Abstract
This study examines the effectiveness of employing Advanced Rider Assistance System (ARAS) for enhancing motorcycle safety by reducing crashes and improving rider safety. The system includes both single solution approaches, like braking systems, and multi-sensor solutions that integrate data from LiDARs, radars, and cameras through sensor fusion. A systematic literature review was conducted to collect data from 2008 to 2024 across various sources related to ARAS. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a comprehensive and transparent process. Data were extracted from the included studies, focusing on study design, sample size, intervention details, and outcomes. The risk of bias was assessed using a customized checklist. The review included 31 studies that met the inclusion criteria. Findings were summarized for single sensor solutions and sensor fusion approaches.The review indicates that single-solution systems are effective ARAS technologies. In contrast, the application of sensor fusion in motorcycles has been only minimally explored, making it difficult to draw definitive conclusions about its impact in this context. Evidence from four-wheeled vehicles, however, shows that sensor fusion can enhance perception robustness, improve performance under adverse conditions, and contribute to measurable safety gains. These results suggest that similar advantages could be realized for motorcycles as fusion-based ARAS technologies become more widely implemented. Moreover, sensor fusion holds the potential to provide riders with broader situational awareness and more comprehensive safety assistance than single-system solutions. Future research should focus on addressing the identified challenges and optimizing these systems for broader implementation. This review underscores the critical role of ARAS in reducing motorcycle-related incidents and improving rider safety, highlighting the need for ongoing research to refine sensor fusion algorithms and address technical challenges for real-world applications.

Abstract
The increasing integration of Artificial Intelligence (AI) into Building Energy Management Systems (BEMS) is revolutionizing energy optimization by enabling real-time monitoring, predictive analytics, and automated control. While these advancements improve energy efficiency and sustainability, the opacity of AI models poses challenges in interpretability, limiting user trust and hindering widespread adoption in operational decisionmaking. Ensuring transparency is crucial for aligning AI insights with building performance requirements and regulatory expectations. This paper presents EI-Build, a novel Explainable Artificial Intelligence (XAI) framework designed to enhance the interpretability of intelligent automated BEMS. EI-Build integrates multiple XAI techniques, including Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Anchors, Partial Dependence Plots, Feature Permutation Importance, and correlation-based statistical analysis, to provide comprehensive explanations of model behavior. By dynamically tailoring the format and depth of explanations, EI-Build ensures that insights remain accessible and actionable for different user profiles, from general occupants to energy specialists and machine learning experts. A case study on photovoltaic power generation forecasting applied to a real BEMS context evaluates EI-Build's capacity to deliver to deliver both global and local explanations, validate feature dependencies, and facilitate cross-comparison of interpretability techniques. The results highlight how EI-Build enhances user trust, facilitates informed decision-making, and improves model validation. By consolidating diverse XAI methods into a single automated framework, EI-Build represents a significant advancement in bridging the gap between complex AI energy models and real-world applications.

Abstract

Abstract
This paper proposes a multi-objective stochastic optimization framework that can be used by governments to run auctions and select the best community energy storage system (CESS) projects to support. The framework enables CESS providers and energy community members to equitably benefit from the economic value generated by CESSs. The auction accepts offers from competing CESS providers that constitute the data of the CESS location, size, install time, technology, provider, investment cost, and energy trading price. The auction is run by a government agency which selects CESS projects that maximize the economic benefits and distribute them equitably among CESS providers and community members. The multi-objective stochastic optimization accounts for the multi-year uncertainties of photovoltaic (PV) generation, real and reactive energy consumption, energy trading prices, and PV installations. We exploit the Monte Carlo simulation and scenario trees to model the aforementioned uncertainties. The K-Means clustering method is used to reduce the number of scenarios, and thereby, lessen the computational burden of the optimization problem. Our experiments on an Australian low-voltage network with a community of prosumers and consumers demonstrate that government financial support can accelerate the installation of CESSs and enhance their business viability. This can be achieved by boosting the economic benefits shared between CESS providers and communities and ensuring these benefits are distributed equitably. Also, our experiments show that the economic benefits of all stakeholders are further improved with a high growth of the number of PV installations, and a slight reduction of energy import and export prices over the planning period.

Abstract
Being a part of society, employees' behavior can't be ignored, and it must be encouraged to sustain nature for the upcoming generation. Following the resource-based view theory, this study aims to identify the factors influencing employees toward sustainable behavior. To meet the objectives, cross-sectional data were collected from employees of manufacturing companies, and structural equation modeling was used for the analysis. The study results show a positive effect of participative decision-making and employee motivation on employees' eco-friendly innovation capabilities and behavior. Additionally, this research reveals that employee motivation partially mediates the link between participative decision-making, eco-friendly innovation capabilities, and behavior. Furthermore, this research evidenced a positive moderation of green culture on the relationship between participative decision-making and eco-friendly innovation capabilities, evidencing that the relationship is stronger when the culture is high. This research contributes to the existing literature by providing a deeper understanding of the factors influencing employees' eco-friendly innovation capabilities and behavior. It highlights the significant roles of green culture as a moderator and employee motivation as a mediator, offering novel perspectives to both theory and practice.

Abstract
Background and objectives: Neurodegenerative diseases, including Alzheimer's disease (AD), mild cognitive impairment (MCI), and frontotemporal dementia (FTD), are a growing public health challenge, with dementia incidence projected to triple in the coming decades. AD is associated with memory impairment, bvFTD with behavioral dysfunction, and MCI as a transitional stage between normal cognition and dementia. While structural brain changes have been widely studied, the role of neurotransmitter pathways remains underexplored. This study aims to correlate gray matter atrophy in AD, bvFTD, and MCI with neurotransmitter pathways to identify distinctive neurochemical impairments. Methods: We included 214 participants (89 CE, 74 bvFTD, 51 MCI) from a single-center cohort. MRI from 3 T scanners was segmented via FreeSurfer. Neurotransmitter maps were sourced from JuSpace. We performed volumetric and whole-brain correlation analyses to evaluate relationships between brain regional volumes (BRVs) and neurotransmitter pathways. Group differences were assessed with Kruskal-Wallis tests followed by post-hoc analyses. Results: Volumetric analysis showed expected atrophy patterns in each group. Correlation analysis indicated distinct neurotransmitter involvement: AD showed significant atrophy correlations with dopamine D2 and GABA A receptor distribution; bvFTD had significant negative correlations with the mu-opioid receptor; MCI exhibited early serotonergic dysregulation. Conclusions: We identified distinct atrophy patterns linked to specific neurotransmitter systems, each showing unique neurochemical profiles. In AD, precuneus and inferior parietal lobules atrophy aligns with dopaminergic and GABAergic receptors, potentially impacting memory and executive functions. In bvFTD, medial orbitofrontal and temporal atrophy, is linked to mu-opioid receptor impairment, possibly contributing to behavioral symptoms. In MCI, early serotonergic dysregulation involving SERT occurs before detectable atrophy.