Impact of spatially varying rock disturbance on rock slope stability

Degradation of rock mass produced by rock blasting,stress relief,and other causes is an important factor in the assessment of rock strength.Quantified as a disturbance factor,such degradation varies depending on blasting control,stress state and stress relief,and rock mass quality.This study focuses on the impact of disturbance on the safety of slopes.The disturbance in the rock mass is characterized by the geometry of the disturbed zone,its size,the magnitude,and the decaying rate with the distance away from the slope surface.A method accounting for decay of rock disturbance is presented.A study of the impact of rock disturbance characteristics on the quantitative stability measures of slopes was carried out.These characteristics included disturbed zone geometry,its thickness,the maximum magnitude of the disturbance factor,and the rate of disturbance decaying.The thickness of the disturbed zone and the maximum factor of disturbance were found to have the greatest impact.For example,the factor of safety for a 45slope in low-quality rock mass can decrease from 1.96 to 1.09 as the thickness of the disturbed zone increases from 1/4 of slope height H to the double of H and the maximum disturbance factor increases from 0.5 to 1.Uniform thickness of a disturbed zone was found to yield more conservative outcomes than the triangular zones did.The critical failure surfaces were found to be shallow for high rates of disturbance decay,and they were the deepest for spatially uniform disturbance factors.

Release characteristics and stabilization of heavy metals in antimony tailings in Yunnan Province,China

The pollution caused by the mining and smelting of heavy metals is becoming an increasingly severe environmental problem.In this study,the environmental risks of mine tailings were explored using typical antimony tailings(the depth of the sample taken from the ground to the deepest position of 120 cm)from the Zuoxiguo mine in Yunnan Province,Southwest China.The tailings were examined to explore the geological background,distribution characteristics,and release characteristics of heavy metals.Additionally,stabilizer treatments for heavy metals were investigated in consideration of waste treatment.The results showed that the contents of Sb and As(8.93×103 and 425 mg/kg,respectively)in the tailings were considerably higher than the local soil background values,suggesting that these metals pose a considerable threat to the surrounding environment.The geological background values of Cr,Cd,Pb,Cu,and Zn were relatively low.The results of static release showed that Sb,As,Cd,and Cr leached from the tailings more easily than Cu,Zn,and Pb under acidic conditions(pH=2.98).Geo-accumulation indices and potential ecological risk indices showed that Sb,As,Cd,and Pb were highly enriched in the tailings,whereas Cu,Cr,and Zn contents were relatively low.The single factor ecological risk index of the mining area showed that Sb and As are high ecological risk factors,whereas Cr,Cu,Zn,Cd,and Pb are not.The results of the orthogonal test results showed that by adding 15.0%(m/m)fly ash and 15.0%(m/m)zeolite powder to the quicklime and curing for 28 d,a significant stabilization effect was observed for Sb,As,and Pb.This study helps determine the priority control components for characteristic heavy metals in antimony tailings,and provides valuable insights regarding the formulation of appropriate mitigation strategies.

Electrochemistry and DFT study of galvanic interaction on the surface of monoclinic pyrrhotite(001)and galena(100)

The electrochemical interaction between galena and monoclinic pyrrhotite was investigated to examine its impact on the physical and chemical properties of the mineral micro-surface.This investigation employed techniques such as electrochemistry,metal ion stripping,X-ray photoelectron spectroscopy,and quantum chemistry.The electrochemical test results demonstrate that the galena surface in the electro-couple system exhibits a lower electrostatic potential and higher electrochemical activity compared to the monoclinic pyrrhotite surface,rendering it more susceptible to oxidation dissolution.Monoclinic pyrrhotite significantly amplifies the corrosion rate of the galena surface.Mulliken charge population calculations indicate that electrons are consistently transferred from galena to monoclinic pyrrhotite,with the number of electron transfers on the mineral surface increasing as the interaction distance decreases.The analysis of state density revealed a shift in the surface state density of galena towards lower energy levels,resulting in decreased reactivity and increased difficulty for the reagent to adsorb onto the mineral surface.Conversely,monoclinic pyrrhotite exhibited an opposite trend.The X-ray photoelectron spectroscopy(XPS)test results indicate that galvanic interaction leads to the formation of hydrophilic substances,PbS_(x)O_(y) and Pb(OH)_(2),on the surface of galena.Additionally,the surface of monoclinic pyrrhotite not only adsorbs Pb^(2+)but also undergoes S^(0) formation,thereby augmenting its hydrophobic nature.

Assessing the Efficacy of Wheat-Soybean Based Intercropping System at Different Plant Densities in Bambili, Cameroon

Wheat is one of the most important cereals in the world, serving as a staple for millions globally. In the wake of the geopolitical crisis between Russia and Ukraine, it has become incumbent for many countries to invest in wheat production. Improving cropping systems for wheat production is paramount. Intercropping cereals with legumes has tremendous advantages. Therefore, this study was designed to optimize wheat production by intercropping it with soybean at different densities. Between March and August 2023, a randomized complete block design trial was conducted in Bambili, North West of Cameroon with treatments T1 (wheat monocrop at 200,000 plants ha−1), T2 (soybean monocrop at 250,000 plants ha−1), T3 (200,000 wheat and 125,000 soybean ha−1), T4 (100,000 wheat and 250,000 soybean ha−1), T5 (200,000 wheat and 250,000 soybean ha−1) and T6 (100,000 wheat and 125,000 soybean ha−1). Results revealed that growth parameters of wheat were not significantly influenced by monocrop or intercrop. The yield of wheat was significantly higher in the monocrop than the intercrop treatments, with slight variation amongst the intercrop treatments. Soybean yield was higher in the monocrop than in the intercrop, with no variations amongst the intercrop treatments. Only the land equivalence ratio (LER) for T5 was greater than 1.0. The competitive ratio for T5 was 0.54 for wheat and 1.90 for soybean, comparatively lower than the other monocrop treatments. Intercropping wheat and soybean at 200,000:250,000 ratio is recommended.

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and AD.However,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)individuals.This paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and NC.To improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized VGG-16.We performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed models.Our experimental results demonstrate superior performance in classifying the three binary classification tasks:NC vs.AD,NC vs.MCI,and MCI vs.AD.Specifically,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,respectively.Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM scans.Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

Physico-Chemical Properties of Soils from Diguel and Koudalwa Tchad,and Phytoremediation Potentials of Acasia and Kinkeliba Plants in Removing Cr and Cu from These Soils

The aim of this study to investigate the physico-chemical properties of soils from Diguel and Koudalwa Tchad,and phytoremediation potentials of acasia and kinkeliba plants in removing Cr and Cu from these soils.Soil was sampled from the tannery plant in Diguel and agricultural land around crude oil activities in Koudalwa.Soils were characterized by the following properties:pH,(Electrical Conductivity),TDS(Total Dissolved Solids),salinity,bulk density,OM(Organic Matter),nitrate,phosphate,clay,silt,sand,and textural class.The acasia and kinkeliba plants were used to decontaminate Cu and Cr from sample soils for 30 days period.The bulk density of all the soil samples from Kouldawa was higher compared to Diguil,ranging from 1.421 to 1.64 g/cm^(3)compared to 1.21 to 1.51 g/cm^(3)for Diguel.Most of the soils in Kouldawa are mineral soils,while those of Diguel are mostly mineral soil with some organics.Diguel soils are richer in nitrate(577.9 to 2,687.32 mg/kg in Diguel and 33.64 to 197.64 mg/kg in Kouldawa)and phosphate than soils from Kouldawa.Soils from the studied areas are not saline except for a subsurface soil in Diguel(salinity,4.21;EC,4.280 dS/m;TDS,3,040 mg/L).The highest amount of Cr and Cu removed by acasia is 78%and 53.12%compared to 73.8%and 52.21%by kinkeliba.Diguel soil can be very suitable for agriculture and Kouldawa soils for construction.Acasia and kinkeliba can be effectively used to decontaminate the studied soils from heavy metal pollution.

Tigris River Water Quality Quantifying Using the Iraq Water Quality Index (IraqWQI) and Some Statistical Techniques

Evaluation of water quality is important for the management of water resources. The current study is focused on the interpretation of the water quality monitoring data of the Tigris River in Iraq by the application of the principal component analysis (PCA), cluster analysis (CA), and water quality index (WQI). Twelve water quality parameters were taken from 14 stations along the river Ca2+, Mg2+, Na+, K+, Cl−, SO42−, HCO3−, NO3−, TH, TDS, BOD5, and EC to apply the PCA and CA. The results show that the mean of all the parameters was under the standards except Ca2+, EC, Mg2+, TH, and SO42−. The amount of EC is the critical factor that affects the river water quality. The PCA obtained one principal component responsible for 97% of the variation caused by different pollution sources. The CA divided the river into three regions of sampling stations with similar water quality, the best in the north, and the worst in the far south. In this paper, the computer-automated tool (IraqWQI) was presented and evaluated, which has been developed by authors to classify and measure the quality of Iraqi surface water. The proposed index is of hundred degrees and includes six variables for drinking water quality Cl−, TH, TDS, COD, DO, and total coliform (TC) according to the Iraqi specifications. The result of the IraqWQI application showed a decrease in the water quality of the river and its suitability for drinking in the south of the country. The best value of the index was (81.48, Good) in Fishkhabour during winter, and the worst value was (46.23, Bad) in Qurnah during summer. The result of this study proved the success and importance of using statistical techniques and WQI as useful tools for the management, control, and conservation of surface water. .

Scoping biology-inspired chemical engineering

Biology is a rich source of great ideas that can inspire us to find successful ways to solve the challenging problems in engineering practices including those in the chemical industry. Bio-inspired chemical engineering(Bio Ch E)may be recognized as a significant branch of chemical engineering. It may consist of, but not limited to, the following three aspects: 1) Chemical engineering principles and unit operations in biological systems; 2) Process engineering principles for producing existing or developing new chemical products through living ‘devices';and 3) Chemical engineering processes and equipment that are designed and constructed through mimicking(does not have to reproduce one hundred percent) the biological systems including their physical–chemical and mechanical structures to deliver uniquely beneficial performances. This may also include the bio-inspired sensors for process monitoring. In this paper, the above aspects are defined and discussed which establishes the scope of BioChE.

The Effect of Training and Community Based Management on Earthquake Impacts on Environmental Resources

Disasters have been defined as ecological disorders or emergencies, which lead to disease incidence, damage or injury and loss of property. Iran is located in a region prone to earthquake hazard and has faced several tragic earthquakes in its history which have led to mortalities and destruction of natural and socio-economic resources. Several researches have shown the positive effect of training in decreasing negative impacts of disasters. In societies where people are properly trained and obtain necessary information about how to react in case of earthquake or other natural disasters, the rate of fatalities would be significantly decreased. Earthquake is not a preventable natural disaster. So the only way to decrease its negative consequences would be effective preparedness. Public training is the main way of enhancing the awareness to the whole society and is the core of disaster planning. In this research, the aim is to evaluate the effect of training on the knowledge and practice of people vulnerable to earthquakes.

Research on the Transformation of Teaching and Research Form of Professional Teachers in Blended Learning at Colleges and Universities - Taking the Java Programming Course as an Example

In view of the current situation that offline teaching is the main mode of teaching Java Programming in higher vocational schools,this paper introduces the online and offline hybrid teaching method and expounds it from the aspects of blended learning design,teaching organization,and implementation.At the same time,combined with the characteristics of blended learning,this paper proposes that under the new mode,teachers should actively change the form of teaching and research,the teaching mode,and the role of teachers,take students as the center,and build an independent and effective classroom.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Predicting pavement condition index based on the utilization of machine learning techniques:A case study

Pavement management systems(PMS)are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost.To accomplish this objective,the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate time.The pavement condition index(PCI)is a commonly usedmetric to evaluate the pavement's performance.This research aims to create and evaluate prediction models for PCI values using multiple linear regression(MLR),artificial neural networks(ANN),and fuzzy logic inference(FIS)models for flexible pavement sections.The authors collected field data spans for 2018 and 2021.Eight pavement distress factors were considered inputs for predicting PCI values,such as rutting,fatigue cracking,block cracking,longitudinal cracking,transverse cracking,patching,potholes,and delamination.This study evaluates the performance of the three techniques based on the coefficient of determination,root mean squared error(RMSE),and mean absolute error(MAE).The results show that the R2 values of the ANN models increased by 51.32%,2.02%,36.55%,and 3.02%compared toMLR and FIS(2018 and 2021).The error in the PCI values predicted by the ANNmodel was significantly lower than the errors in the prediction by the FIS and MLR models.

Rheological and Morphological Characterization of Modified Bitumen with Cup Lump Rubber

Pure bitumen is not suitable for heavy traffic loads;hence modifiers are used to improve the bitumen performance.Recently,cup lump rubber(CLR)has become a preferred modifier due to its outstanding performance and less cost.However,little is known about the interactions between CLR and bitumen.Thus,this study investigates the behavior of bitumen with CLR.Four percentages of CLR(2.5%,5.0%,7.5%,and 10.0%by weight of bitumen)were used to modify conventional 60/70 penetration grade bitumen.The modified bitumen was evaluated through different laboratory testing such as dynamic shear rheometer,rotational viscosity,softening point,bending beam rheometer,ductility,and elastic recovery.The testing results show that the addition of CLR increased the bitumen’s rutting resistance by 3 PG grades at high temperatures.At low pavement temperatures,the cup lump rubber modified bitumen(CMB)can withstand up to−34℃.Fourier Transform Infrared(FTIR)analysis shows that the Aromaticity index at 1600 cm^(−1) rose as the CLR percentage increased,indicating the formation of a binder with a compact structure.This is expected to improve the elasticity of bitumen throughπ-πinteractions.Atomic Force Microscopy(AFM)results showed the Catana phase increased in size and quantity at 5.0%and 7.5%CLR content.While contact angle measurement revealed that the binders are hydrophobic and tend to repel the dropped water on the bitumen surface.

Techniques and Technological Aspects of Palm Oil (Elaeis guineensis) Processing in Cameroon

Crude palm Oil (CPO) processing is very popular in African Countries. In Cameroon, various actors are involved ranging from agro-industrial complexes to traditional processing by smallholders who use very inefficient equipment and thus have low oil extraction rates. Small-scale processing by smallholders dates back to the early 1980s and has witnessed a lot of changes as a result of new actors in the sector, changing technologies and to an extent, government policies. This paper attempts a review of the origins and evolution of small-scale palm oil processing using intermediate technology, highlighting its importance to both the farmer and the national economy. An attempt is made to look into the future of this activity, with proposals for its consolidation.

Production and Evaluation of the Nutritional and Functional Qualities of “Adakwa” Enriched with Waste Biomass of Traditional Brewer’s Spent Grain as a Functional Staple Food

Brewers’ spent grains constitute a nutrient-rich valuable and highly under-utilized by-product of the beer industry produced in large amounts all through the year. This bio-resource is a very good candidate for valorization, due to environmental and economic concerns, using biotechnological processing, particularly for food enrichment. This study was undertaken to evaluate the effect of fortification of Adakwa with traditional brewers’ spent grains (TBSG) on its physicochemical and nutritional properties as well as its acceptability using an experimental design. Four (4) samples of Adakwa were produced with TBSG incorporated rates of 0% (control sample), 10%, 20%, and 30% and evaluated. Using an experimental design, the effect of process parameters, including the TBSG incorporation rate, cooking time, and cooking temperatures on the physicochemical and nutritional properties of the Adakwa were evaluated while the 9-point hedonic scale was used to evaluate the sensory properties and its overall acceptability: carbohydrate, protein, crude fibre, cellulose, polyphenol, antioxidant activity (FRAP and DPPH). The water absorption activity values were 81.2 ± 0.04, 4.55 ± 0.05, 9.73 ± 0.23, 3.31 ± 0.05, 6.73 ± 0.23, 1.60 ± 0.09, 28.85 ± 0.8 and 117 ± 3.54 respectively for 0% TBSG (control);86.8 ± 0.01, 1.81± 0.20, 16.22 ± 0.16, 5.54 ± 0.69, 6.01 ± 0.16, 6.59 ± 0.03, 25.89 ± 0.94 and 475.0 ± 21.21 respectively for 30% TBSG. The sample with a high nutrient content was further produced using a central composite design and the factors studied were temperature and time, with responses, crude fiber, and FRAP content. The optimum production condition was: % TBSG: 47.06%;temperature: 123.17℃;Time: 30.34 mins. The sample with 47% TBSG had the best overall acceptability after sensory evaluation with sensory scores of: 5.45 ± 0.76, 7.9 ± 0.79, 8.0 ± 1.0, 7.10 ± 0.16, 8.5 ± 1.6, 7.6 ± 0.98 for color, taste, aroma, mouthfeel, texture and overall acceptability respectively. Thus TBSG can be used to improve the physicochemical and nutritional properties of adakwa.

An Assessment and Geostatistics of Land-Use and Selected Physico-Chemical Properties of Soils in the Mount Cameroon Area

This work investigated the land-use/land-cover and some physico-chemical properties of the soils of Mt Cameroon and presented same in maps. ArcGIS Pro mapping software, Landsat images, Global Positioning Systems (GPS) coordinates collected from the field combined with updated shape files from competent services were used to produce the location and land-use/land-cover maps. Sixteen topsoil samples (0 - 20 cm) were collected, 4 from each land use/cover category: farmland, forest, plantation and settlement, and analysed for soil pH, cation exchange capacity (CEC), bulk density, moisture content and soil texture, in the laboratory using standard analytical procedures. This data was used to produce spatial distribution maps using ordinary kriging, in ArcGIS Pro. The main terrestrial land use/cover categories comprised of the forest (mangrove, lowland, montane and sub-montane), agroforestry, plantations, grassland, settlement, cropland, shrubby savannah, and bare lava. Bulk density showed the highest values in settlement areas and least values under forest land-use categories. Soil moisture content exhibited a reverse trend compared to that of soil bulk density. Forest soils were the sandiest while soils in plantation agricultural land were the most clayey. The soils were slightly acidic to neutral with soils from agricultural land being more acidic (pH(water) = 5.43). It is discernible from the results that the conversion from forest to other land use/cover classes enhances soil degradation and that soil physico-chemical properties adequately serve as indicators of soil quality in the Mt Cameroon area.

Nitrogen interception in floodwater of rice field in Taihu region of China

A field experiment located in Taihu Lake Basin of China was conducted, by application of urea or a mixture of urea with manure, to elucidate the interception of nitrogen （N） export in a typical rice field through ＂zero-drainage water management＂ combined with sound irrigation, rainfall forecasting and field drying. N concentrations in floodwater rapidly declined before the first event of field drying after three split fertilizations, and subsequently tended to rearm to the background level. Before the first field drying, total particulate nitrogen （TPN） was the predominant N form in floodwater of plots with no N input, dissolved inorganic nitrogen （DIN） on plots that received urea only, and dissolved organic nitrogen （DON） on plots treated with the mixture of urea and manure. Thereafter TPN became the major form. No N export was found from the rice field, but total nitrogen （TN） of 15.8 kg/hm^2 was remained, mainly due to soil N sorption. The results recommended the zero-drainage water management for full-scale areas for minimizing N export.

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.

Ionic liquid based dispersive liquid-liquid microextraction of aromatic amines in water samples

In this work, a new microextraction method termed ionic liquid based dispersive liquid-liquid microextraction （IL-DLLME） was demonstrated for the extraction of 2-methylaniline, 4-chloroaniline, 1-naphthylamine and 4-aminobiphenyl in aqueous matrices. After extraction the ionic liquid （IL） phase was injected directly into the high performance liquid chromatography （HPLC） system for determination. Some parameters that might affect the extraction efficiency were optimized. Under the optimum conditions, good linear relationship, sensitivity and reproducibility were obtained. The limits of detection （LOD, S/N = 3） for the four analytes were in the range of 0.45-2.6 μg L^-1. The relative standard deviations （R.S.D., n = 6） were in the range of 6.2-9.8%. This method was applied for the analysis of the real water samples. The recoveries ranged from 93.4 to 106.4%. The main advantages of the method are high speed, high recovery, good repeatability and volatile organic solvent-free.

Discovery and Analysis of Shale Gas in a Carboniferous Reservoir and its Enrichment Characteristics in the Northern Nanpanjiang Depression, Guizhou Province, China

Shale gas resources are considered to be extremely abundant in southern China,which has dedicated considerable attention to shale gas exploration in recent years.Exploration of shale gas has considerably progressed and several breakthroughs have been made in China.However,shale gas explorations are still scarce.Summary and detailed analysis studies on black shale reservoirs are still to be performed for many areas.This lack of information slows the progress of shale gas explorations and results in low quantities of stored black shale.The Carboniferous Dawuba Formation,which is widely distributed and considerably thick,is one of the black shale formations targeted for shale gas exploration in southern China in the recent years.The acquisition and analysis of total organic carbon,vitrinite reflectance,types of organic matter,mineral composition,porosity,and permeability are basic but important processes.In addition,we analyzed the microscopic pores present in the shale.This study also showesd the good gas content of the Dawuba Formation,as well as the geological factors affecting its gas content and other characteristics.To understand the prospect of exploration,we compared this with other shale reservoirs which have been already successfully explored for gas.Our comparison showesd that those shale reservoirs have similar but not identical geological characteristics.