Group problem solving Laughlin, Patrick R; Laughlin, Patrick R
2011., 20110124, 2011, 2011-01-24
eBook
Experimental research by social and cognitive psychologists has established that cooperative groups solve a wide range of problems better than individuals. Cooperative problem solving groups of ...scientific researchers, auditors, financial analysts, air crash investigators, and forensic art experts are increasingly important in our complex and interdependent society. This comprehensive textbook--the first of its kind in decades--presents important theories and experimental research about group problem solving. The book focuses on tasks that have demonstrably correct solutions within mathematical, logical, scientific, or verbal systems, including algebra problems, analogies, vocabulary, and logical reasoning problems.
Background
Several inventories have been developed to assess social problem‐solving. However, these instruments originally developed for adult or adolescence and do not capture the full range of main ...interpersonal relationships over which elementary students resolve daily life interpersonal problems and apply elementary‐age typical responses. Therefore, the development of a valid scale to measure interpersonal problem‐solving ability in elementary school students is warranted.
Aims
This study aimed to develop and perform a preliminary psychometric evaluation of an interpersonal problem‐solving inventory for elementary school students (IPSIE).
Samples and Methods
The IPSIE was administered to elementary student samples that consist of 516 Vietnamese elementary school students in grades 3–5. This study examined the reliabilities of International problem behaviour (IPB) and interpersonal problem‐solving inventory (IPSI) as well as the construct validity of IPSI. The construct validity of IPSI was investigated by using exploratory factor analysis (EFA) to explore the emerging factor structure of the data. The confirmatory factor analysis (CFA) was utilized to fit the data.
Results
The reliabilities of IPB and IPSI were assessed by calculating internal consistencies (Cronbach’s α = 0.79 vs. 0.90, McDonald's ω = 0.79 vs. 0.82). The EFA results suggested that the IPSI has two‐factor structure. The CFA was reexamined to define theory‐driven five‐factor structure of the IPSI’s data. The CFA findings indicated that the scores of IPSI have the five‐factor structure as expected with acceptable global fit indices (CFI: 0.943, TLI: 0.939, RMSEA: 0.030, and RMR: 0.046). The concurrent validity of IPSI was tested by calculating correlations between the IPSI and SPSI‐R scores (r = .667) and the IPSI and SPSTE‐A scores (r = .482).
Conclusions
These finding figures suggest that overall the scales of IPSIE are well‐functioning measures with good psychometric properties. Caution and limitations of IPSIE are discussed. Future study and possible applicability are suggested.
Stop Rushing In With Advice Stanier, Michael Bungay
MIT Sloan management review,
04/2020, Letnik:
61, Številka:
3
Journal Article
Recenzirano
There's a time and place for advice. But when giving it is your default response to colleagues and friends who face difficult situations, it becomes a problem. Here, Stanier discusses why giving ...advice is not useful or effective.
The cover image is based on the Research Article Spatial abilities associated with open math problem solving by Xinlin Zhou et al., https://doi.org/10.1002/acp.3919.
Abstract Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their ...computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.
Abstract Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited ...amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence–based image segmentation model such as Meta’s Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet
Abstract The emergence and rapid spread of SARS-CoV-2 prompted the global community to identify innovative approaches to diagnose infection and sequence the viral genome because at several points in ...the pandemic positive case numbers exceeded the laboratory capacity to characterize sufficient samples to adequately respond to the spread of emerging variants. From week 10, 2020, to week 13, 2023, Slovenian routine complete genome sequencing (CGS) surveillance network yielded 41 537 complete genomes and revealed a typical molecular epidemiology with early lineages gradually being replaced by Alpha, Delta, and finally Omicron. We developed a targeted next-generation sequencing based variant surveillance strategy dubbed Spike Screen through sample pooling and selective SARS-CoV-2 spike gene amplification in conjunction with CGS of individual cases to increase throughput and cost-effectiveness. Spike Screen identifies variant of concern (VOC) and variant of interest (VOI) signature mutations, analyses their frequencies in sample pools, and calculates the number of VOCs/VOIs at the population level. The strategy was successfully applied for detection of specific VOC/VOI mutations prior to their confirmation by CGS. Spike Screen complemented CGS efforts with an additional 22 897 samples sequenced in two time periods: between week 42, 2020, and week 24, 2021, and between week 37, 2021, and week 2, 2022. The results showed that Spike Screen can be applied to monitor VOC/VOI mutations among large volumes of samples in settings with limited sequencing capacity through reliable and rapid detection of novel variants at the population level and can serve as a basis for public health policy planning.
Abstract Motivation Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. ...However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged due to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but there is still a wide margin for improvement. Results In this work we present sincFold, an end-to-end deep learning approach, that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared with classical methods and recent deep learning models, showing that it can outperform the state-of-the-art methods.
