Theranostics referring to the ingenious integration of diagnostics and therapeutics has garnered tremendous attention in these years as it provides a promising opportunity for modern personalized and precision medicine. By virtue of the good biocompatibility, outstanding fluorescence property, easy processability and functionalization, promoted photosensitizing efficiency, as well as facile construction of multi‐modality theranostics, fluorophores with aggregation‐induced emission (AIE) characteristics exhibit inexhaustible and vigorous vitality in the field of theranostics. Numerous significant breakthroughs and state‐of‐the‐art progression have been witnessed in the past few years. This review highlights the tremendous aggregation‐enhanced superiorities of AIE luminogens (AIEgens) in disease theranostics mainly involving diagnostic imaging (fluorescence and room temperature phosphorescence), therapeutic intervention (photodynamic therapy), and feasibility in construction of multi‐modality theranostics based on the experimental measurements and theoretical simulations. Additionally, the latest and advanced developments of AIEgens in disease theranostics in the aspect of corresponding strategies to design highly effective AIE‐active theranostics through triggering aggregation formation are comprehensively summarized. Moreover, a brief conclusion with the discussion of current challenges and future perspectives in this area is further presented.

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Information and communication technology (ICT) elicited rapidly dissemination over the world. For its impact in Sudan, the national government has been energized the institutions to implement ICT in every sector. This study examined the Sudanese teachers' skills and ICT integration in technical and vocational education and training TVET in Khartoum state. The study directed out of two hundred respondents were sampled randomly, questionnaires were distributed, 168 (84%) were properly filled and returned, 130 were males and 38 females from three various bodies of TVET include (technical secondary schools, artisan institutions, and vocational training centres). A One-Way ANOVA and Independent sample t-test on SPSS version 20 for data analysis were adopted. The results revealed that significantly the respondents are same in terms of demographic information and ICTs usage skills. However, there was a significant difference among the respondents' ICT skill due to their ages and qualifications. Consequently, more training needs to be conducted for TVET teachers in basic skills of computer use. Following the international standards, the right decisions we are strongly recommending train/teach pre-service and in-service teachers on ICTs skills based on 21st-century requirement.

Hierarchical clustering is a classical method to provide a hierarchical representation for the purpose of data analysis. However, in practical applications, it is difficult to deal with massive datasets due to their high computation complexity. To overcome this challenge, this paper presents a novel distributed storage and computation hierarchical clustering algorithm, which has a lower time complexity than the standard hierarchical clustering algorithms. Our proposed approach is suitable for hierarchical clustering on massive datasets, which has the following advantages. First, the algorithm is able to store massive dataset exceeding the main memory space by using distributed storage nodes. Second, the algorithm is able to efficiently process nearest neighbor searching along parallel lines by using distributed computation at each node. Extensive experiments are carried out to validate the effectiveness of the DHC algorithm. Experimental results demonstrate that the algorithm is 10 times faster than the standard hierarchical clustering algorithm, which is an effective and flexible distributed algorithm of hierarchical clustering for massive datasets.