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This paper studies the impact of digital economy development on carbon emissions, which is a hot topic in the field of environmental economics. This topic is within the scope of the Frontiers in Environmental Science and is particularly in line with the theme of Environmental Economics and Management section in the journal. Firstly, this paper constructs the Digital Economy Development Index and its variable indicators to measure the development of the digital economy. Secondly, this paper uses urban panel data to reveal the impact of digital economy development on carbon emissions, and further adopts IV method to deal with endogeneity issues. Thirdly, this paper performs the heterogeneity analyses depending on the characteristics of the city. The results can provide a basis for formulating differentiated policies. Fourthly, this paper further explores the mechanism by which the digital economy affects carbon peaking. The results show that the digital economy may influence carbon peaking through upgrading of the industrial structure, technical innovation, and energy consumption.

Nonadherence to medication among patients with cardiovascular diseases undermines the desired therapeutic outcomes. eHealth interventions emerge as promising strategies to effectively tackle this issue. The aim of this study was to conduct a network meta-analysis (NMA) to compare and rank the efficacy of various eHealth interventions in improving medication adherence among patients with cardiovascular diseases (CVDs). A systematic search strategy was conducted in PubMed, Embase, Web of Science, Cochrane, China National Knowledge Infrastructure Library (CNKI), China Science and Technology Journal Database (Weipu), and WanFang databases to search for randomized controlled trials (RCTs) published from their inception on January 15, 2024. We carried out a frequentist NMA to compare the efficacy of various eHealth interventions. The quality of the literature was assessed using the risk of bias tool from the Cochrane Handbook (version 2.0), and extracted data were analyzed using Stata16.0 (StataCorp LLC) and RevMan5.4 software (Cochrane Collaboration). The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. A total of 21 RCTs involving 3904 patients were enrolled. The NMA revealed that combined interventions (standardized mean difference [SMD] 0.89, 95% CI 0.22-1.57), telephone support (SMD 0.68, 95% CI 0.02-1.33), telemonitoring interventions (SMD 0.70, 95% CI 0.02-1.39), and mobile phone app interventions (SMD 0.65, 95% CI 0.01-1.30) were statistically superior to usual care. However, SMS compared to usual care showed no statistical difference. Notably, the combined intervention, with a surface under the cumulative ranking curve of 79.3%, appeared to be the most effective option for patients with CVDs. Regarding systolic blood pressure and diastolic blood pressure outcomes, the combined intervention also had the highest probability of being the best intervention. The research indicates that the combined intervention (SMS text messaging and telephone support) has the greatest likelihood of being the most effective eHealth intervention to improve medication adherence in patients with CVDs, followed by telemonitoring, telephone support, and app interventions. The results of these network meta-analyses can provide crucial evidence-based support for health care providers to enhance patients' medication adherence. Given the differences in the design and implementation of eHealth interventions, further large-scale, well-designed multicenter trials are needed. INPLASY 2023120063; https://inplasy.com/inplasy-2023-12-0063/.

Machine learning (ML) has achieved substantial success in performing healthcare tasks in which the configuration of every part of the ML pipeline relies heavily on technical knowledge. To help professionals with borderline expertise to better use ML techniques, Automated ML (AutoML) has emerged as a prospective solution. However, most models generated by AutoML are black boxes that are challenging to comprehend and deploy in healthcare settings. We conducted a systematic review to examine AutoML with interpretation systems for healthcare. We searched four databases (MEDLINE, EMBASE, Web of Science, and Scopus) complemented with seven prestigious ML conferences (AAAI, ACL, ICLR, ICML, IJCAI, KDD, and NeurIPS) that reported AutoML with interpretation for healthcare before September 1, 2023. We included 118 articles related to AutoML with interpretation in healthcare. First, we illustrated AutoML techniques used in the included publications, including automated data preparation, automated feature engineering, and automated model development, accompanied by a real‐world case study to demonstrate the advantages of AutoML over classic ML. Then, we summarized interpretation methods: feature interaction and importance, data dimensionality reduction, intrinsically interpretable models, and knowledge distillation and rule extraction. Finally, we detailed how AutoML with interpretation has been used for six major data types: image, free text, tabular data, signal, genomic sequences, and multi‐modality. To some extent, AutoML with interpretation provides effortless development and improves users' trust in ML in healthcare settings. In future studies, researchers should explore automated data preparation, seamless integration of automation and interpretation, compatibility with multi‐modality, and utilization of foundation models. This article systematically reviewed AutoML techniques, summarized interpretation methods, and detailed how AutoML with interpretation has been used for six major healthcare data types: image, free text, tabular data, signal, genomic sequences, and multi‐modality.

Under iodine-catalyzed and visible light-irradiated aerobic conditions, selenoalkoxylation of olefins with diselenides and alcohols can be efficiently achieved to afford the useful α-alkoxyl selenides in the presence of only 0.5 equiv. of H_2O_2. Controlling the sub-stoichiometric H_2O_2 amount is crucial to avoid the non-selective over-oxidation of the diselenides that leads to the ineffective hyper-valent selenium compounds. Meanwhile, under visible light irradiation, the green, safe, and low-cost air can work as a supplemental mild oxidant in the reaction to ensure selective oxidation of the diselenides, full conversion of the reactants, and ultimately good yield of the products.

Salt stress is a critical constraint affecting the cultivation of Tunisian soft-seeded pomegranate ( Punica granatum L.). To elucidate the molecular mechanisms underlying exogenous melatonin (MT)-mediated enhancement of salt tolerance in pomegranate seedlings, this study integrated physiological phenotyping and transcriptome sequencing to systematically investigate MT’s regulatory effects on antioxidant systems, photosynthetic apparatus function, osmotic adjustment, and core metabolic pathways under salt stress. The results demonstrated that 200 mM NaCl treatment induced reactive oxygen species (ROS) overaccumulation, elevating malondialdehyde (MDA) content and relative electrical conductivity (REC) by 0.43 and 0.46 fold, respectively. Concurrently, salt stress severely impaired photosynthetic performance: PSII maximum photochemical efficiency (F V /F M ) decreased by 44.5%, actual photochemical efficiency (Y II ) and photochemical quenching (qP) were reduced, and non-photochemical quenching (NPQ) increased, indicating serious photoinhibition and energy wastage. In contrast, 400 μM MT treatment effectively mitigated oxidative damage by coordinated activation of superoxide dismutase (SOD,+14.3%), peroxidase (POD,+21.7%), and catalase (CAT,+11.7%) activities, thereby stabilizing membrane integrity. Furthermore, MT significantly alleviated photoinhibition: F V / F M increased by 39%, Y II and qP rose, and NPQ decreased compared to salt-stressed plants, reflecting enhanced protection of the PSII reaction center and optimized light energy allocation. Transcriptomic analysis reveals that MT treatment is associated with alterations in the expression of key sucrose metabolism genes, including the upregulation of SUCROSE SYNTHASE ( SUS ) and UDP-GLUCOSE PYROPHOSPHORYLASE ( UGP2 ), as well as the recovery of GLYCOGEN SYNTHASE ( glgA ) expression following salt stress inhibition. These changes suggest a potential role for MT in modulating carbon metabolic homeostasis. Additionally, MT application is linked to expression changes in genes within the Mitogen-Activated Protein Kinase (MAPK) signaling pathway. Concurrently, broad expression variations are observed in genes associated with multiple phytohormone signaling pathways. Weighted gene co-expression network analysis (WGCNA) further identifies two core gene modules: the blue module is enriched with antioxidant-related genes (e.g., LOC116212144 ), while the yellow module is closely associated with genes implicated in membrane stability (e.g., LOC116203737 ). Integrated physiological and transcriptional evidence indicates that exogenous melatonin may enhance salt tolerance in pomegranate seedlings by activating the antioxidant system, protecting photosynthetic apparatus, regulating carbon metabolism, and influencing multiple signal transduction pathways. This study provides a theoretical foundation for further elucidating the mechanistic basis of MT-mediated salt adaptation in plants.

Event-based cameras are suitable for human action recognition (HAR) by providing movement perception with highly dynamic range, high temporal resolution, high power efficiency and low latency. Spike Neural Networks (SNNs) are naturally suited to deal with the asynchronous and sparse data from the event cameras due to their spike-based event-driven paradigm, with less power consumption compared to artificial neural networks. In this paper, we propose two end-to-end SNNs, namely Spike-HAR and Spike-HAR++, to introduce spiking transformer into event-based HAR. Spike-HAR includes two novel blocks: a spike attention branch, which enables model to focus on regions with high spike rates, reducing the impact of noise to improve the accuracy, and a parallel spike transformer block with simplified spiking self-attention mechanism, increasing computational efficiency. To better extract crucial information from high-level features, we modify the architecture of the spike attention branch and extend it in Spike-HAR to a higher dimension, proposing Spike-HAR++ to further enhance classification performance. Comprehensive experiments were conducted on four HAR datasets: SL-Animals-DVS, N-LSA64, DVS128 Gesture and DailyAction-DVS, to demonstrate the superior performance of our proposed model. Additionally, the proposed Spike-HAR and Spike-HAR++ require only 0.03 and 0.06 mJ, respectively, to process a sequence of event frames, with model sizes of only 0.7 and 1.8 M. This efficiency positions it as a promising new SNN baseline for the HAR community. Code is available at Spike-HAR++ .

This study addresses the freeze-up problem in HVAC system heat exchangers of industrial buildings in severely cold regions by proposing a collaborative anti-freeze control strategy based on multi-objective optimization. Taking a diesel engine laboratory as the research case, key freezing-inducing factors were identified through system performance analysis and fault diagnosis. An innovative interlocked anti-freeze control system was developed by integrating electric heating with dynamic regulation of bypass air volume. Utilizing gray relational analysis, the optimal interlock control scheme was selected from four alternatives based on a comprehensive performance evaluation. Multi-objective optimization through the NSGA-II algorithm was performed on parameters including the set temperature, water flow rate, and fresh air volume, achieving coordinated optimization of energy consumption (11.4% reduction compared to pre-optimization) and thermal comfort. TRNSYS-based simulation verification demonstrated that the system maintains a 94.71% freeze protection time assurance rate under extreme operating conditions, effectively resolving the reliability deficiencies of traditional solutions in severely cold environments. This research provides a novel method for industrial building HVAC system anti-freeze design that harmonizes energy efficiency and comfort performance.

Disruptive behaviors in online gaming communities are a growing concern, affecting player experience, retention, and well-being. While previous research has primarily focused on the victims’ experiences, this study examines the psychological mechanisms underlying the attitudinal and behavioral responses to both encountering disruptive behaviors and being flagged for such behaviors, as well as the effects on retention. The study retrieved longitudinal telemetry records of player reporting and gameplay data from the North American server of a popular competitive player vs. player multiplayer online game, coupled with a psychometric survey of a randomly selected sample of 1,217 players. Based on the rejection-disidentification model, this research identifies a shared pathway for both reporting and being reported for disruptive behavior. Our findings support a serial mediation model where both experiences are linked to decreased player engagement. This reduced engagement, reflected in diminished participation in game battles over time, is mediated by perceived discrimination and a reduced sense of community. Moreover, drawing on the concept of procedural justice from the group engagement model, the study delineates unique pathways for the disengagement process for reporters and those reported. Being flagged for disruptive behavior leads to a significant drop in sustained engagement through a decreased sense of community, which is not the case for reporting disruptive behavior. The article concludes with a discussion of the theoretical and practical implications of these findings.

Background Generative artificial intelligence (GAI) has developed rapidly and been increasingly used in scholarly publishing, so it is urgent to examine guidelines for its usage. This cross-sectional study aims to examine the coverage and type of recommendations of GAI usage guidelines among medical journals and how these factors relate to journal characteristics. Methods From the SCImago Journal Rank (SJR) list for medicine in 2022, we generated two groups of journals: top SJR ranked journals ( N  = 200) and random sample of non-top SJR ranked journals ( N  = 140). For each group, we examined the coverage of author and reviewer guidelines across four categories: no guidelines, external guidelines only, own guidelines only, and own and external guidelines. We then calculated the number of recommendations by counting the number of usage recommendations for author and reviewer guidelines separately. Regression models examined the relationship of journal characteristics with the coverage and type of recommendations of GAI usage guidelines. Results A higher proportion of top SJR ranked journals provided author guidelines compared to the random sample of non-top SJR ranked journals (95.0% vs. 86.7%, P  < 0.01). The two groups of journals had the same median of 5 on a scale of 0 to 7 for author guidelines and a median of 1 on a scale of 0 to 2 for reviewer guidelines. However, both groups had lower percentages of journals providing recommendations for data analysis and interpretation, with the random sample of non-top SJR ranked journals having a significantly lower percentage (32.5% vs. 16.7%, P  < 0.05). A higher SJR score was positively associated with providing GAI usage guidelines for both authors (all P  < 0.01) and reviewers (all P  < 0.01) among the random sample of non-top SJR ranked journals. Conclusions Although most medical journals provided their own GAI usage guidelines or referenced external guidelines, some recommendations remained unspecified (e.g., whether AI can be used for data analysis and interpretation). Additionally, journals with lower SJR scores were less likely to provide guidelines, indicating a potential gap that warrants attention. Collaborative efforts are needed to develop specific recommendations that better guide authors and reviewers.

Background Hyperuricemia and gout have garnered increasing attention as significant health concerns in recent years, often associated with damage to multiple bodily systems. Consequently, the reduction of uric acid levels has become particularly crucial. The utilization of dietary supplements presents potential adjunctive treatment options for individuals with gout. Certain dietary supplements are purported to aid in the reduction of uric acid levels and are highly preferred by patients due to their affordability, ease of use, and accessibility. The aim of this article was to compare the efficacy and safety of dietary supplements in modulating uric acid, oxidative stress, and lipid metabolism in patients with hyperuricemia or gout, using a comprehensive network meta-analysis (NMA) approach. Methodology A comprehensive search was performed across both Chinese and English databases to identify randomized controlled trials (RCTs) examining the efficacy of dietary supplements in reducing uric acid levels. Network meta-analysis was conducted using Stata 16.0 software, while RevMan 5.3 software was employed to assess the quality of the literature and evaluate the risk of bias. Result A total of 30 RCTs, encompassing 44,972 patients, were conducted. The findings of the study indicated that folic acid (mean difference [MD] = -57.62 μmol/L, 95% confidence interval [CI] [-107.14, -8.1]) and probiotics (MD = -42.52 μmol/L, 95% CI [-81.95, -3.09]) significantly reduced uric acid levels compared to conventional therapy. Furthermore, Vitamin C (MD = -0.92 nmmol/ml, 95% CI [-1.54, -0.31]) and Vitamin E (MD = -1.05 nmmol/ml, 95% CI [-2.01, -0.1]) were effective in reducing oxidative stress-related malondialdehyde (MDA) levels. In terms of lipid metabolism improvement, DKB114 (MD = -0.45 mmol/L, 95% CI [-0.9, -0.001]) and curcumin (MD = -0.54 mmol/L, 95% CI [-0.89, -0.18]) demonstrated statistically significant reductions in low-density lipoprotein (LDL) levels. Analysis of subgroups revealed that administration of 500 mg of vitamin C resulted in a significant reduce in uric acid levels when compared to conventional treatment (MD =  − 21.67 μmol/L, 95% CI [− 43.01, − 0.33]), indicating statistically significant differences. The safety profile of all dietary supplements has generally been demonstrated to be favorable. Conclusion Dietary supplements hold significant potential for managing gout and hyperuricemia, as well as improving patients' metabolic status. Future research should focus on larger-scale studies to further explore these findings.