Search Results (3,186)

The issue of energy in agriculture is complex and multifaceted. Historically, agriculture was the first producer of energy through the conversion of solar energy into biomass. However, industrial development has made agriculture an important consumer of fossil energy. Although the share of agriculture in the consumption of direct energy carriers is relatively small, today’s agricultural producers use many inputs, the production of which also consumes much energy, mainly from fossil fuels (e.g., synthetic fertilizers).The food security of the world’s growing population does not allow for a radical reduction in direct and indirect energy inputs in agriculturer. Undoubtedly, some opportunities lie in improving energy efficiency in agricultural production, as any waste of inputs is also a waste of energy. In addition to improving efficiency, the agricultural sector has significant opportunities to consume energy for its own use and for other sectors of the economy. Biomass has a wide range of applications and plays a special role here. Other forms of renewable energy, such as increasingly popular agrovoltaics, are also important options. When analyzing the place of agriculture in the energy system, it is therefore worth seeing this sector as a specific energy prosumer, which is essential in the energy transition process. Such a point of view is adopted in this study, which attempts to identify the determinants of agriculture as a consumer and producer of renewable energy. Full article

The identification of dynamical systems from data is essential in control theory, enabling the creation of mathematical models that accurately represent the behavior of complex systems. However, real-world applications often present challenges such as the unknown dimensionality of the system and limited access to measurements, particularly in partially observed systems. The Hankel alternative view of Koopman (HAVOK) method offers a data-driven approach to identify linear representations of nonlinear systems, but it often overlooks the influence of external control signals (inputs) and disturbances. This paper introduces a novel input-aware modeling method for unstable linear systems using data-driven Koopman analysis. By explicitly incorporating the impact of inputs and disturbances, our method enhances the accuracy and robustness of system identification, even in the face of incomplete observations. The proposed approach leverages Koopman operator theory on augmented state-input data to capture both the intrinsic dynamics and the system’s sensitivity to external control. Through extensive numerical examples, we demonstrate the effectiveness of our method in accurately identifying and predicting the behavior of various dynamical systems, including real-world nonlinear systems and simulated unstable linear systems with and without disturbances. The results highlight the potential of our approach to advance the field of system identification and control, offering a powerful tool for modeling and analyzing complex systems in diverse applications. Full article

Pan-sharpening aims to generate high-resolution multispectral (HRMS) images by combining high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) data, while maintaining the symmetry of spatial and spectral characteristics. Traditional convolutional neural networks (CNNs) struggle with global dependency modeling due to local receptive fields, and Transformer-based models are computationally expensive. Recent Mamba models offer linear complexity and effective global modeling. However, existing Mamba-based methods lack sensitivity to local feature variations, leading to suboptimal fine-detail preservation. To address this, we propose a Conditional Skipping Mamba Network (CSMN), which enhances global-local feature fusion symmetrically through two modules: (1) the Adaptive Mamba Module (AMM), which improves global perception using adaptive spatial-frequency integration; and (2) the Cross-domain Mamba Module (CDMM), optimizing cross-domain spectral-spatial representation. Experimental results on the IKONOS and WorldView-2 datasets demonstrate that CSMN surpasses existing state-of-the-art methods in achieving superior spectral consistency and preserving spatial details, with performance that is more symmetric in fine-detail preservation. Full article

Detecting targets in remote sensing imagery, particularly when identifying sparsely distributed materials, is crucial for applications such as defense, mineral exploration, agriculture, and environmental monitoring. The effectiveness of detection and the precision of the results are influenced by several factors, including sensor configurations, platform properties, interactions between targets and their background, and the spectral contrast of the targets. Environmental factors, such as atmospheric conditions, also play a significant role. Conventionally, target detection in remote sensing has relied on statistical methods that typically assume a linear process for image formation. However, to enhance detection performance, it is critical to account for the geometric and spectral variabilities across multiple imaging platforms. In this research, we conducted a comprehensive target detection experiment using a unique benchmark multi-platform hyperspectral dataset, where man-made targets were deployed on various surface backgrounds. Data were collected using a hand-held spectroradiometer, UAV-mounted hyperspectral sensors, and airborne platforms, all within a half-hour time window. Multi-spectral space-based sensors (i.e., Worldview and Landsat) also flew over the scene and collected data. The experiment took place on 23 July 2021, at the Rochester Institute of Technology’s Tait Preserve in Penfield, NY, USA. We validated the detection outcomes through receiver operating characteristic (ROC) curves and spectral similarity metrics across various detection algorithms and imaging platforms. This multi-platform analysis provides critical insights into the challenges of hyperspectral target detection in complex, real-world landscapes, demonstrating the influence of platform variability on detection performance and the necessity for robust algorithmic approaches in multi-source data integration. Full article

The Chilean palm (Jubaea chilensis) is an endangered and culturally important species from central Chile. We studied the Ocoa palm landscape (OPL), which is currently part of a protected area that harbors the largest Chilean palm population where local peasant practices have been excluded and conflict with biodiversity conservation strategies. We explored how human–landscape relationships over time have shaped present conditions and the implications for biocultural conservation. Methods included a review of archaeobotanical and historical records, and a qualitative study focused on local peasants’ perspectives. We reported the uses of J. chilensis and the OPL since pre-Hispanic times. For the last 400 years, these uses have involved important differences between landowners and local peasants in terms of power dynamics, access to the land, and intensity of use. The current palm landscape structure directly responds to past human activities, such as palm felling and agriculture. Also, we explain peasant practices linked to the OPL as ways of resisting cultural homogenization and marginalization associated with reductive conservation approaches and other presses and pulses. Chilean palm conservation can be improved by considering ecological legacies to inform future conservation strategies and adding a biocultural approach that respectfully integrates local peasant knowledge systems and worldviews. Full article

Humanity is currently in the midst of a number of serious ecological crises. Various scientific, philosophical, and religious ideas have been put forth in response to these global crises. Here, I suggest that the solutions to ecological problems can be best achieved when we undergo an essential change in our perspective on the existence and value of the natural world. In this regard, interreligious engagement and research, which address the multiple worldviews that emerge from individual religions and philosophies, have great potential to fundamentally transform our view of ecosystems. The problem is how to conduct such interreligious engagement and research, which has—unfortunately—to this point been overlooked. In this context, I propose the “four-step method of interreligious sympoiesis to address the ecological crisis”. This is a phenomenological–hermeneutic method that involves the following steps: (1) Suspension of Judgment (Epoché): the mind’s performing an epoché, which is taken as an ethical or religious vow; (2) Empathetic Reduction: the mind’s engaging in empathy with non-human beings; (3) Symbiotic Reduction: the mind’s envisioning of proper coexistence between humans and non-human beings in both minimal and maximal ways; (4) Interreligious Hermeneutical Synthesis: the arranging and synthesizing of the ideas obtained from the above reductions in a specific or comprehensive manner from an interreligious perspective. This paper aims to expound and defend these ideas. Full article

The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research has been devoted to breaking through few-sample-driven aircraft detection technology, most algorithms still struggle to effectively solve the problems of missed target detection and false alarms caused by numerous environmental interferences in bird-eye optical remote sensing scenes. To further aircraft detection research, we have established a new dataset, Aircraft Detection in Complex Optical Scene (ADCOS), sourced from various platforms including Google Earth, Microsoft Map, Worldview-3, Pleiades, Ikonos, Orbview-3, and Jilin-1 satellites. It integrates 3903 meticulously chosen images of over 400 famous airports worldwide, containing 33,831 annotated instances employing the oriented bounding box (OBB) format. Notably, this dataset encompasses a wide range of various targets characteristics including multi-scale, multi-direction, multi-type, multi-state, and dense arrangement, along with complex relationships between targets and backgrounds like cluttered backgrounds, low contrast, shadows, and occlusion interference conditions. Furthermore, we evaluated nine representative detection algorithms on the ADCOS dataset, establishing a performance benchmark for subsequent algorithm optimization. The latest dataset will soon be available on the Github website. Full article

This paper critically reviews the convergence between Business Ecosystem Theory and Social Network Theory in sustainability studies. While both frameworks view organizations as part of larger, interconnected systems, they can be differentiated by six key dimensions: unit of analysis, focus, decomposability, types of relationships, market segment, and worldview. To better reflect real-world phenomena, this paper argues for a new stream of theoretical convergence that is practical, reliable, generalizable, and reproducible. Specifically, it proposes shifting from interorganizational networks to interfunctional networks, offering a clearer theoretical framework, reducing strategic bias and complexity, enhancing stability over time, and providing a more objective foundation for diversification strategies. This is illustrated through a case study of Tesla Inc., built from secondary data, which serves as an example of the emergence of a new strategic construct named the Business Ecosystem Footprint. This construct could assist managers in understanding where their organization stands within the network of functions, guiding them in making informed decisions about resource allocation and diversification aimed at supporting financial goals as well as sustainability and decarbonization objectives. The article concludes by suggesting potential research agendas, such as automating ecosystem mapping, exploring constraints of the new construct, and testing hypotheses related to firm performance. Full article

Primary school teachers play a significant role in preparing students to meet the demands of the 21st century. Balancing the integration of work-related skills into classroom learning while maintaining student motivation presents considerable challenges. This study explored teachers’ perceptions of the relationship between student learning and the development of work-related skills through 13 narrative interviews. Data were analysed using phenomenographic and content analyses, revealing three key themes: competence-building, relatedness-focused, and autonomy-related views. Teachers highlighted the importance of developing students’ general competencies and cross-contextual skills while fostering a supportive learning environment and promoting a sense of relatedness. Notably, their perceptions were more influenced by past experiences than by current contexts. These findings suggest that teachers recognise the importance of integrating real-world phenomena into classroom learning to prepare students for future challenges. The implications for teacher training include fostering reflective practices to help educators critically examine the influence of personal history on their teaching approaches. Full article

Autonomous vehicles (AVs) offer significant potential to improve safety, reduce emissions, and increase comfort, drawing substantial attention from both research and industry. A critical challenge in achieving SAE Level 5 autonomy, full automation, is path planning. Ongoing efforts in academia and industry are focused on optimizing AV path planning, reducing computational complexity, and enhancing safety. This paper presents a novel approach using Lyapunov Optimization (LO) for local path planning in AVs. The proposed LO model is benchmarked against two conventional methods: model predictive control and a sampling-based approach. Additionally, an AV prototype was developed and tested in Norman, Oklahoma, where it collected data to evaluate the performance of the three control algorithms used in this study. To minimize costs and increase real-world applicability, a vision-only solution was employed for object detection and the generation of bird’s-eye-view coordinate data. Each control algorithm, i.e., Lyapunov Optimization (LO) and the two baseline methods, were independently used to generate safe and smooth paths for the AV based on the collected data. The approaches were then compared in terms of path smoothness, safety, and computation time. Notably, the proposed LO strategy demonstrated at least a 20 times reduction in computation time compared to the baseline methods. Full article

The world, through the UN and its agencies, is looking for ways to sustain the environment that we live in via the SDGs. The importance of school science in achieving the SDGs cannot be overstressed. The literature abounds on the need for school science to relate to the interests and expectations of students and the importance of teachers and scientists of school science in finding solutions to sustaining our world. However, little is known about the interests and expectations of other members of our society. Consequently, we studied the expectations of members of the non-science community whose children are learning science in schools and colleges. Using a phenomenological approach, we employed interview schedules to study 15 participants, comprising 5 women and 10 men, on what they thought about science and scientists and their expectations regarding learning science in school in the 21st century, saddled with global pandemics, global warming, flooding, and other natural disasters. The thinking and expectations of the participants were analysed using a thematic analysis with constant comparison procedures to arrive at five views and expectations of school science by the non-science community. Although the participants were grateful to the science community for rising to the challenge of COVID-19, they expected school science teachers to use innovative approaches to prepare their students for future challenges, as the world lost many human lives to the COVID-19 pandemic. These findings will inform science curriculum planners about the content and pedagogies to recommend for schools and colleges. Full article

Canada’s world-renowned banking sector is well- regulated, capitalized and one of the world’s most stable. It meets the essential pre-conditions for intellectual property (IP) finance methods such as a strong IP regime and a pool of firms with registered trademarks. In 2018 Canada launched its National IP Policy followed by certain IP finance initiatives led by the Canadian Business Development Bank (BDC) in 2019. However, it is not well understood how the Canadian Constitution structures economic relations. Certain longstanding federal and provincial issues remain to be addressed if trademark-backed finance is to become part of mainstream commercial lending in Canada. This article contributes to the nascent academic interdisciplinary trademark law and finance literature. An in-depth literature review highlights the existing gaps between the Canadian federal and provincial legal frameworks that govern security interests in trademarks, and market needs. The traditional legal research methodology evaluates the impact of relevant case law, public policies and law practice, adopting finance, economic and IP rights theory perspectives. A digital shared ledger system technology law solution is proposed to enhance registration of security interests with the aim of making trademark finance in Canada more effective and efficient. This article is foundational in the sense that it paves the way for recommendations for new policies with a view to normalising trademark-backed debt finance processes in Canada. Full article

Acid Mine Drainage (AMD) presents significant environmental challenges, particularly in regions with extensive mining activities. Effective monitoring and mapping of AMD are crucial for mitigating its detrimental impacts on ecosystems and water quality. This study investigates the application of Machine Learning (ML) algorithms to map AMD by fusing multispectral imagery from Sentinel-2 with high-resolution imagery from WorldView-3. We applied three widely used ML models—Random Forest (RF), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP)—to address both classification and regression tasks. The classification models aimed to distinguish between AMD and non-AMD samples, while the regression models provided quantitative pH mapping. Our experiments were conducted on three lakes in the Outokumpu mining area in Finland, which are affected by mine waste and acidic drainage. Our results indicate that combining Sentinel-2 and WorldView-3 data significantly enhances the accuracy of AMD detection. This combined approach leverages the strengths of both datasets, providing a more robust and precise assessment of AMD impacts. Full article

Accurate human action recognition is becoming increasingly important across various fields, including healthcare and self-driving cars. A simple approach to enhance model performance is incorporating additional data modalities, such as depth frames, point clouds, and skeleton information, while previous studies have predominantly used late fusion techniques to combine these modalities, our research introduces a multi-level fusion approach that combines information at early, intermediate, and late stages together. Furthermore, recognizing the challenges of collecting multiple data types in real-world applications, our approach seeks to exploit multimodal techniques while relying solely on RGB frames as the single data source. In our work, we used RGB frames from the NTU RGB+D dataset as the sole data source. From these frames, we extracted 2D skeleton coordinates and optical flow frames using pre-trained models. We evaluated our multi-level fusion approach with EfficientNet-B7 as a case study, and our methods demonstrated significant improvement, achieving 91.5% in NTU RGB+D 60 dataset accuracy compared to single-modality and single-view models. Despite their simplicity, our methods are also comparable to other state-of-the-art approaches. Full article

Arecaceae (palms) play a crucial role for native communities and wildlife in the Amazon region. This study presents a first-of-its-kind regional-scale spatial cataloging of palms using remotely sensed data for the country of Guyana. Using very high-resolution satellite images from the GeoEye-1 and WorldView-2 sensor platforms, which collectively cover an area of 985 km², a total of 472,753 individual palm crowns are detected with F1 scores of 0.76 and 0.79, respectively, using a convolutional neural network (CNN) instance segmentation model. An example of CNN model transference between images is presented, emphasizing the limitation and practical application of this approach. A method is presented to optimize precision and recall using the confidence of the detection features; this results in a decrease of 45% and 31% in false positive detections, with a moderate increase in false negative detections. The sensitivity of the CNN model to the size of the training set is evaluated, showing that comparable metrics could be achieved with approximately 50% of the samples used in this study. Finally, the diameter of the palm crown is calculated based on the polygon identified by mask detection, resulting in an average of 7.83 m, a standard deviation of 1.05 m, and a range of {4.62, 13.90} m for the GeoEye-1 image. Similarly, for the WorldView-2 image, the average diameter is 8.08 m, with a standard deviation of 0.70 m and a range of {4.82, 15.80} m. Full article