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Air pollution affects the health of people and therefore monitoring of the air quality is important both for the public and policy makers. Efficient monitoring of air quality requires a combination of measurements and modelling. Both current and annual average concentrations as well as future concentrations on all locations where people live are required. This information on exposure to pollutants can only be achieved at high spatial resolution at all locations by using air-quality models. Therefore, model calibration is a major objective in air quality measurement strategies. Measurement results of reference instruments (or equivalent) as defined in the EU air quality directive offer a high-quality basis for model calibration and validation. Over the last years, low-cost sensors/samplers have shown a rapid development and promising results. In this paper, a statistical framework is presented to evaluate measurement strategies that apply a combination of reference measurement instruments and low-cost measurements, like diffusion tubes and sensors. For some practical situations the introduction of sensors at only twenty locations gives a significant improvement of the calibration of an air quality model. The calibration of the low-cost measurements themselves with respect to the reference instruments is critical for any application. This calibration largely determines the model quality improvement due to the addition of low-cost measurements. The results shown in this paper can be used to optimize measurement strategy using low-cost measurements and/or sensors with established performance characteristics. The results can also be used to define the quality of the low-cost measurements that is required for useful applications. Using low-cost measurements can improve the quality of the calibrated model, even with a simultaneous reduction of the number of reference instruments. I.e., improved quality of information, at reduced costs. • Optimizing the combined use of model results, reference - and low-cost measurements. • Combination of citizen science and operational monitoring of air quality. • Minimum uncertainty of model after bias removal. [ABSTRACT FROM AUTHOR]

Covered anaerobic lagoons (CALs) are Latin America's main livestock waste treatment systems. Mexico has 680 CALs that present low biogas yields (0.05 m3 m−3 digester d−1) and low COD removal rates (< 60%). This work focused on diagnosing CAL´s low performance in dairy farms by determining and analyzing operational parameters. Seven CALs located in the main dairy basin of Mexico were analyzed. The sampling areas for each CAL were the supernatant, the active zone, settled sludge, and digester inlet and outlet. The variation of the process parameter values corroborated that CALs appeared stratified and not working as expected. The sludge zone, comprising 50–58% of total solids content and 1–15% of total CALs volume, showed an elemental compounds content suitable for organic fertilizer (340, 48, and 5 kg t−1 of C, N, and S, respectively). However, this zone contained, at least, 85% of the slowly hydrolysable material; the methanogenic potential was less than 87 mL CH4 g VS−1, and the C/N ratio ranged from 4.9 to 17, outside of the optimal range. The biogas produced did not exceed 60% of methane content and more than 3000 ppm of H2S. The sludge zone significantly influences the lagoon's dynamics since it is a nutrient sink. Furthermore, the lack of agitation is the leading cause for the low energy yield and the low removal of organic matter rate. This work provides valuable information to address the operational problems within the CALs improving our understanding that shall allow proposing reactivation alternatives. [ABSTRACT FROM AUTHOR]

Sugarcane yield prediction is an important tool to support the sugar-energy sector. This study aimed to create a regional empirical model, using the random forest algorithm, to predict sugarcane yield in the state of Sao Paulo. For this, we used Sentinel-2 imagery (vegetation indices NDVIRE and CIRE, spectral bands Red-edge and near-infrared arrow), agronomic data (variety and ratoon stage and plant cane), climatic data (temperature, precipitation) and crop water deficit data from three mills. We created two predictive yield model based on three scenarios with different training and testing data: (SI) Scenario I is the regional model considered all data from the three mills, (SII) Scenario II was training similar SI and testing individuals for each mill, (SIII) Scenario III includes regional individual's models for sugarcane ratoon stage and plant cane. In each case, 70% of the dataset was used for training and 30% for testing. SI gave R2 equal to 0.72, while SII R2 was between 0.60 and 0.78; the RMSE for SI was 11.7 tonha - 1 , while for SII from 8.62 to 15.56 tonha - 1 . The rRMSE was 16.5% for SI and from 12.4 to 21.6%, for SII. SIII showed R2 greater than 0.61, and RMSE between 9.6 and 13.5 t o n ha - 1 . The CIRE and NDVIRE vegetation indices, crop water deficit and precipitation were the most important variables to estimate sugarcane yield. The model created considering SI and SII showed potential to be applied to different locals using data from three mills. [ABSTRACT FROM AUTHOR]

Introduction: Optimizing fruit quality and yield in agriculture requires accurately monitoring leaf nitrogen (N) status spatially and temporally throughout the growing season. Standard remote sensing approaches for assessing leaf N rely on proxies like vegetation indices or leaf chlorophyll a + b (Cab) content. However, limitations exist due to the Cab-N relationship’s saturation and early nutrient deficiency insensitivity. Methods: The study utilized Sentinel-2 satellite imagery to estimate a set of plant biochemical traits in large almond orchards in a two-year study. These traits, including leaf dry matter, leaf water content, and leaf Cab retrieved from the radiative transfer model, were used to explain the observed variability of leaf N. Airborne hyperspectral imagery-derived leaf N using Cab and solar-induced fluorescence served as a benchmark for validation. Results: Results demonstrate that plant traits quantified from Sentinel-2 were strongly associated with leaf N variability across the orchard, with a strong contribution from the estimated leaf Cab content and leaf dry matter biochemical constituent, outperforming the consistency of vegetation indices. The Sentinel-2 model explaining leaf N variability yielded r2 = 0.82 and nRMSE = 13% in a two-year dataset, obtaining consistent performance and trait contribution across both years. Conclusion: This study highlights the potential application of Sentinel-2 satellite imagery for monitoring leaf N variability in almond tree orchards. Incorporating plant biochemical traits allows for a more consistent and reliable prediction of leaf N compared to traditional vegetation indices over two years, making it a promising method for precision agriculture applications. [ABSTRACT FROM AUTHOR]

Background: Due to their intrinsic biological activity biocides can pose an unintended threat to various aquatic organisms. Monitoring data on the spatial distribution and temporal trends are needed to evaluate potential risks and the effectiveness of mitigation measures, but these are scarce for biocides in aquatic environments. In particular, even though many biocides tend to sorb to particles, there are only few studies investigating the contamination of suspended particulate matter (SPM). The aim of this study was to obtain an overview of the temporal trends of selected biocides in SPM using German rivers as an example. For this purpose, SPM from the German Environmental Specimen Bank was used for a retrospective trend assessment of a broad spectrum of biocides in integrated SPM samples (yearly composite) in six large German rivers between 2008 and 2021. Results: Overall 16 of 23 analyzed biocides were found, whereof 10 substances were detected in all samples. Highest concentrations were found for quaternary ammonium compounds (QACs, the sum of four analyzed QACs were up to 8.7 µg/g) and methyl-triclosan (up to 280 ng/g), a transformation product of the bactericide triclosan. Considerably lower concentrations in the range of 0.08 to 88 ng/g and < 0.03 to 13 ng/g were detected for azoles and triazines, respectively. The pyrethroid permethrin, which is highly toxic to aquatic organisms (invertebrates: NOEC = 0.0047 µg/L; fish: NOEC = 0.41 µg/L) as well as to sediment-dwelling organisms (Chironomidae: LC50 = 2.1 mg/kg and NOEC 0.1 mg/kg), was detected at several sampling sites (up to 11.2 ng/g). Concentrations of the other analyzed pyrethroids were below the respective quantification or detection limits. In general, for most compounds, concentrations were higher for locations with higher wastewater proportion, but overall no clear differences in biocide concentration pattern between the different sampling locations were observed. For cybutryne and triclosan significant decreasing concentration trends were observed. This is consistent with regulatory use restrictions and confirms their effectiveness. For benthic organisms a toxicological risk from the individual azole fungicides and QACs seems to be low. Conclusions: Explicit differences between sampling sites and temporary changes in local concentrations indicate regional variations of biocide emissions which hamper identification of long-term concentration trends. Moreover, time trends could be affected by remobilization of legacy contamination from contaminated sites. Hence, for biocides a continuous long-term monitoring is crucial to identify the effectiveness of recent restrictions and mitigation measures. [ABSTRACT FROM AUTHOR]

There is a growing interest in estimating urban CO2 emission from spaceborne imagery of the CO2 column-average dry-air mole fraction (XCO2). Emission estimation methods have been widely tested and applied to actual or synthetic images. However, there is still a lack of objective criteria for selecting images that are worth processing. This study analyzes the performances of an automated method for estimating urban emissions as a function of the targeted cities and of the atmospheric conditions. It uses synthetic data experiments with synthetic truth and 9920 synthetic satellite images of XCO2 over 31 of the largest cities across the world generated with a global adaptive-mesh model, the Ocean–Land–Atmosphere Model (OLAM), zoomed in at high resolution over these cities. We use a decision tree learning method applied to this ensemble of synthetic images to define criteria based on these emission and atmospheric conditions for the selection of suitable satellite images. We show that our automated method for the emission estimation, based on a Gaussian plume model, manages to produce estimates for 92 % of the synthetic images. Our learning method identifies two criteria, the wind direction's spatial variability and the targeted city's emission budget, that discriminate images whose processing yields reasonable emission estimates from those whose processing yields large errors. Images corresponding to low spatial variability in wind direction (less than 12°) and to high urban emissions (greater than 2.1 kt CO2 h−1) account for 47 % of the images, and their processing yields relative errors in the emission estimates with a median value of - 7 % and an interquartile range (IQR) of 56 %. Images corresponding to a high spatial variability in wind direction or to low urban emissions account for 53 % of our images, and their processing yield relative errors in the emission estimates with a median value of - 31 % and an IQR of 99 %. Despite such efficient filtering, the accuracy of the estimates corresponding to the former group of images varies widely from city to city. [ABSTRACT FROM AUTHOR]

The highly sensitive Ka-band cloud radar at the Barbados Cloud Observatory (BCO) frequently reveals radar reflectivity signals below - 50 dBZ within the convective sub-cloud layer. These so-called haze echoes are signals from hygroscopically grown sea salt aerosols. Within the Cloudnet target classification scheme, haze echoes are generally misclassified as precipitation (target class: "drizzle or rain"). We present a technique to discriminate between "drizzle or rain" and sea salt aerosols in Cloudnet that is applicable to marine Cloudnet sites. The method is based on deriving heuristic probability functions utilizing a combination of cloud radar reflectivity factor, radar mean Doppler velocity, and the ceilometer attenuated backscatter coefficient. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements at the BCO. The results are validated against the amount of precipitation detected by the Virga-Sniffer tool. We analyze data for measurements at BCO covering 2 years (July 2021–July 2023). A first-ever statistical analysis of the Cloudnet target classification product including the new "haze echo" target over 2 years at the BCO is presented. In the atmospheric column above the BCO, "drizzle or rain" is on average more frequent during the dry season compared to the wet season due to the higher occurrence of warm clouds contributing to the amount of precipitation. Haze echoes are identified about 4 times more often during the dry season compared to the wet season. The frequency of occurrence of "drizzle or rain" in Cloudnet caused by misclassified haze echoes is overestimated by up to 16 %. Supported by the Cloudnet statistics and the results obtained from the Virga-Sniffer tool, 48 % of detected warm clouds in the dry and wet season precipitate. The proportion of precipitation evaporating fully before reaching the ground (virga) is higher during the dry season. [ABSTRACT FROM AUTHOR]

Aluminum and its alloys are widely used in the busbar structures of electrolytic aluminum production. However, they are prone to corrosion and wear damage during use, leading to a decline in current-transmission efficiency and potentially causing safety issues. To repair damaged aluminum busbars, this paper explores the feasibility of using cold spraying technology for surface restoration. Using 6063 aluminum alloy as the substrate, AlZn/nickel-plated graphite composite coatings were applied through cold spraying. The effects of different nickel-plated graphite contents on the microstructure, mechanical properties, and corrosion resistance of the coatings were studied. Annealing treatments (200 °C, 300 °C, 400 °C) were further used to improve the coating's density and performance. The results show that with an increase in the nickel-plated graphite content, the porosity of the coating gradually increases, while the coating's density and bond strength improve. Additionally, the annealing treatment significantly enhanced the uniformity and hardness of the coating. Moreover, the cold-sprayed coatings exhibited excellent corrosion resistance, especially in the annealed coatings, which showed superior microstructural stability and lower corrosion current density. This study provides a new technological approach for the repair of aluminum busbars and offers an in-depth discussion on the application of cold spraying technology in the surface restoration of aluminum-based composite materials. [ABSTRACT FROM AUTHOR]

Somatic coliphages are a group of bacteriophages that infect Escherichia coli and can be used in aquatic environment for risk assessment of viral and fecal pollution. The objective of this work is to isolate bacteriophages specific to E. coli strain ATCC-13706, as well as characterization and evaluation of their stability. In this study, an enrichment method was used to isolate phages against E. coli strain ATCC 13706 from two wastewater treatment plants, and one opened drain. The isolated phages purified by successive single plaque isolation techniques and results indicated isolation of four different phages. The plaque sizes for isolated phages were between 1.0 and 4.0 mm in diameter with high titer ranging from 19 to 55 × 109 PFU/mL. Based on morphology, by Transmission Electron Microscopy, all phages were found to belong to tailed-phages order, Caudovirales, and were identified as members of the Myoviridae and Siphoviridae families. The characterization of these phages revealed high tolerance to temperature degrees ranging from 5 to 75°C, and viability over wide pH values ranging from 3 to 11. Some phages exhibit a wide range of hosts, which could be polyvalent phage, while others were host specific. 1.0 is the optimum ratio of Multiplicity of Infection (MOI) for these phages. In the phage efficiency test, MOI of 1.0 resulted in 63.93, 61.20, 69.39 and 79.14% reduction in bacterial cell count for EcoP1, EcoP2, EcoP3 and EcoP4, respectively, after 5hrs incubation at 37 ℃. In one step growth curve, the latent period and burst size of these phages were between 10 - 20 min and 44 - 62 PFU/cell, respectively. Our results indicated that the isolated phages are abundant in Egyptian sewage, with high diversity, and a wide host range, which can be used for biocontrol of some pathogenic bacteria. The isolated phages exhibit high tolerance to a wide range of pH values and temperature degrees, which supports the notion of using phages as indicators of fecal pollution in the Egyptian aquatic environment. [ABSTRACT FROM AUTHOR]

West Africa faces a complex range of challenges arising from climatic, social, economic, and ecological factors, which pose significant risks. The rapidly growing population, coupled with persistently low agricultural yield, further exacerbates these risks. A state-of-the-art monitoring and data derivation of agricultural systems are crucial for improving livelihoods and enhancing food security. Despite smallholder farming systems accounting for 80% of cultivated cropland area and providing about 42% of the total employment in West Africa, there exists a lack of a comprehensive overview of Remote Sensing (RS) products and studies specifically tailored to smallholder farming systems, which this review aims to address. Through a systematic literature review comprising 163 SCI papers sourced from the Web of Science database (Filter I), followed by a full-text review (Filter II), we analyze the RS sensors, spatiotemporal distribution, temporal scales, the crop types examined, and thematic foci employed in existing research. Our findings highlight the predominance of high to very high-resolution, multispectral sensors as the primary data source and we observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. By highlighting existing knowledge, this study identifies the potential of RS and pinpoints the key research gaps. This sets the stage for future investigations aimed at addressing critical challenges in West African smallholder agricultural systems. [ABSTRACT FROM AUTHOR]

Background: Blockchain technology can transform military operations, increasing security and transparency and gaining efficiency. It addresses many problems related to data security, privacy, communication, and supply chain management. The most researched aspects are its integration with emerging technologies, such as artificial intelligence, the IoT, application in uncrewed aerial vehicles, and secure communications. Methods: A systematic review of 43 peer-reviewed articles was performed to discover the applications of blockchain in defense. Key areas analyzed include the role of blockchain in securing communications, fostering transparency, promoting real-time data sharing, and using smart contracts for maintenance management. Challenges were assessed, including scalability, interoperability, and integration with the legacy system, alongside possible solutions, such as sharding and optimized consensus mechanisms. Results: In the case of blockchain, great potential benefits were shown in enhancing military operations, including secure communication, immutable record keeping, and real-time integration of data with the IoT and AI. Smart contracts optimized resource allocation and reduced maintenance procedures. However, challenges remain, such as scalability, interoperability, and high energy requirements. Proposed solutions, like sharding and hybrid architecture, show promise to address these issues. Conclusions: Blockchain is set to revolutionize the efficiency and security of the military. Its potential is enormous, but it must overcome scalability, interoperability, and integration issues. Further research and strategic adoption will thus allow blockchain to become one of the cornerstones of future military operations. [ABSTRACT FROM AUTHOR]

In the rapidly changing digital economy, manufacturing companies are under growing pressure to adopt new approaches to business management by developing digital capabilities. This research explores the role of digital technologies in enabling these capabilities, using the Digital Capability Model (DCM) as a guiding framework. While previous research often focused on theoretical perspectives, this study operationalizes the DCM by identifying specific applications of digital technologies that enhance business processes. Through a multiple case study methodology, eight manufacturing companies were examined to assess how digital technologies foster the development of digital capabilities. The case studies provide practical insights into the application of these technologies and their impact on organizational resilience and competitiveness, particularly in response to global disruptions such as the COVID-19 pandemic. Our findings reveal that certain technologies are more promising than others for enhancing digital capabilities and that their strategic implementation significantly improves a company's ability to navigate uncertainty. Embracing digital transformation not only mitigates operational risks but also ensures sustainable competitive advantages in an increasingly volatile and complex environment. This research bridges the gap between theory and practice, offering actionable insights for managers to strategically develop and leverage digital capabilities for long-term success. [ABSTRACT FROM AUTHOR]

Retrofitting legacy systems provides significant advantages by addressing compatibility issues with new devices and technologies, meeting current process requirements, and increasing security and regulatory compliance. The process starts by collecting requirements and evaluating the legacy system's attributes and limitations, followed by integrating modern technologies to improve efficiency, streamline processes, and enhancing performance and interoperability while leveraging existing facilities to reduce costs. A systematic approach ensures that updates align with modern technological standards, with performance evaluations conducted via qualitative and quantitative methods and system maturity assessed according to the Reference Architecture Model for Industries 4.0 (RAMI 4.0 model's) criteria for intelligent factories. By incorporating digital twin (DT) capabilities, which replicate the physical state of systems and provide real-time data updates, the retrofit strategy aligns the physical system with Industry 4.0 contexts, facilitating continuous improvement and seamless integration with modern processes. The goal is to advance the legacy system technologically to ensure seamless integration with contemporary processes, validated through RAMI criteria analysis for smart factories. As part of this process, digital twin architecture was built. This architecture was the basis for building and operating digital twins in the process. The methodology was used to enhance and transform legacy systems, creating the foundation for creating a fully digital twin. Using this method, these systems can be updated to meet the requirements of Industry 4.0. This ensures that they can work with new systems and share data in real time, which improves general operations. [ABSTRACT FROM AUTHOR]

With the rapid advancement of modern industries, pressure vessels and piping have become increasingly integral to sectors such as energy, petrochemicals, and process industries. To grasp the research and application status in the field of pressure vessel and piping safety, 670 publications in the Web of Science core database from 2008 to 2024 were taken as data samples in this paper. The knowledge mapping tools were used to carry out co-occurrence analysis, keyword burst detection, and co-citation analysis. The results show that the research in this field presents a multidisciplinary and cross-disciplinary state, involving multiple disciplines such as Nuclear Science and Technology, Engineering Mechanics, and Energy and Fuels. The "International Journal of Hydrogen Energy", "International Journal of Pressure Vessels and Piping", and "Nuclear Engineering and Design" are the primary publication outlets in this domain. The study identifies three major research hotspots: (1) the safety performance of pressure vessels and piping, (2) structural integrity, failure mechanisms, and stress analysis, and (3) numerical simulation and thermal–hydraulic analysis under various operating conditions. The current challenges can be summarized into three aspects: (1) addressing the safety risks brought by new technologies and materials, (2) promoting innovation and the application of detection and monitoring technologies, and (3) strengthening the building capacity for accident prevention and emergency management. Specific to China, the current challenges include the safety and management of aging equipment, the effective detection of circumferential weld cracks, the refinement of risk assessment models, and the advancement of smart technology applications. These findings offer valuable insights for advancing safety practices and guiding future research in this multidisciplinary field. [ABSTRACT FROM AUTHOR]

Floods have catastrophic effects worldwide, particularly in monsoonal Asia. This systematic review investigates the literature from the past two decades, focusing on the use of remote sensing (RS), Geographic Information Systems (GISs), and technologies for flood disaster management in South Asia, and addresses the urgent need for effective strategies in the face of escalating flood disasters. This study emphasizes the importance of tailored GIS- and RS-based flood disaster studies inspired by diverse research, particularly in India, Pakistan, Bangladesh, Sri Lanka, Nepal, Bhutan, Afghanistan, and the Maldives. Our dataset comprises 94 research articles from Google Scholar, Scopus, and ScienceDirect. The analysis revealed an upward trend after 2014, with a peak in 2023 for publications on flood-related topics, primarily within the scope of RS and GIS, flood-risk monitoring, and flood-risk assessment. Keyword analysis using VOSviewer revealed that out of 6402, the most used keyword was "climate change", with 360 occurrences. Bibliometric analysis shows that 1104 authors from 52 countries meet the five minimum document requirements. Indian and Pakistani researchers published the most number of papers, whereas Elsevier, Springer, and MDPI were the three largest publishers. Thematic analysis has identified several major research areas, including flood risk assessment, flood monitoring, early flood warning, RS and GIS, hydrological modeling, and urban planning. RS and GIS technologies have been shown to have transformative effects on early detection, accurate mapping, vulnerability assessment, decision support, community engagement, and cross-border collaboration. Future research directions include integrating advanced technologies, fine-tuning spatial resolution, multisensor data fusion, social–environmental integration, climate change adaptation strategies, community-centric early warning systems, policy integration, ethics and privacy protocols, and capacity-building initiatives. This systematic review provides extensive knowledge and offers valuable insights to help researchers, policymakers, practitioners, and communities address the intricate problems of flood management in the dynamic landscapes of South Asia. [ABSTRACT FROM AUTHOR]

This paper considers the features of a time series of plankton concentrations, which are further compared with such phenomena as the alteration of day and night and tidal processes. The analysis of experimental data recorded as a result of long-term monitoring measurements under field conditions showed that the diurnal variability in plankton concentrations can be described using a model harmonic function. At the same time, based on the parameters of the diurnal variability model, it is possible to build a bioindication system to detect the influence of abnormal environmental factors estimated as pollution. This study discusses the ideology of building such a system based on regular observations of the behavior of autochthonous plankton using a submersible digital holographic camera. [ABSTRACT FROM AUTHOR]

Data-driven models utilizing machine learning algorithms provide an effective approach for predicting power in heavy-duty gas turbines, extracting valuable insights from large-scale operational datasets. However, global unified models often struggle to meet the accuracy requirements of all data when dealing with complex and variable operating conditions, leading to limited prediction accuracy for local conditions. To address this problem, a clustering modification method is introduced to develop a novel power prediction model for heavy-duty gas turbines. In this study, the Support Vector Regression (SVR) prediction model is combined with a k-means clustering modification model, enabling the model to adapt to different operational conditions. Operational data from an E-class gas turbine are carefully preprocessed, including filtering, noise reduction, and steady-state selection, to enhance data quality. Then, the k-means algorithm is employed to classify operational conditions, with tailored modification models trained for each category. These modification models refine predictions to accommodate variations in specific operating states. Experimental results demonstrate that the composite model achieves a 32.66% reduction in MAPE and an increase in R2 to 0.9982 compared to single-model approaches. The analysis further highlights that training the model with 70% of the annual data achieves optimal prediction accuracy and stability. Additionally, the model significantly reduces high-error occurrences, with 75% of predictions having errors below 0.2946%. This method improves the precision and adaptability of power prediction for gas turbines, providing a practical framework that enhances the reliability of real-world applications and supports the advancement of data-driven energy systems. [ABSTRACT FROM AUTHOR]

To address the challenges associated with lightweight design and small object detection in infrared imaging for substation electrical equipment, this paper introduces an enhanced YOLOv8_Adv network model. This model builds on YOLOv8 through several strategic improvements. The backbone network incorporates PConv and FasterNet modules to substantially reduce the computational load and memory usage, thereby achieving model lightweighting. In the neck layer, GSConv and VoVGSCSP modules are utilized for multi-stage, multi-feature map fusion, complemented by the integration of the EMA attention mechanism to improve feature extraction. Additionally, a specialized detection layer for small objects is added to the head of the network, enhancing the model's performance in detecting small infrared targets. Experimental results demonstrate that YOLOv8_Adv achieves a 4.1% increase in mAP@0.5 compared to the baseline YOLOv8n. It also outperforms five existing baseline models, with the highest accuracy of 98.7%, and it reduces the computational complexity by 18.5%, thereby validating the effectiveness of the YOLOv8_Adv model. Furthermore, the effectiveness of the model in detecting small targets in infrared images makes it suitable for use in areas such as infrared surveillance, military target detection, and wildlife monitoring. [ABSTRACT FROM AUTHOR]

The city of Warsaw, Poland, has endured significant damage throughout its history, particularly during World War II. The city was bombed in September 1939, and many buildings were blown up following the Warsaw Ghetto Uprising in May 1943. The Warsaw Uprising in August and September 1944 led to further destruction from artillery bombardments and fires. Even after the surrender and civilian evacuation in October 1944, additional demolitions occurred, leaving almost 90% of Warsaw in ruins. Despite ongoing efforts to rebuild the city, many landmarks could not be fully reconstructed. However, invaluable historical archives preserve remnants of Warsaw's rich history. To reconstruct 3D models of pre-war buildings in Warsaw, a methodology was developed that integrates cartographic materials, spatial data, and results from tachymetric measurements. Historical maps, terrestrial and aerial photographs, and architectural blueprints from the National Archives in Warsaw were used to propose three distinct approaches to 3D modeling. Notable structures such as the Grand Synagogue, the Kamienica Theater building, and the Tłomackie buildings were selected for 3D modeling. These buildings either were destroyed or endured significant damage during the war. The 3D modeling process involved meticulous processing and calibrating of historical photographs alongside tachymetric surveying for accurate measurements. The proposed methodology showcases the feasibility of recreating 3D renderings of historical edifices, even those lost to time, utilizing archival cartographic data and spatial information from diverse sources. By leveraging cartographic heritage with digital advancements, a unique perspective on Warsaw's narrative can be gained, enriching the understanding of its past for both residents and professionals such as historians, archivists, and cartographers. [ABSTRACT FROM AUTHOR]

Multivariate time series (MTS) clustering has been an essential research topic in various domains over the past decades. However, inherent properties of MTS data—namely, temporal dynamics and inter-variable correlations—make MTS clustering challenging. These challenges can be addressed in Grassmann manifold learning combined with state-space dynamical modeling, which allows existing clustering techniques to be applicable using similarity measures defined on MTS data. In this paper, we present a systematic overview of Grassmann MTS clustering from a geometrical perspective, categorizing the methods into three approaches: (i) extrinsic, (ii) intrinsic, and (iii) semi-intrinsic. Consequently, we outline 11 methods for Grassmann clustering and demonstrate their effectiveness through a comparative experimental study using human motion gesture-derived MTS data. [ABSTRACT FROM AUTHOR]

On February 6, 2023 the Kahramanmaraş Earthquake Sequence caused significant ground shaking and catastrophic losses across south-central Türkiye and northwest Syria. These seismic events produced ionospheric perturbations detectable in Global Navigation Satellite System (GNSS) total electron content (TEC) measurements. This work aims to develop and incorporate a near-real-time (NRT) ionospheric disturbance detection method into JPL’s GUARDIAN system. Our method uses a Long Short-Term Memory (LSTM) neural network to detect anomalous ionospheric behavior, such as co-seismic ionospheric disturbances among others. Our method detected an anomalous signature after the second M w 7.5 earthquake at 10:24:48 UTC (13:24 local time) but did not alert after the first M w 7.8 earthquake at 01:17:34 UTC (04:17 local time), which had a visible disturbance of smaller amplitude likely due to lower ionization levels at night and potentially the multi-source mechanism of the slip. Plain Language Summary Seismic activity, including the destructive Kahramanmaraş Earthquake Sequence on February 6, 2023 in the Republic of Türkiye, result in vertical ground displacement that cause atmospheric waves. These waves propagate upwards to the outer atmosphere, disturbing the ionospheric electron content. This disturbance impacts the signals broadcast by positioning satellites (such as GPS) and received by ground-based receivers. If the receiver position is known, the impact to these signals can be used to measure the electron density disturbance caused by these seismically-induced atmospheric waves. Such studies usually rely on being aware of the event a priori. Using deep learning neural networks, we instead aim to detect anomalous signals automatically. We propose to utilise this method to detect seismically-induced disturbances over a large geographical area. The detection method proposed in this paper successfully detected an anomalous event in the ionosphere approximately ten minutes after the second earthquake in the Kahramanmaraş Earthquake Sequence. [ABSTRACT FROM AUTHOR]

Modeling the intricate interactions between fruit trees, their environments, soils, and economic factors continues to be a significant challenge in agricultural research globally, requiring a multidisciplinary approach. Despite advances in agricultural technology and algorithms, significant knowledge gaps persist in understanding and modeling these interactions. This review explores basic concepts related to modeling for tropical fruit production. It explains modeling development from sensor technologies, image analysis, databases, and algorithms for decision support systems while considering climate changes or edaphoclimatic limitations. We report the current fruit modeling tendencies showing a significant increase in publications on these topics starting in 2021, driven by the need for sustainable solutions and access to large agricultural databases. This study emphasizes inherent challenges in tropical fruit modeling, such as fruit tree cycles, costly and time-consuming experimentation, and the lack of standardized data. These limitations are evident in tropical fruit, where few models have been reported or validated for cocoa, avocado, durian, dragonfruit, banana, mango, or passion fruit. This study analyzes the classification of the algorithms related to tropical fruit into three main categories: supervised, unsupervised, and reinforcement learning, each with specific applications in agricultural management optimization. Crop classification and yield prediction use supervised models like neural networks and decision trees. Unsupervised models, like K-Means clustering, allow pattern identification without prior labels, which is useful for area segmentation and pest detection. Automation of irrigation and fertilization systems employs reinforcement learning algorithms to maximize efficiency. This multidisciplinary review discusses recent approaches to 1) Modeling Soil health and plant-soil interaction, 2) Yield prediction in tropical fruit orchards, 3) Integrating meteorological models for enhanced tropical fruit production, and 4) Economics of tropical fruit business through modeling. Furthermore, this review illustrates the complexity and multidisciplinary research on models for tropical fruit and platforms using agricultural models. Further opportunities to advance fruit modeling frameworks are indicated, requiring technical knowledge about the fruit crop requirements with user-friendly platforms to collect and access fruit tree data and site-specific agroecological conditions. [ABSTRACT FROM AUTHOR]