Interfacial Confinement in Semi-Crystalline Shape Memory Polymer Towards Sequentially Dynamic Relaxations

Sequential glass and melting transitions in semi-crystalline shape memory polymers (SMPs) provide great opportunities to design and generate multiple shape-memory effects (SMEs) for practical applications. However, the complexly dynamic confinements of coexisting amorphous and crystalline phases within the semi-crystalline SMPs are yet fully understood. In this study, an interfacial confinement model is formulated to describe dynamic relaxation and shape memory behavior in the semi-crystalline SMPs undergoing sequential phase/state transitions. A confinement entropy model is first established to describe the glass transition behavior of amorphous phase within the SMPs based on the free volume theory, where the free volume is critically confined by the crystalline phase. An extended Avrami model is then formulated using the frozen volume theory to characterize the melting and crystallization transitions of the crystalline phase in the SMPs, whose interfacial confinement with the amorphous phase has been identified as the driving force for the supercooled regime. Furthermore, an extended Maxwell model is formulated to describe the effect of dynamic confinement of two phases on the multiple SMEs and shape recovery behaviors in the semi-crystalline SMPs. Finally, the effectiveness of the newly proposed model is verified using the experimental data reported in the literature. This study aims to provide a new methodology for the dynamic confinements and cooperative principles in the semi-crystalline SMP towards multiple SMEs.

Projecting the Potential Distribution Areas of Ixodes scapularis (Acari: Ixodidae) Driven by Climate Change

Ixodes scapularis is a vector of tick-borne diseases. Climate change is frequently invoked as an important cause of geographic expansions of tick-borne diseases. Environmental variables such as temperature and precipitation have an important impact on the geographical distribution of disease vectors. We used the maximum entropy model to project the potential geographic distribution and future trends of I. scapularis. The main climatic variables affecting the distribution of potential suitable areas were screened by the jackknife method. Arc Map 10.5 was used to visualize the projection results to better present the distribution of potential suitable areas. Under climate change scenarios, the potential suitable area of I. scapularis is dynamically changing. The largest suitable area of I. scapularis is under SSP3-7.0 from 2081 to 2100, while the smallest is under SSP5-8.5 from 2081 to 2100, even smaller than the current suitable area. Precipitation in May and September are the main contributing factors affecting the potential suitable areas of I. scapularis. With the opportunity to spread to more potential suitable areas, it is critical to strengthen surveillance to prevent the possible invasion of I. scapularis.

Modeling the Climate Suitability of Northernmost Mangroves in China under Climate Change Scenarios

Mangroves are important wetland ecosystems on tropical and subtropical coasts. There is an urgent need to better understand how the spatial distribution of mangroves varies with climate change factors. Species distribution models can be used to reveal the spatial change of mangroves; however, global models typically have a horizontal resolution of hundreds of kilometers and more than 1 km, even after downscaling. In the present study, a maximum entropy model was used to predict suitable areas for the northernmost mangroves in China in the 2050s. An approach was proposed to improve the resolution and credibility of suitability predictions by incorporating land-use potential. Predictions were made based on two CMIP6 scenarios (i.e., SSP1-2.6 and SSP5-8.5). The results show that the northern edge of the natural mangrove distribution in China would migrate from 27.20° N to 27.39° N–28.15° N, and the total extent of suitable mangrove habitats would expand. By integrating 30 m resolution land-use data to refine the model’s predictions, under the SSP1-2.6 scenario, the suitable habitats of mangroves are predicted to be 13,435 ha, which would increase by 33.9% compared with the current scenario. Under the SSP5-8.5 scenario, the suitable area would be 23,120 ha, with an increased rate of 96.5%. Approximately 40–44% of the simulated mangrove patches would be adjacent to aquacultural ponds, cultivated, and artificial land, which may restrict mangrove expansion. Collectively, our results showed how climate change and land use could influence mangrove distributions, providing a scientific basis for adaptive mangrove habitat management despite climate change.

A Modified Markov Based Maximum-entropy Model for POS Tagging of Odia Text

POS (Parts of Speech) tagging, a vital step in diverse Natural Language Processing (NLP) tasks has not drawn much attention in case of Odia a computationally under-developed language. The proposed hybrid method suggests a robust POS tagger for Odia. Observing the rich morphology of the language and unavailability of sufficient annotated text corpus a combination of machine learning and linguistic rules is adopted in the building of the tagger. The tagger is trained on tagged text corpus from the domain of tourism and is capable of obtaining a perceptible improvement in the result. Also an appreciable performance is observed for news articles texts of varied domains. The performance of proposed algorithm experimenting on Odia language shows its manifestation in dominating over existing methods like rule based, hidden Markov model (HMM), maximum entropy (ME) and conditional random field (CRF).

Public Emotional and Coping Responses to the COVID-19 Infodemic: A Review and Recommendations

Since its onset in early 2020, the coronavirus disease 2019 (COVID-19) pandemic has adversely affected not only the physical but also the mental health of people worldwide. Healthcare professionals and laypersons have sought to learn more about this novel and highly transmissible disease to better understand its etiology, treatment, and prevention. However, information overload and misinformation related to COVID-19 have elicited considerable public anxiety and created additional health threats. Collectively, these problems have been recognized by the World Health Organization as an “infodemic.” This review provides an overview of the global challenges posed by the COVID-19 infodemic, and used the psychological entropy model as a guiding framework to explicate the potential causes of the infodemic and identify potential solutions to mitigate impacts on public health. We first examine the role of anxiety in information processing and then delineate the adverse impacts of the infodemic. Finally, we propose strategies to combat the infodemic at the public, community, and individual levels.

Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function

Neural activity coordinated across different scales from neuronal circuits to large-scale brain networks gives rise to complex cognitive functions. Bridging the gap between micro- and macro-scale processes, we present a novel framework based on the maximum entropy model to infer a hybrid resting state structural connectome, representing functional interactions constrained by structural connectivity. We demonstrate that the structurally informed network outperforms the unconstrained model in simulating brain dynamics; wherein by constraining the inference model with the network structure we may improve the estimation of pairwise BOLD signal interactions. Further, we simulate brain network dynamics using Monte Carlo simulations with the new hybrid connectome to probe connectome-level differences in excitation-inhibition balance between apolipoprotein E (APOE)-ε4 carriers and noncarriers. Our results reveal sex differences among APOE-ε4 carriers in functional dynamics at criticality; specifically, female carriers appear to exhibit a lower tolerance to network disruptions resulting from increased excitatory interactions. In sum, the new multimodal network explored here enables analysis of brain dynamics through the integration of structure and function, providing insight into the complex interactions underlying neural activity such as the balance of excitation and inhibition.

Eye-tracking as a proxy for coherence and complexity of texts

Reading is a complex cognitive process that involves primary oculomotor function and high-level activities like attention focus and language processing. When we read, our eyes move by primary physiological functions while responding to language-processing demands. In fact, the eyes perform discontinuous twofold movements, namely, successive long jumps (saccades) interposed by small steps (fixations) in which the gaze “scans” confined locations. It is only through the fixations that information is effectively captured for brain processing. Since individuals can express similar as well as entirely different opinions about a given text, it is therefore expected that the form, content and style of a text could induce different eye-movement patterns among people. A question that naturally arises is whether these individuals’ behaviours are correlated, so that eye-tracking while reading can be used as a proxy for text subjective properties. Here we perform a set of eye-tracking experiments with a group of individuals reading different types of texts, including children stories, random word generated texts and excerpts from literature work. In parallel, an extensive Internet survey was conducted for categorizing these texts in terms of their complexity and coherence, considering a large number of individuals selected according to different ages, gender and levels of education. The computational analysis of the fixation maps obtained from the gaze trajectories of the subjects for a given text reveals that the average “magnetization” of the fixation configurations correlates strongly with their complexity observed in the survey. Moreover, we perform a thermodynamic analysis using the Maximum-Entropy Model and find that coherent texts were closer to their corresponding “critical points” than non-coherent ones, as computed from the Pairwise Maximum-Entropy method, suggesting that different texts may induce distinct cohesive reading activities.

EVALUATION OF THE ROOT CAUSE OF FAILURE IN A CRAWLER CRANE MACHINE USING HYBRID MCDM MODEL

A real-life case study has been presented in this paper, where a crawler crane machine is investigated for the root cause of failure, using an expert opinion-based technique which comprises of an integrated model which is based on an Intuitionistic Fuzzy TOPSIS model Exponential related function, and the intuitionistic entropy model. The main contribution and advantages of the proposed approach are in the use of a subjective and objective model for the computation of the criteria weight, which allows for complete assessment of the actual performance and value of each of the criteria. The application of the exponential-related function, which represent the aggregated effect of the positive and negative evaluations in the performance ratings of the alternatives based on the intuitionistic fuzzy set (IFS) data used. And finally, the method ranks all alternatives using the exponential-related function matrix, which accounts for the expert's attitudinal character, which a strong influencing factor in subjective assessments like the one used in some of the root cause of failure/reliability analysis.

Modeling the Potential Distribution of Three Taxa of Akebia Decne. under Climate Change Scenarios in China

Akebia trifoliata (Thunb.) Koidz., Akebia trifoliata subsp. australis (Diels) T. Shimizu and Akebia quinata (Houtt.) Decne. are the source plants of the traditional Chinese medicines AKEBIAE CAULIS and AKEBIAE FRUCTUS, and have high pharmaceutical value. However, the resource reserve of these plants has dramatically declined due to habitat destruction, which has seriously affected their adequate supply and sustainable utilization. A poor knowledge of the potential distribution of these medicinal materials would seriously constrain the protective exploitation of wild resources and the establishment of new cultivations. In this study, based on the scenarios of SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, the maximum entropy model was used to predict the potential distribution of these three Akebia taxa under current and future (2030s, 2050s, 2070s and 2090s) climate conditions. Our findings showed that the potentially suitable areas of these three Akebia taxa were mainly distributed in China at 101.8–121.9° E and 23.5–34.6° N. Temperature played a more significant role than precipitation in affecting the distribution. The dominant bioclimatic variable that affected the distribution of A. trifoliata and A. quinata in China was the minimum temperature of the coldest month (BIO06). For A. trifoliata subsp. australis, the mean diurnal range (BIO02) was the dominant variable influencing its distribution. Compared with current conditions, the moderate- and high-suitability areas of these three Akebia taxa were predicted to shrink towards the core areas, while the low-suitability areas were all observed to increase from the 2030s to the 2090s. With the increase in radiative forcing of SSP, the low-impact areas of these three Akebia taxa showed a decreasing trend as a whole. Our results illustrate the impact of climate change on the distribution of Akebia, and would provide references for the sustainable utilization of Akebia’s resources.