Virtual workshops on the road: Co-designing with drivers, within context in real-time

An investigation was performed of the characteristics of real-time, virtually present, contextual inquiry between automobile drivers and automotive designers. 28 participants were remotely interviewed while they were in one of two contexts: either sitting in a bare isolated meeting room or when operating a driving simulator. Open questions of the type involved in concept design were used as the basis of the real time interview. The interview transcripts were analysed by means of discourse analysis, thematic analysis and evaluations of degree of creativity. The results revealed that the real time automotive interview context provided richer information in terms of the quantity of the words expressed, the variety of the words expressed, and in terms of the judged degree of creativity of the statements

Automated Creativity Assessment Around the World: Validating Semantic Distance Across Multiple Non-English Contexts.

Traditionally, creativity research has involved asking human raters to judge responses to verbal creativity tasks, such as the Alternate Uses Task (AUT). These manual scoring practices have been useful to the field, but they have notable limitations, including labor-intensiveness and subjectivity, which can potentially threaten experimental reliability and validity. To address these challenges, creativity researchers are increasingly employing automated scoring approaches, including computational models of semantic distance. In English samples, semantic distance correlates positively with human ratings of creativity on the AUT, as well as other markers of creativity, such as openness to experience and creative achievement. However, semantic distance has only been validated in English-speaking samples, with very little psychometric work available in the many other languages of the world. In a multi-lab study, we seek to validate semantic distance across many non-English datasets, including Arabic, Chinese, French, German, Hebrew, Italian, Polish, Russian, and Spanish. We gathered AUT responses and human creativity ratings, as well as criterion measures for validation (e.g., openness to experience, creative achievement). We will use a deep learning-based language model, Bidirectional Encoder Representations from Transformers (BERT)—publicly-available in over 100 languages—to compute semantic distance scores and validate this automated metric with our behavioral data. These nine languages will be incorporated into the openly available SemDis platform, with the goal of facilitating greater diversity and accessibility in automated creativity assessment

Grey water footprint for evaluating Zefta wastewater treatment plant: a case study

The numbers of wastewater treatment plants (WWTP) in Egypt are increasing, yet the general level of pollution associated with wastewater discharge after treatment has not been evaluated. Grey water footprint (GWF) was used to assess the effluent discharges from Zefta WWTP. GWF, before and after treatment, was calculated and followed up to determine its impact on the receiving freshwater body. 150 samples were collected and analysed for BOD5 to determine the optimum operating conditions. Averages values were DO = 2.2, SV30 = 500, SVI  = 167, SA = 9.3 d, MLVSS = 2392 mg/L, f/m = 0.16, MLSS in RAS = 7922 mg/L, WAS = 140 m3/d and the HRT = 12 h. The removal efficiency of COD and TSS in the primary settling tank reached 39% and 69%, respectively. Average calculations of removal efficiency percentile reached 90-93%. Average freshwater quantities required to reduce pollutants in the receiving body stream were seasonally determined for Zefta WWTP as 5.3 × 107 m3/year. The average influent BOD5 was 376 mg/L, it was reduced to 47 mg/L in the effluent, wherever the Cmin is 6 mg/L and Cnat is 10 mg/L. Statistical analysis has shown a significant direct relation between ΔWFG,mef and WFG,ref reached 0.952 and a significant inverse relation with Cef −0.982. WFG,T has shown a significant direct relation with Cr 0.974. WFG,T– ref has shown a significant direct relation with Cr as 0.971 and WFG,T as 0.803. It can be concluded that ΔWF is effective in evaluating the efficiency of wastewater treatment and its effect on the quality of receiving water bodies.</p

Multilingual semantic distance: Automatic verbal creativity assessment in many languages

Creativity research commonly involves recruiting human raters to judge the originality of responses to divergent thinking tasks, such as the alternate uses task (AUT). These manual scoring practices have benefited the field, but they also have limitations, including labor-intensiveness and subjectivity, which can adversely impact the reliability and validity of assessments. To address these challenges, researchers are increasingly employing automatic scoring approaches, such as distributional models of semantic distance. However, semantic distance has primarily been studied in English-speaking samples, with very little research in the many other languages of the world. In a multilab study (N = 6,522 participants), we aimed to validate semantic distance on the AUT in 12 languages: Arabic, Chinese, Dutch, English, Farsi, French, German, Hebrew, Italian, Polish, Russian, and Spanish. We gathered AUT responses and human creativity ratings (N = 107,672 responses), as well as criterion measures for validation (e.g., creative achievement). We compared two deep learning-based semantic models—multilingual bidirectional encoder representations from transformers and cross-lingual language model RoBERTa—to compute semantic distance and validate this automated metric with human ratings and criterion measures. We found that the top-performing model for each language correlated positively with human creativity ratings, with correlations ranging from medium to large across languages. Regarding criterion validity, semantic distance showed small-to-moderate effect sizes (comparable to human ratings) for openness, creative behavior/achievement, and creative self-concept. We provide open access to our multilingual dataset for future algorithmic development, along with Python code to compute semantic distance in 12 languages