Abstract
SMEs in developing economies operate under persistently volatile environments where economic instability, regulatory uncertainte and technological disruptions threaten their survival. Here, sustainability shifts from long-term environmental or socioeconomic performance to strategic resilience. In this study, we investigate how dynamic capabilities condition the effect of business environmental forces on SME sustainability in Nigeria. Grounded in contingency and dynamic capability theory, this study adopts a quantitative, cross-sectional survey design using data from 285 Nigerian SMEs. It examines the direct effects of economic, legal and technological environmental forces, as well as the moderating roles of sensing and seizing, and learning and reconfiguration capabilities, on SME strategic resilience using PLS-SEM. The results show that economic, legal and technological turbulence significantly affect SME strategic resilience, with legal turbulence emerging as the strongest constraint. Findings further reveal that dynamic capabilities-sensing and seizing, learning and reconfiguration-significantly moderate the effect of environmental turbulence on SME strategic resilience and strengthen SME capacity in absorbing shocks, reconfiguring resources and sustaining operations under disruptions. This study contributes by reframing SME sustainability as strategic resilience amid environmental turbulence, differentiating external pressures into economic, legal and technological dimensions, and showing how dynamic capability bundles condition SME strategic resilience in a highly volatile developing-economy context. This study offers insights relevant to other emerging economies characterised by institutional instability, policy unpredictability and uneven technological development. It also broadens understanding of contingency and dynamic capability theory in developing economies and positions dynamic capabilities as vital for resilience-building, not just competitive advantage.

Abstract
Route optimization algorithms are essential to calculate the best routes and solve problems that may arise in logistics services. This study focuses on the problem of route optimization for both single and fleets of electrical motorbikes dedicated to urban services. The paper explores the most relevant and widely-known problems within route optimization, which match the requirements of the specified target problem, e.g., traveling salesman problems. This paper also explores the most widely used algorithms for solving the identified problem variations, namely exact methods such as Branch and Bound and Dijkstra's algorithm; and also heuristic and meta-heuristic algorithms. Route optimization issues and problem variations related to vehicle fleets are also addressed, including the Vehicle Routing Problem (VRP), Battery-Constrained Vehicle Routing Problem (BCVRP), and Vehicle Routing Problem with Time Windows (VRPTW). This study concludes that efficient algorithms for solving these problems can have several benefits, including cost reduction, resource optimization, and time efficiency.

Abstract
Counterfactual explanations (CFs) help users understand and act on black-box machine learning decisions by suggesting minimal changes to achieve a desired outcome. However, existing methods often ignore individual feasibility, leading to unrealistic or unactionable recommendations. We propose a personalized CF generation method based on cluster-specific fine-tuning of Generative Adversarial Networks (GANs). By grouping users with similar behavior and constraints, we adapt immutable features and cost weights per cluster, allowing GANs to generate more actionable and user-aligned counterfactuals. Experiments on the German Credit dataset show that our approach achieves a 6x improvement in prediction gain and a 30% reduction in sparsity compared to a baseline CounterGAN, while maintaining plausibility and acceptable latency for online use.

Abstract
Effective warehouse management plays a pivotal role in optimizing supply chain performance, particularly in high-demand, time-sensitive environments. This study introduces a simulation-based decision support system designed to improve the management of Picking-By-Line (PBL) operations in cross-docking distribution centres. Developed in FlexSim and calibrated with empirical data from an industrial case study, the model replicates real-world warehouse conditions and is validated against observed operational performance. The tool supports warehouse managers in evaluating and comparing operational strategies, such as dynamic storage allocation policies and picker routing constraints, with the goal of reducing operator travel distances, mitigating congestion, and enhancing overall efficiency. A key contribution of this work is the integration of congestion-sensitive performance indicators that allow for a detailed analysis of the trade-offs between travel efficiency and localized congestion-an aspect often overlooked in traditional optimization methods. This study demonstrates the value of simulation as a scalable and realistic decision-support tool for optimizing PBL operations in complex and variable environments where human movement is a major cost and performance driver. The proposed tool bridges the gap between theoretical modelling and practical implementation, offering actionable insights for warehouse layout, space utilization, and resource allocation.

Abstract
Haemoglobin (Hb) concentration is a key biomarker for several diseases. Traditional laboratory methods often have limitations due to their time-consuming nature, the need for skilled personnel, or the use of high-cost instrumentation. This work presents a sensing strategy for developing new point-of-care tests (POCTs) for Hb detection via a proof of concept. The proposed sensing approach is implemented using plasmonic plastic optical fiber (POF) sensor chips that integrate an electropolymerized molecularly imprinted polymer (eMIP) film on the plasmonic surface for Hb-selective detection. The developed sensor system demonstrates an ultra-low detection limit of 80 fM in buffer, about five orders of magnitude lower than that of other comparable Hb sensors. Selectivity tests against common interfering proteins, such as bovine serum albumin (BSA) and immunoglobulin G (IgG), confirmed high specificity towards the target analyte. Moreover, the sensor's performance was tested using a whole-blood sample, yielding results consistent with those of standard haematology analysis. The proposed sensor system, based on simple equipment, provides a quick (about 10 min) and cost-effective (about 10 euros per chip) label-free diagnostic tool for POCTs in real-world scenarios, such as finger-prick sampling, offering a less invasive alternative to traditional laboratory methods, towards devices useful for Internet of Medical Things (IoMT).

Abstract