例谈“问题学生”的转化

一直以来,对于"问题学生"的教育和管理是学校和班主任较为棘手的问题。主要对两则典型"问题学生"案例进行了细致的阐述,并基于两则案例的分析,从侧面记录了一位班主任的成长历程。最后总结了转化"问题学生"的积极措施。

Coal mining in northeast India： an overview of environmental issues and treatment approaches

Northeast India has a good deposit of sub-bituminous tertiary coal. The northeast Indian coals have unusual physico-chemical characteristics such as high sulfur, volatile matter and vitrinite content, and low ash content. In addition, many environmental sensitive organic and mineral bound elements such as Fe, Mg, Bi, AI, V, Cu, Cd, Ni, Pb, and Mn etc. remain enriched in these coals. Such characteristics are associated with more severe environmental impacts due to mining and its utilization in coal based industries. Environmental challenges include large scale landscape damage, soil erosion, loss of forest ecosystem and wildlife habitat, air, water and soil pollution. Several physical and chemical methods are reported in literature for the removal of mineral matter, total sulfur and different forms of sulfur from high sulfur coal in northeast India. This paper may help different researchers and stakeholders to understand current state of research in the field. Initiatives may be taken towards sustainable use of coal resources by adopting innovative clean technologies and by implementing effective control measures and regulatory policies.

Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems

Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution （DE） algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the con- vergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model oarameters for a comolex mathematical model.

Effectiveness of Computer Assisted Problem based Learning in Chemistry for Enhancing Thinking Skills among Secondary School Students

Experiencing problems that challenges science and the thought, habits of mind and actions associated with trying to solve them is one of the best ways of learning Chemistry. This impels opportunities for authentic, inquiry based learning. PBL （problem based learning） is a student centered pedagogy in which students learn about a subject through the experience of problem solving. The goals of PBL are to help the students develop flexible knowledge, effective problem solving skills, self-directed learning, effective collaborative skills and intrinsic motivation. CAPBL （computer assisted problem based learning） has been created to expand the boundaries of the creativity and the creativity of students beyond the four walls of the classroom. In the present study the investigator tested the effectiveness of the CAPBL material in Chemistry for enhancing thinking skills among secondary school students. The sample consisted of 100 secondary school students. The study revealed that the CAPBL material in Chemistry is effective in enhancing thinking skills such as critical thinking, creativity and problem solving.

Dolphin swarm algorithm

By adopting the distributed problem-solving strategy, swarm intelligence algorithms have been successfully applied to many optimization problems that are difficult to deal with using traditional methods. At present, there are many well-implemented algorithms, such as particle swarm optimization, genetic algorithm, artificial bee colony algorithm, and ant colony optimization. These algorithms have already shown favorable performances. However, with the objects becoming increasingly complex, it is becoming gradually more difficult for these algorithms to meet human＇s demand in terms of accuracy and time. Designing a new algorithm to seek better solutions for optimization problems is becoming increasingly essential. Dolphins have many noteworthy biological characteristics and living habits such as echolocation, information exchanges, cooperation, and division of labor. Combining these biological characteristics and living habits with swarm intelligence and bringing them into optimization problems, we propose a brand new algorithm named the ‘dolphin swarm algorithm＇ in this paper. We also provide the definitions of the algorithm and specific descriptions of the four pivotal phases in the algorithm, which are the search phase, call phase, reception phase, and predation phase. Ten benchmark functions with different properties are tested using the dolphin swarm algorithm, particle swarm optimization, genetic algorithm, and artificial bee colony algorithm. The convergence rates and benchmark function results of these four algorithms are compared to testify the effect of the dolphin swarm algorithm. The results show that in most cases, the dolphin swarm algorithm performs better. The dolphin swarm algorithm possesses some great features, such as first-slow-then-fast convergence, periodic convergence, local-optimum-free, and no specific demand on benchmark functions. Moreover, the dolphin swarm algorithm is particularly appropriate to optimization problems, with more calls of fitness functions and fewer individuals.

Discovery of ferrocene-carborane derivatives as novel chemical antimicrobial agents against multidrug-resistant bacteria

Antimicrobial resistance has now become a very serious global public health problem. New drug discovery and development are urgently needed to combat the growing threat of multidrug-resistant (MDR) bacteria. The aim of this study was to explore the potential application of three ferrocene-carborane derivatives as new promising agents to confront the problem of increasing antibiotic resistance. The results of agar diffusion bioassay, minimal inhibitory concentrations (MIC) testing and time-kill assay illustrate their broad-spectrum antimicrobial activities to both American Type Culture Collection (ATCC) control strains and MDR clinical isolates. It is evident that the relevant antimicrobial properties are all in a dose-dependent manner and gradually transform into a bactericidal effect from a bacteriostatic effect with the increasing of the drug concentration. Furthermore, these ferrocene-carborane derivatives have no/little toxic effect on normal cells like HELF cells and lead to little hemolysis at their MICs. This raises the possibility to develop novel antimicrobial drugs using these new ferrocene carborane derivants.