Identification Of Drivers Research Articles

Carbon Farming in the EU: a Policy Tool or a Business Opportunity?

The European Green Deal has set ambitious targets to reduce greenhouse gas emissions and achieve climate neutrality by 2050. To achieve these, carbon sequestration and long-term storage must be increased not only through technological carbon capture and storage solutions, but also through nature-based carbon pools. Bioeconomy sectors have an important role to play as they include sustainable practices in agriculture, forestry and marine ecosystems that contribute to carbon sequestration. The new Common Agricultural Policy programming period (2023–2027) sets more ambitious environmental and climate targets than previous periods, including carbon farming practices to reduce greenhouse gas emissions and sequester carbon. This approach prepares and motivates farmers to switch to sustainable practices at an early stage. This is important because once the voluntary EU Certification Framework of the CRCF Regulation (EU) 2024/3012 is fully operational, interested farmers and foresters will be able to actively participate in a new business model by trading carbon credits alongside conventional products. In order to better understand the role of the CRCF Regulation in the context of the EU’s climate neutrality objectives and its interaction with other EU policy planning documents and legal acts, a content analysis was carried out, which led to the identification of key drivers and opportunities of the new CRCF Regulation. The results show that the CRCF Regulation plays an important role in the EU decarbonisation processes and in increasing carbon sequestration, in particular in the agriculture and forestry sectors. Certified activities will contribute to carbon removals and sequestration in both ecosystems and industry, while preserving biodiversity and ecosystem integrity. In addition, carbon sequestration and storage in construction products can contribute to a carbon neutral construction sector. Carbon farming offers a new business model where land managers are rewarded for sustainable management practices that increase carbon sequestration in biomass and soils. Keywords – Carbon credits; carbon farming; carbon removal; certification framework; EU climate policy; EU policy planning; sustainable agriculture

ScMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data

Single-cell transcriptomics data present challenges due to their inherent stochasticity and sparsity, complicating both cell clustering and cell type-specific network inference. To address these challenges, we introduce scMINER (single-cell Mutual Information-based Network Engineering Ranger), an integrative framework for unsupervised cell clustering, transcription factor and signaling protein network inference, and identification of hidden drivers from single-cell transcriptomic data. scMINER demonstrates superior accuracy in cell clustering, outperforming five state-of-the-art algorithms and excelling in distinguishing closely related cell populations. For network inference, scMINER outperforms three established methods, as validated by ATAC-seq and CROP-seq. In particular, it surpasses SCENIC in revealing key transcription factor drivers involved in T cell exhaustion and Treg tissue specification. Moreover, scMINER enables the inference of signaling protein networks and drivers with high accuracy, which presents an advantage in multimodal single cell data analysis. In addition, we establish scMINER Portal, an interactive visualization tool to facilitate exploration of scMINER results.

Behavioral Economics and Sustainability: How Consumer Choices Affect Environmental Policies

Objectives: To explore how insights from behavioral economics can enhance environmental policies and influence consumer behavior toward sustainability. To understand the psychological and social drivers behind sustainable choices. Methods: Applying behavioral economic theories that account for cognitive biases, heuristics, and social influences in consumer decision-making. Utilizing tools such as nudges, incentives, regulations, green labeling, carbon pricing, and public campaigns. Results: Identification of key behavioral drivers like loss aversion, default options, and social norms influencing sustainable behavior. Demonstration of how behavioral interventions (e.g., eco-product subsidies, awareness campaigns) lead to increased adoption of sustainable practices. Conclusion: Behavioral economics provides valuable strategies for designing effective environmental policies. To close the intention-action gap in sustainable behavior, a comprehensive approach is needed involving policy, corporate responsibility, and consumer empowerment. Ultimately, aligning personal behavior with environmental goals is achievable through thoughtful behavioral design.

Oncogenic Signalling Pathways in Cancer Immunotherapy: Leader or Follower in This Delicate Dance?

Immune checkpoint inhibitors have become a mainstay of treatment in many solid organ malignancies. Alongside this has been the rapid development in the identification and targeting of oncogenic drivers. The presence of alterations in oncogenic drivers not only predicts response to target therapy but can modulate the immune microenvironment and influence response to immunotherapy. Combining immune checkpoint inhibitors with targeted agents is an attractive therapeutic option but overlapping toxicity profiles may limit the clinical use of some combinations. In addition, there is growing evidence of shared resistance mechanisms that alter the response to immunotherapy when it is used after targeted therapy. Understanding this complex interaction between oncogenic drivers, targeted therapy and response to immune checkpoint inhibitors is vital for selecting the right treatment, at the right time for the right patient. In this review, we summarise the preclinical and clinical evidence of the influence of four common oncogenic alterations on immune checkpoint inhibitor response, combination therapies, and the presence of shared resistance mechanisms. We highlight the common resistance mechanisms and the need for more randomised trials investigating both combination and sequential therapy.

Research on the Identification of Road Hypnosis Based on the Fusion Calculation of Dynamic Human–Vehicle Data

Driver factors are the main cause of road traffic accidents. For the research of automotive active safety, an identification method for road hypnosis of a driver of a car with dynamic human–vehicle heterogeneous data fusion calculation is proposed. Road hypnosis is an unconscious driving state formed by the combination of external environmental factors and the psychological state of the car driver. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task. The safety of humans and cars is greatly affected. Therefore, the study of the identification of drivers’ road hypnosis is of great significance. Vehicle and virtual driving experiments are designed and carried out to collect human and vehicle data. Eye movement data and EEG data of human data are collected with eye movement sensors and EEG sensors. Vehicle speed and acceleration data are collected by a mobile phone with AutoNavi navigation, which serves as an onboard sensor. In order to screen the characteristics of human and vehicles related to the road hypnosis state, the characteristic parameters of the road hypnosis in the preprocessed data are selected by the method of independent sample T-test, the hidden Markov model (HMM) is constructed, and the identification of the road hypnosis of the Ridge Regression model is combined. In order to evaluate the identification performance of the model, six evaluation indicators are used and compared with multiple regression models. The results show that the hidden Markov-Ridge Regression model is the most superior in the identification accuracy and effect of the road hypnosis state. A new technical scheme reference for the development of intelligent driving assistance systems is provided by the proposed comprehensive road hypnosis state identification model based on human–vehicle data can provide, which can effectively improve the life recognition ability of automobile intelligent cockpits, enhance the active safety performance of automobiles, and further improve traffic safety.

Perception clouding reality? Interactions and opportunities for Andean bear conservation in Colombia

ABSTRACT Habitat loss due to human production practices is one of the main threats that promote negative interactions between humans and Andean bears. These interactions affect the economic incomes of ranchers and generate negative perceptions toward the species, which can lead to retaliatory killings. We aim to characterize conflict drivers, assess perceptions, and evaluate willingness to accept hypothetical conservation scenarios for Andean bears in the eastern range of Colombia. A total of 145 interviews were made, with a positive response rate of 84%. Declining participation was because of negative past experiences. Driver of negative interactions was the lack of cattle surveillance. Ranchers preferred developing conservation projects not paying for the loss of cattle, showing a higher willingness to accept conservation projects that include their knowledge of livestock practices. Identification of drivers of negative human-wildlife interactions and the generation of potential management scenarios are a priority for the facilitation of coexistence between humans and Andean bears.

Abstract 5543: High resolution lineage tracing for the study of cancer drug persistence

Abstract Drug-tolerant persister cells (DTPs) are a subpopulation of cancer cells that survive initial anticancer drug treatment despite lacking genetic resistance-driving alterations. DTPs have been shown to serve as an intermediate stage in the evolution of acquired drug resistance, facilitating the adaptation and survival of cancer cells under sustained therapeutic pressure. Recent studies have shed light on the biological characteristics of DTPs during and following drug treatment, describing the various adaptations that cells undergo while transforming into a DTP state. However, little is known about the biology of persistence potential — the cellular and molecular characteristics that prime certain drug-naïve cells to survive initial drug exposure. The main challenge in studying the persistence potential of drug-naïve cells lies in the need to treat cells to determine whether they will persist. To address this issue, we demonstrated the hereditary nature of persistence potential and adopted a high-resolution lineage tracing technology, to develop an assay for the study of persistence potential. This method, utilizing CRISPR-targeted evolving barcodes to reconstruct a lineage tree from a single-cell-derived clone, allows for the placement of persister cells along drug-naïve cells on the lineage tree. Utilizing this technology to study persistence to EGFR inhibitors in EGFR-positive non-small-cell lung cancer cells, we identified events on the lineage tree that alter the persistence potential of specific clades and derived the persistence potential of drug-naïve cells. Combining single-cell RNAseq data with cell persistence potential, we developed a computational pipeline to study the molecular mechanisms that govern the probability of drug-naïve cells to persist through drug treatment. We demonstrated that upregulation of genes related to oxidative phosphorylation correlates with a significant increase in persistence potential. These results agree with previous studies that demonstrate that cells in the DTP state are less dependent on glycolysis and rely more on oxidative phosphorylation for energy production and suggests that drug-naïve cells with elevated persistence potential are primed towards a high oxidative phosphorylation state. Intriguingly, we also described a module of translation-related genes that are negatively correlated with persistence potential, in agreement with recent results that show that persister cells undergo reversible remodeling of mRNA translation. This work introduces a novel approach and computational framework to study persistence potential and allows for the identification of potential drivers of persistence. A better understanding of the unique cellular and molecular traits that drive persistence potential is crucial for the study of drug persistence and development of targeted therapies preventing persistence and enhancing the likelihood of curative treatment. Citation Format: Binyanim Zhitomirsky, Jideofor Ezike, Brian P. Danysh, Gad Getz. High resolution lineage tracing for the study of cancer drug persistence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5543.

Abstract 2508: A multiscale framework combining mechanistic models and LSTM-GCN networks for predicting drug sensitivity

Abstract Understanding tumor evolution and treatment resistance requires an integrative approach that combines cellular dynamics with molecular interactions. While mechanistic models excel in capturing physiological processes and predicting behaviors beyond the original data, their specificity limits their generalizability. Conversely, deep learning enables universal predictions but often lacks interpretability, posing challenges for application in cancer research. To address these limitations, we propose a novel computational framework that integrates mechanistic simulations, graph-based modeling, Long Short-Term Memory (LSTM) networks, and Bayesian inference to predict the sensitivity of six cancer types to targeted therapies, chemotherapy agents, immunotherapies, and their combinations. In this framework, mechanistic models provide an individualized baseline simulation for different cancer types, while deep learning integrates these outputs with drug-related information to generate robust and interpretable predictions tailored to specific types. Our hybrid framework leverages a mechanistic pan-cancer model that simulates molecular signaling pathways, cell cycle dynamics, and apoptosis across four biological compartments. The model is parameterized using published gene expression profiles and mutation data from xenograft models, including key mutations such as KRAS, EGFR, and TP53. Simulations are performed for six cancer types, including breast carcinoma, colorectal cancer, and pancreatic carcinoma, to approximate patient-specific tumor biology. Tumor cell populations generated from these simulations are represented as graphs, with each node corresponding to an individual cell characterized by proliferation status and dynamic molecular states (e.g., protein concentrations and phosphorylation levels). Edges are weighted and defined based on molecular similarity between cells. The graph is processed using a Graph Convolutional Network (GCN), which generates embeddings that capture tumor population heterogeneity. These embeddings are then fed into an LSTM to extract the temporal dynamics of the tumor population. The final hidden states computed by the LSTM are further combined with embeddings of drug chemical structure and drug-target interactions to make drug sensitivity predictions. To enhance the robustness of predictions, Bayesian inference is incorporated to quantify uncertainty in estimated drug responses. This framework bridges the gap between mechanistic modeling and machine learning, offering a scalable and interpretable approach to studying tumor evolution under the selective power of drug treatment. By integrating the physiological insights of mechanistic models with the predictive power of deep learning, it enables the identification of molecular drivers of resistance and informs the development of personalized therapies. Citation Format: Chenhui Ma, Evren Gurkan-Cavusoglu. A multiscale framework combining mechanistic models and LSTM-GCN networks for predicting drug sensitivity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2508.

CD40 promotes AML survival via non-canonical NF-κB signaling and aberrant lipid metabolism.

CD40 promotes AML survival via non-canonical NF-κB signaling and aberrant lipid metabolism.

Experimental insights on antisocial behavior: two meta-analyses

Abstract We report two meta-analyses on the determinants of antisocial behavior in experimental settings in which such behavior is not rationally motivated by pecuniary incentives. The first meta-analysis employs aggregate data from 95 published and unpublished studies (24,086 participants), using laboratory, field and online experiments carried out since 2000. We find that antisocial behavior depends significantly on the experimental setting, being highest in vendetta games and lowest in social dilemmas. As we find significant heterogeneity across the studies, including across game classes, in the second meta-analysis, we focus only on “Joy of Destruction” (JoD) and money-burning (MB) experiments, for which we have the most observations, 51 studies and around 16,784 participants. Overall, our findings suggest that procedural fairness and being observed by others reduce the frequency of antisocial behavior. Online and field experiments display more antisocial behavior than laboratory experiments. We also find that the strategy method biases antisocial behavior upward. However, we do not find evidence for a positive publication bias being correlated with higher destructive behavior, either in the general meta-analysis or in relation to JoD/MB experiments; if anything, there is evidence of a negative publication bias. The JoD/MB meta-analysis finds evidence of a price effect for destruction frequency, negative discrimination against outsiders, within-subject designs underestimating destructive behavior, and more antisocial behavior in one-shot interactions. Collectively, our results point to the value of more laboratory experiments that systematically build on paradigmatic experimental designs to enable comparability and the identification of key economic drivers of antisocial behavior.

Why Micro-influencers are Sharing Digital Brand Content in a B2B Context

This research aims to contribute in understanding micro-influencers’ behaviour in sharing digital brand content by focusing on business networking sites (LinkedIn, Xing). This choice was influenced by two considerations: first, marketing influence literature which is highly dominated by studies of B2C digital context to the detriment of B2B digital context despite their crucial importance in the marketing arena. And second, given their particularity, micro-influencers are more likely to have motivations that are totally or partially different from what the literature suggests for all influencers in general. Based on a mixed-technique analysis (MLM) and bias-corrected bootstrap method, this research was conducted by administering a large survey to 312 micro-influencers, who were selected according to a new methodological algorithmic approach called metric-behavioural. The results not only cover the identification of salient drivers that lead the sharing of digital brand content by micro-influencers in a specific area but also validate the robustness of the use of the theory of planned behaviour in a B2B social media context. By considering these results, the paper provides a practical guidance for companies, urging them to tailor their influence marketing strategies for effective engagement in B2B and B2G contexts.

Identification of Key Drivers of Land Surface Temperature Within the Local Climate Zone Framework

The surface urban heat island (SUHI) effect, driven by human activities and land cover changes, leads to elevated temperatures in urban areas, posing challenges to sustainability, public health, and environmental quality. While SUHI drivers at large scales are well-studied, finer-scale thermal variations remain underexplored. This study employed the Local Climate Zones (LCZs) framework to analyze land surface temperature (LST) dynamics in Zhengzhou, China. Using 2022 mean LST data derived from a single-channel algorithm, combined with field surveys and remote sensing techniques, we examined 30 potential driving factors spanning natural and anthropogenic conditions. Results show that built-type LCZs had higher average LSTs (31.10 °C) compared with non-built LCZs (28.91 °C), with non-built LCZs showing greater variability (10.48 °C vs. 6.76 °C). Among five major driving factor categories, landscape pattern indices dominated built-type LCZs, accounting for 44.5% of LST variation, while Tasseled Cap Transformation indices, particularly brightness, drove 42.8% of the variation in non-built-type LCZs. Partial dependence analysis revealed that wetness and landscape fragmentation reduce LST in built-type LCZs, whereas GDP, imperviousness, and landscape cohesion increase it. In non-built LCZs, population density, connectivity, and brightness raise LST, while wetness and atmospheric dryness provide cooling effects. These findings highlight the need for LCZ-specific SUHI mitigation strategies. Built-type LCZs require urban form optimization, enhanced landscape connectivity, and expanded green infrastructure to reduce heat accumulation. Non-built LCZs benefit from maintaining soil moisture, addressing atmospheric dryness, and optimizing vegetation configurations. This study provides actionable insights for sustainable thermal environment management and urban resilience.

ХАРАКТЕРНЫЕ ОСОБЕННОСТИ ЭКОНОМИЧЕСКОГО РОСТА КАЗАХСТАНА

The article analyzes the dynamics of Kazakhstan's economic growth over a ten-year period, which has allowed for the identification of the main drivers and the determination of the barriers affecting its dynamics. An analysis of the structure of Kazakhstan's GDP was conducted, revealing the predominant share of trade in the services sector and the share of the manufacturing sector in production. However, this sector does not make a significant contribution to the country's GDP due to the structural characteristics of the economy, which is dominated by high-income sectors dependent on resource extraction. It was found that the country's GDP growth, supported by high oil prices and transfers from the National Fund of Kazakhstan. The predominant share of household expenditures on food in the structure of consumer spending indicates an existing irrational consumption model in society. As a result, there is a noted direct dependence of Kazakhstan's economy on the export of natural resources and global oil prices, and a characteristic pro-cyclical budget policy for Kazakhstan's economy with a significant share of government spending financed from the National Fund of Kazakhstan. The presented article employs methods of desk research, statistical analysis, induction and deduction, content analysis of documents and research publications.

Widespread Multi‐Year Droughts in Italy: Identification and Causes of Development

ABSTRACTMulti‐year droughts pose a significant threat to the security of water resources, putting stress on the resilience of hydrological, ecological and socioeconomic systems. Motivated by the recent multi‐year drought that affected Southwestern Europe and Italy from 2021 to 2023, here we utilise two indices—the Standardised Precipitation Evapotranspiration Index (SPEI) and the Standardised Precipitation Index (SPI)—to quantify the temporal evolution of the percentage of Italian territory experiencing drought conditions in the period 1901–2023 and to identify Widespread Multi‐Year Drought (WMYD) events, defined as multi‐year droughts affecting at least 30% of Italy. Seven WMYD events are identified using two different precipitation datasets: 1921–1922, 1942–1944, 1945–1946, 2006–2008, 2011–2013, 2017–2018 and 2021–2023. Correlation analysis between the time series of Italian drought areas and atmospheric circulation indicates that the onset and spread of droughts in Italy are related to specific phases of the winter North Atlantic Oscillation (NAO), the Scandinavian Pattern (SCAND), East Atlantic/Western Russia (EAWR) pattern and the summer East Atlantic (EA) and East Atlantic/Western Russia (EAWR) patterns. Event‐based analysis of these drought episodes reveals a variety of atmospheric patterns and combinations of the four teleconnection modes that contribute to persistently dry conditions in Italy during both winter and summer. This study offers new insights into the identification and understanding of the meteorological drivers of Italian WMYD events and serves as a first step toward a better understanding of the impacts of anthropogenic climate change on them.

Machine learning-based identification of key biotic and abiotic drivers of mineral weathering rate in a complex enhanced weathering experiment

Background The optimization of enhanced mineral weathering as a carbon dioxide removal technology requires a comprehensive understanding of what drives mineral weathering. These drivers can be abiotic and biotic and can interact with each other. Therefore, in this study, an extensive 8-week column experiment was set up to investigate 29 potential drivers of mineral weathering simultaneously. Methods The setup included various combinations of mineral types and surface areas, irrigation settings, biochar and organic amendments, along with various biota and biotic products such as earthworms, fungi, bacteria and enzymes; each varying in type or species and quantity. The resulting changes in dissolved, solid, and total inorganic carbon (∆TIC), and total alkalinity were calculated as indicators of carbon dioxide removal through mineral weathering. Three machine learning models, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest and eXtreme Gradient Boosting (XGB) regression, were used to predict these indicators. Dominant drivers of the best performing model were investigated using SHapley Additive exPlanations (SHAP). Results SHAP analysis revealed that each CDR indicator was influenced by different factors. However, key drivers were consistently abiotic, though biota also made a significant contribution to the predictions. The most representative CDR indicator, ∆TIC, was predominantly driven by steel slag addition and mixed mineral grain sizes but was also substantially impacted by earthworms and microbes. Conclusions These findings provide valuable insights into the complex interplay of numerous abiotic and biotic factors that affect mineral weathering, highlighting the potential of machine learning to unravel complex relationships in biogeochemical systems.

Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression

Scientific management and environmental regulation of facility strawberries depends on the level of accurate prediction and forecasting of low temperature freezes in plastic greenhouses during winter and spring strawberry cultivation. Accurate identification of potential factors affecting layer-by-layer minimum temperatures in plastic greenhouses and selection of optimal forecasting methods are important for safe strawberry production. However, the identification of important drivers of minimum temperatures in plastic greenhouses and the prediction of potential drivers of use are still unclear. In this study, we used Classification and Regression Tree (CART) to identify the importance of the potential factors affecting the minimum temperatures at different depths and different heights of plastic greenhouses. Random forest (RF), back-propagation (BP), and multiple linear regression (MLR) were used to establish the minimum temperature prediction models for plastic greenhouses at different depths and heights, respectively. The results showed that Tsmin10, Tsmin25, Tamin150, Tamin320, and Tamin150 were the most important variables explaining the changes in minimum temperatures at heights Tsmin25, Tsmin10, Tsmin2, Tamin150, and Tamin320 respectively. RF, BP performed much better than MLR, as it showed much lower error indices (AE and RMSE) and higher R2 than MLR. The superiority of RF and BP in predicting minimum temperatures is related to their ability to deal with non-linear and hierarchical relationships between minimum temperatures and predictors. The low-temperature frost protection and fine management of strawberries in the Changfeng area can be related to the prediction method of minimum temperature in plastic greenhouses constructed in this study.

Genomic Insights into the Successful Invasion of the Avian Vampire Fly (Philornis downsi ) in the Galápagos Islands.

Invasive species pose significant threats to island ecosystems, often leading to the decline of native species and the disruption of ecological balance. The avian vampire fly (Philornis downsi), introduced to the Galápagos Islands of Ecuador, has emerged as a major threat to the endemic avifauna, parasitizing multiple species of Darwin's finches and other passerines. Yet, the genetic mechanisms of its invasion remain unclear. In this study, we conducted the first whole-genome sequencing analysis of P. downsi populations from the Galápagos Islands and their native range in mainland Ecuador. Our results reveal genomic signatures of a founder effect, with reduced genetic diversity in the Galápagos populations, indicative of a recent population bottleneck. We found a lack of significant genetic differentiation and evidence of ongoing gene flow among island populations. Despite low genetic diversity in island populations, we identified adaptive genetic changes, including regions possibly under positive selection near genes related to neural signaling, muscle development, and metabolic processes, which may have contributed to the fly's invasion success. Additionally, we uncovered genetic changes associated with precipitation-related climate adaptation, highlighting the possible role of environmental factors in shaping genetic variation in P. downsi. Our findings provide crucial insights into the invasion dynamics of P. downsi in Galápagos, emphasizing the importance of genomic research in informing conservation strategies. The identification of key adaptive genomic loci and potential environmental drivers of genetic change will aid in the development of targeted management practices to mitigate the impact of this invasive species on the unique biodiversity of the Galápagos Islands.

Identification and prioritization of drivers for successful green manufacturing implementation employing the Best-Worst Method: evidence from Iranian manufacturing industries

PurposeGreen manufacturing (GM) has emerged as a vital strategy to minimize environmental impacts and maximize resource efficiency in industrial production. The main aim of this work is to identify and validate essential criteria for GM and prioritize drivers of successful GM implementation frameworks for the manufacturing industry based on Best–Worst Methodology (BWM).Design/methodology/approachThis work explores the essential factors required to achieve long-term success in GM, followed by their comparison using the BWM to determine the most and least important indicators. The study conducted purposive sampling to gather data from 15 experts representing diverse industries in the manufacturing sector. The research methodology consists of three main steps: criteria identification through literature review, criteria validation using the content validity ratio (CVR) method and the BWM application to rank the indicators.FindingsThe main success factors identified included top management commitment, organizational culture, employee training, cost saving, investment in innovation and technology, environmental regulation, zero-emission and waste management. The results obtained through BWM indicated top management commitment, investment in innovation and technology and organizational culture as the most critical factors for successful GM implementation. Other factors, such as zero-emission, waste management and cost savings, were also significant but ranked lower in significance. In conclusion, this study highlights the importance of top management commitment to successfully adopting GM initiatives.Originality/valueThis research provides insights into the key success factors, through which decision-makers are assisted in prioritizing efforts and implementing sustainable and eco-friendly practices in manufacturing processes. However, further research is recommended to address existing gaps and foster a deeper understanding of crucial success factors for successful GM implementation.

TAF7 directly targets SAA1 to enhance triple-negative breast cancer metastasis via phosphorylating E-cadherin and N-cadherin.

TAF7 directly targets SAA1 to enhance triple-negative breast cancer metastasis via phosphorylating E-cadherin and N-cadherin.

Identification of direct and indirect drivers of land use and land cover changes from agriculture to Eucalyptus plantation using the DPSIR framework in Sinan and Mecha Districts of Northwestern Ethiopia

Identification of direct and indirect drivers of land use and land cover changes from agriculture to Eucalyptus plantation using the DPSIR framework in Sinan and Mecha Districts of Northwestern Ethiopia