Search Results (13,726)

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

The oxygen evolution reaction (OER) plays a central role as an anode in electrocatalytic processes such as energy conversion and storage and the generation of molecular oxygen from the electrolysis of water. Currently, precious metal oxides such as IrO₂ and RuO₂ are recognized as reference OER electrocatalysts with reasonably high activity; however, their widespread use in practical devices has been severely hindered by their high cost and scarcity. It is essential to design alternative OER electrocatalysts made of low-cost and abundant earth elements with significant activity and robustness. We report four new nanocellulose-derived Fe–zeolite nanocomposites, namely Fe/Zeolite@CCNC (1), Fe/Zeolite@CCNF (2), Fe/Zeolite/CNT@CCNC (3), and Fe/Zeolite/CNT@CCNF (4). Two different types of nanocellulose were investigated: nanocellulose nanofibrils and nanocellulose nanocrystals. Characterization with TEM, SEM-EDS, PXRD, and XPS is reported. The nanocomposites exhibited electrocatalytic activity for OER that varies based on the origin of biocarbon and the composition content. The effect of adding carbon nanotubes to the nanocomposites was studied, and an improvement in OER catalysis was observed. The electrochemical double-layer capacitance and electrochemical impedance spectroscopy of the nanocomposites are reported. The nanocomposite 3 exhibited the highest performance, with an onset potential value of 1.654 V and an overpotential of 551 mV, which exceeds the activity of RuO₂ for OER catalysis at 10 mA/cm² in the glassy carbon electrode. A 24 h chronoamperometry study revealed that the catalyst is active for ~2 h under continuous operating conditions. BET surface analysis showed that the crystalline nanocellulose-derived composite exhibited 301.47 m²/g, and the fibril nanocellulose-derived composite exhibited 120.39 m²/g, indicating that the increased nanoporosity of the former contributes to the increase in OER catalysis. Full article

This paper investigates the problem of tracking targets with unknown but fixed 3D star-convex shapes using point cloud measurements. While existing methods typically model shape parameters as random variables evolving according to predefined prior models, this evolution process is often unknown in practice. We propose a particular approach within the Expectation Conditional Maximization (ECM) framework that circumvents this limitation by treating shape-defining quantities as parameters estimated directly via optimization. The objective is the joint estimation of target kinematics, extent, and orientation in 3D space. Specifically, the 3D shape is modeled using a radial function estimated via double Fourier series (DFS) expansion, and orientation is represented using the compact, singularity-free axis-angle method. The ECM algorithm facilitates this joint estimation: an Unscented Kalman Smoother infers kinematics in the E-step, while the M-step estimates DFS shape parameters and rotation angles by minimizing regularized cost functions, promoting robustness and smoothness. The effectiveness of the proposed algorithm is substantiated through two experimental evaluations. Full article

Molasses, a by-product of raw sugar production, is widely used as a cost-effective carbon and nutrient source for industrial fermentations, including the production of baker’s yeast (Saccharomyces cerevisiae). Due to the cost and limited availability of molasses, efforts have been made to replace molasses with cheaper and more readily available substrates such as corn syrup. However, the quality of dry yeast drops following the replacement of molasses with corn syrup, despite the same amount of total sugar being provided. Our understanding of how molasses replacement affects yeast physiology, especially during the dehydration step, is limited. Here, we examined changes in gene expression of a strain of baker’s yeast during fermentation with increasing corn syrup to molasses ratios at the transcriptomic level. Our findings revealed that the limited availability of the key metal ions copper, iron, and zinc, as well as sulfur from corn syrup (i) reduced their intracellular storage, (ii) impaired the synthesis of unsaturated fatty acids and ergosterol, as evidenced by the decreasing proportions of these important membrane components with higher proportions of corn syrup, and (iii) inactivated oxidative stress response enzymes. Taken together, the molecular and metabolic changes observed suggest a potential reduction in nutrient reserves for fermentation and a possible compromise in cell viability during the drying process, which may ultimately impact the quality of the final dry yeast product. These findings emphasize the importance of precise nutrient supplementation when substituting molasses with cheaper substrates. Full article

To enhance agricultural soil health and soil organic carbon (SOC) sequestration, it is important to accurately measure SOC. The aim of this study was to compare common methods for measuring SOC in soils in order to determine the most effective approach among different agricultural land types. The measurement methods of loss-on-ignition (LOI), automated dry combustion (Dumas), and real-time near-infrared spectroscopy (NIRS) were compared. A total of 95 soil core samples, ranging in clay and calcareous content, were collected across a range of agricultural land types from forty-eight fields across five farms in the Southwest of England. There were similar and positive correlations between all three methods for measuring SOC (ranging from r = 0.549 to 0.579; all p < 0.001). On average, permanent grass fields had higher SOC content (6.6%) than arable and temporary ley fields (4.6% and 4.5%, respectively), with the difference of 2% indicating a higher carbon storage potential in permanent grassland fields. Newly predicted conversion equations of linear regression were developed among the three measurement methods according to all the fields and land types. The correlation of the conversation equations among the three methods in permanent grass fields was strong and significant compared to those in both arable and temporary ley fields. The analysed results could help understand soil carbon management and maximise sequestration. Moreover, the approach of using real-time NIRS analysis with a rechargeable portable NIRS soil device can offer a convenient and cost-saving alternative for monitoring preliminary SOC changes timely on or offsite without personnel risks from the high-temperature furnace and chemical reagent adopted in the LOI and Dumas processes, respectively, at the laboratory. Therefore, the study suggests that faster, lower-cost, and safer methods like NIRS for analysing initial SOC measurements are now available to provide similar SOC results as traditional soil analysis methods of the LOI and Dumas. Further studies on assessing SOC levels in different farm locations, land, and soil types across seasons using NIRS will improve benchmarked SOC data for farm stakeholders in making evidence-informed agricultural practices. Full article

This study evaluates the efficiency of sub-stoichiometric Ti₄O₇ titanium oxide anodes for the electrochemical degradation of glyphosate, a persistent herbicide classified as a probable carcinogen by the World Health Organization. After optimizing the process operating parameters (pH and current density), the mineralization efficiency and fate of degradation by-products of the treated solution were determined using a total organic carbon (TOC) analyzer and HPLC/MS, respectively. The results showed that at pH = 3, glyphosate degradation and mineralization are enhanced by the increased generation of hydroxyl radicals (^●OH) at the anode surface. A current density of 14 ${\mathrm{mA} \mathrm{cm}}^{-2}$ enables complete glyphosate removal with 77.8% mineralization. Compared with boron-doped diamond (BDD), ${\mathrm{Ti}}_4{\mathrm{O}}_7$ shows close performance for treatment of a concentrated glyphosate solution (0.41 mM), obtained after nanofiltration of a synthetic ionic solution (0.1 mM glyphosate), carried out using an NF-270 membrane at a conversion rate (Y) of 80%. At 10${ \mathrm{mA} \mathrm{cm}}^{-2}$ for 8 h, ${\mathrm{Ti}}_4{\mathrm{O}}_7$ achieved 81.3% mineralization with an energy consumption of 6.09 ${\mathrm{kWh} \mathrm{g}}^{-1}$ TOC, compared with 90.5% for BDD at 5.48 ${\mathrm{kWh} \mathrm{g}}^{-1}$ TOC. Despite a slight yield gap, ${\mathrm{Ti}}_4{\mathrm{O}}_7$ demonstrates notable efficiency under demanding conditions, suggesting its potential as a cost-effective alternative to BDD for glyphosate electro-oxidation. Full article

Drug repurposing or drug repositioning is the process of identifying new therapeutic uses for approved or investigational drugs beyond the original treatment indication. The discovery of new drugs for cancer therapy needs this cost-effective and time-saving alternative strategy to traditional drug development for a rapid clinical translation in Phase II/III studies, especially for unmet medical needs and rare diseases. Neuroendocrine tumors (NETs) are a heterogeneous group of rare neoplasms arising from cells of the neuroendocrine system that, though often indolent, can be aggressive and metastatic. In this context, drug repurposing has emerged as a promising strategy to improve treatment options due to the limited number of effective treatments and the heterogeneity of the disease. Indeed, a large number of non-oncology drugs have the potential to address more than one target that could be therapeutic for cancer patients. Although many repurposed drugs are used off-label, efficacy for the new use must be demonstrated in clinical trials. Within regulatory frameworks, both the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have procedures to reduce the need for extensive new studies and to expedite the review of drugs for serious conditions when preliminary evidence indicates substantial clinical improvement over available therapy. In spite of several advantages, including reduced development time, lower costs, known safety profiles, and faster regulatory approval, difficulty in obtaining new patents for old drugs with limited protection for intellectual property may reduce commercial returns and disincentivize investments. This review aims to provide comprehensive information on some marketed drugs currently under investigation to be repurposed or used in clinical practice for NETs and to discuss the major clinical challenges. Although drug repurposing is a useful strategy for early access to medicines, the monitoring of the clinical benefit of oncologic drugs during the post-marketing authorization is crucial to support the safety and effectiveness of treatments. Full article

In the task of PolSAR image classification, effectively utilizing convolutional neural networks and vision transformer models with limited labeled data poses a critical challenge. This article proposes a novel method for PolSAR image classification that combines channel-wise convolutional local perception, detachable self-attention, and a residual feedforward network. Specifically, the proposed method comprises several key modules. In the channel-wise convolutional local perception module, channel-wise convolution operations enable accurate extraction of local features from different channels of PolSAR images. The local residual connections further enhance these extracted features, providing more discriminative information for subsequent processing. Additionally, the detachable self-attention mechanism plays a pivotal role: it facilitates effective interaction between local and global information, enabling the model to comprehensively perceive features across different scales, thereby improving classification accuracy and robustness. Subsequently, replacing the conventional feedforward network with a residual feedforward network that incorporates residual structures aids the model in better representing local features, further enhances the capability of cross-layer gradient propagation, and effectively alleviates the problem of vanishing gradients during the training of deep networks. In the final classification stage, two fully connected layers with dropout prevent overfitting, while softmax generates predictions. The proposed method was validated on the AIRSAR Flevoland, RADARSAT-2 San Francisco, and RADARSAT-2 Xi’an datasets. The experimental results demonstrate that the proposed method can attain a high level of classification performance even with a limited amount of labeled data, and the model is relatively stable. Furthermore, the proposed method has lower computational costs than comparative methods. Full article

This study presents a cost-effective, carbon-negative construction material for affordable housing, developed entirely from locally available agricultural wastes: rice husk ash, wood ash, and rice straw—materials often problematic to dispose of in many African regions. Rice husk ash provides high amorphous silica, acting as a strong pozzolanic agent. Wood ash contributes calcium oxide and alkalis to serve as a reactive binder, while rice straw functions as a lightweight organic filler, enhancing thermal insulation and indoor climate comfort. These materials undergo natural pozzolanic reactions with water, eliminating the need for Portland cement—a major global source of anthropogenic CO₂ emissions (~900 kg CO₂/ton cement). This process is inherently carbon-negative, not only avoiding emissions from cement production but also capturing atmospheric CO₂ during lime carbonation in the hardening phase. Field trials in Kenya confirmed the composite’s sufficient structural strength for low-cost housing, with added benefits including termite resistance and suitability for unskilled laborers. In a collaboration between the University of Tartu and Kenyatta University, a semi-automatic mixing and casting system was developed, enabling fast, low-labor construction of full-scale houses. This innovation aligns with Kenya’s Big Four development agenda and supports sustainable rural development, post-disaster reconstruction, and climate mitigation through scalable, eco-friendly building solutions. Full article

The paper investigates the process of degumming substandard soybean oil using an enzyme complex of phospholipases to prepare it as a feedstock for biodiesel production. Dehumidification is an important refining step aimed at reducing the phosphorus content, which exceeds the permissible limits according to ASTM, EN, and ISO standards, by re-moving phospholipids. The enzyme complex of phospholipases includes phospholipase C, which specifically targets phosphatidylinositol, and phospholipase A2, which catalyzes the hydrolysis of phospholipids into water-soluble phosphates and lysophospholipids. This process contributes to the efficient removal of phospholipids, increased neutral oil yield, and reduced residual oil in the humic phase. The use of an enzyme complex of phospholipases provides an innovative, cost-effective, and environmentally friendly method of oil purification. The results of the study demonstrate the high efficiency of using the phospholipase enzyme complex in the processing of substandard soybean oil, which allows reducing the content of total phosphorus to 0.001% by weight, turning it into a high-quality raw material for biodiesel production. The proposed approach contributes to increasing the profitability of agricultural raw materials and the introduction of environmentally friendly technologies in the field of renewable energy. Full article

This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically. Full article

The growing adoption of serverless computing has highlighted critical challenges in resource allocation, policy fairness, and energy efficiency within multitenancy cloud environments. This research proposes a reinforcement learning (RL)-based adaptive resource allocation framework to address these issues. The framework models resource allocation as a Markov Decision Process (MDP) with dynamic states that include latency, resource utilization, and energy consumption. A reward function is designed to optimize the throughput, latency, and energy efficiency while ensuring fairness among tenants. The proposed model demonstrates significant improvements over heuristic approaches, achieving a 50% reduction in latency (from 250 ms to 120 ms), a 38.9% increase in throughput (from 180 tasks/s to 250 tasks/s), and a 35% improvement in energy efficiency. Additionally, the model reduces operational costs by 40%, achieves SLA compliance rates above 98%, and enhances fairness by lowering the Gini coefficient from 0.25 to 0.10. Under burst loads, the system maintains a service level objective success rate of 94% with a time to scale of 6 s. These results underscore the potential of RL-based solutions for dynamic workload management, paving the way for more scalable, cost-effective, and sustainable serverless multitenancy systems. Full article

Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation. Full article

Photoelectrochemical devices have garnered extensive research attention in the field of smart and multifunctional photoelectronics, owing to their lightweight nature, eco-friendliness, and cost-effective manufacturing processes. In this work, Bi₂S₃/Bi₂O₃/TiO₂ heterojunction film was successfully fabricated and functioned as the photoelectrode of photoelectrochemical devices. The designed Bi₂S₃/Bi₂O₃/TiO₂ photoelectrochemical photodetector possesses a broad light detection spectrum ranging from 400 to 900 nm and impressive self-powered characteristics. At 0 V bias, the device exhibits an on/off current ratio of approximately 1.3 × 10⁶. It achieves a commendable detectivity of 5.7 × 10¹³ Jones as subjected to a 0.8 V bias potential, outperforming both bare TiO₂ and Bi₂O₃/TiO₂ photoelectrochemical devices. Moreover, the Bi₂S₃/Bi₂O₃/TiO₂ photoelectrode film shows great promise in pollutant decomposition, achieving nearly 97.7% degradation efficiency within 60 min. The appropriate band energy alignment and the presence of an internal electric field at the interface of the Bi₂S₃/Bi₂O₃/TiO₂ film serve as a potent driving force for the separation and transport of photogenerated carriers. These findings suggest that the Bi₂S₃/Bi₂O₃/TiO₂ heterojunction film could be a viable candidate as a photoelectrode material for the development of high-performance photoelectrochemical optoelectronic devices. Full article

With the rapid development of vehicular network technology, data sharing and collaborative training among vehicles have become key to enhancing the efficiency of intelligent transportation systems. However, the heterogeneity of data and potential Byzantine attacks cause the model to update in different directions during the iterative process, causing the boundary between benign and malicious gradients to shift continuously. To address these issues, this paper proposes a privacy-preserving Byzantine-tolerant federated learning scheme. Specifically, we design a gradient detection method based on median absolute deviation (MAD), which calculates MAD in each round to set a gradient anomaly detection threshold, thereby achieving precise identification and dynamic filtering of malicious gradients. Additionally, to protect vehicle privacy, we obfuscate uploaded parameters to prevent leakage during transmission. Finally, during the aggregation phase, malicious gradients are eliminated, and only benign gradients are selected to participate in the global model update, which improves the model accuracy. Experimental results on three datasets demonstrate that the proposed scheme effectively mitigates the impact of non-independent and identically distributed (non-IID) heterogeneity and Byzantine behaviors while maintaining low computational cost. Full article