Combining Ability and Heterotic Effects in Newly Developed Early Maturing and High-Yielding Maize Hybrids under Low and Recommended Nitrogen Conditions

Nitrogen (N) is a crucial nutrient vital for the growth and productivity of maize. However, excessive nitrogen application can result in numerous environmental and ecological problems, such as water pollution, biodiversity loss, and greenhouse gas emissions. Therefore, breeding maize hybrids resilient to low nitrogen conditions is crucial for sustainable agriculture, especially under low nitrogen conditions. Consequently, this study aimed to evaluate the combining ability and heterosis of maize lines, recognize promising hybrids, and study gene action controlling key traits under low and recommended N stress conditions. The half-diallel mating design hybridized seven maize inbreds, resulting in 21 F1 hybrids. These hybrids, along with two high-yielding commercial hybrids (SC10 and TWC310), were evaluated in field trials under recommended (290 kg/ha) and low N (166 kg N/ha) conditions. Significant variations were observed among assessed hybrids for all measured traits, with non-additive gene action being predominant for grain yield and its related characteristics under recommended and low N conditions. Inbred lines P105 and P106 were recognized as effective combiners for earliness, with P105 also excelling in shorter plant height and lower ear placement. In addition, P101, P102, and P104 were identified as good combiners for increasing grain yield and related attributes under low N conditions. The crosses P105 × P106 and P106 × P107 demonstrated outstanding heterotic effects for earliness, while hybrids P101 × P102 and P102 × P104 exhibited remarkable heterotic effects for grain yield low nitrogen stress conditions. These promising hybrids could be considered for commercial use after further evaluation. Strong positive correlations were found between grain yield and ear height, plant height, number of kernels per row, and 1000-grain weight, highlighting their importance for indirect selection to enhance the grain yield of maize under low N stress conditions.

Ternary Hybrid Nanofluid with First and Second Order Velocity Slips: Dual Solutions with Stability Analysis

Modeling the boundary layer flow of ternary hybrid nanofluids is important for understanding and optimizing their thermal performance,particularly in applications where enhanced heat transfer and fluid dynamics are essential.This study numerically investigates the boundary layer flow of alumina-copper-silver/water nanofluid over a permeable stretching/shrinking sheet,incorporating both first and second-order velocity slip.The mathematical model is solved in MATLAB facilitated by the bvp4c function that employs the finite difference scheme and Lobatto IIIa formula.The solver successfully generates dual solutions for the model,and further analysis is conducted to assess their stability.The findings reported that only one of the solutions is stable.For the shrinking sheet case,increasing the first-order velocity slip delays boundary layer separation and enhances heat transfer,while,when the sheet is stretched,the second-order velocity slip accelerates separation and improves heat transfer.Boundary layer separation is most likely to occur when the sheet is shrinking;however,this can be controlled by adjusting the velocity slip with the inclusion of boundary layer suction.

Multi-Objective Hybrid Sailfish Optimization Algorithm for Planetary Gearbox and Mechanical Engineering Design Optimization Problems

This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.

Sub-6GHz Assisted mmWave Hybrid Beamforming with Self-Supervised Learning

In this paper,we propose a sub-6GHz channel assisted hybrid beamforming(HBF)for mmWave system under both line-of-sight(LOS)and non-line-of-sight(NLOS)scenarios without mmWave channel estimation.Meanwhile,we resort to the selfsupervised approach to eliminate the need for labels,thus avoiding the accompanied high cost of data collection and annotation.We first construct the dense connection network(DCnet)with three modules:the feature extraction module for extracting channel characteristic from a large amount of channel data,the feature fusion module for combining multidimensional features,and the prediction module for generating the HBF matrices.Next,we establish a lightweight network architecture,named as LDnet,to reduce the number of model parameters and computational complexity.The proposed sub-6GHz assisted approach eliminates mmWave pilot resources compared to the method using mmWave channel information directly.The simulation results indicate that the proposed DCnet and LDnet can achieve the spectral efficiency that is superior to the traditional orthogonal matching pursuit(OMP)algorithm by 13.66% and 10.44% under LOS scenarios and by 32.35% and 27.75% under NLOS scenarios,respectively.Moreover,the LDnet achieves 98.52% reduction in the number of model parameters and 22.93% reduction in computational complexity compared to DCnet.

Enhancing Evapotranspiration Estimation: A Bibliometric and Systematic Review of Hybrid Neural Networks in Water Resource Management

Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.

A Novel Control Strategy Based onπ-VSG for Inter-Face Converter in Hybrid Microgrid

The rapid development of new energy power generation technology and the transformation of power electronics in the core equipment of source-grid-load drives the power system towards the“double-high”development pattern of“high proportion of renewable energy”and“high proportion of power electronic equipment”.To enhance the transient performance of AC/DC hybrid microgrid(HMG)in the context of“double-high,”aπtype virtual synchronous generator(π-VSG)control strategy is applied to bidirectional interface converter(BIC)to address the issues of lacking inertia and poor disturbance immunity caused by the high penetration rate of power electronic equipment and new energy.Firstly,the virtual synchronous generator mechanical motion equations and virtual capacitance equations are used to introduce the virtual inertia control equations that consider the transient performance of HMG;based on the equations,theπ-type equivalent control model of the BIC is established.Next,the inertia power is actively transferred through the BIC according to the load fluctuation to compensate for the system’s inertia deficit.Secondly,theπ-VSG control utilizes small-signal analysis to investigate howthe fundamental parameters affect the overall stability of the HMG and incorporates power step response curves to reveal the relationship between the control’s virtual parameters and transient performance.Finally,the PSCAD/EMTDC simulation results show that theπ-VSG control effectively improves the immunity of AC frequency and DC voltage in the HMG system under the load fluctuation condition,increases the stability of the HMG system and satisfies the power-sharing control objective between the AC and DC subgrids.

Blood-based magnetohydrodynamic Casson hybrid nanofluid flow on convectively heated bi-directional porous stretching sheet with variable porosity and slip constraints

Fluid flow through porous spaces with variable porosity has wide-range applications,notably in biomedical and thermal engineering,where it plays a vital role in comprehending blood flow dynamics within cardiovascular systems,heat transfer and thermal management systems improve efficiency using porous materials with variable porosity.Keeping these important applications in view,in current study blood-based hybrid nanofluid flow has considered on a convectively heated sheet.The sheet exhibits the properties of a porous medium with variable porosity and extends in both the x and y directions.Blood has used as base fluid in which the nanoparticles of Cu and Cu O have been mixed.Thermal radiation,space-dependent,and thermal-dependent heat sources have been incorporated into the energy equation,while magnetic effects have been integrated into the momentum equations.Dimensionless variables have employed to transform the modeled equations into dimensionless form and facilitating their solution using bvp4c approach.It has concluded in this study that,both the primary and secondary velocities augmented with upsurge in variable porous factor and declined with escalation in stretching ratio,Casson,magnetic,and slip factors along x-and y-axes.Thermal distribution has grown up with upsurge in Casson factor,magnetic factor,thermal Biot number,and thermal/space-dependent heat sources while has retarded with growth in variable porous and stretching ratio factors.The findings of this investigation have been compared with the existing literature,revealing a strong agreement among present and established results that ensured the validation of the model and method used in this work.

新型倒置A^(2)/O耦联MBR组合工艺处理农村低C/N废水的研究

针对农村低C/N污水污染物和营养盐去除率差的问题,以倒置A^(2)/O耦联膜生物反应器(MBR)组合工艺为探究对象,通过控制进水污染物浓度,在中温条件下考察了有机负荷(OLR)对倒置A^(2)/O耦联MBR组合工艺处理农村低C/N污水的影响。结果表明,OLR由150 mg/L提高至450 mg/L时,总氮(TN)和溶解性磷酸盐(SOP)去除率分别由67.6%和86.6%提高至72.4%和94.3%,进一步提高OLR降低了组合工艺对污染物和营养盐的去除。此外,OLR能影响新工艺内污泥特征,提高OLR促进了胞外聚合物(EPS)分泌,尤其在OLR为600 mg/L组别内,EPS含量提高至139.6 mg/g。进水OLR对缺氧池内EPS的影响要大于其对厌氧池内EPS的影响。OLR能影响新工艺内污染物和营养盐去除相关关键酶的活性,当OLR为450 mg/L时,污染物和营养盐去除相关关键活性酶最强。研究结果为农村低C/N污水的高效处理提供了理论依据和数据支撑。

倒置A^(2)/O+A生化法+膜法+磁混凝法污水处理工程实例

采用“倒置A^(2)/O+A生化法+膜法+磁混凝物化法”组合工艺处理污水,分析了工艺特点,并详述了主要构筑物及设备参数。该组合工艺处理效果优良,除磷脱氮效果好,出水COD、TP、NH_(3)-N、TN月均值分别为28、0.2、0.3、6.06mg/L,达到了DB32/1072—2018《太湖地区城镇污水处理厂及重点工业行业主要水污染物排放限值》其他区域污染物排放标准,其中COD、TP和NH_(3)-N三个指标达到了GB3838—2002《地表水环境质量标准》Ⅳ类水排放标准。

谈汉语拼音的字母“o”和元音“o”

本文从八个方面讨论汉语拼音中“o”的读音,以把字母“o”和元音“o”区分开来。一、制定《汉语拼音方案》有语音学原则、文字学原则和兼顾传统的做法。二、普通话基础音系没有“o”元音音位。三、汉语拼音中的字母“o”有三种不同的发音。四、中国学生的语音直觉就是“bo、duo”的韵母相同。五、教外国学生汉语拼音时要注意说明并操练字母“o”在不同条件下的实际发音。六、语文字典可以兼顾韵母“o”的读音和叹词“喔”的规范。七、可以适当编制相应的使用说明以妥善处理《汉语拼音方案》使用中的问题。八、建议完善《汉语拼音方案》,以便更好地发挥汉语拼音的作用。

基于Dynamo的Revit-Midas/Civil斜拉桥模型信息转换

BIM模型不支持有限元计算,且BIM模型与有限元分析模型数据交互困难,故BIM技术正向设计过程中存在建模效率低、模型修改困难等问题,无法做到BIM结构设计与有限元力学分析一体化,增加了结构模型建模与纠错成本.本文依托Revit和Midas/Civil软件平台,在Dynamo环境下采用IronPython语言设计了一套Revit-Midas/Civil的模型信息转换程序.以博士大桥主桥为对象,通过程序自动实现:1)Revit模型桥梁构件分解、截面特性计算、拉索及梁塔弹性连接处理,并转换成适用于Midas/Civil的语言格式MCT文件,实现了Revit向Midas/Civil模型信息自动转换;2)将有限元计算结果反馈到Revit模型中,对作用效应信息按数值大小赋予渐变颜色,实现了在BIM模型中显示有限元分析结果的展示功能.本文程序可实现Revit-Midas/Civil模型信息转换,有效提高了BIM正向应用效率,弥补了BIM技术在桥梁结构分析方面的不足.

基于改进K-means算法的室内可见光通信O-OFDM系统信道均衡技术

在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随机生成足够长的训练序列,然后将训练序列每一簇的均值作为K-means聚类中心,避免了传统K-means反复迭代寻找聚类中心。进一步,提出了基于神经网络的IC-Kmeans(Neural Network Based IC-Kmeans,NNIC-Kmeans)算法,使用反向传播神经网络将接收端二维数据映射至三维空间,以增加不同簇之间混合数据的距离,提高了分类准确性。蒙特卡罗误码率仿真表明,IC-Kmeans均衡和传统K-means算法的误码率性能相当,但可以显著降低复杂度,特别是在信噪比较小时。同时,在室内多径信道模型下,与IC-Kmeans和传统Kmeans均衡相比,NNIC-Kmeans均衡的光正交频分复用系统误码率性能最好。

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

Regulatable Orthotropic 3D Hybrid Continuous Carbon Networks for Efficient Bi-Directional Thermal Conduction

Vertically oriented carbon structures constructed from low-dimen-sional carbon materials are ideal frameworks for high-performance thermal inter-face materials(TIMs).However,improving the interfacial heat-transfer efficiency of vertically oriented carbon structures is a challenging task.Herein,an orthotropic three-dimensional(3D)hybrid carbon network(VSCG)is fabricated by depositing vertically aligned carbon nanotubes(VACNTs)on the surface of a horizontally oriented graphene film(HOGF).The interfacial interaction between the VACNTs and HOGF is then optimized through an annealing strategy.After regulating the orientation structure of the VACNTs and filling the VSCG with polydimethylsi-loxane(PDMS),VSCG/PDMS composites with excellent 3D thermal conductive properties are obtained.The highest in-plane and through-plane thermal conduc-tivities of the composites are 113.61 and 24.37 W m^(-1)K^(-1),respectively.The high contact area of HOGF and good compressibility of VACNTs imbue the VSCG/PDMS composite with low thermal resistance.In addition,the interfacial heat-transfer efficiency of VSCG/PDMS composite in the TIM performance was improved by 71.3%compared to that of a state-of-the-art thermal pad.This new structural design can potentially realize high-performance TIMs that meet the need for high thermal conductivity and low contact thermal resistance in interfacial heat-transfer processes.

Ag-integrated mixed metallic Co-Fe-Ni-Mn hydroxide composite as advanced electrode for high-performance hybrid supercapacitors

Direct growth of redox-active noble metals and rational design of multifunctional electrochemical active materials play crucial roles in developing novel electrode materials for energy storage devices.In this regard,silver(Ag)has attracted great attention in the design of efficient electrodes.Inspired by the house/building process,which means electing the right land,it lays a strong foundation and building essential columns for a complex structure.Herein,we report the construction of multifaceted heterostructure cobalt-iron hydroxide(CFOH)nanowires(NWs)@nickel cobalt manganese hydroxides and/or hydrate(NCMOH)nanosheets(NSs)on the Ag-deposited nickel foam and carbon cloth(i.e.,Ag/NF and Ag/CC)substrates.Moreover,the formation and charge storage mechanism of Ag are described,and these contribute to good conductive and redox chemistry features.The switching architectural integrity of metal and redox materials on metallic frames may significantly boost charge storage and rate performance with noticeable drop in resistance.The as-fabricated Ag@CFOH@NCMOH/NF electrode delivered superior areal capacity value of 2081.9μA h cm^(-2)at 5 mA cm^(-2).Moreover,as-assembled hybrid cell based on NF(HC/NF)device exhibited remarkable areal capacity value of 1.82 mA h cm^(-2)at 5 mA cm^(-2)with excellent rate capability of 74.77%even at 70 mA cm^(-2)Furthermore,HC/NF device achieved maximum energy and power densities of 1.39 mW h cm^(-2)and 42.35 mW cm^(-2),respectively.To verify practical applicability,both devices were also tested to serve as a self-charging station for various portable electronic devices.

p-d Orbital Hybridization Engineered Single-Atom Catalyst for Electrocatalytic Ammonia Synthesis

The rational design of metal single-atom catalysts(SACs)for electrochemical nitrogen reduction reaction(NRR)is challenging.Two-dimensional metal-organic frameworks(2DMOFs)is a unique class of promising SACs.Up to now,the roles of individual metals,coordination atoms,and their synergy effect on the electroanalytic performance remain unclear.Therefore,in this work,a series of 2DMOFs with different metals and coordinating atoms are systematically investigated as electrocatalysts for ammonia synthesis using density functional theory calculations.For a specific metal,a proper metal-intermediate atoms p-d orbital hybridization interaction strength is found to be a key indicator for their NRR catalytic activities.The hybridization interaction strength can be quantitatively described with the p-/d-band center energy difference(Δd-p),which is found to be a sufficient descriptor for both the p-d hybridization strength and the NRR performance.The maximum free energy change(ΔG_(max))andΔd-p have a volcanic relationship with OsC_(4)(Se)_(4)located at the apex of the volcanic curve,showing the best NRR performance.The asymmetrical coordination environment could regulate the band structure subtly in terms of band overlap and positions.This work may shed new light on the application of orbital engineering in electrocatalytic NRR activity and especially promotes the rational design for SACs.

大学生信息成瘾行为的触发路径与干预策略:基于S-O-R理论视角

[目的/意义]文章以大学生为研究对象,探究该群体信息成瘾行为的影响机制,为大学生信息成瘾行为的预防和管理提供价值参考。[方法/过程]引入刺激-机体-反应(S-O-R)理论,利用文献梳理总结大学生信息成瘾过程中的外部刺激,深度剖析机体在成瘾过程中产生的认知心理,构建大学生信息成瘾行为理论模型,运用结构方程模型实证分析大学生信息成瘾的触发路径。[结果/结论]研究结果表明,信息过载、间歇性奖励和行为管理会显著正向影响大学生的不确定性回避,进而导致大学生信息成瘾。同时,信息过载也会使大学生产生信息焦虑,信息焦虑显著正向影响大学生信息成瘾行为。面向高等院校、大学生群体和科技企业,提出针对性建议与对策,有助于完善高等院校管理体系,促进学生适应信息化环境,推动高等教育事业更好发展。

Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

Enhancing rock fragmentation prediction in mining operations:A hybrid GWO-RF model with SHAP interpretability

In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.