Recent Submissions
Integrating industry 4.0 and 5.0 technologies in luxury fashion retail interiors: A systematic review of digital transformation, sensory design, and brand storytelling
(Elsevier Ltd, 2026-05-12) Ratnayake, Janitha C; Jayasuriya, N; Suraweera, T; De Silva, L
Emerging developments in Industry 4.0 and Industry 5.0 are transforming the cultural, sensory, and experiential character of luxury fashion retail interiors. These changes, driven by artificial intelligence, the Internet of Things, augmented and virtual reality, influence how individuals perceive space, form emotional connections, and engage with brand narratives across both physical and digitally enriched environments. Against this backdrop, this review aims to systematically synthesise how Industry 4.0 and Industry 5.0 technologies intersect with sensory experience, spatial design, and narrative expression in luxury fashion retail interiors, addressing the tendency of prior reviews to examine digital technologies or experiential aspects of retail in isolation. The study adopts a systematic literature review approach guided by the SPIDER framework and the PRISMA protocol. Fifty peer reviewed publications published between 2014 and 2025 were analysed to examine how digital transformation, sensory experience, and narrative expression intersect within luxury retail culture. The thematic analysis identified three closely connected areas: branding and customer experience, the interior environment of luxury stores, and the integration of advanced technologies within retail spaces. Together, these themes illustrate how contemporary luxury interiors operate as culturally expressive and emotionally charged settings shaped by new forms of technological mediation. Building on these insights, the study introduces the Multi-Layered Integration Framework, which explains the interaction between digital systems, spatial atmospheres, and brand storytelling in the creation of culturally responsive and human-centred retail interiors. The review contributes to the social sciences and humanities by demonstrating how emerging technologies reshape sensory engagement, symbolic identity, and cultural expression in luxury retail settings, while offering an expanded understanding of human experience within digitally influenced interior environments.
Multimodal AI Framework for Personalized and Context-Aware Skin Disease Diagnosis, Monitoring, and Treatment Support
(Institute of Electrical and Electronics Engineers, 2026-05-22) Wijesinghe H.W.M.O.P.; Laksopan R; Mihisandali W.K.M.; Devindi K.P.T.; Weerasinghe, L; De Silva, A
Dermatoscopic assessment of skin diseases based on visual morphology may not provide sufficient discrimination due to differences in cutaneous appearance, the severity of disease symptoms and individual biological or environmental factors. Current artificial intelligence (AI)-based dermatological systems mainly integrate unimodal image-based data which is constrained by comparative diagnostic performance in visually ambiguous conditions and across different skin complexions. Image-only approaches also do not utilize patient-reported symptoms needed to tailor treatment plans. In this study, we explore the potential of a unified multimodal AI framework towards robust, context-aware and patient- centric skin disease diagnosis, monitoring and treatment support. The framework combines deep learning-based image analysis with symptom-aware inputs extracted from voice recordings and structured text, which allows for improved diagnostic reliability. The proposed framework also introduces an explainable severity assessment module which evaluates disease progression via interpretable features and rule-based score. Domain adaptation methods further employed lead to better generalization for out-of-distribution data originating from different populations and reduce model bias. A knowledge-driven recommendation module generates context-aware personalized treatment recommendations based on predicted disease categories and patient-related information. Experimental results demonstrate that the proposed multimodal framework improves contextual understanding and robustness in visually ambiguous cases while enhancing interpretability, improved generalization, and practical applicability in teledermatology environments, while adding interpretability, fairness and real-world applicability of teledermatology systems.
Determinants of under-five mortality in Africa: evidence from a two-decade panel analysis for public health policy
(BioMed Central Ltd, 2026-06-17) Rathnasekara, H; Jayathilaka, R
Background: Child survival is a critical indicator for a nation’s health and its progress is important in attaining the Sustainable Development Goals. Understanding the regional and country-specific dynamic and interplay of various determinants of under-five child mortality is vital for the African continent, which remains one of the most vulnerable regions for child mortality globally. Methods: This study investigates the association of economic, health-related, social and demographic, environmental, and infrastructure-related factors with under-five child mortality. It integrates generalisable regional associations using a standard fixed-effects panel model and examines illustrative country-specific associations of the selected determinants through multiple linear regression, based on a balanced panel dataset of 45 countries over a 22-year period. Results: Fixed-effect analysis reveals that the diphtheria-tetanus-pertussis (DTP) immunisation and total fertility rate (TFR) are robust regional determinants of under-five mortality across specifications. While health expenditure, sanitation services, and malaria incidence show significant associations in the baseline model, these findings are sensitive to the inclusion of year fixed effects, suggesting they are influenced by broader temporal trends or common regional shocks rather than serving as stable independent factors within the study period. Conclusion: Regional analysis, which controls for unobserved country-specific heterogeneity over an extended period and is complemented by country-specific analysis, facilitates the formulation of policy implications at both national and international levels. Recommended policy measures include increasing immunisation coverage, implementing malaria control programmes, strengthening community health infrastructure, enhancing girls’ education, promoting widespread access to modern family planning, and improving sanitation services. These strategies are expected to contribute to the progress toward Sustainable Development Goal 3.2 in the African region.
MISO M-Ppm for Integrated-Receiver SWIPT with Pulse Shaping and PSO-Based Beamforming
(Institute of Electrical and Electronics Engineers, 2026-06-12) Vithanage, G. S; Jayakody, D. N.K; Sabapathy, S
Integrated-receiver (IntRx) simultaneous wireless information and power transfer (SWIPT) enables low-power Internet of Things devices by eliminating energy-intensive radio frequency (RF) front-end components at the receiver. This paper investigates a multiple-input single-output (MISO) SWIPT system employing M-ary pulse position modulation (M-PPM), where high-amplitude time-localized pulses exploit rectifier nonlinearity via increased peak-to-average power ratio (PAPR). Transmit beamforming is employed to enhance harvested DC power, and the impact of pulse shaping is analyzed using rectangular and raised-cosine (RC) pulses with varying roll-off factors. Monte Carlo simulations under random channel realizations show that the proposed MISO M-PPM architecture achieves harvested energy gains of up to 37.8% compared to single-antenna transmission. Furthermore, RC pulse shaping consistently outperforms rectangular pulses, with harvested energy increasing with the roll-off factor. To enable beamforming without increasing receiver complexity, a particle swarm optimization (PSO)-based transmit beamforming method is proposed, using received power as the sole fitness metric and requiring no phase estimation at the receiver. Beamforming coherence is characterized by using the standard deviation of received signal phases. An analytical model is developed to estimate the expected number of PSO iterations required to satisfy a target coherence level as a function of swarm size, enabling efficient allocation of computational resources.
Ai-Based Urine Microscopy Image Analysis for Predicting Urinary Tract and Renal Diseases
(Institute of Electrical and Electronics Engineers, 2026-05-29) Senanayake, K; Panagoda, P; Dharmapriya, S; Chathurya, R; Wijendra, D; De Silva, H; Jayawardana, D
The manual microscopic examination of urine is a crucial step in diagnosis of urinary tract and renal diseases. However, the process is time-consuming and operator dependent. Most of the existing automated urinalysis techniques only consider the microscopic examination of individual components or the black-box-based prediction models. There is a lack of a comprehensive framework that incorporates the microscopic examination of the microscopic components with the clinical diagnostic logic. In this regard, the present work proposes an artificial intelligence-based urinalysis system for the microscopic examination of the components in the urine sample to generate diagnostic outcomes. In the proposed system, the microscopic components like white blood cells, red blood cells, bacteria, yeast, crystals, and casts are detected and analyzed to generate the diagnostic outcomes for the diagnosis of urinary tract infection, kidney stone risk, hematuria causes, and casts-related renal diseases. In the proposed system, efficient lightweight models ensure precision and effectiveness in identifying various biological entities. White blood cells are detected with a mAP@0.5 score of 0.96, yeast with over 0.94, and crystals with more than 0.91, yielding a classification accuracy of 99.41% for crystals. The system detects microscopic elements like casts with a mAP@0.5 score of 0.80. The system also incorporates auxiliary clinical data to enhance diagnostic results for various diseases.
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