Search Results (141)

The Carbon Emissions Trading System (ETS) serves as a market-based mechanism to drive renewable energy (RE) investments, yet its heterogeneous impacts on different stakeholders remain underexplored. This paper treats the carbon market as an exogenous shock and develops a multi-agent equilibrium model incorporating carbon pricing, encompassing power generation enterprises, power transmission enterprises, power consumers, and the government, to analyze how carbon prices reshape RE investment layouts under dual-carbon goals. Using panel data from Zhejiang Province (2017–2022), a high-energy-consumption region with 25% net electricity imports, we simulate heterogeneous responses of agents to carbon price fluctuations (CNY 50–250/ton). The results show that RE on-grid electricity increases (+0.55% to +2.89%), while thermal power declines (–4.98% to −15.39%) on the generation side. Transmission-side RE sales rise (+3.25% to +9.74%), though total electricity sales decrease (−0.49% to −2.22%). On the consumption side, RE self-generation grows (+2.12% to +5.93%), yet higher carbon prices reduce overall utility (−0.44% to −2.05%). Furthermore, external electricity integration (peaking at 28.5% of sales in 2020) alleviates provincial entities’ carbon cost pressure under high carbon prices. This study offers systematic insights for renewable energy investment decisions and policy optimization. Full article

Background and Objective: Intervertebral disc degeneration (IVDD) is a leading cause of lower back pain with limited regenerative treatments. Among emerging regenerative approaches, growth factor-based therapies, such as recombinant human transforming growth factor-beta (Rh-TGF-β), have shown potential for disc regeneration but are hindered by rapid degradation and uncontrolled release by direct administration. Additionally, mechanical stress elevates heat shock protein 90 (HSP-90), impairing cell function and extracellular matrix (ECM) production. This study aimed to investigate a dual self-cross-linked alginate di-aldehyde (ADA) hydrogel system for the sustained delivery of Rh-TGF-β and epigallocatechin gallate (EGCG) to enhance protein stability, regulate release, and promote disc regeneration by targeting both regenerative and stress-response pathways. Methods: ELISA and UV-Vis spectrophotometry assessed Rh-TGF-β and EGCG release profiles. A rat tail IVDD model was established with an Ilizarov-type external fixator for loading, followed by hydrogel treatment with or without bioactive agents. Disc height, tissue structure, and protein expression were evaluated via radiography, histological staining, immunohistochemistry, and Western blotting. Results: The hydrogel demonstrated a biphasic release profile with 100% Rh-TGF-β released over 60 days and complete EGCG release achieved within 15 days. Treated groups showed improved disc height, structural integrity, and proteoglycan retention revealed by histological analysis and elevated HSP-90 expression by immunohistochemistry. In contrast, Western blot analysis confirmed that EGCG effectively downregulated HSP-90 expression, suggesting a reduction in mechanical stress-induced degeneration. Conclusions: ADA hydrogel effectively delivers therapeutic agents, offering a promising strategy for IVDD treatment. Full article

Indonesia’s dependence on fossil fuel exports, particularly coal and crude oil, presents a dual challenge: sustaining economic growth while addressing rising CO₂ emissions. Despite significant attention to domestic energy consumption, the environmental implications of export activities remain underexplored. This study examines the dynamic relationship between energy exports, crude oil prices, and CO₂ emissions in Indonesia using a Vector Autoregressive (VAR) model with annual data from 2002 to 2022. The analysis incorporates Impulse Response Functions (IRFs) and Forecast Error Variance Decomposition (FEVD) to trace short- and long-term interactions among variables. Findings reveal that coal exports are strongly persistent and positively linked to past emission levels, while oil exports respond negatively to both coal and emission shocks—suggesting internal trade-offs. CO₂ emissions are primarily self-driven yet increasingly influenced by oil export fluctuations over time. Crude oil prices, in contrast, have limited impact on domestic emissions. This study contributes a novel export-based perspective to Indonesia’s emission profile and demonstrates the value of dynamic modeling in policy analysis. Results underscore the importance of integrated strategies that balance trade objectives with climate commitments, offering evidence-based insights for refining Indonesia’s nationally determined contributions (NDCs) and sustainable energy policies. Full article

Using panel data spanning 2004–2023 of 21 countries in the MENA (Middle East and North Africa) region, we measure systemic risk and assess its influence on key banking sector performance indicators, including financial stability (proxied by commercial bank branches per 100,000 adults), providing evidence from the emerging market context. One of the key findings of the study is the pivotal role played by financial access in promoting banking stability. In particular, the density and outreach of commercial banking branches were shown to have a stabilizing effect on the banking system. Also, findings reveal that systemic risk significantly undermines bank stability and operational efficiency while constraining financial depth. The study contributes to the literature by offering empirical evidence on the adverse effects of systemic risk in a region characterized by financial volatility and structural vulnerabilities. These findings align with existing global evidence that links financial development with reduced systemic risk, yet they also offer new empirical insights that are contextually relevant to the MENA region. The findings provide actionable recommendations for policymakers. Regulatory authorities in the MENA region should consider strategies that not only enhance the robustness of financial institutions but also promote inclusive access to banking services. The dual focus on institutional soundness and outreach could serve as a cornerstone for sustainable financial stability. Tailored policies that encourage branch expansion in underserved areas, coupled with incentives for inclusive banking practices, may yield long-term benefits by reducing the concentration of risk and improving the responsiveness of the financial system to external shocks. Full article

This study investigates the effects of Laser Shock Peening (LSP) on residual stress distribution and surface deformation using a Finite Element Method (FEM) model. LSP is a surface treatment process that generates compressive residual stress by applying high-energy laser pulses over nanosecond timescales. The study aims to analyze the impact of key parameters, specifically laser spot overlap rate and power density, on the induced residual stress and surface deformation. A Design of Experiment (DOE) approach was used to systematically vary these parameters. These simulations were performed using the ANSYS Explicit Dynamics FEM with a Johnson–Cook material model to capture the nonlinear constitutive behavior. The research analyzes the distribution of residual stress and surface deformation caused by LSP. Increasing laser spot overlap and power density leads to higher compressive residual stress and surface deformation, revealing two distinct behavioral outcomes: either deep compressive stress with minimal deformation or a transition from compressive to tensile stress followed by significant surface deformation and a subsequent return to compressive stress. The results demonstrate strong agreement with existing experimental data presented in the literature. This study contributes novel insights into the interaction between LSP parameters and their effects on material properties, with implications for understanding LSP techniques in practical applications. The triangular pulse model and dual-overlap analysis offer a novel simulation strategy for optimizing LSP parameters in stainless steel. Full article

Background: Influenza B usually causes mild illness in children. Severe and fatal cases can occur when complicated by secondary Staphylococcus aureus (S. aureus) pneumonia, including community-acquired methicillin-resistant Staphylococcus aureus (MRSA). We present a rare, rapidly progressive fatal case in an adolescent with no known medical history to highlight diagnostic and therapeutic pitfalls. Case Presentation: A 16-year-old boy with no known underlying conditions (unvaccinated for influenza) presented critically ill at “Sf. Ioan” Clinical Emergency Pediatric Hospital in Galați after one week of high fever and cough. He was in respiratory failure with septic shock, requiring immediate intubation and vasopressors. Chest X-ray (CXR) showed diffuse bilateral infiltrates (acute respiratory distress syndrome, ARDS). Initial laboratory tests revealed leukopenia, severe thrombocytopenia, disseminated intravascular coagulation (DIC), rhabdomyolysis, and acute kidney injury (AKI). Reverse transcription polymerase chain reaction (RT-PCR) confirmed influenza B, and blood cultures grew MRSA. Despite maximal intensive care, including mechanical ventilation, antibiotics (escalated for MRSA), antiviral therapy, and cytokine hemoadsorption therapy, the patient developed refractory multi-organ failure and died on hospital day 6. Autopsy revealed bilateral necrotizing pneumonia (NP) without radiographic cavitation, underscoring the diagnostic challenge. Discussion: The initial chest radiography showed diffuse bilateral pulmonary infiltrates, predominantly in the lower zones, with an ill-defined, patchy, and confluent appearance. Such appearance, in our case, was more suggestive of rapid progressive NP caused by MRSA rather than the typical pneumococcal one. This is one of the few reported cases of influenza B–MRSA coinfection with fulminant rhabdomyolysis and autopsy-confirmed necrosis. Our fulminant case illustrates the synergistic virulence of influenza and MRSA. Toxin-producing MRSA strains can cause NP and a “cytokine storm,” causing capillary leak, ARDS, shock, and DIC. Once multi-organ failure ensues, the prognosis is grim despite aggressive care. The absence of early radiographic necrosis and delayed anti-MRSA therapy (initiated after culture results) likely contributed to the poor outcome. Conclusions: Influenza B–MRSA co-infection, though rare, demands urgent empiric anti-MRSA therapy in severe influenza cases with leukopenia or shock, even without radiographic necrosis. This fatal outcome underscores the dual imperative of influenza vaccination and early, aggressive dual-pathogen targeting in high-risk presentations. Full article

Amid the rapid advancement of artificial intelligence (AI) and increasing environmental uncertainty, enterprises are facing unprecedented challenges in sustaining innovation. As a key enabler of digital transformation, AI enhances resource allocation efficiency and knowledge acquisition, offering new avenues for continuous innovation under dynamic conditions. Innovation resilience—defined as a firm’s ability to maintain and restore innovation activities during external shocks—has emerged as a critical indicator of organizational adaptability. Leveraging its advantages in data processing, process optimization, and organizational learning, AI is increasingly regarded as a pivotal driver of innovation resilience. This study develops a theoretical framework linking AI technology, dynamic capabilities, and innovation resilience. Using panel data from Chinese A-share listed companies between 2013 and 2023, we conduct an empirical analysis with a two-way fixed effects model. The results reveal that AI technology significantly enhances innovation resilience; dynamic capabilities partially mediate this relationship; and financial constraints positively moderate the effect of AI on innovation resilience. By adopting a dual perspective of technological enablement and capability construction, this research uncovers the internal mechanism through which AI fosters resilient innovation and provides practical insights for enterprises seeking capability upgrading under resource limitations. Full article

The prognosis of advanced (UICC IIb-IV) primary colorectal cancer (pCRC) remains poor. More effective targeted therapies are needed. Heat shock protein 90 alpha/beta (Hsp90α/β) expression was immunohistologically quantified in 89 pCRCs and multivariately correlated with survival. Pimitespib (Pim, TAS-116), a Hsp90α/β-specific inhibitor, was tested in pCRC cell lines and patient-derived cancer spheroids (PDCS) and referenced to the pan-Hsp90 inhibitor ganetespib (Gan, STA-9090) and standard-of-care therapies. A total of 26.97% pCRCs showed strong tumoral Hsp90α/β expression (Hsp90α/β > 40%), which correlated with reduced PFS (HR: 3.785, 95%CI: 1.578–9.078, p = 0.003) and OS (HR: 3.502, 95%CI: 1.292–9.494, p = 0.014). Co-expression of Hsp90α/β > 40% with its clients BRAF-V600E and Her2/neu aggravated the prognosis (BRAF-V600E mutated: PFS, p = 0.002; OS, p = 0.012; Her2/neu score3: PFS, p = 0.029). The prognostic cut-off Hsp90α/β > 40% was also a predictor for response to Pim-based therapy. Pim efficacy was increased in combination with 5-FU, 5-FU + oxaliplatin, and 5-FU + irinotecan (all p < 0.001). Pim induced sensitization to all chemotherapies in HT-29 (p < 0.001), Caco-2 (p < 0.01), and HCT116 (p < 0.05) cells. Pim combined with encorafenib in HT-29 and with trastuzumab in Caco-2 cells was most effective in dual-target inhibition approaches (HT-29: p < 0.005; Caco-2: p < 0.05). The anti-cancer effect and chemosensitization of Pim-based therapy were prospectively confirmed in PDCS directly generated from Hsp90α/β > 40% pCRCs. Protein profiling combined with functional drug testing stratifies Hsp90α/β > 40% pCRC patients diagnosed with UICC IIb-IV for effective Pim-based therapy. Full article

Helicobacter pylori (H. pylori) is a Gram-negative bacterium that colonizes the human stomach and is associated with several gastric diseases, including gastritis, peptic ulcer disease, and gastric cancer. The bacterium’s ability to thrive in the harsh gastric environment is due, to some extent, to its stress response mechanisms, with its heat shock proteins (HSPs) playing a putative, yet not fully understood, role in these adaptive processes. HSPs are a family of molecules, highly conserved throughout phylogenesis, that assist in protein folding, prevent aggregation, and ensure cellular homeostasis under stressful conditions. In H. pylori, HSPs contribute to survival in the stomach’s acidic environment and oxidative stress. Furthermore, they aid in the bacterium’s ability to adhere to gastric epithelial cells, modulate the host immune response, and form biofilms, all contributing to chronic infection and pathogenicity. The role of microbial HSPs in antibiotic resistance has also emerged as a critical area of research, as these proteins help stabilize efflux pumps, protect essential proteins targeted by antibiotics, and promote biofilm formation, thereby reducing the efficacy of antimicrobial treatments. Among bacterial HSPs, GroEL and DnaK are probably the major proteins that control most of the H. pylori’s functioning. Indeed, both proteins possess remarkable acid resistance, high substrate affinity, and dual roles in protein homeostasis and host interaction. These features make them critical for H. pylori’s adaptation, persistence, and pathogenicity in the gastric niche. In addition, recent findings have also highlighted the involvement of HSPs in the crosstalk between H. pylori and gastric epithelial cells mediated by the release of bacterial outer membrane vesicles and host-derived exosomes, both of these extracellular vesicles being part of the muco-microbiotic layer of the stomach and influencing cellular signalling and immune modulation. Considering their critical role in the survival and persistence of bacteria, microbial HSPs also represent potential therapeutic targets. Strategies aimed at inhibiting microbial HSP function, combined with conventional antibiotics or developing vaccines targeting microbial HSPs, could provide new avenues for the treatment of H. pylori infections and combat antibiotic resistance. This review explores the multifaceted roles of microbial HSPs in the pathogenesis of H. pylori, highlighting their contributions to bacterial adhesion, immune evasion, stress response, and antibiotic resistance. Full article

The prenatal environment critically influences sow and offspring health, with the liver being highly susceptible to heat stress (HS) and vital for antioxidant defense. However, mechanisms underlying HS impacts on early pregnancy and hepatic adaptation remain unclear. This study applied multi-omics to analyze chronic HS responses in early-pregnancy sows. Results demonstrated that HS reduced blood oxygen (PO₂) and basophils while elevating red blood cell parameters (RBC, HGB, and HCT). Endocrine disruptions included upregulated adrenal hormones (ACTH and cortisol) and suppressed thyroid (T3 and TSH) and reproductive hormones (LH1 and FSH). Liver dysfunction was evident through elevated biomarkers (AST, ALT, and TBIL) and pro-inflammatory IL-6, coupled with reduced anti-inflammatory IL-10. HS induced oxidative stress, marked by increased total antioxidant capacity (T-AOC) but decreased SOD and MDA levels. Liver tissue exhibited apoptosis (Bax/CD8 upregulated and Bcl-2 downregulated) and upregulated heat shock proteins (HSP70/90). Multi-omics analysis demonstrated that under heat stress conditions, the pyrimidine metabolism, oxidative phosphorylation, and tryptophan metabolism pathways were significantly upregulated in the liver. This upregulation may be mediated by key metabolites, including AMP, NAD, and UMP. These metabolites likely contribute to the body’s adaptation to heat stress. Chronic HS impaired liver function and anti-inflammatory responses but triggered compensatory antioxidant and metabolic reprogramming. These findings underscore the liver’s dual characteristics of vulnerability and resilience under high-temperature stress, offering valuable mechanistic insights that can inform strategies to enhance heat tolerance in pregnant sows. Full article

Large-inertia pump-controlled grinding machines experience significant energy loss and potential hydraulic shock during frequent high-speed table reciprocation. Traditional control methods often neglect to address efficient energy recovery during the dynamic commutation phase. This study proposes and investigates a dual-mode synergistic energy recovery system that combines motor regeneration and accumulator storage for pump-controlled grinders. The primary focus of this study is on developing a maximum regenerative energy commutation control strategy. A mathematical model of the system was established, and extensive simulations were performed to analyze the energy recovery process under varying load mass, initial velocity, and leakage coefficient conditions. Machine learning models were compared for predicting the peak time of total recovered energy, with a neural network (NN) demonstrating superior accuracy (R² ≈ 0.99997). An adaptive commutation strategy was designed, utilizing the NN prediction corrected by a confidence score based on historical and test data ranges, to determine the optimal moment for initiating reverse motion. The strategy was validated using Simulink–Amesim co-simulation and experiments conducted on a 10-ton test bench. The results show that the proposed strategy effectively maximizes energy capture; experiments indicate a 14.3% increase in energy recovery efficiency and a 25% reduction in commutation time compared to a fixed timing approach. The proposed commutation strategy also leads to faster settling to steady-state velocity and smoother operation, while the accumulator demonstrably reduces pressure peaks. This research provides a robust method for enhancing energy efficiency and productivity in pump-controlled grinding applications by improving regenerative braking control through a predictive commutation strategy. Full article

Amidst global economic fluctuations and escalating geopolitical risks, gold futures, as a pivotal safe-haven asset, demonstrate price dynamics that directly impact investor decision-making and risk mitigation effectiveness. Traditional forecasting models face significant limitations in capturing long-term trends, addressing abrupt volatility, and mitigating multi-source noise within complex market environments characterized by nonlinear interactions and extreme events. Current research predominantly focuses on single-model approaches (e.g., ARIMA or standalone neural networks), inadequately addressing the synergistic effects of multimodal market signals (e.g., cross-market index linkages, exchange rate fluctuations, and policy shifts) and lacking the systematic validation of model robustness under extreme events. Furthermore, feature selection often relies on empirical assumptions, failing to uncover non-explicit correlations between market factors and gold futures prices. A review of the global literature reveals three critical gaps: (1) the insufficient integration of temporal dependency and global attention mechanisms, leading to imbalanced predictions of long-term trends and short-term volatility; (2) the neglect of dynamic coupling effects among cross-market risk factors, such as energy ETF-metal market spillovers; and (3) the absence of hybrid architectures tailored for high-frequency noise environments, limiting predictive utility for decision support. This study proposes a three-stage LSTM–Transformer–XGBoost fusion framework. Firstly, XGBoost-based feature importance ranking identifies six key drivers from thirty-six candidate indicators: the NASDAQ Index, S&P 500 closing price, silver futures, USD/CNY exchange rate, China’s 1-year Treasury yield, and Guotai Zhongzheng Coal ETF. Second, a dual-channel deep learning architecture integrates LSTM for long-term temporal memory and Transformer with multi-head self-attention to decode implicit relationships in unstructured signals (e.g., market sentiment and climate policies). Third, rolling-window forecasting is conducted using daily gold futures prices from the Shanghai Futures Exchange (2015–2025). Key innovations include the following: (1) a bidirectional LSTM–Transformer interaction architecture employing cross-attention mechanisms to dynamically couple global market context with local temporal features, surpassing traditional linear combinations; (2) a Dynamic Hierarchical Partition Framework (DHPF) that stratifies data into four dimensions (price trends, volatility, external correlations, and event shocks) to address multi-driver complexity; (3) a dual-loop adaptive mechanism enabling endogenous parameter updates and exogenous environmental perception to minimize prediction error volatility. This research proposes innovative cross-modal fusion frameworks for gold futures forecasting, providing financial institutions with robust quantitative tools to enhance asset allocation optimization and strengthen risk hedging strategies. It also provides an interpretable hybrid framework for derivative pricing intelligence. Future applications could leverage high-frequency data sharing and cross-market risk contagion models to enhance China’s influence in global gold pricing governance. Full article

The shock wave/boundary layer interaction phenomenon in hypersonic inlets, affected by background waves, may induce the formation of multiple separation zones. Existing theories prove insufficient in explaining the underlying flow mechanisms behind complex phenomena arising from multi-separation zone interactions, which necessitates further investigation. To clarify the governing factors in multi-separation zone interactions, this study developed a simplified dual-separation-zone model derived from inlet flow field characteristics. A series of numerical simulations were conducted under an incoming flow at Mach 3 to systematically analyze the effects of internal contraction ratio, the influencing locations of expansion waves, and incident shock wave intensity on the mergence and re-separation of dual separation zones. The results demonstrate that both the expansion wave impingement position and incident shock intensity significantly influence specific transition points in dual-separation-zone flow states, though they do not fundamentally alter the evolutionary patterns governing the merging/re-separating processes. Furthermore, increasing incident shock intensity leads to the expansion of separation zone scales and prolongation of the dual-separation-zone merging distance. Full article

In recent years, China has actively advanced the new energy vehicle industry to achieve its “dual carbon” objectives via a green revolution. The growth of green technical innovation by new energy vehicle enterprises has emerged as a significant national support project, and it has implemented a number of new energy vehicle promotion policies. Therefore, it is essential to investigate if promotional policies encourage the development of green technologies in businesses. China’s 2016 “New Energy Vehicle Promotion Catalogue” serves as the policy’s temporal shock point, and data from Chinese-listed new energy vehicle companies from 2011 to 2022 are used in this study. The effect and mechanism of the new energy vehicle promotion strategy on developing green technologies in businesses are investigated using a double difference model. As per the research, the promotion policy substantially enhances the green technological innovation of new energy vehicle firms. It can augment the level of R&D investment and alleviate financing constraints for enterprises, and enterprises’ social responsibility can act as a positive moderator for the promotion policy and enterprise green technological innovation. Finally, it has a more apparent positive impact on the green technological innovation of major companies and non-state-owned enterprises compared to state-owned firms. Additionally, it is more evident that enterprises are raising green technology innovation in the eastern and central regions. Full article

Under the rapid advancements in information technology, the complex network characteristics of agricultural product trade relationships among global economies have exhibited increasing prominence. This study takes the soybean trade market as an empirical case, employing a combination of social network analysis to investigate the dynamic evolution of agricultural trade network structures; then, the Temporal Exponential Random Graph Model (TERGM) is adopted to analyse the factors influencing the soybean trade network. Based on comprehensive empirical data encompassing soybean trade data among 126 economies from 2000 to 2022, this research demonstrates several key findings: Firstly, the soybean trade network is characterised by pronounced trade agglomeration effects and “small-world” properties, accompanied by heightened trade substitutability. Secondly, the network’s structural configuration has undergone a distinct transformation, shifting from a traditional single-core–periphery structure to a more complex multi-core–periphery architecture. Thirdly, in response to external shocks impacting network topology, the core structure exhibits greater resilience and stability, whereas the periphery displays heterogeneous responses. Finally, the evolution of soybean trade relations is governed by a dual mechanism involving both endogenous dynamics and exogenous influences. Full article