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Prior theory suggests that reputation spreading (e.g., gossip) and punishment are two key mechanisms to promote cooperation in groups, but no behavioral research has yet examined their relative effectiveness and efficiency in promoting and maintaining cooperation. To examine these issues, we observed participants interacting in a four-round public goods game (PGG) with or without gossip and punishment options and a subsequent two-round trust game (TG). We manipulated gossip as the option to send notes about other group members to these members’ future partners and punishment as the option to assign deduction points to reduce other group members’ outcomes with a fee-to-fine ratio of 1:3. Findings revealed that in the four-round PGG, the option to gossip increased both cooperation and individual earnings, whereas the option to punish had no overall effect on cooperation (but a positive effect on cooperation in the last two rounds of the PGG) and significantly decreased individual earnings. Importantly, the initial option to gossip made people more trusting and trustworthy in the subsequent TG when gossip was no longer possible, compared to the no-gossip condition. Thus, we provide some initial evidence that gossip may be more effective and efficient than punishment to promote and maintain cooperation.

The signature of stochastic resonance is that additional noise surprisingly enhances the signal-to-noise ratio (SNR). A noise-adaptive system that learns to add an optimal amount of noise to trigger stochastic resonance and improve SNR is known as adaptive stochastic resonance. However, the current stochastic resonance mechanism fails when environmental noise exceeds the optimal noise level, as any additional noise merely worsens the SNR. In this case, instead of adding noise, stochastic resonance can be facilitated by adapting the potential energy landscape of the bistable system. Here, we propose a novel approach to enhance SNR in noisy environments, involving a potential adjustable microelectromechanical systems resonator. A periodic signal with an amplitude of 0.28 Vrms is buried in ambient noise, emulated by a white noise signal with amplitude ranging from 0.7 Vrms to 4 Vrms. Experimental results show that when the ambient noise exceeds 1 Vrms, adding additional noise leads to a decline in SNR. However, SNR enhancement induced by stochastic resonance is experimentally demonstrated by tuning the potential well of the resonator. This advancement highlights the feasibility of potential adjustable systems to overcome the limitations of conventional noise adjustable stochastic resonance methods in noisy environments. The proposed mechanism is further applied to detect the frequency of 2.7 nN periodic forces with various waveforms.

Gossip—a sender communicating to a receiver about an absent third party—is hypothesized to impact reputation formation, partner selection, and cooperation. Laboratory experiments have found that people gossip about others' cooperativeness and that they use gossip to condition their cooperation. Here, we move beyond the laboratory and test several predictions from theories of indirect reciprocity and reputation-based partner selection about the content of everyday gossip and how people use it to update the reputation of others in their social network. In a Dutch community sample (N = 309), we sampled daily events in which people either sent or received gossip about a target over 10 days (ngossip = 5284). Gossip senders frequently shared information about targets’ cooperativeness and did so in ways that minimize potential retaliation from targets. Receivers overwhelmingly believed gossip to be true and updated their evaluation of targets based on gossip. In turn, a positive shift in the evaluation of a target was associated with higher intentions to help them in future interactions, and with lower intentions to avoid them in the future. Thus, gossip is used in daily life to impact and update reputations in a way that enables partner selection and indirect reciprocity. This article is part of the theme issue ‘The language of cooperation: reputation and honest signalling’.

Optimization of alkaline hydrogen peroxide (AHP) pretreatment and enzymatic hydrolysis of wheat straw (WS) was carried out to enhance fermentable sugar yields with the use of glucose and xylose yields from the pretreated WS as responses. In the first step, variables including temperature, hydrogen peroxide concentration and time during pretreatment were detected to have significant effects on the sugar yields. The results indicate that maximal sugar yields could be obtained while the WS was pretreated using 71 g/L hydrogen peroxide solution with 200 g/L of solid loading at 50 °C for 7.6 h. The corresponding cellulose recovery, hemicellulose recovery and lignin removal were 97.5%, 84.3% and 75.0%, respectively. In the second step, enzymatic hydrolysis of the pretreated WS was optimized. The results show that the reaction time, enzyme loading and biomass loading during enzymatic hydrolysis also had significant effects on the sugar yields. The final maximum yields of glucose (552.7 mg/gds (mg/g dry substrate)) and xylose (223.6 mg/gds) could be obtained while enzymatic hydrolysis was carried out at 50 °C for 37.0 h using 10.8 FPU/gds (filter paper activity unit per gram dry substrate) of enzyme loading, 88 g/L of biomass loading and 0.3% (w/v) of Tween-80. The corresponding cellulose conversion and hemicellulose conversion were 94.0% and 83.5%, respectively.

To improve sugar yields from wheat straw (WS), response surface methodology (RSM) was adopted to optimize potassium hydroxide combined urea pretreatment and enzymatic hydrolysis of WS. Glucose and xylose yields from the pretreated WS were used as responses during the whole optimization. Potassium hydroxide concentration, time and temperature during pretreatment were found to have significant effects on sugar yields. Sugar yields could be enhanced while WS was pretreated using 45 g/L potassium hydroxide solution containing 15 g/L urea with solid to liquid ratio of 1:5 (g/mL) at 74.0 °C for 50 min. Cellulose recovery, hemicellulose recovery, and lignin removal after optimization were 98.1%, 72.6%, and 75.8%, respectively. In addition, enzyme loading, biomass loading, and reaction time during enzymatic hydrolysis also had significant effects on sugar yields. Maximal yields of glucose (610.25 mg/gds, miligram per gram dry substrate) and xylose (221.26 mg/gds) could be achieved while hydrolysis was carried out at 50 °C for 32.8 h with 141 g/L of biomass loading, 8.1 FPU/gds (filter paper activity unit per gram dry substrate) of enzyme loading and 0.4% (w/v) of polysorbate 80. The corresponding cellulose conversion and hemicellulose conversion were 97.2% and 90.4%, respectively.

With the addition of Tween 80, potassium hydroxide pretreatment and enzymatic hydrolysis were statistically optimized to maximize sugar yields from corn cob (CC). The results indicated that the sugar yields from CC could be influenced significantly by the potassium hydroxide concentration, temperature and time during pretreatment. The optimized pretreatment conditions were as follows: potassium hydroxide, 46 g·L−1; Tween 80, 3.0 g·L−1; solid dose, 200 g·L−1; temperature, 78 °C; and time, 50 min. After optimization, the lignin reduction and recoveries of cellulose and hemicellulose were 89.7%, 97.8% and 68.0%, respectively. In addition, sugar production could also be influenced by the biomass loading, enzyme loading and reaction time. A maximal glucose production (518.48 mg·gds−1, milligrams per gram of dry substrate) and xylose production (351.14 mg·gds−1), 97.2% cellulose conversion and 82.9% hemicellulose conversion from CC could be obtained when the biomass loading was 195 g·L−1 and the enzyme loading was 8.9 FPU·gds−1 (filter paper activity units per gram of dry substrate) and when the Tween 80 concentration was 3.0 g·L−1 at 50 °C for 30.4 h during hydrolysis. This is the first systematic study of combined Tween 80 pretreatment of CC by potassium hydroxide and hydrolysis of CC by cellulase preparation to increase sugar production.

MEMS stiffness-tunable devices, owing to their low resonant frequency and high sensitivity, have been widely adopted in fields such as biological force sensing, vibration sensing, and inertial sensing. However, traditional stress-effect-based stiffness-adjustment methods offer limited tuning range. This paper introduces a novel stiffness-tuning mechanism based on the principle of stiffness compensation, integrating positive stiffness springs with V-shaped negative stiffness springs in a parallel configuration. A self-locking mechanism enables precise control of the mechanical preloading on the negative stiffness structures to realize stiffness adjustment. This design is prototyped by microscale fabrication techniques and is suitable for miniaturization. The experimental results confirm a stiffness reduction of over 90% and demonstrate bistability. These findings highlight the potential of the design for high-sensitivity MEMS accelerometers and dual-mode optical switches with low switching voltage.

Knee osteoarthritis (KOA) combined with depressive symptoms is prevalent and leads to poor outcomes and significant financial burdens. However, practical tools for identifying at-risk patients remain limited. A robust prediction model is needed to address this gap. This study aims to develop and validate a predictive model to identify KOA patients at risk of developing depressive symptoms. The China Health and Retirement Longitudinal Survey (CHARLS) data were used for model development and the Osteoarthritis Initiative (OAI) for external validation. 18 potential predictors were selected using LASSO regression. 4 machine learning models—logistic regression, decision tree, random forest, and artificial neural network—were developed. Model performance was assessed using the area under the operating characteristic curve (AUC), calibration curves, and decision curve analysis. The most important features were extracted from the optimal model on external validation. A total of 469 individuals were included, with 70% used for training and 30% for testing. The random forest model achieved the best performance, with an AUC of 0.928 in the test set, outperforming logistic regression (AUC 0.622), decision tree (AUC 0.611), and neural network models (AUC 0.868). External validation revealed an AUC of 0.877 (95% CI: 0.864–0.889) for the adjusted random forest model. Pain severity was the most significant predictor, followed by the five-time sit-to-stand test (FTSST) and sleep problems. This study is the first in China to apply a predictive model for depressive symptoms in KOA patients, offering a practical tool for early risk identification using routinely available data.

Objectives Abundant evidence has demonstrated that self-compassion robustly contributes to individuals’ psychological well-being. Yet, less is known about the social benefits of self-compassion. The present research sought to test whether self-compassion is related to less unethical behavior, as well as the mediating role of moral disengagement in this relation. Methods Across two studies in China, a college student sample (study 1, N  = 222; 24% men, M age  = 19.44 years) and a large adolescent sample (study 2, N  = 3236; 46% boys, M age  = 15.66 years) were recruited to complete measures of self-compassion, moral disengagement, and unethical behavior. Results Structural equation models revealed that self-compassion was negatively associated with unethical behavior (study 1, β  = − 0.23, p  = 0.006; study 2, β  = − 0.19, p  < 0.001) and that a lower level of moral disengagement explained the negative association between self-compassion and unethical behavior (study 1, indirect effect = − 0.14, p  = 0.012, 95% CI [− 0.24, − 0.03]; study 2, indirect effect = − 0.08, p  < 0.001, 95% CI [− 0.11, − 0.06]). Conclusions The findings support the notion that self-compassion decreases unethical behavior tendency through a lower level of moral disengagement, which expands our understanding of the adaptive functions of self-compassion in the social context.

Phenanthraquinone-doped polymethyl methacrylate (PQ/PMMA) photopolymers are considered to be the most promising holographic storage media due to their unique properties, such as high stability, a simple preparation process, low price, and volumetric shrinkage. This paper reviews the development process of PQ/PMMA photopolymers from inception to the present, summarizes the process, and looks at the development potential of PQ/PMMA in practical applications.