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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]

This paper addresses the challenges posed by frequent memory access during simulations of large-scale spiking neural networks involving synaptic plasticity. We focus on the memory accesses performed during a common synaptic plasticity rule since this can be a significant factor limiting the efficiency of the simulations. We propose neuron models that are represented by only three state variables, which are engineered to enforce the appropriate neuronal dynamics. Additionally, memory retrieval is executed solely by fetching postsynaptic variables, promoting a contiguous memory storage and leveraging the capabilities of burstmode operations to reduce the overhead associated with each access. Different plasticity rules could be implemented despite the adopted simplifications, each leading to a distinct synaptic weight distribution (i.e., unimodal and bimodal). Moreover, our method requires fewer average memory accesses compared to a naive approach. We argue that the strategy described can speed up memory transactions and reduce latencies while maintaining a small memory footprint. [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]

This article evaluates the performance of a 20 MW grid-connected photovoltaic system installed in the Naama region, southwestern highlands of Algeria. The production database used in this work was recorded between January 1, 2017, and December 2022, from the plant's SCADA system. During the first 5 years of production, the final yield varied from 3.88 in winter 2020 to 6.30 h/day in summer 2017, while the reference yield varied from 4.45 to 7.05 h/day, the annual average performance ratio is equal to 83.35%, and the annual average capacity factor was found to be 20.05% for the monitored period. These data are then compared with the predicted outcomes by a detailed difference analysis using three widely used PV simulation tools: PVsyst, SAM, and PVSol. Results indicate that the actual data strongly coincide with those predicted by the PVsyst simulation, as confirmed by statistical indices. However, the degradation of the PV plants over the monitored period is quantified using a linear model and a CSD model and is estimated at −1.43 and −1.55 %/year, respectively. In summary, the photovoltaic system performance indices obtained are satisfactory compared to large-scale photovoltaic systems reported in other studies. Practical applications: This study evaluates the performance and degradation of a 20 MWp photovoltaic system located in the highlands of Algeria. The results of this article will serve as a baseline for the future construction of PV plants to raise awareness among decision-makers about the beginnings of the deterioration of installations to allocate a budget for the maintenance and operation of these installations. The findings of this study have the potential to contribute to the wider adoption of Photovoltaic systems as a viable alternative to traditional energy in Algeria and especially in the Highlands. [ABSTRACT FROM AUTHOR]

This study employed a comprehensive methodology, which combines the use of multispectral images and field inclinometer data. The unique approach involves using multispectral images to monitor the topographic and hydrological changes caused by a large-scale landslide at Caoling in Taiwan. The field inclinometer data, collected at regular intervals, were then integrated into a geographic information system. This system was used to estimate the potential sliding volume of the landslide, which was then compared with changes in the digital terrain model data for Caoling. The multispectral images of Caoling were examined to determine the changes in its topographic and hydrological indices after the landslide. The convex slope at Caoling moved in the progressive backward mode with increased groundwater level and slope displacement under rainfall conditions. The potential sliding volume of the landslide was 6.4 × 106 m3. The results of this study indicate that the changes in topographic and hydrological indices after a landslide enable the identification of the local surficial changes caused by the landslide. Changes in the rate of slope movement show a high correlation to the fluctuation of groundwater level. Reactivation of the large-scale landslide is attributable to hillslope, geologic and topographic characteristics, including toe creek incision, surficial erosion, and torrential rainfall brought by typhoons. Moreover, post landslide topographic monitoring through drone images, which provide high-resolution and real-time data, plays a crucial role in identifying a landslide's reactivation mechanism. This, in turn, enables effective disaster mitigation strategies to be implemented. [ABSTRACT FROM AUTHOR]

In a complex and dynamic job shop containing logistics factor, schedule needs to be generated rapidly, so the real-time scheduling method is more suitable for such scenario. Such method takes advantage of local information within a short time due to the rapid changes of information under uncertain environment. Therefore, how to make use of the future information by prediction while ensuring the robustness of schedule is a valuable problem. To solve it, firstly, a new real-time scheduling model and algorithm is proposed. There is a new kind of release moment of task information which can give AGVs the longest time to prepare for the task than existing research. Secondly, a real-time information update mechanism is designed to increase schedule's robustness. Finally, a large-scale and dynamic job shop simulation experimental platform is developed. Dynamic factors include the random insertion of orders and failures of equipment. Results show that the method proposed outperforms existing research in terms of customer satisfaction, equipment utilisation and energy consumption. The robustness of schedule can also be acceptable. This paper also finds a rule that in job shop with the large proportion of logistics transportation time, the above method can achieve more competitive results. [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]

Wind farm layout optimization (WFLO) is focused on utilizing algorithms to devise a more rational turbine layout, ultimately maximizing power generation efficiency. Traditionally, genetic algorithms have been frequently employed in WFLO due to the inherently discrete nature of the problem. However, in recent years, researchers have shifted towards enhancing continuous optimization algorithms and incorporating constraints to address WFLO challenges. This approach has shown remarkable promise, outperforming traditional genetic algorithms and gaining traction among researchers. To further elevate the performance of continuous optimization algorithms in the context of WFLO, we introduce a multi-local search-based SHADE, termed MS-SHADE. MS-SHADE is designed to fine-tune the trade-off between convergence speed and algorithmic diversity, reducing the likelihood of convergence stagnation in WFLO scenarios. To assess the effectiveness of MS-SHADE, we employed a more extensive and intricate wind condition model in our experiments. In a set of 16 problems, MS-SHADE's average utilization efficiency improved by 0.14% compared to the best algorithm, while the optimal utilization efficiency increased by 0.3%. The results unequivocally demonstrate that MS-SHADE surpasses state-of-the-art WFLO algorithms by a significant margin. [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]

The continuous supply of drinking water for human life is essential to ensure the sustainability of cities, society, and the environment. At a time when water scarcity is worsening due to climate change, the construction of an optimized water supply infrastructure is necessary. In this study, an improved version of the Harmony Search Algorithm (HSA), named the Maisonette-type Harmony Search Algorithm (MTHSA), was developed. Unlike the HSA, the MTHSA has a two-floor structure, which increases the optimizing efficiency by employing multiple explorations on the first floor and additional exploitations of excellent solutions. Parallel explorations enhance the ability in terms of exploration (global search), which is the tendency to uniformly explore the entire search space. Additional exploitations among excellent solutions also enhance the ability of local searches (effective exploitation), which is the intensive exploration of solutions that seem to have high possibilities. Following the development of the improved algorithm, it was applied to water distribution networks in order to verify its efficiency, and the numerical results were analyzed. Through the considered applications, the improved algorithm is shown to be highly efficient when applied to large-scale optimization problems with large numbers of decision variables, as shown in comparison with the considered optimizers. [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]

Recycling plastics on an industrial scale is a key approach to the circular economy. This study presents a techno-economic analysis aimed at recycling polypropylene waste, one of the main consumer plastics. Specifically, it evaluates the technical and economic feasibility of achieving a large-scale cracking process that converts polypropylene waste into an alternative fuel. Pyrolysis is considered as a promising technique to convert plastic waste into liquid oil and other value-added products, with a dual benefit of recovering resources and providing a zero-waste solution. This study concerns a fast catalytic pyrolysis in a fluidized bed reactor, with the presence of a fluid catalytic cracking catalyst of low acidity for high heat transmission, for an industrial plant with a capacity of 1 t/h of polypropylene waste provided by the Greek Petroleum Industry. From the international literature, the operational conditions were chosen pyrolysis temperature at 430 °C, pressure at 1atm, heating rate at 5 °C/min, and yields of products to 71, 14, and 15 wt.%, for liquid fuel, gas, solid product, respectively. The plant design includes a series of apparatuses, with the main one to be the pyrolyzer. The catalytic method is selected over the non-catalytic because the presence of catalyst increases the quantity and quality of the liquid product, which is the main product of the plant. The energy loops of recycling pyrolysis gas and char as a low-carbon fuel in the plant were considered. The production cost, annual revenue, for 2023, are anticipated to reach €13.7 million (115 €/t) and €15 million (15 €/t), respectively, with an estimated investment equal to €5.3 million. The Payback Time is estimated to 2.4 years to recover the cost of investment. The endeavor is rather economically sustainable. A critical parameter for large scale systems is securing feedstock with low or negligible price. [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]

This paper addresses the challenges posed by frequent memory access during simulations of large-scale spiking neural networks involving synaptic plasticity. We focus on the memory accesses performed during a common synaptic plasticity rule since this can be a significant factor limiting the efficiency of the simulations. We propose neuron models that are represented by only three state variables, which are engineered to enforce the appropriate neuronal dynamics. Additionally, memory retrieval is executed solely by fetching postsynaptic variables, promoting a contiguous memory storage and leveraging the capabilities of burst mode operations to reduce the overhead associated with each access. Different plasticity rules could be implemented despite the adopted simplifications, each leading to a distinct synaptic weight distribution (i.e., unimodal and bimodal). Moreover, our method requires fewer average memory accesses compared to a naive approach. We argue that the strategy described can speed up memory transactions and reduce latencies while maintaining a small memory footprint.

Semiconductor single-crystal thin films (SCTFs) with nanoscale thickness play an important role in high-performance electronics and optoelectronic devices, owing to the excellent electrical, optical, thermal, and mechanical performance. Although enormous progress has been made in preparing two-dimensional (2D) material SCTFs, it is still challenging to grow SCTFs based on 3D materials with high efficiency. Herein, we report the efficient growth of centimeter-scale CdS SCTFs with the thickness of ∼95.41 nm on the mica substrates using a chemical vapor deposition method. The controlled experiments and the structure characterizations indicate that the SCTFs are formed by the seamless coalescence of the hexagonal CdS nanosheet domains initially grown in the same orientation on the substrate, with the epitaxial relationship of (100)CdS//(110)mica and (110)CdS//(200)mica. Importantly, the CdS SCTF-based photodetectors (PDs) demonstrate a photoswitching (Ion/Ioff) ratio up to 105 and a fast photoresponse speed (trise ≈ 0.5 ms, tdecay ≈ 1 ms). Furthermore, the PDs exhibit excellent stability to at least 2 months of storage in an ambient atmosphere and 1000-time bending. This work paves the way to synthesizing the 3D semiconductor SCTFs on a large scale and promotes their practical application in multifunctional photoelectric devices. [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]

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Our study introduces a methodology to improve large-scale seismic damage assessment by incorporating site-specific fragility curves, considering soil–structure interaction (SSI) and site amplification (SAmp) effects. The proposed method proposes an enhanced building exposure model, using publicly available data and the open-source OpenQuake Engine software. The objective is to determine whether a more refined approach incorporating SSI and SAmp can impact the final damage calculation. We evaluate our approach by estimating the damage distribution for the Thessaloniki 1978 earthquake scenario using the actual building stock of Thessaloniki. We present several maps with aggregated damages at different levels to investigate the spatial variability of SSI and SAmp, and their influence on the resulting damages. Our estimated physical damages have been compared with those obtained using approaches from the existing literature. Apparently, using an updated building exposure model to assess damages makes any comparison with past observed damages challenging. Nevertheless, incorporating SSI and SAmp in large-scale damage assessment can provide valuable support for strategic decision-making in cities and improve the accuracy of the expected loss assessment due to a seismic event. [ABSTRACT FROM AUTHOR]

INTRODUCTION: We investigated the validity, feasibility, and effectiveness of a voice recognition‐based digital cognitive screener (DCS), for detecting dementia and mild cognitive impairment (MCI) in a large‐scale community of elderly participants. METHODS: Eligible participants completed demographic, cognitive, functional assessments and the DCS. Neuropsychological tests were used to assess domain‐specific and global cognition, while the diagnosis of MCI and dementia relied on the Clinical Dementia Rating Scale. RESULTS: Among the 11,186 participants, the DCS showed high completion rates (97.5%) and a short administration time (5.9 min) across gender, age, and education groups. The DCS demonstrated areas under the receiver operating characteristics curve (AUCs) of 0.95 and 0.83 for dementia and MCI detection, respectively, among 328 participants in the validation phase. Furthermore, the DCS resulted in time savings of 16.2% to 36.0% compared to the Mini‐Mental State Examination (MMSE) and Montral Cognitive Assessment (MoCA). DISCUSSION: This study suggests that the DCS is an effective and efficient tool for dementia and MCI case‐finding in large‐scale cognitive screening. Highlights: To our best knowledge, this is the first cognitive screening tool based on voice recognition and utilizing conversational AI that has been assessed in a large population of Chinese community‐dwelling elderly.With the upgrading of a new multimodal understanding model, the DCS can accurately assess participants' responses, including different Chinese dialects, and provide automatic scores.The DCS not only exhibited good discriminant ability in detecting dementia and MCI cases, it also demonstrated a high completion rate and efficient administration regardless of gender, age, and education differences.The DCS is economically efficient, scalable, and had a better screening efficacy compared to the MMSE or MoCA, for wider implementation. [ABSTRACT FROM AUTHOR]

A central goal of ecological restoration is to promote diverse ecosystems dominated by native species, but restorations are often plagued by exotic species. A better understanding of factors underlying positive correlations between native and exotic species richness, a pattern that is nearly ubiquitous at large scales in plant communities, may help managers modify these correlations to favor native plant species during restoration. Across 29 tallgrass prairie sites restored through seed sowing onto former agricultural lands, we examined whether the relationship between native and exotic richness is (1) altered by management, such as seed additions and prescribed fire; (2) controlled instead by environmental conditions and successional processes; or (3) altered by management in certain environments and not in others. As is commonly found, native and exotic richness were positively correlated at large scales (i.e., across sites) in this study. Management actions explained much of the remaining variation in native richness, while environmental conditions explained very little. Sites sown with more species at higher seeding rates, especially forb species, had higher native richness than predicted by the native–exotic richness relationship. In contrast, native richness was lower in older restorations than predicted by the native–exotic richness relationship, because native richness, and not exotic richness, declined with restoration age. We show that management actions such as seed sowing can modify the native–exotic richness relationship to favor native species during restoration. The development of management actions that mitigate native species richness declines over time will further benefit native species restoration. [ABSTRACT FROM AUTHOR]

Snowmelt floods are highly hazardous meteorological disasters that can potentially threaten human lives and property. Hence, snowmelt susceptibility mapping (SSM) plays an important role in flood prevention systems and aids emergency responders and flood risk managers. In this paper, a method of identifying snowmelt flood hazards is proposed, and a large‐scale snowmelt flood hazard zonation scheme based on historical recordings and multisource remote sensing data is established. To assess the quality of our approach, the proposed model was tested in the cold and arid region of Xinjiang, China. Overall, 140 historical snowmelt flood events and 27 explanatory factors were selected to construct a geospatial dataset for SSM of the contemporary period. GridSearchCV was used to comprehensively search the candidate parameters from the grid of given parameters obtained with the random forest (RF) algorithm. Then, the geospatial dataset was divided into two subsets: 70% for training and 30% for testing. Next, SSM results were obtained with the RF algorithm using optimized parameters. The results indicate that our optimized RF classifier performs well for the task of SSM, with a high AUC value (0.975) for the test dataset. The validation and analysis suggest that the proposed method can efficiently identify snowmelt flood hazards in undersampled arid areas at a regional scale. [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]

Creating model systems that replicate in vivo tissues is crucial for understanding complex biological pathways like drug response and disease progression. Three-dimensional (3D) in vitro models, especially multicellular spheroids (MCSs), offer valuable insights into physiological processes. However, generating MCSs at scale with consistent properties and efficiently recovering them pose challenges. We introduce a workflow that automates large-scale spheroid production and enables parallel harvesting into individual wells of a microtiter plate. Our method, based on the hanging-drop technique, utilizes a non-contact dispenser for dispensing nanoliter droplets of a uniformly mixed-cell suspension. The setup allows for extended processing times of up to 45 min without compromising spheroid quality. As a proof of concept, we achieved a 99.3% spheroid generation efficiency and maintained highly consistent spheroid sizes, with a coefficient of variance below 8% for MCF7 spheroids. Our centrifugation-based drop transfer for spheroid harvesting achieved a sample recovery of 100%. We successfully transferred HT29 spheroids from hanging drops to individual wells preloaded with collagen matrices, where they continued to proliferate. This high-throughput workflow opens new possibilities for prolonged spheroid cultivation, advanced downstream assays, and increased hands-off time in complex 3D cell culture protocols. [ABSTRACT FROM AUTHOR]

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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]

Large-scale distillation is a challenge in many respects. Particularly difficult is the purification by distillation of a compound with limited thermal stability. This article describes various aspects of these difficulties with some possible solutions. Special emphasis is placed on the collaboration of different disciplines to find pragmatic solutions to these challenges. The purification of diketene in quantities of several 1000 ta–1 is an excellent example to illustrate the different requirements. Although the distillation of diketene has been carried out by several companies for many years, there are still some aspects that deserve special attention.

Abstract A central goal of ecological restoration is to promote diverse ecosystems dominated by native species, but restorations are often plagued by exotic species. A better understanding of factors underlying positive correlations between native and exotic species richness, a pattern that is nearly ubiquitous at large scales in plant communities, may help managers modify these correlations to favor native plant species during restoration. Across 29 tallgrass prairie sites restored through seed sowing onto former agricultural lands, we examined whether the relationship between native and exotic richness is (1) altered by management, such as seed additions and prescribed fire; (2) controlled instead by environmental conditions and successional processes; or (3) altered by management in certain environments and not in others. As is commonly found, native and exotic richness were positively correlated at large scales (i.e., across sites) in this study. Management actions explained much of the remaining variation in native richness, while environmental conditions explained very little. Sites sown with more species at higher seeding rates, especially forb species, had higher native richness than predicted by the native–exotic richness relationship. In contrast, native richness was lower in older restorations than predicted by the native–exotic richness relationship, because native richness, and not exotic richness, declined with restoration age. We show that management actions such as seed sowing can modify the native–exotic richness relationship to favor native species during restoration. The development of management actions that mitigate native species richness declines over time will further benefit native species restoration.

Abstract Snowmelt floods are highly hazardous meteorological disasters that can potentially threaten human lives and property. Hence, snowmelt susceptibility mapping (SSM) plays an important role in flood prevention systems and aids emergency responders and flood risk managers. In this paper, a method of identifying snowmelt flood hazards is proposed, and a large‐scale snowmelt flood hazard zonation scheme based on historical recordings and multisource remote sensing data is established. To assess the quality of our approach, the proposed model was tested in the cold and arid region of Xinjiang, China. Overall, 140 historical snowmelt flood events and 27 explanatory factors were selected to construct a geospatial dataset for SSM of the contemporary period. GridSearchCV was used to comprehensively search the candidate parameters from the grid of given parameters obtained with the random forest (RF) algorithm. Then, the geospatial dataset was divided into two subsets: 70% for training and 30% for testing. Next, SSM results were obtained with the RF algorithm using optimized parameters. The results indicate that our optimized RF classifier performs well for the task of SSM, with a high AUC value (0.975) for the test dataset. The validation and analysis suggest that the proposed method can efficiently identify snowmelt flood hazards in undersampled arid areas at a regional scale.

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.

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.

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.

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).

The effective collaboration between crop habitat adaptability evaluation and yield prediction is of great significance. Climate factors occupy an indispensable role, but the contributions of different climate factors to crop growth present spatiotemporal heterogeneity and confusion, amplifying the challenges associated with large-scale dynamic evaluations. Additionally, the mounting input parameters in yield prediction compound the uncertainty and intricacy of modeling. To address these challenges, a climate-driven dynamic habitat adaptability evaluation indicator (HAEI) was developed, capable of forecasting county-level winter wheat yields in China. First, the distribution characteristics and matching relationship between climate and yield variability were explored from multi-source data in the long time series, and a novel method of multiple-factor adaptive matching habitat membership degree was proposed. Second, considering the interaction and contribution differences between multiple-factor at different phenological periods, a comprehensive HAEI suitable for the entire growth period of winter wheat is constructed. The results showed that HAEI can integrate climate information that has a greater impact on yield variability and has a significant correlation with yield in various regions and periods, with an average correlation of 0.70. Remarkably, the predictive models incorporating HAEI consistently outperformed other yield prediction algorithms, demonstrating superior accuracy (R2 = 0.62–0.76, nRMSE = 0.1517–0.2031). Even in the least favorable scenario, involving a linear model with HAEI input, satisfactory results were achieved. This comprehensive framework effectively mitigates the adverse consequences of widespread agricultural climate heterogeneity and evaluates the habitat adaptability and yield status of wheat at the county level in China.