Gradual pollen presentation in Vaccinium corymbosum 'Bluecrop': an adaptive mechanism to improve pollination efficiency and outcrossing.

Gradual pollen presentation is a plant reproductive mechanism to improve pollination efficiency and accuracy and promote outcrossing. Vaccinium corymbosum 'Bluecrop' has a typical gradual pollen presentation mechanism. 'Bluecrop' exhibits an inverted bell-shaped flower with a white coloration. By investigating the flower syndrome, pollination characteristics, pollination efficiency, and breeding system of 'Bluecrop', this study aims to explore the adaptive significance of these traits. The results showed 'Bluecrop' released pollen gradually through anther poricidal dehiscence. Among different pollinators, Apis mellifera and Bombus can pollinate effectively, and the mechanism of gradual pollen presentation significantly improved the efficiency of pollen transfer. This characteristic limits the amount of pollen removed by the pollinators and prolongs pollen presentation, thus attracting more pollinators and thereby increasing male fitness. The nectar secretion of 'Bluecrop' is gradual, with a large nectar production and a long phase of nectar secretion, enhance visitation frequencies and the chances of successful pollination. At the same time, campanulate corolla can protect pollen as well as nectar from waste due to environmental factors and other effects. The breeding system of 'Bluecrop' relies mainly on outcrossing because of its low affinity for self-fertilization and good interaction with pollinating insects. Thus, the special floral syndrome and the mechanism of secondary pollen presentation are significant in improving pollination efficiency and promoting the reproductive success of 'Bluecrop' by outcrossing. It can provide a certain theoretical basis for the future propagation breeding of 'Bluecrop'.

The relationship between academic stress and depression among college students during the COVID-19 pandemic: a cross-sectional study from China.

BACKGROUND: The COVID-19 pandemic is a global public health crisis. During the COVID-19 pandemic, mental health has attracted great attention. However, there is a lack of research on the relationship between academic stress and depression in Chinese college students and its mechanisms. Therefore, this study investigated the mechanisms of coping style, sleep quality, and interpersonal relationship in academic stress and depression among college students. METHODS: The cross-sectional study was conducted from May to June 2022 through face-to-face questionnaires with college students in Anhui Province, China. The questionnaires included sociodemographic information, the Simplified Coping Style Questionnaire, and the Self-Rating Depression Scale. Ordered logistic regression model was used to study the relationship between academic stress and depression of college students during the COVID-19 pandemic through the mechanism analysis of coping style, sleep quality and interpersonal relationship. RESULTS: Two thousand thirty-three Chinese college students participated in the study, including 1,285 female and 748 male college students, with an average age 19.81 years old (SD = 1.22 years old). The results showed that (1) Academic stress had a significant impact on depression in college students under the background of COVID-19 (p < 0.01); (2) The influence of academic stress on depression had a difference in work experience as student cadres, which showed that college students who had served as student cadres were less affected by academic stress (p < 0.10), college students who had not served as student cadres were greatly affected by the academic stress (p < 0.05); (3) College students' attitudes toward COVID-19 significantly affected depression (p < 0.01); (4) Counselors' concern had a significant impact on college students' depression (p < 0.01); (5) Positive coping style, high quality sleep and good interpersonal relationship were the important mechanisms of the impact of academic stress on college students' depression. CONCLUSIONS: This study provides new findings for in-depth understanding of the relationship between academic stress and depression among college students in China during the COVID-19 pandemic, which is conducive to the provision of targeted intervention measures for the mental health of college students.

A new multiregional carbon emissions forecasting model based on a multivariable information fusion mechanism and hybrid spatiotemporal graph convolution network.

Developing scientific and effective carbon emissions reduction policies relies heavily on precise carbon emission trend prediction. The existing complex spatiotemporal correlation and diverse range of influencing factors associated with multi-regional carbon emissions pose significant challenges to accurately modeling these trends. Under this constraint, this study is inspired by graph learning to establish a hybrid dynamic and static graph-based regional carbon emission network framework, which introduces a novel research standpoint for investigating short-term carbon emissions prediction (CEP). Specifically, a parallel framework of attribute-augmented dynamic multi-modal graph convolutional neural networks (ADMGCN) and temporal convolutional networks with adaptive fusion multi-scale receptive fields (AFMRFTCN) is proposed. The proposed model is evaluated against nineteen state-of-the-art models using daily carbon emission data from 30 regions in China, demonstrating its effectiveness in accurately predicting the trends of multi-regional carbon emissions. Conclusions are drawn as follows: First, especially in regions with marked periodicity, compared with the best baseline model, the mean absolute percentage error (MAPE) of our model is reduced by 20.19%. Second, incorporating graph convolutional neural networks (GCNs) with dynamic and static graphs is advantageous in extracting the spatial features of China's carbon emission network, which are influenced by geographical, economic, and industrial factors. Third, the parallel ADMGCN-AFMRFTCNs framework effectively captures the influence of external information on carbon emissions while mitigating the issue of low prediction accuracy resulting from univariate information. Fourth, the analysis reveals significant differences in the short-term (30-day) growth rate of carbon emissions among different regions. For example, Henan exhibits the highest growth rate (37.38%), while Guizhou has the lowest growth rate (-7.46%). It is valuable for policymakers and stakeholders seeking to identify regions with distinct emission patterns and prioritize mitigation efforts accordingly.

Phenylalanine-Arginine-ß-Naphthylamide Enhances the Photobactericidal Effect of Methylene Blue on Pseudomonas aeruginosa.

Objective: To investigate the effectiveness, dosing sequence, concentration, and mechanism of antimicrobial photodynamic inactivation (aPDI) using methylene blue (MB) plus phenylalanine-arginine-ß-naphthylamide (PAßN) against Pseudomonas aeruginosa. Methods: P. aeruginosa bacterial suspension was incubated with MB for different times (5-240 min), and then, 10 J/cm2 red light was irradiated. The efflux pump inhibitor (EPI) PAßN (10-100 µg/mL) was combined with MB (1-20 µM) in different sequences (PAßN-first, PAßN+MB, PAßN-after). Colony-forming units were then determined by serial dilution. Results: Using MB 10 µM plus 10 J/cm2, the killing effect of MB-aPDI on P. aeruginosa increased first and then decreased with longer incubation time. The killing effect of MB+PAßN-aPDI on P. aeruginosa was better than that of MB-aPDI (p < 0.05) by up to 2 logs. PAßN-first had the best killing effect, whereas PAßN-after had the worst killing effect. The killing effect increased with PAßN concentration and at 100 µg/mL reached 5.1 logs. Conclusions: The EPI PAßN enhanced the bactericidal effect of MB-aPDI on P. aeruginosa, especially when added before MB. It is proposed that MB is a substrate of the resistance-nodulation-division family efflux pump.

Three-Dimensional Collision Avoidance Method for Robot-Assisted Minimally Invasive Surgery.

In the robot-assisted minimally invasive surgery, if a collision occurs, the robot system program could be damaged, and normal tissues could be injured. To avoid collisions during surgery, a 3-dimensional collision avoidance method is proposed in this paper. The proposed method is predicated on the design of 3 strategic vectors: the collision-with-instrument-avoidance (CI) vector, the collision-with-tissues-avoidance (CT) vector, and the constrained-control (CC) vector. The CI vector demarcates 3 specific directions to forestall collision among the surgical instruments. The CT vector, on the other hand, comprises 2 components tailored to prevent inadvertent contact between the robot-controlled instrument and nontarget tissues. Meanwhile, the CC vector is introduced to guide the endpoint of the robot-controlled instrument toward the desired position, ensuring precision in its movements, in alignment with the surgical goals. Simulation results verify the proposed collision avoidance method for robot-assisted minimally invasive surgery. The code and data are available at https://github.com/cynerelee/collision-avoidance.

The impact of family functioning on depression in college students: A moderated mediation model.

BACKGROUND: The depression of college students is increasing. Family dysfunction is a potential risk factor for depression. More research is needed to uncover the relationship and influencing mechanism. Based on this, this study examined the mediating effect of coping style and the moderating effect of gender in family functioning and depression among college students. METHODS: From May to June 2022, a cross-sectional survey was conducted among 2033 college students (16-24 years old) from universities in Anhui Province, China, including 1285 females (63.21 %) and 748 males (36.79 %), with an average age of 19.81 years old (SD = 1.22 years old). There were 651 (32.02 %) only child. Family functioning was assessed by Family Assessment Device, coping style was assessed by Simplified Coping Style Questionnaire and depression was assessed by Self-rating Depression Scale. Common method bias was performed by Harman's single-factor test. Mediating effect was analyzed by stepwise regression. Moderating effect was analyzed by moderated multiple regression. RESULTS: There was no serious common method bias in this study. Good family functioning had a negative predictive effect on depression in college students (r = -0.56, p < 0.001). Coping style partially mediated the predictive effect of family functioning on depression, and the mediating effect accounted for 33.73 % of the total effect. The interaction term of family functioning and gender was significant predictor of coping style (ß = 0.33, t = 2.69, p < 0.05) and depression (ß = -1.98, t = -2.46, p < 0.05). CONCLUSIONS: Good family functioning is a negative predictor of depression in college students. Coping style plays a partial mediating role between family functioning and depression. The first half path and the direct path of the mediation model are modulated by gender.

Biohybrid magnetic microrobots: An intriguing and promising platform in biomedicine.

Biohybrid magnetic microrobots (BMMs) have emerged as an exciting class of microrobots and have been considered as a promising platform in biomedicine. Many microorganisms and body's own cells show intriguing properties, such as morphological characteristics, biosafety, and taxis abilities (e.g., chemotaxis, aerotaxis), which have made them attractive for the fabrication of microrobots. For remote controllability and sustainable actuation, magnetic components are usually incorporated onto these biological entities, and other functionalized non-biological components (e.g., therapeutic agents) are also included for specific applications. This review highlights the latest developments in BMMs with a focus on their biomedical applications. It starts by introducing the fundamental understanding of the propulsion system at the microscale in a magnetically driven manner, followed by a summary of diverse BMMs based on different microorganisms and body's own cells along with their relevant applications. Finally, the review discusses how BMMs contribute to the advancements of microrobots, the current challenges of using BMMs in practical clinical settings, and the future perspectives of this exciting field. STATEMENT OF SIGNIFICANCE: Biohybrid magnetic microrobots (BMMs), composed of biological entities and functional parts, hold great potential and serve as a novel and promising platform for biomedical applications such as targeted drug delivery. This review comprehensively summarizes the recent advancements in BMMs for biomedical applications, mainly focused on the representative propulsion modalities in a magnetically propelled manner and diverse designs of BMMs based on different biological entities, including microorganisms and body's own cells. We hope this review can provide ideas for the future design, development, and innovation of micro/nanorobots in the field of biomedicine.

Carbon reduction behavior of waste power battery recycling enterprises considering learning effects.

The carbon reduction behavior of waste power battery recycling (WPBR) enterprises is essential for promoting resource conservation and environmental protection. Introducing the learning effects of carbon reduction research and development (R&D) investment, this study constructs an evolutionary game model between local governments and WPBR enterprises to study the behavior choice of carbon reduction. The paper explores the evolutionary process and factors affecting carbon reduction behavior choices of WPBR enterprises from internal R&D motivation and external regulation perspectives. The critical results reveal that the existence of learning effects significantly reduces the probability of environmental regulation by local governments while effectively increasing the probability of WPBR enterprises implementing carbon reduction. The learning rate index positively correlates with the likelihood of enterprises implementing carbon emissions reduction. In addition, carbon reduction subsidies considerably maintain considerably negative relation with the probability of enterprise carbon reduction behavior. The following conclusions are drawn: (1) The learning effect of carbon reduction R&D investment is the intrinsic driving force for WPBR enterprises' carbon reduction behavior, which can promote enterprises to proactively implement carbon reduction under fewer constraints of government environmental regulation; (2) Pollution fines and carbon trade prices in environmental regulation can promote enterprises carbon reduction, while carbon reduction subsidies inhibit their reduction behavior; (3) There exists an evolutionarily stable strategy between government-enterprise game only under the dynamic mechanism. The research provides insights for decision-making on enterprises' carbon reduction R&D investment and local government environmental regulation policy under carbon reduction targets.

Physician scheduling problem in Mobile Cabin Hospitals of China during Covid-19 outbreak.

In this paper, we investigate a novel physician scheduling problem in the Mobile Cabin Hospitals (MCH) which are constructed in Wuhan, China during the outbreak of the Covid-19 pandemic. The shortage of physicians and the surge of patients brought great challenges for physicians scheduling in MCH. The purpose of the studied problem is to get an approximately optimal schedule that reaches the minimum workload for physicians on the premise of satisfying the service requirements of patients as much as possible. We propose a novel hybrid algorithm integrating particle swarm optimization (PSO) and variable neighborhood descent (VND) (named as PSO-VND) to find the approximate global optimal solution. A self-adaptive mechanism is developed to choose the updating operators dynamically during the procedures. Based on the special features of the problem, three neighborhood structures are designed and searched in VND to improve the solution. The experimental comparisons show that the proposed PSO-VND has a significant performance increase than the other competitors.

Discovery of Orally Bioavailable N-Benzylpiperidinol Derivatives as Potent and Selective USP7 Inhibitors with In Vivo Antitumor Immunity Activity against Colon Cancer.

USP7 emerges as a potential therapeutic target for cancers, as it plays an important role in the development of tumorigenesis by stabilizing multiple cancer-relevant proteins. Nevertheless, the discovery of drug-like USP7 inhibitors remains challenging. Herein, we report a series of N-benzylpiperidinol derivatives as potent and selective USP7 inhibitors (e.g., X20 and X26: IC50 = 7.6 and 8.2 nM), whose binding modes were revealed by crystallographic studies to be distinct from the known N-acylpiperidinol USP7 inhibitors. Among them, X36 with good oral PK profiles (rat: F = 40.8% and T1/2 = 3.5 h) exhibited significant antitumor efficacy in the MC38 colon cancer syngeneic mouse model, at least partly through upregulating the tumor infiltration of CD8+ T, NK, and NKT cells and downregulating that of Tregs and MDSCs. These findings may further pave the way for the development of USP7 inhibitors as novel cancer immunotherapy drugs.

The driving effect of technological innovation on green development: dynamic efficiency spatial variation.

In order to further explore the internal transmission mechanism between technological innovation and green development in manufacturing industry under the background of obvious development characteristics in the new era, this paper constructed an integrated methodology system to evaluate the internal impact mechanism of technological innovation value chain efficiency on green development efficiency based on spatial perspective. First, the Network Slack-based model and Global Malmquist-Luenberger model are constructed to reveal the internal development law of technological innovation and green development of manufacturing industry. Secondly, the spatial Dubin model is employed to analyze the impact of current development characteristics and technological innovation on green development. The results show that innovation value chain efficiency is higher than technological innovation efficiency, and economic transformation efficiency is lower than that of technological innovation value chain. During the study period, the efficiency of technological innovation value chain in the four economic regions present fluctuant growth trend, and the eastern region has the highest value. The green development efficiency in the east, central, west, and northeast regions of manufacturing industry is higher than 1, and it shows an obvious spatial agglomeration effect. Besides, the efficiency of technological innovation, information and communication technology, urbanization, and the advanced industrial structure are all conducive to the improvement of green development in manufacturing industry. This paper studies the influence mechanism of technological innovation value chain efficiency on green development based on spatial perspective and puts forward relevant countermeasures and suggestions to effectively promote green development of manufacturing industry, providing relevant theoretical research for green and high-quality development.

Design, Synthesis, and Biological Evaluation of Triazolone Derivatives as Potent PPARα/δ Dual Agonists for the Treatment of Nonalcoholic Steatohepatitis.

Peroxisome proliferator-activator receptors α/δ (PPARα/δ) are regarded as potential therapeutic targets for nonalcoholic steatohepatitis (NASH). However, PPARα/δ dual agonist GFT-505 exhibited poor anti-NASH effects in a phase III clinical trial, probably due to its weak PPARα/δ agonistic activity and poor metabolic stability. Other reported PPARα/δ dual agonists either exhibited limited potency or had unbalanced PPARα/δ agonistic activity. Herein, we report a series of novel triazolone derivatives as PPARα/δ dual agonists. Among them, compound H11 exhibited potent and well-balanced PPARα/δ agonistic activity (PPARα EC50 = 7.0 nM; PPARδ EC50 = 8.4 nM) and a high selectivity over PPARγ (PPARγ EC50 = 1316.1 nM) in PPAR transactivation assays. The crystal structure of PPARδ in complex with H11 revealed a unique PPARδ-agonist interaction. H11, which had excellent PK properties and a good safety profile, showed potent in vivo anti-NASH effects in preclinical models. Together, H11 holds a great promise for treating NASH or other inflammatory and fibrotic diseases.

An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios.

Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis.

Antimicrobial photodynamic therapy for oral Candida infection in adult AIDS patients: A pilot clinical trial.

BACKGROUND: Antimicrobial photodynamic therapy (aPDT) using methylene blue (MB) plus potassium iodide (KI) has been shown to be effective in killing Candida albicans in many in vitro and in vivo studies, however, there are limited reports of clinical investigations. This study aimed to explore the clinical application of aPDT with MB plus KI for the treatment of oral infection caused by C. albicans in adult acquired immune deficiency syndrome (AIDS) patients. METHODS: A total of 21 adult AIDS patients with C. albicans oral candidiasis were divided into two groups according to MB concentration and received two consecutive aPDT treatments. Immediately before and after the aPDT treatments, C. albicans yeast isolates were recovered to measure the colony-forming units per mL (CFU/mL), biofilm formation, and to analyze the 25S rDNA genotype. Patients were assessed for the clinical recovery of oral lesions and improvement of symptoms. RESULTS: The Log10 CFU/mL of C. albicans decreased significantly after the second aPDT but not the first aPDT. There was no significant difference between the two MB concentrations. Both aPDT protocols decreased the oral lesions and clinical symptoms with no significant difference after 2-fraction aPDT. The biofilm formation of C. albicans isolates did not change before and after aPDT. The killing efficiency of 2-fraction-aPDT was not associated with either biofilm formation or 25S rDNA genotype. CONCLUSIONS: Two-fraction-aPDT with MB plus KI could reduce the number of viable C. albicans fungal cells and improve the clinical symptoms of oral candidiasis in adult AIDS patients, regardless of the biofilm formation or 25S rDNA genotype of infected C. albicans isolates.

Automatic Acetowhite Lesion Segmentation via Specular Reflection Removal and Deep Attention Network.

Automatic acetowhite lesion segmentation in colposcopy images (cervigrams) is essential in assisting gynecologists for the diagnosis of cervical intraepithelial neoplasia grades and cervical cancer. It can also help gynecologists determine the correct lesion areas for further pathological examination. Existing computer-aided diagnosis algorithms show poor segmentation performance because of specular reflections, insufficient training data and the inability to focus on semantically meaningful lesion parts. In this paper, a novel computer-aided diagnosis algorithm is proposed to segment acetowhite lesions in cervigrams automatically. To reduce the interference of specularities on segmentation performance, a specular reflection removal mechanism is presented to detect and inpaint these areas with precision. Moreover, we design a cervigram image classification network to classify pathology results and generate lesion attention maps, which are subsequently leveraged to guide a more accurate lesion segmentation task by the proposed lesion-aware convolutional neural network. We conducted comprehensive experiments to evaluate the proposed approaches on 3045 clinical cervigrams. Our results show that our method outperforms state-of-the-art approaches and achieves better Dice similarity coefficient and Hausdorff Distance values in acetowhite legion segmentation.

SCNET: A Novel UGI Cancer Screening Framework Based on Semantic-Level Multimodal Data Fusion.

Upper gastrointestinal (UGI) cancer has been identified as one of the ten most common causes of cancer deaths globally. UGI cancer screening is critical to improving the survival rate of UGI cancer patients. While many approaches to UGI cancer screening rely on single-modality data such as gastroscope imaging, limited studies have been dedicated to UGI cancer screening exploiting multisource and multimodal medical data, which could potentially lead to improved screening results. In this paper, we propose semantic-level cancer-screening network (SCNET), a framework for UGI cancer screening based on semantic-level multimodal upper gastrointestinal data fusion. Specifically, the proposed SCNET consists of a gastrointestinal image recognition flow and a textual medical record processing flow. High-level features of upper gastrointestinal data are extracted by identifying effective feature channels according to the correlation between the textual features and the spatial structure of the image features. The final screening results are obtained after the data fusion step. The experimental results show that the improvement of our approach over the state-of-the-art ones reached 4.01% in average. The source code of SCNET is available at https://github.com/netflymachine/SCNET.

Data-Driven Analysis of Radiologists' Behavior for Diagnosing Thyroid Nodules.

Thyroid nodule has been a common and serious threaten to human health. With the identification and diagnosis of thyroid nodules in the general population, large volumes of examination reports in clinical practice have been accumulated. They provide data basics of analyzing radiologists' behavior of diagnosing thyroid nodules. To conduct data-driven analysis of radiologists' behavior, an experimental framework is designed based on belief rule base, which is essentially a white box for knowledge representation and uncertain reasoning. Under the framework, with 2744 examination reports of thyroid nodules in the period from January 2012 to February 2019 that have been collected from a tertiary hospital located in Hefei, Anhui, China, experimental results are obtained from conducting missing validation, self-validation, and mutual validation. Three principles are then concluded from the results and corresponding analysis. The first is that missing features on some criteria are considered as benign ones by default, the second is that there is generally inconsistency between the recorded features on criteria and the overall diagnosis, and the third is that different radiologists have different diagnostic preferences. These three principles reflect three diagnostic behavioral characteristics of radiologists, namely reliability, inconsistency, and independence. Based on the three principles and radiologists' behavioral characteristics, managerial insights in a general case are concluded to make the findings in this study available in other situations.

Elaborated pollen packaging and dispensing mechanism induced by petal architecture from a Papaveraceae species.

Secondary pollen presentation (SPP) is a reproductive strategy that enhances the efficiency of pollen transfer, which has been explored for more than 200 years, resulting in 10 identified types of SPP. The ephemeral plant Hypecoum erectum L. (Papaveraceae) has an elaborate petal structure. The middle lobe is a key functional organ in SPP. To explore the importance of the middle lobe structure, we measured the flowering process, the curling movement and growth of the middle lobe, pollination characteristics, pollination efficiency, and the mating system in H. erectum in the field. The yellow middle lobe structure had an important role in attracting pollinators. The middle lobes on the inner petals function as a redundant cucullate structure and wrapped about 84% of the total pollen grains as soon as the anthers dehisced. These then grew upward and gradually presented pollen to pollinators via the roll out of the middle lobes. One bee species, Colletes vestitus from Colletidae, was the only effective pollinator of H. erectum. The SPP mechanism increased the efficiency of pollen transfer by C. vestitus. The middle lobes, which wrapped pollen and grew upward, contacted the stigma and provided an advantage for self-pollination and outcrossing by growing upward higher than the corolla. Hypecoum erectum L. has a mixed mating system with selfing and outcrossing. Thus, the SPP mechanism plays a key role during the pollination process and is necessary for improving pollination efficiency and promoting reproductive success.

Decomposition analysis of China's CO2 emissions (2000-2016) and scenario analysis of its carbon intensity targets in 2020 and 2030.

To address the unprecedented increase in China's CO2 emissions over the past decades, the Chinese government has implemented many policies that are aimed at reducing carbon intensity. Applying the LMDI method, this study conducts a decomposition analysis of the drivers influencing China's CO2 emissions by examining the details of 41 industry sub-sectors during 2000-2016; further, it predicts the carbon intensity reduction potential in 2020 and 2030 based on various official policies and documents. We conclude that energy intensity was the primary indicator that reduced CO2 emissions, whereas the effects of carbon intensity, energy mix, and industrial structure were relatively minor. During the study period, the effect of industrial structure optimization on the change in CO2 emissions shifted from the promotion of emissions to their suppression, with the inhibiting influence becoming greater over time. Finally, scenario analysis indicated that CO2 intensity would decrease 21.5% by 2020 compared to the 2015 level, and the reduction target of 65% would be achieved fully in 2030 in the outlook scenario. Energy intensity is the largest contributor to the decrease in CO2 emissions during 2016-2020, whereas industrial structure optimization shows the greatest potential for environmental improvement during 2020-2030. This paper concludes that more stringent policies are essential to reducing CO2 emissions in the near future.

Blockchain-Based Medical Records Secure Storage and Medical Service Framework.

Accurate and complete medical data are one valuable asset for patients. Privacy protection and the secure storage of medical data are crucial issues during medical services. Secure storage and making full use of personal medical records has always been a concern for the general population. The emergence of blockchain technology brings a new idea to solve this problem. As a hash chain with the characteristics of decentralization, verifiability and immutability, blockchain technology can be used to securely store personal medical data. In this paper, we design a storage scheme to manage personal medical data based on blockchain and cloud storage. Furthermore, a service framework for sharing medical records is described. In addition, the characteristics of the medical blockchain are presented and analyzed through a comparison with traditional systems. The proposed storage and sharing scheme does not depend on any third-party and no single party has absolute power to affect the processing.