Search

Flexible perovskite solar cells (FPSCs) have gained considerable attention for potential applications in portable and wearable electronics. However, the design principles governing FPSCs remain incompletely understood. In this study, two critical factors—thermal conductivity and elastic modulus—that significantly influence the thermal and mechanical stabilities of FPSCs are identified. Achieving stress‐less conditions is crucial for enhancing the performance of FPSCs. To address this, a liquid crystal elastomer (LCE) is employed as a buffer interlayer to effectively mitigate residual thermal stress. This is achieved by improving the thermal conductivity of the electron transport layer from 0.76 to 1.07 W mK−1 and softening the perovskite layer, reducing the Young's modulus from 50 to 42 GPa. The optimized thin films are utilized in both rigid and flexible PSCs, resulting in efficiencies of 24.5% and 22.8%, respectively. Remarkably, these devices demonstrated excellent thermal stability, with unpackaged LCE rigid PSCs retaining 85.6% of their initial efficiency after 504 h of aging at 85 °C. Moreover, robust mechanical stability in FPSCs is exhibited, with 88.4% of the original efficiency retained after 5400 bending cycles. This investigation elucidates the profound impact of thermal conductivity and Young's modulus on the efficiency and stability of flexible electronics. [ABSTRACT FROM AUTHOR]

Simple Summary: Rumination behavior is a crucial indicator of cattle health and welfare. The timely monitoring and analysis of this behavior can provide valuable insights into the physiological status of the animals. Distinguishing from manual observation and wearable devices, this study proposes a method using video technology for monitoring cattle rumination behavior. This method aims to track physiological indicators during rumination, including chewing count, rumination duration, and chewing frequency. This approach can help livestock managers promptly understand the health status of cattle. Furthermore, this research method offers a new perspective for the construction of smart farming, providing technical support for the intelligent transformation of the livestock industry. Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a non-contact method for monitoring cattle rumination behavior, utilizing an improved YOLOv8-pose keypoint detection algorithm combined with multi-condition threshold peak detection to automatically identify chewing counts. First, we tracked and recorded the cattle's rumination behavior to build a dataset. Next, we used the improved model to capture keypoint information on the cattle. By constructing the rumination motion curve from the keypoint information and applying multi-condition threshold peak detection, we counted the chewing instances. Finally, we designed a comprehensive cattle rumination detection framework to track various rumination indicators, including chewing counts, rumination duration, and chewing frequency. In keypoint detection, our modified YOLOv8-pose achieved a 96% mAP, an improvement of 2.8%, with precision and recall increasing by 4.5% and 4.2%, enabling the more accurate capture of keypoint information. For rumination analysis, we tested ten video clips and compared the results with actual data. The experimental results showed an average chewing count error of 5.6% and a standard error of 2.23%, verifying the feasibility and effectiveness of using keypoint detection technology to analyze cattle rumination behavior. These physiological indicators of rumination behavior allow for the quicker detection of abnormalities in cattle's rumination activities, helping managers make informed decisions. Ultimately, the proposed method not only accurately monitors cattle rumination behavior but also provides technical support for precision management in animal husbandry, promoting the development of modern livestock farming. [ABSTRACT FROM AUTHOR]

The inorganic-carbonate dual-phase membrane represents a class of dense membranes that are fabricated using diverse support materials, ranging from metals to ceramics. This dual-phase membrane consists of a porous metal or ceramic support with an introduced carbonate phase within the support pores. Compared with polymer and zeolite membranes, inorganic-carbonate dual-phase membranes exhibit exceptional CO2 selectivity at elevated temperatures (>500 °C), making them an ideal choice for high-temperature CO2 separation in power plant systems. The present paper provides a comprehensive overview of the separation principle, significant models, and preparation techniques employed in carbonate dual-phase membranes for CO2 separation. The present study aims to discuss key factors that limit the CO2 permeation performance and stability of membranes, while also exploring the potential applications of dual-phase membranes in various fields. The identification of key challenges in the future development of the carbonate dual-phase membrane has been highlighted in this work. The future trajectory of research and development should be directed toward overcoming these challenges, encompassing the synthesis technology of membranes, balance optimization of membrane structure and performance, modification of physical and chemical properties of molten carbonate, and advancement in high-temperature sealing techniques, as well as exploration of diverse membrane reactors based on carbonate dual-phase membranes for prospective applications. [ABSTRACT FROM AUTHOR]

The elite bull plays an extremely important role in the genetic progression of the dairy cow population. The previous results indicated the potential positive relationship of large scrotal circumference (SC) with improved semen volume, concentration, and motility. In order to improve bull's semen quantity and quality by selection, it is necessary to estimate the genetic parameters of semen traits and their correlations with other conformation traits such as SC that could be used for an indirect selection. In this study, the genetic parameters of seven semen traits (n = 66,260) and nine conformation traits (n = 3,642) of Holstein bulls (n = 453) were estimated by using the bivariate repeatability animal model with the average information-restricted maximum likelihood (AI-REML) approach. The results showed that the estimated heritabilities of semen traits ranged from 0.06 (total number of motile sperm, TNMS) to 0.37 (percentage of abnormal sperm, PAS) and conformation traits ranged from 0.23 (pin width, PW) to 0.69 (hip height, HH). The highest genetic correlations were found between semen volume per ejaculation (SVPE), semen concentration per ejaculation (SCPE), total number of sperm (TNS), and TNMS traits that were 0.97, 0.98, 1.00, and 0.99, respectively. Phenotypic correlations between SC and SVPE, SCPE, TNS, and TNMS were 0.35, 0.35, 0.48, and 0.42, respectively. In summary, the moderate or high heritability of semen traits indicates that genetic improvement of semen quality by selection is feasible, where SC could be a useful trait for indirect selection or as correlated information to improve semen quantity and production in the practical bull breeding programs. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) with excellent photoelectric properties have attracted much attention in recent years. However, the solution manufacturing of PSCs inevitably introduces a large number of defects (both bulk and surface defects), which seriously affect the performance of PSCs. Defect passivation from additive engineering is an effective and simple strategy. Among these additives, polymers are increasingly attracting attention due to their excellent properties, such as adjustable structures, multiple functional groups, excellent stability, and low cost, which can further promote the commercialization of PSC technology. However, the application of polymers in PSCs have encountered some obstacles due to a lack of systematic studies, such as unelucidated interactions between polymers and perovskites, the frequent trial‐and‐error process used in material selection, and the lack of effective guidelines. In this review, the application of polymers in various layers of PSCs devices is first summarized. Then, three main roles of polymers in PSCs are summarized, including crystallization regulation, the mechanical stability enhancement of flexible perovskite solar cells (FPSCs), and their use as undoped hole transport materials (HTMs). More importantly, machine learning (ML) is proposed to design and select polymers as passivators and HTMs for PSCs. Finally, promising guidelines and recommendations are provided for the commercialization of PSCs. [ABSTRACT FROM AUTHOR]

In this study, an electronic nose model composed of seven kinds of metal oxide semiconductor sensors was developed to distinguish the milk source (the dairy farm to which milk belongs), estimate the content of milk fat and protein in milk, to identify the authenticity and evaluate the quality of milk. The developed electronic nose is a low-cost and non-destructive testing equipment. (1) For the identification of milk sources, this paper uses the method of combining the electronic nose odor characteristics of milk and the component characteristics to distinguish different milk sources, and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis , LDA) for dimensionality reduction analysis, and finally use three machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) to build a milk source (cow farm) Identify the model and evaluate and compare the classification effects. The experimental results prove that the classification effect of the SVM-LDA model based on the electronic nose odor characteristics is better than other single feature models, and the accuracy of the test set reaches 91.5%. The RF-LDA and SVM-LDA models based on the fusion feature of the two have the best effect Set accuracy rate is as high as 96%. (2) The three algorithms, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are used to construct the electronic nose odor data for milk fat rate and protein rate. The method of estimating the model, the results show that the RF model has the best estimation performance( R2 =0.9399 for milk fat; R2=0.9301for milk protein). And it prove that the method proposed in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of dairy products.