Space Distribution Research Articles

Intelligent Lost Circulation Monitoring Method Based on Data Augmentation and Temporal Models

Deep and offshore drilling operations face complex geological formations, uncertain formation pressures, and narrow safety density windows, making them susceptible to lost circulation risks. To address these challenges, this paper introduces an innovative, intelligent lost circulation monitoring model that incorporates geological lithology information. This model not only utilizes real-time drilling parameters, but also encodes geological information such as rock type as inputs to the model. By combining these key lithological features, the model can comprehensively assess wellbore stability and reduce the lost circulation risks. In this paper, the Conditional Tabular Generative adversarial network (CTGAN) model is used to enhance the data of small-sample risk data, which can effectively expand the data distribution space and improve the performance of the model. This paper conducts a comparative analysis of intelligent monitoring results using artificial neural networks (ANNs), long short-term memory (LSTM), and temporal convolutional networks (TCNs). The results show that the TCN achieves an identification accuracy of 93.7%. Furthermore, the analysis reveals that the inclusion of lithology information significantly enhances the model’s performance, resulting in a 7.1% increase in accuracy. The false alarm rate of the model can be reduced by 10.2%, considering the fluctuation of the logging curve caused by the on/off condition of the pump. This indicates that the introduction of lithology information and the condition of the pump on−off provide advantages in monitoring and identifying lost circulation risks, enabling a more precise assessment of wellbore stability and a reduction in lost circulation incidents. The method of lost circulation monitoring proposed in this paper provides an important safety guarantee for the oil drilling industry.

Rock Fracturing Characteristics and Roadway Expansion Application of Static Crushing Agent under Multi-Row Drilling Condition

Static crushing agent (SCA) offers advantages such as good safety, no harmful gases, and minimal vibration, making SCA ideal for rock crushing in mines or underground engineering. To scientifically implement the section expansion of mine roadway, the rock mechanical parameters, SCA expansion properties, and cracking behavior of surrounding rock were investigated using laboratory tests, numerical simulations, and field experiments. Results indicate that the smaller hole spacing of SCA improves the fracturing effect on rock mass, reduces the required fracture initiation stress and penetration stress, and the presence of a free surface aids in fracture development in the rock mass. Optimal borehole design parameters and spacing distributions of the SCA were determined, and corresponding construction techniques of roadway expansion were proposed. Field tests demonstrate that static crushing technology, under multi-row drilling conditions, is highly effective for rock roadway expansion projects. These findings provide valuable guidance for non-explosive expansion and other roof breaking treatment projects.

Evaluation of ‘In-Parlour Scoring’ (IPS) to Detect Lameness in Dairy Cows during Milking

The objective of this study was to evaluate the potential of ‘In-Parlour Scoring’ (IPS) as an alternative to locomotion scoring in herringbone, side-by-side, and tandem milking parlours in Austria. Between January and May 2023, a total of 990 observations were conducted on 495 cows across eleven Austrian dairy farms by two investigators working simultaneously but independently of each other. The observation criteria included shifting weight, claw conformation, and obvious disorders of the distal limb. Locomotion scoring was conducted on all cows within 24 h of assessment in the milking parlour using a scale of 1 to 5 (LCS 1: not lame; LCS 5: severely lame). Functional hoof trimming was performed within ten days after IPS. The following indicators were identified as useful for predicting lameness (LCS ≥ 3): shifting weight, abnormal weight distribution, swollen heel, hock joint or interdigital space, skin lesion on the lateral hock, claw position score, digital dermatitis lesions, short dorsal claw wall, and hyperextension of one claw. The reliability of the individual indicators for intra- and inter-rater assessment exhibited considerable variation ((weighted) kappa values: −0.0020–0.9651 and −0.0037–1.0, respectively). The specificity and sensitivity for the prediction of lame cows were calculated to be ≥ 96% and ≤ 24%, respectively. It was demonstrated that a one-time IPS has limited suitability for lameness assessment on Austrian dairy farms with herringbone, side-by-side, and tandem milking parlours.

IPSRM: An intent perceived sequential recommendation model

Objectives:Sequential recommendation aims to recommend items that are relevant to users’ interests based on their existing interaction sequences. Current models lack in capturing users’ latent intentions and do not sufficiently consider sequence information during the modeling of users and items. Additionally, noise in user interaction sequences can affect the model’s optimization process. Methods:This paper introduces an intent perceived sequential recommendation model (IPSRM). IPSRM employs the generalized expectation–maximization (EM) framework, alternating between learning sequence representations and optimizing the model to better capture the underlying intentions of user interactions. Specifically, IPSRM maps unlabeled behavioral sequences into frequency domain filtering and random Gaussian distribution space. These mappings reduce the impact of noise and improve the learning of user behavior representations. Through clustering process, IPSRM captures users’ potential interaction intentions and incorporates them as one of the supervisions into the contrastive self-supervised learning process to guide the optimization process. Results:Experimental results on four standard datasets demonstrate the superiority of IPSRM. Comparative experiments also verify that IPSRM exhibits strong robustness under cold start and noisy interaction conditions. Conclusions:Capturing latent user intentions, integrating intention-based supervision into model optimization, and mitigating noise in sequential modeling significantly enhance the performance of sequential recommendation systems.

Tight conic approximation of testing regions for quantum statistical models and measurements

Quantum statistical models (i.e., families of normalized density matrices) and quantum measurements (i.e., positive operator-valued measures) can be regarded as linear maps: the former, mapping the space of effects to the space of probability distributions; the latter, mapping the space of states to the space of probability distributions. The images of such linear maps are called the testing regions of the corresponding model or measurement. Testing regions are notoriously impractical to treat analytically in the quantum case. Our first result is to provide an implicit outer approximation of the testing region of any given quantum statistical model or measurement in any finite dimension: namely, a region in probability space that contains the desired image, but is defined implicitly, using a formula that depends only on the given model or measurement. The outer approximation that we construct is minimal among all such outer approximations, and close, in the sense that it becomes the maximal inner approximation up to a constant scaling factor. Finally, we apply our approximation to provide sufficient conditions, that can be tested in a semi-device-independent way, for the ability to transform one quantum statistical model or measurement into another.

How Green Space Justice in urban built-up areas affects public mental health: a moderated chain mediation model.

Green and blue spaces, as crucial components of urban ecosystems, significantly impact the physical and mental health of residents. However, the mechanisms through which Green/Blue Space Justice influence residents' health remain unclear. This study aims to explore the impact of green spaces on public psychological responses, physical activity, and mental health from a justice perspective, and to examine the moderating role of blue spaces in this relationship. The research was conducted in selected communities within the Chang-Zhu-Tan urban agglomeration in Hunan Province, China. A total of 801 valid questionnaires were collected through field visits and online surveys. The study uses an improved Gaussian-based two-step floating catchment area (2SFCA) method to assess green space accessibility. Data were analyzed using structural equation modeling (SEM) and moderation effect analysis to reveal the relationships between variables. The findings indicate that Green Space Justice has a significant positive impact on psychological responses, physical activity, and mental health; psychological responses and physical activity play crucial mediating roles between Green Space Justice and mental health; and Green Space Justice significantly affects mental health through a chain mediation path involving psychological responses and physical activity. Moreover, Blue Space Justice significantly moderates the impact of Green Space Justice on psychological responses and physical activity, but does not have a significant direct impact on mental health. This study enriches the theory of Green Space Justice by revealing the mechanisms through which it influences mental health via psychological responses and physical activity. It provides a scientific basis for the development of healthy cities. Additionally, it recommends that urban planning should prioritize the equitable distribution and high accessibility of both green and blue spaces to comprehensively enhance residents' physical and mental well-being. Policymakers should consider prioritizing the accessibility of high-quality green spaces for vulnerable communities during urban renewal and expansion processes to reduce social health inequalities and promote broader public health outcomes.

Probabilistic stability analysis of small-angle marine slopes considering hydrate decomposition and seismic excitation

The submarine massive mud transports of several seas around the world, including the Shenhu Sea, are thought to relate with hydrate decomposition, which occur even at small gradients. This article investigates the stability and reliability of marine slopes considering three factors related to hydrate decomposition and earthquake, namely decrease in sediment strength, increase in excess pore pressure, and activation by external seismic force. Taking the SH2 hole, implemented by the first Chinese offshore hydrate drilling mission (GMGS-1), as an example, the impacts of three factors mentioned above on marine slopes stability are analyzed with infinite slope theory. In addition, the space distribution of physical and mechanical parameters in hydrate deposits is under consideration. The cohesion, friction angle and buoyant unit weight in a non-stationary random field are treated as spatial random variables, where the means of the first two increase linearly with depth. Variation characteristics of the failure probability for various slope angles are analyzed under different scenarios. The outcomes reveal that the safety factor of the slopes at 30% potential for damage is around 1.25 within a given variant characterization. The proposed integrated stability and reliability analysis can effectively provide early warning for collapse of marine hydrate-bearing slopes.

Effect of infusion direction on convection-enhanced drug delivery to anisotropic tissue.

Convection-enhanced delivery (CED) can effectively overcome the blood-brain barrier by infusing drugs directly into diseased sites in the brain using a catheter, but its clinical performance still needs to be improved. This is strongly related to the highly anisotropic characteristics of brain white matter, which results in difficulties in controlling drug transport and distribution in space. In this study, the potential to improve the delivery of six drugs by adjusting the placement of the infusion catheter is examined using a mathematical model and accurate numerical simulations that account simultaneously for the interstitial fluid (ISF) flow and drug transport processes in CED. The results demonstrate the ability of this direct infusion to enhance ISF flow and therefore facilitate drug transport. However, this enhancement is highly anisotropic, subject to the orientation of local axon bundles and is limited within a small region close to the infusion site. Drugs respond in different ways to infusion direction: the results of our simulations show that while some drugs are almost insensitive to infusion direction, this strongly affects other compounds in terms of isotropy of drug distribution from the catheter. These findings can serve as a reference for planning treatments using CED.

Exploring the equitable inclusion of diverse voices in urban green design, planning and policy development: a scoping review protocol

IntroductionDespite the increasingly evidenced positive impacts of green space on human physical and mental health, green spaces remain inequitably distributed across different socioeconomic groups. Urban planning and design should prioritise...

Urban greenery distribution and its link to social vulnerability

Urban greenery plays a pivotal role in urban environments, impacting the environmental well-being and people’s comfort. Several studies have demonstrated a strong link between urban greenery and socioeconomic status but still lack an analysis of greenery on uneven distribution in social vulnerability. This study assesses how multi-level greenery rates distribute and associate the social vulnerability of people in 429 census tracts in the Seattle metropolitan area. It integrates multi-source urban informatics data, including remote sensing data and street view imagery, to identify various vegetation types. Then, it uses the interpretable machine learning model to explore the relationship between street-level green space distribution and community vulnerability. The results show a serious problem of uneven distribution of green spaces in urban centers since urban areas are built up and fragmented the landscape. Areas with low urban greening in the Seattle area have higher rates of poverty, unemployment, racial segregation, and housing overcrowding. Besides, greening features like street green views, which are more related to human perception, have a great association with social vulnerability. These findings contribute to the urban green spaces to better promote community equity and vulnerability.

Denoising for balanced representation: A diffusion-informed approach to causal effect estimation

Estimating individual treatment effects with observational data is crucial in causal inference. While it faces two major challenges: the absence of counterfactual outcomes and selection bias due to non-random assignment and intervention of confounding variables. This paper specifically addresses these by quantifying the distribution shift between the treated and control groups from a generative view to achieve balanced representation and then predict causal effects. We propose a novel method called Denoising for a Balanced Representation in Treatment Effect Estimation (DBRT). Motivated by the aim to generate analogous distributions in the Diffusion model, which aligns with our goal of achieving similar distributions in the latent space, our approach considers the divergence between the treated and control groups as’noise’ that needs to be removed incrementally. We then construct a network to fit and then denoise it step by step, ensuring a consistent reduction of discrepancies between the two groups. Additionally, to enhance the representation of input features, we incorporate memory vectors into the attention mechanism to capture prior information and combined correlations between covariates. Furthermore, we utilize the Hilbert–Schmidt Independence Criterion (HSIC) within the loss function to constrain the learned representation, ensuring its relevance to the original data. DBRT has demonstrated superior performance on different datasets compared to previous classic works, highlighting its effectiveness in treatment effect estimation.

Gender Dynamics in the Built Environment: A Study of Madurese Taneyan Lanjhang Houses

Abstract Gender norms as a subset of societal norms are manifested in vernacular households. In some aspects, Madurese traditional houses (Taneyan Lanjhang) define spaces according to the domains of men and women. This division implies that roles assigned to men and women are closely tied to whether those activities take place in the public or private domain. The actual experience of women and men nowadays, however, is more complex. The aim of this study is to investigate the roles of men’s and women’s familial roles in shaping their dwellings as well as inhibition of domestic space within contemporary Taneyan Lanjhang homes. This study adopts a qualitative approach with empirical and descriptive methodology. The findings are derived from Taneyan Lanjhang houses in Alang-Alang Village, Bangkalan Regency, Madura through in-depth interviews to homeowners’ activities and alterations made to the house as well as visual observation. The results suggest that there are variations in how households implement gender structural roles. The community is more inclined to egalitarian distribution of roles and spaces. In certain areas traditionally expected to be occupied by either men or women, instances of overlap and blending are evident, suggesting that some gender roles are subject to interpretation, negotiation, and changes. A strict adherence to dichotomy of gender roles is proven to be not sustainable. This study contributes to the theoretical discussion of infrastructure and sustainability by examining how gender dynamics influence the design, utilisation, and sustainability of the built environment within Taneyan Lanjang community.

Dual-space distribution metric-based evolutionary algorithm for multimodal multi-objective optimization

Multimodal multi-objective optimization problems (MMOPs) are characterized by having multiple global or local Pareto solution sets. In most of multimodal multi-objective evolutionary algorithms, the distribution of population in objective space and decision space cannot be taken into account, simultaneously. In this paper, an innovative evolutionary algorithm based on dual-space distribution metric for multimodal multi-objective optimization (DsDMEA) is designed to solve this problem. Specifically, the two enhanced performance metrics are coupled together as a single dual-space distribution metric (DsDM) serving as the criterion for environmental selection. DsDMEA explores solutions on Pareto front and Pareto solutions sets that are not associated with the reference point, which show limited contributions to the distribution in dual spaces. Then DsDMEA removes any bi-noncontributing solutions using DsDM during environmental selection until the population size is met. This achieves a balance between the distribution of solutions and convergence in decision space. At the same time, this paper introduces a novel fitness computation method, enabling the precise localization of local Pareto fronts within the population. Finally, eight state-of-the-art multimodal multi-objective evolutionary algorithms are adopted to make a comparison on two types of benchmark to verify the performance of the DsDMEA. The experimental results demonstrated the DsDMEA effectiveness in solving MMOPs with local problems and traditional MMOPs.

A New Cyclo‐Synchrotron Model for Lunar‐Based Observations of Earth's Radiation Belts

AbstractThe energetic electrons trapped in the terrestrial radiation belts spontaneously emit radiation due to magnetic deflection. The observation of this radiation can offer insights into the space and energy distribution of the electrons, as well as their temporal variability. Given the energy range of the electrons in the Earth's radiation belts (0.1 keV to 10 MeV), the emission corresponds to cyclo‐synchrotron radiation, an intermediate radiation regime between the low‐energy cyclotron radiation and the extremely relativistic synchrotron emission. Terrestrial cyclo‐synchrotron radiation cannot be observed from Earth as it is emitted at frequencies below the ionospheric cutoff frequency (f < 10 MHz). However, a lunar near‐side observation could provide real time measurements of the dynamics of the Earth radiation belts. In this study, we present the development of a full cyclo‐synchrotron model for the electron radiation belt emissions, building upon a preliminary synchrotron model. While previous simulations focused on >1 MeV electrons, our new model is able to simulate the radiation of >100 keV electrons which have much larger fluxes than MeV electrons. This new model accurately simulates the cyclo‐synchrotron radiation emitted by Earth's radiation belts as observed from the lunar surface, which would help the design of lunar‐based radio interferometers and would offer a new perspective on radiation belts research and monitoring.

Jump around: Selecting Markov Chain Monte Carlo parameters and diagnostics for improved food web model quality and ecosystem representation

Capturing ecological data variability in food web models is an important step for improving model representation of empirical systems. One approach is to use linear inverse modelling and Markov Chain Monte Carlo (LIM-MCMC) techniques to set up an inverse LIM problem using empirical data constraints, and then sample multiple plausible food webs from the inverse problem using an MCMC algorithm. We describe the set of plausible food webs as an ‘ensemble’ of solutions to the inverse problem sampled with the LIM-MCMC algorithm. The extent of data variability eventually integrated into an ensemble depends on how well the LIM-MCMC algorithm samples the solution space. Algorithm quality can be adjusted via user-defined parameters describing starting points, jump sizes, and number of iterations or food webs produced. However, little information exists on how each LIM-MCMC algorithm parameter affects the degree of empirical data variability introduced into the ensemble. Further, post hoc algorithm quality diagnostics with commonly used trace plots and the coefficient of variation (CoV) rarely address critical aspects of algorithm quality, such as (1) if the returned ensemble successfully targeted the solution space distribution (stationarity), (2) correlation between ensemble solutions (mixing), and (3) if the ensemble contains enough solutions to adequately capture input data variability (sampling efficiency). Therefore, we used several established MCMC convergence diagnostics to (1) quantify how algorithm parameters affect ensemble flow values and if these differences propagate to ecological indicators and (2) evaluate algorithm quality and compare to current evaluation and ecosystem modelling methods. We applied 30 LIM-MCMC algorithm combinations of varying starting points, jump sizes, and number of iterations to solve food web ensembles from a single food web model. We analysed ensembles with Ecological Network Analysis (ENA) to calculate indicators describing system function. Results show that LIM-MCMC algorithm parameters, in particular the jump size, affect ensemble flow values, which propagate to ecological indicators describing different ecosystem function of the same model. Thereafter, comparisons of post hoc diagnostics show that MCMC convergence diagnostics provided more robust estimates of algorithm quality than trace plots and CoV. Together, these findings underpin several novel recommendations to enhance LIM-MCMC algorithm parameter selection and quality assessments applicable to any ecological ensemble network study.

Optimization of fast-ion diagnostic sets in tokamaks and stellarators using diagnostic weight functions.

The fast-ion phase-space distribution function in the magnetic fusion devices is always underdiagnosed, and every new fast-ion diagnostic should be carefully assessed before installation to minimize redundancies in measurements and maximize the information from the yet undiagnosed part of the fast-ion phase space distribution function. Here, we present a novel method of assessing the added value of a considered fast-ion diagnostic, taking actual geometry and an existing set of fast-ion diagnostics into account. The new method is based on a reformulation of the diagnostic weight functions in constants of motion (COM). We compare the proposed method with the previous approach using Monte Carlo simulations.

Scaling properties of particle density fluctuations at LHC energies

The availability of high-multiplicity events at LHC energies provides a unique opportunity to examine the nature of phase-transition (PT) in heavy-ion collisions. Within the framework of HYDJET++ model, a systematic study of particle density fluctuations in narrow phase-space bins in Pb-Pb collisions at and 5.02 TeV energies is performed using the method of scaled factorial moments (SFM). The findings reveal the presence of intermittency in the 2-dimensional (2D) phase space distribution of charged particles at the LHC energies. The anomalous fractal dimensions, d q , are observed to increase with the order of the moments q, suggesting the multifractal nature of the charged particle production. The parameter λ q is found to exhibit a monotonically decreasing trend with the order of the moments but with no clear minima in λ q values. Furthermore, the value of the critical exponent is estimated and compared with those predicted by other models reported at RHIC and LHC energies. The value of the critical exponent, ν, obtained in the present study, shows significant departure from the value ∼1.304, as expected for the Ginzberg-Landau (GL) type second-order PT but exhibits a good level of agreement with the results obtained by other models at LHC energies.

Application of Machine Learning Methods in Predicting Lysine-specific Histone Demethylase 1 (LSD1) Inhibitors

Background: Lysine-specific histone demethylase 1 (LSD1) is a well-known anti-cancer target for drug discovery. Novel reversible inhibitors of LSD1 are desirable to be developed. Objective: This study aimed to build reliable predictive models to evaluate the antineoplastic efficacy of compounds and reveal the structural foundation underlying the inhibitory activity of LSD1. Methods: Multiple artificial intelligence algorithms were utilized in the development of quantitative structure-activity relationship (QSAR) models. A dataset comprising 915 compounds with welldefined IC50 values against LSD1 was assembled for analysis. The structures of these compounds were described by different descriptor packages. Principal component analysis (PCA) was performed to explore the chemical space distribution of each dataset. Y-randomization test and applicability domain (AD) analysis were deployed to validate the reliability of models. Results: For regression models, a consensus model was constructed by integrating the predictions of four top-performing individual models (SVM_ECFP4, RFR_PyDescriptor, RFR_RDKIT, and TRANSNNI), resulting in enhanced predictive performance as compared to the individual models. The consensus model achieved a determination coefficient (r2, for the test set) value of 0.82. For classification models, the consensus model was derived from the amalgamation of three individual models (ASNN_RDKIT, ASNN_ECFP4, and RFR_ECFP4), and the overall prediction accuracy of the model was 0.96 for external validation. Conclusion: The reliable models could be used to identify highly active LSD1 inhibitors for the purpose of efficient virtual screening. In addition, the important molecular properties and structural fragments derived from this work can provide guidance for the structural optimization of novel LSD1 inhibitors.

Optimization of an Urban Microgreen Space Distribution Based on the PS-ACO Algorithm: A Case Study of Shenyang, China

In this study, optimization of the microgreen space distribution through multistage regulation is investigated, with the goal of alleviating the imbalance between the supply and demand of green resources in the central urban area of Shenyang. An optimized evaluation model of green space supply and demand is employed to calculate the green space accessibility index at a 100-m grid scale and identify different levels of green space resource supply and demand. Priority is given to supplementing resources for the elderly population by balancing the green space supply in vulnerable areas. Particle swarm—ant colony optimization (PS-ACO) is used to select microgreen space sites within each priority level. On the basis of the “important-urgent” quadrant analysis, S1-priority residential areas account for 8.12% of the grid, S2-priority areas account for 67.01%, and S3-priority areas account for 24.87%. The PS-ACO algorithm outputs potential microgreen space sites within each priority level to accurately regulate the green space distribution in residential areas with different supply pressures and limited land availability. A spatial correlation analysis of the new sites reveals good spatial dispersion within service units, effectively alleviating demand pressures, and good aggregation at a regional scale to address imbalances in the supply of green space in a targeted manner. Thus, the optimized results of the PS-ACO algorithm are effective, providing reliable site-selection references for subsequent urban microgreen space distributions.

Effects of Varying Nano-Montmorillonoid Content on the Epoxy Dielectric Conductivity

This study investigates the correlation between the interface structure and macroscopic dielectric properties of polymer-based nanocomposite materials. Utilizing bisphenol-A (BPA) epoxy resin (EP) as the polymer matrix and the commonly employed layered phyllosilicate montmorillonoid (MMT) as the nanometer-scale dispersive phase, nano-MMT/EP composites were synthesized using composite technology. The microstructure of the composite samples was characterized through XRD, FTIR, SEM, and TEM. Changes in the morphology of the nanocomposite interface were observed with varying MMT content, subsequently impacting dielectric polarization and loss. Experimental measurements of the dielectric spectrum of the nano-MMT/EP were conducted, and the influence of the material interface, at different nano-MMT contents, on the dielectric relaxation was analyzed. The study delves into the effect of the nanocomposite interface structure on ion dissociation and migration barriers, exploring the ionic conductivity of nano-MMT/EP. Lastly, an analysis of the impact of different nano-MMT contents on the dielectric conductivity is presented. From the experimental results, the arranging regularity of polymer molecules in the interface area raises. In the matrix, the ion migration barriers decrease significantly. The higher the MMT content in the interface, the lower the migration barrier is. Until the MMT content exceeds the threshold, the agglomerated micro-particles form, which decreases the polymers’ space distribution regularity, and the ions migration barrier raises. According to the changes in the rule of the ions migration barrier with the composite interface structure content, the reason for dielectric conductivity changes can be judged.