Research Article
Centralized Faith-based Pharmaceutical Supply Chain Performance in Cameroon: A System-level Audit
Issue:
Volume 12, Issue 3, June 2026
Pages:
39-53
Received:
16 June 2026
Accepted:
30 June 2026
Published:
22 July 2026
Abstract: Background: Centralized pharmaceutical procurement can strengthen medicine quality assurance and reduce exposure to substandard and falsified medicines in low- and middle-income countries. However, medicine availability depends on how well procurement, forecasting, warehousing, inventory management, information systems, and distribution function as an integrated system. This study assessed the system-level performance of a centralized, faith-based pharmaceutical supply chain in Cameroon to identify operational strengths, performance gaps, and governance-related risks that affect sustained access to quality-assured medicines. Methods: A cross-sectional pharmaceutical supply chain audit was conducted over a 1-year period using the Pharmaceutical Supply Chain Initiative tripartite audit framework. Performance was assessed across forecasting, procurement, supplier sourcing, warehousing, inventory management, use of logistics management information systems, and distribution, using adapted USAID-recommended indicators. Quantitative benchmarking was complemented by participatory root-cause analysis using the Ishikawa framework. Results: Procurement-level quality assurance was strong: all products underwent quality testing, and 94.6% met pharmacopoeial standards. Internal warehouse handling accuracy and electronic order processing through the logistics management information system also met benchmark targets. In contrast, major downstream gaps were identified. Forecasting discrepancies reached 49.5%; supplier on-time delivery was low; warehouse storage utilization exceeded recommended capacity; inventory accuracy remained below target; and order-filling performance was poor at 38%. These gaps were associated with high stockout rates of 49.7% and inconsistent medicine availability across service delivery points, indicating misalignment between governance functions and downstream operational performance. Conclusions: Centralized procurement strengthened upstream quality assurance but did not ensure reliable availability of medicines when downstream supply chain functions were weak. Persistent gaps in forecasting, supplier performance, warehousing, inventory management, and distribution were associated with reduced service readiness and greater reliance on emergency or decentralized procurement pathways, which may increase risks to medicine quality. Strengthening pharmaceutical supply chains requires integrated end-to-end performance management, supported by routine system-level audits, to align governance and operational functions and sustain access to quality-assured medicines.
Abstract: Background: Centralized pharmaceutical procurement can strengthen medicine quality assurance and reduce exposure to substandard and falsified medicines in low- and middle-income countries. However, medicine availability depends on how well procurement, forecasting, warehousing, inventory management, information systems, and distribution function as...
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Research Article
AMAtt: Manifold Attention Network with Adaptive Log-Euclidean Metrics for Brain Signals
Syed Mohamed Syed Abubakar Shaihan*
Issue:
Volume 12, Issue 3, June 2026
Pages:
54-61
Received:
5 April 2026
Accepted:
20 April 2026
Published:
27 August 2026
Abstract: In this paper, we discuss the recognition of electroencephalographic (EEG) signals, which is crucial in order to improve the performance of non-invasive brain-computer interfaces (BCIs). Although deep learning (DL) has achieved considerable advancements in the decoding of EEG signals, it frequently encounters difficulties related to noisy data and non-stationarity challenges. We discuss geometric learning which offers a more robust way to handle EEG signals by leveraging the mathematical structure of the data. We extended the existing Manifold Attention Network (MAtt), a novel deep learning model that applies a manifold attention mechanism to better capture the spatiotemporal patterns of EEG signals. Instead of using traditional Euclidean space, we mapped the data onto a Riemannian symmetric positive definite (SPD) manifold, which allows for more effective feature extraction. One major issue with existing SPD-based deep learning approaches is that they rely on fixed Riemannian metrics, which can be suboptimal. To solve this, we integrate Adaptive Log-Euclidean Metrics (ALEMs)---a learnable metric framework into the MAtt network that adapts to the specific structure of EEG data, improving model performance with minimal extra computation. AMAtt achieves 63.19% accuracy on BCIC-IV-2a and 46.00% on MAMEM-SSVEP-II, outperforming the MAtt baseline by 5.55% and 4.60% respectively. This adaptive geometric approach opens new possibilities for robust, subject-independent EEG decoding, paving the way towards practical BCI systems.
Abstract: In this paper, we discuss the recognition of electroencephalographic (EEG) signals, which is crucial in order to improve the performance of non-invasive brain-computer interfaces (BCIs). Although deep learning (DL) has achieved considerable advancements in the decoding of EEG signals, it frequently encounters difficulties related to noisy data and ...
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