution-dependent stochastic differential equations (SDEs, in short). By introducing isolated subsystems, under certain hypotheses added on the interconnected terms and using the moment estimation, exponential stability of second moments of isolated subsystems implies exponential stability of second moments of large-scale distribution-dependent SDEs. Specially, we provide sufficient and necessary conditions for the exponential stability of large-scale distribution-dependent SDEs with triangular structure and apply this results to study stabilization of large-scale distribution-dependent SDEs. Finally, the main results are illustrated by several examples and applications. [ABSTRACT FROM AUTHOR]

Green hydrogen (GH 2) is produced using renewable energy resources (RERs) such as solar photovoltaic (PV) and wind energy. However, relying solely on a single source, H 2 production systems may encounter challenges due to the intermittent nature, time-of-day variability, and seasonal changes associated with these energies. This paper addresses the assessment of mega-scale solar-wind complementarity and the economic viability of large-scale H 2 production and storage in Algeria, considering various climatic conditions. A techno-economic feasibility analysis is conducted by examining various factors, including resource complementarity, electricity and hydrogen productivity, solar and wind contributions, capacity factors (CFs), total net present cost (TNPC), the levelized cost of hydrogen (LCOH) production, and storage (LCOS). The findings reveal that among the studied locations (Tamanrasset, M'sila, Hassi R'mel, and Adrar), Tindouf ranked first in H 2 production, with the highest amount at 11,267 kg and the lowest LCOH at 2.999 $/kg. Wind energy accounted for 69% (3,846,922 kWh) of the total (5,572,571 kWh), while solar energy contributed 31% (1,738,171 kWh). Economically, the LCOH-TNPC for the various locations ranged from 2.99 $/kg-77,042,696 $ to 3.70 $/kg-99,000,488 $. The total electricity-hydrogen generated is estimated at 25,840.251 MWh-529,395 kg. Wind power makes the major contribution, accounting for 63%–70%, compared to the PV system's share, ranging from 31% to 37%. Compared to relying on a single source, leveraging solar-wind complementarity presents opportunities for more consistent energy utilization and reliable hydrogen production. The obtained results are essential for decision-makers and policymakers in Algeria, indicating that the country can successfully achieve energy diversification and transition for future sustainability, and become a major player in the international hydrogen market. [Display omitted] • Mega-scale solar-wind assessment for energy-H 2 production and storage in Algeria. • Feasibility analysis with techno-economic criteria (H 2 productivity, CF, TNPC, LCOH). • LCOH ranges from 2.99 to 3.707 $/kg and TNPC from 77,042,696 to 99,000,488 $. • Tindouf site achieves highest H 2 production and lowest LCOH: 114,267 kg, 2.999 $/kg. • Wind CF is nearly double that of PV, ranging from 34.3 to 44.1%, contributing 63–70%. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has been a significant manufacturing technology and adopted in various industries for years. Although different types of AM technologies are developed to fulfill people's customed manufacturing scenarios, the ability of AM to fabricate large scale mechanical parts is still limited, since there are specific maximum values of building volumes of most AM platforms. In this paper, we proposed a segment-by-segment printing strategy to print components with large scale using a robot-based Wire Arc Additive Manufacturing (WAAM) manufacturing and machining platform. The consumed wire was Aluminum 5356 (Al5356). In this project, three segments were sequentially printed on an aluminum alloy substrate in a non-enclosed working platform. After printing, a series of following tests such as hardness testing, tensile testing, and sectional observation were conducted. It was found that the printed segments could be deposited to the previous segments and combined into a continuous single part without any cracks or cavity at the jointed areas. Also, the mechanical properties revealed through these tests suggested that the welding joints between segments were as strong as normal areas. Thus, the proposed segment-by-segment strategy can be extended to 3D print large-scale metal components in the future more efficiently and will provide a feasible solution to break the limitation of the maximum building volume of AM devices which used to be a challenge in previous studies about large-scale component printing. [ABSTRACT FROM AUTHOR]

Studies addressing the supervised extraction of geospatial elements from aerial imagery with semantic segmentation operations (including road surface areas) commonly feature tile sizes varying from 256 × 256 pixels to 1024 × 1024 pixels with no overlap. Relevant geo-computing works in the field often comment on prediction errors that could be attributed to the effect of tile size (number of pixels or the amount of information in the processed image) or to the overlap levels between adjacent image tiles (caused by the absence of continuity information near the borders). This study provides further insights into the impact of tile overlaps and tile sizes on the performance of deep learning (DL) models trained for road extraction. In this work, three semantic segmentation architectures were trained on data from the SROADEX dataset (orthoimages and their binary road masks) that contains approximately 700 million pixels of the positive "Road" class for the road surface area extraction task. First, a statistical analysis is conducted on the performance metrics achieved on unseen testing data featuring around 18 million pixels of the positive class. The goal of this analysis was to study the difference in mean performance and the main and interaction effects of the fixed factors on the dependent variables. The statistical tests proved that the impact on performance was significant for the main effects and for the two-way interaction between tile size and tile overlap and between tile size and DL architecture, at a level of significance of 0.05. We provide further insights and trends in the predictions of the extensive qualitative analysis carried out with the predictions of the best models at each tile size. The results indicate that training the DL models on larger tile sizes with a small percentage of overlap delivers better road representations and that testing different combinations of model and tile sizes can help achieve a better extraction performance. [ABSTRACT FROM AUTHOR]

This paper focuses on large-scale landslide susceptibility modelling in NW Croatia. The objective of this research was to provide new insight into stable and unstable area sampling strategies on a representative inventory of small and shallow landslides mainly occurring in soil and soft rock. Four strategies were tested for stable area sampling (random points, stable area polygon, stable polygon buffering and stable area centroid) in combination with four strategies for unstable area sampling (landslide polygon, smoothing digital terrain model derived landslide conditioning factors, polygon buffering and landslide centroid), resulting in eight sampling scenarios. Using Logistic Regression, Neural Network, Random Forest and Support Vector Machine algorithm, 32 models were derived and analysed. The main conclusions reveal that polygon sampling of unstable areas is an imperative in large-scale modelling, as well as that subjective and/or biased stable area sampling leads to misleading models. Moreover, Random Forest and Neural Network proved to be more favourable methods (0.804 and 0.805 AUC, respectively), but also showed extreme sensitivity to the tested sampling strategies. In the comprehensive comparison, the advantages and disadvantages of 32 derived models were analysed through quantitative and qualitative parameters to highlight their application to large-scale landslide zonation. The results yielded by this research are beneficial to the susceptibility modelling step in large-scale landslide susceptibility assessments as they enable the derivation of more reliable zonation maps applicable to spatial and urban planning systems. [ABSTRACT FROM AUTHOR]

The accurate large-scale classification of tree species is crucial for the monitoring, protection, and management of the Earth's invaluable forest ecosystems. Numerous previous studies have recognized the suitability of satellite imagery, particularly Sentinel-2 imagery, for this task. In this study, we utilized a dense phenology Sentinel-2 time series, which offered consistent data across multiple granules, to map tree species across the entire forested area in Austria. Aiming for the classification scheme to more accurately represent actual forest conditions, we included mixed tree species and sparsely populated classes (classes with sparse canopy cover) alongside pure tree species classes. To enhance the training data for the mixed and sparse classes, synthetic data creation was employed. Autocorrelation has significant implications for the validation of thematic maps. To investigate the impact of spatial dependency on validation data, two methods were employed at numerous split and buffer distances: spatial split validation and a validation method based on a buffered ground reference probability samples provided by the National Forest inventory (NFI). While a random training data holdout set yielded 99% accuracy, the spatial split validation resulted in 74% accuracy, emphasizing the importance of accounting for spatial autocorrelation when validating with holdout sets derived from polygon-based training data. The validation based on NFI data resulted in 55% overall accuracy, 91% post-hoc pure class accuracy, and 79% accuracy when confusions in phenological proximity were disregarded (e.g., spruce–larch confused with spruce). The significant differences in accuracy observed between spatial split and NFI validation underscore the challenge for polygon-based training data to capture ground reference forest complexity, particularly in areas with diverse forests. This hardship is further accentuated by the pure class accuracy of 91%, revealing the substantial impact of mixed stands on the accuracy of tree species maps. [ABSTRACT FROM AUTHOR]

Mountain hazards involve complex multiphase media composed of granular materials and fluids. The challenges related to scale effects and similarity issues in physical modelling of these hazards are key problems in both fundamental research on dynamics of granular materials and applied research in disaster prevention and mitigation. Supported by the Chinese Academy of Sciences' key scientific infrastructure construction project at the field station network, the National Dongchuan Debris Flow Observation and Research Station in Jiangjia Gully, Dongchuan District, Kunming City completed the construction of the large-scale experimental platform on dynamic simulation of mountain hazards (LEADS) in 2024. This platform is the world's largest, most automated, and best synchronized system for data collection among international advanced experimental platforms for physical modelling of mountain hazards. The LEADS platform fully adheres to the similarity criteria for simulating multiphase media in mountain hazards, accurately reveals the dynamic evolution mechanisms of these media as well as the regulation mechanisms of mitigation structures, and effectively aids in the engineering design. LEADS serves as a national treasure in China's disaster reduction field and is set to become a demonstration base for basic and fundamental research on mountain hazards dynamics and for the development, testing, and promotion of new disaster prevention and control structures. It will significantly promote the research level of mountain hazards in China and will lead the development of the relevant scientific fields. [ABSTRACT FROM AUTHOR]

Measuring the dynamic displacements of a structure provides a comprehensive understanding of the structure, especially when subjected to different types of dynamic loading (i.e., wind, traffic, impact loads, blast loads, etc.). Despite their usefulness, direct displacement measurements are typically not collected due to the cumbersome logistical issues of sensor placement and maintenance and the impracticality of instrumenting contact-based sensors across all significant structures. In this context, this study proposes a novel dual stereovision technique to measure the dynamic displacement of structures using a portable, noncontact measurement system that involves an uncrewed aerial system (UAS) and four optical cameras. One pair of cameras tracks the three-component (x , y , and z) motion of a region of interest (ROI) on a structure with respect to the UAS system, and the other pair of cameras measure the six degrees of freedom motion (6-DOF) (both rotational and translational motion) of the UAS system by tracking a stationary reference. The motion of the UAS is then compensated for to recover the true dynamic displacement of the ROI. The proposed dual stereovision technique realizes simultaneous measurement of all three components of displacements of the structure and 6-DOF of UAS motion through a mathematically elegant process. The unique dual stereovision technique allows flexibility in choosing a global reference coordinate system, greatly enhancing the feasibility of applying the new technology in various field environments. This new technique has overcome the major challenge of significant UAS motions in full-scale applications. Furthermore, this technique relies on natural features and eliminates the requirement of artificial targets on the structure, permitting applications to difficult-to-access structures. [ABSTRACT FROM AUTHOR]

Metal halide perovskites have shown unique advantages compared with traditional optoelectronic materials. Currently, perovskite films are commonly produced by either multi-step spin coating or vapor deposition techniques. However, both methods face challenges regarding large-scale production. Herein, we propose a straightforward in situ growth method for the fabrication of CsPbBr3 nanocrystal films. The films cover an area over 5.5 cm × 5.5 cm, with precise thickness control of a few microns and decent uniformity. Moreover, we demonstrate that the incorporation of magnesium ions into the perovskite enhances crystallization and effectively passivates surface defects, thereby further enhancing luminous efficiency. By integrating this approach with a silicon photodiode detector, we observe an increase in responsivity from 1.68 × 10−2 A/W to 3.72 × 10−2 A/W at a 365 nm ultraviolet wavelength. [ABSTRACT FROM AUTHOR]

California has experienced a surge in wildfires, prompting research into contributing factors, including weather and climate conditions. This study investigates the complex, multiscale interactions between large-scale climate patterns, such as the Boreal Summer Intraseasonal Oscillation (BSISO), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) and their influence on moisture and temperature fluctuations, and wildfire dynamics in California. The combined impacts of PDO and BSISO on intraseasonal fire weather changes; the interplay between fire weather index (FWI), relative humidity, vapor pressure deficit (VPD), and temperature in assessing wildfire risks; and geographical variations in the relationship between the FWI and climatic factors within California are examined. The study employs a multi-pronged approach, analyzing wildfire frequency and burned areas alongside climate patterns and atmospheric conditions. The findings reveal significant variability in wildfire activity across different climate conditions, with heightened risks during specific BSISO phases, La-Niña, and cool PDO. The influence of BSISO varies depending on its interaction with PDO. Temperature, relative humidity, and VPD show strong predictive significance for wildfire risks, with significant relationships between FWI and temperature in elevated regions (correlation, r > 0.7, p ≤ 0.05) and FWI and relative humidity along the Sierra Nevada Mountains (r ≤ −0.7, p ≤ 0.05). [ABSTRACT FROM AUTHOR]

The recent methods that guarantee the stability of the reduced-order models are Mihailov stability, improved Pade approximation and truncation, improved generalized pole clustering, moment matching and salp swarm optimization. Further, these methods could overcome the limitations such as non-uniqueness, pole clustering, gain adjustment and difficulty to maintain the dominant roots in the lower order system for non-minimum higher order plants. This research emphasizes the design of the proposed compensating algorithm by using an additional open loop zero for the stable reduced-order models of large-scale single-input single-output linear time-invariant continuous time systems. The results of the proposed algorithm are compared with the existing compensating methods and the design is validated and illustrated numerically. [ABSTRACT FROM AUTHOR]

Urban informal settlements (UISs) are densely populated and poorly developed residential areas in urban areas. The mapping of UISs using remote sensing is crucial for urban planning and management. However, the large-scale extraction of UISs is impeded by the labor-intensive task of collecting numerous training samples and the lack of automatic and effective city partition. To overcome these challenges, we proposed a large-scale extraction framework for UISs based on semantic segmentation of highresolution remote sensing images. Utilizing Deeplab V3 Plus as the foundational extraction model, the proposed framework introduces fast sample collection based on GLCM features. Besides, an automatic city partition approach combined with clustering and fine-tuning was proposed to enhance the performance on extracting a specific category of UISs. The results of the case study conducted in 36 major Chinese cities show that the proposed framework achieved good performance, with an overall F1 score of 85.76%. Furthermore, comparative assessments were performed to demonstrate the effectiveness of automatic city partition. The proposed framework offers a practical approach for the largescale extraction of UISs, which holds great significance for sustainable development, poverty estimation, infrastructure construction, and urban planning. [ABSTRACT FROM AUTHOR]

A series of trials were conducted to investigate the repeatability of furniture-scale calorimetry experiments. Twenty-five identical upholstered chairs were ignited under the same experimental conditions. Experimental results of heat release rate (HRR), mass loss rate (MLR), and emission yields (CO, CO2, N2O, NO, CH4, HCN) are presented. Discrepancies were observed between the time resolved evolution of the various recorded data. However the development of each fire was observed to be tied to common events. By accounting for these events, a more consistent representation of the burning behaviour can be expressed. Each experiment displayed distinct burning regimes (i.e., pyrolysis, flaming, and smouldering) which were identified through visual observation and through analysing the emission data. Some species yields were found to be approximately constant over some burning regimes (e.g., CO2 yield over the flaming regime) while others displayed highly transient behaviour (e.g., CO yield was found to be burning regime dependent). Results from upholstered furniture scale experiments, including HRR and emission yields, are commonly used in various engineering applications; this study lends insight into the variability that can be observed for such data and the implications in applying this data in further analysis. [ABSTRACT FROM AUTHOR]

Broad-area lasers (BALs) have found applications in a variety of crucial fields on account of their high output power and high energy transfer efficiency. However, they suffer from poor spatial beam quality due to multi-mode behavior along the waveguide transverse direction. In this paper, we propose a novel metasurface waveguide structure acting as a transverse mode selective back-reflector for BALs. In order to effectively inverse design such a structure, a digital adjoint algorithm is introduced to adapt the considerably large design area and the high degree of freedom. As a proof of the concept, a device structure with a design area of 40 × 20 μm2 is investigated. The simulation results exhibit high fundamental mode reflection (above 90%), while higher-order transverse mode reflections are suppressed below 0.2%. This is, to our knowledge, the largest device structure designed based on the inverse method. We exploited such a device and the method and further investigated the device's robustness and feasibility of the inverse method. The results are elaborately discussed. [ABSTRACT FROM AUTHOR]

Circulating fluidized bed (CFB) boilers offer a technically viable and environmentally friendly means for the clean and efficient utilization of solid fuels. However, the complex gas–solid two-phase flow processes within them have hindered a thorough resolution of prediction issues related to coupled combustion, heat transfer, and pollutant generation characteristics. To address the deficiencies in scientific research, meet the practical operational needs of CFB boilers, and comply with new carbon emission policies, conducting full-scale field tests on large-scale CFB boilers is needed, so that the complex gas–solid flow, combustion, and heat transfer mechanisms in the furnace can be comprehended. In this paper, issues related to large-scale CFB boilers, including the uniformity of air distribution, secondary air injection range, spatial distribution of oxygen consumption and combustion reactions, distribution of pollutant generation, hydrodynamic and heat transfer characteristics, coal feeding distribution characteristics, coal diffusion characteristics under thermal operating conditions, and engineering research on anti-wear technology, are reviewed. By integrating practical engineering applications, the basic methods and measurement techniques used in full-scale field tests for large-scale CFB boilers are summarized, providing a practical reference for conducting engineering tests with large-scale CFB boilers. [ABSTRACT FROM AUTHOR]

Large-scale natural frequency-related topology optimization problems pose a great challenge due to the high computational cost required by the iterative computation of the system eigenvalues and their sensitivities in each design iteration. This paper presents a new framework based on a multi-step relay strategy in conjunction with the idea of successive iteration of analysis and design (SIAD), to find high-quality solutions at affordable computational costs. The method starts from a relatively coarse finite element discretization, and then use gradually refined meshes to improve the design resolution. For a given level of mesh resolution, the method interleaves the eigenvalue solution routine with the optimization iterations to achieves sequential approximations of the eigenpairs along with the structural design evolution, thus avoiding the time-consuming eigenpair analysis in each optimization iteration. By sequentially solving the optimization problem and projecting intermediate designs and the corresponding approximate eigenmodes from a coarser mesh onto finer meshes, the proposed multi-step relay method can further substantially alleviate the computational burden and generate high-resolution boundaries in the final design. Numerical examples show that this method can be used to solve natural frequency maximization topology optimization problems with millions of degrees of freedom on a desktop workstation, and is much more efficient than the conventional double-loop method. [ABSTRACT FROM AUTHOR]

Copper sulfide (CuS), a copper‐deficient p‐type semiconductor material, has been widely utilized due to its unique optical properties, low toxicity, and cost‐effectiveness. Although many studies have been conducted on synthesizing CuS nanoparticles, harsh synthetic conditions and low yield must be solved. This study presents a new methodology that can synthesize CuS nanoparticles in large quantities at room temperature and pressure using high‐concentration Cu complex ion precursors. This methodology is based on the theory that the critical nucleus radius and the critical nucleation free energy decrease as the concentration of the precursor increases to synthesize a large number of nanoparticles by applying low energy. In addition, it is possible to minimize the aggregation of nanoparticles, which is a problem of nanoparticles synthesized at a high precursor concentration through complex ions in the solution. We synthesized nanoparticles by controlling the precursor concentration from 0.1 to 2.5 M to confirm the effect of the precursor concentration on the size, shape, and yield of nanoparticles. As the precursor concentration increased, the particle size decreased, and the yield improved. The CuS nanoparticles synthesized at the highest concentration had a size of about 17 nm without a strong agglomeration and a yield of about 213.9 g/L. Furthermore, the nanoparticles showed excellent photothermal performance due to their high near‐infrared absorption. When about 0.1 g of the nanoparticles were irradiated with a Xenon lamp and an 808 nm laser, the maximum temperatures and rising rates were 53.7°C and 172.1°C and 13.8°C/mg and 33°C/mg, respectively. The excellent photothermal properties of CuS nanoparticles suggest the potential of this material for various applications. [ABSTRACT FROM AUTHOR]

Extreme precipitation shows non-stationarity, meaning that its distribution can change with time or other large-scale variables. For a classical frequency-intensity analysis this effect is often neglected. Here, we propose a model including the influence of North Atlantic Oscillation, time, surface temperature and a blocking index. The model features flexibility to use annual maxima as well as seasonal maxima to be fitted in a generalized extreme value setting. To further increase the efficiency of data usage, maxima from different accumulation durations are aggregated so that information for extremes on different time scales can be provided. Our model is trained to individual station data with temporal resolutions ranging from one minute to one day across Germany. Models are chosen with a stepwise BIC model selection and verified with a cross-validated quantile skill index. The verification shows that the new model performs better than a reference model without large-scale information. Also, the new model enables insights into the effect of large-scale variables on extreme precipitation. Results suggest that the probability of extreme precipitation increases with time since 1950 in all seasons. High probabilities of extremes are positively correlated with blocking situations in summer and with temperature in winter. However, they are negatively correlated with blocking situations in winter and temperature in summer. [ABSTRACT FROM AUTHOR]

Large-scale group decision-making (LSGDM) has become common in the new era of technology development involving a large number of experts. Recently, in the use of social network analysis (SNA), the community detection method has been highlighted by researchers as a useful method in handling the complexity of LSGDM. However, it is still challenging to deal with the reliability and hesitancy of information as well as the interpretability of the method. For this reason, we introduce a new approach of a Z-hesitant fuzzy network with the community detection method being put into practice for stock selection. The proposed approach was subsequently compared to an established approach in order to evaluate its applicability and efficacy. [ABSTRACT FROM AUTHOR]

The previous literature has focused almost exclusively on understanding cooperative degeneration, paying little attention to the phenomenon of regeneration, which has primarily been addressed from a theoretical standpoint. This article provides a longitudinal analysis of a regenerative process undertaken within the Mondragon group, one of the most relevant and influential cooperative groups in the world. The large organizational size and geographical dispersion that characterize most cooperatives in this group imply a much more complex regeneration process than that involved in small- and medium-sized cooperatives that operate exclusively on a local scale. The theoretical framework of the research is based on the cooperative degeneration and regeneration theses, as well as on recent contributions in the field of the cooperative life cycle theory. The study highlights the challenges of reintroducing democratic governance mechanisms in large cooperatives competing in global markets, as well as the effect of geographical dispersion on the decline of a sense of belonging among the parent coop worker-members. Overall, this article contributes to a fundamental line of research focused on the organizational evolution of cooperatives in the face of a rapidly changing, competitive, and globalized market context. [ABSTRACT FROM AUTHOR]

2‐(3‐Bromophenyl)‐2H‐1,2,3‐triazole is a low molecular‐weight building block that is not readily accessible in pure form by standard synthetic approaches due to competing formation of the triazol‐1‐yl regioisomer. After investigation of a wide range of synthetic routes, a process based on construction of the triazole heterocycle by cyclization of an activated dihydrazone was developed. A suitable isolation method had to be identified before embarking on a demonstration run on >100 g scale without the need for chromatography. The new process delivered the desired building block in 48 % overall yield over four telescoped steps with excellent purity (99 % a/a). [ABSTRACT FROM AUTHOR]

The ready-mixed concrete delivery (RMC) problem is a scheduling problem, where multiple trucks deliver concrete to order sites abiding by hard constraints in a dynamic environment. It is an NP-hard problem, impractical to solve using exhaustive methods. Thus, it requires heuristic-based approaches for generating sub-optimal schedules. Due to its distributed nature, we address this problem using a decentralised, scalable, cooperative MAS (multiagent system) that dynamically generates schedules. We explore the impact of teamwork by trucks on schedule optimisation. This work illustrates two novel approaches that address the dynamic RMC problem; a Delegate MAS approach and a team-extended approach. We present an empirical study, comparing our novel approaches with existing ones. The evaluation is performed by classifying the RMC case study scenarios into unique stress, scale, and dynamism characteristics. With 40% to 70% improvement over different metrics, the results show that both approaches generate better schedules, and using agent teams augments the performance. Thus, such decentralized MAS with the appropriate coordination approach and teamwork can be used for solving constrained dynamic scheduling problems. [ABSTRACT FROM AUTHOR]

The problem of balancing the average weighted completion times of two classes of jobs is an NP-hard scheduling problem that was very recently introduced in the literature. Interpreted as a cooperative-type two-agent single-machine problem, its applications are in various practical contexts such as in logistics for balancing the delivery times, in manufacturing for balancing the assembly lines and in services for balancing the waiting times of groups of people. The only solution technique currently existing in the literature is a Lagrangian heuristic, based on solving a finite number of successive linear assignment problems, whose dimension depends on the total number of jobs. Since the Lagrangian approach has not appeared to be particularly suitable for solving large-scale problems, to overcome this drawback, we propose to face the problem by means of a genetic algorithm. Differently from the Lagrangian heuristic, our approach is found to be effective also for large instances (up to 2000 jobs), as confirmed by numerical experiments. [ABSTRACT FROM AUTHOR]

[Display omitted] Knowledge of the extent and distribution of irrigated and rainfed croplands is critical in providing the necessary baseline data for enhancing agricultural efficiency and making informed policy decisions. Accurately identifying and mapping irrigated and rainfed croplands can hasten the attainment of Sustainable Development Goals (SDGs) 1 and 2, aimed at reducing poverty and hunger, respectively. However, traditional methods employed to identify and map cropland areas are expensive and require substantial labour, particularly in extensive environments. As a result, this study presents a comprehensive and spatially explicit methodological framework for identifying and mapping national-scale irrigated and rainfed croplands in South Africa. This framework leverages low-cost earth observation technologies (Sentinel-2 MSI) and employs highly accurate classification algorithms, namely Deep Learning Neural Network (DNN) and Random Forest (RF). The proposed methodology strategically integrates data from multiple sources, including public repositories (e.g., cropland data, evapotranspiration), ongoing research (e.g., land cover maps), and field data, to enhance the accuracy and reliability of the results. The methodology begins by employing a robust random forest model to classify the study area into distinct land cover types. Leveraging the power of a deep learning neural network (DNN), the method accurately distinguishes between irrigated and rainfed croplands in South Africa. The random forest model achieved a notable classification accuracy of 0.77 when identifying the main land-use and land cover types. Meanwhile, the deep learning neural network (DNN) model achieved an accuracy of 0.71 in differentiating rainfed and irrigated croplands at a national scale. These results highlight the effectiveness of the proposed methodology in providing baseline information relevant to crop monitoring, yield forecasting, and understanding agricultural food supply systems. Furthermore, the proposed methodology has the potential to offer timely and accurate information on cropland areas and their extent which could assist in implementing targeted interventions for optimising agricultural productivity. With its potential to be upscaled to other sub-Saharan countries, this methodology enriches agricultural decision-making and plays a vital role in bolstering food security and advancing the attainment of SDGs. [ABSTRACT FROM AUTHOR]

Metal materials play an important role in electromagnetic interference (EMI) shielding due to their ultra‐high electrical conductivity, but they are rigid and inflexible, as well as of high density, which limits their application in some areas. In this study, a novel and simple method to fabricate the composites of metal foil and polymer is reported. A copper foil (Cuf) with micropores on the surface is used as a shielding matrix, then interpenetrating and interlocking nanostructure as well as reactive groups are introduced on its surface, and then silicone rubber (SiR) is coated on both surfaces to form a sandwich structure, and the obtained Cuf/SiR composites possesses high EMI shielding efficiency (SE), strong interface adhesion, and low density. The average EMI shielding efficiency of the obtained Cuf/SiR composites in the range of 1.13–18 GHz is 111.5 dB, with a high interface adhesion strength of 5.2 N cm−1 and low density of 1.3 g cm−3, and it also possesses bending and splicing properties, which exhibits broad potential application in the field of EMI shielding tents and shielding rooms. This preparation method of the composites is simple and efficient, and provides a guideline for the design and fabrication of metal foil/polymer composites in molding method. [ABSTRACT FROM AUTHOR]

Metal halide perovskite photodetector arrays have demonstrated great potential applications in the field of integrated systems, optical communications, and health monitoring. However, the fabrication of large‐scale and high‐resolution device is still challenging due to their incompatibility with the polar solvents. Here, a universal fabrication strategy that utilizes ultrathin encapsulation‐assisted photolithography and etching to create high‐resolution photodetectors array with vertical crossbar structure is reported. This approach yields a 48 × 48 photodetector array with a resolution of 317 ppi. The device shows good imaging capability with a high on/off ratio of 3.3 × 105 and long‐term working stability over 12 h. Furthermore, this strategy can be applied to five different material systems, and is fully compatible with the existing photolithography and etching techniques, which are expected to have potential applications in the other high‐density and solvent‐sensitive devices array, including perovskite‐ or organic semiconductor‐based memristor, light emitting diode displays, and transistors. [ABSTRACT FROM AUTHOR]

Clustering is used to analyze the intrinsic structure of a dataset based on the similarity of datapoints. Its widespread use, from image segmentation to object recognition and information retrieval, requires great robustness in the clustering process. In this paper, a novel clustering method based on adjacent grid searching (CAGS) is proposed. The CAGS consists of two steps: a strategy based on adaptive grid-space construction and a clustering strategy based on adjacent grid searching. In the first step, a multidimensional grid space is constructed to provide a quantization structure of the input dataset. The noise and cluster halo are automatically distinguished according to grid density. Moreover, the adaptive grid generating process solves the common problem of grid clustering, in which the number of cells increases sharply with the dimension. In the second step, a two-stage traversal process is conducted to accomplish the cluster recognition. The cluster cores with arbitrary shapes can be found by concealing the halo points. As a result, the number of clusters will be easily identified by CAGS. Therefore, CAGS has the potential to be widely used for clustering datasets with different characteristics. We test the clustering performance of CAGS through six different types of datasets: dataset with noise, large-scale dataset, high-dimensional dataset, dataset with arbitrary shapes, dataset with large differences in density between classes, and dataset with high overlap between classes. Experimental results show that CAGS, which performed best on 10 out of 11 tests, outperforms the state-of-the-art clustering methods in all the above datasets. [ABSTRACT FROM AUTHOR]

Reliable shear strength determination of large in situ discontinuities is still a challenge faced by the rock mechanics field. This is principally due to the limited availability of surface roughness and morphology information of in situ discontinuities and the unresolved management of the 'scale effect' phenomenon. Recently, a stochastic approach for predicting the shear strength of large-scale discontinuities was established, encompassing random field theory, a semi-analytical shear strength model, and a stochastic analysis framework. A key aspect of the new approach is the application at field scale, thereby minimising or bypassing the scale effect. The approach has been validated at laboratory scale and an initial large-scale deterministic-based validation showed promising results. However, to date, no large-scale experimental-based validation has been undertaken. This paper presents the first rigorous application of the employed semi-analytical shear strength model and the stochastic approach on a 2 m-by-2 m discontinuity surface, with comparison of prediction to experimental shear strength data. The shear strength model was found to generally produce peak and residual predictions within a ± 10% relative error range, with good agreement between predicted and observed damage areas. It was observed that, applying the stochastic approach to seed traces with gradient statistics equivalent to that of the surface, produced predictions that closely resemble the experimental results. Whereas, predicting shear strength from different seed traces results in more variability of predictions, with many falling within ± 20% of the experimental data. The predictions of residual shear strength tended to be more accurate than peak shear strength. Highlights: The stochastic approach for discontinuity shear strength was applied to a 2 m per 2 m rock surface replica made of mortar. A sensitivity analysis conducted on the analytical shear strength model highlights the parameter influencing the most the shear strength prediction. The predictions of peak and residual strengths fall within 20% of the experimental data. The accuracy of prediction depends on the trace used to define the input statistics. [ABSTRACT FROM AUTHOR]

The systematic identification of tumour vulnerabilities through perturbational experiments on cancer models, including genome editing and drug screens, is playing a crucial role in combating cancer. This collective effort is known as the Cancer Dependency Map (DepMap). The 1st European Cancer Dependency Map Symposium (EuroDepMap), held in Milan last May, featured talks, a roundtable discussion, and a poster session, showcasing the latest discoveries and future challenges related to the DepMap. The symposium aimed to facilitate interactions among participants across Europe, encourage idea exchange with leading experts, and present their work and future projects. Importantly, it sparked discussions on future endeavours, such as screening more complex cancer models and accounting for tumour evolution. [ABSTRACT FROM AUTHOR]

With the development of Internet-of-Things (IoT), our modern life has been greatly facilitated, while an exponentially growing number of vulnerable devices also breed a wonderful ground for botnet controllers,. However, existing detection approaches developed for individual traditional network area neglect cross-area privacy issue and resource restraint nature of IoT network and therefore impede their effectiveness of mitigating IoT botnet. In this work, we present a lightweight and privacy-preserving system, namely PPBotHunter, to detect botnet across multiple network areas. PPBotHunter implements a fuzzy matrix algorithm to retrieve effective bot similarity computation while ensuring a high privacy degree. This algorithm is designed based on a privacy-preserving scalar product computation technique (PPSPC) which enables PPBotHunter to be lightweight yet efficient. We utilize only time series feature to build the fuzzy matrices, which further improve the compatibility, energy-efficacy and resistance against heterogeneity. The theoretical analysis and detailed simulations illustrate the efficacy and effectiveness of our proposed botnet detection system. [ABSTRACT FROM AUTHOR]

Penetrating neural electrodes provide a powerful approach to decipher brain circuitry by allowing for time-resolved electrical detections of individual action potentials. This unique capability has contributed tremendously to basic and translational neuroscience, enabling both fundamental understandings of brain functions and applications of human prosthetic devices that restore crucial sensations and movements. However, conventional approaches are limited by the scarce number of available sensing channels and compromised efficacy over long-term implantations. Recording longevity and scalability have become the most sought-after improvements in emerging technologies. In this review, we discuss the technological advances in the past 5–10 years that have enabled larger-scale, more detailed, and longer-lasting recordings of neural circuits at work than ever before. We present snapshots of the latest advances in penetration electrode technology, showcase their applications in animal models and humans, and outline the underlying design principles and considerations to fuel future technological development. [ABSTRACT FROM AUTHOR]

Objective: Major depressive disorder (MDD) and bipolar disorder (BD) are considered whole‐brain disorders with some common clinical and neurobiological features. It is important to investigate neural mechanisms to distinguish between the two disorders. However, few studies have explored the functional dysconnectivity between the two disorders from the whole brain level. Methods: In this study, 117 patients with MDD, 65 patients with BD, and 116 healthy controls completed resting‐state functional magnetic resonance imaging (R‐fMRI) scans. Both edge‐based network construction and large‐scale network analyses were applied. Results: Results found that both the BD and MDD groups showed decreased FC in the whole brain network. The shared aberrant network across patients involves the visual network (VN), sensorimotor network (SMN), dorsal attention network (DAN), and ventral attention network (VAN), which is related to the processing of external stimuli. The default mode network (DMN) and the limbic network (LN) abnormalities were only found in patients with MDD. Furthermore, results showed the highest decrease in edges of patients with MDD in between‐network FC in SMN–VN, whereas in VAN–VN of patients with BD. Conclusions: Our findings indicated that both MDD and BD are extensive abnormal brain network diseases, mainly aberrant in those brain networks correlated to the processing of external stimuli, especially the attention network. Specific altered functional connectivity also was found in MDD and BD groups, respectively. These results may provide possible trait markers to distinguish the two disorders. [ABSTRACT FROM AUTHOR]

During the past decade, more and more large-scale pragmatic clinical trials have been carried out in orthopedic trauma surgery. This trend is fueled by the common belief that the larger the numbers in a trial, the broader the eligibility criteria, and the less strict the regimentation of local treatment standards by protocol, the more trustworthy the findings would be. However, it must also be taken into account that the precision of an outcome measurement does not depend on the sample size alone, but the homogeneity of the studied population. Consequently, a small trial with stringent entry and assessment criteria may offer similarly precise answers as a large trial with less strict entry and assessment criteria because of the basic mathematical correlation between standard deviation and standard error of the mean. There is now a lively and controversial debate about the role of randomized controlled trials (RCT) in an era of stratified medicine driven by the ever increasing understanding and clinical measurability of molecular pathways, making a certain intervention more effective in patients who show a distinct genetic variant. Cluster and pattern recognition by artificial intelligence (AI) and its methodological variety applied to huge datasets and population-based cohorts further propel the spiral of knowledge. Advanced adaptive RCT concepts like enrichment designs, basket and bucket trials, master protocols etc. were developed to combine classic principles of the scientific method with big data, the latter of which have not arrived yet in trauma care. In spite of all biomedical and methodological achievements made, surprisingly such key questions remain unanswered as a) is a certain treatment causally responsible for making a difference in patient-centered outcomes compared to placebo, a control treatment, or the standard of care, b) do the results of a controlled experiment are relevant enough to change clinical practice, and c) under which conditions and assumptions shall we conduct large-scale pragmatic RCTs, focused confirmatory RCTs, or personalized analyses with or without AI support. [ABSTRACT FROM AUTHOR]

Raceways are widely used as microalgae culture systems due to their low cost, but they are not the best option for biomass yield. Understanding in situ photosynthetic performance can be a first step to increase their biomass productivity. This study aimed at comparing the real time photosynthetic activity in a greenhouse raceway culture (250 L) with discrete measurements under laboratory conditions. We evaluated the photophysiology and biochemical composition of Chlorella fusca culture up to 120 h. In situ photosynthetic activity was continuously monitored and compared to discrete ex situ measurements; biochemical compounds were measured daily. The results showed a final biomass density of 0.45 g L–1 (5 days – 120 h) and an increase of the electron transport rate (ETR) up to 48 h but decreased thereafter. When the relative ETR was estimated considering the absorption coefficient (a) positive correlations of this parameter with photosynthetic capacity, cell density, biomass, biocompounds and antioxidant activity were obtained, whereas no correlation was detected without considering a. In situ photosynthesis monitoring showed higher absolute maximal ETR (10 – 160 μmol m–3s–1) than discrete ex situ measurements. We demonstrated the importance of considering the light absorption coefficient for expressing photosynthetic capacity and showed that C. fusca can produce, in the short-term, bioactive compounds that are correlated to photosynthetic conditions. • RLC is more sensitive to cell physiology information than Fv/Fm. • Calculated rETR considering the absorption coefficient supports culture physiology. • Chlorella fusca , with its biocompounds, is a commercially promising microalga. [ABSTRACT FROM AUTHOR]

Laboratory experiments form a vital part of any engineering course and consequently, students can spend a lot of their time on them. Most laboratory experiences are run independently of their associated lectures and tend to be free-standing. This is due to the way that students typically circulate through a limited number of sets of equipment over the duration of the course. There has been little work done on the organisation of labs and how the spaces might better support the learning experience. This article describes a novel approach using large laboratories with multiples of equipment which are shared between several departments to get both good equipment usage and the positioning of labs in the timetable to coincide with the associated topics being introduced to the students. This is reinforced with a few equations to allow the design of laboratories to take place. Example numbers using the Sheffield University Lab size (quantum) of 80 are used to show the power of this approach. Finally, the author uses his experience of operating this approach at the University of Sheffield for the past 7 years to show that this system has worked well. He also discusses some of the difficulties of this system so others can follow this method or a variation of it. [ABSTRACT FROM AUTHOR]

Tailings ponds' failure and environmental pollution make tailings monitoring very important. Remote sensing technology can quickly and widely obtain ground information and has become one of the important means of tailings monitoring. However, the efficiency and accuracy of traditional remote sensing monitoring technology have difficulty meeting the management needs. At the same time, affected by factors such as the geographical environment and imaging conditions, tailings have various manifestations in remote sensing images, which all bring challenges to the accurate acquisition of tailings information in large areas. By improving You Only Look Once (YOLO) v5s, this study designs a deep learning-based framework for the large-scale extraction of tailings ponds information from the entire high-resolution remote sensing images. For the improved YOLOv5s, the Swin Transformer is integrated to build the Swin-T backbone, the Fusion Block of efficient Reparameterized Generalized Feature Pyramid Network (RepGFPN) in DAMO-YOLO is introduced to form the RepGFPN Neck, and the head is replaced with Decoupled Head. In addition, sample boosting strategy (SBS) and global non-maximum suppression (GNMS) are designed to improve the sample quality and suppress repeated detection frames in the entire image, respectively. The model test results based on entire Gaofen-6 (GF-6) high-resolution remote sensing images show that the F1 score of tailings ponds is significantly improved by 12.22% compared with YOLOv5, reaching 81.90%. On the basis of both employing SBS, the improved YOLOv5s boots the mAP@0.5 of YOLOv5s by 5.95%, reaching 92.15%. This study provides a solution for tailings ponds' monitoring and ecological environment management. [ABSTRACT FROM AUTHOR]

Perovskite solar cells are promising candidates for next-generation photovoltaic devices with certified power-conversion efficiency for single-junction perovskite-based devices exceeded 25%. However, it remains challenging to fabricate a large-area and dense perovskite film using solution-based deposition methods. This is due to perovskite wet films being highly sensitive and unstable, and thus having only a narrow processing window. There is therefore a demand for ways to expand the processing window in the fabrication of perovskite films. Herein, we systematically review research on the role of precursor solutions and antisolvents during the perovskite formation process, and reveal the fundamental factors governing the width of the perovskite film-processing window. Then, we give an overview of current strategies for enlarging the processing window, which includes solvent and antisolvent engineering strategies. We conclude by summarizing current challenges in the development of perovskite solar modules with a wide processing window and provide perspectives for future development. [ABSTRACT FROM AUTHOR]

Accurate and efficient large-scale mapping of P. euphratica distribution is of great importance for managing and protecting P. euphratica forests, policy making, and realizing sustainable development goals in the ecological environments of desert areas. In large regions, numerous types of vegetation exhibit spectral characteristics that closely resemble those of P. euphratica, such as Tamarix, artificial forests, and allée trees, posing challenges for the accurate identification of P. euphratica. To solve this issue, this paper presents a method for large-scale P. euphratica distribution mapping. The geographical distribution characteristics of P. euphratica were first utilized to rapidly locate the appropriate region of interest and to further reduce background complexity and interference from other similar objects. Spectral features, indices, phenological features, and backscattering features extracted from all the available Sentinel-2 MSI and Sentinel-1 SAR data from 2021 were regarded as the input for a random forest model used to classify P. euphratica in the GEE platform. The results were then compared with the results from the method using only spectral features and index features, the results from the method that only added phenological features, and the results from the method that added phenological features and backscattering features by visually and quantitatively referencing field-surveyed samples, UAV data, and high-spatial-resolution data from Google Earth Data and Map World. The comparison indicated that the proposed method, which adds both phenological and time-series backscattering features, could correctly distinguish P. euphratica from other types of vegetation that have spectral information similar to P. euphratica. The rates of omission errors (OEs), commission errors (CEs), and overall accuracy (OA) for the proposed method were 12.53%, 11.01%, and 89.32%, respectively, representing increases of approximately 9%, 17%, and 13% in comparison with the method using only spectral and index features. The proposed method significantly improved the accuracy of P. euphratica classification in terms of both omission and, especially, commission. [ABSTRACT FROM AUTHOR]

Aflatoxins (AFs) are a group of secondary metabolites that cause several diseases in both animals and humans. Since the discovery of this group of toxins, several effects were revealed, such as hepatic changes, carcinoma, failure, and cancer of the liver. In the European Union, there are concentration limits for this group of mycotoxins in food and feed products; thus, these substances are required in their pure forms to prepare reference standards or certified reference materials. In our present work, a liquid–liquid chromatographic method utilizing a toluene/acetic acid/water ternary system was improved. In order to enhance the purification and gain a higher amount of pure AFs in one separation run, a scale-up of the previous separation was carried out. In several scale-up steps—including the determination of the maximum concentration and volume to load on a 250 mL rotor via a loop and via a pump as well, and the quadruplication of the entire separation procedure to a 1000 mL rotor—an efficient scale-up was achieved. Utilizing a 250 mL rotor in an 8-hour workday, altogether approximately 2.2 g of total AFs could be purified with 8.2 liters of solvent, while on a 1000 mL column, approximately 7.8 g AFs could be prepared, utilizing around 31 liters of solvents. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to evaluate the association between the degree of religiosity and subsequent fractures and a decrease in bone mineral density in a Japanese population. Methods: We conducted a retrospective longitudinal study at St. Luke's International Hospital in Tokyo, Japan, from 2005 to 2018. All participants who underwent voluntary health check-ups were included. Our outcomes were any fractures and the change in T-score from baseline to each visit. We compared these outcomes by the self-reported degree of religiosity (not at all; slightly; somewhat; very) and adjusted for potential confounders. Results: A total of 65,898 participants were included in our study. Their mean age was 46.2(SD:12.2) years, and 33,014(50.1%) were male. During a median follow-up of 2,500 days (interquartile range (IQR):987–3,970), 2,753(4.2%) experienced fractures, and their mean delta T-score was -0.03%(SD:18.3). In multivariable longitudinal analyses, the slightly religious group had a statistically lower adjusted odds ratio (AOR) for a fracture than the nonreligious group(AOR:0.81,95% confidence interval(CI):0.71 to 0.92). Conclusions: We demonstrated that slightly religious people, but not somewhat or very religious people, had a lower incidence of fracture than nonreligious individuals, although the T-scores were similar regardless of the degree of religiosity. [ABSTRACT FROM AUTHOR]

Traffic replay can effectively improve the fidelity of network emulation and can support emerging network verification and network security evaluation. A distributed traffic replay framework for network emulation, named DTRF, is designed for large-scale and high-fidelity network traffic emulation on the cloud platform. A fast traffic replay strategy bypassing the kernel is proposed to improve the traffic replay performance in the single node by alleviating the problem of high overhead from the kernel protocol stack. To conveniently expand the emulation scale, an extendable method of workflow orchestration that can realize the flexible control and configuration of the execution process of emulation tasks is proposed. The experimental results show that, compared with that of the traditional method, the throughput of traffic replay is increased by 3.71 times on average, the number of concurrent streams is increased by 4.18 times on average, and the timestamp error of the replayed packets is reduced by 97.8% on average. The DTRF can expand the emulation scenario to a massive heterogeneous network. [ABSTRACT FROM AUTHOR]

Microalgal biomass can be used to derive many different types of biofuels. In order to widely commercialize this technology, its limitations and weaknesses must be eliminated. Many technical and economic issues also need to be clarified and unknowns answered. Microalgae-based technologies have been shown to be versatile, efficient and suitable for practical and commercial use. However, the current technological readiness level (TRL) of most microalgae-based bioenergy production systems precludes their quick and widespread implementation. Their development is limited by a combination of factors that must be precisely identified, after which their negative impact on scale-up prospects can be eliminated or minimized. The present study identifies the main barriers to the development of industrial microalgae-production and microalgae-to-biofuel systems. In addition, it proposes measures and efforts necessary to achieve a higher TRL, which will provide investors with sought-after performance and cost-efficiency data for the given project. The main barriers to the development of microalgae cultivation and processing systems have been identified to include: the complex nature of the cultivation process with multiple variables involved; lack of sufficient data from pilot-scale and near-full-scale plants, which often precludes reliable life cycle assessment (LCA); and insufficient legal assistance, advisory assistance, subsidies and funding for innovative projects. Potential ways of improving performance and competitiveness of microalgae-based systems include: process optimization techniques, genetic engineering, yield improvement through advanced process control and monitoring, use of waste as feedstock and dedicated support programs. The presented summary of the current stage of microalgal biofuel production technology development indicates the directions for further research and implementation work, which are necessary for the final verification of the application potential of these solutions. [ABSTRACT FROM AUTHOR]

• An efficient solution method for large scale matrix estimation problems is proposed. • The proposed solution method is solves a series of convex and smooth subproblems. • The method can include observed queuing delays and congestion patterns. • The added value of these inclusions are demonstrated on the small Sioux Falls model. • The added value and scalability of the method is demonstrated on the large BBMB model. This paper presents an efficient solution method for the matrix estimation problem using a static capacity constrained traffic assignment (SCCTA) model with residual queues. The solution method allows for inclusion of route queuing delays and congestion patterns besides the traditional link flows and prior demand matrix whilst the tractability of the SCCTA model avoids the need for tedious tuning of application specific algorithmic parameters. The proposed solution method solves a series of simplified optimization problems, thereby avoiding costly additional assignment model runs. Link state constraints are used to prevent usage of approximations outside their valid range as well as to include observed congestion patterns. The proposed solution method is designed to be fast, scalable, robust, tractable and reliable because conditions under which a solution to the simplified optimization problem exist are known and because the problem is convex and has a smooth objective function. Four test case applications on the small Sioux Falls model are presented, each consisting of 100 runs with varied input for robustness. The applications demonstrate the added value of inclusion of observed congestion patterns and route queuing delays within the solution method. In addition, application on the large scale BBMB model demonstrates that the proposed solution method is indeed scalable to large scale applications and clearly outperforms the method mostly used in current practice. [ABSTRACT FROM AUTHOR]

Building with Nature (BwN), is a concept that has been applied in various projects around the world in the broader context of nature based solutions for climate resilience and adaptation. Most of these projects are regional or local in nature and are implemented on a smaller scale. Larger scale projects are more rare. The aim of this article is to highlight a number of large‐scale projects that have been completed or are in the developing stage. The focus will be on coastal regions, from temperate locations in the Netherlands and from developing countries in the tropics including Bangladesh. The value of implementing the principle of BwN for a sustainable future is underlined and described. Certain aspects of governance are highlighted to point out the differences and challenges, but also similarities, of carrying out such projects in different parts of the world. [ABSTRACT FROM AUTHOR]

The study presents a landslide susceptibility assessment on a large scale in the City of Zagreb (Croatia). The susceptibility analysis was performed using the Weight of Evidence model in the pilot area (21 km² ) and applying the obtained weight values for each class of conditioning factors in the study area (130 km² ). The input data were LiDAR-based landslide inventory and six conditioning factors derived from 5 m LiDAR DTM, 5 m SfM DEM, and geological and land-use maps. The validation of the susceptibility assessment for the study area was evaluated with a ROC curve, which showed a high prediction rate (AUC = 84.4%), similar to the prediction rate for the pilot area (AUC = 86.9%). Based on the results, it can be concluded that the proposed method for large-scale landslide susceptibility assessment, where susceptibility conditions are defined in smaller pilot areas, can be applied to larger research areas with similar geomorphological and geological conditions. [ABSTRACT FROM AUTHOR]

Although climatologically dry, the western slopes of the southern Andes of Peru (WSA) can experience precipitation extremes (PEs) during the summer (December–February) resulting in great economic and human losses. Generally, WSA has a positive upslope gradient in precipitation, meaning more rain falls at higher elevations, but observations have shown this gradient can become negative with higher rainfall near the coastal cities. In this study we analyse 2000–2019 regional atmospheric patterns associated with different upslope precipitation gradients and PEs in WSA using principal component analysis methods and surface station observations. Results show important changes in the atmospheric circulation patterns during the occurrence of PE events. A prevailing pattern of negative southerly wind anomalies and regional warming of the southeastern Pacific Ocean leads to significant increases in moisture along the coast of WSA. Eastern moisture flows associated with the presence of the Bolivian High are observed at upper levels of the atmosphere and transport water vapour from the Amazon to the western side of the Andes. Additionally, there is a blocking effect aloft in response to an intense gradient of geopotential height that attenuates the easterly circulations. These large‐scale mechanisms act to concentrate high precipitable water amounts and high levels of convective available potential energy in the troposphere which favours the vertical velocities essential to trigger PEs. These results increase our knowledge of the large‐scale characteristics of PEs to help with forecasting these impactful events and protecting the more than 1.8 million people living in WSA. [ABSTRACT FROM AUTHOR]

Low accuracy of global land cover (LC) products at local and regional scales is concerning, because of its huge impact on downstream applications. Therefore, developing a method for high accuracy LC maps at local and regional scale is of utmost importance. Taking Mongolia as a case study, we proposed a simple, fast, and accurate method to produce annual LC maps with 250 m spatial resolution from 2001 to 2020 using MODIS data. Our products have higher spatial resolution and higher accuracy (Overall Accuracy ~90%) compared to MCD12Q1. These new maps are critical e.g., for land degradation research, and desertification/forest cover monitoring. Especially, information on grassland ecosystems is of utmost importance for Mongolia since more than half of the country economically depends on grassland resources. Therefore, Mongolia will benefit from the new dataset. Furthermore, due to the simplicity of the method, it can be easily applied and transferred to other regions. [ABSTRACT FROM AUTHOR]

Thermoset composites are strong candidates for large-scale material extrusion additive manufacturing (AM). However, uncured thermoset composites become unstable as print height increases. Here, chemical initiation of vinyl ester immediately before deposition was used to suppress collapse and enable large-scale thermoset printing. Structural stability was assessed by printing thin walls at various layer times and monitoring thermal gradients with an infrared camera. Stable printing was observed at a layer time of 4.50 min, approximately half the gel time of the material (8 min). Self-weight collapse occurred at short layer time (0.68 min), whereas warpage occurred at long layer time (6.50 min). A new behavior was discovered at intermediate layer time (2.25 min) where the heat generated by the reaction causes new, un-gelled layers to flow. Forced convection eliminates this behavior, stabilizing the 2.25-min layer time print. These findings motivated development of a less exothermic material system, which was used to print a large-scale mold and demonstrate the value of this understanding. By presenting these challenges of large-scale reactive thermoset AM for the first time, this work motivates focused studies of the complex interplay between rheological, thermal, and chemical behaviors to improve the feasibility of large-scale thermoset AM. Tensile properties of the printed material were also measured. Longitudinal and transverse elastic moduli are 3.79 and 2.95 GPa, respectively, and corresponding tensile strengths are 36.11 and 18.83 MPa. The glass transition temperature is 93.13 °C. [ABSTRACT FROM AUTHOR]

Cellulose molecules, as the basic unit of biomass cellulose, have demonstrated advancements in versatile engineering and modification of cellulose toward sustainable and promising materials in our low-carbon society. However, harvesting high-quality cellulose molecules from natural cellulosic fibers (CF) remains challenging due to strong hydrogen bonds and unique crystalline structure, which limit solvents (such as ionic liquid, IL) transport and diffusion within CF, making the process energy/time-intensively. Herein, we superfast and sustainably engineer biomass fibers into high-performance cellulose molecules via ethanol pre-swelling of CF followed by IL treatment in the microwave (MW) system. Ethanol-pre-swelled cellulosic fibers (SCF) feature modified morphological and structural distinctions, with improved fiber width, pore size, and specific surface area. The ethanol in the SCF structure is appropriately removed through MW heating and cooling, leaving transport and diffusion pathways of IL within the SCF. Such strategy enables the superfast (140 s) and large-scale (kilogram level) harvesting of cellulose molecules with high molecular weight, resulting in high-performance, versatile cellulose ionogel with a 300 % increase in strength and 1027 % in toughness, monitoring human movement, external pressure, and temperature. Our strategy paves the way for time/energy-effectively, sustainably harvesting high-quality polymer molecules from natural sources beyond cellulose toward versatile and advanced materials. [ABSTRACT FROM AUTHOR]