Recent Submissions
Rising from the crisis by raising their businesses exploring the social media strategies for micro-level women entrepreneur success
(Emerald Publishing Limited, 2025-10-03) Nisha Jayasuriya, Krishantha Wisenthige, Naduni Kalansuriya, Sumudu Munasinghe, Pubuddi Shamila, Suranjith Swaris; Jayasuriya, Nisha; Wisenthige, Krishantha; School of Business
Purpose – This study examines the social media strategies adopted by micro-level women entrepreneurs who
successfully transitioned their businesses from “brick and mortar” to online operations during the coronavirus
(COVID-19) pandemic.
Design/methodology/approach – The research consisted of two phases: semi-structured interviews
followed by focus group discussions (FGDs). Twelve participants were selected through a judgmental
sampling method for the interviews. The sample included contemporary successful micro-level
female entrepreneurs who shifted to online platforms during the crisis. New factors were identified as
determinants of success for online-based small businesses. The FGDs provided insights into these
determinants, which were analyzed thematically.
Findings – The findings indicate that the success of online businesses is influenced by an active presence on
social media, the maintenance of a strong online network with customers and industry partners, vigilance in
seizing strategic opportunitiesin a diverse environment, and the generation of powerful word-of-mouth (WOM)
through excellent customer service. In contrast to gender inequality typically observed in business across Asian
regions, no such gender stereotype issues were found among micro-level Sri Lankan women entrepreneurs.
Originality/value – Thisstudy enhancesthe understanding ofsocial media strategiesin micro-entrepreneurship
by identifying key successfactorsthat have been underexplored in existing literature. It challenges assumptions
about gender barriers in Asian entrepreneurship, demonstrating that Sri Lankan women entrepreneurs navigate
digital business environments effectively. Unlike prior studies focused on larger firms or developed economies,
this research highlights unique success determinants in an emerging market. The study introduces a conceptual
framework that integrates social media engagement, strategic adaptability and network strength as critical
components for online business success. These insights provide a foundation for future empirical studies in
digital entrepreneurship.
SmartSphere: Bridging AI-Powered Automation and Tesla Coil Wireless Energy for Smarter Living
(Institute of Electrical and Electronics Engineers Inc., 2025) Jurison Jenaraj, J.K; Umasuthan, A; Vigneswaran, V; Raveendran, K; Thiruthanigesan, K; Kasthuriarachchi, S
This paper presents SmartSphere, an intelligent, secure, and wire-free home automation system that synergizes artificial intelligence, multi-factor access control, and resonant wireless power transmission. Built on an edge-computing architecture using ESP32 and Arduino Mega 2560 microcontrollers, SmartSphere integrates facial recognition, fingerprint authentication (96.7% accuracy), and IR-based presence detection to reduce false activations by 75%. The system employs OpenCV and TensorFlow Lite for real-time anomaly detection and environmental personalization, including weather-responsive adjustments and emotion-aware lighting via facial expression analysis. A key innovation is the incorporation of a Tesla coil-based wireless power transmission module, which eliminates conventional wiring constraints and reduces installation cabling by 86%. Experimental validation demonstrates a 14% improvement in energy efficiency, 75% faster response time (0.3 s), and seamless compatibility with 92% of tested IoT devices. Through comprehensive testing and validation, this research establishes SmartSphere as a secure, intelligent, and sustainable solution for next-generation smart homes, addressing the limitations of traditional wired systems while enhancing the user experience through AI-driven personalization.
Adaptive AI-Based Enhancement of Critical External Sounds in Insulated Vehicle Cabins for Improved Safety
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Rathnayaka D.B; Wickramasuriya L.H.N.Y; Walpalage J.V; Rathnayake, S
The increasing acoustic insulation in modern and electric vehicles improves passenger comfort but unintentionally suppresses critical external sounds such as ambulance sirens, car horns, and train alarms, creating potential safety risks. While existing research has explored sound detection or localization in isolation, few systems integrate both capabilities in a unified framework for real-time vehicular deployment. This research proposes an adaptive AI-based system that detects, classifies, and selectively enhances these critical sounds in real time while providing directional awareness. Using a convolutional recurrent neural network (CRNN) trained on the UrbanSound8K dataset, the system processes incoming audio from external microphones, extracts Mel-frequency cepstral coefficients (MFCCs), and distinguishes safety-relevant cues from non-essential background noise. A dual-microphone setup enables the estimation of sound direction (left or right), providing additional spatial awareness to the driver. Detected signals are isolated through spectral filtering and relayed into the cabin with sub-30 ms latency, ensuring timely driver and passenger awareness without compromising comfort. Experimental results achieved 91.2% classification accuracy and 87.4% directional accuracy,confirming the system's feasibility for enhancing safety in insulated vehicle cabins and supporting future autonomous driving environments.
Fidelity-Driven Physically Constrained Adaptive Code Distance for Surface Codes under Biased Circuit-Level Noise
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Hettiarachchi, A; Dissanayaka, K
Surface codes are leading candidates for fault-tolerant quantum computing, but conventional implementations use fixed code distances that result in conservative resource allocation during varying noise conditions. We propose a physically-constrained, fidelity-driven adaptive surface code framework that dynamically adjusts code distance at scheduled intervals to maintain target logical fidelity while minimizing resource overhead. Our approach introduces a syndrome-based logical error rate estimation algorithm using rolling-window statistics, a scheduled adaptation policy operating within pre-allocated qubit pools, and detailed modeling of adaptation latency and reconfiguration-induced errors for superconducting qubit devices. Through large-scale Monte Carlo simulations under realistic biased circuit-level noise, we demonstrate that hardware-constrained adaptive codes achieve 43-62% success rate improvements over fixed-distance strategies, reaching 72-82% success rates compared to 5̃0% for fixed approaches. Oracle adaptive strategies with perfect error knowledge achieve 91-94% success rates, indicating substantial theoretical potential. Our results show that adaptive surface codes provide significant performance improvements even under realistic hardware constraints, with oracle strategies consuming only 37% of maximum resources while achieving 86% higher success rates for shorter circuits.
AI-Assisted Criminal Investigations: Enhancing Testimony Analysis and Case Correlation
(Institute of Electrical and Electronics Engineers Inc., 2025-12-08) Karunarathna K.A.D; Ediriweera E.R.L.C; Fernando N.D.R.N; Abeywardana B.A.I.S; Abeywardhana, L
This paper presents Lexa AI, a holistic AIpowered solution for improving criminal investigative practices through four phases that target key drawbacks found in traditional practices. The first model provides an automated data/information collection stage that accepts legal documents in multiple formats and utilizes a three-stage processing pipeline based on Gemini 2.0 Flash model, which performs Optical Character Recognition (OCR) with a higher level of accuracy and speed compared to alternative approaches. The collected data will be forwarded to the next phase by leveraging dynamic question generation that uses Reinforcement Learning (RL), instantaneous multilingual capabilities, and real-time scoring of relevance. The third phase conducts Multimodal Behavioral and Physiological Analysis (MBPA), which includes facial signals, speech signals, and heart rate signals to create a combined Stress Index as an objective indicator and avoids subjective judgment. Finally, semantic similarity will be measured to correlate incidents, assess risk for victims, and provide explainable predictions
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