Emotional Intelligence of Large Language Models

Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and complex reasoning. However, their alignment with human emotions and values, which is critical for real-world applications, has not been systematically evaluated. Here, we assessed LLMs' Emotional Intelligence (EI), encompassing emotion recognition, interpretation, and understanding, which is necessary for effective communication and social interactions. Specifically, we first developed a novel psychometric assessment focusing on Emotion Understanding (EU), a core component of EI, suitable for both humans and LLMs. This test requires evaluating complex emotions (e.g., surprised, joyful, puzzled, proud) in realistic scenarios (e.g., despite feeling underperformed, John surprisingly achieved a top score). With a reference frame constructed from over 500 adults, we tested a variety of mainstream LLMs. Most achieved above-average EQ scores, with GPT-4 exceeding 89% of human participants with an EQ of 117. Interestingly, a multivariate pattern analysis revealed that some LLMs apparently did not reply on the human-like mechanism to achieve human-level performance, as their representational patterns were qualitatively distinct from humans. In addition, we discussed the impact of factors such as model size, training method, and architecture on LLMs' EQ. In summary, our study presents one of the first psychometric evaluations of the human-like characteristics of LLMs, which may shed light on the future development of LLMs aiming for both high intellectual and emotional intelligence. Project website: https://emotional-intelligence.github.io/Comment: 34 pages, 4 figure

The Effects of Floods on the Incidence of Bacillary Dysentery in Baise (Guangxi Province, China) from 2004 to 2012

Research shows potential effects of floods on intestinal infections. Baise, a city in Guangxi Province (China) had experienced several floods between 2004 and 2012 due to heavy and constant precipitation. This study aimed to examine the relationship between floods and the incidence of bacillary dysentery in Baise. A mixed generalized additive model and Spearman correlation were applied to analyze the relationship between monthly incidence of bacillary dysentery and 14 flood events with two severity levels. Data collected from 2004 to 2010 were utilized to estimate the parameters, whereas data from 2011 to 2012 were used to validate the model. There were in total 9255 cases of bacillary dysentery included in our analyses. According to the mixed generalized additive model, the relative risks (RR) of moderate and severe floods on the incidence of bacillary dysentery were 1.40 (95% confidence interval (CI): 1.16–1.69) and 1.78 (95% CI: 1.61–1.97), respectively. The regression analysis also indicated that the flood duration was negatively associated with the incidence of bacillary dysentery (with RR: 0.57, 95% CI: 0.40–0.86). Therfore, this research suggests that floods exert a significant part in enhancing the risk of bacillary dysentery in Baise. Moreover, severe floods have a higher proportional contribution to the incidence of bacillary dysentery than moderate floods. In addition, short-term floods may contribute more to the incidence of bacillary dysentery than a long-term flood. The findings from this research will provide more evidence to reduce health risks related to floods

The Effects of Floods on the Incidence of Bacillary Dysentery in Baise (Guangxi Province, China) from 2004 to 2012

Research shows potential effects of floods on intestinal infections. Baise, a city in Guangxi Province (China) had experienced several floods between 2004 and 2012 due to heavy and constant precipitation. This study aimed to examine the relationship between floods and the incidence of bacillary dysentery in Baise. A mixed generalized additive model and Spearman correlation were applied to analyze the relationship between monthly incidence of bacillary dysentery and 14 flood events with two severity levels. Data collected from 2004 to 2010 were utilized to estimate the parameters, whereas data from 2011 to 2012 were used to validate the model. There were in total 9255 cases of bacillary dysentery included in our analyses. According to the mixed generalized additive model, the relative risks (RR) of moderate and severe floods on the incidence of bacillary dysentery were 1.40 (95% confidence interval (CI): 1.16–1.69) and 1.78 (95% CI: 1.61–1.97), respectively. The regression analysis also indicated that the flood duration was negatively associated with the incidence of bacillary dysentery (with RR: 0.57, 95% CI: 0.40–0.86). Therfore, this research suggests that floods exert a significant part in enhancing the risk of bacillary dysentery in Baise. Moreover, severe floods have a higher proportional contribution to the incidence of bacillary dysentery than moderate floods. In addition, short-term floods may contribute more to the incidence of bacillary dysentery than a long-term flood. The findings from this research will provide more evidence to reduce health risks related to floods

Influence of Heat Waves on Daily Hospital Visits for Mental Illness in Jinan, China—A Case-Crossover Study

Background: Given that more frequent and intensive extreme heat events have been projected based on climate change modeling, it is of significance to have a better understanding of the association between heat waves and mental illnesses. This study aimed to explore the effects of heat waves on daily hospital visits for mental illness in the summer of 2010 in Jinan, China. Methods: A symmetric bidirectional case-crossover study was firstly conducted to determine the relationship between daily hospital visits for mental illness and heat waves in Jinan in 2010. Multifactor logistic regression analysis was then used to analyze the influencing factors for daily hospital visits for mental illness during the heat wave periods. Results: Multivariable analysis showed that the heat wave events were associated with an increased risk of mental illness. The largest odds ratios (ORs) of the heat waves for daily hospital visits for mental illness were 2.231 (95% confidence interval (CI): 1.436–3.466) at a 3-day lag, 2.836 (95% CI: 1.776–4.525) at a 2-day lag, 3.178 (95% CI: 1.995–5.064) at a 3-day lag, and 2.988 (95% CI: 2.158–4.140) at a 2-day lag for the first, second, third, and fourth heat waves, respectively. The elderly, urban residents, outdoor workers, and singles may be high-risk populations for developing heat wave-related mental illness. Conclusions: Our study has supported that there is a positive association between heat waves and hospital visits for mental illness in the study site. Age, home address, occupation, and marital status were associated with daily hospital visits for mental illness during the heat wave periods

Reconstruction of metabolic module with improved promoter strength increases the productivity of 2-phenylethanol in Saccharomyces cerevisiae

Abstract Background 2-phenylethanol (2-PE) is an important aromatic compound with a lovely rose-like scent. Saccharomyces cerevisiae is a desirable microbe for 2-PE production but its natural yield is not high, and one or two crucial genes’ over-expression in S. cerevisiae did not improve 2-PE greatly. Results A new metabolic module was established here, in which, permease Gap1p for l-phenylalanine transportation, catalytic enzymes Aro8p, Aro10p and Adh2p in Ehrlich pathway respectively responsible for transamination, decarboxylation and reduction were assembled, besides, glutamate dehydrogenase Gdh2p was harbored for re-supplying another substrate 2-oxoglutarate, relieving product glutamate repression and regenerating cofactor NADH. Due to different promoter strengths, GAP1, ARO8, ARO9, ARO10, ADH2 and GDH2 in the new modularized YS58(G1-A8-A10-A2)-GDH strain enhanced 11.6-, 15.4-, 3.6-, 17.7-, 12.4- and 7.5-folds respectively, and crucial enzyme activities of aromatic aminotransferases and phenylpyruvate decarboxylase were 4.8- and 7-folds respectively higher than that of the control. Conclusions Under the optimum medium and cell density, YS58(G1-A8-A10-A2)-GDH presented efficient 2-PE synthesis ability with ~ 6.3 g L−1 of 2-PE titer in 5-L fermenter reaching 95% of conversation ratio. Under fed-batch fermentation, 2-PE productivity at 24 h increased 29% than that of single-batch fermentation. Metabolic modularization with promoter strategy provides a new prospective for efficient 2-PE production

A Novel Geometric Algorithm for Blind Image Restoration Based on High-Dimensional Space

A novel geometric algorithm for blind image restoration is proposed in this paper, based on High-Dimensional Space Geometrical Informatics (HDSGI) theory. In this algorithm every image is considered as a point, and the location relationship of the points in high-dimensional space, i.e. the intrinsic relationship of images is analyzed. Then geometric technique of "blurring-blurring-deblurring" is adopted to get the deblurring images. Comparing with other existing algorithms like Wiener filter, super resolution image restoration etc., the experimental results show that the proposed algorithm could not only obtain better details of images but also reduces the computational complexity with less computing time. The novel algorithm probably shows a new direction for blind image restoration with promising perspective of applications

Speciation of chromium in chromium yeast

High-performance liquid chromatography was used to separate Cr(III) and Cr(VI) in samples with detection by inductively coupled plasma mass spectrometry(ICP-MS). The separation was achieved on a weak anion exchange column. The mobile phase was pH 7.0 ammonium nitrate solution. The redox reaction between Cr(III) and Cr(VI) was avoided during separation and determination. This separation method could be used to separate the samples with large concentration differences between Cr(III) and Cr(VI). The alkaline digestion was used to extract chromium in solid sample, which had no effect on the retention time and the peak area of the Cr(VI). However, the conversion of Cr(VI) from Cr(III) was observed during alkaline digestion, which displayed positive relation with the ratio of Cr(III) and Cr(VI) in samples. Both Cr(III) and Cr(VI) contents of chromium yeasts cultured in media with different chromium additions were determined. The spike recoveries of Cr(VI) for chromium yeasts were in the range of 95-108 %

A PAM4 transceiver design scheme with threshold adaptive and tap adaptive

Abstract To meet the demand of low bit error rate and high bandwidth for high-speed links, a reliable 112 Gb/s four-level pulse amplitude modulation (PAM4) transceiver design scheme with adaptive threshold voltage and adaptive decision feedback equalizer is proposed in this paper. In this scheme, three continuous time linear equalizers (CTLEs) at the front end of receiver are used to compensate the high-frequency, mid-frequency and low-frequency signals, respectively, and the variable gain amplifier (VGA) and saturation amplifier (SatAmp) are used to scale the signal amplitude. In addition to the three data samplers, four auxiliary samplers are also used for threshold adaptation. The sign-sign least mean squares algorithm uses the offset between the data sampler and the auxiliary sampler at the receiver side to drive the auxiliary reference voltage to converge to the signal constellation level, thus ensuring that the eye diagram of the PAM4 received signal has equal spacing and a constant signal–noise ratio for the three eyes in the vertical direction. In addition, the adaptive DFE for PAM4 signaling allows the transceiver to better adapt to the channel and thus achieve better equalizer performance. The simulation results show that the PAM4 transceiver design can compensate up to 25 dB of channel loss with an average eye height of 59.6 mv and an average eye width of 0.27 UI at a bit error rate of 10−12 under the condition of 3-tap feedforward equalizer (FFE) transmitter

Preparation and Characterization of Extruded Yam Starch–Soy Protein Isolate Complexes and Their Effects on the Quality of Dough

Extrusion is a method of processing that changes the physicochemical and rheological properties of starch and protein under specific temperature and pressure conditions. In this study, twin-screw extrusion technology was employed to prepare yam starch–soy protein isolate complexes. The structure and properties of the complexes and their effects on the quality of dough were studied. The results showed changes in the X-ray diffraction, rheology, and in vitro digestibility of the complexes. The extruded starch–protein complex formed an A+V-type crystal structure with the addition of soy protein isolate. A small amount of soy protein isolate could improve the complex’s viscoelasticity. As the content of soy protein isolate increased, the content of slow-digesting starch and resistant starch in the complexes increased, and the digestibility decreased. The microstructure of the dough indicated that the network structure of the puffed yam starch–protein complex dough was more uniform than that of the same amount of puffed yam starch. The moisture distribution of the dough showed that with the addition of extruded flour, the closely bound water content of the dough increased, and the weakly bound water content decreased. The hardness, gumminess, chewiness, and resilience of the dough decreased. In conclusion, extruded starch–protein complexes can improve dough quality and provide technical support for the broad application of yam