IILM Institute for Higher Education

The purpose of this study is to conduct a comprehensive two-level analysis of the literature on employee green behaviour in the hospitality industry. It seeks to map current research trends and identify future directions, thereby offering a holistic understanding of the scholarly landscape. Using a two-tier approach, the study integrates bibliometric analysis with thematic content review to capture the breadth and depth of existing academic work across disciplines. A key finding is the interdisciplinary nature of research on green behaviour, which reveals how multiple fields converge on this topic. This study contributes by synthesizing fragmented insights, enhancing the understanding of green behaviour among hospitality employees and outlining new avenues for future inquiry.

New medical technologies are having a significant influence on our healthcare system, transforming how we manage and preserve patient medical data. A significant advantage is the ability to track and validate this data before saving the information to the cloud for future use. However, due to the fast advancement of current medical technology, effective security measures are required to ensure the protection of all stored data. It is critical to encrypt and properly manage sensitive information, such as a patient's medication history. However, striking a balance between using data for legitimate objectives and protecting patient privacy is difficult. To develop a safe and trustworthy data environment, it is critical to understand the limitations of present technologies and design future research pathways. Integrating blockchain, a decentralized digital ledger, with cybersecurity, particularly with the use of artificial intelligence (AI), helps to create trustworthy communication. This study supports the use of blockchain-integrated cybersecurity in healthcare, highlighting its ability to reliably discover and correct serious, possibly life-threatening errors in the medical industry. Additionally, this research emphasizes the growing need for real-time monitoring and validation of medical data to prevent unauthorized access and ensure data integrity. By leveraging blockchain technology alongside AI-driven cybersecurity measures, the healthcare industry can strengthen its defense against cyberthreats while fostering a more transparent and secure data management system. These advancements not only protect patient privacy but also pave the way for innovative healthcare solutions, facilitating the development of a safer and more efficient digital health ecosystem.

Image dehazing plays a critical role in understanding complex real-world systems such as transportation, environmental monitoring, and healthcare imaging, where haze significantly degrades visual information and hinders decision-making. Traditional deep learning models emphasize accuracy but often neglect interpretability and resource efficiency, limiting their applicability in sustainable multi-modal fusion frameworks. We introduce SFX-GAN, a Sustainable and eXplainable Frequency-domain GAN that employs a novel channel-wise spectral fusion strategy, treating low, mid, and high-frequency components across RGB channels as complementary modalities. This multi-modal spectral integration enables the model to capture both global context and fine-grained structural cues, facilitating more reliable restoration of haze-free images. For interpretability, SFX-GAN incorporates a Concept Bottleneck Layer and Grad-CAM supervision, aligning network attention with haze-affected regions and enhancing trust in automated dehazing for safety-critical applications. Designed with sustainability in mind, SFX-GAN leverages frequency-domain processing to deliver efficient inference with reduced computational overhead. Experiments on RESIDE-SOTS, NH-HAZE, I-HAZE, O-HAZE, and D-HAZY datasets demonstrate state-of-the-art performance under both full-reference (PSNR, SSIM) and no-reference (NIQE, BRISQUE, FADE, SSEQ) metrics. Ablation studies confirm the effectiveness of each module in the fusion pipeline. Overall, SFX-GAN contributes to the intelligent fusion of spectral-spatial modalities for complex imaging systems, advancing responsible and explainable AI aligned with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities).

Predictive Maintenance (PdM) plays a critical role in reducing unplanned downtime and extending asset longevity in industrial systems like aircraft engines, manufacturing equipment, and energy infrastructure. Proper estimation of the Remaining Useful Life (RUL) of parts allows for preventive maintenance scheduling and increased operational efficiency, safety, and cost savings. However, the development of robust RUL prediction models remains challenging due to high variability in operating conditions and fault modes. Existing deep learning models tend to demonstrate exceptionally good performance on individual datasets but are unable to generalize across diverse regimes without retraining, which makes them less reliable and scalable for real-world deployment. To overcome existing constraints, this study proposes STARNet, a robust RUL prediction framework that incorporates a multi-scale Conv–BiLSTM with attention pooling, window statistics fusion, and mixture-of-experts encoding. The framework incorporates phase-aware normalization and delta features to improve adaptability across multi-regime subsets (FD002/FD004) of the NASA CMAPSS dataset. Furthermore, self-supervised pretraining and targeted transfer learning between subsets are employed to improve representation quality and reduce overfitting, while Monte Carlo dropout provides reliable uncertainty-aware inference. A multi-seed ensemble with nonlinear stacking ensures stable and accurate predictions across all four subsets. The model is thoroughly tested using a multi-metric approach consisting of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R²) and NASA Score. In this study, STARNet, a robust RUL prediction network that illustrates steady performance gains across all subsets of C-MAPSS, with average RMSE and score improvements of 5.91% and 13.23%, respectively, has been proposed over existing state-of-the-art models. The findings consistently surpass traditional and cutting-edge deep learning benchmarks, confirming the efficacy, generality, and deployment suitability of the developed framework for real-world predictive maintenance tasks.

Urban last mile delivery is a key part of smart city logistics, but one that is hampered by delays and has a major impact on service reliability and operational efficiency - and sustainability. Despite the progress made in Internet of Things (IoT) technologies and artificial intelligence, transportation delays are still rampant, as a result of urban congestion, spatial heterogeneity and demand volatility. This study examines how big data in the logistics industry, made available through IoT, can be harnessed using machine learning algorithms to predict and minimize last mile delivery delays. Using the LaDe dataset that contains more than 4.4 million real-world delivery records across five major Chinese cities, the study combines a quantitative data-driven method combining exploratory analysis, supervised learning, hyperparameters optimization, and simulation-based intervention evaluation. Logistic Regression, Random Forest and LightGBM models were created and tested, with a random forest model fine-tuned showing the best results, (80% accuracy and 0.857 ROC-AUC). Spatial and temporal features proved to be the most influential predictors, and thus the importance of local urban context. A risk-based simulation showed that targeted interventions for higher-risk deliveries are likely to enable a reduction of overall delay rates by over 8% with city-specific improvements of more than 20% in heavily congested areas. The findings confirm the value of operationalizing predictive analytics to provide meaningful reductions in delays and enable proactive and data-driven decision making in smart urban logistics systems.

ABSTRACT Food is one of the most basic needs of humans and cannot exist without it. On the other hand, predicting the number of COVID-19 cases in 2020 made the problems with food supply and contamination worse. Customers' concerns about food and the demand for reliable information on food quality are increased by these issues.Food packaging plays an important part in the food supply chain in the food business by acting as a barrier against undesirable elements and preserving the food's quality.he objective of this paper is to emphasize the significance of packaging in the food sector, paying special attention to how packaging affects sustaining enhancing the quality and safety of the product and lowering the rate of food waste and losses after harvest. The focus is on recent developments in clever and smart packaging that minimize some of the harmful effects of packaging on the environment and food waste. We look at how food packaging affects the environment and talk about ways to cut down on packaging waste.In this paper we describe the various formats and types of materials used in food packaging. KEYWORDS:packaging,packaging material,food waste,smart packaging