Local Anomalies Research Articles

Single-session Preoperative Embolization and Surgical Resection of Vascular Anomalies: A Case Series

Summary: Vascular anomalies encompass 2 broad classifications including vascular tumors and vascular malformations. The management of vascular anomalies is best approached by a multidisciplinary team. Surgical treatment can offer definitive treatment, but carries several risks such as intraoperative hemorrhage, recurrence, and collateral vascular formations from incomplete resections. Here, we present a case report of 3 patients with different vascular anomalies who were all treated by a multidisciplinary team including interventional radiology and plastic surgery. All underwent preoperative embolization before surgical excision, accomplished under a single anesthetic. Here, we discuss the surgical planning and overall approach to patients presenting with localized vascular anomalies and support the use of preoperative embolization with surgical excision under a single anesthetic as a treatment approach.

3D reconstruction and depth estimation method for local anomalies of rail surface based on multi-view stereo matching

Abstract Detecting rail surface anomalies has become crucial for ensuring the safety of train operations. However, manual detection methods are time-consuming and labor-intensive. Although traditional detection models based on one-dimensional signals or two-dimensional images can effectively identify the existence of defects, they are difficult to use to obtain local depth characteristic information, leading to difficulties in accurately assessing the extent of damage in abnormal regions. Rail track maintenance strategies are typically developed based on the detected different depth ranges of defects. To address this issue, this paper proposes a local depth estimation method based on the point cloud of the target rail surface reconstructed by PatchmatchNet. Firstly, according to the acquisition method of multi-view images and the sampling environment at the site, a practical data collection protocol is established for generating a multi-view dataset of rail surface. Next, dense point cloud of the target rail surface were reconstructed by PatchmatchNet. Subsequently, a method for estimating the local anomaly depth is developed based on the point cloud. Experimental results demonstrate that the proposed method achieves a minimum error of approximately 5.5% within a maximum depth range of 0.35mm, when compared to depth measurements obtained using a structured light camera. Finally, this paper presents a more comprehensive three-dimensional representation of the local abnormal physical structure, surpassing traditional one-dimensional and two-dimensional forms. This enhanced representation offers richer information for the effective determination of whether the anomalies constitute defects and aids in distinguishing true defects from other surface irregularities. Such advancement significantly improves the precision of defect assessment and supports the development of highly precise repair strategies.

Палеотектонический анализ отложений нижнеапшеронской литофациальной пачки Астраханского Прикаспия

The paleotectonic analysis of the Apsheron deposits of the Astrakhan Caspian Sia is considered. The main at-tention is paid to the analysis of the deposits of the lower Apsheron lithofacies unit, which includes the study of the thickness of the deposits, the construction of maps of thicknesses and trend values, as well as the analysis of the wave pattern of seismic works carried out in the territory of the Astrakhan Caspian Sia. The results of the study showed that during the Lower Apsheron time, significant tectonic processes took place in the region, including the growth of salt domes and the formation of alluvial fans. These processes had a significant impact on sedimentation and the distribution of sediment thicknesses. The conclusions about the influence of salt dome tectonics on sedimentation, as well as the role of paleorelief in the formation of local thickness anomalies are presented as well. The results obtained are important for understanding the geological history of the region and assessing its oil and gas potential. The data presented in the article are based on the analysis of geological data, including seismic data, and the use of paleotectonic analysis methods. The article describes in detail the research methods and provides examples of specific results obtained during the work.

A method for structural framework variation of a multilayer field associated with complicated geology

Background. During the probabilistic resource base estimation, the largest uncertainties are associated with the wide range of the gross rock volume. In the course of the formation area work, the resulting maps of gross thickness are constructed incorrectly. Objective. To take into account facies zoning when varying the structural framework if working with the Achimov strata of a multilayer field. Materials and methods. The applied approach implies accounting of the errors in structural constructions in proportion to the power depending on facies zones. The authors proposed an additional stage of mapping using proportion maps to avoid obtaining geologically unrealistic local power anomalies. Results. A set of modified maps of the total thickness of clinocyclites was obtained, reflecting the key features of the geological structure of the modeling object, particularly facies differentiation and related lateral heterogeneity. Conclusions. The chosen approach to constructing maps of gross thickness allowed to obtain geologically realistic maps of effective thickness, which, in turn, formed the basis for mapping of productive volume.

Negative Surface Chlorophyll Concentration Anomalies in the Southeast Arabian Sea During Summer in 2015 and 2019

AbstractSatellite observations revealed two extremely low surface chlorophyll concentration (SCC) events with a warm sea surface temperature anomaly in the southeastern Arabian Sea (SEAS, 6°–15°N, 72°–77°E) during the summer (July–August–September) in 2015 and 2019. We find that the physical processes leading to these two similar low SCC events are remarkably different. The low SCC in the SEAS during summer 2019 is mainly related to the weakened upwelling and deepening of the thermocline depth due to the combined effects of the local wind anomalies and the arrival of westward‐propagating downwelling coastal Kelvin wave driven by easterly anomalies near the eastern Sri Lanka during an extreme positive Indian Ocean Dipole (IOD) event. In summer 2015, a weaker positive IOD‐induced easterly anomalies in the southern Bay of Bengal also drives downwelling coastal Kelvin waves westward, deepening the thermocline in the SEAS. But unlike that in summer 2019, the local wind stress curl anomalies in the SEAS during summer 2015 favors upwelling, which counteracts the downward motion of the coastal Kelvin waves, leading to weaker downward transport (one‐third of that in 2019). Meanwhile, the upper ocean layer in the SEAS experiences extreme warming during summer owing to the development of 2015/2016 super El Niño. This substantial warming enhances upper oceanic stratification, which results in weaker vertical mixing and reduces the SCC to an extremely low level despite the much weaker IOD strength in 2015.

Строение центральной части палеопротерозойского Лосевского террейна по данным плотностного моделирования

The results of detailed three-dimensional density modeling of the upper crust of the central part of the Losevsky terrane located in the northern part of the Voronezh crystal massif are considered. The distribution of absolute crustal density values of to a depth of 16 km was obtained as a result of the inversion of local anomalies of the gravitational field in the Bouguer reduction. The solution of the inverse problem of gravimetry was implemented within the framework of the initial model, which was formed using digital geological and geophysical data from the study area: the regional density model of the East European Platform lithosphere and its corresponding regional gravity field; the field of local gravity anomalies, representing the difference between the observed and regional fields of the lithosphere model; generalized information on the density of sedimentary cover rocks and the crystal basement; the thickness scheme of the «gravity-active» layer, obtained on the basis of statistical analysis of the anomalous field; geological and topographic information. Quantitative interpretation of the gravimetry data allowed us to obtain fundamentally new information about the geological structure of the upper crust of the study area. The reliability of the obtained results is confirmed by the consistency of a priori data and the correspondence of the total model (local plus regional) and observed gravitational fields.

Assessment of soil pollution with polycyclic aromatic hydrocarbons in Ulan-Ude

: For the first time, polycyclic aromatic hydrocarbons (PAH) contamination of the soil cover of Ulan-Ude, the capital of the Republic of Buryatia, was studied. The content of 16 individual polyarenes was analyzed in the upper horizon of background chestnut and urban soils, sampled during soil and geochemical survey in July–August 2022. The average concentration of total PAHs in the soil cover of Ulan-Ude was 735 ng/g, which was 8 times as much as the concentration in the background soils (87 ng/g). The PAH amount in the soil of various functional zones decrease in the following order: railway transport > motor traffic > industrial > one-story residential > multi-story residential > recreational. At the same time, the amount of PAHs in the railway transport zone was 2.6–5.2 times higher than in other functional zones, which indicates that railway transport is the most powerful source of PAHs in the city. Contamination of soils with polyarenes in all functional zones is determined primarily by medium- (46%) and high-molecular-weight (41%) compounds. Among PAHs with low molecular weight, phenanthrene prevailed (9% of the total PAHs), but medium-molecular-weight fluoranthene (18%) and pyrene (13%) showed higher share; high-molecular-weight compounds dominated by benzo(ghi)perylene (12%), benzo(b)fluoranthene (10%), indeno(1,2,3-cd)pyrene (8%) and benzo(a)pyrene (6%). The total amount of 16 PAHs in the soil cover of the city varied within the range of 17–9 540 ng/g. The highest levels of pollution (3 226–9 540 ng/g) were recorded at 9 sampling points (4% of the city), which form the most contrasting local PAH anomalies. In more than half of the territory of Ulan-Ude, the total amount of PAHs did not exceed 500 ng/g. The indicator ratios of individual PAHs made it possible to determine the dominant types of sources, which include railway transport and coal combustion. The contribution to the environmental hazard of soil contamination with PAHs in Ulan-Ude was mainly made by benzo(a)pyrene (64%) and, to a lesser extent, by benzo(b)fluoranthene (9.6%), indeno(1,2,3-cd)pyrene (7.2%), dibenzo(ah)anthracene (6.5%) and benzo(a)anthracene (6.1%).

Unsupervised Machine Learning-Based Singularity Models: A Case Study of the Taiwan Strait Basin

The identification of geochemical anomalies in oil and gas indicators is a fundamental task in oil and gas exploration, as the process of oil and gas accumulation is a low probability event. Machine learning algorithms for anomaly detection are applicable to the identification of oil and gas geochemical anomalies related to oil and gas accumulation. However, when using oil and gas indicators for anomaly detection, the diversity of these indicators often leads to the influence of the indicator redundancy on the identification of such features. Therefore, it is particularly important to select appropriate oil and gas indicators for anomaly detection. In this study, a hybrid model combining unsupervised machine learning methods and singularity analysis methods was used to evaluate oil and gas indicator anomalies using geochemical data from the Taiwan Strait Basin. The models used in this study include the singularity index model (LSP), the principal component model combined with the singularity index model (PCA and LSP), and the cluster analysis combined with the principal component model and the singularity index model (CLA-PCA-LSP). PCA models can reduce the dimensions of the data and retain as much information as possible. CLA divides data samples into different groups, so that samples within the same group are more similar and samples between different groups are less similar. LSP is mainly used for measuring the setting and singular degree of local anomalies in multi-scale geochemistry, geophysics, and other types of local anomalies, and it has a unique advantage in extracting low and weak anomalies and nonlinear characteristics. The results of the study show that the results obtained using the CLA-PCA-LSP hybrid model are very similar to those obtained by performing PCA on the entire index and then calculating the singularity index. This also verifies that, for the study areas of the Jiulongjiang Depression and Jinjiang Depression, we can select oil and gas indicators that are favorable for exploration analysis, without including all indicators in the analysis scope, thereby improving the computational efficiency. The application of a singularity analysis method and generalized self-similarity principle in extracting the geochemical information of oil and gas indicators in the Taiwan Strait Basin highlights key technologies such as the identification of weak anomalies, decomposition of composite anomalies, and integration of spatial information. The combination anomalies delineated by the singularity analysis method and S-A method not only reflect the spatial relationship with known oil and gas reservoir distribution, but also show the multiple combination anomalies in unknown areas, providing favorable guidance for the next exploration direction in the Taiwan Strait Basin.

Ecological and Geochemical Assessment of the State of Soils in the City of Baikalsk According to the Content of Polycyclic Aromatic Hydrocarbons

The pollution of the topsoils of the city of Baikalsk (Irkutsk region) under the influence of industrial emissions and wastes of the Baikal Pulp and Paper Mill (BPPM) was studied. The content of 16 individual PAH structures in samples of urban and background soils taken during the soil geochemical survey in the summer of 2019 was analyzed. Relatively low levels of PAH content were found in the lignin sludge from the BPPM and СHP ash. The concentration of total PAHs in CHP ash reaches 46 mg/ kg with a predominance of low molecular weight compounds (the proportion of naphthalene and its homologues is 24% and 34% of the total PAHs, respectively), among high molecular weight PAHs, 5-nuclear benzo(b)fluoranthene dominates (16%). In lignin sludge, the amount of PAHs is 7.16 mg/kg with the predominance of benzo(b)fluoranthene (83%). In the soils of Baikalsk, the average total content of PAHs (38.4 mg/kg) is 5 times higher than the background content. In urban soils, 4–5-nuclear fluoranthene (61.1%) and benzo(b)fluoranthene (29.4%) prevail. This makes it possible to attribute soil pollution to the fluoranthene type. The soils of the motor transport (total PAH 105 mg/kg) and industrial (59.5 mg/ kg) zones are the most polluted, where the most contrasting PAH anomalies were formed. In descending order of the amount of PAHs, the land use zones of the city form a series: motor transport industrial residential one-storey railway transport residential multi-storey recreational zone. Several local anomalies in the amount of PAHs are distinguished, forming two large pollution halos in the western and eastern parts of the city. The leading factors in the accumulation of high molecular weight PAHs in soils are acid-alkaline conditions and soil organic matter, while the accumulation of low molecular weight polyarenes is mainly controlled by pH. The environmental hazard of pollution of Baikalsk soils with polyarenes is due to benzo(b)fluoranthene, its contribution is 83.5%.

Microplastic and heavy metal contamination in sediments of the high-altitude Nundkol Lake of northwestern Himalayas-Kashmir

This study investigates heavy metal and microplastic contamination in the sediment of Nundkol Lake, a high-altitude glacial lake in the northwestern Himalayas. Sediment analysis revealed heavy metal concentrations significantly exceeding background levels, with average concentrations of cobalt (Co), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), zinc (Zn), and chromium (Cr) recorded at 40.991 ppm, 54.434 ppm, 87,843.59 ppm, 1544.566 ppm, 83.288 ppm, 16.312 ppm, 104.571 ppm, and 106.514 ppm, respectively. Notably, the highest levels of Cu, Fe, Mn, and Pb were observed in sample GL2, indicating potential local geochemical anomalies. The trace elements were quantified using a Graphite Furnace - Atomic Absorption Spectrophotometer (Perkin Elmer-PinAAcle 900AA) for high precision.Microplastic analysis revealed an average abundance of 76±12.08 items/kg dry weight, predominantly fibers. The microplastics were mostly red (55.26 %), blue (36.84 %), orange (5.26 %), and green (2.63 %). Raman spectroscopy identified Polyamides (PA) as the predominant type (86.5 %), with the remainder being Polyethylene (PE) and Polypropylene (PP). The results suggest tourism, fishing, and trekking activities as major contributors to microplastic presence. This study uniquely combines the assessment of heavy metal and microplastic contamination in a high-altitude lake, providing a comprehensive view of pollutant dynamics. The findings highlight the need for tailored mitigation strategies and comprehensive monitoring programs to protect this fragile region. Applications include informing environmental policy, guiding pollution mitigation efforts, and promoting sustainable tourism practices. This study offers valuable insights into the environmental health of high-altitude lakes and underscores the need for efforts to mitigate anthropogenic impacts.

A Model‐Based Investigation of the Recent Rebound of Shelf Water Salinity in the Ross Sea

AbstractIntense atmosphere‐ocean‐ice interactions in the Ross Sea play a vital role in global overturning circulation by supplying saline and dense shelf waters. Since the 1960s, freshening of the Ross Sea shelf water has led to a decline in Antarctic Bottom Water formation. However, during 2012–2018, salinity of the western Ross Sea has rebounded. This study adopts a global ocean‐sea ice model to investigate the causes of this salinity rebound. Model‐based surface salinity budget analysis indicates that the salinity rebound was driven by increased brine rejection from sea ice formation, triggered by nearly equal effects of local anomalous winds and surface heat flux. The local divergent wind anomalies promoted local sea ice formation by creating a thin ice area, while cooling heat flux anomaly decreased the surface temperature, increasing sea ice production as well. This highlights the importance of understanding local climate variability in projecting future dense shelf water change.

Detecting 3D Salinity Anomalies from Soil Sampling Points: A Case Study of the Yellow River Delta, China

Rapidly capturing the spatial distribution of soil salinity plays important roles in saline soils’ management. Existing studies mostly focus on the macroscopic distribution of soil-salinity changes, lacking effective methods to detect the structure of micro-regional areas of soil-salinity anomalies. To overcome this problem, this study proposes a 3D Soil-Salinity Anomaly Structure Extraction (3D-SSAS) methodology to discover soil-salinity anomalies and step forward in revealing the irregular 3D structure of soil-anomaly salinity areas from limited sampling points. We first interpolate the sampling points to soil voxels using 3D EBK. A novel concept, the Local Anomaly Index (LAI), is developed to identify the candidate soil-salinity anomalies with the greatest amplitude of change. By performing differential calculations on the LAI sequence to determine the threshold, the anomaly candidates are selected. Finally, we adopt 3D DBSCAN to construct anomalous candidates as a 3D soil-salinity anomaly structure. The experimental results from the Yellow River Delta data set show that 3D-SSAS can effectively identify the 3D structure of salinity-anomaly areas, which are highly correlated with the geographical distribution mechanism of soil salinity. This study provides a novel method for soil science, which is conducive to further research on the complex variation process of soil salinity’s spatial distribution.

Dominant modes of interannual variability in spring compound dry and hot events over Northern Asia and the possible mechanisms

In this study, spatial and temporal variations of spring compound dry and hot events (CDHEs) over northern Asia (NA) during 1950–2020 and the related possible mechanisms are investigated on the interannual timescale. The standardized compound event indicator (SCEI) is used to represent compound dry and hot conditions over NA, which is validated by the observed summer case of CDHEs over western Russia in 2010. The first empirical orthogonal function (EOF1) mode of the SCEI over NA presents a monopole pattern, while the EOF2 mode shows an east-west dipole pattern. Possible mechanism analysis indicates that the CDHEs tend to be modulated by local high-pressure anomalies, accompanied by reduced cloud cover and more solar radiation. The high-pressure anomalies can lead to warm temperature and water vapor divergence, which synergistically contributes to the occurrence of CDHEs. Further analysis shows that the EOF1 mode is jointly affected by the Scandinavian (SCAND) pattern and the Arctic Oscillation (AO); while the EOF2 mode is influenced by the Atlantic-Eurasian (AEA) teleconnection. Moreover, the North Atlantic tripolar pattern of sea surface temperature (SST) can influence the EOF1 mode through changing the AO. And the diagonal tripolar SST pattern over the North Atlantic affects the EOF2 mode via altering the AEA pattern. The influence of the SST patterns is further confirmed by numerical experiments using the Community Atmospheric Model version CAM-5.3.

Electricity theft detection model based on the CEEMDAN-CNN-ViT

Abstract Most of the traditional power theft detection methods construct the model directly on the basis of the original power sequences, and do not simultaneously consider the long-period dependencies in the long-time sequences and the local connectivity features between periods, which limits the deep excavation of the behavioral laws of power users. In order to further improve the accuracy of power theft detection, this paper proposes a high-precision power theft detection model that integrates local anomaly filtering, energy consumption decomposition, and multi-feature fusion strategies. First, local anomaly filtering is used to eliminate local anomalies in the normalized energy consumption data to avoid the misleading effect of short-term abnormal behavior on the model. Then, the energy consumption decomposition based on CEEMDAN selects specific frequency band data that can accurately characterize the pattern of power theft users to improve the accuracy of power theft detection. Next, the long-time periodic features in the two-dimensional data and the short-time local features in the one-dimensional sequences are integrated by multi-feature fusion to enhance the adaptability of the model. The results show that the proposed model can effectively improve the detection accuracy, detection completeness, F1 score, and accuracy compared with the existing methods.

Deep Geological Structure Analysis of the Dongyang Area, Fujian, China: Insights from Integrated Gravity and Magnetic Data

To explore the deep geological structure of the Dongyang area in Fujian, China, gravity data from the area and its surroundings were collected and processed. Additionally, a high-precision magnetic survey was conducted in the Zhongxian region of this area, with subsequent analysis of the magnetic anomalies. Through the integration of regional geological data, a comprehensive analysis was carried out on the characteristics of gravity–magnetic anomalies and deep geological structures in the Dongyang area. The study indicates that the primary portion of the Dongyang area lies southwest of the expansive circular volcanic structure spanning Dehua to Yongtai. Two significant residual gravity anomalies were identified within the region, interpreted as the Xiaoban-Shuangqishan and Dongyang-Lingtouping residual gravity-positive anomalies. In the Zhongxian region, the magnetic field exhibits complexity with notable amplitude variations. Positive anomalies predominate in the western and northern sectors, while localized positive anomalies are prominent in the eastern region. The central area portrays a circular and disordered mix of positive and negative anomalies. Particularly distinctive are the band-shaped and fan-shaped negative anomalies curving from northeast to southeast through the central region. Various positive and negative anomalies of varying strengths, gradients, and orientations overlay both positive and negative magnetic backgrounds in specific locales. Moreover, the Dongyang area showcases well-developed fault structures, primarily oriented in northeast and northwest directions. Leveraging the regional magnetic attributes in conjunction with regional geological data, 39 faults were deduced in the Zhongxian region of the Dongyang area, delineating three promising mineralization zones.

Hotter weather, less of a hoax? Testing the longitudinal association between experience of temperature anomalies and belief in climate change conspiracy theories

While people across the world are experiencing hotter summers, there is still a considerable number of people who believe that climate change is a hoax. It thus raises the question of whether the experience of local temperature anomalies would influence people's beliefs about climate change conspiracy. To address this question, we conducted a two-wave longitudinal study with participants from the U.S. (Study 1) and mainland China (Study 2), respectively. We expect that the experience of temperature anomalies would serve as a cognitive proxy that counteracts the hoax conspiracy narratives and thus would be negatively related to belief in such narratives. We further explored whether this negative association would be related to less perceived psychological distance, stronger negative emotions, and more climate change-related information exposure; and whether it would be weaker among people who tended to interpret things in a conspiracist manner (i.e., conspiracy mentality). In Study 1, our results support the hypothesized links cross-sectionally and longitudinally on the subjective experience of hotter summer, except for climate change-related information exposure. In Study 2, we found only cross-sectional associations between the constructs, except for climate change-related information exposure. Lastly, we found mixed evidence regarding the moderating role of conspiracy mentality between the studies. Our findings provide initial support that climate change conspiracy beliefs are malleable to people's subjective experience of temperature anomalies. The happening of temperature anomalies could be a crucial opportunity for promoting scientific understanding of climate change.

Structural Controls on Fault Slip Models of the 6 February 2023 Kahramanmaraş, Türkiye Earthquake Doublet With Finite Element Analyses

AbstractTwo major earthquakes of Mw7.8 and Mw7.5 ruptured the Southern East Anatolian Fault (SEAF) and the Savrun‐Çardak‐Sürgü fault (SCSF), devastating southeast Türkiye and northwest Syria on 6 February 2023. We adopt innovative nonlinear and linear approaches to analyze the coseismic ground displacements and estimate the complex slip geometry. Unlike conventional analytical solutions that simplify crust heterogeneity, finite‐element fault models invert the displacement data and simulate the dual‐fault geometry with non‐uniformly distributed shallow crustal materials. Our results suggest the west‐dipping SEAF and north‐dipping SCSF accommodate earthquake slips of >10 m. Their respective slip distributions and proximal aftershocks correlate spatially with local seismic velocity anomalies (i.e., ΔVp and ΔVs), which implies differences in structural control along these two faults and provides insights into assessing the seismic hazard of mixed incipient‐mature fault systems.

Building the fracture network model for the Okuaizu geothermal field based on microseismic data analysis

Understanding flow behavior in a geothermal reservoir is important for managing sustainable geothermal energy extraction. Fluid flow in geothermal reservoirs generally occurs in complex existing fracture systems in which the reservoirs are situated in highly fractured rocks. To simulate a discrete fracture model, the location and orientation of the fracture were computed using statistical processes and observational data. In many cases, estimating the location and orientation of fractures from 1D borehole logging data is challenging. In this study, we used microseismic data to build the fracture network systems and extract the detailed positions and dimensions of the fractures. We used the microseismic data recorded at the Okuaizu Geothermal Field, Fukushima Prefecture, Japan, from 2019 to 2021. First, we located the hypocenters, removing the effect of uncertainty in the velocity structure of the geothermal fluids. We relocated and clustered the seismic events based on waveform similarity. We analyzed each cluster to define the fracture orientation using principal component analysis (PCA) and focal mechanism (FM) analysis. We used the P polarity with the S/P ratio as a constraint for a better fault-plane solution. With PCA, we can extract the fracture dimension of each cluster. Our cluster analysis showed that the clusters were not always planar fractures, and we interpreted them as fracture zones. Based on the consistency between PCA and FM, each cluster/fracture zone was classified into three conceptual models to characterize the fracture network system in this field. This model showed variations in the orientation of small fractures within the fracture zone. We characterized the spatial variation in fracture distribution and orientations in the reservoir and demonstrated the fracture network system of this field. The fracture zone near the injection well has a N–S strike, and the dip is above 80°; however, the fracture zone in the northeastern part of the injection well has a NW–SE strike with a dip between 60° and 80°. The fracture network system estimated in this study is crucial for robust reservoir modeling because our model is more realistic, observation-orientated, and includes local anomalies of reservoir properties.Graphical

Influence of material and geometry parameters on resonance linewidths of plasmonic modes in gratings made from highly doped Ge1− x Sn x

Highly doped group IV semiconductors such as Ge or GeSn are promising candidates for plasmonic mid infrared applications. The lower effective mass of GeSn alloys in comparison to pure Ge can result in lower plasma wavelengths and extend the application wavelength range. Devices made from doped GeSn alloys, therefore, are one interesting route towards plasmonic applications in the mid-infrared wavelength range, possibly extending to the NIR. Here, we specifically explore how spectrally narrow plasmonic resonances can be obtained in comb-like grating antennas by combining aspects of material growth with geometry optimization. We investigate both in simulation and in experiment how the interplay of localised surface plasmon resonances and Rayleigh anomalies can be tuned to achieve narrow extinction peaks originating from the resulting surface lattice resonances generated in our antennas made from highly doped Ge1−x Sn x .

Header Height Detection and Terrain-Adaptive Control Strategy Using Area Array LiDAR

During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is an urgent need to enhance the automation level. Conventional methods frequently employ single-point measurements and lack extensive area coverage, which means their results do not fully represent the terrain’s variations in the area and are prone to local anomalies. Given the inherently undulating terrain of farmland during harvesting, a control strategy that does not adjust for minor undulations but only for significant ones proves to be more rational. To this end, a sine wave superposition model was established to simulate three-dimensional ground elevation changes, and an area array LiDAR was used to collect 8 × 8 data for the header height. The effects of mounds and stubble on the measurement results were analyzed, and a dynamic process simulation model for the solenoid valve core was developed to analyze the on/off delay characteristics of a three-position four-way electromagnetic directional valve. Moreover, a physical model of the hydraulic system was constructed based on the Simscape module in Simulink, and the Bang Bang switch predictive control system based on position threshold was introduced to achieve early switching of the electromagnetic directional valve circuit. In addition, an automatic control system for cutting platform height was designed based on an STM32 microcontroller. The control system was tested on the hydraulic automatic control test rig developed by Shanxi Agricultural University. The simulation and experimental results demonstrated that the control system and strategy were robust to output disturbances, effectively enhancing the intelligence and environmental adaptability of agricultural machinery operations.