Partitioning Model Research Articles

Predicting therapeutic clinical trial enrollment for underrepresented patients in neuro-oncology using supervised machine learning.

1614 Background: Despite the unique ability of machine learning (ML) techniques to uncover complex interactive effects in datasets, studies exploring predictors of clinical trial enrollment have largely used standard statistical methods to discern associations. Furthermore, there have been limited applications of ML to close existing disparities in clinical trial enrollment of underrepresented patients in neuro-oncology. This study aims to use supervised machine learning to define and validate primary contributors of therapeutic clinical trial enrollment for a) all patients, b) women and c) NIH-designated minority patients with low- and high-grade glioma. Methods: Adult glioma patients who received care from the UCSF Brain Tumor Center between 1997- 2017 were identified in a prospective registry. Bootstrap forest (BF) and Recursive Partitioning (RP) models were created by randomly dividing patients in a 70/30 split into development (DEV) and validation (VAL) cohorts among all patients, women, and NIH-designated minority patients, separately. Model performance was assessed using the area under the curve (AUC) in the DEV and VAL cohorts. Results: Among 1042 patients, 350 patients (33.6%) enrolled in a therapeutic clinical trial. There were 445 women (42.7%), of which 144 (32.4%) enrolled, and 141 minority patients (13.5%), of which 39 (27.7%) enrolled. For all patients, in order of decreasing influence, the BF model selected median neighborhood household income, age, neighborhood poverty level, distance from hospital, tumor location, tumor volume, treatment with chemotherapy, occupation, KPS, insurance status, employment status, seizure at presentation, and volumetric extent of resection (EOR) to predict trial enrollment (DEV AUC: 0.984; VAL AUC: 0.746). For women, the BF model selected distance from hospital, age, household income, neighborhood poverty level, tumor location, treatment with chemotherapy, and KPS for trial prediction (DEV AUC: 0.972; VAL AUC: 0.746), whereas for minority patients, the BF model selected neighborhood poverty level, age, occupation, distance from hospital, tumor volume, insurance status, employment status, and EOR to predict trial enrollment (DEV AUC: 0.9830; VAL AUC: 0.769). The RP model for women designated those with non-White race as less likely to enroll (DEV AUC: 0.735; VAL AUC: 0.665) while the RP model for minority patients characterized those who preferred a non-English language or who were divorced or widowed as less likely to enroll (DEV AUC: 0.775; VAL AUC: 0.601). Conclusions: Supervised machine learning models achieved similar predictive performance among patient subgroups, while uncovering interactions between gender identity, minority status, and sociodemographic variables. These findings can guide targeted recruiting efforts to advance equitable trial enrollment for women and minorities.

Identifying uncertainty in physical–chemical property estimation with IFSQSAR

This study describes the development and evaluation of six new models for predicting physical–chemical (PC) properties that are highly relevant for chemical hazard, exposure, and risk estimation: solubility (in water SW and octanol SO), vapor pressure (VP), and the octanol–water (KOW), octanol–air (KOA), and air–water (KAW) partition ratios. The models are implemented in the Iterative Fragment Selection Quantitative Structure–Activity Relationship (IFSQSAR) python package, Version 1.1.0. These models are implemented as Poly-Parameter Linear Free Energy Relationship (PPLFER) equations which combine experimentally calibrated system parameters and solute descriptors predicted with QSPRs. Two other ancillary models have been developed and implemented, a QSPR for Molar Volume (MV) and a classifier for the physical state of chemicals at room temperature. The IFSQSAR methods for characterizing applicability domain (AD) and calculating uncertainty estimates expressed as 95% prediction intervals (PI) for predicted properties are described and tested on 9,000 measured partition ratios and 4,000 VP and SW values. The measured data are external to IFSQSAR training and validation datasets and are used to assess the predictivity of the models for “novel chemicals” in an unbiased manner. The 95% PI intervals calculated from validation datasets for partition ratios needed to be scaled by a factor of 1.25 to capture 95% of the external data. Predictions for VP and SW are more uncertain, primarily due to the challenges in differentiating their physical state (i.e., liquids or solids) at room temperature. The prediction accuracy of the models for log KOW, log KAW and log KOA of novel, data-poor chemicals is estimated to be in the range of 0.7 to 1.4 root mean squared error of prediction (RMSEP), with RMSEP in the range 1.7–1.8 for log VP and log SW. Scientific contributionNew partitioning models integrate empirical PPLFER equations and QSARs, allowing for seamless integration of experimental data and model predictions. This work tests the real predictivity of the models for novel chemicals which are not in the model training or external validation datasets.Graphical

Partitioning of water vapor and CO2 fluxes and underlying water use efficiency evaluation in a Brazilian seasonally dry tropical forest (Caatinga) using the Fluxpart model

The Fluxpart partitioning model was employed to assess transpiration, photosynthesis and evaporation, respiration based exclusively through high-frequency eddy covariance (EC) data. The model was implemented across three sites with two vegetation cover conditions (dense and sparse) during the drought episode between 2012 and 2015 in the Caatinga biome in Northeast Brazil. Fluxpart, an open-source Python software, partitions data by analyzing the flux variance similarity (FVS) relationship and conducting correlation analyses of EC data. The main contribution to the evapotranspiration (ET) process was transpiration (T), representing about 64 and 67% of the amount ET in areas with dense and sparse vegetation cover, respectively. When the Caatinga biome is in a good state of conservation, it behaves as a carbon sink; the net ecosystem exchange average was −6 g C m−2 day−1 (gram of carbon per square meter daily) for dense vegetation and 3 g C m−2 day−1 for sparse vegetation. Through ET, vapor pressure deficit (VPD), and gross primary production (GPP) via Fluxpart, an assessment was made concerning the underlying water use efficiency (uWUE) to understand the nonlinear effects of VPD on the carbon-water coupling at the ecosystem scale. The overvalues of 60 g C hPa0.5 kg−1 H2O−1 (grams of carbon times square root of hectopascals per kilogram of water) prevailing in the rainy season and below 20 g C hPa0.5 kg−1 H2O−1 were mostly in the dry season. uWUE was an important indicator of the ability of the Caatinga biome to optimize water loss and carbon gain according to the water available in the soil-plant-atmosphere system to withstand water stress, especially at dense vegetation covers.

Graph-Aligned Random Partition Model (GARP)

Bayesian nonparametric mixtures and random partition models are powerful tools for probabilistic clustering. However, standard independent mixture models can be restrictive in some applications such as inference on cell lineage due to the biological relations of the clusters. The increasing availability of large genomic data requires new statistical tools to perform model-based clustering and infer the relationship between homogeneous subgroups of units. Motivated by single-cell RNA data we develop a novel dependent mixture model to jointly perform cluster analysis and align the clusters on a graph. Our flexible graph-aligned random partition model (GARP) exploits Gibbs-type priors as building blocks, allowing us to derive analytical results for the probability mass function (pmf) on the graph-aligned random partition. We derive a generalization of the Chinese restaurant process from the pmf and a related efficient and neat MCMC algorithm to implement Bayesian inference. We illustrate posterior inference under the GARP using single-cell RNA-seq data from mice stem cells. We further investigate the performance of the model in recovering the underlying clustering structure as well as the underlying graph by means of simulation studies. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

The impact of gaseous degradation on the gas–particle partitioning of methylated polycyclic aromatic hydrocarbons

Abstract. The partitioning of semi-volatile organic compounds (SVOCs) between gas and particle phases plays a crucial role in their long-range transport and health risk assessment. However, the accurate prediction of the gas–particle (G–P) partitioning quotient (KP′) remains a challenge, especially for the light-molecular-weight (LMW) SVOCs due to their upward deviation from equilibrium state. In this study, the phenomenon with the influence of gaseous degradation on G–P partitioning was observed. Based on the diurnal study of concentrations and KP′ values for methylated polycyclic aromatic hydrocarbons (Me-PAHs), it was found that the KP′ values of methylated naphthalenes (Me-Naps; one type of LMW SVOC) during the daytime were higher than during the nighttime, and the regression lines of log KP′ versus log KOA (octanol–air partitioning coefficient) for daytime and nighttime were non-overlapping, which were different from other Me-PAHs. Compared with other diurnal influencing factors, the higher gaseous degradation of Me-Naps in the daytime than in the nighttime should partially explain their special diurnal variation in KP′, which provided a new explanation for the non-equilibrium behavior of KP′ for LMW SVOCs. Moreover, the influence of gaseous degradation on the deviation of KP′ from equilibrium state was deeply studied based on the steady-state G–P partitioning model considering particulate proportion in emission (ϕ0). The increasing times of KP′ influenced by the gaseous degradation deviated from equilibrium state can be calculated by 1 + 13.2ϕ0 × kdeg (kdeg, gaseous degradation rate). The increase in KP′ along with the increase in kdeg proved that higher gaseous degradation in the daytime could increase the KP′ value. Furthermore, an amplification in KP′ ranging from 1.11 to 5.58 times (90 % confidence interval: 1.01 to 14.4) under different ϕ0 values (0 to 1) in the temperature range of −50 to 50 °C was estimated by the Monte Carlo analysis. In summary, it can be concluded that the influence of gaseous degradation should also be considered in the G–P partitioning models of SVOCs, especially for the LMW SVOCs, which provided new insights into the related fields.

Model for partitioning the non-phytoplankton absorption coefficient of seawater in the ultraviolet and visible spectral range into the contributions of non-algal particulate and dissolved organic matter

Non-algal particles and chromophoric dissolved organic matter (CDOM) are two major classes of seawater constituents that contribute substantially to light absorption in the ocean within the ultraviolet (UV) and visible (VIS) spectral regions. The similarities in the spectral shape of these two constituent absorption coefficients, ad(λ) and ag(λ), respectively, have led to their common estimation as a single combined non-phytoplankton absorption coefficient, adg(λ), in optical remote-sensing applications. Given the different biogeochemical and ecological roles of non-algal particles and CDOM in the ocean, it is important to determine and characterize the absorption coefficient of each of these constituents separately. We describe an ADG model that partitions adg(λ) into ad(λ) and ag(λ). This model improves upon a recently published model [Appl. Opt. 58, 3790 (2019)APOPAI0003-693510.1364/AO.58.003790] through implementation of a newly assembled dataset of hyperspectral measurements of ad(λ) and ag(λ) from diverse oceanic environments to create the spectral shape function libraries of these coefficients, a better characterization of variability in spectral shape of ad(λ) and ag(λ), and a spectral extension of model output to include the near-UV (350–400 nm) in addition to the VIS (400–700 nm) part of the spectrum. We developed and tested two variants of the ADG model: the ADG_UV-VIS model, which determines solutions over the spectral range from 350 to 700 nm, and the ADG_VIS model, which determines solutions in the VIS but can also be coupled with an independent extrapolation model to extend output to the near-UV. This specific model variant is referred to as ADG_VIS-UVExt. Evaluation of the model with development and independent datasets demonstrates good performance of both ADG_UV-VIS and ADG_VIS-UVExt. Comparative analysis of model-derived and measured values of ad(λ) and ag(λ) indicates negligible or small median bias, generally within ±5% over the majority of the 350–700 nm spectral range but extending to or above 10% near the ends of the spectrum, and the median percent difference generally below 20% with a maximum reaching about 30%. The presented ADG models are suitable for implementation as a component of algorithms in support of satellite ocean color missions, especially the NASA PACE mission.

Effects of spruce budworm defoliation on in-stream algal production and carbon use by food webs

Insect defoliation is a widespread cause of forest disturbance in Canada, and it has the potential to alter terrestrial carbon contributions to stream consumers and thus affect stream ecosystems. Although defoliation by spruce budworms ( Choristoneura fumiferana) is considered a natural disturbance with importance to forest succession, the magnitude and duration of outbreaks are increasing, and there is a growing need to understand their downstream consequences. To examine this, algal productivity and autochthonous (in-stream) energy supporting macroinvertebrate and fish consumers (using δ13C, δ15N) were examined in 12 streams in the Gaspé Peninsula, Québec, Canada, that ranged in watershed defoliation from spruce budworm. Hierarchical partitioning models were used to assess the contributions of watershed and stream variables to these endpoints. Total seasonal algal production ranged fourfold among sites, and cumulative defoliation was the strongest predictor. Consumers from streams with greater defoliation (cumulative or annual) had lower reliance on autochthonous carbon, despite increased algal production, and this effect was greater for brook trout than macroinvertebrates. These findings demonstrate for the first time that insect outbreaks—which alter forest structure and function—also similarly affect downstream aquatic ecosystems by causing changes in the production and contributions of carbon sources. This suggests that insect control strategies and forest management decisions should also consider implications for downstream aquatic ecosystems.

Urban air PCDD/Fs: Atmospheric concentrations, temporal changes, gas/particle partitioning, possible sources and cancer risks

Polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) are pollutants of concern due to their toxic effects. No active sampling study on PCDD/Fs has been conducted in Bursa. This study aimed to fill this gap by measuring PCDD/F levels in the region. Accordingly, the samples were collected from an urban area in Bursa, covering four seasons between June 2022 and April 2023. The total (gas+particulate) ambient air concentrations were between 312.23 and 829.80 fg/m3 (mean: 555.05 ± 173.62 fg/m3). In terms of toxic equivalents (TEQ), the average concentration was 43.29 ± 9.18 fg WHOTEQ/m3. Based on the concentration values obtained, cancer and non-carcinogenic risk values of PCDD/Fs were calculated for three different age groups. The results indicated negligible health risks for all age groups. In addition, a seasonal assessment was also made and it was observed that PCDD/F concentration values varied with the ambient air temperatures. In general, higher values were measured in colder months compared to warmer months. This was probably due to the additional sources and adverse meteorological conditions. Moreover, the gas/particle partitioning of PCDD/Fs was investigated in detail. The average gas and particulate phase concentrations for PCDD/Fs were 101.81 ± 20.77 and 453.24 ± 172.50, respectively. It was found that an equilibrium state was not reached in the gas/particle partitioning. Two different gas/particle partition models based on adsorption and absorption mechanisms were compared, and the absorption model gave more consistent predictions. The Principal Component Analysis (PCA) was employed to identify the possible PCDD/F sources. The results indicated that the region was influenced by vehicle emissions, residential heating, organized industrial zones and metal recycling facilities. In addition, 72-hour backward air mass trajectory analyses were performed to understand the long-range transported air masses. However, it was found that the transported air masses did not significantly affect the concentration values measured in the sampling site.

Volatility forecasts by clustering: Applications for VaR estimation

It is well known that volatility has time-varying and clustering characteristics. The information content of volatility clustering is particularly important in turbulent periods, such as the stage of financial crisis. How to fully mine the implicit information within clusters to predict the volatility in the future is a rarely discussed issue. In this paper, we put forward a partition model to segment volatility into non-overlapping clusters by Fisher’s optimal dissection methodology. Using this model, we can quickly identify the points of structural changes in volatility. By utilizing the information of the nearest cluster, we can perform point estimation and interval estimation on future volatility. In the end, we conduct some empirical examples based on the returns of S&P 500, DAX 30 and FTSE 100 index. We find that our method can improve the volatility forecast and VaR estimations.

A Novel RSS Fingerprint to Locate User Equipment UE in Remote Area

Location determination algorithms are widely used in cellular networks, especially in the long-term evolution (LTE) network, to enable the provision of location-based services (LBS). The increasing global demand for cellular networks has resulted in the creation of new user equipment (UE) positioning systems that align with the network's momentum. Regrettably, all of these technologies are hindered by their incapacity to ascertain the location of the UE in distant regions. This article introduces a novel method utilizing Radio Frequency (RF) fingerprinting to precisely locate UEs in remote areas. The approach entails employing a proposed partitioning model with a high level of precision, incorporating artificial intelligence and machine learning AI/ML in its fundamental state to reduce the search area. Furthermore, two algorithms are suggested: The first aims to enhance the efficiency of the battery with limited capacity by decreasing the frequency of measurements transmission. The second utilizes Jaccard similarity and incorporates the prefix filtering technique to determine matches. The algorithm is used to speed up the process of matching the fingerprint recorded in the fingerprint database with the fingerprint captured in real time. The results shows that it can reduces the transmission rate by 77.08% and achieves the lowest error rate of 35.34 m. Additionally, it exhibits a response time of 8 seconds.

An enhanced exact algorithm for the multi-trip vehicle routing problem with time windows and capacitated unloading station

This paper introduces a vehicle routing problem that simultaneously considers multiple trips, time windows, and a capacitated unloading station. This problem is a generalization of the multi-trip vehicle routing problem with time windows, which determines a set of least-cost vehicle routes to fulfill all customer demands while respecting the constraints of vehicle capacity and time windows. Due to restricted resources (e.g., equipment and labor force) at the depot, vehicles may need to wait in a queue for being unloaded when they arrive. This unloading capacity constraint significantly complicates the problem, as it causes a trip to involve three stages—traveling, waiting, and unloading. We formulate this problem as an arc flow model and a trip-based set partitioning model, where the latter is solved by a branch-price-and-cut (BPC) algorithm. To improve the computational aspect of the BPC framework, a two-phase column generation (CG) algorithm is designed. First, a bidirectional labeling algorithm is tailored to solve the pricing problem, where two accelerating strategies are employed to speed up the resolution process. Meanwhile, k-path inequalities and limited-memory subset row inequalities are utilized to tighten the linear relaxation of the master problem. Computational results based on the instances adapted from the well-known Solomon’s benchmark show that the developed BPC algorithm can solve most instances within 50 customers to optimality in a short time frame and some instances of 100 customers to optimality within a 3-hour time limit. Moreover, our BPC algorithm performs better than exact algorithms in the literature for similar problem variants in both solution quality and computing time.

Critical Casimir effect in a disordered O(2)-symmetric model.

The critical Casimir effect appears when critical fluctuations of an order parameter interact with classical boundaries. We investigate this effect in the setting of a Landau-Ginzburg model with continuous symmetry in the presence of quenched disorder. The quenched free energy is written as an asymptotic series of moments of the model's partition function. Our main result is that, in the presence of a strong disorder, Goldstone modes of the system contribute either with either an attractive or a repulsive force. This result was obtained using the distributional zeta-function method without relying on any particular ansatz in the functional space of the moments of the partition function.

Major axes of variation in tree demography across global forests

The future trajectory of global forests is closely intertwined with tree demography, and a major fundamental goal in ecology is to understand the key mechanisms governing spatio‐temporal patterns in tree population dynamics. While previous research has made substantial progress in identifying the mechanisms individually, their relative importance among forests remains unclear mainly due to practical limitations. One approach to overcome these limitations is to group mechanisms according to their shared effects on the variability of tree vital rates and quantify patterns therein. We developed a conceptual and statistical framework (variance partitioning of Bayesian multilevel models) that attributes the variability in tree growth, mortality, and recruitment to variation in species, space, and time, and their interactions – categories we refer to as organising principles (OPs). We applied the framework to data from 21 forest plots covering more than 2.9 million trees of approximately 6500 species. We found that differences among species, the species OP, proved a major source of variability in tree vital rates, explaining 28–33% of demographic variance alone, and 14–17% in interaction with space, totalling 40–43%. Our results support the hypothesis that the range of vital rates is similar across global forests. However, the average variability among species declined with species richness, indicating that diverse forests featured smaller interspecific differences in vital rates. Moreover, decomposing the variance in vital rates into the proposed OPs showed the importance of unexplained variability, which includes individual variation, in tree demography. A focus on how demographic variance is organized in forests can facilitate the construction of more targeted models with clearer expectations of which covariates might drive a vital rate. This study therefore highlights the most promising avenues for future research, both in terms of understanding the relative contributions of groups of mechanisms to forest demography and diversity, and for improving projections of forest ecosystems.

Using recursive partitioning to predict presence and severity of suicidal ideation amongst college students

Objective Predicting the presence and severity of suicidal ideation in college students is important, as deaths by suicide amongst young adults have increased in the past 20 years. Participants We recruited college students (N = 5494) from ten universities across eight states. Method Participants answered three questionnaires related to lifetime and past month suicidal ideation, and an indicator of suicidal ideation in a DSM-5 symptom measure. We used recursive partitioning to predict the presence, absence, and severity, of suicidal ideation. Results Recursive partitioning models varied in their accuracy and performance. The best-performing model consisted of predictors and outcomes measured by the DSM-5 Level 1 Cross-Cutting Symptom Measure. Sexual orientation was also an important predictor in most models. Conclusions A single measure of DSM-5 symptom severity may help universities understand suicide severity to promote targeted interventions. Though further work is needed, as similar scaling amongst predictors could have influenced the model.

Incremental Fuzzy Clustering-Based Neural Networks Driven With the Aid of Dynamic Input Space Partition and Quasi-Fuzzy Local Models.

Fuzzy clustering-based neural networks (FCNNs) based on information granulation techniques have been shown to be effective Takagi-Sugeno (TS)-type fuzzy models. However, the existing FCNNs could not cope well with sequential learning tasks. In this study, we introduce incremental FCNNs (IFCNNs), which could dynamically update themselves whenever new learning data (e.g., single datum or block data) are incorporated into the dataset. Specifically, we employ dynamic (incremental) fuzzy C-means (FCMs) clustering algorithms to reveal a structure in data and divide the entire input space into several subregions. In the aforementioned partition, the dynamic FCM adaptively adjusts the position of its prototypes by using sequential data. Due to the time-sharing arrival of training data, compared with batch learning models, incremental learning methods may lose classification (prediction) accuracy. In order to tackle this challenge, we utilize quasi-fuzzy local models (QFLMs) based on modified Schmidt neural networks to replace the popular linear functions in TS-type fuzzy models to refine and enhance the ability to represent the behavior of fuzzy subspaces. Meanwhile, the recursive least square error (LSE) estimation is utilized to update the weights of QFLMs from one-by-one or block-by-block (fixed or varying block size) learning data. In addition, the L2 regularization is considered to ameliorate the deterioration of generalization abilities caused by potential overfitting when carrying out weight estimation. The proposed method leads to the construction of FCNNs in a new way, which can effectively deal with incremental data as well as deliver sound generalization capability. Extensive machine-learning datasets and a real-world application are employed to show the validity and performance of the presented methods. From the experimental results, we show that the proposal can maintain sound classification accuracy when effectively processing sequential data.

Exploring different classification-dependent QSAR modelling strategies for HDAC3 inhibitors in search of meaningful structural contributors.

Histone deacetylase 3 (HDAC3), a Zn2+-dependent class I HDACs, contributes to numerous disorders such as neurodegenerative disorders, diabetes, cardiovascular disease, kidney disease and several types of cancers. Therefore, the development of novel and selective HDAC3 inhibitors might be promising to combat such diseases. Here, different classification-based molecular modelling studies such as Bayesian classification, recursive partitioning (RP), SARpy and linear discriminant analysis (LDA) were conducted on a set of HDAC3 inhibitors to pinpoint essential structural requirements contributing to HDAC3 inhibition followed by molecular docking study and molecular dynamics (MD) simulation analyses. The current study revealed the importance of hydroxamate function for Zn2+ chelation as well as hydrogen bonding interaction with Tyr298 residue. The importance of hydroxamate function for higher HDAC3 inhibition was noticed in the case of Bayesian classification, recursive partitioning and SARpy models. Also, the importance of substituted thiazole ring was revealed, whereas the presence of linear alkyl groups with carboxylic acid function, any type of ester function, benzodiazepine moiety and methoxy group in the molecular structure can be detrimental to HDAC3 inhibition. Therefore, this study can aid in the design and discovery of effective novel HDAC3 inhibitors in the future.

Study of nucleate boiling growth regime on thin surfaces.

In a context of energy transition, heat exchanges are a key for energy sobriety. Among those exchanges, nucleate boiling is the preferred heat transfer method for high heat flux. Vaporisation plays an important role in the nuclear industry, but also in thermal machines such as cooling systems in data centers. In spite of decades studying boiling thermodynamics, modelling heat transfers in the nucleate boiling is still a major difficulty because of its numerous parameters. In the 1960s, scientists developed promising models called heat flux partitioning models. These models have been improved over the years, but they need closure laws about bubbles dynamics and nucleation site density. For the purpose of improving those models, we took part in an international collaboration (ANR TraThI) to study interface heat transfer. Project focuses on boiling with a controlled nucleation site density with a strong emphasis on studying isolated bubbles in water pool boiling, and later addressing multiple bubble interactions in pool and flow boiling. This work focuses on the bubble growth regimes on thin metallic foil observed in a water pool boiling experiment, with a distinction between microlayer and contact line growth regime. Pulsed nanosecond laser was used to create active nucleation site, while high-speed infrared thermography and shadowgraphy were implemented to record transient wall temperatures and bubble dynamics, respectively. This work shows that our experimental configuration induced two different bubble growth regimes, depending on the imposed heat flux and the recent past at the nucleation site and its vicinity. Study provides a framework for further in-depth investigations with different experimental configurations.

Research on joint scheduling method of order grading and machine maintenance

AbstractIn the multi‐variety and large‐scale order production mode, enterprises must balance delivery deadlines and maintain customer satisfaction while also considering the health status of machines. Therefore, the authors propose a method for jointly optimising production scheduling and machine maintenance. Before machine processing, an order value grading and sorting model and a machine health‐status group partitioning model are constructed to classify orders into different production value levels and machines into different health‐status groups, respectively. During machine processing, based on the Weibull distribution theory, a ‘health evaluation function value’ constraint machine preventive maintenance (PM) model and PM strategy are proposed to account for the changing health status of machines; these are integrated with the order allocation machine strategy as decision‐making elements in the production schedule. Finally, two case studies are used to verify the effectiveness of this proposed model and method. The results show that compared to general scheduling schemes, the proposed method can reduce total delay and improve customer satisfaction. Additionally, the PM plan proposed in this method can improve production efficiency and line stability compared to periodic maintenance.

Investigation of the fracture behavior of cemented waste rock-tailing backfill by digital image correlation technique and discrete element modeling

Cemented waste rock-tailing backfill (CWTB), as an artificial false roof in the downward-filling mining method, with its fracture mechanical properties, directly affects the safety of workers. However, few studies have been conducted on CWTB. Therefore, in this study, three-point bending tests and digital image correlation were used to systematically investigate the effects of waste rock aggregate content (10, 20, 30, 40, and 50 %) on the bending mechanical properties (bending strength, post-peak toughness, and fracture toughness) and deformation characteristics (crack extension length and crack opening length) of CWTB. In addition, based on the partition modelling method, a mesoscopic DEM model of CWTB was proposed to reveal the crack extension mechanism and contact force evolution trends of CWTB beams. The results showed that increasing the waste rock aggregate content decreased the flexural strength and increased the post-peak toughness of CWTB. The waste rock aggregate content affected the contact force distribution characteristics and contact force variation was the mesoscopic reason for the evolution of the mechanical properties of CWTB. This study provided important experimental data and simulation basis for the engineering application of CWTB.

Input of high-quality litter reduces soil carbon losses due to priming in a subtropical pine forest

To date, it is unclear how differences in litter quality affect soil organic matter (SOM) decomposition through a phenomenon called 'priming effects' (PEs), especially for low-fertility forest soils under field conditions. Here, the effects of low- and high-quality leaf litter on PE and microbial metabolism of litter-derived carbon (C) were explored in a low-fertility pine (Pinus massoniana) plantation. A 185-day in situ incubation experiment was carried out by adding two 13C-labeled leaf litters to the pine soil—a low-quality (high lignin: nitrogen ratio) litter sourced from pine and a broadleaved species Schima superba produced high-quality (low lignin: nitrogen ratio) litter. To determine the key microbial groups contributing to PEs, the abundance of 13C-labeled litter enrichment in soil phospholipid fatty acids (13C-PLFAs) was quantified. We found that high-quality litter decomposed more rapidly than low-quality litter, with both litter-derived CO2 efflux reaching a plateaued level during the experimental period. Low-quality litter induced net positive PEs, while high-quality litter induced net negative PEs during the litter decomposition processes. The 13C-PLFAs results showed that bacterial groups governed the negative PEs induced by high-quality litter, whereas fungal communities targeted the positive PEs induced by low-quality litter. Random forest model and variation partitioning analysis demonstrated that the direction and magnitude of PEs were driven by litter-induced changes in key microbial groups rather than the structure of the microbial community. Our results demonstrate that microorganisms preferentially utilized litter-derived C in high-quality litter treatment and SOM in low-quality litter treatment, respectively. In contrast to low-quality litter, adding high-quality litter promoted the microbial metabolism of litter-derived C, reducing SOM decomposition (strong negative PEs). Taken together, this study provides isotope-based suggestions for the improvement of degraded pine forests—introducing tree species that produce high-quality litter may benefit soil C sequestration by reducing soil C losses due to PE in nutrient-poor pine forests.