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Triple‐negative breast cancer (TNBC) is often considered one of the most aggressive subtypes of breast cancer, characterized by a high recurrence rate and low overall survival (OS). It is notorious for posing challenges related to drug resistance. While there has been progress in TNBC research, the mechanisms underlying chemotherapy resistance in TNBC remain largely elusive. We collect single‐cell RNA sequencing (scRNA‐seq) data from five TNBC patients susceptible to chemotherapy and five resistant cases. Comprehensive analyses involving copy number variation (CNV), pseudotime trajectory, cell–cell interactions, pseudospace analysis, as well as transcription factor and functional enrichment are conducted specifically on macrophages and malignant cells. Furthermore, we performed validation experiments on clinical samples using multiplex immunofluorescence. We identified a subset of SPP1+ macrophages that secrete SPP1 signals interacting with CD44 on malignant cell surfaces, potentially activating the PDE3B pathway within malignant cells via the integrin pathway, leading to chemotherapy resistance. The abnormally enhanced SPP1 signal between macrophages and malignant cells may serve as a factor promoting chemotherapy resistance in TNBC patients. Therefore, SPP1+ macrophages could potentially serve as a therapeutic target to reduce chemotherapy resistance. [ABSTRACT FROM AUTHOR]

The direct discharge of boron mud poses significant environmental hazards to soil and groundwater. Despite extensive research efforts, the reprocessing of boron mud has not yielded significant advancements. Recently, the development of magnesium cement has spurred interest in the reutilization of boron mud. However, the direct treatment of boron mud remains challenging, necessitating pre-treatment in most studies to achieve substantial results. Consequently, research on the direct incorporation of untreated boron mud is scarce. This study explores the feasibility of using uncalcined boron mud as a base material in basic magnesium sulfate cement (BMSC), composed of lightly calcined magnesia and magnesium sulfate heptahydrate. The effects of varying boron mud content on the compressive strength of the BMSC system were investigated. The results indicate that the 5·1·7 phase is the primary strength phase of BMSC. When the boron mud content is 30%, the uncalcined boron mud has a minimal impact on the formation of the 5·1·7 phase. Additionally, the 28 days compressive strength of BMSC-B30 showed a slight difference compared to the control group BMSC-C, registering at 66.7 MPa. TG-DSC analysis revealed that the presence of a small amount of boron mud inhibits the micro-expansion trend of the BMSC structure. Furthermore, XRD and SEM analyses confirmed that the addition of uncalcined boron mud does not significantly alter the phase structure of the 5·1·7 phase in BMSC. This study provides a foundational basis for the long-term development of direct boron mud treatment. [ABSTRACT FROM AUTHOR]

Photon management strategies are crucial to improve the efficiency of perovskite thin film (PTF) solar cell. In this work, a nano-cone (NC) based 2D photonic nanostructure is designed and simulated aiming at achieve superior light trapping performance by introducing strong light scattering and interferences within perovskite active layer. Compared to the planar PTF solar cell, the NC nanostructured device with 45 degrees half apex angle obtains highest short-circuit current density, which improved over 20% from 15.00 mA/cm2 to 18.09 mA/cm2. This work offers an alternative design towards effective light trapping performance using 2D photonic nanostructure for PTF solar cell and could potentially be adopted as the nano-structuring strategy for the future perovskite solar cell industry. [ABSTRACT FROM AUTHOR]

This study investigated the effects of lithium slag and iron tailings on the hydration mechanism of Basic Oxygen Furnace (BOF) slag cement paste with the addition of 0.06% diethanol-isopropanolamine (DEIPA). This study examined the fluidity, compressive strength, pore solution pH, and hydration products of BOF slag-based composite cementitious materials. The results showed that DEIPA facilitated the conversion from ettringite (AFt) to monosulphate (AFm) and improved the early compressive strength of the BOF slag–cement mortar. Incorporating lithium slag into the DEIPA-containing BOF slag–cement system promoted AFt formation, increased calcium-silicate-hydrate production, and enhanced the microstructure. BOF slag, lithium slag, and iron tailings exhibit synergistic effects in cement pastes. BOF slag and lithium slag provided the reactive components SiO2 and Al2O3. In the early hydration stages, the iron tailings primarily served as fillers, accelerating the system's reactions. [ABSTRACT FROM AUTHOR]

Alkali-activated cementitious materials, as an environmentally friendly cementitious material, can effectively reduce carbon emissions and improve the utilisation of solid wastes. However, traditional strong alkali activators have limitations such as high carbon emissions and poor safety. In order to overcome the defects of traditional strong alkaline activators and realise the high value-added use of calcium carbide residue (CCR), this paper adopts CCR as an alkaline activator to activate granulated blast furnace slag (GBFS)-steel slag (SS) cementitious systems for the preparation of alkaline-activated cementitious materials. The effects of CCR content and SS content on the compressive strength and working performance of CCR-GBFS-SS cementitious systems are analysed, along with the hydration process of CCR-GBFS-SS cementitious systems and the mechanism of action through the hydration products, their chemical structure and their microscopic morphology. The research results show that CCR-GBFS-SS cementitious systems have a 28-day compressive strength of 41.5 MPa and they can be controlled by the setting time; however, the flow performance is poor. The SS content can be increased to improve the flow performance; however, this will reduce the compressive strength. In CCR-GBFS-SS cementitious systems, CCR is the main driving force of hydration reactions, GBFS mainly provides active silica and aluminium and the amorphous C-(A)-S-H gel and ettringite formed by the synergistic action of multiple solid wastes are the main sources of compressive strength. With the extension of the curing time, the amount of hydration products in the cementitious systems gradually increases and the matrix of the cementitious systems gradually becomes denser. This study will provide a reference for the consumption of low-value solid waste such as CCR and the preparation of low-carbon cementitious materials from multi-component solid wastes. [ABSTRACT FROM AUTHOR]

The preparation of cement-based supplementary cementitious materials is an important method for the efficient use of iron tailings and the reduction in CO2 emissions. The aim of this study is to improve the reactivity of iron tailings by mixing them with steel slag, slag, and fly ash through orthogonal tests to solve the problem that iron tailings cannot be utilised on a large scale. The compressive strength, hydration products, and microstructure of the iron tailings–cement-based supplementary cementitious materials were investigated using ICP-OES, XRD, TG, FTIR, and SEM. The results revealed that each solid waste raw material played a distinct role in the hydration reaction. In the iron tailings–cement-based supplementary cementitious materials system, steel slag provided Ca2+, OH−, and Si4+ ions, slag provided Ca2+ and Al3+ ions, fly ash contributed a significant amount of Ca2+ and Al3+ ions, and iron tailings offered more nucleation sites and some Si4+ ions for the hydration products. Moreover, there was a synergistic effect among these four materials, promoting the formation of hydration products such as ettringite, C-(A)-S-H gels, and others. When the proportion of IOTs:SS:FA:SL was 9:8:8:2, the highest 7 d compressive strength of cementitious material was 24.8 MPa. When the proportion of IOTs:SS:FA:SL was 9:6:8:4, the highest 28 d compressive strength of cementitious material was 35.0 MPa. This study provides a comprehensive solution for the utilisation of iron tailings and contributes to the high-value green utilisation of solid waste. [ABSTRACT FROM AUTHOR]

Utilizing a variety of solid wastes to prepare alkali-activated cementitious materials is one of the principal trends in the development of cementitious materials. Commonly used alkali activation precursors such as granulated blast furnace slag (GBFS) and fly ash (FA) will be less available due to resource pressures. Supply limitation is an important reason to research alternative precursors. To realize the high value-added utilization of copper–molybdenum tailings (CMTs), this study adopted the modified sodium silicate solution as an alkaline activator to activate GBFS-FA-CMTs cementitious system to prepare alkali-activated cementitious materials. The influence of CMTs content on the compressive strength of GBFS-FA-CMTs cementitious system was analyzed, and the mechanism of GBFS-FA-CMTs cementitious system was also analyzed through hydration product types, physical phase composition, and microscopic morphology. The results indicated that a paste with the incorporation of CMTs, S50F30C20 (50% GBFS, 30% FA, 20% CMTs), achieved the highest compressive strength of 79.14 MPa, which was due to the filling effect of the CMTs and the degree of participation in the reaction. Pastes with different contents of CMTs, while maintaining a constant CBFS content, exhibited similar strength development. Excessive amounts of CMTs could result in reduced compressive strength. Microstructural analysis revealed that the hydration products were structurally altered by the addition of CMTs. In addition to ettringite, quartz, C(-N)-S-H gel, and calcite, gaylussite was also formed; moreover, the mass of chemically bound water increased, and the microstructure of reaction products became denser. An excess of CMTs may restrict the growth of the hydration gel, leading to more microstructural defects. The study suggests that CMTs could enhance the compressive strength of hardened paste within an alkali-activated slag-fly ash system, possibly due to a filling effect and participation in the chemical reaction. This research confirms the feasibility of using CMTs in alkali-activated cementitious materials. [ABSTRACT FROM AUTHOR]

The preparation of iron ore tailings (IOTs) into supplementary cementitious materials (SCMs) is an effective approach to achieve value-added utilization of industrial solid waste. This study systematically investigates the hydration pattern and strength development of Portland cement systems with the incorporation of IOTs, steel slag (SS), granulated blast-furnace slag (GBFS), and fly ash (FA) under the action of different chemical additives. The hydration products, and microstructure and pore structure of the SCMs are analyzed using X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, and mercury intrusion porosimetry. The findings of this study demonstrate that chemical activation plays a significant role in the strength development of SCMs. Among the five chemical activators tested, Triethanolamine (TEA) had the greatest influence on mechanical properties. The maximum compressive strength of the SCMs at 28 days was 42.9 MPa at a dosage of 1%. Specifically, the addition of TEA promotes volcanic ash reactions, and the high fineness of SCM provides nucleation sites for hydration products. Interactions between the volcanic ash reaction and the complexation reaction of TEA have a positive effect on compressive strength development. This research expands the potential for IOTs SCMs through chemical activation methods for value-added applications. [ABSTRACT FROM AUTHOR]

There are some limitations in the application of tuff powder as a supplementary cementitious material (SCM). Exploring its feasibility in new fields will consume a large amount of silica-alumina mine solid wastes. This study has investigated the mechanical properties and mechanism in contact-hardening of tuff powder with a method of compression molding. The compressive strength of specimens was tested, and the X-ray diffraction (XRD), thermogravimetric analysis (TG), scanning electron microscopy (SEM), and Mercury intrusion porosimetry (MIP) methods were used to reveal the mechanism of contact-hardening of tuff powder from a micro-perspective. The results indicated that the compressive strength of specimens was higher when activated by sodium hydroxide compared to calcium hydroxide. Compared to calcium hydroxide, the compressive strength of TFS20 and TFF20 activated by sodium hydroxide was improved by 20% and 23%, respectively. The hydration degree of tuff powder was very low, with a water–cement ratio (w/c) of 0.15, while the hydration degree of coal gangue powder was higher. The results of TGA and SEM indicated that the sodium hydroxide had a better activating effect on slag and fly ash. Therefore, more C-S-H gels were generated in those samples activated by sodium hydroxide. Furthermore, the structure of samples was more compacted, and there was a reduction of porosity by 10% and 11% for TFS20 and TFF20, respectively, especially the proportion of harmful pores. [ABSTRACT FROM AUTHOR]

Solid waste management is a critical issue worldwide. Effectively utilizing these solid waste resources presents a viable solution. This study focuses on Iron ore tailings (IOTs), a solid waste generated during iron ore processing, which can be used as supplementary cementitious materials (SCMs) but have low reactivity, hindering their large-scale application in concrete production. To address this, ternary SCMs were prepared using ceramic powder (CP) and steel slag (SS) to enhance the performance of concrete incorporating IOTs. The study found that the synergistic effect of CP and SS significantly improved the compressive strength of concrete, with a notable increase of up to 21% compared to concrete with IOTs alone. Mercury intrusion porosimetry (MIP) and backscattering electron (BSE) analyses revealed that the ternary SCMs significantly optimized the characteristics of the interfacial transition zone (ITZ), which in turn enhanced the compressive properties of the concrete. This contributed to maintaining the structural integrity of the concrete, even amidst variations in the pore structure. Importantly, the incorporation of ternary SCMs led to a 23% reduction in carbon emissions, from 400.01 kg CO2/m3 to 307.48 kg CO2/m3, and elevated eco-strength efficiency from 0.1 to 0.14. The study highlights the role of multi-material synergy in developing composite SCMs systems, fostering the sustainable advancement of green building materials. [ABSTRACT FROM AUTHOR]

Few studies have shown that radial artery (RA), which is used as a secondary arterial graft, offers superior results compared with right internal thoracic artery (RIMA) in coronary artery bypass grafting (CABG). In a meta‐analysis of observational studies starting in 2023, we looked at the effect of re‐operation on postoperative infection and haemorrhage in CABG with RA vs. RIMA. The electronic database up to October 2023 was examined in the course of the research. Analysis was carried out on the clinical trials of postoperative wound infections and haemorrhage re‐surgery. Among 912 trials associated with CABG, we selected 8 trials to be included in the final data analysis. The main results were secondary wound infection and re‐operation after surgery. The odds ratios (OR) and confidence intervals (CIs) were computed on the basis of a randomized or fixed‐effect model of wound infection and re‐operation. Seven trials showed a significant reduction in the risk of wound infection in RA treated as a secondary artery transplant compared with RIMA (OR, 1.60; 95% CI, 1.03, 2.47 p = 0.04); Four trials showed that RIMA was not significantly different from RA in the rate of re‐operation for postoperative bleeding (OR, 1.31; 95% CI, 0.60, 2.88 p = 0.50). In CABG, RA is used as a secondary arterial conduit graft to lower the risk of wound infection in CABG patients. [ABSTRACT FROM AUTHOR]

Climatic conditions in plateau areas can enormously affect the properties and microstructure of cement-based materials. This research investigates the strength development and microstructural changes in magnesium phosphate cement (MPC) mortars in a plateau environment. Experiments were conducted in parallel in a plateau area (Lhasa) and a plain area (Chengdu) to evaluate the effects of the water-to-binder ratio (w/b = 0.12, 0.14 and 0.16) and sand-to-binder ratio (s/b = 0.5, 0.75 and 1) on the compressive and flexural strength of MPC mortars. At the same time, hydration products were characterized via XRD, TGA, and SEM/EDX micro-analyses, and the porosity of the materials was also analyzed via MIP. The results demonstrated that curing in a plateau environment resulted in a decrease in workability and yielded higher strength at an early age (before 1 day) but degraded the long-term (180-day) strength of MPC mortars when compared with curing in a plain environment, irrespective of w/b and s/b ratios. Unlike the plain group, the plateau group revealed the deterioration of microstructures over time, including the decrease in struvite content, the morphology change in struvite crystals, and the increase in porosity, which resulted in the degradation of mechanical properties between 1 and 180 days. The strength loss can be effectively alleviated at lower w/b and s/b ratios. [ABSTRACT FROM AUTHOR]

This study proposes to construct a model using random forest method, an efficient machine learning‐based method, to predict the spatial structure and temporal evolution of the sea surface temperature (SST) cooling induced by northwest Pacific tropical cyclones (TCs), a process of the so‐called wind pump. The predictors in use include 12 predictors related to TC characteristics and pre‐storm ocean conditions. The model is shown to skillfully predict the spatiotemporal evolutions of the cold wake generated by TCs of different intensity groups, and capture the cross‐case variance in the observed SST response. Another model is further built based on the same method to assess the relative importance of the 12 predictors in determining the magnitude of the maximum cooling. Computations of feature scores of those predictors show that TC intensity, translation speed and size, and pre‐storm mixed layer depth and SST dominate, depending on the area where the cooling is considered. Plain Language Summary: While many studies have been devoted to understanding the processes and mechanisms underlying the sea surface temperature (SST) cooling induced by tropical cyclones (TCs), few studies have attempted to predict the spatial and temporal evolution of the sea surface temperature (SST) cooling triggered by TCs. In this study, we proposed to achieve this goal by building a model using an efficient and robust machine learning‐based method. The constructed model uses 12 predictors associated with TC characteristics (e.g., intensity, and translation speed) and pre‐storm ocean states (e.g., mixed layer depth). The model performs well in producing the TC‐induced spatial structure and temporal evolution of the cold wake and can capture most of the variance in the observed SST response. We quantified the relative importance of the 12 predictors, and found that TC intensity, translation speed and size, and pre‐storm mixed layer depth and SST dominate in deciding the magnitude of the SST response. The results and proposed method have important implications for predicting the response of ocean primary production to the TC wind pump effects. Key Points: A machine learning‐based model is built to predict the spatiotemporal evolution of the tropical cyclone‐induced sea surface temperature responseThe model well predicts the spatial structure and temporal evolution of the observed response and captures the observed cross‐case varianceFeature scores are computed to assess the relative importance of the predictors in determining the magnitude of the SST response [ABSTRACT FROM AUTHOR]

Objective: The objective of this research was to analyze the prognostic significance of the minimum count of lymphocytes (LY) and the corresponding ratio of neutrophil‐to‐lymphocyte (NLR) in cervical cancer (CC) patients receiving radiotherapy. Methods: We retrospectively collected data from 202 CC patients who received concurrent chemoradiotherapy or radiotherapy alone at our hospital. Statistical methods including the Kaplan–Meier method, log‐rank test and the Cox proportional hazards model were included to examine survival differences and identify independent factors that may affect overall survival (OS) and progression‐free survival (PFS). Results: The research enrolled a total of 202 patients. Patients with higher LY levels and lower NLR values during radiotherapy had significantly better survival prognosis than those with lower LY levels and higher NLR values. Multivariate COX regression analysis revealed that FIGO stage I, pathological types of SqCC, absence of lymph node metastasis, concurrent chemoradiotherapy, higher LY levels during radiotherapy, and lower NLR values before radiotherapy were independently associated with poorer PFS. Similarly, FIGO stage I, absence of lymph node metastasis and lower NLR values during and before radiotherapy were independently linked with poorer OS. Conclusion: Minimum LY value and its corresponding NLR during radiotherapy serve as prognostic factors for CC. [ABSTRACT FROM AUTHOR]

As one of the three pillars in computational intelligence, fuzzy systems are a powerful mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy systems take the form of linguistic IF-THEN fuzzy rules that are easy to understand for human. In this sense, fuzzy inference mechanisms have been developed to mimic human reasoning and decision-making. From a data analytic perspective, fuzzy systems provide an effective solution to build precise predictive models from imprecise data with great transparency and interpretability, thus facilitating a wide range of real-world applications. This paper presents a systematic review of modern methods for autonomously learning fuzzy systems from data, with an emphasis on the structure and parameter learning schemes of mainstream evolving, evolutionary, reinforcement learning-based fuzzy systems. The main purpose of this paper is to introduce the underlying concepts, underpinning methodologies, as well as outstanding performances of the state-of-the-art methods. It serves as a one-stop guide for readers learning the representative methodologies and foundations of fuzzy systems or who desire to apply fuzzy-based autonomous learning in other scientific disciplines and applied fields. [ABSTRACT FROM AUTHOR]

On the basis of analyzing present situation and research progress of field scientific observation in water conservancy, this paper discusses the construction and operation management of field scientific observation and research stations in water conservancy sector. In recent years, through long-term fixed-point observations and researches, a certain amount of continuous field observation and experimental data have been accumulated, which plays an important role in understanding natural laws, guiding water conservancy practices, and promoting the development of disciplines. However, the stations still have shortcomings in terms of planning layout, support guarantee, and operation mechanism. Focusing on the four main goals and six implementation paths of promoting the high-quality development of water conservancy in the new stage, this paper proposes measures such as optimizing the regional layout of disciplines, improving the research ability, and improving the management and operation mechanism in order to promote the long-term stable development of the field scientific observation and research station and provide foundational support of science and technology for ensuring national water security. [ABSTRACT FROM AUTHOR]

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology. This study presents a deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine resolution satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types. [ABSTRACT FROM AUTHOR]

Exposure of concrete to acidic environments can cause the degradation of concrete elements and seriously affect the durability of concrete. As solid wastes are produced during industrial activity, ITP (iron tailing powder), FA (fly ash), and LS (lithium slag) can be used as admixtures to produce concrete and improve its workability. This paper focuses on the preparation of concrete using a ternary mineral admixture system consisting of ITP, FA, and LS to investigate the acid erosion resistance of concrete in acetic acid solution at different cement replacement rates and different water–binder ratios. The tests were performed by compressive strength analysis, mass analysis, apparent deterioration analysis, and microstructure analysis by mercury intrusion porosimetry and scanning electron microscopy. The results show that when the water–binder ratio is certain and the cement replacement rate is greater than 16%; especially at 20%, the concrete shows strong resistance to acid erosion; when the cement replacement rate is certain and the water–binder ratio is less than 0.47; especially at 0.42, the concrete shows strong resistance to acid erosion. Microstructural analysis shows that the ternary mineral admixture system composed of ITP, FA, and LS promotes the formation of hydration products such as C-S-H and AFt, improves the compactness and compressive strength of concrete, and reduces the connected porosity of concrete, which can obtain good overall performance. In general, concrete prepared with a ternary mineral admixture system consisting of ITP, FA, and LS has better acid erosion resistance than ordinary concrete. The use of different kinds of solid waste powder to replace cement can effectively reduce carbon emissions and protect the environment. [ABSTRACT FROM AUTHOR]

Ultra-High Performance Concrete (UHPC) was hindered by the use of non-renewable quartz sand, so there was a need to find a new sustainable material to replace quartz sand. This study investigates the possibility of using copper slag as an aggregate instead of quartz sand to make sustainable UHPC. To ensure the workability and good particle build-up of UHPC, the copper slag particle size was differentiated into three classes to replace quartz sand of the same particle size. UHPC mortars were prepared at 0, 25%, 50%, 75%, and 100% substitution rates and tested for flow, compressive, flexural, and pore structure. The test results showed that copper slag has a dense structure and good compression properties. The smooth surface and poor water absorption of copper slag can significantly improve the flowability of UHPC mortar. The inherent high density, high abrasiveness, high compressibility, and sharp edges of copper slag material can improve the strength of UHPC mortar. A good particle gradation allows the material to build up more compactly and reduces porosity. The results of the study show that it is feasible to use copper slag as a fine aggregate to replace quartz sand at a substitution level of 50%. [ABSTRACT FROM AUTHOR]

Supplementary cementitious material (SCM) plays an important role in blended cement, and the effect of the particle size and morphology of siliceous supplementary cementitious material on hydration should not be ignored. In this study, 0.5 h and 1 h of wet grinding was applied to pretreat iron ore tailing powder (TP), and the divergence in pozzolanic behavior and morphology were investigated. Then, the treated TPs were used to replace the 30% cement contents in preparing blended cementitious paste, and the impact mechanism of morphology on performance was studied emphatically. M, the autogenous shrinkages of pastes were tested. Finally, hydration reaction kinetics was carried out to explore the hydration behavior, while X-ray diffraction (XRD) and thermogravimetric analysis (TGA) were used to characterize the hydration product properties, respectively. Meanwhile, microscopy intrusion porosimetry (MIP) was also carried out to characterize the pore structures of hardened specimens. Results indicated that wet grinding has a dramatic effect on particle size and morphology, but hardly affects the phase assemblages and pozzolanic reactivity of TP, while the particle shape of TP changes from sub-circular to clavate and, finally, back to sub-circular. The results of hydration reaction kinetics, representing the morphology of particles, had a significant effect on hydration rate and total heat, and compared with the sub-circle one, the clavated particle could inhibit the hydration procedure. With the increasing grinding time, the compressive strength of cementitious paste was increased from 17.37% to 55.73%, and the micro-pore structure became denser; however, the autogenous shrinkage increased. [ABSTRACT FROM AUTHOR]

It is difficult to test the engineering properties of undisturbed cohesionless coarse-grained fillers in the laboratory, especially those with low water contents because most sampling methods disturb the natural structure of the filler. This study proposes a novel sampling method that uses menthol as a temporary cement to obtain samples that are more resistant to disturbance. Liquid menthol infiltrates the interparticle spaces and cements the particles together as it cools, forming a high-strength structure. The samples can then be cut with a cutting ring (for grain sizes ≤ 0.5 mm) or a cutting machine (for grain sizes > 0.5 mm) to prepare the final samples, from which the menthol is removed by heating. To demonstrate the feasibility of the method, a number of laboratory experiments were performed, including sampling tests, confined compressive strength tests, vibration tests, drop tests, and CT scans. The optimal particle temperature for introducing the liquid menthol is 70°C, while 90°C is optimal for removing the cemented menthol. For both cost and anti-disturbance reasons, 20°C is suggested as a suitable storage temperature for menthol-cemented samples. The interspace structure undergoes less change with increases in sample depth and with larger grain sizes. We recommend that the lower two-third of the depth of a menthol-cemented block is used as the final sample for laboratory testing. [ABSTRACT FROM AUTHOR]

The optimization of open pit mine production scheduling is not only a multistage decision-making problem but also involves space–time dynamic action among multiple factors, which makes it difficult to optimize production capacity, mining sequence, mining life, and other factors simultaneously in optimizing design. In addition, the production capacity is disorderly expanded, the calculation scale is large, and the optimization time is long. Therefore, this article designs a mobile capacity search domain method to improve computing efficiency without omitting the optimal production capacity. At the same time, taking the maximum net present value as the objective function, an enumeration method is used to optimize the possible paths in different capacity domains and calculate the infrastructure investment and facility idle cost required to meet the maximum production capacity on each possible path to control the disorderly expansion and violent fluctuation of production capacity. The research shows that the open pit mine production scheduling optimization algorithm proposed in this article can not only realize the simultaneous optimization of the three elements of production capacity, mining sequence, and mining life but also improve the computing efficiency by 200 times. Furthermore, the production capacity fluctuation is less than 1.4%. The mining life of the mine is extended by 13 years, and the overall economic benefit is increased by 18%. [ABSTRACT FROM AUTHOR]

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this article, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the interclass overlaps in a natural way and better capture the underlying multimodel structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based if–then fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule-based construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems. [ABSTRACT FROM AUTHOR]

Monitoring disturbances in tropical forests is important for assessing disturbance-related greenhouse gas emissions and the ability of forests to sequester carbon, and for formulating strategies for sustainable forest management. Thanks to a long-term observation history, large spatial coverage, and support from powerful cloud platforms such as Google Earth Engine (GEE), remote sensing is increasingly used to detect forest disturbances. In this study, three types of forest disturbances (abrupt, gradual, and multiple) were identified since the late 1980s on Hainan Island, the largest tropical island in China, using an improved LandTrendr algorithm and a dense time series of Landsat and Sentinel-2 satellite images on the GEE cloud platform. Results show that: (1) the algorithm identified forest disturbances with high accuracy, with the R2 for abrupt and gradual disturbance detection reaching 0.92 and 0.83, respectively; (2) the total area in which forest disturbances occurred on Hainan Island over the past 30 years accounted for 10.84% (2.33 × 105 hm2 in total area, at 0.35% per year) of the total forest area in 2020 and peaked around 2005; (3) the areas of abrupt, gradual, and multiple disturbances were 1.21 × 105 hm2, 9.96 × 104 hm2, and 1.25 × 104 hm2, accounting for 51.93%, 42.75%, and 5.32% of the total disturbed area, respectively; and (4) most forest disturbance occurred in low-lying (<600 m elevation accounts for 97.42%) and gentle (<25° slope accounts for 94.42%) regions, and were mainly caused by the rapid expansion of rubber, eucalyptus, and tropical fruit plantations and natural disasters such as typhoons and droughts. The resulting algorithm and data products provide effective support for assessments of such things as tropical forest productivity and carbon storage on Hainan Island. [ABSTRACT FROM AUTHOR]

In this study, a type of fly ash and iron tailing powder were used as a binary supplementary cementitious material (SCM) and two different mechanically activated procedures, named coalescent activation and separated activation, were carried out to improve the pozzolanic reactivity. Then, three binary supplementary cementitious materials were used to replace 30 wt% of ordinary Portland cement to develop cemented mortars, and the compressive strength, hydration products, and micro-structure were studied. The experimental results indicated that the activated supplementary cementitious materials increased the compressive strengths of the mortars by 5.4% and 13.2%, negligibly changed the hydration product types, and impacted the quantity only. The application of SCMs also prolonged the setting time and decreased the hydration rate, profiting the application. Simultaneously, the micro-pore structure was ameliorated, and the porosity decreased. Therefore, comprehensively considering the improved mechanical behavior and inexpensive cost, the activated binary SCM can be considered an ecological and economical admixture, especially for the coalescent activation procedure. [ABSTRACT FROM AUTHOR]

Recently, the increasing of ultrafine-tailings increases the amount of ordinary Portland cement (OPC) in cemented paste backfill (CPB), which leads to the rise of CPB cost and carbon emission. As a result, it is necessary to develop alternative binders. The present work focuses on the preparation of a new binder, which is activated by a mixture of calcined quarry dust (CQD) and NaOH at a mass ratio of 1:1. The results indicated that CQD/NaOH was more effective than using NaOH or CQD alone in activating blast furnace slag (BFS) and also showed better performance than OPC. The compressive strength of the CPB samples using 10% CQD/NaOH was around 3.78 MPa after curing for 90 days, around 42% higher than the OPC-based CPB samples. The reaction products of CQD/NaOH-activated BFS consisted mainly of C-(A)-S–H, hydrotalcite like phases (Ht), and M-S–H. The generation of Ht phases lowered the Al incorporation into the structure of C-S–H, resulting in lower average Al/Si ratio and mean chain length. [ABSTRACT FROM AUTHOR]

Adaptive boosting (AdaBoost) is a widely used technique to construct a stronger ensemble classifier by combining a set of weaker ones. Zero-order fuzzy inference systems (FISs) are very powerful prototype-based predictive models for classification, offering both great prediction precision and high user interpretability. However, the use of zero-order FISs as base classifiers in AdaBoost has not been explored yet. To bridge the gap, in this article, a novel multiclass fuzzily weighted AdaBoost (FWAdaBoost)-based ensemble system with a self-organizing fuzzy inference system (SOFIS) as the ensemble component is proposed. To better incorporate the SOFIS, FWAdaBoost utilizes the confidence scores produced by the SOFIS in both sample weight updating and ensemble output generation, resulting in more accurate classification boundaries and greater prediction precision. Numerical examples on a wide range of benchmark classification problems demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

At present, the stability study of the flywheel battery system under the influence of road conditions is only limited to the analysis of dynamic characteristics but also lacks effective controller considering road disturbances. In order to solve this defect and further improve the robust performance of vehicular flywheel battery systems under road disturbances, a robust controller considering interference of road surface roughness is proposed. Firstly, on the basis of a brief introduction to flywheel battery system structure, the influence degree of the flywheel by road condition is compared to find the key areas most affected by road disturbance factors. Then the nonlinear dynamic model of the axial suspension system is constructed, and the actual road surface roughness is regarded as the unmodeled dynamics of the external disturbance emphatically. Following, the established unknown system dynamics is approximated by radial basis function (RBF) neural network based on the minimum parameter learning method, the control input is generated by sliding-mode control law, and the weight adjustment of neural network is replaced by the designed adaptive law of parameter estimation. The effects of different levels of road surface roughness on the system are simulated, and the robustness of the proposed controller is verified based on the Lyapunov method. Finally, the experimental platform to simulate road disturbances is designed ingeniously. Experimental results show that the proposed controller can make the flywheel battery system have good robustness under different road conditions. [ABSTRACT FROM AUTHOR]

In this article, a new data-driven autonomous fuzzy clustering (AFC) algorithm is proposed for static data clustering. Employing a Gaussian-type membership function, AFC first uses all the data samples as microcluster medoids to assign memberships to each other and obtains the membership matrix. Based on this, AFC chooses these data samples that represent local models of data distribution as cluster medoids for initial partition. It then continues to optimize the cluster medoids iteratively to obtain a locally optimal partition as the algorithm output. Moreover, an online extension is introduced to AFC enabling the algorithm to cluster streaming data chunk-by-chunk in a “one pass” manner. Numerical examples based on a variety of benchmark problems demonstrate the efficacy of the AFC algorithm in both offline and online application scenarios, proving the effectiveness and validity of the proposed concept and general principles. [ABSTRACT FROM AUTHOR]

How to treat the iron tailings of mining solid waste with high value is an urgent problem on a global scale. In recent years, the application of iron tailings in the building materials industry has attracted the attention of many scholars. The conversion of iron tailings into green building materials helps achieve carbon neutrality and high-value utilization of solid waste, and promotes sustainable development. Although iron tailings have been extensively studied as supplementary cementitious materials, the performance of concrete is not ideal due to its low activity. In this study, the hybrid supplementary cementitious materials system was prepared by iron tailings, phosphorus slag, and steel slag, and the effects of supplementary cementitious materials type, iron tailings content, iron tailings grinding time, and supplementary cementitious materials content on concrete performance were studied. The compressive properties, iron tailings properties, pore structure, interfacial transition zone, and element distribution of hydration products of concrete were tested by compressive strength tests, X-ray Diffractometer (XRD), X-ray Photoelectron Spectroscopy (XPS), Mercury Intrusion Porosimetry (MIP), Backscattering Electron Tests (BSE), and Energy Dispersive Spectrometer (EDS). The results show that further grinding improves the iron tailings activity. There is a synergistic mechanism between steel slag and phosphorus slag in the composite supplementary cementitious materials, which overcomes the low activity defect of iron tailings and produces concrete with a compressive strength exceeding 40 MPa. The composite supplementary cementitious materials can optimize the interfacial transition zone of the concrete interface and reduce the calcium–silicon ratio of the hydration products. However, it will deteriorate the pore structure of the concrete matrix, cause part of the concrete matrix to be damaged and lead to a loss of compressive strength, and the loss is acceptable. This work broadens the methods of comprehensive utilization of iron tailings and also provides a reference for a more detailed understanding of the properties of iron tailings-based concrete. [ABSTRACT FROM AUTHOR]

In order to control energy consumption and reduce pollution, the use of supplementary cementitious materials (SCMs) instead of cement to produce green cementitious materials can save energy, reduce emissions and achieve sustainable development. This study demonstrates the possibility of developing SCMs with iron tailings (IOTs), fly ash (FA) and ceramic powder (CP) ternary system, as well as the optimization and improvement scheme of gelation activation. The effects of activator dosage, mix ratio and substitution rate on mechanical properties of ternary SCMs system were investigated. The formation and evolution of hydration products were analyzed by differential thermogravimetric analysis (DTA) and scanning electron microscopy (SEM). The results of the study show that there is synergy in the system. The results show that there is synergy in the system and the hydration reaction is sufficient. At the substitution rate of 30%, the doping ratio of IOTs, CP and FA is 1:2:2 and the Ca(OH)2 is 0.6%, the strength reaches 39.9 MPa and the activity index is 91.5%, which can provide a basis for the application and more in-depth study of IOTs multi SCMs. [ABSTRACT FROM AUTHOR]

This paper aimed to improve the thermal and waterproof properties of foamed concrete through the synergic work of nano SiO2 aerogel powder (NSAP) and organosilicon waterproofing agent (OWA). Hence, several series of foamed concrete with a 0–3.0% of OWA and with 1–4% of NSAP addition were developed. The results show that OWA can decrease the dry bulk density, compressive strength, water absorption rate, and thermal conductivity. With the addition of NSAP, the water absorption rate reached a minimum value of 9.2% at 3.0% NSAP, while the thermal conductivity continued decreasing. Through the microscopic pore size distribution, XRD, and SEM, it was found that the addition of NSAP had no effect on the type of hydration product, but it can increase the average pore size and the amount of macropore and weaken the interfacial adhesion between hydration products. Comparing the heat transmission model, the presence of NSAP increased the heat transmission pathway and resistance, resulting in lower thermal conductivity. [ABSTRACT FROM AUTHOR]

Large-scale (large-area), fine spatial resolution satellite sensor images are valuable data sources for Earth observation while not yet fully exploited by research communities for practical applications. Often, such images exhibit highly complex geometrical structures and spatial patterns, and distinctive characteristics of multiple land-use categories may appear at the same region. Autonomous information extraction from these images is essential in the field of pattern recognition within remote sensing, but this task is extremely challenging due to the spectral and spatial complexity captured in satellite sensor imagery. In this research, a semi-supervised deep rule-based approach for satellite sensor image analysis (SeRBIA) is proposed, where large-scale satellite sensor images are analysed autonomously and classified into detailed land-use categories. Using an ensemble feature descriptor derived from pre-trained AlexNet and VGG-VD-16 models, SeRBIA is capable of learning continuously from both labelled and unlabelled images through self-adaptation without human involvement or intervention. Extensive numerical experiments were conducted on both benchmark datasets and real-world satellite sensor images to comprehensively test the validity and effectiveness of the proposed method. The novel information mining technique developed here can be applied to analyse large-scale satellite sensor images with high accuracy and interpretability, across a wide range of real-world applications. [ABSTRACT FROM AUTHOR]

A considerable amount of carbon dioxide is released during the preparation of traditional Portland cement, which is not in conformity with the sustainable road. Developing supplementary cementitious material (SCMs) and reducing cement consumption are safe ways to solve this problem. Iron tailings (IOTs), a type of mining waste from the beneficiation process of iron ore concentrate, can be used as SCMs in concrete. In order to tackle the problem of low compressive performance of IOTs as SCMs in concrete, this study develops a multi-SCMs system based on IOTs. Mechanical properties and microstructure of samples with different activator contents and ratios were analyzed by compressive strength, differential thermal–thermal gravimetric analysis (DTA-TG), and scanning electron microscopy (SEM). The results show that the compressive strength of the composite gel blocks in different gel systems changed as follows: ternary SCMs system > binary SCMs system > unary SCMs system. The ternary SCMs system (IOTs: Steel slag(SS):Phosphorus slag(PS) = 1:2:2) without activator was added so that it resulted in the highest compressive strength in multi-SCMs system. With the increase in the content of activators, the compressive strength with ternary SCMs system shows a decreasing trend. It is well known from DTA-TG, as well as SEM, that NaOH and Na2SiO3 have an inhibitory effect on the secondary hydration reaction and inhibit the formation of C-S-H gel. [ABSTRACT FROM AUTHOR]

Gonad development is a highly regulated process that coordinates cell specification and morphogenesis to produce sex‐specific organ structures that are required for fertility, such as testicular seminiferous tubules and ovarian follicles. While sex determination occurs within specialized gonadal supporting cells, sexual differentiation is evident throughout the entire organ, including within the interstitial compartment, which contains immune cells and vasculature. While immune and vascular cells have been traditionally appreciated for their supporting roles during tissue growth and homeostasis, an increasing body of evidence supports the idea that these cell types are critical drivers of sexually dimorphic morphogenesis of the gonad. Myeloid immune cells, such as macrophages, are essential for multiple aspects of gonadogenesis and fertility, including for forming and maintaining gonadal vasculature in both sexes at varying stages of life. While vasculature is long known for supporting organ growth and serving as an export mechanism for gonadal sex steroids in utero, it is also an important component of fetal testicular morphogenesis and differentiation; additionally, it is vital for ovarian corpus luteal function and maintenance of pregnancy. These findings point toward a new paradigm in which immune cells and blood vessels are integral components of sexual differentiation and organogenesis. In this review, we discuss the state of the field regarding the diverse roles of immune and vascular cells during organogenesis of the testis and ovary and highlight outstanding questions in the field that could stimulate new research into these previously underappreciated constituents of the gonad. [ABSTRACT FROM AUTHOR]

Iron ore tailings are mainly composed of SiO2 and iron, whose content determines the potential reuse strategy of the tailings. Compared with the traditional wet chemistry approach, spectroscopy has proven its superior effectiveness in characterizing and predicting minerals, such as iron oxides, clay, and SiO2. This study aims to estimate the content of SiO2 and TFe in iron ore tailings based on visible–near infrared (VIS–NIR, 350–2500 nm) and thermal infrared (TIR, 8–14 μm) spectroscopy. The outer product analysis (OPA) method is used to combine VIS–NIR and TIR spectral domains, from which an outer product matrix of fusion data can be generated. The study area is the iron ore tailing dam from Waitoushan, which is one of the super-large iron deposits in the Anshan–Benxi iron cluster of northeastern China. The spectral analysis results demonstrated the following: (1) The reflectance feature at 1163–2499 nm in the VIS–NIR range correlates with TFe and the emissivity feature at 8–9.4 and 10.7–12 μm in the TIR range correlates with SiO2. (2) Compared with the original absorbance spectra, the correlation coefficients of fusion spectra improve from 0.66 to 0.87 for TFe and from 0.64 to 0.84 for SiO2. (3) The partial least squares regression, random forest (RF), and extreme learning machine exploiting particle swarm optimization modeling methods are established for SiO2 and TFe estimation. The prediction accuracy results indicate that the prediction model with OPA-fused spectra performs significantly better than with individual VIS–NIR and TIR spectra. The RF model with input-fused spectra provides the highest accuracy with the coefficients of determination of 0.95 and 0.91, root mean square errors of 0.97% and 0.96%, and ratios of performance to interquartile distance of 6.49 and 2.31 for SiO2 and TFe content estimation, respectively. These outcomes provide a theoretical basis and technical support for tailing composition estimation using spectroscopy. [ABSTRACT FROM AUTHOR]

The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this article introduces a particle swarm-based approach for the EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the “one pass” learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without full retraining. The experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms. [ABSTRACT FROM AUTHOR]

Rainfall infiltration is the main factor of hydraulic erosion and deformation instability of tailings reservoir slope. Through the hydraulic erosion test of tailings reservoir slope based on rainfall, the parameters of slope internal moisture content, runoff sediment concentration, runoff, start time of surface erosion and gully erosion and slope velocity were tested. The slope protection effect of ecological base material of tailings reservoir slope determined in the previous study was evaluated. The results show that the vegetation allocation model combined with grass, shrub and flower is effective in hydraulic erosion protection of tailings reservoir slope. The critical slope range of ecological slope model of tailings reservoir is 37.5°–42.3° under three kinds of rain intensity. The research results can provide certain theoretical and technical support for the safe operation of tailings reservoir and the development of ecological slope protection theory of tailings reservoir. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to compare the efficacy of osimertinib (OSI) versus afatinib (AFA) in patients with T790M-positive, non-small-cell lung cancer (NSCLC) and multiple central nervous system (CNS) metastases after failure of initial epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) treatment.Methods: Consecutive patients with T790M-positive NSCLC and multiple CNS metastases after failure of initial EGFR-TKI treatment were retrospectively identified from our medical institution during 2016-2018 and underwent either oral 80 daily OSI or oral 40 daily AFA every 3 weeks for up to 6 cycles, until disease progression, intolerable adverse events (AEs), or death. The co-primary endpoints were overall survival (OS) and progression-free survival (PFS).Results: The cohort consisted of 124 patients (OSI: n = 60, mean age = 64.24 years [SD: 12.33]; AFA: n = 64, mean age = 64.13 years [SD: 13.72]). After a median follow-up of 24 months (range, 3 to 28), a significant improvement in OS was detected (hazard ratio [HR] 0.59, 95% confidence interval [CI], 0.39-0.91; p = 0.0160; median, 13.7 months [95% CI, 11.1-14.8] for OSI vs 9.6 months [95% CI, 8.4-10.2] for AFA). The median duration of PFS was significantly longer with OSI than with AFA (HR 0.62; 95% CI, 0.41-0.91; p = 0.014; median, 4.5 months [95% CI, 3.5-5.7] vs 3.9 months [95% CI, 3.1-4.8]). The proportion of grade 3 or higher adverse events (AEs) was lower with OSI (22.4%) than with AFA (39.4%).Conclusions: In patients with T790M-positive NSCLC and multiple CNS metastases after failure of initial EGFR-TKI treatment, OSI may be associated with significantly improved survival benefit compared with AFA, with a controllable tolerability profile. [ABSTRACT FROM AUTHOR]

This research investigates the effects of Na2SO4 and Na2CO3 on the mechanical and hydration properties of high-volume fly ash (FA) cement (HFC) at three curing temperatures: 25°C, 40°C, and 60°C. Setting time, compressive strength, reaction kinetics, phase assembles, and pore structure were explored. The research found that the hydration of HFC was significantly promoted by the chemical activators at all temperatures. At 25°C, the incorporation of Na2CO3 and Na2SO4 decreased the initial setting time by 59% and 50%, respectively, compared to the control sample. This was accompanied by increased heat release from the isothermal calorimetry tests. At the same time, the introduction of Na2CO3 (HFC-C) increased the early compressive strength by 40%, higher than that of samples with Na2SO4 addition (HFC-S). While reduced compressive strength was noticed in HFC-C after curing for a long time, resulting from the increased porosity. Furthermore, temperature rise reduced the contribution index (Ci) to the strength development of both HFC-C and HFC-S. On the other hand, the presence of Na2SO4 contributed to the formation of more ettringite (AFt), while no AFt was detected after the addition of Na2CO3. Increasing the curing temperature did not strongly change the phase assembles of the hydration products, although the amount of AFt content was observed to increase. [ABSTRACT FROM AUTHOR]

This paper presents a phase planning method specially designed for coal deposits with nearly horizontal, bedded coal seams. The geology of this type of deposit is modeled into a column model, instead of a block model, to avoid coal-rock mixing in blocks. A nested pit generation algorithm is developed for producing a series of nested, least-strip ratio pits with a column model as its input. The algorithm completely overcomes the troublesome gap problem. Taking the least-strip ratio pits as possible phase states, a dynamic programming formulation is proposed to simultaneously optimize the number of phases, the phase-pits, and the ultimate pit, with an objective of maximizing the net present value. The merits and capability of the proposed method are demonstrated through a case study on a large coal deposit. [ABSTRACT FROM AUTHOR]

In view of the problem of soil erosion control and ecological restoration of the tailings reservoir slope, the characteristics of runoff, sediment yield, and velocity of sheet flow of the tailings reservoir slope under four conditions (goosegrass, vetiver, clover, and bare slope) were studied by conducting a laboratory test of hydraulic erosion. Based on the direct shear test and triaxial shear test of the root–soil composites, the optimum range of root–soil composites was determined, and the stress–strain relationship, strength characteristics, and failure form of root–soil composites under different confining pressure were studied. The results show that as a whole, the characteristics of runoff, sediment yield, and velocity of sheet flow in the tailings slope under the condition of vetiver are more advantageous than other vegetation to the reinforcement of the slope. The shear strength of the root–soil composites increases with the increase of the root content, and the shear strength of the root–soil composites remains unchanged or decreased when it reaches a certain range; the interval of the root content 0.3–0.4% is the best root content of the root–soil composites. This study's results can provide an important theoretical basis and reference value for comprehensive control of a tailings reservoir slope and restoration of ecological stability. [ABSTRACT FROM AUTHOR]

Due to the differentiation between training and testing data in the feature space, cross‐project defect prediction (CPDP) remains unaddressed within the field of traditional machine learning. Recently, transfer learning has become a research hot‐spot for building classifiers in the target domain using the data from the related source domains. To implement better CPDP models, recent studies focus on either feature transferring or instance transferring to weaken the impact of irrelevant cross‐project data. Instead, this work proposes a dual weighting mechanism to aid the learning process, considering both feature transferring and instance transferring. In our method, a local data gravitation between source and target domains determines instance weight, while features that are highly correlated with the learning task, uncorrelated with other features and minimizing the difference between the domains are rewarded with a higher feature weight. Experiments on 25 real‐world datasets indicate that the proposed approach outperforms the existing CPDP methods in most cases. By assigning weights based on the different contribution of features and instances to the predictor, the proposed approach is able to build a better CPDP model and demonstrates substantial improvements over the state‐of‐the‐art CPDP models. [ABSTRACT FROM AUTHOR]

The article presents a commentary on a study by Ishida et al., which has found a fast and robust protocol to directly induce the differentiation of human induced pluripotent stem cells (hiPSC) into functional testosterone-producing Leydig-like cells. Topics covered include a strategy developed by the authors in which hiPSCs can be differentiated into human Leydig-like or adrenal-like cells, and evidence that the induction of tissue-specific cell types from hiPSCs may be associated with gender.

In recent years, numerous techniques have been proposed for human activity recognition (HAR) from images and videos. These techniques can be divided into two major categories: handcrafted and deep learning. Deep Learning-based models have produced remarkable results for HAR. However, these models have several shortcomings, such as the requirement for a massive amount of training data, lack of transparency, offline nature, and poor interpretability of their internal parameters. In this paper, a new approach for HAR is proposed, which consists of an interpretable, self-evolving, and self-organizing set of 0-order If...THEN rules. This approach is entirely data-driven, and non-parametric; thus, prototypes are identified automatically during the training process. To demonstrate the effectiveness of the proposed method, a set of high-level features is obtained using a pre-trained deep convolution neural network model, and a recently introduced deep rule-based classifier is applied for classification. Experiments are performed on a challenging benchmark dataset UCF50; results confirmed that the proposed approach outperforms state-of-the-art methods. In addition to this, an ablation study is conducted to demonstrate the efficacy of the proposed approach by comparing the performance of our DRB classifier with four state-of-the-art classifiers. This analysis revealed that the DRB classifier could perform better than state-of-the-art classifiers, even with limited training samples. [ABSTRACT FROM AUTHOR]

The cyclic wetting-drying phenomenon is a complicated physical and chemical process. This kind of process will weaken the properties of rock to some extent. Some fundamental physical parameters of sandstone were first tested to study the weakening influence of wetting-drying cycles on the physical characteristics. Then, the water weakening effect on the mechanical and acoustic emission (AE) characteristics of rock on account of wetting-drying cycles was experimentally studied. Laboratory test results showed that when the number of cycles increased from 0 to 25, the density, P-wave velocity, uniaxial compressive strength (UCS), (elastic modulus (E)) and total AE counts of the rock specimens continuously decreased. However, the water absorption would increase as the number of cycles increased. The functional relationships between the total AE counts and the mechanical parameters (UCS and E) of sandstone after each cyclic number were established based on the test results. In addition, the chemical components and microstructure variations of rock undergoing wetting-drying cycles were examined. From the results, it can be concluded that microcrack growth and expansion inside the rocks undergoing wetting-drying cycles are the main reasons for the attenuation of the rock properties. Moreover, with increasing loading rate, the UCS, E and total AE counts of sandstone after each cyclic number would increase. [ABSTRACT FROM AUTHOR]

This study considers the finite time consensus tracking problems for second‐order non‐linear multi‐agent system with the unknown dynamics. Firstly, the radial basis function neural networks are utilised to approximate the lumped unknown dynamic model. And then, to achieve the finite‐time consensus tracking, a distributed non‐singular fast terminal sliding mode control strategy is designed which can speed up the convergence of the multi‐agent system. Moreover, the consistency under the proposed control strategy is theoretically analysed and the upper bound of setting time is explicitly obtained. Finally, two simulation examples are provided. [ABSTRACT FROM AUTHOR]