电商ODM模式下的竞争性供应链定价及品质决策研究

在消费升级的过程中,电商企业纷纷涉足ODM模式,相对于成熟品牌产品,新兴的电商自营品牌产品如何定价及品质决策是其成功关键。通过构建一个制造商与两个相互竞争的品牌销售商的二级供应链,探讨了集中式与品牌销售商不同权力结构下的定价决策问题,分析了产品品质敏感度对于电商ODM品牌定价、品质水平以及供应链利润的影响。研究表明,不同品牌产品的采购价、销售价、销售商利润以及供应链总利润均与产品品质敏感度正相关;当国际知名品牌销售商处于市场主导地位时,不同品牌销售商以及供应链总利润均较优;与此同时,当电商自营品牌销售商处于市场主导地位时,容易导致供应链整体利润处于较低水平,不利于供应链的长期运营。

Sustainable Mining in the Era of Artificial Intelligence

The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread use.Over-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.

Instability mechanism of mining roadway passing through fault at different angles in kilometre-deep mine and control measures of roof cutting and NPR cables

The angle α between the fault strike and the axial direction of the roadway produces different damage characteristics. In this paper, the research methodology includes theoretical analyses, numerical simulations and field experiments in the context of the Daqiang coal mine located in Shenyang, China. The stability control countermeasure of "pre-splitting cutting roof + NPR anchor cable"(PSCR-NPR) is simultaneously proposed. According to the different deformation characteristics of the roadway, the faults are innovatively classified into three types, with α of type I being 0°-30°, α of type II being 30°-60°, and α of type III being 60°-90°. The full-cycle stress evolution paths during mining roadway traverses across different types of faults are investigated by numerical simulation. Different pinch angles α lead to high stress concentration areas at different locations in the surrounding rock. The non-uniform stress field formed in the shallow surrounding rock is an important reason for the instability of the roadway. The pre-cracked cut top shifted the high stress region to the deep rock mass and formed a low stress region in the shallow rock mass. The high prestressing NPR anchor cable transforms the non-uniform stress field of the shallow surrounding rock into a uniform stress field. PSCR-NPR is applied in the fault-through roadway of Daqiang mine. The low stress area of the surrounding rock was enlarged by 3-7 times, and the cumulative convergence was reduced by 45%-50%. It provides a reference for the stability control of the deep fault-through mining roadway.

A Quarterly High RFM Mining Algorithm for Big Data Management

In today’s highly competitive retail industry,offline stores face increasing pressure on profitability.They hope to improve their ability in shelf management with the help of big data technology.For this,on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase behavior.RFM(recency,frequency,andmonetary)patternmining is a powerful tool to evaluate the value of customer behavior.However,the existing RFM patternmining algorithms do not consider the quarterly nature of goods,resulting in unreasonable shelf availability and difficulty in profit-making.To solve this problem,we propose a quarterly RFM mining algorithmfor On-shelf products named OS-RFM.Our algorithmmines the high recency,high frequency,and high monetary patterns and considers the period of the on-shelf goods in quarterly units.We conducted experiments using two real datasets for numerical and graphical analysis to prove the algorithm’s effectiveness.Compared with the state-of-the-art RFM mining algorithm,our algorithm can identify more patterns and performs well in terms of precision,recall,and F1-score,with the recall rate nearing 100%.Also,the novel algorithm operates with significantly shorter running times and more stable memory usage than existing mining algorithms.Additionally,we analyze the sales trends of products in different quarters and seasonal variations.The analysis assists businesses in maintaining reasonable on-shelf availability and achieving greater profitability.

Preface:Heavy metal(loid)s at mining&metallurgical sites:Fate,risk and remediation

The contamination of heavy metal(loid)s at mining&metallurgical sites has been a major environmental challenge worldwide[1].Typically,large amounts of metal(loid)s-bearing wastes are generated at these sites,such as smelting slag,combustion residues,mine tailings,wastewater,and exhaust gas[2].Due to their high mobility in the environment,the released heavy metal(loid)s can easily enter the soil and water environment,posing long-term and widespread threats to ecological and human health[3].

Using X Social Networks (formerly Twitter) and web news mining to predict the measles outbreak

Measles,an infectious disease caused by the measles virus,remains a significant public health concern worldwide due to its highly contagious nature and potential for severe complications[1].In addition to symptoms such as high fever,cough,Koplik spots,and rash,measles can lead to serious complications including pneumonia and myocarditis,particularly in vulnerable populations such as young children[1,2].

Integrated biomarker response to assess toxic impacts of iron and manganese on deep-sea mussel Gigantidas platifrons under a deep-sea mining activity scenario

Deep-sea mining activities can potentially release metals,which pose a toxicological threat to deep-sea ecosystems.Nevertheless,due to the remoteness and inaccessibility of the deep-sea biosphere,there is insufficient knowledge about the impact of metal exposure on its inhabitants.In this study,deep-sea mussel Gigantidas platifrons,a commonly used deep-sea toxicology model organism,was exposed to manganese(100,1000μg/L)or iron(500,5000μg/L)for 7 d,respectively.Manganese and iron were chosen for their high levels of occurrence within deep-sea deposits.Metal accumulation and a battery of biochemical biomarkers related to antioxidative stress in superoxide dismutase(SOD),catalase(CAT),malondialdehyde(MDA);immune function in alkaline phosphatase(AKP),acid phosphatase(ACP);and energy metabolism in pyruvate kinase(PK)and hexokinase(HK)were assessed in mussel gills.Results showed that deep-sea mussel G.platifrons exhibited a high capacity to accumulate Mn/Fe.In addition,most tested biochemical parameters were altered by metal exposure,demonstrating that metals could induce oxidative stress,suppress the immune system,and affect energy metabolism of deep-sea mussels.The integrated biomarker response(IBR)approach indicated that the exposure to Mn/Fe had a negative impact on deep-sea mussels,and Mn demonstrated a more harmful impact on deep-sea mussels than Fe.Additionally,SOD and CAT biomarkers had the greatest impact on IBR values in Mn treatments,while ACP and HK were most influential for the low-and high-dose Fe groups,respectively.This study represents the first application of the IBR approach to evaluate the toxicity of metals on deep-sea fauna and serves as a crucial framework for risk assessment of deep-sea mining-associated metal exposure.

Dust-Holding Capacity and Bio-Chemical Changes of Plant Species Growing in an Around Opencast Mining Area of Bundelkhand Region of Uttar Pradesh, India

The present study has been carried out on a total of 50 available plant species to assess their dust-capturing capacity and biochemical performances in and around open cast granite mine areas of Jhansi district and Bundelkhand University campus treated as control site. Plant species existing under a polluted environment for a long time may be considered as potentially resistant species and recommended for green belt design in mining areas, especially to cope with dust pollution. Results showed the pollution level, especially of mining-originated dust particles holding capacity of leaves and effects of different biochemical parameters (Total Chlorophyll, Protein and Carotenoid) of existing plant species both from mining areas as well as from Bundelkhand University campus. Based on their performances, Tectona grandis L., Ficus hispida L., Calotropis procera Aiton., Butea monosperma Lam. and Ficus benghalensis L., etc. are highly tolerant species while Ficus infectoria L., Artocarpus heterophyllus Lam., Ipomoea purpurea L., Allianthus excelsa Roxb. and Bauhinia variegata L. are intermediate tolerant species. T. grandis had shown the highest dust-holding capacity (2.566 ± 0.0004 mg/cm2) whereas Albizia procera (0.018 ± 0.0002 mg/cm2) was found to be the lowest dust-holding capacity. Our findings also showed that the T. grandis and F. hispida have significant dust deposition with minimal effect of dust on their leaf chlorophyll (17.447 ± 0.019 mg/g and 14.703 ± 0.201 mg/g), protein (0.699 ± 0.001 mg/g and 0.604 ± 0.002 mg/g) and carotenoid (0.372 ± 0.003 mg/g and 0.354 ± 0.003 mg/g) content respectively among all selected plant species. Therefore, in the present investigation, plant species with high tolerance to high dust-holding capacity on their leaf surfaces are preferable for green corridors as open cast granite mines and their adjacent areas.

PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining performance,but they still require huge computational resource and may miss many HUIs.Due to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded processors.Experiments show that the mining performance of PHUI-GA outperforms the existing EAs.When mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

Experimental Investigation on Vertical Hydraulic Transport of Ores in Deepsea Mining

Deepsea mining has been proposed since the 1960s to alleviate the lack of resources on land.Vertical hydraulic transport of collected ores from the seabed to the sea surface is considered the most promising method for industrial applications.In the present study,an indoor model test of the vertical hydraulic transport of particles was conducted.A noncontact optical method has been proposed to measure the local characteristics of the particles inside a vertical pipe,including the local concentration and particle velocity.The hydraulic gradient of ore transport was evaluated with various particle size distributions,particle densities,feeding concentrations and mixture flow velocities.During transport,the local concentration is larger than the feeding concentration,whereas the particle velocity is less than the mixture velocity.The qualitative effects of the local concentration and local fluid velocity on the particle velocity and slip velocity were investigated.The local fluid velocity contributes significantly to particle velocity and slip velocity,whereas the effect of the local concentration is marginal.A higher feeding concentration and mixture flow velocity result in an increased hydraulic gradient.The effect of the particle size gradation is slight,whereas the particle density plays a crucial role in the transport.

Spatial distribution and potential ecological and health risks associated with heavy metals in the Ijero-Ekiti mining site,Nigeria

Artisanal gold mining,a labor-intensive and antiquated technique,is a growing industry and the source of income for rural communities all over the world.However,artisanal gold mining has potential negative and long-term effects on economy,environment,and society.This study collected soil samples from 16 sample points including a control point to examine the pollution degrees and spatial distribution of heavy metals,as well as ecological and health risks associated with heavy metal pollution in the Ijero-Ekiti mining site,Nigeria.Geographical Information System(GIS)and remote sensing technologies were used to identify regions with high concentrations of heavy metals and assess the environmental impact of gold mining activities.The results show that the mean heavy metal concentrations of 16 soil pointa are 8.94(±5.97)mg/kg for As,0.18(±0.54)mg/kg for Cd,0.11(±1.06)mg/kg for Co,14.32(±3.43)mg/kg for Cr,6.89(±0.64)mg/kg for Cu,48.92(±11.77)mg/kg for Fe,135.81(±30.75)mg/kg for Mn,5.92(±0.96)mg/kg for Ni,5.72(±1.66)mg/kg for Pb,and 13.94(±1.38)mg/kg for Zn.The study reveals that heavy metal concentration in soils follows the order of Mn>Fe>Cr>Zn>As>Cu>Ni>Pb>Co>Cd.An analysis of soil samples indicates that 3 principal components(PCs)account for 70.008%of the total variance and there are strong positive correlations between various pairs of heavy metals.The total potential ecological risk index(309.599)in the study area is high.Non-carcinogenic risk suggests that there may be long-term health impacts on people who work in the mining areas due to chronic exposure to the environment.Based on the study,the hazard index of carcinogenic health risks associated with heavy metals through ingestion is 520.00×10^(–4).Dermal contact from As and Cr also increases the risk of cancer,with the highest hazard index value of 18.40×10^(–4).The lowest exposure pathway,with the hazard index value of 0.68×10^(–4),indicates that the inhalation of heavy metals has a comparatively low risk of cancer.This study recommends the formulation of policies to monitor the Ijero-Ekiti mining site and other regions in Nigeria where indiscriminate artisanal gold mining activities exist.

Coordinated Mining Procedures of Open Pit Mines Based on River Management

This paper primarily concerns the effective coordination of the procedures and methods employed in open pit mining operations under the background of river management.The central objective of this study is to identify a viable approach for ensuring rational and efficient development of open pit mineral resources while simultaneously protecting and restoring the ecological environment of the river.This approach should facilitate the realization of a harmonious symbiosis between mining and river management.The intricate mutual influence relationship between river management and open pit mining is first analyzed in depth,which provides a solid foundation for the subsequent coordination strategy development.In light of the aforementioned considerations,a set of coordination procedures for open pit mining based on river management conditions is proposed.These procedures emphasize the integration of river protection into the overall layout of mining at the planning stage.The implementation of scientific mining schemes,accompanied by rigorous control of the scope and depth of mining operations,has proven to be an effective means of reducing the impact of mining activities on river environments.This approach has also facilitated the achievement of a balance and coordination between mining and river management.

Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

A semi-infinite beam theoretical model on predicting rock slope subsidence induced by underground mining

When the mining goaf is close to the cliff,rock slope subsidence induced by underground mining is significantly affected by its boundary conditions.In this study,an analytical method is proposed by considering the key strata as a semi-infinite Euler-Bernoulli beam rested on a Winkler foundation with a local subsidence area.The analytical solutions of deflection are derived by analyzing the boundary and continuity conditions of the cliff.Then,the analytical solutions are verified by the results from experimental tests,FEM and InSAR,respectively.After that,the influence of changing parameters on deflections is studied with sensitivity analysis.The results show that the distance between goaf and cliff significantly affects the deflection of semi-infinite beam.The response of semi-infinite beam is obviously determined by the length of goaf and the bending stiffness of beam.The comparisons between semi-infinite beam and infinite beam illustrate the ascendancy of the improved model in such problems.

Binary Program Vulnerability Mining Based on Neural Network

Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.

Mobility of Major and Trace Elements during the Bauxitization Processes in Ngaoundal Area (Adamawa Cameroon): Implication on Mining Perspectives

This study was focused to assess major and trace elements in bauxitic duricrusts from Ngaoundal and its surroundings in order to establish their mining interest. To this end, fieldworks, mineralogical and geochemical analyses were carried out. Four facies of duricrust were identified and characterized from the summit to the top of the slope of the Ngaoundal mountain: scoriaceous, pisolitic, nodular and massive. Mineralogical and geochemical analyses performed on 16 samples, revealed a significant concentration of Al2O3 mainly in the scoriaceous facies (over 45% in grade), moderate in Fe2O3 (averaging 23.69%) and SiO2 (averaging 21.7%). Trace elements were generally low, excluding Cr (421 ppm on average), Zr (327 ppm on average and V (213 ppm on average). In addition, the limited quantities of alkalis (Na2O, K2O) and alkaline earths metals (MgO, CaO) coupled with the very high values of Chemical Index of Alteration (CIA) and Mineralogical Index of Alteration (MIA), (more than 99%) attest to the intense weathering of the studied materials. Allitization and monosiallitization constituted the crystallochemical phenomena that have led to the development of bauxitic minerals. More than 90% of gibbsite in scoriaceous facies, 52.21% - 76.01% of kaolinite in pisolitic facies and more than 40% of hematite in nodular facies were quantified. The relationships between the constitutive components indicated their interdependency during the bauxitization phenomenon. The mineralogical and geochemical properties highlighted the mining interest of the studied duricrusts to be valorized.

Assessing trends in wildland-urban interface fire research through text mining: a comprehensive analysis of published literature

Research on fires at the wildland-urban inter-face(WUI)has generated significant insights and advance-ments across various fields of study.Environmental,agri-culture,and social sciences have played prominent roles in understanding the impacts of fires in the environment,in protecting communities,and addressing management challenges.This study aimed to create a database using a text mining technique for global researchers interested in WUI-projects and highlighting the interest of countries in this field.Author’s-Keywords analysis emphasized the dominance of fire science-related terms,especially related to WUI,and identified keyword clusters related to the WUI fire-risk-assessment-system-“exposure”,“danger”,and“vulnerability”within wildfire research.Trends over the past decade showcase shifting research interests with a growing focus on WUI fires,while regional variations highlighted that the“exposure”keyword cluster received greater atten-tion in the southern Europe and South America.However,vulnerability keywords have relatively a lower representation across all regions.The analysis underscores the interdisci-plinary nature of WUI research and emphasizes the need for targeted approaches to address the unique challenges of the wildland-urban interface.Overall,this study provides valu-able insights for researchers and serves as a foundation for further collaboration in this field through the understanding of the trends over recent years and in different regions.

Study on the Safety and Prevention Technology of Coal Mining under the River in Xingyuan Coal Mine

Coal mining-induced surface subsidence poses significant ecological and infrastructural challenges, necessitating a comprehensive study to ensure safe mining practices, particularly in underwater conditions. This project aims to address the extensive impact of coal mining on the environment, infrastructure, and overall safety, focusing on the Shigong River area above the working face. The study employs qualitative and quantitative analyses, along with on-site engineering measurements, to gather data on crucial parameters such as coal seam characteristics, roof rock lithology, thickness, water resistance, and structural damage degree. The research encompasses a multidisciplinary approach, involving mining, geology, hydrogeology, geophysical exploration, rock mechanics, mine surveying, and computational mathematics. The importance of effective safety measures and prevention techniques is emphasized, laying the foundation for research focused on the Xingyun coal mine. The brief concludes by highlighting the potential economic and social benefits of this project and its contribution to valuable experience for future subsea coal mining.

Land Use and Land Cover Dynamics in the Eseka Alluvial Gold Mining District, Centre Region, Cameroon

Local populations in Cameroon thrive on forest resources and the flow of ecosystem services they provide are pivotal in sustaining national economy, improving people’s lives, safeguarding biodiversity, and mitigating the impacts of environmental changes. The exploitation of these resources invariably leads to deforestation and forest degradation. This study was designed to evaluate land use land cover change (LULCC) in the Eseka alluvial gold mining district with the aid of Landsat images. In the investigation of forest cover change, four Landsat satellite images for (1990, 2002, 2015 and 2022) were used. Ground-truthing also helped to identify the activities carried out by the local population and to determine agents, drivers and pressures of land use and land cover change. Four main land cover classes namely: forest, agricultural land, settlement/mining camps and water bodies were selected. Between 1990 and 2022, the proportion of forest decreased from 98% to 34% while those of agricultural land and settlement/mining camps increased from 2% to 60% and 0.54% to 6% respectively. Analysis showed ongoing deforestation with forest cover loss of ~98,263 ha in 32 years giving a cover change percentage of 63.94%. Kappa coefficient for the study period ranged from 0.92 to 0.99. Forest cover loss could be attributed to farming activities, wood extraction and alluvial gold mining activities. Economic motives notably the need to increase household income from a frequent demand for farm and wood products in neighbouring towns and the quest for gold were the main drivers of these activities. Hence, this study assesses the impact of human activities from the mining sector on the forest ecosystem in a bid to inform mitigation policies.

Experimental investigation of the inhibition of deep-sea mining sediment plumes by polyaluminum chloride

Deep-sea sediment disturbance may occur when collecting polymetallic nodules,resulting in the creation of plumes that could have a negative impact on the ecological environment.This study aims to investigate the potential solution of using polyaluminum chloride(PAC)in the water jet.The effects of PAC are examined through a self-designed simulation system for deep-sea polymetallic nodule collection and sediment samples from a potential deep-sea mining area.The experimental results showed that the optimal PAC dose was found to be 0.75 g/L.Compared with the test conditions without the addition of PAC,the presence of PAC leads to a reduction in volume,lower characteristic turbidity,smaller diffusion velocity,and shorter settling time of the plume.This indicates that PAC inhibits the entire development process of the plume.The addition of PAC leads to the flocculation of mm-sized particles,resulting in the formation of cm-sized flocs.The flocculation of particles decreases the rate of erosion on the seabed by around 30%.This reduction in erosion helps to decrease the formation of plumes.Additionally,when the size of suspended particles increases,it reduces the scale at which they diffuse.Furthermore,the settling velocity of flocs(around 10^(-2) m/s)is much higher that of compared to sediment particles(around 10^(-5) m/s),which effectively reduces the amount of time the plume remains in suspension.