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The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems. Full article

As a vital component of the terrestrial ecosystem, grassland accounts for one-third of the global vegetation system. Grassland degradation has been exacerbated due to extreme overgrazing in China’s Inner Mongolia Autonomous Region (IMAR). While conservation was carried out via the Ecological Subsidy and Award Program (ESAP) to mitigate grassland degradation, little is known about its effectiveness in improving the biophysical conditions of grassland. This paper integrates the conceptual frameworks of total socio-environmental systems (TSESs) to assess how ecological systems respond to the ESAP, investigate the spatial heterogeneity of the ESAP, and explore the meddling effects of socio-environmental interactions on the ESAP. We integrated ecological, climate, and socioeconomic data and developed several hierarchical linear mixed models (HLMMs) to investigate how these factors interact with the ESAP in the IMAR. Our findings prove that the above-ground biomass between 2011 and 2015 responds significantly to variations in socioeconomic conditions and ecological communities. Available land resources, hospital and medical facilities, and net farmer and herdsman income are the most critical factors positively related to grassland productivity. Primary industries like mining, total consumer retail value, farming, forestry, animal husbandry, fishery productions, and GDP are the most damaging factors affecting biomass. Our study recommends a regionally or locally tailored ecological recovery policy, instead of a generalized one, in future efforts to conserve grassland. Full article

In this paper, we first prove the existence and uniqueness of the solution to a variable-order Caputo–Fabrizio fractional stochastic differential equation driven by a multiplicative white noise, which describes random phenomena with non-local effects and non-singular kernels. The Euler–Maruyama scheme is extended to develop the Euler–Maruyama method, and the strong convergence of the proposed method is demonstrated. The main difference between our work and the existing literature is the fact that our assumptions on the nonlinear external forces are those of one-sided Lipschitz conditions on both the drift and the nonlinear intensity of the noise as well as the proofs of the higher integrability of the solution and the approximating sequence. Finally, to validate the numerical approach, current results from the numerical implementation are presented to test the efficiency of the scheme used in order to substantiate the theoretical analysis. Full article

According to research, there is a disparity in the aesthetic design preferences of the public and architects. This is especially noticeable in urban areas environments, where there is a wide range of demographic variables and a lack of active public involvement in decision-making. The study included a visual preference survey to evaluate the perception and ranking of two dominant color schemes in local development projects in Stockholm across different age, ethnicity, and gender groups from Sweden. Over 400 people participated in the survey, which used photo-elicitation and a literature review. The objective was to determine the extent of divergence and disparities in the ranking of color schemes. The first dominant color schemes are a group of four analogous colors related to the Natural Color System’s (NSC’s) basic colors. The second is a group of four neutral colors that are related to grayscale colors. The study demonstrates that one could also measure building color schemes using visual preference ranking. Moreover, the research introduced a unique method for city planners to understand diverse ethnic, gender, and age group perceptions of color schemes in current projects, promoting active participation in decision-making and providing valuable insights. These insights can enhance people’s sense of belonging, a prerequisite for social sustainability, and improve the quality and profitability of future projects. Full article

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that presents significant diagnostic challenges due to its varied symptoms and nature. This study aims to improve ASD classification using advanced deep learning techniques applied to neuroimaging data. We developed an automated system leveraging the ABIDE-1 dataset and a novel lightweight quantized one-dimensional (1D) Convolutional Neural Network (Q-CNN) model to analyze fMRI data. Our approach employs the NIAK pipeline with multiple brain atlases and filtering methods. Initially, the Regions of Interest (ROIs) are converted into feature vectors using tangent space embedding to feed into the Q-CNN model. The proposed 1D-CNN is quantized through Quantize Aware Training (QAT). As the quantization method, int8 quantization is utilized, which makes it both robust and lightweight. We propose a federated learning (FL) framework to ensure data privacy, which allows decentralized training across different data centers without compromising local data security. Our findings indicate that the CC200 brain atlas, within the NIAK pipeline’s filt-global filtering methods, provides the best results for ASD classification. Notably, the ASD classification outcomes have achieved a significant test accuracy of 98% using the CC200 and filt-global filtering techniques. To the best of our knowledge, this performance surpasses previous studies in the field, highlighting a notable enhancement in ASD detection from fMRI data. Furthermore, the FL-based Q-CNN model demonstrated robust performance and high efficiency on a Raspberry Pi 4, underscoring its potential for real-world applications. We exhibit the efficacy of the Q-CNN model by comparing its inference time, power consumption, and storage requirements with those of the 1D-CNN, quantized CNN, and the proposed int8 Q-CNN models. This research has made several key contributions, including the development of a lightweight int8 Q-CNN model, the application of FL for data privacy, and the evaluation of the proposed model in real-world settings. By identifying optimal brain atlases and filtering methods, this study provides valuable insights for future research in the field of neurodevelopmental disorders. Full article

The logistics industry (LI) is a key pillar of the global economy, and its carbon emission efficiency (CEE) is crucial for achieving carbon neutrality. The rapid development of the digital economy (DE) has had a profound impact on the LI, but the spatial impact on its CEE is currently unclear and requires further research. Firstly, based on the collection of relevant data, we use the entropy weight method and linear weighted sum method to measure the level of development of the DE. Secondly, the SBM model is used to measure the CEE level of the LI. Using Moran’s I index model and OLS and GWR models, we analyze the impact and spatial distribution characteristics of the DE on the CEE of the LI and propose development strategies. The article uses statistical data from 30 provinces in China from 2013 to 2022 as an example to demonstrate the implementation process of the method. The results show that the DE has a positive impact on the CEE of the LI, and there are spatial differences. Based on this, this article proposes policy recommendations for the development of green and low-carbon logistics and digital logistics that are tailored to local conditions, providing theoretical and methodological support for low-carbon research in the LI, and providing reference for other countries and regions to explore the path of green and low-carbon transformation. Full article

In the framework of fully vertical GaN-on-Silicon device technology development, we report on the optimization of non-alloyed ohmic contacts on the N-polar n+-doped GaN face backside layer. This evaluation is made possible by using patterned TLMs (Transmission Line Model) through direct laser writing lithography after locally removing the substrate and buffer layers in order to access the n+-doped backside layer. As deposited non-alloyed metal stack on top of N-polar orientation GaN layer after buffer layers removal results in poor ohmic contact quality. To significantly reduce the related specific contact resistance, an HCl treatment is applied prior to metallization under various time and temperature conditions. A 3 min HCl treatment at 70 °C is found to be the optimum condition to achieve thermally stable high ohmic contact quality. To further understand the impact of the wet treatment, SEM (Scanning Electron Microscopy) and XPS (X-ray Photoelectron Spectroscopy) analyses were performed. XPS revealed a decrease in Ga-O concentration after applying the treatment, reflecting the higher oxidation susceptibility of the N-polar face compared to the Ga-polar face, which was used as a reference. SEM images of the treated samples show the formation of pyramids on the N-face after HCl treatment, suggesting specific wet etching planes of the GaN crystal from the N-face. The size of the pyramids is time-dependent; thus, increasing the treatment duration results in larger pyramids, which explains the degradation of ohmic contact quality after prolonged high-temperature HCl treatment. Full article

High-temperature adhesives find extensive application in diverse domains, encompassing repairs, production processes, and material joining. However, achieving their curing at ambient temperatures remains a formidable challenge due to the inherent requirement of elevated temperatures, typically exceeding 500 °C, for the curing reaction. To overcome this limitation, in this study, we developed a distinctive inorganic phosphate-based composite adhesive by incorporating dual-functional calcium monoaluminate (CA) into a traditional adhesive blend comprising Al(H₂PO₄)₃ and MgO. This distinctive approach significantly diminishes the curing temperature, enabling it to occur at room temperature. Firstly, CA’s facile hydration reaction effectively scavenges surrounding water molecules, thereby accelerating the dehydration curing process of Al(H₂PO₄)₃. Secondly, as hydration is an exothermic process, it locally generates heat around the Al(H₂PO₄)₃, fostering optimal conditions for its curing reaction. Moreover, the adhesive’s strength is substantially bolstered through the strategic inclusion of Nano-Al₂O₃ (enhancing the availability of reaction sites) and Nano-SiO₂ (improving overall stability). As a demonstration, the adhesive formulation with added CA containing 2% Nano-Al₂O₃ and 2% Nano-SiO₂ achieved a remarkable tensile strength of 32.48 MPa at room temperature, underscoring its potential as an efficient solution for various practical adhesive applications. The adhesive prepared in this study harnesses the hydration properties of CA to absorb moisture and release substantial heat, introducing a novel method for ambient temperature curing. This development promises to broaden its applications in refractory materials, coatings, and equipment repair. Full article

The construction industry is a key driver of environmental change due to its extensive use of resources and high emissions, thus significantly burdening global efforts towards sustainable development targets. A large portion of the environmental footprint of buildings results from the energy required to sustain indoor comfort levels. Thus, enhancing the energy efficiency of existing buildings becomes critical in reducing their environmental impact. This study explores the impact of thermal performance improvements on the heating demand, employing numerical modeling and two energy performance methodologies, PHPP and Mc001-2022, across various climatic datasets and case studies in Romania. The results show substantial variability in heating demand predictions: Mc001-2022 predicts up to 27.2% higher continuous heating demands and 21.0% higher intermittent demands compared to PHPP in one case study. In the second case study, the differences range from 8.1% higher to 6.9% lower for continuous heating and from 3.3% higher to 9.9% lower for intermittent heating, depending on the scenario. These findings underscore the importance of the methodological choice and localized climatic data in heating demand assessments, highlighting the need for a tailored, context-specific approach to energy performance assessment, integrating multiple energy efficiency measures suited to the unique characteristics of each building. Full article

The pursuit of an improved quality of life is a major trend in the food market. This is driving the reformulation of the industry’s product portfolio, with the aim of providing nourishment while also contributing to beneficial health metabolic processes. In this context, the use of local biodiversity and the recovery of the traditional knowledge associated with the consumption of vegetables that grow spontaneously in nature emerge as more sustainable and nutritionally adequate concepts. The Amazon region is known for its abundant biodiversity, housing numerous unconventional food plants whose nutritional and biological properties remain unknown due to a lack of research. Among the different species are Xanthosoma sagittifolium, Acmella oleracea, Talinum triangulare, Pereskia bleo, Bidens bipinnata, and Costus spiralis. These species contain bioactive compounds such as apigenin, syringic acid, spilanthol, and lutein, which provide various health benefits. There are few reports on the biological effects, nutritional composition, bioactive compounds, and market prospects for these species. Therefore, this review provides an overview of their nutritional contribution, bioactive compounds, health benefits, and current market, as well as the use of new technologies that can contribute to the development of functional products/ingredients derived from them. Full article

This paper focuses on studying the concept of walkability in Riyadh, Saudi Arabia, after the implementation of the Humanizing Neighborhoods Initiative. Al-Falah neighborhood was selected as a case study because it was one of the first neighborhoods to implement walkability strategies. By adopting a qualitative critical approach, this research collected data through a literature review and on-site observation to assess walkability in the neighborhood. Analysis of the observations highlighted the challenges and opportunities of walkability in Riyadh and the wider Gulf cities. Recommendations for promoting walkable communities in Riyadh include enhancing pedestrian infrastructure, incorporating mixed-use developments, integrating green infrastructure, implementing smart urban planning principles, and integrating public transportation systems. This paper emphasizes the importance of community engagement, preserving local identity, and involving various stakeholders to transform Riyadh into a pedestrian-friendly city that prioritizes public health and sustainability. Full article

Enhancing the sustainability of the non-timber forest products industry has dual significance for both the management of local forest resources and socio-economic development. This paper adopts a systems theory perspective to construct an analytical model for the sustainable development of non-timber forest products, based on a “social-economic-natural” framework. By analyzing case studies of non-timber forest products industry sustainability from four underdeveloped counties in China, the paper derives the following main conclusions and insights: The sustainability of non-timber forest products development models is influenced by factors such as resource endowments and institutional environments and includes both single and composite models. Underdeveloped regions can achieve considerable sustainability in the development of non-timber forest products, but this requires a rational allocation of six key elements—policy, model, stakeholders, natural resources, funding, and technology—to stimulate industry growth. To promote the sustainable development of this industry, optimization should be pursued across five aspects: “policy leadership and top-level design to guide industry development”, “selection of appropriate development models based on local natural endowments and socio-economic foundations”, “large enterprise-driven mechanisms to form multi-stakeholder interest connections”, “focus on product technology research and development, and establishment of technical training mechanisms”, and “market-driven funding to develop product sales markets”. Full article

Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously. As a result, they require more memory usage and increased inference time. Also, less attention is paid to other domains, such as animals and cartoon characters. To address these challenges, we propose an input-agnostic face model, AnyFace++, to perform multiple face-related tasks concurrently. The tasks are face detection and prediction of facial landmarks for human, animal, and cartoon faces, including age estimation, gender classification, and emotion recognition for human faces. We trained the model using deep multi-task, multi-domain learning with a heterogeneous cost function. The experimental results demonstrate that AnyFace++ generates outcomes comparable to cutting-edge models designed for specific domains. Full article

This contribution proposes a hybrid approach integrating transient test-based techniques with machine learning for automatic leak detection in water transmission mains. Transient numerical simulations calibrated using experimental tests are used to develop a data-driven method based on neural networks to identify leak locations and characteristics. The accuracy of leak localization is demonstrated using three different degrees of noise in terms of mean absolute error, ranging between 0.54 m and 2.1 m. This proposed hybrid approach shows prospects for in-field applications. Full article

Wound infections caused by Staphylococcus aureus often result in localized suppurative lesions that severely impede the healing process, so it is urgent to develop a dress with efficient antimicrobial and pro-healing functions. In this study, the bifunctional injectable hydrogel lactoferrin (Lf)/NZ2114/lithium magnesium silicate hydrogel (LMSH) was first successfully prepared through the electrostatic interaction method. The physical, biological, and efficacy properties are systematically analyzed with good shear-thinning capacity and biocompatibility. More importantly, it inhibits infection and promotes wound healing in a mouse wound infection model after 14 d treatment, and the bactericidal rate and healing rate were over 99.92% and nearly 100%, respectively. Meanwhile, the massive reduction of inflammatory cells, restoration of tissue structure, and angiogenesis in mice showed the anti-inflammatory and pro-healing properties of the hydrogel. The healed wounds showed thickening with more hair follicles and glands, suggesting that the hydrogel Lf/NZ2114/LMSH (Three in One) could be a better dressing candidate for the treatment of S. aureus-induced wound infections. Full article