Small and medium-sized enterprises (SMEs) face substantial barriers in accessing reliable and cost-effective IT infrastructure, primarily due to economic constraints and limited technical resources. Hybrid cloud computing solutions, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS), offer potential to overcome these barriers by providing scalable, flexible IT services. This study systematically reviews the impact of cloud computing on SME performance, focusing on key performance metrics such as cost-efficiency, operational reliability, and competitive advantage. A systematic literature review was conducted using the PRISMA 2020 framework. Inclusion criteria included studies published in English from 2014 to 2024, focusing on cloud computing for SMEs and presenting clear analytical frameworks for evaluating performance. Out of an initial pool of 18,570 studies, 90 met the criteria for detailed analysis. Findings show that cloud computing adoption enhances SME performance, with approximately 82% of studies reporting improvements in operational efficiency and 76% noting cost savings. Competitive advantage was identified as a key benefit in 64% of studies, driven by cloud-enabled scalability and access to advanced technology. Key adoption drivers include management support (cited by 68% of studies), service quality (56%), and perceived risks (54%), while barriers such as initial cost concerns and data security risks were also prevalent, affecting 48% and 45% of SMEs, respectively. This review provides strategic insights for SMEs and policymakers, emphasizing the importance of tailored cloud strategies that align with specific operational needs and budget constraints. By addressing key predictors and challenges, this study offers a roadmap for SMEs to leverage cloud computing to improve performance, sustainability, and competitiveness.

In recent times, the adoption of cloud computing by small and medium-sized 9 enterprises (SMEs) has become a crucial issue. These businesses face substantial difficulties in 10 accessing reliable and affordable IT infrastructure due to economic constraints and technological 11 restrictions. Hybrid cloud computing solutions which incorporate Infrastructure as a Service 12 (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) offer a feasible approach to 13 overcoming these challenges. This study proposes a systematic literature review of cloud 14 computing's impact on SME performance emphasizing cost-efficiency, reliability, and 15 competitive advantages, using the 2020 Preferred Reporting Items for Systematic Reviews and 16 Meta-Analyses (PRISMA) framework. The inclusion criteria are bounded by (1) publication date 17 between 2014 and 2024, (2) English-language publications, (3) research focused on cloud 18 computing for SMEs, and (4) studies with a clear analytical framework for evaluating SME 19 performance. Following this process, 90 eligible research studies were included. The average 20 total cost for cloud services varies significantly, with operational costs reflecting the scalability 21 of the services and the specific needs of SMEs. This analysis offers valuable guidance for SMEs 22 and stakeholders in optimizing cloud-based strategies for enhanced performance.

Regenerative braking recovers vehicle kinetic energy during deceleration, but realised battery-side recovery depends on braking demand and battery charge-acceptance conditions. This study characterises real-world regenerative braking behaviour and evaluates a dual-output event-level estimator using the public Real-World Electric Vehicle Data: Driving and Charging dataset. Negative-current intervals from eight drive records were segmented into 8181 valid events using hysteresis, short-gap bridging, acquisition-dropout splitting, and long-stretch exclusion. The events recovered 288.7 kWh and returned 683.4 Ah; the median event recovered 19.7 Wh over 3.6 s, and the maximum observed peak regenerative power was 224.1 kW. Event duration and peak current showed the strongest associations with recovered energy (Spearman rho = 0.802 and 0.871, respectively), while SoC, temperature, and voltage showed weak direct global associations. The maximum peak power decreased to 155.53 kW in the 90–100% SoC band, which indicates a constrained high-SoC charge-acceptance envelope. A 10-input, 12-hidden-neuron, two-output feedforward neural network (158 parameters) was optimised using HPWOA and benchmarked against PSO, WOA, and SFSA. On the reported held-out event set, HPWOA achieved R² = 0.9784 and RMSE = 1.839 Wh for recovered energy and R² = 0.9819 and RMSE = 4.891 kW for peak power. The framework is interpreted as retrospective event-level estimation rather than prebraking forecasting.

Pack-side current, voltage and battery-state data can quantify charging episodes during driving, but an event-level model must not be credited for reconstructing its target from post-event electrical summaries. We re-examined 8181 negative-current intervals from one Audi e-tron recorded in the San Francisco Bay Area. The original eight-regressor comparison used a partial target-specific feature screen and five-fold gap-grouped and event-ordered validation; those scores are retained only as a shortcut-prone reference. A conservative reanalysis excludes every event-current, mean/end-state and target-derived feature, retaining state of charge, temperature and voltage at the first detected event sample, clock variables, and completed-event duration. Across five 120 s-gap-grouped test folds, the highest mean R² was 0.511 for energy (Random Forest), 0.132 for peak power (Gradient Boosting), and 0.522 for charge (XGBoost); five forward, gap-group-separated folds provide an additional temporal check. These are post-event estimates rather than forecasts before braking. The 280 gap-defined groups are not verified independent trips, and the dataset contains no brake-pedal, speed, grade or motor-torque signal with which to confirm every charging interval as friction-brake deceleration. No independent vehicle or laboratory experiment was available. The revised benchmark therefore supports only within-vehicle, battery-side inference under the stated feature and grouping rules; it does not support not cross-vehicle deployment or causal claims.