Method For Quantitative Interpretation Research Articles

Long-Term Ground Deformation Monitoring and Quantitative Interpretation in Shanghai Using Multi-Platform TS-InSAR, PCA, and K-Means Clustering

Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals’ lives and assets. Shanghai is a megacity of China, and it has a long history of ground subsidence due to the overexploitation of groundwater and urban expansion. Time Series Synthetic Aperture Radar Interferometry (TS-InSAR) is a highly effective and widely used approach for monitoring urban ground deformation. However, it is difficult to obtain long-term (such as over 10 years) deformation results using single-platform SAR satellite in general. To acquire long-term surface deformation monitoring results, it is necessary to integrate data from multi-platform SAR satellites. Furthermore, the deformations are the result of multiple factors that are superimposed, and relevant studies that quantitatively separate the contributions from different driving factors to subsidence are rare. Moreover, the time series cumulative deformation results of massive measurement points also bring difficulties to the deformation interpretation. In this study, we have proposed a long-term surface deformation monitoring and quantitative interpretation method that integrates multi-platform TS-InSAR, PCA, and K-means clustering. SAR images from three SAR datasets, i.e., 19 L-band ALOS-1 PALSAR, 22 C-band ENVISAT ASAR, and 20 C-band Sentinel-1A, were used to retrieve annual deformation rates and time series deformations in Shanghai from 2007 to 2018. The monitoring results indicate that there is serious uneven settlement in Shanghai, with a spatial pattern of stability in the northwest and settlement in the southeast of the study area. Then, we selected Pudong International Airport as the area of interest and quantitatively analyzed the driving factors of land subsidence in this area by using PCA results, combining groundwater exploitation and groundwater level change, precipitation, temperature, and engineering geological and human activities. Finally, the study area was divided into four sub-regions with similar time series deformation patterns using the K-means clustering. This study helps to understand the spatiotemporal evolution of surface deformation and its driving factors in Shanghai, and provides a scientific basis for the formulation and implementation of precise prevention and control strategies for land subsidence disasters, and it can also provide reference for monitoring in other urban areas.

An Effective Method for Quantitative Interpretation of Seawater Intrusion in Shallow Aquifers from Electrical Resistivity Data

Groundwater in coastal areas is vulnerable to saltwater intrusion. Electrical resistivity is commonly used in order to detect saltwater intrusion due to its ability to distinguish the occurrence of saltwater in the pore water. In this research, a correlational approach was used to estimate percentage seawater content that was mixed with fresh water in a shallow aquifer. The study area was located in Dumai City, which was covered mainly by peat soil, but some areas were covered by sand and silt. A ground surface resistivity survey and direct soil resistivity measurements were used in the study. For both experiments, the Wenner configuration of electrical resistivity was employed. In order to determine the direct soil resistivity values, measurements of the soil's fluid content and character were made. The findings indicate that the resistivity value in the aquifer increased to 5–10 Wm at a 25% seawater content, from roughly 2–5 Wm at a 50% seawater content. The pore soil's rising salt content was the primary source of the sharp decline in resistivity values. By measuring the electrical resistivity on the surface, it is possible to accurately forecast the percentage of seawater mixture in the pore soil fluid.

Возможности искусственных нейронных сетей при решении обратных задач электроразведки методом вертикального электрического зондирования

An algorithm for solving the inverse problem of electrical prospecting by the method of vertical electrical sounding (VES) using neural networks (NN) is presented. The use of NN is aimed at identifying complex patterns and dependencies that may not be available for traditional methods of quantitative interpretation of electrical prospecting data. The algorithm includes the formation of a training sample; training NN and directly obtaining solutions; combination of solutions found with the help of NN and selection of the optimal solution to the inverse problem of VES. The algorithm is tested on model data and practical materials in order to evaluate its capabilities. Geoelectric sections constructed using NN based on the results of field observations were compared with the results of quantitative interpretation performed in the «ZOND» program. The advantages and disadvantages of the created algorithm for solving the inverse problem of VES, as well as ways for its further development are characterized.

Quantitative Analysis Method of the Organizational Characteristics and Typical Types of Landscape Spatial Sequences Applied with a 3D Point Cloud Model

(1) Background: Sequential landscape changes give people experiences of dynamic beauty, and the key to creating spatial sequences lies in the organization of spatial changes. The purpose of this study is to use a 3D point cloud model to achieve a refined description of spatial sequences’ organizational characteristics from information acquisition to description and explore the quantitative interpretation methods of typical sequence organizational characteristics. (2) Methods: The proposed model contains three main steps: data acquisition and extraction, characteristic index system construction and data processing, and quantitative characterization analysis. A typology research method that combines quantitative induction with qualitative statistical verification is proposed. (3) Results: Seventy-two spatial sequence point cloud models of study cases are obtained; 4 indicators are established; 3 typical organization types are summarized, namely fluctuating, reversal and moderate type; and the characterization factors and threshold intervals for each sequence organization type are analyzed to validate the type classification result. (4) Conclusions: This research improves the accuracy of spatial data, the comprehensiveness of sequence organization characterization factors, and the reliability of classification results. It supplements the existing spatial sequence theoretical knowledge system and provides parameters that can be referred to in practical design.

Quantitative interpretations of potential fields: from parametric to geostructural recalculations

The relevance. Need to development of an applied algorithm for combined interpretation of heterogeneous geophysical data based on unified physical analogy, focused on solving the inverse problem of potential theory. The wave approximation of the cross-section of mining massif fits into the paradigm of dissipative structuring of nonequilibrium media acts as a special case of the above analogy. The task of the study is to adapt this algorithm to recalculations of both potential and non-potential geofields. In the list of nonpotential geofields the remote sensing data and digital terrain model are the most interesting as the data with non-regulated access. Practical significance of the research is associated with the approbation of a family of algorithms in production projects of both geological and engineering nature. The aim. To minimize the family of equivalent solutions to the inverse problem by mutual verification of heterogeneous models of the anomaly-forming source. The objects. Development of the geofields combined interpretation algorithm and recalculation of geofields in depth for tracing elements of stratification and geological fractures. The methods. Formed by software and algorithmic development, which is based on deterministic analytical representations: computation of Cauchy integral, iterative application of direct solution, spectrum estimation. The results. Reduced to the set of parametric cross-sections derived with recalculations with minimum a priori geological information with final geological consideration. The combination of visualizations of analytical continuation, singular points and results of automated fitting admits the reconstruction of geostructural model of parametric cross-section with significant reliability. Extension of standard methods of quantitative interpretation by means of wave approximations includes quasi-seismic halftone visualizations verifying the mentioned reconstructions.

A quantitative analysis framework of placenta accreta spectrum: placenta subtype, intraoperative bleeding, and hysterectomy risk evaluation based on magnetic resonance imaging-anatomical-clinical features.

Placenta accreta spectrum (PAS) is a significant contributor to maternal morbidity and mortality. Our objective was to develop a quantitative analysis framework utilizing magnetic resonance imaging (MRI)-anatomical-clinical features to predict 3 clinically significant parameters in patients with PAS: placenta subtype (invasive vs. non-invasive placenta), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy). A total of 125 pregnant women with PAS from 2 medical centers were enrolled into an internal training set and an external testing set. Some 21 MRI-anatomical-clinical features were integrated as input into the framework. The proposed quantitative analytic framework contains mainly 3 classifiers built by extreme gradient boosting (XGBoost) and their testing in external datasets. We also further compared the accuracy of placenta subtype prediction between the proposed model and 4 radiologists. A quantitative model interpretation method called SHapley Additive exPlanations (SHAP) was conducted to explore the contribution of each feature. The placenta subtype (invasive vs. non-invasive), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy) demonstrated impressive area under the receiver operating characteristic curve (AUROC) values of 0.93, 0.88, and 0.90, respectively, in the internal validation set. Even in the external testing set, these metrics maintained their strength, achieving AUROC values of 0.91, 0.82, and 0.82, respectively. Comparing our proposed framework to the 4 radiologists, our model exhibited superior accuracy, specificity, and sensitivity in predicting placental subtypes within the external testing cohort. The features associated with intraplacental dark T2 bands played a crucial role in the decision-making process of all 3 prediction models. The quantitative analysis framework can provide a robust method for classification of placenta subtype (invasive vs. non-invasive placenta), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy) based on MRI-anatomical-clinical features in PAS.

An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT.

To determine whether the texture feature analysis of multi-phase abdominal CT can provide a robust prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. A total of 1051 participants with renal tumor were split into the internal cohort (850 patients from four different hospitals) and the external testing cohort (201 patients from another local hospital). The proposed framework comprised a 3D-kidney and tumor segmentation model by 3D-UNet, a feature extractor for the regions of interest based on radiomics and image dimension reduction, and the six classifiers by XGBoost. A quantitative model interpretation method called SHAP was usedto explore the contribution of each feature. The proposed multi-phase abdominal CT model provides robust prediction for benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in the internal validation set, with the AUROC values of 0.88 ± 0.1, 0.90 ± 0.1, 0.91 ± 0.1, 0.89 ± 0.1, 0.84 ± 0.1, and 0.88 ± 0.1, respectively. The external testing set also showed impressive results, with AUROC values of 0.83 ± 0.1, 0.83 ± 0.1, 0.85 ± 0.1, 0.81 ± 0.1, 0.79 ± 0.1, and 0.81 ± 0.1, respectively. The radiomics feature including the first-order statistics, the tumor size-related morphology, and the shape-related tumor features contributed most to the model predictions. Automatic texture feature analysis of abdominal multi-phase CT provides reliable predictions for multi-tasks, suggesting the potential usage of clinical application. The automatic texture feature analysis framework, based on multi-phase abdominal CT, provides robust and reliable predictions for multi-tasks. These valuable insights can serve as a guiding tool for clinical diagnosis and treatment, making medical imaging an essential component in the process. • The automatic texture feature analysis framework based on multi-phase abdominal CT can provide more accurate prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. • The quantitative decomposition of the prediction model was conducted to explore the contribution of the extracted feature. • The study involving 1051 patients from 5 medical centers, along with a heterogeneous external data testing strategy, can be seamlessly transferred to various tasks involving new datasets.

An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma

ObjectivesTo determine whether 3D-CT multi-level anatomical features can provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma.MethodsThis is a retrospective study based on multi-center cohorts. A total of 473 participants with pathologically proved renal cell carcinoma were split into the internal training and the external testing set. The training set contains 412 cases from five open-source cohorts and two local hospitals. The external testing set includes 61 participants from another local hospital. The proposed automatic analytic framework contains the following modules: a 3D kidney and tumor segmentation model constructed by 3D-UNet, a multi-level feature extractor based on the region of interest, and a partial or radical nephrectomy prediction classifier by XGBoost. The fivefold cross-validation strategy was used to get a robust model. A quantitative model interpretation method called the Shapley Additive Explanations was conducted to explore the contribution of each feature.ResultsIn the prediction of partial versus radical nephrectomy, the combination of multi-level features achieved better performance than any single-level feature. For the internal validation, the AUROC was 0.93 ± 0.1, 0.94 ± 0.1, 0.93 ± 0.1, 0.93 ± 0.1, and 0.93 ± 0.1, respectively, as determined by the fivefold cross-validation. The AUROC from the optimal model was 0.82 ± 0.1 in the external testing set. The tumor shape Maximum 3D Diameter plays the most vital role in the model decision.ConclusionsThe automated surgical decision framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features exhibits robust performance in renal cell carcinoma. The framework points the way towards guiding surgery through medical images and machine learning.Clinical relevance statementWe proposed an automated analytic framework that can assist surgeons in partial or radical nephrectomy decision-making. The framework points the way towards guiding surgery through medical images and machine learning.Key Points• The 3D-CT multi-level anatomical features provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma.• The data from multicenter study and a strict fivefold cross-validation strategy, both internal validation set and external testing set, can be easily transferred to different tasks of new datasets.• The quantitative decomposition of the prediction model was conducted to explore the contribution of each extracted feature.

Recovering density disturbance spectra from FLDI. Part 2: comparisons with previous methods.

An exact analytical method for recovering density disturbance spectra in multi-frequency, multi-dimensional fields from focused laser differential interferometry (FLDI) measurements, developed in Part 1 [Appl. Opt.62, 3042 (2023)APOPAI0003-693510.1364/AO.480352], is compared with previous methods for quantitative interpretation of FLDI. It is shown that previous exact analytical solutions can be recovered as special cases of the more general present method. It is also found that despite outwards dissimilarity, a previous approximate method that is becoming widely used can be related to the general model. It is demonstrated that the previous approach-while a suitable approximation for spatially restricted disturbance fields such as conical boundary layers it was originally applied to-does not work well in general applications. While corrections can be made, informed by results from the exact method, doing so offers no computational or analytical advantages.

Adaptive Feature Map-Guided Well-Log Interpolation

As an irreplaceable quantitative interpretation method, prestack seismic inversion enables the effective estimation of subsurface elastic parameters for reservoir prediction. However, for the model-driven prestack seismic inversion, the band-limited characteristics and noise interference of observed seismic data result in its high dependence on the initial models. This suggests that reasonable initial models act as a supplement to reliable variation trends in formation and can reduce the non-uniqueness of inversion results. In this article, we introduce a well-log interpolation method with a feature map-guided non-local means algorithm, which is for establishing high-fidelity initial models used for prestack seismic inversion. First, we briefly review the basic theory of general model-driven prestack seismic inversion. Then, we use dictionary learning to split the poststack seismic record into patches, and represent them with sparse vectors, instead of directly using seismic record. The advantage of dictionary learning is that it can adaptively extract useful signals from noisy observed data and provide fine structures by sparse reconstruction. Therefore, the proposed feature extraction method can improve the noise immunity and reliability of the well-log interpolation. More accurate initial models are pre-constructed efficiently by our feature extraction method, which improves the reliability of prestack seismic inversion results. Two kinds of observed seismic data are used, including the poststack seismic record for well-log interpolation and prestack seismic data used for inversion. Synthetic and field data tests both demonstrate the favorable performance of the proposed well-log interpolation method. In summary, a novel and convenient initial model building approach is provided, which contributes to seismic exploration and geologic modeling.

Applications of high-resolution seismic frequency and phase attribute analysis techniques

Seismic prospecting for oil and gas exploration and development is limited by seismic data resolution. Improving the accuracy of quantitative interpretation of seismic data in thin layers, thereby identifying effective reservoirs and delineating favorable areas, can be a key factor for successful exploration and development. Historically, the limit of seismic resolution is usually assumed to be about 1/4 wavelength of the dominant frequency of the data in the formation of interest. Constrained seismic reflectivity inversion can resolve thinner layers than this assumed limit. This leads to a series of highresolution quantitative interpretation methods and techniques have been developed. Case studies in carbonates, clastic, and unconventional reservoirs indicate that the application of quantitative interpretation techniques such as high-resolution seismic frequency and phase attribute analysis can resolve and allow/or allow quantitative estimation of rock and fluid properties in such seismically thin layers. Band recovery using high resolution seismic processing technology can greatly improve the ability to recognize geological details such as thin layers, faults, and karst caves. Multiscale fault detection technology can effectively detect small-scale faults in addition to more readily recognized large-scale faults. Based on traditional seismic amplitude information, high-resolution spectral decomposition and phase decomposition technology expands seismic attribute analysis to the frequency and phase dimensions, boosting the interpretable geological information content of the seismic data including subsurface geological characteristics and hydrocarbon potential and thereby improving the reliability of seismic interpretation. These technologies, based on high-resolution quantitative interpretation techniques, make the identification of effective reservoirs more efficient and accurate.

3-D Joint Inversion of Gravity and Magnetic Data Using Data-Space and Truncated Gauss–Newton Methods

Gravity and magnetic inversion are important methods for comprehensive quantitative interpretation of data obtained in, e.g., mineral, oil and gas, and geothermal exploration. At present, the 3-D joint inversion technology of gravity and magnetic data is facing challenges from large-scale data exploration applications. In this letter, a new algorithm for 3-D joint inversion of gravity and magnetic data with high accuracy and low computational cost is presented. We use the geometric trellis method to perform fast forward calculations and then introduce the sparse constraint and adaptive sensitivity matrix into the model constraint terms. The inexact structural resemblance method is then used to add the cross-gradient constraint penalty term to the objective function. Finally, an algorithm (DS-TGN) combining data-space (DS) and truncated Gauss–Newton (TGN) methods is used to solve the joint inversion objective function. Numerical experiments with synthetic data show that the proposed algorithm can significantly reduce the computational cost and obtain high accuracy density and magnetization models with structural resemblance and sharp boundaries. We also apply the DS-TGN algorithm to data obtained in the area of Greater Khingan in northwestern Heilongjiang, China. The underground density and magnetization distribution results provide a high-resolution geological model for the detection of skarn-type deposits.

Восприятие масштабов распространения коррупции региональным сообществом

The article examines the manifestations of corruption practices in the regional government. The results of a sociological survey of 623 residents of the Belgorod Region from the 16 settlements were analyzed in order to identify the actual values of the parameters for assessing corruption. Using the method of qualitative and quantitative interpretation of empirical data, the authors determine the scale and structure of corruption, and identify the factors that determine the nature and content of corruption practices in the region. They show that the main trigger of corruption processes is the lack of formation of anti-corruption attitudes and behavior among the majority of the regional community members. The article contains recommendations that can help minimizing corruption practices and increasing the level of anti-corruption culture of the regional community.

Methods and future directions for evaluation of Tau PET signal

AbstractBackgroundAlzheimer’s disease (AD) is defined in vivo by evidence of amyloid and tau, usually accompanied by neurodegeneration, in accordance with the ATN criteria.MethodDeposition of amyloid is thought to begin early in the disease process and, by the time symptoms become evident, can usually be detected as elevated PET signal in a stereotypical pattern across regions. In contrast tau deposition begins later in disease and the pattern and timing of evolution of tau deposition may vary across individuals. Thus, it is more difficult to assign a classification of tau positive that fits all purposes.ResultStudies in autopsy confirmed patients indicate that tau PET visual interpretation methods may be effective in identifying subjects at the B3 (Braak V/VI) stage of deposition. B3 tau deposition is typically also associated with amyloid positivity, thus predicting a likely pathological diagnosis of high AD neuropathologic change at autopsy. However, a negative visual interpretation cannot rule out the presence of amyloid and tau levels sufficient to support a classification of intermediate AD neuropathological change. Quantitative methods focused on temporal lobe (particularly inferior and mesial temporal lobe) may be more sensitive to earlier stages of AD tau deposition, but confirmation of amyloid status may be required to rule out primary age‐related tauopathy.ConclusionTogether visual and quantitative interpretation methods may identify patterns of tau distribution and density with implications for the expression of symptoms, and future progression of impairment, and may be of value in stratifying/selecting patients most likely to respond in clinical trials of AD therapeutics.

Using EEG in Resource-Limited Areas: Comparing Qualitative and Quantitative Interpretation Methods in Cerebral Malaria

BackgroundOur goal was to compare the strength of association and predictive ability of qualitative and quantitative electroencephalographic (EEG) factors with the outcomes of death and neurological disability in pediatric cerebral malaria (CM). MethodsWe enrolled children with a clinical diagnosis of CM admitted to Queen Elizabeth Central Hospital (Blantyre, Malawi) between 2012 and 2017. A routine-length EEG was performed within four hours of admission. EEG data were independently interpreted using qualitative and quantitative methods by trained pediatric neurophysiologists. EEG interpreters were unaware of patient discharge outcome. ResultsEEG tracings from 194 patients were reviewed. Multivariate modeling revealed several qualitative and quantitative EEG variables that were independently associated with outcomes. Quantitative methods modeled on mortality had better goodness of fit than qualitative ones. When modeled on neurological morbidity in survivors, goodness of fit was better for qualitative methods. When the probabilities of an adverse outcome were calculated using multivariate regression coefficients, only the model of quantitative EEG variables regressed on the neurological sequelae outcome showed clear separation between outcome groups. ConclusionsMultiple qualitative and quantitative EEG factors are associated with outcomes in pediatric CM. It may be possible to use quantitative EEG factors to create automated methods of study interpretation that have similar predictive abilities for outcomes as human-based interpreters, a rare resource in many malaria-endemic areas. Our results provide a proof-of-concept starting point for the development of quantitative EEG interpretation and prediction methodologies useful in resource-limited settings.

Real magnetic stripping method in unexploded ordnance detection and remediation – a case study from Rohožník military training range in SW Slovakia

In this contribution we present results from a case-study, which was performed in collaboration between geophysicists and explosive ordnance disposal technicians at the Rohožník military training range in SW Slovakia. The aim of this study was to locate a deep-penetrated unexploded Mk-82 aerial bomb using high-definition digital magnetometry. The location where this bomb had entered the ground was known but its final position needed to be determined so that a safe excavation and disposal could be conducted. However, the detection of this unexploded ordnance object was complicated by the presence of intense magnetic interference from a number of near surface ferrous items including non-explosive test bombs, fragmentation and other iron junk. These items contributed a localised, high amplitude of magnetic clutter masking any deeper source. Our strategy was to approach the problem in three stages. First, we used magnetic data to locate the near surface items. After the detection and before the excavation of the searched objects, two quantitative interpretation methods were used. These involved an optimised modelling of source bodies and the application of a 3D Euler deconvolution. Both methods yielded acceptable results, but the former was found to be more accurate. After the interpretation phase, many of the items were then safely excavated and removed individually. A second magnetic mapping was then performed and from this data which was now significantly less cluttered, we were able to identify but not quantify, two deep source items and to confirm that all remaining near surface items were significantly smaller in size than a Mk-82 bomb. As the remaining near surface sources were interpreted as being contained within the surface one metre of soil and being small they could be assured to be non-explosive, it was considered most practical to mechanically excavate and remove this soil and the remaining objects contained.

Automatic detection of epileptiform potentials and seizures in the EEG

Automatic computer-based algorithms for the detection of epileptiform potentials and seizure patterns on EEG facilitate a time-saving, objective method of quantitative EEG interpretation which is available 7/24. For the automatic detection of interictal epileptiform potentials sensitivities range from 65 to 99% with false positive detections of 0,09 to 13,4 per minute. Recent studies documented equal or even better performance of automatic spike detection programs compared with experienced human EEG readers. The seizure detection problem-one of the major problems in clinical epileptology-consists of the fact that the majority of focal onset seizures with impaired awareness and of seizures arising out of sleep occur unnoticed by patients and their caregivers. Automatic seizure detection systems could facilitate objective seizure documentation and thus help to solve the seizure detection problem. Furthermore, seizure detection systems may help to prevent seizure-related injuries and sudden unexpected death in epilepsy (SUDEP), and could be an integral part of novel, seizure-triggered on-demand therapies in epilepsy. During long-term video-EEG monitoring seizure detection systems could improve patient safety, provide a time-saving objective and reproducible analysis of seizure patterns and facilitate automatic computer-based patient testing during seizures. Sensitivities of seizure detection systems range from 75 to 90% with extratemporal seizures being more difficult to detect than temporal seizures. The false positive alarm rate ranges from 0,1 to 5 per 24 hours. Finally, machine learning algorithms, especially deep learning approaches, open a new highly promising era in automatic spike and seizure detection.

Factors of Ethnic Conflict in the Ethiopian Federation

Since 1991 Ethiopia has made a change in thinking favoring federalism against the centralized hierarchical power to radically respond to the problem of diversity and better recognize and accommodate the country's ethnolinguistic and cultural diversity. Paradoxically, Ethiopia had experienced more ethnic-based conflict in its post-1991 existence than ever before. Among others, the Somali-Oromo conflict is the worst ethnic-based conflict in the country’s history. Though the two communities, have a long tradition of co-existence and strong socio-cultural integrations due to their shared Muslim-Cushitic identity, economic interdependence, and shared cultural practices; antagonistic relationships, and intermittent conflicts due to resource competition, territorial expansion, bad governance, and other factors have prevailed in the last three decades. It is the objective of this paper, therefore, to investigate and analyze factors of ethnic conflict along the shared border of the Somali and Oromia regional administrations, specifically Bable and Bobas districts, within the context of Ethiopian federalism. Methodologically, the study employed a comparative research approach and made use of key informant interview and survey questionnaires' techniques in gathering the relevant data, and in effect, both qualitative and quantitative data interpretation and analysis methods were utilized in the analysis section. The findings of this study demonstrate that the Somali-Oromo conflict is complex &amp; dynamics and the result of the interplay of historical, institutional/structural/political, economic, socio-cultural, and environmental factors. Furthermore, the result of the study reveals that major drivers of ethnic conflict in both areas are similar.

Current Status and Development Trend Analysis of Neutron Logging in Uranium Mines in China

Neutron logging is a method of measuring uranium fission neutrons by bombarding uranium in ore beds with neutrons emitted by neutron sources or neutron generators. The pulsed neutron logging technology of uranium ore has been popularized and applied in the United States, Russia, Australia and other countries. The key technology is almost monopolized by these countries, but the technology is prohibited in China. In the field of uranium logging, natural gamma logging has always been used in China for uranium quantification, which is characterized by large error, low efficiency, high cost and technical and economic bottlenecks. Due to the fact that the research and application of pulsed neutron log in the field of petroleum exploration and production is relatively mature in China, it is urgent to develop relevant logging instruments and uranium quantitative interpretation methods as independent intellectual property rights. This will lead to a change in the backward appearance of uranium logging. This paper analyzes the research status and development trend of neutron logging in uranium at home and abroad.

Evaluating the Accuracy of Reconstruction of the Electrical and Geometric Parameters of Multilayer Dielectric Coatings by a Multifrequency Radio-Wave Method for Slow Surface Electromagnetic Waves

One of the most important problems in the diagnostics of multilayer dielectric materials and coatings is the development of methods for quantitative interpretation of the results of monitoring the electrical and geometric parameters of these materials. Results are presented from a study of the potential informativeness of a multifrequency radio-wave method for slow surface electromagnetic waves for reconstruction of the electrical and geometric parameters of multilayer dielectric coatings. A simulation model is shown for evaluating the accuracy of reconstructing the electrical and geometric parameters of multilayer dielectric coatings. The model takes into account the electrical and geometric parameters of the coating, the level of noise in the measurement data, and the measurement bandwidth. Simulation modeling and experimental test data for the reconstruction of the relative dielectric constants and thicknesses of one- and two-layer equal-thickness dielectric coatings based on polymethyl methacrylate, ftoroplast (teflon) F-4D, and RO3010 for different values of the mean square deviation of the noise level in the measured attenuation coefficients for the field of a slow surface electromagnetic wave. The accuracy of the reconstruction of the geometric and electrical parameters of the layers is found to decrease as the number of estimated parameters and the noise level increase, as well as when the dielectric constant and thickness of the layers are reduced. Experimental data confirm the adequacy of the simulation model developed here. This model can be used for a specific measurement complex employing a multifrequency radio-wave technique for slow surface electromagnetic waves to estimate quantitatively the potential possible accuracy of reconstructing the geometric and electrical parameters of multilayer dielectric materials and coatings. A simulation model and experimental study of a multilayer dielectric coating show that for a measurement bandwidth of 1 GHz the errors in estimating the dielectric constants and thicknesses of the layers do not exceed 10% with a confidence coefficient of 0.95 for a mean square deviation of 0.003–0.004 in the noise level.