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Optimizing the spatial patterns of rural settlements is crucial for rural development and revitalization. Enhancing the internal spatial configuration of these settlements necessitates a comprehensive understanding of their micro-scale spatial characteristics. This study develops evaluation indicators and methodologies to quantify rural settlement spatial patterns by analyzing their multidimensional aspects. The research utilizes Huanglong Town in Dayu County, Jiangxi Province, as a case study for exploring micro-scale spatial patterns and proposing corresponding optimization models. The research employs remote sensing image processing and GIS spatial analysis to collect data on the study area. The results indicate that rural settlements in Huanglong Town generally form clustered patterns with moderate spatial structure intensity and order. Notably, spatial heterogeneity is observed across the northern mountainous area, the central plain and low hilly region, and the southern hilly area. Based on these findings, the study categorizes rural settlements in Huanglong Town into four optimization models: stable improvement, internal potential exploitation, controlled expansion, and relocation and withdrawal. Each model is associated with differentiated optimization strategies. By integrating analyses of spatial form, structure, and order, this study reveals the intrinsic spatial characteristics of rural settlements, offering a systematic approach to guide their spatial optimization.

Abstract Background Accurate estimation of the effective reproductive number ( $$R_e$$ R e ) of epidemic outbreaks is of central relevance to public health policy and decision making. We present estimateR, an R package for the estimation of the reproductive number through time from delayed observations of infection events. Such delayed observations include confirmed cases, hospitalizations or deaths. The package implements the methodology of Huisman et al. but modularizes the $$R_e$$ R e estimation procedure to allow easy implementation of new alternatives to the currently available methods. Users can tailor their analyses according to their particular use case by choosing among implemented options. Results The estimateR R package allows users to estimate the effective reproductive number of an epidemic outbreak based on observed cases, hospitalization, death or any other type of event documenting past infections, in a fast and timely fashion. We validated the implementation with a simulation study: estimateR yielded estimates comparable to alternative publicly available methods while being around two orders of magnitude faster. We then applied estimateR to empirical case-confirmation incidence data for COVID-19 in nine countries and for dengue fever in Brazil; in parallel, estimateR is already being applied (i) to SARS-CoV-2 measurements in wastewater data and (ii) to study influenza transmission based on wastewater and clinical data in other studies. In summary, this R package provides a fast and flexible implementation to estimate the effective reproductive number for various diseases and datasets. Conclusions The estimateR R package is a modular and extendable tool designed for outbreak surveillance and retrospective outbreak investigation. It extends the method developed for COVID-19 by Huisman et al. and makes it available for a variety of pathogens, outbreak scenarios, and observation types. Estimates obtained with estimateR can be interpreted directly or used to inform more complex epidemic models (e.g. for forecasting) on the value of $$R_e$$ R e .

Abstract The coal mining process is affected by various water sources such as groundwater and coal seam water injection. Understanding the dynamic mechanical parameters of water-immersed coal is helpful for coalmine safe production. The impact compression tests were performed on coal with different moisture contents by using the ϕ50 mm Split Hopkinson Pressure Bar (SHPB) experimental system, and the dynamic characteristics and energy loss laws of water-immersed coal with different compositions and water contents were analyzed. Through analysis and discussion, it is found that: (1) When the moisture content of the coal sample is 0%, 30%, 60%, the stress, strain rate and energy first increase and then decrease with time. (2) When the moisture content of the coal sample increases from 30% to 60%, the stress “plateau” of the coal sample becomes more obvious, resulting in an increase in the compressive stress stage and a decrease in the expansion stress stage. (3) The increase of moisture content of the coal sample will affect its impact deformation and failure mode. When the moisture content is 60%, the incident rod end and the transmission rod end of the coal sample will have obvious compression failure, and the middle part of the coal sample will also experience expansion and deformation. (4) The coal composition ratio suitable for the coal immersion softening impact experiment is optimized.

BackgroundLymph node status is an important factor in determining the prognosis of patients with early gastric cancer (EGC) and preoperative diagnosis of lymph node metastasis (LNM) has some limitations. This study explored the risk factors and independent prognostic factors of LNM in EGC patients and constructed a clinical prediction model to predict LNM.MethodsClinicopathological data of EGC patients was collected from the public Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression was used to identify risk factors for LNM in EGC patients. The performance of the LNM model was evaluated by C-index, calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis (DCA) curve, and clinical impact curve (CIC) based on the results of multivariate regression to develop a nomogram. An independent data set was obtained from China for external validation. The Kaplan-Meier method and Cox regression model were used to identify potential prognostic factors for overall survival (OS) in EGC patients.ResultsA total of 3993 EGC patients were randomly allocated to a training cohort (n=2797) and a validation cohort (n=1196). An external cohort of 106 patients from the Second Hospital of Lanzhou University was used for external validation. Univariate and multivariate logistic regression showed that age, tumor size, differentiation, and examined lymph nodes count (ELNC) were independent risk factors for LNM. Nomogram for predicting LNM in EGC patients was developed and validated. The predictive model had a good discriminatory performance with a concordance index (C-index) of 0.702 (95% CI: 0.679-0.725). The calibration plots showed that the predicted LNM probabilities were the same as the actual observations in both the internal validation cohort and external validation cohort. The AUC values for the training cohort, internal validation cohort and external validation cohort were 0.702 (95% CI: 0.679-0.725), 0.709 (95% CI: 0.674-0.744) and 0.750(95% CI: 0.607-0.892), respectively, and the DCA curves and CIC showed good clinical applicability. The Cox regression model identified age, sex, race, primary site, size, pathological type, LNM, distant metastasis, and ELNC were prognostic factors for OS in EGC patients, while a year at diagnosis, grade, marital status, radiotherapy, and chemotherapy were not independent prognostic factors.ConclusionIn this study, we identified risk factors and independent prognostic factors for the development of LNM in EGC patients, and developed a relatively accurate model to predict the development of LNM in EGC patients.

One of the most essential inherent characteristics of a combustible mixture is laminar burning velocity. Because of its relevance, many methods for measuring laminar flame velocity have been devised. One advanced procedure was used to derive laminar burning velocity in a constant volume vessel for blends of iso-octane and n-heptane/air mixtures in this study: particle imaging velocimetry. Based on a precise flow field with vectors, particle imaging velocimetry (PIV) provides highly accurate laminar burning velocities. The results of the PIV method were compared with Schlieren data and simulations based on detailed LLNL gasoline surrogate chemical kinetics. It was found that the measured laminar burning velocities using the PIV method were more consistent. To predict the laminar burning velocity of the binary mixture of primary reference fuel (PRF) investigated in this study, five blending laws were used and all of them demonstrated strong application and high accuracy. The Leeds Q/k law based on the variation curve of the heat release rate/reaction process was found to have a strong predictive capability as well.

The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

Multi-station joint localization has important practical significance. In this paper, phase difference of arrival (PDOA) information is introduced into the joint time difference of arrival (TDOA) and frequency difference of arrival (FDOA) localization method to improve the target localization accuracy. First, the Cramer–Rao lower bound (CRLB) of the joint TDOA, FDOA and PDOA localization approach with multi-station precise phase synchronization is derived. Then, the CRLB of the joint TDOA, FDOA and differential PDOA (dPDOA) localization method for the case of phase asynchronization between observation stations is also presented. Furthermore, the authors analyze the influence of the phase wrapping problem on localization accuracy and propose solutions to solve the phase wrapping problem based on cost functions of grid search. Finally, iterative localization algorithms based on maximum likelihood (ML) are proposed for both TDOA/FDOA/PDOA and TDOA/FDOA/dPDOA scenarios, respectively. Simulation results demonstrate the localization performance of the proposed approaches.

This study is aimed at detecting small faults in coal seams. With coal seams of different buried depths in Luling Coal Mine, Yuenan Coal Mine, and Sihe Coal Mine with obvious difference in hardness and outburst hazards selected as the research objects, the morphological characteristics of a sample particle size such as the Rosin-Rammler distribution function, average particle size, cumulative mass distribution, and mechanical parameters such as particle strength and F value were statistically analyzed through drill cuttings related to sampling points at different positions of faults already known. The results show that the effect of tectonic stress reduces the strength and permeability of the coal after the borehole enters the fault damage zone, resulting in abnormal fluctuations in the morphological characteristics of the drill cuttings. Correspondingly, large-size particles account for a high percentage. The weight analysis discloses that the characteristic particle size of the Rosin-Rammler distribution function has the greatest influence on the determination of fault existence. The analysis of drill cutting characteristics conduces to detecting the distribution of small faults, helps to improve the utilization of mine information, and is of great significance for accident prevention and control.

Due to the development of manufacturing and launch technologies for satellites, there are now more and more satellite networks. Hence, cooperative reconnaissance is possible to implement among satellite networks, aerial vehicles and ground stations. In this paper, we study the method of geolocation and tracking by time difference of arrival (TDOA) measurements based on space–air–ground integrated (SAGI) network. We first analyze the Cramer Rao lower bound (CRLB) for the source localization accuracy in different coordinate systems. Then, we compare the effects of different system errors, such as clock synchronization error, position bias of the observers, elevation bias of the target and non-horizontal velocity of the target. Further, we also develop a maximum likelihood (ML) estimator for target position and velocity. Finally, the theoretical performance of the proposed estimator is validated via computer simulations.

The permeability of the coal body is the key parameter restricting the efficient extraction of coalbed methane, and scholars have analysed it from two angles of the change of stress state and porosity of the coal body. However, there is still a lack of study on the mechanism of gas migration and movement in soft coalbed methane reservoir under the coupling between the true triaxial stress field (maximum principal stress σ1 > intermediate principal stress σ2 > minimum principal stress σ3) and the gas pressure field. In this paper, the coal gas adsorption and seepage experiments are conducted through the self-developed true triaxial ‘gas–solid’ coupled coal mass seepage system with gas as the adsorption and seepage medium and coal briquette taking the place of soft coalbed methane reservoirs. Furthermore, the coal gas adsorption deformation model and the permeability evolution model taking gas adsorption into account are developed. Through analysis of both experimental and theoretic results, the main conclusions are drawn as follows: (i) With the increase in gas pressure, the adsorption deformation variation of coal mass is divided into a slow growth zone, a stable growth zone and a rapid growth zone. (ii) The gas adsorption deformation model developed can predict the variation trend of coal mass adsorption volumetric strains for different types of soft coalbeds, and the fitting variance of experimental and theoretical volumetric strains is above 98%. (iii) With the increase in maximum principal stress difference, the coal permeability variation curve shows two obvious turning points, which can be divided into a slow reduction zone, a rapid reduction zone and a steady reduction zone. (iv) The permeability model of coal mass considering the gas adsorption effect can reflect the variation characteristics of permeability in the rapid reduction zone, and the overall fitting variance of experimental and theoretical permeabilities is above 91%. The above results could provide a reliable experimental and theoretical basis for improving coalbed methane extraction rates.

Background. Cutaneous squamous cell carcinoma (cSCC) and cutaneous basal cell carcinoma (cBCC) are the most common types of non-melanoma skin cancer (NMSC). The NACHT, LRR and PYD domains-containing protein 1 (NLRP1) protein is considered to be inhibited in NMSC, although clinical evidence is still lacking. Objectives. To investigate the clinical significance of NLRP1 in cSCC and cBCC patients. Materials and methods. This prospective observational study enrolled 199 cases of cBCC and cSCC patients who reported to our hospital from January 2018 to January 2019. Additionally, 199 blood samples from healthy individuals were collected as the control. Serum NLRP1 and cancer biomarkers of CEA and CYFRA21-1 were then measured using enzyme-linked immunosorbent assay (ELISA). Clinical characteristics collected from patients included age, sex, BMI, TNM stage, cancer type, lymph node metastasis, and myometrial infiltration conditions. All patients were followed up for 1-3 years. Results. Of all patients, 23 died during the follow-up period, with a mortality rate of 11.56%. Serum NLRP1 showed markedly lower levels in cancer patients compared with healthy controls. Furthermore, the expression of NLRP1 was significantly higher in cBCC patients compared with cSCC patients. The deceased patients, together with those with lymph node metastasis and myometrial infiltration, also showed significantly lower NLRP1 levels. Moreover, lower NLRP1 levels were associated with higher frequencies of tumor-nodule-metastasis (TNM) III-IV stage, lymph node metastasis and myometrial infiltration, as well as higher mortality and recurrence rates. The curvilinear regression showed the relationship between NLRP1 and CEA/or CYFRA21-1 was most appropriate for the reciprocal. Receiver operating characteristic (ROC) curves showed NLRP1 was a potential biomarker for lymph node metastasis, myometrial infiltration and prognosis in NMSC patients, and the Kaplan-Meier analysis found NLRP1 was associated with 1-3-year mortality and recurrence of NMSC. Conclusions. Lower NLRP1 level is associated with worse clinical outcomes and poorer prognosis in cSCC and cBCC patients. [ABSTRACT FROM AUTHOR]

Multi-source sensor information fusion technology plays an important role in many application fields in the era of “Industry 4.0”. Generally, in practical engineering applications, the obtained information is inevitably uncertain due to the degradation of sensor performance, environmental interference, abnormal communication transmission process, etc. Compared with the strict requirements of probability density function for data quantity, the use of interval numbers to characterize the uncertainty of information has obvious advantages. Classical Dempster-Shafer(D-S) evidence theory can effectively deal with uncertain information, whereas its evidence structure is single-valued, making it difficult to fuse interval uncertainty information. Therefore, it is necessary to adopt an interval-valued evidence structure to carry out interval uncertainty information fusion. Current researches on interval-valued belief structure mainly focus on the modification of evidence combination rules but neglect the key problem of how to generate interval-valued evidence structure from original data obtained from sensors. Aiming at the problem of classification and discrimination based on multi-source interval-valued sensor information fusion, this paper adopts the theory of interval-valued evidence and focuses on the problem of generating a reasonable belief structure based on original interval-valued sensor information by using a multistage correction framework. Firstly, an interval-valued Lance and Williams Distance(ILWD) is proposed, and an initial belief structure is generated based on the proposed ILWD using the original sensor information. Secondly, by considering the interval length effect and based on Jaccard similarity coefficient, the initial interval-valued belief structure is corrected by a two-stage process. Then, in order to alleviate the impact of evidence conflict on the combination results, the weight of each evidence is assigned by an optimization method (the third stage of the belief structure modification). Further, the evidence is combined based on the method proposed by Wang et al. (2007). Finally, a practical case study is carried out to verify the rationality and effectiveness of the proposed method.