Delineation Of Areas Research Articles

Delineation of Vulnerable Areas Affected by the Eruption of Mount Agung, Bali

Vulnerability is a condition of a community or society that causes the inability to face the threat of disaster. Geographic information systems (GIS) have become essential tools in evaluating natural hazards and risks which is an aspect to define vulnerability of the volcano eruption-affected area. Based on activity of volcano, it is necessary to delineate the vulnerability of areas affected by the eruption of Mount Agung by integrating the Mount Agung Area Network Map and Geological Map with physical vulnerability data and social vulnerability. This research using processing methods and spatial analysis of Geographic Information Systems (GIS) and remote sensing methods. Geographic Information System (GIS) is used to create vulnerability maps using scoring, weighting, and overlay methods. The data used for making the vulnerability map include administrative boundaries, DEM (Digital Elevation Model), secondary data from the BPS 2020 Karangasem Regencyt in Figures, geological maps, settlement data and land cover maps. The results based on these data show the number of areas affected by the eruption of Mount Agung, the data states that the data area with low vulnerability has an area of 0.001490 Ha. Medium vulnerability data has an eruption affected area of 47.024493897 Ha. While the high vulnerability has an affected area of 36.878843877 Ha. Differences in the level of vulnerability are influenced by social vulnerability, physical vulnerability, and environmental vulnerability.Keywords: Delineation, Eruption, GIS, Mount Agung, Vulnerability

Assessment of lymph node area coverage with total marrow irradiation and implementation of total marrow and lymphoid irradiation using automated deep learning-based segmentation.

Total marrow irradiation (TMI) and total marrow and lymphoid irradiation (TMLI) have the advantages. However, delineating target lesions according to TMI and TMLI plans is labor-intensive and time-consuming. In addition, although the delineation of target lesions between TMI and TMLI differs, the clinical distinction is not clear, and the lymph node (LN) area coverage during TMI remains uncertain. Accordingly, this study calculates the LN area coverage according to the TMI plan. Further, a deep learning-based model for delineating LN areas is trained and evaluated. Whole-body regional LN areas were manually contoured in patients treated according to a TMI plan. The dose coverage of the delineated LN areas in the TMI plan was estimated. To train the deep learning model for automatic segmentation, additional whole-body computed tomography data were obtained from other patients. The patients and data were divided into training/validation and test groups and models were developed using the "nnU-NET" framework. The trained models were evaluated using Dice similarity coefficient (DSC), precision, recall, and Hausdorff distance 95 (HD95). The time required to contour and trim predicted results manually using the deep learning model was measured and compared. The dose coverage for LN areas by TMI plan had V100% (the percentage of volume receiving 100% of the prescribed dose), V95%, and V90% median values of 46.0%, 62.1%, and 73.5%, respectively. The lowest V100% values were identified in the inguinal (14.7%), external iliac (21.8%), and para-aortic (42.8%) LNs. The median values of DSC, precision, recall, and HD95 of the trained model were 0.79, 0.83, 0.76, and 2.63, respectively. The time for manual contouring and simply modified predicted contouring were statistically significantly different. The dose coverage in the inguinal, external iliac, and para-aortic LN areas was suboptimal when treatment is administered according to the TMI plan. This research demonstrates that the automatic delineation of LN areas using deep learning can facilitate the implementation of TMLI.

Observations of the effectiveness, dosage, and prognosis of intensity-modulated radiation therapy under ultrasonic guidance for cervical cancer patients.

Volumetric modulated arc therapy (VMAT) guided by ultrasound is a novel radiation therapy technique that facilitates the delineation of the tumor target area under image guidance, enhancing the precision of radiation therapy and maximizing the protection of surrounding tissues. The objective of this paper is to investigate the effectiveness of VMAT under ultrasonic guidance for cervical cancer patients and its impact on radiotherapy dosage and prognosis. A retrospective analysis encompassed 128 instances of cervical cancer patients who were admitted to our medical facility between April 2019 and April 2021. The patients were categorized into an observation cohort and a control cohort, depending on variations in treatment modalities post-admission. The control group underwent conventional radiotherapy, whereas the observation group received VMAT guided by ultrasound. Clinical efficacy, average radiation dosages (in the radiotherapy target area, rectum, and bladder), radiotherapy-related toxicities during treatment, and one-year survival rates were compared between the two groups. Additionally, variances in pre- and post-treatment serum levels of squamous cell carcinoma antigen (SCC-Ag), carcinoembryonic antigen (CEA), and carbohydrate antigen 724 (CA724) were subjected to assessment. When compared to the control group (64.52%), the observation cohort's comprehensive effectiveness rate was considerably greater (80.30%). The observation group saw lower average radiation exposures and a reduction in the post-treatment concentrations of CEA, SCC-Ag, and CA724. The overall incidence of adverse effects from radiation treatment also declined. The observation group had a greater one-year survival rate (90.48%) than the control group (73.33%). When comparing the observation cohort to the control group, Kaplan-Meier survival analysis showed a significantly higher one-year survival rate (Log-Rank = 6.530, P= 0.011). VMAT guided by ultrasound for patients with cervical cancer demonstrates promising short- and long-term treatment outcomes. It also leads to improvements in serum CEA, SCC-Ag, and CA724 levels, as well as reductions in the average radiation dosages to the radiotherapy target area, rectum, and bladder. This approach warrants attention from clinicians in clinical practice.

An Optimized Smoke Segmentation Method for Forest and Grassland Fire Based on the UNet Framework

Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification—an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle–high-resolution satellite datasets for forest and grassland fire smoke, and the associated challenges in identifying smoke, the CAF_SmokeSEG dataset was constructed for smoke segmentation. The dataset was created based on GF-6 WFV smoke images of forest and grassland fire globally from 2019 to 2022. Then, an optimized segmentation algorithm, GFUNet, was proposed based on the UNet framework. Through comprehensive analysis, including method comparison, module ablation, band combination, and data transferability experiments, this study revealed that GF-6 WFV data effectively represent information related to forest and grassland fire smoke. The CAF_SmokeSEG dataset was found to be valuable for pixel-level smoke segmentation tasks. GFUNet exhibited robust smoke feature learning capability and segmentation stability. It demonstrated clear smoke area delineation, significantly outperforming UNet and other optimized methods, with an F1-Score and Jaccard coefficient of 85.50% and 75.76%, respectively. Additionally, augmenting the common spectral bands with additional bands improved the smoke segmentation accuracy, particularly shorter-wavelength bands like the coastal blue band, outperforming longer-wavelength bands such as the red-edge band. GFUNet was trained on the combination of red, green, blue, and NIR bands from common multispectral sensors. The method showed promising transferability and enabled the segmentation of smoke areas in GF-1 WFV and HJ-2A/B CCD images with comparable spatial resolution and similar bands. The integration of high spatiotemporal multispectral data like GF-6 WFV with the advanced information extraction capabilities of deep learning algorithms effectively meets the practical needs for pixel-level identification of smoke areas in forest and grassland fire scenarios. It shows promise in improving and optimizing existing forest and grassland fire monitoring systems, providing valuable decision-making support for fire monitoring and early warning systems.

Structure and Technology of Structuring Marine Areas Using Remote Sensing Data in Semi-Arid Conditions

Correctly distinguishing urbanized marine areas from bare ground is becoming increasingly important in the context of urbanization and environmental management. This study explores the feasibility of using spectral indices to distinguish urbanized marine areas from bare ground with similar spectral signatures. The Landsat-8 data were analyzed and different spectral indices were calculated and tested for their effectiveness in identifying urban areas. The results show that the Normalized Difference Built-up Index (NDBI) and the Vegetation Blending Unit (VBU) have promising potential for distinguishing urban areas from bare ground. The identification of category boundaries based on the distribution of minimum and maximum values of different spectral indices allows a clear delineation of urbanized areas. This study highlights the usefulness of spectral indices in extracting urbanized marine areas from remote sensing data and has practical implications for urban planners, decision makers, and stakeholders involved in urban planning, land use management, and environmental protection. However, caution is needed to avoid misclassification, and careful selection of appropriate indices is crucial to achieve correct classification results.

Optimizing Recharge Area Delineation for Small- to Medium-Sized Groundwater Systems through Coupling Methods and Numerical Modeling: A Case Study of Linfen City, China

In previous investigations, the demarcation of capture zones within a specific research area predominantly relied on a singular method, leading to pronounced limitations and uncertainties. To address this challenge, an extensive field survey was conducted, focusing on the systematic classification of water sources in the Linfen City region. Building upon this classification, an intricate fusion of a hydrogeological analysis and formulaic methodology was employed. This integrated approach, coupled with independent numerical simulation methods, was applied to delineate recharge areas for both alluvial fan pore water in piedmont regions and exposed karst water in small- to medium-sized water sources. Simultaneously, precise spatial interpolation was carried out on water quality monitoring data from supply wells within the water source area for the year 2020. This meticulous analysis facilitated the determination of the spatial distribution characteristics of hydrochemical elements. To assess the water quality within the capture zone, the class III groundwater quality standards of China were employed as a pivotal tool for validating the results of the delineation of water source recharge areas. In the final analysis, a comparative study between the integrated coupling method and numerical simulation outcomes revealed the successful delineation of the boundaries for the water supply areas of Tumen and Caojiapo in Linfen City, covering areas of 5.5 km2 and 22.29 km2, respectively. Simultaneously, the combination of the three methods accurately outlined the boundary of the Hexi water supply area, encompassing an area of 2.5224 km2. These results vividly illustrate that the amalgamation of various methodologies proves more beneficial for the precise delineation of capture zones, particularly in diverse types and scales of groundwater sources. The synergy exhibited by these three methods underscores their collective efficacy, providing a more comprehensive and intuitive delineation of the recharge areas for small- to medium-sized water sources. Consequently, these findings significantly enhance the practical application value of the study and hold promise in making substantial contributions to local groundwater security and management initiatives.

Delineation of riparian areas based on the application of two-dimension hydraulic modelling

This paper presents a proposal for riparian area delineation relying on topographical, hydrological, vegetation, and soil data together with numerical modelling of the river hydrodynamics. The two-dimensional model Iber is used to simulate 2.5, 10, 50, and 100-years return period flood events, and new code is developed to simulate the main hydrological processes of the river-riparian system to generate riparian zone maps. Results show that changes in topography and discharge direction between river and groundwater both influence the riparian area extent, and that temporal evolution of the riparian zone is much slower than that of the flood, and its extension can continue to increase while the flood recedes, but only to a certain extent, conditioned by topography, soil characteristics, and vegetation. A simple but efficient numerical code for understanding and simulating the riparian dynamics has been developed, which constitutes a proposal for a new riparian delineation approach useful for research and management applications, and which can also be a useful tool for gaining a better understanding of the riparian boundary behavior under different ecohydrological conditions.

A Spatial Optimization Model for Delineating Metropolitan Areas

A metropolitan area comprises a collection of cities and counties bound by strong socioeconomic ties. Despite the pivotal role that metropolitan areas play in regional economics, their delineation remains a challenging task for researchers and urban planners. Current threshold-based delineation methods select counties based on their connection strength with prespecified core counties. Such an approach often neglects potential interactions among outlying counties and fails to identify polycentric urban structures. The delineation of a metropolitan area is fundamentally a spatial optimization problem, whose objective is to identify a set of counties with high interconnectivity while also meeting specific constraints, such as area, contiguity, and shape. In this study, we present a novel spatial optimization model designed for metropolitan area delineation. This model aims to maximize intercounty connection strength in terms of both industry and daily life. This approach ensures a more accurate representation of the multicore structure that is commonly seen in developed metropolitan areas. Additionally, our model avoids the possibility of holes in metropolitan area delineation, leading to more coherent and logical metropolitan boundaries. We provide a mixed-integer programming formulation for the proposed model. Its efficacy is demonstrated by delineating the boundaries of the Nanjing and Lhasa metropolitan areas. This study also delves into discussions and policy implications pertinent to both of these metropolitan areas.

The Sea Cliffs Eposure and Management in 2 Mai – Vama Veche Shore Sector

A major concern for coastal communities is shoreline change. This leads to the progressive loss of beaches. In order to anticipate the development of coastal management plans, it is important to understand the causes of coastal erosion. In the absence of development and consolidation works, the Tourist Area of National Interest, 2 Mai - Vama Veche, is undergoing progressive degradation. Due to the advanced erosion areas, the coastal cliffs are in danger of collapsing more quickly. The delineation of the affected coastal area, as well as the highlighting of methodological criteria, especially at the sea-land interface, assumes the use of the coastline as a key element in the delineation of beach and cliff areas, and the use of the crest of the cliff slope in the delineation of cliff areas. This takes into account the natural variability of geomorphological processes, but also the vulnerability of sectors and subsectors of the southern Romanian coast.

Detection of Benggang in Remote Sensing Imagery through Integration of Segmentation Anything Model with Object-Based Classification

Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a large spatial scale, offering essential data for aiding in the remediation efforts for these areas. Nevertheless, traditional image segmentation methods may face challenges in accurately delineating Benggang areas. Consequently, the extraction of spatial and textural features from these areas can be susceptible to inaccuracies, potentially compromising the detection accuracy of Benggang areas. To address this issue, this study proposed a novel approach that integrates Segment Anything Model (SAM) and OBC for Benggang detection. The SAM was used to segment HR remote sensing imagery to delineate the boundaries of Benggang areas. After that, the OBC was employed to identify Benggang areas based on spectral, geometrical, and textural features. In comparison to traditional pixel-based classification using the random forest classifier (RFC-PBC) and OBC based on the multi-resolution segmentation (MRS-OBC), the proposed SAM-OBC exhibited superior performance, achieving a detection accuracy of 85.46%, a false alarm rate of 2.19%, and an overall accuracy of 96.48%. The feature importance analysis conducted with random forests highlighted the GLDV Entropy, GLDV Angular Second Moment (ASM), and GLCM ASM as the most pivotal features for the identification of Benggang areas. Due to its inability to extract and utilize these textural features, the PBC yielded suboptimal results compared to both the SAM-OBC and MRS-OBC. In contrast to the MRS, the SAM demonstrated superior capabilities in the precise delineation of Benggang areas, ensuring the extraction of accurate textural and spatial features. As a result, the SAM-OBC significantly enhanced detection accuracy by 34.12% and reduced the false alarm rate by 2.06% compared to the MRS-OBC. The results indicate that the SAM-OBC performs well in Benggang detection, holding significant implications for the monitoring and remediation of Benggang areas.

Cellular automata-based simulators for the design of prescribed fire plans: the case study of Liguria, Italy

Abstract Background Socio-economic changes in recent decades have resulted in an accumulation of fuel within Mediterranean forests, creating conditions conducive to potential catastrophic wildfires intensified by climate change. Consequently, several wildfire management systems have integrated prescribed fires as a proactive strategy for land management and wildfire risk reduction. The preparation of prescribed fires involves meticulous planning, entailing the identification of specific objectives, verification of prescriptions, and the definition of various scenarios. During the planning phase, simulation models offer a valuable decision-support tool for the qualitative and quantitative assessment of different scenarios. In this study, we harnessed the capabilities of the well-established wildfire simulation tool , to identify areas where prescribed fires can be performed, optimizing the wildfire risk mitigation and the costs. We selected a case study in the Liguria region, Italy, where the model is utilized operationally by the regional wildfire risk management system in emergency situations. Results Initially, we employed the propagation model to simulate a historical wildfire event, showcasing its potential as an emergency response tool. We focused on the most significant fire incident that occurred in the Liguria region in 2022. Subsequently, we employed to identify optimal areas for prescribed fires with the dual objectives of maximizing the mitigation of wildfire risk and minimizing treatment costs. The delineation of potential areas for prescribed fires has been established in accordance with regional regulations and expert-based insights. The methodology put forth in this study is capable of discerning the most suitable areas for the implementation of prescribed burns from a preselected set. A Monte Carlo simulation framework was employed to evaluate the efficacy of prescribed burns in mitigating the spread of wildfires. This assessment accounted for a variety of conditions, including fuel loads, ignition points, and meteorological patterns. The model was utilized to simulate the progression of wildfire spread. Conclusions This study underscores the utility of in offering both quantitative and qualitative insights that can inform prescribed fire planning. Our methodology has been designed to involve active engagement with subject matter experts throughout the process, to develop scenarios grounded in their expert opinions. The ability to assess diverse scenarios and acquire quantitative information empowers decision-makers to make informed choices, thereby advancing safer and more efficient fire management practices.

Study on the radiotherapy effect and serum neutral granulocyte lymphocyte ratio and inflammatory factor expression of nasopharyngeal carcinoma.

To compare target area delineation schemes in intensity-modulated radiotherapy (IMRT) effect on patients with locally advanced nasopharyngeal carcinoma (NPC). A total of 88 NPC patients received IMRT and were assigned into control group (n = 44) and observation group (n = 44) based on MRI and CT imaging. In the control group, the treatment range was determined as the clinical target volume (CTV) as the gross tumor volume (GTV) + 5 mm. In the observation group, high-risk target areas CTVp1 was GTVp + 5 mm, lymphatic drainage area CTVn1 was GTVn + 5 mm, medium-risk CTVp2 was CTVp1 + 5 mm margin + the whole nasopharyngeal area, CTVn2 was CTVn1 + 5 mm. Radiotherapy treatment course was 6-8 weeks, four times a week. The observation group had higher total effective rate, with fewer complications. Neutrophil lymphocyte ratio (NLR), interleukin (IL)-6, and tumor necrosis factor (TNF)-α levels were lower after radiotherapy in both groups compared to before radiotherapy, with the observation group demonstrating lower levels than the control group. The effective group exhibited lower serum NLR, IL-6, and TNF-α compared to the non-effective group. T stage, target location, serum NLR, IL-6, and TNF-α were risk factors for the effect of radiotherapy. Serum NLR, IL-6, and TNF-α have predictive significance for radiotherapy effect.

Dislocation determination and quality control of industrial casting monocrystalline silicon

By analyzing dislocation ratio data of casting monocrystalline silicon wafers and photovoltaic conversion efficiency of the fabricated solar cells and comparing analyzing growth height and dislocation ratio of silicon crystalline, we derived the calculation equation of dislocation growth in the vertical direction. According to this, the determination software for the dislocation area of casting monocrystalline silicon blocks has been developed, and we realized the adequate delineation and interception of serious dislocation area in casting monocrystalline silicon blocks. The batch conversion efficiency gap between the casting monocrystalline silicon and the Czochralski (CZ) monocrystalline silicon has been narrowed from 0.51 % to 0.19 %, and the low efficiency ratio of casting monocrystalline silicon cells has been reduced from 6.98 % to 0 %. Finally, the stability and competitiveness of casting monocrystalline silicon wafer products have been improved.

Assessment of spatiotemporal variability of ultraviolet index (UVI) over Kerala, India, using satellite remote sensing (OMI/AURA) data.

The spatial and temporal dynamics of daily ultraviolet index (UVI) for a period of 18 years (2004-2022) over the Indian state of Kerala were statistically characterised in the study. The UVI measurements used for the study were derived from the ultraviolet-B (UVB) irradiance measured by the Ozone Monitoring Instrument (OMI) of the AURA satellite and classified into different severity levels for analysis. Basic statistics of daily, monthly and seasonal UVI as well as Mann-Kendall (MK) statistical trend characteristics and the rate of change of daily UVI using Theil-Sen's slope test were also evaluated. A higher variability of UVI characteristics was observed in the Kerala region, and more than 79% of the measurements fell into the categories of very high and extreme UVI values, which suggests the need of implementation of appropriate measures to reduce health risks. Although the UVI measured during the study period shows a slight decrease, most of the data show a seasonal variation with undulating low and peak values. Higher UVI are observed during the months of March, April and September. The region also has higher UVI during the southwest monsoon (SWM) and summer seasons. Although Kerala region as a single whole unit, UVI show a non-significant decreasing trend (-0.83), the MK test revealed the increasing and decreasing trends of UVI ranging from -1.96 to 0.41 facilitated the delineation of areas (domains) where UVI are increasing or decreasing. The domain of UVI increase occupies the central and southern (S) parts, and the domains of decrease cover the northern (N) and S parts of the Kerala region. The rate of change of daily UVI in domain of increase and decrease shows an average rate of 0.34 × 10-5 day-1 and -2 × 10-5 day-1, respectively. The parameters (rainfall, air temperature, cloud optical depth (COD) and solar zenith angle (SZA)) that affect the strength of UV rays reaching the surface indicate that a cloud-free atmosphere or low thickness clouds prevails in the Kerala region. Overall, the study results indicate the need for regular monitoring of UVI in the study area and also suggest appropriate campaigns to disseminate information and precautions for prolonged UVI exposure to reduce the adverse health effects, since the study area has a high population density.

Analysis Options for Urban Green Spaces Based on Unified Urban Masks: Selected Results for European Cities

At a time of rising urbanisation and climate change, urban green spaces (UGSs) are an essential element to help adapt to extreme weather events. Especially in urban core areas, heat and drought are regarded as human stress factors. The delineation of such areas constitutes an important reference geometry in topographic geodata (urban mask). This article deals with possibilities for investigating UGSs in European cities—based on unified urban masks—by applying city-wide metrics to Copernicus data (Urban Atlas including the Street Tree Layer). Both public and tree-covered urban green spaces are examined in detail. Selected results are presented for 30 European cities that display a wide range of urban structures. The spatial reference to uniformly delineated urban masks places the analytical focus of city-wide metrics onto corresponding core areas. In general, the values of UGS metrics vary considerably between cities, indicating the strong influence of city-specific factors on urban structures in Europe. For the comparative analysis of tree-covered urban areas, the Urban Green Raster Germany and a municipal tree register are used to provide additional data sources. The regular updating of the Copernicus dataset means that green spaces in European cities can be monitored, also using urban masks.

Quantitative Analysis of Heavy Metal Pollution Sources, Influencing Factors, and Delineation of Pollution Risk Areas in Industrial Soil: A Combined Application of Receptor Models and GeoDetector

Quantitative Analysis of Heavy Metal Pollution Sources, Influencing Factors, and Delineation of Pollution Risk Areas in Industrial Soil: A Combined Application of Receptor Models and GeoDetector

Optimization of supraclavicular lymph node clinical target volume delineation in high-risk breast cancer: a single center experience and recommendation

BackgroundProphylactic irradiation of supraclavicular lymph node drainage areas can improve the regional control rate of lymph node-positive or lymph node-negative disease but a locally-advanced stage breast cancer, and it can reduce breast cancer-related mortality. However, many controversies exist in the clinical target volume delineation of supraclavicular lymph node drainage in patients with breast cancer.MethodsWe retrospectively analyzed 42 patients with breast cancer and supraclavicular lymph node metastasis at our hospital between January 2017 and December 2021. Among these cases, 32 were locally advanced and 10 were stage IV at initial treatment. A patient with breast cancer who did not undergo dissection of the supraclavicular and infraclavicular lymph nodes at our hospital was selected as a standard patient. A contrast-enhanced computed tomography (CT) scan for positioning was used as a template image, and blood vessels, muscles, and bony landmarks were used as references for positioning. The metastatic supraclavicular lymph nodes were identified in all enrolled patients and projected into the template CT images.ResultsThe metastastic pattern of supraclavicular lymph node in breast cancer was proposed: distribution along the posterolateral border of the internal jugular vein (medial supraclavicular group) and along the transverse jugular vein (lateral supraclavicular group). We theorized that the lateral and posterior borders of the clinical target volume in the supraclavicular region should include the lymph nodes in the posterior triangle of the neck (level V) in high-risk individuals. If the metastatic axillary lymph node is extensive, then the superior border of the supraclavicular region should be moved upward appropriately.ConclusionsThis study analyzed patients with breast cancer and supraclavicular lymph node metastasis at initial treatment, explored the metastastic pattern of supraclavicular lymph node, and applied anatomical knowledge to further optimize the target volume delineation of supraclavicular lymph node drainage area in high-risk breast cancer.

Optimized Drainage System Design for Andesite Mining at PT. Rolas Nusantara Tambang, Kejayan, East Java

This study presents a systematic approach to designing a filtration system aimed at efficiently managing rainwater and surface runoff outside mining areas. Utilizing a quantitative method, the data collection predominantly involves secondary sources. The data is processed through a series of steps: rainfall projection, delineation of rainwater catchment areas, calculation of rainfall intensity and inflow rate, and determination of open channel and settling pond dimensions. The designed open channels have a trapezoidal shape with specific dimensions including a base width of 0.51 m, a side length of 0.62 m, a slope angle of 60°, and a channel depth of 0.54 m. These channels span a total length of 2.216 m. The accompanying settling ponds are rectangular with dimensions encompassing a length of 31 m, a width of 11 m, a depth of 6 m, and an area of 341 m². These ponds have a volume capacity of 2,046 m³, achieving an impressive 90.89% efficiency. Notably, maintenance of the settling pond involves periodic dredging using the Kobelco SK200 Excavator at intervals of roughly 16 years (5,817 days). Given that this maintenance duration surpasses the expected mine lifespan, no further interventions are required for the pond during the mine's operational period.

Non-invasive diffused reflected/transmitted signature accompanied with hyperspectral imaging for breast cancer early diagnosis

IntroductionBreast cancer (BC) is a prevalent form of cancer worldwide and a leading cause of cancer-related deaths. There is an urgent need for the development of new methods to detect, diagnose, and monitor BC at an early stage. Light propagation and interaction with molecular species, could be utilized for diagnostic purposes. Incident light reflection and transmission are influenced by the changes of cell structure and dielectric properties. Any variations in these components could impact the reflected and transmitted signals, offering potential diagnostic applications. MethodsIn this study, non-invasive, polychromatic light source over the visible range (400–900 nm) was employed to illuminate controlled normal and BC tissue samples. The resulted diffused reflected and transmitted spectra were captured and analyzed using hyperspectral (HS) camera and designed cross-correlation algorithm. ResultsDiffused reflected signature demonstrated significant difference between normal and BC tissues over the band 500–700 nm. At 600 nm, normal tissue demonstrated diffused reflection intensity of 2304 relative to 764 for malignant tissues. Transmitted signature exhibited good discrimination capabilities over the band 600–900 nm. At 750 nm, normal tissue demonstrated transmission signal intensity of 34 relative to 21 for malignant tissues.Results were validated via generated HS 2D images, for diffused reflected and transmitted signals. Noise reduction, using moving average filter (K = 10), was adopted to enhance HS images. Contrast was improved through de-convolution with reference. Magnitude was calculated using fast Fourier transform for the concated diffused reflected and transmitted HS images. Additionally, phase shift analysis was performed to assess the differences in arrival time. ConclusionThe optimal wavelength to distinguish between normal and malignant breast tissues was 600 nm for the diffused reflected HS images; Furthermore, the transmitted HS images at 750 nm demonstrated high contrast and specificity. The developed HS imaging technique secured high levels of accuracy, specificity, and sensitivity for diffused reflected and transmitted images at 600 nm and 750 nm, respectively; K-means clustering algorithm offered precise outline and delineation of malignant areas at these wavelengths.

Uniportal thoracoscopic extended right apical segmentectomy with virtual-assisted lung mapping: a case report

BackgroundPulmonary extended segmentectomy is an optional surgical treatment for early-stage non-small cell lung cancer that helps to achieve optimal surgical margins. Here, we describe a challenging instance of extended segmentectomy via uniportal video-assisted thoracic surgery with virtual-assisted lung mapping, a preoperative bronchoscopic dye marking procedure.Case presentationA 72-year-old woman presented with two tumors that were clinically diagnosed as early-stage lung cancer; extended right apical segmentectomy was indicated. Because the tumors had appeared unidentifiable intraoperatively, we performed virtual-assisted lung mapping for tumor localization and delineation of the optimal resection area. Surgery was conducted through a single port. All virtual-assisted lung mapping markings were visible. After dissection of the apical vessels and bronchi, a putative intersegmental line was determined using collateral ventilation. Based on the putative intersegmental plane identified by collateral ventilation and the virtual-assisted lung mapping markings, the resection line was delineated. Extended apical segmentectomy along the resection line was successfully performed via uniportal video-assisted thoracic surgery. The postoperative course was uneventful. The pathological diagnosis was minimally invasive adenocarcinoma and adenocarcinoma in situ.ConclusionsVirtual-assisted lung mapping can help to achieve optimal extended segmentectomy via uniportal video-assisted thoracic surgery.