When human civilization is thriving to the rapid economic and social development, the deteriorating ecological environment has also pressured the society to put environmental protection issues on the agenda future development. Therefore, green innovation is not merely a requirement for a corporate’s long-term development but the basics of sustainable development of human society. As an objective indicator to measure a company's commitment to social responsibility, corporate environmental responsibility has an extremely important impact on a corporate's business philosophy and innovation strategy. However, the existing literature rarely studies between these two variables. This paper collects the data of listed companies from 2010 to 2019 and empirically tests the impact of corporate environmental responsibility on corporate green innovation. The study finds that corporate environmental responsibility can promote green innovation by reducing financing constraints, improving corporate governance and increasing government subsidies and R&D investment, and the result is still significant under a series of robustness checks. The placebo test suggests that the Jiangsu enterprise responsibility construction pilot has facilitated the development of enterprises' green innovation to a great extent. The conclusions enrich existing literature on corporate environmental responsibility and corporate green innovation, providing implications for government policy orientation, social atmosphere guidance and corporate strategic decision-making process.

Economy has various stages which have been described as economic cycle and due to that the circulatory movement observed, some have faster cycle some have longer cycle which depends on numbers of factors and from that inflation is an important segment. Various terms have been described for inflation, in simple meaning when price of different commodities have been raised compared to previous stage due to depreciation of currency having lesser purchasing power. Perception of people towards inflation and its impact is the main crux of the study, because how the people think about technical terms is vital matter. The present study is based on primary data collected from 277 respondents through questionnaire from the Gujarat state India. The perception has been analysed from four statement have been asked in Lickert’s five scale form. The one-way ANOVA has been used to test the hypotheses and it has been found from that in first statement regarding price of commodity once increase then it will not decrease due to inflation has significant difference with place, education and age of the respondents and in rest three statement no significant differences have been observed.

Banks operating within a free economic environment and with constructive regulatory frameworks can function with enhanced efficiency, augmenting their profitability. This study aimed to examine the influence of economic freedom and financial restrictions on banks' profitability within the Organization of Islamic Cooperation member nations. The study used a sample of 1453 banks, collecting panel data spanning 13 years. The System-GMM methodology was justified based on the enduring characteristics of the data and the profitability metrics. The dependent variables, namely the Net Income Margin, Return on Average Assets, and Return on Average Equity were individually examined. The research findings indicate that most economic freedom indicators, except for investment freedom, harm bank profitability. The influence of the rule of law on profitability indicators is mostly adverse, although regulatory quality within the financial system has a comparatively favorable effect on profitability. Furthermore, bank-specific performance indicators had a negative impact on profitability, except for bank size. Moreover, country-specific variables substantially impact banks' profitability, including inflation, tax, and interest rates. The presence of Islamic banking has been shown to substantially impact the profitability performance of banks within the Organization of Islamic Cooperation. OIC banks' profitability is adversely impacted by the ongoing health crises, including the Covid-19 pandemic.

A differences-in-differences (DID) model and financial data from Chinese listed firms from 2011 to 2019 are used to empirically investigate the effects of the digital economy on corporate financial asset holdings using the "Broadband China" policy as a quasi-natural experiment. After robustness testing and heterogeneity effects are disposed of, the digital economy dramatically boosts corporate financial asset holdings. According to the channel analysis, the digital economy can reduce corporate financing constraints and boost corporate financial asset holdings, notably based on speculative demand. Heterogeneity analysis shows that the digital economy has a greater impact on corporate financial asset holdings for firms with higher market competition, small-scale firms with a short-listed age, and firms in the western region. This paper provides policy guidance for enterprises returning to the real economy.

Achieving food security through improved agricultural technology is one of the greatest challenges of our time. Indeed, it is one of the elements explicitly mentioned in the Sustainable Development Goals (SDGs). One factor that can lead to improved production and distribution systems is the availability of data of all kinds on the sector. In this paper we study what is happening in this respect in two Mediterranean olive oil producing countries, and show how available data can facilitate the coordination and communication of technical developments and improve the competitiveness of producers.

Purpose: To realize the benefits a Quality Management System (QMS), an organization needs to effectively implement its principles. This includes embracing these principles within an enabling environment. Hence, organizational structure is a critical success factor for adopting quality management systems. This paper aims at understanding how the organizational structure impacts ISO 9001 QMS implementation and vise versa, besides exploring how quality management initiatives affect organizational resilience through parts. Design/methodology/approach: The research deploys a qualitative methodology, with an outsider-insider approach. 32 semi-structured interviews were conducted, plus one final reflective interview. Interview questions dealt with structural constructs: communication, employee engagement, employee empowerment, process/routine-orientation, multidisciplinarity, cross-functionality, expertise-utilization and change-readiness. The questions also dealt with how the organizational structure impacted ISO 9001 implementation and vice versa. Findings: The results show that ISO 9001 implementation was more successful and fruitful under process-orientation and to some degree under semi-process orientation, while it looked inconsistent under routine-orientation. This indicates a significant role played by the organizational structure when it comes to ISO 9001 implementation. More importantly, the results also demonstrate that ISO 9001 implementation managed to improve organizational resilience by consistently driving the organizational structure toward higher levels of process-orientation for process-oriented units, whereas it failed to realize such impact for routine-oriented units. Originality/value: This study is unique as it is the first - within the reviewed literature - to examine the moderating role of the organizational structure between the ISO 9001 standard and organizational resilience. It draws a roadmap for the successful realization of organizational resilience through quality management systems, considering organizational structure constructs.

Background: Hepatocellular carcinoma (HCC) stands out as one of the most lethal cancers globally, given its complexity, recurrence following surgical resection, metastatic potential, and inherent heterogeneity. In recent years, researchers have systematically elucidated the significance of long non-coding RNA (lncRNA) in the initiation and progression of HCC. The introduction of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases has significantly enhanced the prognostic assessment of HCC. However, the association between HCC and cell senescence has been infrequently explored in the literature. Method: We downloaded liver hepatocellular carcinoma (LIHC)-related messenger RNA and lncRNA expression levels from TCGA. Correlation analysis, Cox regression, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to validate the lncRNA risk model associated with cellular aging. Comparing the infiltration of diverse immune cells enabled the identification of distinct differences in the immunological microenvironments of the two risk groups. Subsequently, we conducted a real-time polymerase chain reaction (qPCR) experiment to confirm the accuracy of the selected lncRNAs. Results: A predictive framework for HCC was constructed based on the expression levels of five lncRNAs. Multivariate and univariate Cox regression analyses revealed that lncRNA signatures associated with senescence were independently correlated with an increased risk of HCC. Additionally, the nomogram also provides a more refined and sensitive model. Further investigation into the variations in immune cells and functions between the high-risk and low-risk groups was conducted. Subsequently, a qPCR experiment results revealed underexpression of AC068756.1, AC090578.1, AC145343.1, and LINC0022 in Huh7 and LM3 cells. In contrast, AP003392.4 did not exhibit a significant difference between Huh7 and control cells. Conclusion: The prognostic features and nomogram, consisting of five aging-related lncRNAs (AC068756.1, AC090578.1, AC145343.1, AP003392.4, and LINC00221), may be useful in predicting the overall survival of HCC.

In the context of digital economy, Hebei Province, as an important province in central China, has a huge manufacturing base and potential, and the development of digital economy has driven the transformation and upgrading of manufacturing industry to a certain extent. In order to better play the driving role of digital economy in the transformation and upgrading of manufacturing industry, this paper establishes an intermediary effect model based on three intermediary variables: enterprise resource allocation ability, enterprise cost and enterprise innovation ability, and conducts an empirical study on the panel data of 11 prefecture-level cities in Hebei Province from 2017 to 2022. The mechanism of digital economy driving manufacturing upgrading in Hebei Province was discussed. The results show that: (1) there is a significant positive correlation between the development of digital economy and the transformation and upgrading of manufacturing industry, which indicates that the development of digital economy has a direct driving effect on the transformation and upgrading of manufacturing industry; (2) There is also a positive correlation between the development of digital economy and the resource allocation ability and innovation ability of enterprises, indicating that the development of digital economy can improve the resource allocation ability and innovation ability of enterprises, and is conducive to the stable development of enterprises in the future; (3) Enterprise resource allocation ability and enterprise innovation ability play a significant intermediary role in the relationship between digital economy development and the transformation and upgrading of manufacturing industry, indicating that digital economy can indirectly accelerate the transformation of manufacturing enterprises' R & D achievements by improving enterprises' resource allocation ability and innovation ability, thus improving enterprises' market competitiveness and increasing enterprises' earnings. Drive the transformation and upgrading of the manufacturing industry. Based on this research conclusion, the government should give full play to the role of guidance and support, and introduce relevant policies to help the digital economy drive the transformation and upgrading of manufacturing enterprises. Manufacturing enterprises should seize the tide of the development of the digital economy, use digital technology to improve their resource allocation ability and innovation ability, enhance core competitiveness, and accelerate the transformation and upgrading of enterprises.

Given the rapid development of live streaming commerce in China, this study focuses on the interactivity and sociability of live streaming shopping activities and explores online consumers’ real-time interaction intentions and trust-building behaviours with online merchants. To discover the real-time interaction between online consumers and online merchants, this study builds a research model based on the Theory of Planned Behaviour (TPB). Through the data analysis based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the key findings state that, three factors, including attitude, subject norm, and perceived control, positively affect online consumers’ real-time interaction intentions and lead them to build trust with online merchants. Meanwhile, control variables, including gender, age, and educational background, demonstrate insignificant effects across the model. Unlike existing literature, the current study pays much attention to the interactive characteristics of live streaming shopping activities and can provide some valuable suggestions both for online consumers and online merchants, which can promote the co-development of the commercial and social aspects of live streaming platforms.

Cloud computing is one of the fastest growing domains of the IT industry, however, the growth rates for each of the models on a global and local (Russian) scale have not been generalized, despite the obvious value of such analysis. Modern business and industry in Russia is going through another period of instability and at the same time is forced to follow the trend of digitalization. That is why a balanced scientific analysis and forecast of the development of cloud technologies is needed, as the main factor in improving business efficiency. The purpose of the study: is to review the main models of cloud services for modern business and industry, describing their capabilities separately and the prospects for the market as a whole. The materials for the study were analytical reports of leading consulting companies and materials from specialized web-sites. Result: The article considers cloud technologies as the main global technological trend, describes popular models of cloud services, their opportunities for business and industry. Russian and global leaders in the provision of cloud services are also described, the growth rates of these markets for each of the models are indicated. The most popular and promising cloud service models in 2022 are: UCaaS, CaaS, SaaS, IaaS, RPAaaS, PaaS. The cloud technology market will show strong growth until 2030, generating both hybrid and absolutely new service models. The private business, industrial and public sectors will increasingly move to cloud services. The main trends in the development of cloud technologies until 2030 are: hybrid cloud, artificial intelligence (AI) and cloud computing, deployment of cloud services in the planetary orbit. The results obtained are very valuable for analysts and industry experts. The above trend analysis provides valuable predictive data to research economists, investors, business and industry leaders and other stakeholders on how the cloud services market will grow in the next 5-7 years. The results of the study also have a general theoretical nature, substantiating the importance of further forecasting the development of this market (especially using mathematical methods).

Background: Bladder cancer (BLCA), particularly invasive BLCA, has become a medical burden worldwide as it is associated with recurrence and easy metastasis. There are specific differences in the expression of various miRNAs in tumor and normal tissues. Hence, miRNAs can be used as biomarkers for tumor diagnosis and prognostic evaluation. The current study aimed to predict the downstream target genes of BLCA-related miRNAs and explore their association with immune infiltration. Method: Data on BLCA-related mRNA and miRNA expression levels were downloaded from The Cancer Genome Atlas. Correlation analysis and Cox regression analysis were performed to validate the miRNA risk model. The infiltration of various immune cells should be compared to determine the distinct differences between the immunological microenvironment of the two risk groups. Results: A predictive framework of BLCA was established using the expression levels of two miRNAs. Cox regression analysis showed that the low-risk group had a better prognosis. Then, the target genes of miRNA were predicted, and the target genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Moreover, variations in immune cells and functions between the high- and low-risk groups were assessed. Conclusion: The prognostic features composed of two associated miRNAs (MIR-25, MIR-548AN) may help predict the overall survival of BLCA.

The COVID-19 pandemic wreaked havoc on economies everywhere, affecting everything from supply chain networks to international trade as a direct result of the increased vulnerability of more nations and the disruption of the activities of key traders. This research study examines how COVID-19 affected the supply chain on a worldwide and regional scale, as well as the systemic policy and economic steps governments and businesses took to rebound and maintain resilience. COVID-19 has had a significant influence, prompting governments and businesses to take steps to fortify their operations and businesses against disruptions, highlight the necessity for resilience in supply chains and emphasize the importance of managing risk. The supply value chain is at risk from the prolonged implementation of COVID-19 trade measures like border closure, export restrictions, and import sanctions. Many businesses are transitioning from a "recovery mode" to a resilient and sustainable mode" and are beginning to plan for the future. The study suggests that corporate and government leaders should prioritize proactive and adaptable policy, economic, and structural adjustment. This research will be useful to policymakers and industry leaders in their efforts to lessen the effect of COVID-19 on the global supply chain by enforcing governmental, economic, and business reforms built on resilient supply chains and mitigating economic risks during tough times.

Cancer, a global health menace, continues to pose significant challenges in terms of incidence and mortality, necessitating innovative therapeutic strategies. Despite existing treatments, the limitations persist, prompting a quest for novel approaches. The emergence of immunotherapy marked a transformative era in solid tumor treatments, yet its efficacy is constrained by adverse effects. Concurrently, the integration of advanced technologies into cancer treatment explores the vast potential residing at the molecular level through gene analysis and manipulation. This review articulates the role of state-of-the-art genome editing technology, notably clustered regularly interspaced short palindromic repeats (CRISPR-Cas9), in overcoming the constraints of immunotherapy for cancers. Unveiling the intricacies of CRISPR-Cas9-mediated genome editing, the review introduces the formidable CRISPR toolbox. A spotlight is cast on the transformative impact of CRISPR-induced double-strand breaks (DSBs) on cancer immunotherapy, encompassing knockout and knock-in strategies. The utilization of CRISPR/Cas9 technology in pre-clinical cancer research has demonstrated notable success; however, its transition to the clinical setting remains in the nascent stages of development. This review aims to elucidate the fundamental aspects of CRISPR technology and offer a comprehensive survey of its existing applications while outlining its prospective role in the realm of cancer therapies. Through an exploration of CRISPR's mechanisms, current applications, and anticipated future potentials, this review provides valuable insights into the evolving landscape of CRISPR-based cancer treatment strategies.

The enactment of policies that bolster new research and development (R&D) institutions stands as a pivotal strategy to catalyze urban innovation and development. Adopting a strategic lens of innovation chain management and employing the Differences-in-Differences (DID) method to scrutinize panel data from 43 Chinese cities spanning 2008 to 2019, this study probes the efficacy and underlying mechanisms of policies designed to support nascent R&D institutions in facilitating urban innovation. Empirical findings reveal that policies in support of new R&D institutions have markedly enhanced the three integral stages of the urban innovation chain: research and development, transfer, and application. Furthermore, the innovation ambiance within cities and the innovative activities of enterprises emerge as significant mediators between support policies and the output of urban innovation across these stages. A regional heterogeneity analysis unveils that the impact of support policies on the output of urban innovation diverges across regions, with a notably more pronounced effect observed in the eastern region compared to central and western regions. An objective appraisal of the policy's impact on urban innovation not only aids in delving into the profound implementation effects of policy instruments but also furnishes policy-makers with decision-making references for optimizing the utilization of support policies for new R&D institutions to advance the edification of the national innovation system.

Cancer, a pervasive threat to human health, presents formidable challenges to traditional treatment approaches. Tumor immunotherapy has emerged as a promising strategy for combating malignancies by bolstering the body's immune response to thwart tumor metastasis and recurrence. Nonetheless, the intricacies of tumors, patient heterogeneity, and the presence of tumor-immunosuppressive microenvironments have limited the overall efficacy of immunotherapy, achieving only approximately a 20% success rate. In recent years, nanomaterials have garnered increasing attention in the realm of tumor immunotherapy due to their inherent advantages, such as excellent biocompatibility, precise targeting, and controlled drug release. Nanomaterials empower immunostimulatory molecules and therapeutic agents with the ability to specifically target tumors, amplify drug accumulation at tumor sites, facilitate local immune modulation, alleviate immunosuppressive microenvironments, and thereby enhance the effectiveness of tumor immunotherapy. This review provides an overview of the current state of immunotherapy and offers insights into the ongoing research progress surrounding various nanomaterials aimed at augmenting the efficacy of immunotherapy.

Parkinson’s Disease (PD) is a prevalent progressive neurodegenerative condition affecting millions globally. Research has found that individuals with PD have a reduced risk of certain cancers, such as colon, lung, and rectal cancers, but an increased risk of brain cancer. Therefore, there is an urgent need for the development of advanced PD diagnostic methods and for investigating the relationships between risk factors, such as lifestyle due to handedness associated with various types of cancers. Recent ad- vancements in magnetic resonance imaging have enhanced PD diagnosis, reducing misdiagnosis and facilitating more accurate disease progression monitoring. Nevertheless, challenges exist, particularly in the distinction of PD between left-handed and right-handed patients over time. This survey provides an overview of contemporary deep learning-based imag- ing analysis methodologies, encompassing both non-longitudinal and lon- gitudinal contexts. We also explore existing limitations and prospects for refinement to gain deeper insights. These insights are poised to inform the development of personalized treatment strategies for PD patients while elucidating the current disparities between deep learning models and their efficacious implementation in clinical practice.

The latent transforming growth factor-beta (TGF-β) binding protein 1 (LTBP1) has been implicated in various cellular processes, but its role in triple-negative breast cancer (TNBC) remains unclear. In this study, we investigated the impact of LTBP1 on TNBC progression and its underlying mechanisms. Analysis of online datasets revealed elevated LTBP1 mRNA expression in breast cancer tissues compared to normal adjacent tissues. Kaplan-Meier Plotter analysis indicated that high LTBP1 expression was negatively correlated with relapse-free survival (RFS), distant-metastasis free survival (DMFS), and overall survival (OS) of breast cancer patients. Additionally, LTBP1 mRNA levels were associated with chemotherapy resistance. Functional assays in TNBC cells demonstrated that LTBP1 knockdown suppressed cell proliferation, induced apoptosis, and attenuated migration and invasion. In vivo studies confirmed that LTBP1 knockdown inhibited tumor growth in a xenograft mouse model. Mechanistically, LTBP1 positively correlated with genes involved in signaling regulation and organelle organization, with significant associations to GTPase binding and the RhoA/ROCK pathway. LTBP1 knockdown reduced RhoA activity and phosphorylation of Myosin Light Chain 2 (MLC2), suggesting inhibition of the RhoA/ROCK signaling pathway. Moreover, activation of the RhoA/ROCK pathway partially rescued the effects of LTBP1 knockdown on TNBC cell proliferation, apoptosis, migration, and invasion. In conclusion, our findings suggest that LTBP1 promotes TNBC progression through activation of the RhoA/ROCK signaling pathway, highlighting its potential as a therapeutic target for TNBC.

The clean energy revolution is a global concern. Cross-border collaboration is crucial for sharing knowledge, pooling resources, and tackling complex challenges on a global scale. International partnerships can accelerate innovation by drawing on diverse knowledge and experience from different regions. Access to capital is a key driver of innovation and entrepreneurship in clean energy. Institutional investors and financial institutions increasingly recognize the potential for high returns from the clean energy sector. This research aimed to understand whether "dirty" energies, such as the Brent Crude Spot, WTI, FTSE 350 Oil, Gas & Coal, EURO STOXX Oil & Gas, influence the formation of sustainable energy prices, namely Geothermal Index, Solar Energy Index, NASDAQ OMX Bio Clean Fuels Index, Wind Energy Index, WilderHill Clean Energy Index, during the 2020 Covid-19 pandemic and the Russian invasion of Ukraine in 2022. When comparing the two sub-periods, the results show that the level of autocorrelation in price formation has increased, i.e., the mean rhoDCCA went from 32 to 45, the weak correlation coefficients without trend decreased from 30 to 19, and the anti-persistence has also decreased from 8 to 4. The strong rhoDCCA increased from 2 to 4; during the Tranquil period, the FTSE 350 / EURO STOXX Oil index pairs had a rhoDCCA of 0.76. While in the Stress subperiod, the NASDAQ OMX Bio / Solar Energy pairs had a slope of 0.70, and the FTSE 350 / EURO STOXX Oil indices had a rhoDCCA of 0.91. Evidence shows a notable dissociation between fossil energy prices and sustainable energy sources. This dissociation is characterized by various degrees of influence between fossil energy prices and sustainable energy indices, from low to moderate. This dissociation between fossil energy prices and sustainable energy prices suggests that investments in sustainable energy can offer diversification opportunities.

Self-employment is often associated with entrepreneurship and regarded as a driver of innovation, job creation and economic growth. As such, many countries have policies to promote and support self-employment. One mechanism for self-employment to drive job growth is for sole traders to become an employer through hiring employees. However, there are few studies that investigate if solo self-employment helps the transitions into employership. Using the Understanding Society data, this study shows that in the UK labour market solo self-employment does not work as a ‘steppingstone’ to employership. This raises the question why self-employment should be promoted through public policy as in the UK and many other developed countries.

The aim of this study is to solve the problem of labor allocation in cases where the company has fewer employees than the number of existing jobs based on the evaluation of work quality according to multiple objectives. Optimizing labor allocation not only benefits the company by maximizing the use of human resources, but also saves employees' energy. In addition, employees are assigned to tasks that match their skills and qualifications, maximizing their productivity. The research results show that the multi-objective decision-making algorithm based on the Hungarian algorithm is a suitable method to help leaders of companies solve the aforementioned problem text.