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Nowadays, climate change is recognized as one of the biggest problems the world is facing, posing a potential threat to the environment and almost all aspects of human life. Since the United Nations Framework Convention on Climate Change in 1992, many efforts have been made to mitigate climate change, with no considerable results. According to climate change projections, temperatures will continue to rise, and extreme weather events will become more frequent, prolonged, and intense. Reflecting these concerns, the 2015 Paris Agreement was adopted as the cornerstone for reducing the impact of climate change, aiming to limit global warming below 2 °C and even keep the temperature rise below 1.5 °C. To achieve this international goal, focused mitigation actions will be required. Climate change has a strong impact on forests, enhancing their growth but also posing risks to them. Conversely, forests can mitigate climate change, as they have a considerable impact on global surface temperatures through their influence on the land–atmosphere energy exchange and the absorption of vast amounts of CO₂ through photosynthesis. Consequently, afforestation and reforestation have become integral components of climate change mitigation strategies worldwide. This review aims to summarize the cutting-edge knowledge on the role of forests in climate change mitigation, emphasizing their carbon absorption and storage capacity. Overall, the impact of afforestation/reforestation on climate change mitigation hinges on strategic planning, implementation, and local forest conditions. Integrating afforestation and reforestation with other carbon removal technologies could enhance long-term effectiveness in carbon storage. Ultimately, effective climate change mitigation entails both restoring and establishing forests, alongside reducing greenhouse gas emissions. Full article

Landraces are considered a crucial component of biodiversity conservation, serving as a reservoir of genetic diversity. Consequently, the collection, cultivation, and detailed characterization of such landraces constitute an inherent aspect of the world’s natural resource heritage. This effort holds promise for the development of elite varieties capable of thriving amidst continuous global climate fluctuations. In this context, we conducted a comprehensive assessment of the main agronomic attributes, physico-chemical properties, and functional quality traits of the major hot pepper landraces adapted to diverse climatic conditions in Tunisia. These landraces include ‘Dhirat’, ‘Semmane’, ‘Beldi’, ‘Nabeul’, ‘Jerid’, ‘Mahdia’, ‘Cayenne’, ‘Kairouan’, and ‘Baklouti’. Most of the pepper landraces exhibited satisfactory yields, ranging from 1163.25 to 1841.67 g plant⁻¹ in ‘Jerid’ and ‘Kairouan’, respectively, indicating robust productivity, especially under prevailing climatic changes and high temperatures during both growing cycles. The levels of antioxidants comprising capsaicinoids, carotenoids, phenolics, and tocopherols, as well as radical scavenging activity, emerged as key discriminating factors among pungent pepper landraces. Irrespective of genotype, capsaicin and dihydrocapsaicin constituted the major capsaicinoids, accounting for 44–91% of the total capsaicinoids content. Total capsaicinoids ranged from 1.81 µg g⁻¹ fw to 193.71 µg g⁻¹ fw, with ‘Baklouti’ and ‘Jerid’ identified as the most pungent landraces. Total carotenoids ranged from 45.94 µg g⁻¹ fw to 174.52 µg g⁻¹ fw, with ‘Semmane’ and ‘Jerid’ exhibiting the highest levels. Considerable variation was observed in β-carotene content, spanning from 3% to 24% of the total carotenoids. α-Tocopherol content ranged from 19.03 µg g⁻¹ fw in ‘Kairouan’ to 30.93 µg g⁻¹ fw in ‘Beldi’, exerting a notable influence on the overall tocopherol content. Conversely, the β- and γ-tocopherol isomers were detected at very low concentrations. The total vitamin C content ranged from 132 mg 100g⁻¹ fw in ‘Mahdia’ to 200 mg 100 g⁻¹ fw in ‘Nabeul’, indicating relatively low genetic variability. However, large variability was detected in total phenolics content, ranging from 168.58 mg GAE kg⁻¹ fw in ‘Beldi’ to 302.98 mg GAE kg⁻¹ fw in ‘Cayenne’. Landraces such as ‘Dhirat’, ‘Nabeul’, ‘Semmane’, ‘Kairouan’, ‘Cayenne’, and ‘Mahdia’ appear suitable for both fresh consumption and processing, owing to their favorable average fruit weight, soluble solids content, and bioactive content. Among the pepper landraces tested, ‘Cayenne’ achieved the highest value of radical scavenging activity in both hydrophilic and lipophilic fractions (RSAHF and RSALF), with variations ranging from 59% to 120% for RSAHF and from 4% to 63% for RSALF. This study aims to preserve and enhance the value of local genetic resources and contribute to identify desirable traits for incorporation into breeding programs to develop high-quality, high-yielding landraces and elite lines. Full article

Change detection (CD) is the main task in the remote sensing field. Binary change detection (BCD), which only focuses on the region of change, cannot meet current needs. Semantic change detection (SCD) is pivotal for identifying regions of change in sequential remote sensing imagery, focusing on discerning “from-to” transitions in land cover. The emphasis on features within these regions of change is critical for SCD efficacy. Traditional methodologies, however, often overlook this aspect. In order to address this gap, we introduce a change-aware guided multi-task network (CGMNet). This innovative network integrates a change-aware mask branch, leveraging prior knowledge of regions of change to enhance land cover classification in dual temporal remote sensing images. This strategic focus allows for the more accurate identification of altered regions. Furthermore, to navigate the complexities of remote sensing environments, we develop a global and local attention mechanism (GLAM). This mechanism adeptly captures both overarching and fine-grained spatial details, facilitating more nuanced analysis. Our rigorous testing on two public datasets using state-of-the-art methods yielded impressive results. CGMNet achieved Overall Score metrics of 58.77% on the Landsat-SCD dataset and 37.06% on the SECOND dataset. These outcomes not only demonstrate the exceptional performance of the method but also signify its superiority over other comparative algorithms. Full article

Today, with the presence of 5G communication systems, including Internet of Things (IoT) technology, there is a high demand for mobile devices (especially smartphones, tablets, wearable technology, and so on). Regarding this proliferation and high demand, the massive adoption of mobile devices (MDs) has led to an exponential increase in network latency; the heavy demand for cloud servers causes the degradation of data traffic, which considerably impacts the real-time communication and computing aspects of mobile devices. As a result, mobile edge computing (MEC), an efficient framework capable of enhancing processing, optimizing energy usage, and offloading computation tasks, is considered a promising solution. In current research, numerous models have been implemented to achieve resource allocation and task offloading. However, these techniques are ineffective due to privacy issues and a lack of sufficient resources. Hence, this study proposes secure task offloading and resource allocation strategies in mobile devices using the Probit Mish–Gated Recurrent Unit (PM-GRU) and Entropic Linear Interpolation-Serval Optimization Algorithm (ELI-SOA). Primarily, the tasks to be offloaded and their attributes are gathered from mobile users and passed to a local computing model to identify the edge server. Here, the task attributes and the server attributes are compared with a cache table using the Sorensen–Dice coefficient. If the attributes match, then details about the appropriate edge server are produced. If the attributes do not match, then they are inputted into a global scheme that analyzes the attributes and predicts the edge server based on the Probit Mish-Gated Recurrent Unit (PM-GRU). Then, the server information is preserved and updated in the cache table in the local scheme. Further, the attributes, along with the predicted edge server, are inputted into a system for privacy-preserving smart contract creation by using Exponential Earth Mover’s Distance Matrix-Based K-Anonymity (EEMDM-KA) to develop a secure smart contract. Subsequently, the traffic attributes in the smart contract are extracted, and the request load is balanced by using HCD-KM. Load-balanced requests are assigned to the edge server, and the optimal resources are allocated in the cloud server by using the Entropic Linear Interpolation-Serval Optimization Algorithm (ELI-SOA). Finally, the created smart contract is hashed based on KECCAK-512 and stored in the blockchain. With a high accuracy of 99.84%, the evaluation results showed that the proposed approach framework performed better than those used in previous efforts. Full article

The simulation analogy presented in this work enhances the accessibility of abstract quantum theories, specifically the stochastic hydrodynamic model (SQHM), by relating them to our daily experiences. The SQHM incorporates the influence of fluctuating gravitational background, a form of dark energy, into quantum equations. This model successfully addresses key aspects of objective-collapse theories, including resolving the ‘tails’ problem through the definition of quantum potential length of interaction in addition to the De Broglie length, beyond which coherent Schrödinger quantum behavior and wavefunction tails cannot be maintained. The SQHM emphasizes that an external environment is unnecessary, asserting that the quantum stochastic behavior leading to wavefunction collapse can be an inherent property of physics in a spacetime with fluctuating metrics. Embedded in relativistic quantum mechanics, the theory establishes a coherent link between the uncertainty principle and the constancy of light speed, aligning seamlessly with finite information transmission speed. Within quantum mechanics submitted to fluctuations, the SQHM derives the indeterminacy relation between energy and time, offering insights into measurement processes impossible within a finite time interval in a truly quantum global system. Experimental validation is found in confirming the Lindemann constant for solid lattice melting points and the ⁴He transition from fluid to superfluid states. The SQHM’s self-consistency lies in its ability to describe the dynamics of wavefunction decay (collapse) and the measure process. Additionally, the theory resolves the pre-existing reality problem by showing that large-scale systems naturally decay into decoherent states stable in time. Continuing, the paper demonstrates that the physical dynamics of SQHM can be analogized to a computer simulation employing optimization procedures for realization. This perspective elucidates the concept of time in contemporary reality and enriches our comprehension of free will. The overall framework introduces an irreversible process impacting the manifestation of macroscopic reality at the present time, asserting that the multiverse exists solely in future states, with the past comprising the formed universe after the current moment. Locally uncorrelated projective decays of wavefunction, at the present time, function as a reduction of the multiverse to a single universe. Macroscopic reality, characterized by a foam-like consistency where microscopic domains with quantum properties coexist, offers insights into how our consciousness perceives dynamic reality. It also sheds light on the spontaneous emergence of gravity in discrete quantum spacetime evolution, and the achievement of the classical general relativity limit in quantum loop gravity and causal dynamical triangulation. The simulation analogy highlights a strategy focused on minimizing information processing, facilitating the universal simulation in solving its predetermined problem. From within, reality becomes the manifestation of specific physical laws emerging from the inherent structure of the simulation devised to address its particular issue. In this context, the reality simulation appears to employ an optimization strategy, minimizing information loss and data management in line with the simulation’s intended purpose. Full article

Pepper is a high-economic-value agricultural crop that faces diverse disease challenges such as blight and anthracnose. These diseases not only reduce the yield of pepper but, in severe cases, can also cause significant economic losses and threaten food security. The timely and accurate identification of pepper diseases is crucial. Image recognition technology plays a key role in this aspect by automating and efficiently identifying pepper diseases, helping agricultural workers to adopt and implement effective control strategies, alleviating the impact of diseases, and being of great importance for improving agricultural production efficiency and promoting sustainable agricultural development. In response to issues such as edge-blurring and the extraction of minute features in pepper disease image recognition, as well as the difficulty in determining the optimal learning rate during the training process of traditional pepper disease identification networks, a new pepper disease recognition model based on the TPSAO-AMWNet is proposed. First, an Adaptive Residual Pyramid Convolution (ARPC) structure combined with a Squeeze-and-Excitation (SE) module is proposed to solve the problem of edge-blurring by utilizing adaptivity and channel attention; secondly, to address the issue of micro-feature extraction, Minor Triplet Disease Focus Attention (MTDFA) is proposed to enhance the capture of local details of pepper leaf disease features while maintaining attention to global features, reducing interference from irrelevant regions; then, a mixed loss function combining Weighted Focal Loss and L2 regularization (WfrLoss) is introduced to refine the learning strategy during dataset processing, enhancing the model’s performance and generalization capabilities while preventing overfitting. Subsequently, to tackle the challenge of determining the optimal learning rate, the tent particle snow ablation optimizer (TPSAO) is developed to accurately identify the most effective learning rate. The TPSAO-AMWNet model, trained on our custom datasets, is evaluated against other existing methods. The model attains an average accuracy of 93.52% and an F1 score of 93.15%, demonstrating robust effectiveness and practicality in classifying pepper diseases. These results also offer valuable insights for disease detection in various other crops. Full article

Computational intelligence technologies have been extensively applied for the interpretation of remote sensing imagery. Recently, the computational-intelligence-based Transformer change detection (CD) approach has attracted increasing attention. However, the current Transformer-based CD method can better capture global features, but there is no good solution for the loss of local detail information. For this reason, introducing semantic and frequency information from the perspective of a dual-domain can be beneficial for improving the representation of detailed features to improve CD performance. To overcome this limitation, a dual-domain Transformer (D²Former) is proposed for CD. Firstly, we adopt a semantic tokenizer to capture the semantic information, which promotes the enrichment and refinement of semantic change information in the Transformer. Secondly, a frequency tokenizer is introduced to acquire the frequency information of the features, which offers the proposed D²Former another aspect and dimension to enhance the ability to detect change information. Therefore, the proposed D²Former employs dual-domain tokenizers to acquire and fuse the feature representation with rich semantic and frequency information, which can refine the features to acquire more fine-grained CD ability. Extensive experiments on three CD benchmark datasets demonstrate that the proposed D²Former obviously outperforms some other existing approaches. The results present the competitive performance of our method on the WHU-CD, LEVIR-CD, and GZ-CD datasets, for which it achieved F1-Score metrics of 92.85%, 90.60%, and 87.02%, respectively. Full article

The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, the process is often inaccurate and far from perfect. This paper explores the potential of using expert forecasts to enhance the crop yield predictions of our global-to-local XGBoost machine learning system. Additionally, it investigates the ERA5 climate model’s viability as an alternative data source for crop yield forecasting in the absence of on-farm weather data. We find that, by combining both the expert’s pre-season forecasts and the ERA5 climate model with the machine learning model, we can—in most cases—obtain better forecasts that outperform the growers’ pre-season forecasts and the machine learning-only models. Our expert-informed model attains yield forecasts for 4 weeks ahead with an average RMSE of 0.0855 across all the plots and an RMSE of 0.0872 with the ERA5 climate data included. Full article

Green supply chain management is critical for driving sustainable development and addressing escalating environmental challenges faced by companies. However, due to the multidimensionality of cost–benefit analysis and the intricacies of supply chain operations, strategic decision-making regarding green supply chains is inherently complex. This paper proposes a green supply chain optimization framework based on a two-stage heuristic algorithm. First, anchored in the interests of intermediary core enterprises, this work integrates upstream procurement and transportation of products with downstream logistics and distribution. In this aspect, a three-tier green complex supply chain model incorporating economic and environmental factors is developed to consider carbon emissions, product non-conformance rates, delay rates, and transportation costs. The overarching goal is to comprehensively optimize the trade-off between supply chain costs and carbon emissions. Subsequently, a two-stage heuristic algorithm is devised to solve the model by combining the cuckoo search algorithm with the brainstorming optimization algorithm. Specifically, an adaptive crossover–mutation operator is introduced to enhance the search performance of the brainstorming optimization algorithm, which caters to both global and local search perspectives. Experimental results and comparison studies demonstrate that the proposed method performs well within the modeling and optimization of the green supply chain. The proposed method facilitates the efficient determination of ordering strategies and transportation plans within tight deadlines, thereby offering valuable support to decision-makers in central enterprises for supply chain management, ultimately maximizing their benefits. Full article

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

In recent years, multi-view graph clustering algorithms based on representations learning have received extensive attention. However, existing algorithms are still limited in two main aspects, first, most algorithms employ graph convolution networks to learn the local representations, but the presence of high-frequency noise in these representations limits the clustering performance. Second, in the process of constructing a global representation based on the local representations, most algorithms focus on the consistency of each view while ignoring complementarity, resulting in lower representation quality. To address the aforementioned issues, a local-global representation enhancement for multi-view graph clustering algorithm is proposed in this paper. First, the low-frequency signals in the local representations are enhanced by a low-pass graph encoder, which yields smoother and more suitable local representations for clustering. Second, by introducing an attention mechanism, the local embedded representations of each view can be weighted and fused to obtain a global representation. Finally, to enhance the quality of the global representation, it is jointly optimized using the neighborhood contrastive loss and reconstruction loss. The final clustering results are obtained by applying the k-means algorithm to the global representation. A wealth of experiments have validated the effectiveness and robustness of the proposed algorithm. Full article

Osteoarthritis (OA) stands as a prevalent and progressively debilitating clinical condition globally, impacting joint structures and leading to their gradual deterioration through inflammatory mechanisms. While both non-modifiable and modifiable factors contribute to its onset, numerous aspects of OA pathophysiology remain elusive despite considerable research strides. Presently, diagnosis heavily relies on clinician expertise and meticulous differential diagnosis to exclude other joint-affecting conditions. Therapeutic approaches for OA predominantly focus on patient education for self-management alongside tailored exercise regimens, often complemented by various pharmacological interventions primarily targeting pain alleviation. However, pharmacological treatments typically exhibit short-term efficacy and local and/or systemic side effects, with prosthetic surgery being the ultimate resolution in severe cases. Thus, exploring the potential integration or substitution of conventional drug therapies with natural compounds and extracts emerges as a promising frontier in enhancing OA management. These alternatives offer improved safety profiles and possess the potential to target specific dysregulated pathways implicated in OA pathogenesis, thereby presenting a holistic approach to address the condition’s complexities. Full article

In recent decades, an important number of regional and global digital elevation models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs need to be expressed in the same coordinate reference system (CRS) and sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention) with heights relative to the same vertical reference system (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements or disparities may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect, or curvature. Therefore, before any comparison, the control of DEM planimetric accuracy is certainly a very important task to perform. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have already been computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been introduced through bicubic interpolation in an 11 × 11 grid with a 0.1-pixel step in both directionsThis validation method has been applied in four approximately 10 km × 10 km DEMIX tiles showing different roughness (height distribution). Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with typical errors (e_b) ranging from 12 to 20% of the pixel size (with the worst case in Croatia). These errors in displacement retrievals are not equally distributed in the 11 × 11 grid, and the overall error E_b depends on the roughness encountered in the different tiles. The second aim of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d = 1 of the interpolated point) on the accuracy of the displacement retrieval. By considering E_b as a quality indicator, tests have been performed in the four DEMIX tiles, making the bicubic parameter vary between −1.5 and 0.0 by a step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four E_b minimal values. This BBC parameter b* is low for flat areas (around −0.95) and higher in mountainous areas (around −0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r = 0.717). The logarithmic regression formula $\tilde{b}\left({\sigma }_{slope}\right)$ estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC $\tilde{b}$. Full article

Photovoltaic (PV) power prediction plays a significant role in supporting the stable operation and resource scheduling of integrated energy systems. However, the randomness and volatility of photovoltaic power generation will greatly affect the prediction accuracy. Focusing on this issue, a prediction framework is proposed in this research by developing an improved sparrow search algorithm (ISSA) to optimize the hyperparameters of long short-term memory (LSTM) neural networks. The ISSA is specially designed from the following three aspects to support a powerful search performance. Firstly, the initial population variety is enriched by using an enhanced sine chaotic mapping. Secondly, the relative position of neighboring producers is introduced to improve the producer position-updating strategy to enhance the global search capabilities. Then the Cauchy–Gaussian variation is utilized to help avoid the local optimal solution. Numerical experiments on 20 test functions indicate that ISSA could identify the optimal solution with better precision compared to SSA and PSO algorithms. Furthermore, a comparative study of PV power prediction methods is provided. The ISSA-LSTM algorithm developed in this paper and five benchmark models are implemented on a real dataset gathered from the Alice Springs area in Australia. In contrast to the SSA-LSTM model, most MAE, MAPE, and RMSE values of the proposed model are reduced by 20∼60%, demonstrating the superiority of the proposed model under various weather conditions and typical seasons. Full article

In recent times, there has been a significant shift from centralized energy systems to decentralized ones. These systems aim to satisfy local energy needs using renewable resources within the community. This approach leads to decreased complexity and costs, improved efficiency, and enhanced local resilience and supports energy independence, thereby advancing the transition toward zero carbon emissions. Community energy plays a pivotal role globally, particularly in European countries, driven by citizen engagement in generating power from renewable sources. The European Union, known for its focus on social innovation and citizen participation, recognizes the essential role of energy communities in its latest energy strategy. The concept for creating local energy communities or community-based energy projects has gained worldwide attention, demonstrating the economic, environmental, and efficiency benefits for using renewable energy sources. However, there is a noticeable gap in research covering all the updated aspects of renewable energy communities. This article provides an in-depth review of energy communities, especially renewable energy communities, exploring their concepts, scope, benefits, and key activities. It also sheds light on their progress by presenting results and analyses. Some countries have shown significant advancement, others are in the initial stages, and a few have partially adopted REC implementation according to the Renewable Energy Directive II. Additionally, it discusses the main challenges and potential recommendations to enhance the growth of renewable energy communities. This work is a valuable resource, emphasizing the importance of citizen involvement and offering insights into various aspects of community energy for sustainable energy transition. It also provides practical insights and valuable information for policymakers, researchers, industry professionals, and community members who are keen on promoting sustainable, community-driven energy systems. Full article