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Climate change traps heat, affecting various species in previously dry areas. Climate change brought on by emissions of greenhouse gases exacerbates problems such as severe storms, earthquakes, epidemics, and food distribution. The group of developed and developing countries, the world’s biggest carbon emitters and most significant economies, is expertly planning to lessen its environmental challenges and contribute to achieving Sustainable Development Goals 7 and 13 set by the United Nations. This study uses the novel econometric methodologies of the dynamic ordinary least square (DOLS) estimator, the augmented mean group (AMG) estimator, and the fully modified ordinary least square (FMOLS) estimate to examine the influence of economic policy uncertainty, renewable energy consumption, geopolitical risk, non-renewable energy consumption, and economic growth on ecological footprint from 2000 to 2021. The results reveal that the variables are co-integrated; REC reduces carbon emissions, EPU, geopolitical risk, and economic growth contribute to increasing carbon emissions, while urbanization improves carbon emission. Finally, the results suggest that the developed and developing economies can progress toward SDGs 7 and 13 by using renewable energy, lowering the geopolitical risk, effectively handling policy uncertainty, and reducing urbanization.

Background: The formin family proteins play an important role in guiding the assembly and nucleation of linear actin and can promote the formation of actin filaments independently of the Arp2/3 complex. As a key protein that regulates the cytoskeleton and cell morphological structure, the formin gene family has been widely studied in plants such as Arabidopsis thaliana and rice. Methods: In this study, we conducted comprehensive analyses, including phylogenetic tree construction, conserved motif identification, co-expression network analysis, and transcriptome data mining. Results: A total of 18 MtFH gene family members were identified, and the distribution of these genes on chromosomes was not uniform. The phylogenetic tree divided the FH proteins of the four species into two major subgroups (Clade I and Clade II). Notably, Medicago truncatula and soybean exhibited closer phylogenetic relationships. The analysis of cis-acting elements revealed the potential regulatory role of the MtFH gene in light response, hormone response, and stress response. GO enrichment analysis again demonstrated the importance of FH for reactions such as actin nucleation. Expression profiling revealed that MtFH genes displayed significant transcriptional responsiveness to cold, drought, and salt stress conditions. And there was a temporal complementary relationship between the expression of some genes under stress. The protein interaction network indicated an interaction relationship between MtFH protein and profilin, etc. In addition, 22 miRNAs were screened as potential regulators of the MtFH gene at the post-transcriptional level. Conclusions: In general, this study provides a basis for deepening the understanding of the physiological function of the MtFH gene and provides a reference gene for stress resistance breeding in agricultural production.

Modern electrical vehicles (EVs) are equipped with sizable batteries that possess significant potential as energy prosumers. EVs are poised to be transformative assets and pivotal contributors to the virtual power plant (VPP), enhancing the performance and profitability of VPPs. The number of household EVs is increasing yearly, and this poses new challenges to the optimization of VPP operations. The computational cost increases exponentially as the number of decision variables rises with the increasing participation of EVs. This paper explores the role of a large number of EVs as prosumers, interacting with a VPP consisting of a photovoltaic system and battery energy storage system. To accommodate the large quantity of EVs in the modeling, this research adopts the decentralized control structure. It optimizes EV operations by regulating their charging and discharging behavior in response to pricing signals from the VPP. A two-stage optimization framework is proposed for VPP-EV operation using a reinforcement algorithm and gradient-based programming. Action masking for reinforcement learning is explored to eliminate invalid actions, reducing ineffective exploration, thereby accelerating the convergence of the algorithm. The proposed approach is capable of handling a substantial number of EVs and addressing the stochastic characteristics of EV charging and discharging behaviors. Simulation results demonstrate that the VPP-EV operation optimization increases the revenue of the VPP and significantly reduces the electricity costs for EV owners. Through the optimization of EV operations, the charging cost of 1000 EVs participating in the V2G services is reduced by 26.38% compared to those that opt out of the scheme, and VPP revenue increases by 27.83% accordingly.

PurposeEmployee’s innovative behavior as a team allows the organization to achieve its goals; however, team green creativity requires transformational and entrepreneurial leader support. Therefore, the study explores the impact of green transformational and entrepreneurial leadership on team innovative behavior and green new product development with the mediating role of team green creativity.Design/methodology/approachA survey was conducted to collect data from 455 employees working in the hospitality industry via a self-administered questionnaire, and hypotheses were analyzed using the partial least squares structural equation modeling PLS-SEM technique using Smart-PLS 4.0.FindingsThe results indicate that green transformational and entrepreneurial leadership styles positively and significantly affect team innovative behavior and new green product development performance. Furthermore, findings show that team green creativity partially mediates the relationship between green transformational and entrepreneurial leadership on team innovative behavior, and new green product development performance.Research limitations/implicationsThe results of this study provide insights to hospitality professionals pursuing the improvement of team innovative behavior and new green product development performance through team green creativity and leadership styles.Practical implicationsThis study is useful for organizations that target new green product development performance and establish higher green innovative behavior cohesively among its team members through these robust leadership styles.Originality/valueThis study is the first attempt to provide a valuable contribution to the growing field of green leadership styles on team innovative behavior and new green product development performance through team green creativity.

This study extended the research on the association between cognitive flexibility and entrepreneurial intention by developing a moderated mediation model. This research examined whether entrepreneurial alertness mediates this association. This study also investigated whether entrepreneurial self-efficacy moderates this mediation model by conducting a moderated mediation model. The sample of this study comprised 486 medical university students of Pakistan. Data gathered using a self-report administered questionnaire and hypotheses were tested with SEM structural equation modeling technique through AMOS user-defined estimates and developed a syntax based on Hayes model 15 of process macro. The results revealed that cognitive flexibility is positively related to entrepreneurial alertness and entrepreneurial intentions. Furthermore, findings showed that the indirect relationship of entrepreneurial alertness via entrepreneurial self-efficacy on cognitive flexibility and the entrepreneurial intention was also significant. This study contributes to the emerging research on psychology and entrepreneurship as well as concludes that individuals with a high level of cognitive flexibility, entrepreneurial alertness, and entrepreneurial self-efficacy are more inclined to pursue a career in entrepreneurship.

The continuous generalized wavelet transform (GWT) which is regarded as a kind of time-linear canonical domain (LCD)-frequency representation has recently been proposed. Its constant-Q property can rectify the limitations of the wavelet transform (WT) and the linear canonical transform (LCT). However, the GWT is highly redundant in signal reconstruction. The discrete linear canonical wavelet transform (DLCWT) is proposed in this paper to solve this problem. First, the continuous linear canonical wavelet transform (LCWT) is obtained with a modification of the GWT. Then, in order to eliminate the redundancy, two aspects of the DLCWT are considered: the multi-resolution approximation (MRA) associated with the LCT and the construction of orthogonal linear canonical wavelets. The necessary and sufficient conditions pertaining to LCD are derived, under which the integer shifts of a chirp-modulated function form a Riesz basis or an orthonormal basis for a multi-resolution subspace. A fast algorithm that computes the discrete orthogonal LCWT (DOLCWT) is proposed by exploiting two-channel conjugate orthogonal mirror filter banks associated with the LCT. Finally, three potential applications are discussed, including shift sampling in multi-resolution subspaces, denoising of non-stationary signals, and multi-focus image fusion. Simulations verify the validity of the proposed algorithms.

In response to preharvest priming with exogenous methyl jasmonate (MeJA), tea plants adjust their physiological behavior at the molecular level. The whole-organism reconfiguration of aroma formation from the precursor to storage is poorly understood. In this study, we performed iTRAQ proteomic analysis and identified 337, 246, and 413 differentially expressed proteins in tea leaves primed with MeJA for 12 h, 24 h, and 48 h, respectively. Furthermore, a total of 266 nonvolatile and 100 volatile differential metabolites were identified by utilizing MS-based metabolomics. A novel approach that incorporated the integration of extended self-organizing map-based dimensionality was applied. The vivid time-scale changes tracing physiological responses in MeJA-primed tea leaves are marked in these maps. Jasmonates responded quickly to the activation of the jasmonic acid pathway in tea leaves, while hydroxyl and glycosyl jasmonates were biosynthesized simultaneously on a massive scale to compensate for the exhausted defense. The levels of α-linolenic acid, geranyl diphosphate, farnesyl diphosphate, geranylgeranyl diphosphate, and phenylalanine, which are crucial aroma precursors, were found to be significantly changed in MeJA-primed tea leaves. Green leaf volatiles, volatile terpenoids, and volatile phenylpropanoids/benzenoids were spontaneously biosynthesized from responding precursors and subsequently converted to their corresponding glycosidic forms, which can be stably stored in tea leaves. This study elucidated the physiological response of tea leaves primed with exogenous methyl jasmonate and revealed the molecular basis of source and sink changes on tea aroma biosynthesis and catabolism in response to exogenous stimuli. The results significantly enhance our comprehensive understanding of tea plant responses to exogenous treatment and will lead to the development of promising biotechnologies to improve fresh tea leaf quality.

Background According to the selection criteria outlined in China’s current basic medical insurance system, the majority of drugs for rare diseases fail to meet the requirements for inclusion. Consequently, patients with rare diseases lack sufficient protection for their fundamental rights to life and health. The purpose of this paper is to explore the construction of a multi-criteria decision analysis (MCDA) system for the inclusion of rare disease drugs in medical insurance. Methods A preliminary evaluation system was constructed through a systematic literature review, followed by the establishment of a formal evaluation system through expert investigation. Based on the formal system, expert surveys were conducted to score the system attributes, and the pairwise comparison method was used to calculate the weight contribution value of each attribute in the system. The robustness of the system was tested using the bootstrap method. Results A multi-criteria decision value evaluation system for the inclusion of rare disease drugs in medical insurance, comprising four primary attributes and 16 secondary attributes, was developed. The four primary attributes and their respective weight contributions are as follows: disease-related aspects (26.6%), treatment-related aspects (25.8%), economic-related aspects (24.0%), and social-related aspects (23.6%). Bootstrap validation confirmed the stability of the system results ( p  < 0.05). Conclusion This study initially constructed a value evaluation system for the inclusion of rare disease drugs in medical insurance, based on MCDA and multi-stakeholder perspectives. However, the system relied heavily on expert opinions and lacks empirical analysis. Given the complexity of real-world applications, further validation is necessary to assess the system’s applicability and feasibility.

Citrus blend black teas are popular worldwide, due to its unique flavor and remarkable health benefits. However, the aroma characteristics, aroma profiles and key odorants of it remain to be distinguished and cognized. In this study, the aroma profiles of 12 representative samples with three different cultivars including citrus (Citrus reticulata), bergamot (Citrus bergamia), and lemon (Citrus limon) were determined by a novel approach combined head space-solid phase microextraction (HS-SPME) with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS). A total of 348 volatile compounds, among which comprised esters (60), alkenes (55), aldehydes (45), ketones (45), alcohols (37), aromatic hydrocarbons (20), and some others were ultimately identified. The further partial least squares discrimination analysis (PLS-DA) certified obvious differences existed among the three groups with a screening result of 30 significant differential key volatile compounds. A total of 61 aroma-active compounds that mostly presented green, fresh, fruity, and sweet odors were determined in three groups with gas chromatography-olfactometry/mass spectrometry (GC-O/MS) assisted analysis. Heptanal, limonene, linalool, and trans-β-ionone were considered the fundamental odorants associated with the flavors of these teas. Comprehensive analysis showed that limonene, ethyl octanoate, copaene, ethyl butyrate (citrus), benzyl acetate, nerol (bergamot) and furfural (lemon) were determined as the characterized odorants for each type.

Medullary thyroid carcinoma (MTC) is a rare, aggressive neuroendocrine tumor with limited treatment options and frequent recurrence. Comprehensive recurrence risk stratification remains lacking. Here, we profile 482 MTC samples from 452 patients across ten Chinese clinical centers, identifying 10,092 proteins and mutations in 87.0% of patients. Clinically, MTC grading, concurrent papillary thyroid carcinoma, and lymph node metastasis are significant recurrence risk factors, whereas at the genetic level, RET M918T and RET S891A mutations are correlated with high recurrence risk in sporadic and hereditary MTC, respectively. Ubiquitinomics show downregulated E3 ligases CUL4B and TRIM32 are associated with structural recurrence. We define three molecular subtypes with distinct outcomes and present an integrative machine learning model combining clinical, genomic, and proteomic features, validated in an independent test dataset of 105 patients and a published dataset. This multi-center, multi-omics study enhances the understanding of MTC heterogeneity and facilitates personalized patient management. Medullary thyroid carcinoma (MTC) is a rare and aggressive neuroendocrine tumor with limited risk stratification. Here, the authors perform multi-omic analysis of MTC samples from 452 patients across 10 Chinese clinical centers, identify molecular subtypes and develop a prediction model.