One-pot bioprocessing (OPB) represents an integrated strategy for biomass valorization within a single reactor rather than across sequential, isolated units. By consolidating formation-stage steps that are traditionally separated, OPB can lower capital intensity and reduce intermediate losses. It may also improve carbon utilization under specific conditions compared with conventional modular biorefineries optimized around a single product. Despite these advantages, OPB has yet to achieve robust scalability. This review examines the dynamic processes governing biomass fractionation into individual constituents and biocatalytic transformation in single-reactor systems. It synthesizes recent advances in metabolic engineering, process intensification, and dynamic flux control to assess how biological network behavior, thermodynamic feasibility, and reactor-scale transport phenomena jointly constrain feasible product combinations within integrated one-pot systems. Persistent limitations arising from metabolic trade-offs, physicochemical incompatibilities, and increasing control complexity are evaluated alongside emerging enabling strategies, including dynamic metabolic regulation, hybrid one-pot architectures, and digital bioprocess twins. This work provides a data-informed framework indicating that effective one-pot bioprocess design depends on aligning biological compatibility, control capacity, and operational robustness to support adaptive and anticipatory control of multiproduct formation dynamics.
This study examines the impact of risk attitudes on the profit efficiency of smallholder agricultural enterprises. Using output and input quantity and price data from smallholder maize farmers in Ghana, this study employs a three-stage feasible generalized least square (3SFGLS) framework to determine farmers' risk attitude; data envelopment analysis to estimate farmers' profit efficiency; and the endogenous switching regression (ESR) model to measure the impact of risk attitude on profit efficiency. Our analysis leads to three key findings. First, smallholder maize farmers in our sample are risk-loving, and their socioeconomic characteristics and some institutional factors influence their risk attitudes. Second, farmers are generally inefficient, with an average profit efficiency score of 53.3%, indicating potential to increase their profits. Third, our analysis suggest that risk-averse farmers could improve their profit efficiency by 7% if they took more productive risks.
PurposeEnvironmental variables like natural resource availability, distance and landlockedness are important locational determinants of foreign direct investment (FDI). This paper aims to determine the effect of environmental variables on African countries' attractiveness to Chinese FDI.Design/methodology/approachA two-stage approach was used to analyze and explain the attractiveness of African countries to Chinese FDI using averaged annual data from 2005 to 2022. In the first stage of the analysis, this paper employed data envelopment analysis (DEA) to estimate the technical efficiency of FDI flow to 42 African countries. In this case, the estimated technical efficiency represents the attractiveness of countries to FDI. In stage two of the analysis, they applied a Tobit model to analyze the environmental determinants of the estimated efficiency scores.FindingsBased on constant returns to scale technology, five of the 42 countries analyzed are technically efficient, and the average efficiency score is 85%. The Tobit model shows that distance has a negative and significant effect, whereas landlockedness has a positive and significant effect on the attractiveness of Chinese FDI to Africa. The effect of natural resource availability is not significant.Research limitations/implicationsTwo major weaknesses can be identified. First, focusing on Chinese FDI does not give a general picture of the efficiency of FDI flow to Africa. Extant empirical literature has shown that Chinese FDI is peculiar owing to, inter alia, the significant component of state-owned enterprises among Chinese MNCs, which makes it sensitive not only to the economic circumstances of host countries but also to China's political objectives. Empirical analysis focusing on FDI from the global north is, therefore, required for comparative purposes.Practical implicationsThe findings suggest that policies seeking to promote Chinese FDI should focus on ameliorating the negative effects of geographical distance, while acknowledging the confounding effect of sovereign debt.Social implicationsThe findings of this study have implications for the formulation and targeting of investment promotion policies. Through the DEA-based performance ranking, the less efficient or less attractive countries have an opportunity to learn from the more efficient peers. The other implication for policy is that FDI promotion strategies targeting Chinese FDI should focus more on ameliorating the adverse effects of distance as the other environmental variables have no significant influence.Originality/valueThe use of efficiency analysis to estimate the attractiveness of countries to FDI is still new in the FDI literature. The contribution of this paper is twofold: the use of data envelopment analysis to estimate attractiveness of host countries to FDI, and the use of environmental variables to explain the observed attractiveness and efficiency.
This article examines how the rise of FinTech companies has affected the power of commercial banks. Specifically, we employ comparative case studies of Kenya and Nigeria to explore how the rise of FinTechs has affected banks' ability to exercise control over the emerging digital financial infrastructure. We find that cross-national variation in the relationship between the state and private sector in the allocation of economic resources has led to cross-national differences in the regulatory framework for digital financial services, which in turn explain differences in banks' ability to control the emerging digital financial infrastructure. Our article makes an empirical contribution by studying how banks' power is affected by digitalisation in under-researched middle-income country contexts and contributes to broader theoretical discussions about the changing power and purpose of banks in the digital era.
Breast cancer is among the most common cancers in women worldwide, and outcomes improve with early detection. As machine learning enters routine care, data driven diagnostic systems may support earlier risk estimation. We present a compact pipeline that uses Principal Component Analysis for dimensionality reduction and Borderline-SMOTE for imbalance correction, followed by classification with Light Gradient Boosting Machine. Using the standardized Wisconsin Breast Cancer Diagnostic dataset, we retain 20 features to capture key variance while limiting redundancy and noise. Borderline-SMOTE is applied within each training fold to refine class boundaries. Performance is evaluated with stratified 10-fold cross validation and compared with seven alternatives: XGBoost, Support Vector Machines, Random Forests, Logistic Regression, Gaussian Naive Bayes, k Nearest Neighbor, and a Multilayer Perceptron. With 20 components, the proposed model attains accuracy 0.993, precision 1, recall 0.986, F10.993, and AUC 1.000 for distinguishing benign from malignant cases, outperforming baselines. These findings suggest that coupling dimensionality reduction, boundary focused resampling, and gradient boosted trees can enhance diagnostic performance and may inform clinical decision support.
