Authors - Neeraj Mathur, Jiby Mariya Jose Abstract - Material Control Systems (MCS) serve as a critical software layer that coordinates material flow by issuing transport commands, tracking material lo-cations, and interfacing with factory equipment and automated handling systems. Although the term may appear to focus primarily on inventory management, it is most commonly used in high-tech environments such as semiconductor manufacturing to describe the software layer that manages, directs, and optimizes the movement, storage, and routing of materials (e.g., wafers and carriers) within a production or logistics environment. This paper presents the development and implementation of a novel Physical AI–based Material Control System. Unlike traditional MCS architectures that rely on rigid rule-based dispatching, the proposed approach leverages a Physical AI plat-form to enable unified and adaptive control across heterogeneous hardware, including stockers, Autonomous Mobile Robots (AMRs), and Overhead Hoist Transport (OHT) systems. By integrating real-time sensor fusion and adaptive motion planning, the proposed system enhances process logistics in semiconductor backend facilities, where high-mix production requires highly dynamic coordination between storage and transport resources.
Authors - Maryam Ghazi Ali, Bindu V. R Abstract - The Internet of Things (IoT) has spread rapidly, significantly increasing several secu-rity vulnerabilities, as traditional detection systems are becoming insufficient to manage the vol-ume and diversity of traffic that characterizes modern networks. The review provides a compre-hensive analysis of recent advances in learning-based intrusion detection systems (IDS), focusing primarily on deep learning, traditional learning, machine learning, and hybrid frameworks. Through critically evaluating a diverse range of state-of-the-art studies, the review explores dif-ferent methodological solutions, data, and performance measurement in the field. The available empirical results show that, although deep learning models are better at identifying complex pat-terns in the data, traditional machine learning algorithms require less computational power. In addition, hybrid and ensemble models often outperform single-method options, but often with high computational cost. The review outlines a number of important challenges, including the issue of class imbalance and the fact that models are not very interpretable. It argues that light-weight and interpretable AI systems should be a priority in future studies, and the gap between theoretical academic frameworks and practical IoT applications would be minimized.
Authors - Aditi Jha, Ravi Shankar Pandey Abstract - Indoor air quality (IAQ) is a frequently overlooked determinant of health in rural villages, where the extensive use of solid fuels for cooking and space-heating generates elevated concentrations of airborne pollutants. This study presents an integrated, low-cost protocol for improving IAQ in rural dwellings, combining real-time environmental monitoring, simplified digital modelling and passive strategies of ventilation and biophilic design. The methodology can be structured into three steps: Conceptual digital twin, feedback interface, ventilation strategies, biophilic integration. Conceptual digital twin is based on the mapping of each dwelling linked to Arduino low-cost, stand-alone sensors (CO₂, PM₂.₅, temperature and relative humidity) that collect data at temporal resolution of one minute. An immediate feedback interface based on visual and/or acoustic indicators that prompt residents to take corrective actions (selective opening of windows, activation of cross-breezes), when exposure thresholds - derived from WHO Air Quality Guidelines - are exceeded. Data-driven natural-ventilation strategies – optimal ventilation windows identified through time-series analysis of sensor data, calibrated to local weather conditions and occupancy profiles to maximise air exchange while minimising heat losses. Biophilic integration implies the introduction of resilient plant species with proven phytoremediation capacity, as Epipremnum aureum) which could reduce CO₂ level, with quantitative guidance on density (two to three plants per main room) and optimal placement. Using low-cost IoT sensors, the protocol monitors environmental parameters and pollutant concentrations in real time. The system targets specific safety and comfort thresholds, aiming to maintain CO₂ levels below 700 ppm and PM₂.₅ below 50 μg/m³ to optimize occupant health (Wu et al, 2021). These thresholds, derived from World Health Organization (WHO) guidelines, are essential to ensure occupant satisfaction and well-being. The ultimate objective is to define a scalable and replicable intervention model capable of combining digital technologies and natural solutions for the sustainable regeneration of fragile territories.
Authors - Atul Pawar, Ganesh Deshmukh, Rajesh Lomte, Sahil Ambokar, Vedant Bankewar, Sanket Ahirrao Abstract - This study explored teachers’ perspectives on the need for an interac tive digital storytelling application to support English language learning at the primary level. Using a teacher-based needs analysis, data were collected through expert review of research instruments and in-depth interviews with English teachers working in international school contexts. The findings reveal that teach ers perceive digital storytelling as an effective approach for enhancing student engagement, motivation, and contextualized language learning. Teachers high lighted the importance of integrating interactive elements such as narrative audio, visuals, game-based tasks, immediate feedback, and reward systems to support vocabulary development, comprehension, and learner autonomy. The results also indicate a need for applications that are curriculum-aligned, age-appropriate, and easy to use in classroom settings. Based on the identified needs, the study pro vides design implications for the development of an interactive digital storytell ing application that combines storytelling and game-based learning principles. This research contributes to the growing body of literature on digital storytelling and offers practical guidance for educators and developers seeking to design ef fective language learning applications.
Authors - Veenu Singh, Saurabh Singhal Abstract - Many AI agents store observations, summaries, and retrieved content in persistent memory, then reuse that material in later planning and action. This creates a failure mode that standard incident response does not fully address. If malicious content is written into durable memory, patching the vulnerable component, rotating credentials, and restarting the agent do not remove the poisoned state. The agent can restart clean, retrieve the same memory, and act on it again. We call this provenance laundering: external-origin content is later consumed with authority it should not have. We formalize this mechanism, show that remediation without memory purge leaves residual impact over time, and examine seven production memory architectures against this threat model. We then define a containment primitive based on provenance metadata, namespace separation, and an inference-time non-escalation gate, and evaluate it with ablation across two frameworks. In our experiments, unauthorized behavior persisted after standard remediation and stopped only after memory purge. These results suggest that incident response for persistent-memory agents should treat purge as a required step rather than an optional cleanup action.
Authors - Nitesh Varman V R, Sanjith Ganesa P, Rahul Veeramachaneni, Korapati Mohan Aditya, Bagavathi Sivakumar Abstract - With the development of cloud computing and big data technology, data handling particularly in handling big data, while also mentioning the dangers of privacy and security violations in delegating the processing of sensitive data to cloud computing has increased. The conventional encryption method that demands the decryption of data for processing, which could result in the leakage of sensitive data and performance inefficiencies are no longer valid. The paper introduces the Optimized Privacy-Preserving Cryptographic Processing Algorithm (OPCPA), which reduces computational complexity through the use of light-weight encryption, adaptive data partitioning, hierarchical key management, and parallel processing of encrypted data. The proposed algorithm is compared to conventional methods using the KDD Cup 1999 dataset and outperforms them in terms of processing speed, throughput, and resource utilization.
Authors - Kashish Goyal, Parteek Kumar, Karun Verma Abstract - The clinical deployment of continuous epileptic seizure forecasting systems is severely hindered by the cold-start problem. Current state-of-the-art deep learning models require patient-specific fine-tuning, necessitating the recording of multiple seizures from a newly admitted patient before the system becomes operational. To achieve immediate clinical utility, forecasting models must operate in a zero-shot capacity. This paper presents a Zero-Shot Cross-Patient Transfer Framework, leveraging the Horizon-Aware Graph Transformer as a universal feature extractor, coupled with the Strict Discipline Protocol as a rigid domain adaptation layer. By anchoring the batch normalization layers to a global source distribution and utilizing a brief interictal calibration phase, the framework mitigates the severe covariate shift inherent in cross-patient electroencephalogram signals. Experimental validation on the CHB-MIT dataset demonstrates a sensitivity of 87.3% with a false alarm rate of 0.28 per hour, achieving a Time-to-Utility of exactly 10 minutes, a 99.9% reduction compared to conventional patient-specific approaches requiring 5-14 days of monitoring. The framework successfully bypasses patientspecific training, offering immediate clinical interoperability while minimizing alarm fatigue through disciplined feature scaling.
Authors - The Quan Trong, Nguyen Trong Nhan Abstract - The integration of large language models (LLMs) into primary educa tion remains limited in low resource, diglossic languages like Sinhala. General purpose models often produce grammatically inconsistent or cognitively over whelming output for young learners. This paper introduces a grade-adaptive, con straint-driven framework for automated Sinhala story and quiz generation target ing Grades 1-5. Building upon an 8-billion-parameter Sinhala-adapted LLaMA 3 model, we apply Quantized Low-Rank Adaptation (QLoRA) using a curated multi-task educational dataset. The system enforces tier-specific linguistic con straints separating conversational Sinhala for lower grades from formal written Sinhala for upper grades while embedding strict structural rules such as con trolled sentence counts (5-6 vs. 7-8) and validated multiple-choice formats (3 vs. 4 options). Evaluation on 100 structured prompts demonstrated substantial im provements over a zero-shot baseline: structural compliance increased from 64% to 93%, and hallucination-related failures decreased from 31% to 8%. Further more, evaluation against 50 unseen real-world classroom prompts yielded a 0.0% crash rate and 95% register adherence, confirming robust qualitative perfor mance. Results demonstrate that diglossia-aware dataset engineering and con straint-aware fine-tuning enable reliable, pedagogically aligned deployment of LLMs in low-resource primary learning environments.
Authors - Maria Veronica Alderete Abstract - This study extends the empirical literature on the relationship between intention to use Artificial Intelligence (AI), the digital divide, and regional ine-qualities in Latin America. To the best of our knowledge, no prior research has examined the AI gap by combining data at the subnational (regional) level across countries. The analysis relies on a sample of 208 regions from 10 Latin American countries. A structural equation model is estimated to assess the relationships among digital infrastructure, socioeconomic factors, and intention to use ChatGPT. The results show that household internet access has a positive and statistically significant effect on intention to use ChatGPT. Data center presence indirectly re-inforces AI intention use through its positive association with internet access, while rurality exerts a negative effect. Education levels and platform-based em-ployment (e.g., Uber) are also positively associated with intention to use AI. The findings suggest that AI adoption is structurally conditioned by foundational digi-tal infrastructure, regional human capital, and exposure to platform-based labor markets. Although the expansion of the gig economy fosters intention to use AI, AI diffusion simultaneously increases the importance of formal education.
Authors - Donald Flywell Malanga, Wallace Chigona Abstract - Mobile Health (mHealth) has been regarded as a potentially transform-ative element for enhancing health service delivery in low-income nations. The effective integration of technology relies on ongoing usage rather than just initial acceptance. While the body of literature on factors influencing continued mHealth use is expanding, post-adoption expectations are proposed as indicators of the success or failure of mHealth implementation. There is limited research on how community health workers' post-adoption expectations influence their inten-tions to persist in using mHealth in developing regions. Consequently, this study explores the effect of post-adoption expectations on satisfaction and ongoing us-age behaviour regarding mHealth among community health workers in Malawi, which represents a developing country context. The research introduces a frame-work that builds upon the expectation confirmation model and incorporates ele-ments from the updated information success model. A mixed-methods conver-gent design was utilised for the study. Data were collected through surveys and semi-structured interviews with community health workers who utilise Cstock. Cstock is an mHealth application that facilitates the ordering of medical supplies via text message. The findings generally support the notion that post-usage use-fulness, along with information quality, system quality, and service quality, pos-itively influences community health workers’ satisfaction and their intention to continue using the Cstock application. The results indicate that the ongoing usage behaviour of mHealth among community health workers is shaped not solely by behavioural expectation beliefs (i.e., post-usage usefulness) but also by objective expectation beliefs, including system quality, service quality, and information quality. Therefore, these findings provide valuable insights to policymakers, practitioners, mHealth developers, and other relevant parties regarding the post-user expectations essential for maintaining future mHealth solutions in develop-ing countries, particularly in Malawi.
