Spatial Patterns of Car Sales and Their Socio-economic Attributes in China

Using data from the Economic Advisory Center of the State Information Center(SIC), we examined the spatial patterns of car sales in China at the prefectural level in 2012. We first analyzed the spatial distributions of car sales of different kinds of automakers(foreign automakers, Sino-foreign joint automakers, and Chinese automakers), and then identified spatial clusters using the local Moran's indexes. Location quotient analysis was applied to examine the relative advantage of each type of automaker in the local markets. To explain the variations of car sales across cities, we collected several socioeconomic variables and conducted regression analyses. Further, factor analysis was used to extract independent variables to avoid the problem of multicollinearity. By incorporating a spatial lag or spatial error in the models, we calibrated our spatial regression models to address the spatial dependence problem. The analytical results show that car sales varied significantly across cities in China, and most of the cities with higher car sales were the developed cities. Different automakers exhibit diverse spatial patterns in terms of car sales volume, spatial clusters, and location quotients. The scale and incomes factor were extracted and verified as the two most significant and positive factors that shape the spatial distributions of car sales, and together with the spatial effect, explained most of the variations of car sales across cities.

Machine learning for carbonate formation drilling: Mud loss prediction using seismic attributes and mud loss records

Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.

Aboveground biomass stocks of species-rich natural forests in southern China are influenced by stand structural attributes,species richness and precipitation

Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biomass,their influence on biomass stocks in species-rich forests in southern China,a biodiversity hotspot,has rarely been investigated.In this study,we characterized the effects of environmental factors,forest structure,and species diversity on aboveground biomass stocks of 30 plots(1 ha each) in natural forests located within seven nature reserves distributed across subtropical and marginal tropical zones in Guangxi,China.Our results indicate that forest aboveground biomass stocks in this region are lower than those in mature tropical and subtropical forests in other regions.Furthermore,we found that aboveground biomass was positively correlated with stand age,mean annual precipitation,elevation,structural attributes and species richness,although not with species evenness.When we compared stands with the same basal area,we found that aboveground biomass stock was higher in communities with a higher coefficient of variation of diameter at breast height.These findings highlight the importance of maintaining forest structural diversity and species richness to promote aboveground biomass accumulation and reveal the potential impacts of precipitation changes resulting from climate warming on the ecosystem services of subtropical and northern tropical forests in China.Notably,many natural forests in southern China are not fully stocked.Therefore,their continued growth will increase their carbon storage over time.

A risk assessment method considering risk attributes and work safety informational needs and its application

The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility.

National Social Investment Programmes in Nigeria:A Descriptive Analysis of the Socio-Economic Contributions of N-Power Programme in Benue State

This study analysed the socio-economic contributions of N-Power programme amongst the beneficiaries of the scheme in Benue State.Prior to the introduction of N-Power programme,successive administrations in Nigeria have made concerted efforts towards improving the standard of living of the citizenry through the execution of various welfare or social intervention programmes,but not much successes were recorded.Learning from the mistakes of the past regimes,and by way of deliberate state policy,the Buhari’s government initiated a multi-pronged social investment policy,one of which is the N-power programme that came onboard in 2016,which also doubles as the subject of this study.To achieve the goal of this study,a combination of desktop research and survey design was employed.Questionnaires were administered to 390 respondents through a combination of stratified and random sampling techniques.The results of the survey were matched with that of the secondary data obtained through online websites and other related sources.The result indicated that N-Power made positive contributions to the socio-economic life of the beneficiaries in Benue State:specifically,the scheme contributed in poverty eradication,employment generation,skills acquisitions and capacity building.However,some aspect of our findings revealed that the programme has a number of challenges such as:inadequate cash support,delay in monthly cash transfer to beneficiaries,distance participants had to move to their work stations,absence of posting in N-Teach scheme,and lack of adequate working tools amongst others.To salvage this problem the paper recommended the following solutions:expansion of the scheme to cover N-Teach and other aspects,increment in the monthly cash transfer to cushion the high rate of inflation,support for the participants/beneficiaries in transportation and logistics,enrolment of more youth into the various schemes,proper monitoring and evaluation of the implementation of the schemes amongst others.

Attributes of Domestic Spaces for Contemporary Habitation-A Secondary Publication

The domestic space can be defined as the sphere that articulates the needs for subjective containment and contextual stimuli.In this sense,questions arise about the indispensable attributes that spaces must possess for this articulation to take place adequately.Architecture,as the discipline in charge of satisfying the specific spatial needs of those who inhabit these spaces and,in a broader sense,as a concrete contribution to society,must address this relationship in all its complexity and generate concrete responses that incorporate the appropriate spatial attributes during the design processes.The design processes that shape living spaces confront this dialectic,and the manner in which they do so brings identity and character to them.It is believed that the higher the level of variables that are contemplated and weighted,the greater the adequacy of spaces to the changing dynamics of the people who inhabit them.This article focuses on a thorough analysis of these spatial attributes,in parallel to the definition of each one as a particular condition for design,based on their conceptualization,breakdown,and articulation.Conceptually,the following attributes are addressed:flexibility,adaptability,variability,versatility,multiplicity,plurality,integrality,gradualness,incrementality,progressiveness,independence,connectivity,intimacy,and privacy.Each of these attributes is valued as a contribution to creating adequate habitability in contextual terms,with consideration to possible integrations and combinations.

Quantitative evaluation of gas hydrate reservoir by AVO attributes analysis based on the Brekhovskikh equation

AVO (Amplitude variation with offset) technology is widely used in gas hydrate research. BSR (Bottom simulating reflector), caused by the huge difference in wave impedance between the hydrate reservoir and the underlying free gas reservoir, is the bottom boundary mark of the hydrate reservoir. Analyzing the AVO attributes of BSR can evaluate hydrate reservoirs. However, the Zoeppritz equation which is the theoretical basis of conventional AVO technology has inherent problems: the Zoeppritz equation does not consider the influence of thin layer thickness on reflection coefficients;the approximation of the Zoeppritz equation assumes that the difference of wave impedance between the two sides of the interface is small. These assumptions are not consistent with the occurrence characteristics of natural gas hydrate. The Brekhovskikh equation, which is more suitable for thin-layer reflection coefficient calculation, is used as the theoretical basis for AVO analysis. The reflection coefficients calculated by the Brekhovskikh equation are complex numbers with phase angles. Therefore, attributes of the reflection coefficient and its phase angle changing with offset are used to analyze the hydrate reservoir's porosity, saturation, and thickness. Finally, the random forest algorithm is used to predict the reservoir porosity, hydrate saturation, and thickness of the hydrate reservoir. In the synthetic data, the inversion results based on the four attributes of the Brekhovskikh equation are better than the conventional inversion results based on the two attributes of Zoeppritz, and the thickness can be accurately predicted. The proposed method also achieves good results in the application of Blake Ridge data. According to the method proposed in this paper, the hydrate reservoir in the area has a high porosity (more than 50%), and a medium saturation (between 10% and 20%). The thickness is mainly between 200m and 300m. It is consistent with the previous results obtained by velocity analysis.

Modelling of Active and Latent Attributes Based on Traveler Perspectives: Case of Port City of Douala

A growing stream of study stresses the relevance of subjective elements in understanding the hierarchy of preferences that underpin individual travel behavior. The purpose of this study is to evaluate the impact of various factors on mode choice. To achieve this, a multinomial logit model (MNL) was used to analyze the relationships between mode choice and three classes of attributes;Combined Active and Latent, Active only and Latent only attributes. The data used are derived from surveys in the port city of Douala, Cameroon as a case study. Results stipulated that, the combined attributes model performed better than both active only attributes and latent only attributes models. Likewise, latent only attributes model performed better than active only attributes model. The advantage of modelling all three groups is for better selection of the most relevant attributes, and this is very relevant in understanding travel behavior of individuals and mode choice decisions.

Socio-Economic and Technical Characteristics of Beef Cattle Breeding in Ndé Division, West Region of Cameroon

Insight is here provided into the socio-economic and technical characteristics of cattle production in the Ndé Division (West Cameroon region). Using a semi-structured questionnaire, information on the activity was assessed following onsite visits and interviewing breeders. Through a random sampling scheme, 110 breeders in the four subdivisions of the Ndé Division were shadowed. The majority (98.18%) of breeders were men aged 20 to 40 and married (91.82%). They belong to the Mbororo ethnic group, having cumulated more than 10 years in the activity. Most respondents (50.91%) did not attend school and earn their living mainly from livestock (78.18%). Cattle were raised for sale and to feed the breeder’s family (77.27%). The main mode of acquisition of animals was through inheritance (81.82%). The cattle breeds were mostly made up of white Fulani (70%), living in private farms using a combination of stake and barbed wires (67.27%) as fences. The combination of natural fodder and cooking salt was used by most breeders (59.09%) as a daily ration. Reproduction was by natural mating (97.07%). Parasites (internal and external) and foot-and-mouth disease were the main diseases reported by the majority of farmers. Self-treatment (50%) was the main prophylactic measure taken by farmers in case of disease symptoms. Cattle herders faced several challenges, such as insufficient pasture (67.27%), agro-pastoral conflicts (76.36%) and diseases (90.91%). Cattle breeding was rather a widespread activity with no particular restriction.

Classifying Big Medical Data through Bootstrap Decision Forest Using Penalizing Attributes

Decision forest is a well-renowned machine learning technique to address the detection and prediction problems related to clinical data.But,the tra-ditional decision forest(DF)algorithms have lower classification accuracy and cannot handle high-dimensional feature space effectively.In this work,we pro-pose a bootstrap decision forest using penalizing attributes(BFPA)algorithm to predict heart disease with higher accuracy.This work integrates a significance-based attribute selection(SAS)algorithm with the BFPA classifier to improve the performance of the diagnostic system in identifying cardiac illness.The pro-posed SAS algorithm is used to determine the correlation among attributes and to select the optimum subset of feature space for learning and testing processes.BFPA selects the optimal number of learning and testing data points as well as the density of trees in the forest to realize higher prediction accuracy in classifying imbalanced datasets effectively.The effectiveness of the developed classifier is cautiously verified on the real-world database(i.e.,Heart disease dataset from UCI repository)by relating its enactment with many advanced approaches with respect to the accuracy,sensitivity,specificity,precision,and intersection over-union(IoU).The empirical results demonstrate that the intended classification approach outdoes other approaches with superior enactment regarding the accu-racy,precision,sensitivity,specificity,and IoU of 94.7%,99.2%,90.1%,91.1%,and 90.4%,correspondingly.Additionally,we carry out Wilcoxon’s rank-sum test to determine whether our proposed classifier with feature selection method enables a noteworthy enhancement related to other classifiers or not.From the experimental results,we can conclude that the integration of SAS and BFPA outperforms other classifiers recently reported in the literature.

Structural attributes,evolution and petroleum geological significances of the Tongnan negative structure in the central Sichuan Basin,SW China

The Tongnan secondary negative structure in central Sichuan Basin has controls and influences on the structural framework and petroleum geological conditions in the Gaoshiti-Moxi area.To clarify the controls and influences,the deformation characteristics,structural attributes and evolution process of the Tongnan negative structure were investigated through a series of qualitative and quantitative methods such as balanced profile restoration,area-depth-strain(ADS)analysis,and structural geometric forward numerical simulation,after comprehensive structural interpretation of high-precision 3D seismic data.The results are obtained in three aspects.First,above and below the P/AnP(Permian/pre-Permian)unconformity,the Tongnan negative structure demonstrates vertical differential structural deformation.It experiences two stages of structural stacking and reworking:extensional depression(from the Sinian Dengying Formation to the Permian),and compressional syncline deformation(after the Jurassic).The multi-phase trishear deformation of the preexisting deep normal faults dominated the extensional depression.The primary depression episodes occurred in the periods from the end of Late Proterozoic to the deposition of the 1st–2nd members of the Dengying Formation,and from the deposition of Lower Cambrian Longwangmiao Formation–Middle–Upper Cambrian until the Ordovician.Second,the multi-stage evolution process of the Tongnan negative structure controlled the oil and gas migration and adjustment and present-day differential gas and water distribution between the Tongnan negative structure and the Gaoshiti and Moxi-Longnüsi structural highs.Third,the Ordovician,which is limitedly distributed in the Tongnan negative structure and is truncated by the P/AnP unconformity on the top,has basic geological conditions for the formation of weathering karst carbonate reservoirs.It is a new petroleum target deserving attention.

Profiling the effects of microwave-assisted and soxhlet extraction techniques on the physicochemical attributes of Moringa oleifera seed oil and proteins

There is a constant search for biomaterials from natural products like plants for food and industrial applications.The work embodied in this report aimed at investigating the effects of microwave-assisted and soxhlet extraction(MAE and SE) techniques on the functional physicochemical quality characteristics of Moringa oleifera seed oil and proteins extracts. M. oleifera seeds were ground to fine powders and oil was extracted by microwave-assisted and soxhlet extraction techniques using petroleum ether. Quality attributes including yield percent, moisture content,iodine, saponification, specific gravity, viscosity, p H, thiobarbituric acid, acid and peroxide values were measured. Mineral and vitamin contents, chemical/functional groups, fatty acid(FA) composition, and reducing power of the oil were evaluated. Metabolomics of protein extracted from the defatted powders were analyzed by nuclear magnetic resonance(NMR). M. oleifera oil from MAE and SE methods had good yield(34.25 ± 0.0%,28.75 ± 0.0%), low moisture content(0.008 ± 0.0%, 0.011 ± 0.0%), non-drying and unsaturated, moderately saponified, less dense(0.91 ± 0.01, 0.92 ± 0.02 g m L^(-1)), had Newtonian flow, were weakly acidic, showed good content of FAs, recorded strong potential for long shelf-life, showed stability against oxidative rancidity and enzymatic hydrolysis, had very rich deposits of micro-and macro-nutrients as well as water-soluble and lipidsoluble vitamins, and functional groups in the oil were reflective of its content of long-and medium-chain triglycerides(LCT and MCT). Monounsaturated and saturated fatty acids(MUFA and SFA) were detected and the oil has excellent ferric ion reducing power. NMR metabolomic assay revealed the presence of nine essential amino acids(EAAs) in the protein extract. MAE technique is a feasible and acceptable alternative for high throughput extraction of M. oleifera oil with high yield and excellent quality attributes. The study revealed that MAE did not impart any remarkable advantage(s) on the physicochemical properties of M. oleifera seed oil and protein compared to SE technique.

Socio-Economic Characteristics of Three Artisanal Fisheries Communities in the Northern Sudanese Red Sea Coast

Socio-economic characteristics related to fishing gears and boats from three selected small-scale fishers’ communities (Oseif, Dungonab and Mohammed Qol) in the northern part of the Sudanese Red Sea coast were studied. The study was designed to provide base line data and information on the current socio-economic situation, factors that determine the use of fishing gear, types of boats and fishing equipment, challenges facing fishers and the extent to which fishing gear and boats are related with the socio-economic characteristics of the fishers community in the study sites. Fieldwork was carried out during 2016. The study methods included a series of interviews based on questionnaires, field observations and focus group discussions. The results of the study showed that the most common fishing gears are handlines, cast nets and gillnets. The two types of boats used at the study sites are motorized fiberglass and houri (motorized and paddled) with a V-shaped hull. Boat ownership varies among fishers. Each fisher can have several types of gears in different numbers. Target fish species, season, efficiency of gear and fishing location were the main factors determining the use of fishing gear at study sites, respectively. All fishers are men and most have families of at least four people. The majority of fishers are between 21 and 50 years old and work mainly in fishing. They inherited fishing knowledge within the family, and most of them have been working in artisanal fishing for more than 10 years. Fishers have relatively good net incomes compared to other government employers in the study sites. Most fishers work full time in fishing. A small number of fishers work part-time with the possibility of undertaking other income-generating activities such as animal husbandry and seasonal agriculture. The main challenges identified by fishers at the study sites were fish prices, sea surface roughness and high cost of fishing materials, respectively. Most fishers perceived a recent decline in both catch size and abundance.

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

Not in Control,but Liable?Attributing Human Responsibility for Fully Automated Vehicle Accidents

Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities.For instance,human occupants are passengers and do not have direct vehicle control in fully automated cars(i.e.,driverless cars).An interesting question is whether users are responsible for the accidents of these cars.Normative ethical and legal analyses frequently argue that individuals should not bear responsibility for harm beyond their control.Here,we consider human judgment of responsibility for accidents involving fully automated cars through three studies with seven experiments(N=2668).We compared the responsibility attributed to the occupants in three conditions:an owner in his private fully automated car,a passenger in a driverless robotaxi,and a passenger in a conventional taxi,where none of these three occupants have direct vehicle control over the involved vehicles that cause identical pedestrian injury.In contrast to normative analyses,we show that the occupants of driverless cars(private cars and robotaxis)are attributed more responsibility than conventional taxi passengers.This dilemma is robust across different contexts(e.g.,participants from China vs the Republic of Korea,participants with first-vs third-person perspectives,and occupant presence vs absence).Furthermore,we observe that this is not due to the perception that these occupants have greater control over driving but because they are more expected to foresee the potential consequences of using driverless cars.Our findings suggest that when driverless vehicles(private cars and taxis)cause harm,their users may face more social pressure,which public discourse and legal regulations should manage appropriately.

Attribute Reduction Method Based on Sequential Three-Branch Decision Model

Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

Attribute Reduction of Neighborhood Rough Set Based on Discernment

For neighborhood rough set attribute reduction algorithms based on dependency degree,a neighborhood computation method incorporating attribute weight values and a neighborhood rough set attribute reduction algorithm using discernment as the heuristic information was proposed.The reduction algorithm comprehensively considers the dependency degree and neighborhood granulation degree of attributes,allowing for a more accurate measurement of the importance degrees of attributes.Example analyses and experimental results demonstrate the feasibility and effectiveness of the algorithm.

The Location Differences of Attributes in EST Translation

There are similarities and differences about the order of the attributes between English sentences and Chinese sentenc es. As a result, mistakes can often be observed; those mistakes are the results of unfamiliarity with the proper order of the attri butes. But here, special attention is paid to the EST(English for Science and Technology) translation in which their differences are much more distinct. Generally speaking, the attributes in English can be divided into pre-attributes and post-attributes in terms of their positions in a sentence. But in Chinese an attribute is usually put before a noun, and rarely put after it, so several simple comparisons are tried to find out some rules to translate English attributes into Chinese.

A Look at Contrastive Linguistics--Differences and Similarities between English Attributes and Chinese Attributes

Contrastive linguistics is a branch of linguistics which mainly involves contrast or comparison,and it can leave us some useful insights into our problems,especially for translation work.This paper discusses similarities and differences between English attributes and Chinese attributes from the perspective of the location,the composition,and the function,for the purpose of presenting a sound version for the original text.